diff --git a/ppcpy/calibration/lidarconstant.py b/ppcpy/calibration/lidarconstant.py index 8ccb2d2..e3a0d52 100644 --- a/ppcpy/calibration/lidarconstant.py +++ b/ppcpy/calibration/lidarconstant.py @@ -39,11 +39,14 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li .. TODO:: Check if LC's are normalized with respect to the mean of the profiles. .. TODO:: Add option for Aeronet and rotational Raman retrieved LC. + .. TODO:: insted of performing an additional check for if the raman channels was off here. we could just pass on the information form the quality flag and use it when choosing the best LC. >-- What did I mean here??? + .. TODO:: Add a check at the statr if to see if the channels where on more than x% of the time during each cloud free preiod. Give a warning if it was less than ...% and an error/skipp the period for the channel if it was less than ...%. **History** xxxx-xx-xx: First edition by ... 2026-03-18: Changed beta_mol for inelastic wavelengths and added the 'flagUseRetrievedExt4LCCalc' variable. + """ logging.info(f'LC retrieval: {retrieval} method') @@ -75,7 +78,7 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li # Elastic signal: sig = profiles[channel]['signal'] - signal = np.nanmean(np.squeeze( + signal = np.nanmean(np.squeeze( # TODO: try to use PCR --> normalized data_cube.retrievals_highres[f'sig{sig}'][slice(*cldFree), :, data_cube.gf(wv, t, tel)]), axis=0) molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :].copy() @@ -139,7 +142,7 @@ def lc_for_cldFreeGrps(data_cube, retrieval:str, collect_debug:bool=False) -> li wv_r = elastic2raman[int(wv)] ## Inelastic signal, backscatter and extinction: - signal_r = np.nanmean(np.squeeze( + signal_r = np.nanmean(np.squeeze( # TODO: try to use PCR --> normalized data_cube.retrievals_highres[f'sig{sig}'][slice(*cldFree), :, data_cube.gf(wv_r, t, tel)]), axis=0) molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :].copy() molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :].copy() diff --git a/ppcpy/calibration/polarization.py b/ppcpy/calibration/polarization.py index daf7729..164619d 100644 --- a/ppcpy/calibration/polarization.py +++ b/ppcpy/calibration/polarization.py @@ -1,7 +1,6 @@ import logging from collections import defaultdict -import pprint import numpy as np from ppcpy.misc.helper import uniform_filter @@ -14,6 +13,7 @@ def onemx_onepx(x:float|np.ndarray) -> float|np.ndarray: """Calculate the fraction of (1-x)/(1+x)""" return (1-x)/(1+x) + def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: """Uniformly smooth the input signal @@ -47,22 +47,43 @@ def loadGHK(data_cube): ---------- data_cube : object Main PicassoProc object. + + Yields + ------ + data_cube.polly_config_dict : dict + Updated to parameters: + TR --> Removed + G --> filled and converted to array + H --> filled and converted to array + K --> filled and converted to array + voldepol_error_355 --> converted to array + voldepol_error_532 --> converted to array + voldepol_error_1064 --> converted to array + + Notes + ----- + .. TODO:: + - Write a proper docstring. + + ** History ** + + - xx-xx-xxxx: First edition by ... + """ - print('starting loadGHK') - #print('flag_532_total', flag_532_total_FR) - #print('flag_532_cross', flag_532_cross_FR) - - print('data_cube keys ', data_cube.__dict__.keys()) - G = np.array(data_cube.polly_config_dict['G']).astype(float) - H = np.array(data_cube.polly_config_dict['H']).astype(float) - K = np.array(data_cube.polly_config_dict['K']).astype(float) - #print(TR[flag_532_total_FR]) - #print(TR[flag_532_cross_FR]) - #if data_cube.polly_config_dict['H'][0] == -999: + logging.info("Starting loadGHK") + # print('flag_532_total', flag_532_total_FR) + # print('flag_532_cross', flag_532_cross_FR) + + G = np.asarray(data_cube.polly_config_dict['G'], dtype=float) + H = np.asarray(data_cube.polly_config_dict['H'], dtype=float) + K = np.asarray(data_cube.polly_config_dict['K'], dtype=float) + # print(TR[flag_532_total_FR]) + # print(TR[flag_532_cross_FR]) + # if data_cube.polly_config_dict['H'][0] == -999: if (np.all(np.isclose(H, -999))): - TR = np.array(data_cube.polly_config_dict['TR']).astype(float) - print('H is empty -> calculate parameters') + TR = np.asarray(data_cube.polly_config_dict['TR'], dtype=float) + logging.info('H is empty -> calculate parameters') K[data_cube.flag_355_total_FR] = 1.0 K[data_cube.flag_532_total_FR] = 1.0 @@ -83,92 +104,82 @@ def loadGHK(data_cube): H[data_cube.flag_1064_cross_FR] = onemx_onepx(TR[data_cube.flag_1064_cross_FR]) if np.any(data_cube.flag_532_total_NR): - print("GHK for 532NR") + logging.info("GHK for 532 NR") K[data_cube.flag_532_total_NR] = 1.0 G[data_cube.flag_532_total_NR] = 1.0 H[data_cube.flag_532_total_NR] = onemx_onepx(TR[data_cube.flag_532_total_NR]) - print("H", H[data_cube.flag_532_total_NR]) + logging.info(f"G: {G[data_cube.flag_532_total_NR]}, H {H[data_cube.flag_532_total_NR]}, K: {K[data_cube.flag_532_total_NR]}.") if np.any(data_cube.flag_532_cross_DFOV): - print("GHK for 532DFOV") + logging.info("GHK for 532 DFOV") K[data_cube.flag_532_cross_DFOV] = 1.0 G[data_cube.flag_532_cross_DFOV] = 1.0 H[data_cube.flag_532_cross_DFOV] = onemx_onepx(TR[data_cube.flag_532_cross_DFOV]) - print("H", H[data_cube.flag_532_cross_DFOV]) - print('TR', TR) + logging.info(f"G: {G[data_cube.flag_532_cross_DFOV]}, H: {H[data_cube.flag_532_cross_DFOV]}, K: {K[data_cube.flag_532_cross_DFOV]}.") + logging.info(f"TR: {TR}") else: - print("Using GHK from config file") - #print('TR', TR) - #print('TR from H', (1-H)/(1+H)) - print('G', G) - print('H', H) - print('K', K) + logging.info("Using GHK from config file") + # print('TR', TR) + # print('TR from H', (1-H)/(1+H)) + logging.info(f"G: {G}, H: {H}, K: {K}") data_cube.polly_config_dict.pop('TR', None) # remove the TR from the config to avoid inconsistencies - data_cube.polly_config_dict['G'] = np.array(G) - data_cube.polly_config_dict['H'] = np.array(H) - data_cube.polly_config_dict['K'] = np.array(K) - data_cube.polly_config_dict['voldepol_error_355'] = np.array(data_cube.polly_config_dict['voldepol_error_355']) - data_cube.polly_config_dict['voldepol_error_532'] = np.array(data_cube.polly_config_dict['voldepol_error_532']) - data_cube.polly_config_dict['voldepol_error_1064'] = np.array(data_cube.polly_config_dict['voldepol_error_1064']) - - -""" -.. TODO:: can this be removed? -"TR": [0.898, 1086, 1, 1, 1.45, 778.8, 1, 1, 1, 1, 1, 1, 1], -"G": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"H": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"K": [-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999 ], -"voldepol_error_355": [0.003, 0, 0], -"voldepol_error_532": [0.004, 0, 0], -"voldepol_error_1064": [0.005, 0, 0], -""" - + data_cube.polly_config_dict['G'] = np.asarray(G) + data_cube.polly_config_dict['H'] = np.asarray(H) + data_cube.polly_config_dict['K'] = np.asarray(K) + data_cube.polly_config_dict['voldepol_error_355'] = np.asarray(data_cube.polly_config_dict['voldepol_error_355']) + data_cube.polly_config_dict['voldepol_error_532'] = np.asarray(data_cube.polly_config_dict['voldepol_error_532']) + data_cube.polly_config_dict['voldepol_error_1064'] = np.asarray(data_cube.polly_config_dict['voldepol_error_1064']) def calibrateGHK(data_cube) -> dict: - """Estimate the polarization calibration from the delta 90 Method [1]_ + """Estimate the polarization calibration from the delta 90 Method [1] Parameters ---------- - data_cube - the input data cube + data_cube : object + Main PicassoProc object Returns ------- pol_cali : dict polarization factors from delta 90 for each wavelength containing sub-dicts with 'eta', 'eta_std', 'time_start', 'time_end', 'status' - - Notes - ----- - **History** - - Function is called here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5f5e4d0fd3dcebe7f87220cf802fcd6f414fe235/lib/interface/picassoProcV3.m#L548 - The two most relevant functions here are https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/pollyPolCaliGHK.m - which also calls https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/depolCaliGHK.m - References ---------- .. [1] Freudenthaler 2016 + + Notes + ----- + - Compleate the reference. + - Currently only implemented in the far-range receiver. + - Matlab relevent functions: + Function is called here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5f5e4d0fd3dcebe7f87220cf802fcd6f414fe235/lib/interface/picassoProcV3.m#L548 + The two most relevant functions here are https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/pollyPolCaliGHK.m + which also calls https://github.com/PollyNET/Pollynet_Processing_Chain/blob/dev/lib/calibration/depolCaliGHK.m + + **History** + + - xxxx-xx-xx: First edition by ... """ pol_cali = {} - tel = 'FR' # currently only implemented in the far range receiver + tel = 'FR' # currently only implemented in the far-range receiver for wv in [355, 532, 1064]: if np.any(data_cube.gf(wv, 'total', tel)) and np.any(data_cube.gf(wv, 'cross', tel)): logging.info(f'and even a {wv} channel') + # Extracting necessary data sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, data_cube.gf(wv, 'total', tel)]) bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'total', tel)]) sigBGCor_cross = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, data_cube.gf(wv, 'cross', tel)]) bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'cross', tel)]) pol_cali[f"{wv}_{tel}"] = depol_cali_ghk( - signal_t=sigBGCor_total, bg_t=bg_total, - signal_x=sigBGCor_cross, bg_x=bg_cross, + signal_t=sigBGCor_total, bg_t=bg_total, + signal_x=sigBGCor_cross, bg_x=bg_cross, time=data_cube.retrievals_highres['time'], pol_cali_pang_start_time=data_cube.retrievals_highres['depol_cal_ang_p_time_start'], pol_cali_pang_stop_time=data_cube.retrievals_highres['depol_cal_ang_p_time_end'], @@ -184,9 +195,8 @@ def calibrateGHK(data_cube) -> dict: rel_std_dminus=data_cube.polly_config_dict[f'rel_std_dminus_{wv}'], segment_len=data_cube.polly_config_dict[f'depol_cal_segmentLen_{wv}'], smooth_win=data_cube.polly_config_dict[f'depol_cal_smoothWin_{wv}'], - collect_debug=False + collect_debug=False, flagPicassoComparison=data_cube.polly_config_dict['flagPicassoComparison'] ) - print(pol_cali[f'{wv}_{tel}']) logging.info(f"pol_cali_{wv} {pol_cali[f'{wv}_{tel}']}") else: logging.warning(f'calibrateGHK no {wv} channel') @@ -196,12 +206,13 @@ def calibrateGHK(data_cube) -> dict: return pol_cali -def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg_x:np.ndarray, +def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg_x:np.ndarray, time:np.ndarray, pol_cali_pang_start_time:np.ndarray, pol_cali_pang_stop_time:np.ndarray, pol_cali_nang_start_time:np.ndarray, pol_cali_nang_stop_time:np.ndarray, K:float, cali_h_indx_range:list|tuple, SNRmin:list, sig_max:list, rel_std_dplus:float, rel_std_dminus:float, - segment_len:int, smooth_win:int, collect_debug:bool=False) -> dict: + segment_len:int, smooth_win:int, collect_debug:bool=False, + flagPicassoComparison:bool=False) -> dict: """Polarization calibration for PollyXT lidar system. Parameters @@ -251,8 +262,31 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg 1 if calibration is successful, 0 otherwise. global_attri : dict, optional Information about the depolarization calibration. + + Notes + ----- + .. TODO:: + - Decide on the output format. Current procedure is to return a + list containing one dict with the element 'status':0 when no eta + was found. When eta was succesfully retieved status is 1 and only + the periods with a succesfull retrival is returned. The other possible + option would be to always return a list with as many elements as the + number of polarization calibration periods, both when eta is succesfully + retrived and not (NaN-filling the non-existing values such as eta, or + returning {'status':0} N times). The same system should also be used for + the Lidar constant calculations. Test which system works best with the + reading / writing to db. procedures. + - Check the input shapes defined in the docstring. I do not believe they are correct. + + ** History ** + + - xx-xx-xxxx: First edition by ... + - 30-06-2026: Made output datatype consistant and changed from `nanmean` to + `nansum` in signal aggregation to be consistant with SNR requierment. + """ - # Initialize outputs and intermediate storage + + ## Initialize outputs and intermediate storage pol_cali_eta, pol_cali_eta_std = [], [] mean_dplus, mean_dminus, std_dplus, std_dminus = [], [], [], [] pol_cali_start_time, pol_cali_stop_time = [], [] @@ -261,12 +295,10 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if signal_t.size == 0 or signal_x.size == 0: logging.warning("Warning: No data for polarization calibration.") - #return pol_cali_eta, pol_cali_eta_std, pol_cali_start_time, pol_cali_stop_time, 0, global_attri - return {'status': 0} + # return [{'status': 0} for i in range(len(pol_cali_nang_start_time))] + return [{'status': 0}] # the iteration of days can be omitted if unixtimestamps are used - print('pol_cali_nang_start_time', pol_cali_nang_start_time) - time = np.array(time) for i_depol_cal in range(len(pol_cali_nang_start_time)): indx_45p = np.where( @@ -276,41 +308,46 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg indx_45m = np.where( (time >= pol_cali_nang_start_time[i_depol_cal]) & (time <= pol_cali_nang_stop_time[i_depol_cal]))[0] + if len(indx_45p) < 4 or len(indx_45m) < 4: logging.warning(f'calibrateGHK array to short {len(indx_45p)}{len(indx_45m)} in period {i_depol_cal}') break + this_cali_start_time = min(pol_cali_pang_start_time[i_depol_cal], pol_cali_nang_start_time[i_depol_cal]) this_cali_stop_time = max(pol_cali_pang_stop_time[i_depol_cal], pol_cali_nang_stop_time[i_depol_cal]) - # Exclude the first and last profiles + + ## Exclude the first and last profiles indx_45m = indx_45m[1:-1] indx_45p = indx_45p[1:-1] - # matlab -> python swap from signal_t[:, indx_45p] to signal_t[indx_45p,:] - # to be a profile - sig_t_p = np.nanmean(signal_t[indx_45p, :], axis=0) - bg_t_p = np.nanmean(bg_t[indx_45p], axis=0) + ## Calculating SNR (only sum should be used to aggregate the signal for SNR calculations!) + func = np.nansum + if flagPicassoComparison: + func = np.nanmean + + sig_t_p = func(signal_t[indx_45p, :], axis=0) + bg_t_p = func(bg_t[indx_45p], axis=0) snr_t_p = calc_snr(sig_t_p, bg_t_p) indx_bad_t_p = (snr_t_p <= SNRmin[0]) | (sig_t_p >= sig_max[0]) - sig_t_m = np.nanmean(signal_t[indx_45m, :], axis=0) - bg_t_m = np.nanmean(bg_t[indx_45m], axis=0) + sig_t_m = func(signal_t[indx_45m, :], axis=0) + bg_t_m = func(bg_t[indx_45m], axis=0) snr_t_m = calc_snr(sig_t_m, bg_t_m) indx_bad_t_m = (snr_t_m <= SNRmin[1]) | (sig_t_m >= sig_max[1]) - sig_x_p = np.nanmean(signal_x[indx_45p, :], axis=0) - bg_x_p = np.nanmean(bg_x[indx_45p], axis=0) + sig_x_p = func(signal_x[indx_45p, :], axis=0) + bg_x_p = func(bg_x[indx_45p], axis=0) snr_x_p = calc_snr(sig_x_p, bg_x_p) indx_bad_x_p = (snr_x_p <= SNRmin[2]) | (sig_x_p >= sig_max[2]) - sig_x_m = np.nanmean(signal_x[indx_45m, :], axis=0) - bg_x_m = np.nanmean(bg_x[indx_45m], axis=0) + sig_x_m = func(signal_x[indx_45m, :], axis=0) + bg_x_m = func(bg_x[indx_45m], axis=0) snr_x_m = calc_snr(sig_x_m, bg_x_m) indx_bad_x_m = (snr_x_m <= SNRmin[3]) | (sig_x_m >= sig_max[3]) - # Calculate dplus and dminus - #print('smooth_win', smooth_win) + ## Calculate dplus and dminus dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win) dminus = smooth_signal(sig_x_m, smooth_win) / smooth_signal(sig_t_m, smooth_win) dplus = np.where(np.isfinite(dplus), dplus, np.nan) @@ -318,36 +355,35 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg dplus[indx_bad_t_p | indx_bad_x_p] = np.nan dminus[indx_bad_t_m | indx_bad_x_m] = np.nan - # Subset the calibration range - dplus = dplus[cali_h_indx_range[0]:cali_h_indx_range[1]] - dminus = dminus[cali_h_indx_range[0]:cali_h_indx_range[1]] + ## Subset the calibration range + dplus = dplus[cali_h_indx_range[0]:cali_h_indx_range[1]+1] + dminus = dminus[cali_h_indx_range[0]:cali_h_indx_range[1]+1] if np.all(np.isnan(dplus)) or np.all(np.isnan(dminus)): - logging.warning(f'calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}') - print(f'calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}, len(dplus) {len(dplus)}') - print(' snr_t_p ', np.sum((snr_t_p <= SNRmin[0])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_t_p ', np.sum((sig_t_p >= sig_max[0])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_t_p in height interval', np.sum(indx_bad_t_p[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_t_m ', np.sum((snr_t_m <= SNRmin[1])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_t_m ', np.sum((sig_t_m >= sig_max[1])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_t_m in height interval', np.sum(indx_bad_t_m[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_x_p ', np.sum((snr_x_p <= SNRmin[2])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_x_p ', np.sum((sig_x_p >= sig_max[2])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_x_p in height interval', np.sum(indx_bad_x_p[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' snr_x_m ', np.sum((snr_x_m <= SNRmin[3])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print(' sig_x_m ', np.sum((sig_x_m >= sig_max[3])[cali_h_indx_range[0]:cali_h_indx_range[1]])) - print('> indx_bad_x_m in height interval', np.sum(indx_bad_x_m[cali_h_indx_range[0]:cali_h_indx_range[1]])) + logging.warning(f"CalibrateGHK all values in dplus or dminus masked in period {i_depol_cal}") + logging.debug(f"calibrateGHK all values in dplus or dminus masked in period {i_depol_cal}, len(dplus) {len(dplus)}") + logging.debug(f" snr_t_p {np.sum((snr_t_p <= SNRmin[0])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_t_p {np.sum((sig_t_p >= sig_max[0])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_t_p in height interval {np.sum(indx_bad_t_p[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_t_m {np.sum((snr_t_m <= SNRmin[1])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_t_m {np.sum((sig_t_m >= sig_max[1])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_t_m in height interval {np.sum(indx_bad_t_m[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_x_p {np.sum((snr_x_p <= SNRmin[2])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_x_p {np.sum((sig_x_p >= sig_max[2])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_x_p in height interval {np.sum(indx_bad_x_p[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" snr_x_m {np.sum((snr_x_m <= SNRmin[3])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f" sig_x_m {np.sum((sig_x_m >= sig_max[3])[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") + logging.debug(f"> indx_bad_x_m in height interval {np.sum(indx_bad_x_m[cali_h_indx_range[0]:cali_h_indx_range[1]+1])}") continue - # Analyze segments for stability - #print('before analyze segments', len(dplus), segment_len) + ## Analyze segments for stability seg = analyze_segments(dplus, dminus, segment_len, rel_std_dplus, rel_std_dminus) if seg.shape[0] == 0: logging.warning(f'calibrateGHK no stable segment found in period {i_depol_cal}') continue - # translate manually - # min(sqrt((std_dplus_tmp./mean_dplus_tmp).^2 + (std_dminus_tmp./mean_dminus_tmp).^2)); + ## Translate manually + # Matlab code: min(sqrt((std_dplus_tmp./mean_dplus_tmp).^2 + (std_dminus_tmp./mean_dminus_tmp).^2)); indx_best_seg = np.argmin(np.sqrt((seg[:, 1]/seg[:, 0])**2 + (seg[:, 3]/seg[:, 2])**2)) # the best segment searching was flawed by the AI translate best_segment = seg[indx_best_seg] @@ -376,8 +412,8 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if not mean_dplus or not mean_dminus: logging.warning("Plus or minus 45° calibration is missing.") - #return pol_cali_eta, pol_cali_eta_std, pol_cali_start_time, pol_cali_stop_time, 0, global_attri - return {'status': 0} + # return [{'status': 0} for i in range(len(pol_cali_nang_start_time))] + return [{'status': 0}] pol_cali_eta = [float(1 / K * np.sqrt(dp * dm)) for dp, dm in zip(mean_dplus, mean_dminus)] pol_cali_eta_std = [float(0.5 * (dp * std_dm + dm * std_dp) / np.sqrt(dp * dm)) for @@ -389,6 +425,7 @@ def depol_cali_ghk(signal_t:np.ndarray, bg_t:np.ndarray, signal_x:np.ndarray, bg if collect_debug: results['global_attri'] = dict(global_attri) + return results @@ -419,45 +456,25 @@ def analyze_segments(dplus:np.ndarray, dminus:np.ndarray, segment_len:int, results = [] for i in range(len(dplus) - segment_len): - #print(i, i+segment_len) + # print(i, i+segment_len) seg_dplus = dplus[i:i + segment_len] seg_dminus = dminus[i:i + segment_len] + if np.sum(~np.isnan(seg_dplus)) <= segment_len / 4 or np.sum(~np.isnan(seg_dminus)) <= segment_len / 4: continue + mean_dp = np.nanmean(seg_dplus) std_dp = np.nanstd(seg_dplus) mean_dm = np.nanmean(seg_dminus) std_dm = np.nanstd(seg_dminus) - #print('mean_dp', mean_dp, 'std_dp', std_dp, '-> ', std_dp / mean_dp, rel_std_dplus) - #print('mean_dm', mean_dm, 'std_dm', std_dm, '-> ', std_dm / mean_dm, rel_std_dminus) + # print('mean_dp', mean_dp, 'std_dp', std_dp, '-> ', std_dp / mean_dp, rel_std_dplus) + # print('mean_dm', mean_dm, 'std_dm', std_dm, '-> ', std_dm / mean_dm, rel_std_dminus) if std_dp / mean_dp <= rel_std_dplus and std_dm / mean_dm <= rel_std_dminus: results.append([mean_dp, std_dp, mean_dm, std_dm]) - return np.array(results) - -""" - [data.polCaliEta532, data.polCaliEtaStd532, data.polCaliTime, data.polCali532Attri] = - pollyPolCaliGHK(data, PollyConfig.K(flag532t), flag532t, flag532c, wavelength, ... - 'depolCaliMinBin', PollyConfig.depol_cal_minbin_532, ... - 'depolCaliMaxBin', PollyConfig.depol_cal_maxbin_532, ... - 'depolCaliMinSNR', PollyConfig.depol_cal_SNRmin_532, ... - 'depolCaliMaxSig', PollyConfig.depol_cal_sigMax_532, ... - 'relStdDPlus', PollyConfig.rel_std_dplus_532, ... - 'relStdDMinus', PollyConfig.rel_std_dminus_532, ... - 'depolCaliSegLen', PollyConfig.depol_cal_segmentLen_532, ... - 'depolCaliSmWin', PollyConfig.depol_cal_smoothWin_532, ... - 'dbFile', dbFile, ... - 'pollyType', CampaignConfig.name, ... - 'flagUsePrevDepolConst', PollyConfig.flagUsePreviousDepolCali, ... - 'flagDepolCali', PollyConfig.flagDepolCali, ... - 'default_polCaliEta', PollyDefaults.polCaliEta532, ... - 'default_polCaliEtaStd', PollyDefaults.polCaliEtaStd532); - %print_msg('eta532.\n', 'flagTimestamp', true); - %data.polCaliEta532 - %Taking the eta with lowest standard deviation - [~, index_min] = min(data.polCali532Attri.polCaliEtaStd); - data.polCaliEta532=data.polCali532Attri.polCaliEta(index_min); -""" + + return np.asarray(results) + def calibrateMol(data_cube) -> dict: """Calibrate the polarization with the molecular signal. @@ -469,57 +486,70 @@ def calibrateMol(data_cube) -> dict: Returns ------- - dict - ... - + eta : ndarray + Polarization calibration eta. + etaStd : ndarray + Uncertainty of polarization calibration eta. + fac : array + Polarization calibration factor. + facStd : ndarray + Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good + time_start : int + Start time of the cloud free segment for the retrieval in unixtime. + end_time : int + End time of the cloud free segment for the retrieval in unixtime. + Notes ----- - - Converted from the matlab code to the best knowledge, but not cross-validated yet + - Converted from the matlab code to the best knowledge, but not cross-validated yet. - .. TODO:: had to calculate TR_t, TR_c again when calling depol_cali_mol() - .. TODO:: Finish docstring. - """ + .. TODO:: + - had to calculate TR_t, TR_c again when calling depol_cali_mol(). + - Finish docstring. + + ** History ** - #temp = {'eta': [], 'eta_std': [], 'fac': [], 'fac_std': [], - # 'time_start': [], 'time_end': [], 'status': 0} - pol_cali = defaultdict(lambda: defaultdict(list)) + - xx-xx-xxxx: First edition by ... + - xx-xx-xxxx: Translated to python + + """ + # temp = {'eta': [], 'eta_std': [], 'fac': [], 'fac_std': [], + # 'time_start': [], 'time_end': [], 'status': 0} + pol_cali = defaultdict(list) config_dict = data_cube.polly_config_dict for i, cldFree in enumerate(data_cube.clFreeGrps): - print(i, cldFree) cldFreeTime = np.array(data_cube.retrievals_highres['time'])[cldFree] - print(cldFreeTime) - #for wv in [355, 532, 1064]: + # for wv in [355, 532, 1064]: for wv, t, tel in [(532, 'total', 'FR'), (355, 'total', 'FR')]: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv, 'cross', tel)): logging.info(f'and even a {wv} channel') - sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][slice(*cldFree), :, data_cube.gf(wv, 'total', 'FR')]) - bg_total = np.squeeze(data_cube.retrievals_highres['BG'][slice(*cldFree), data_cube.gf(wv, 'total', 'FR')]) - sigBGCor_cross = np.squeeze(data_cube.retrievals_highres['sigBGCor'][slice(*cldFree), :, data_cube.gf(wv, 'cross', 'FR')]) - bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][slice(*cldFree), data_cube.gf(wv, 'cross', 'FR')]) - + # Extracting necessary data + sigBGCor_total = np.squeeze(data_cube.retrievals_profile['sigBGCor'][i, :, data_cube.gf(wv, 'total', 'FR')]).copy() + bg_total = np.squeeze(data_cube.retrievals_profile['BG'][i, data_cube.gf(wv, 'total', 'FR')]).copy() + sigBGCor_cross = np.squeeze(data_cube.retrievals_profile['sigBGCor'][i, :, data_cube.gf(wv, 'cross', 'FR')]).copy() + bg_cross = np.squeeze(data_cube.retrievals_profile['BG'][i, data_cube.gf(wv, 'cross', 'FR')]).copy() refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] - print(f'referenceH {wv} {t} {tel}', refHInd) + # Check for valid reference height if np.any(np.isnan(refHInd)): logging.info(f"skiping {wv} channel") continue - + ret = depol_cali_mol( - signal_t=sigBGCor_total[:, slice(*refHInd)], - background_t=bg_total, - signal_c=sigBGCor_cross[:, slice(*refHInd)], - background_c=bg_cross, - TR_t=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, t, tel)])), - TR_t_std=0, - TR_c=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, 'cross', tel)])), - TR_c_std=0, - minSNR=10, - mdr=config_dict[f'molDepol{wv}'], - mdrStd=config_dict[f'molDepolStd{wv}'], + signal_t=sigBGCor_total[refHInd[0]:refHInd[1]+1], background_t=bg_total, + signal_c=sigBGCor_cross[refHInd[0]:refHInd[1]+1], background_c=bg_cross, + TR_t=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, t, tel)])), TR_t_std=0, + TR_c=onemx_onepx(np.squeeze(data_cube.polly_config_dict['H'][data_cube.gf(wv, 'cross', tel)])), TR_c_std=0, + minSNR=10, mdr=config_dict[f'molDepol{wv}'], mdrStd=config_dict[f'molDepolStd{wv}'], + flagPicassoComparison=config_dict['flagPicassoComparison'] ) ret['time_start'] = int(cldFreeTime[0]) ret['time_end'] = int(cldFreeTime[1]) @@ -530,44 +560,49 @@ def calibrateMol(data_cube) -> dict: def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.ndarray, background_c:np.ndarray, - TR_t:float, TR_t_std:float, TR_c:float, TR_c_std:float, minSNR:float, mdr:float, mdrStd:float) -> dict: + TR_t:float, TR_t_std:float, TR_c:float, TR_c_std:float, minSNR:float, mdr:float, mdrStd:float, + flagPicassoComparison:bool=False) -> dict: """Molecular polarization calibration. Parameters ---------- - signal_t: numeric - Total signal (photon count). - background_t: numeric - Background at total channel (photon count). - signal_c: numeric - Cross signal (photon count). - background_c: numeric - Background at cross channel (photon count). - TR_t: scalar + signal_t : ndarray + Total signal [photon count]. + background_t : ndarray + Background at total channel [photon count]. + signal_c : ndarray + Cross signal [photon count]. + background_c : ndarray + Background at cross channel [photon count]. + TR_t : float Transmission ratio at total channel. - TR_t_std: scalar + TR_t_std : float Uncertainty of the transmission ratio at total channel. - TR_c: scalar + TR_c : float Transmission ratio at cross channel. - TR_c_std: scalar + TR_c_std : float Uncertainty of the transmission ratio at cross channel. - minSNR: float + minSNR : float The SNR constraint for the signal strength at reference height. - mdr: float + mdr : float Default molecular depolarization ratio. - mdrStd: float + mdrStd : float Default standard deviation of molecular depolarization ratio. Returns ------- - polCaliEta: array + eta : ndarray Polarization calibration eta. - polCaliEtaStd: array + etaStd : ndarray Uncertainty of polarization calibration eta. - polCaliFac: array + fac : array Polarization calibration factor. - polCaliFacStd: array + facStd : ndarray Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good References ---------- @@ -576,6 +611,8 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda Notes ----- + .. TODO:: + - The inputs TR_t_std & TR_c_std are currnetly not used in the function! **History** @@ -583,17 +620,15 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda - 2024-12-23: converted to python """ - polCaliEta = [] - polCaliEtaStd = [] - polCaliFac = [] - polCaliFacStd = [] - - #print(signal_t, signal_t) - sig_t = np.nansum(signal_t[:, :], axis=0) - bg_t = np.nansum(background_t[:], axis=0) * signal_t.shape[1] + + # Summing signal and background along height dim. + sig_t = np.nansum(signal_t, keepdims=True) + bg_t = background_t * signal_t.shape[0] + sig_c = np.nansum(signal_c, keepdims=True) + bg_c = background_c * signal_c.shape[0] + + # Calculate SNR SNR_TSig = calc_snr(sig_t, bg_t) - sig_c = np.nansum(signal_c[:, :], axis=0) - bg_c = np.nansum(background_c[:], axis=0) * signal_c.shape[1] SNR_CSig = calc_snr(sig_c, bg_c) # Check validity of signals @@ -601,16 +636,14 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda flagValidCSig = (SNR_CSig >= minSNR) if not np.all(flagValidTSig) or not np.all(flagValidCSig): - print("Too noisy at the reference height to enable molecular polarization calibration.") + logging.warning("Too noisy at the reference height to enable molecular polarization calibration.") return {'status': 0} - sig_t = np.nansum(sig_t) - bg_t = np.nansum(bg_t) - sig_c = np.nansum(sig_c) - bg_c = np.nansum(bg_c) - - std_sig_t = np.sqrt(sig_t + bg_t) - std_sig_c = np.sqrt(sig_c + bg_c) + std_sig_t = np.sqrt(sig_t + 2*bg_t) + std_sig_c = np.sqrt(sig_c + 2*bg_c) + if flagPicassoComparison: + std_sig_t = np.sqrt(sig_t + bg_t) + std_sig_c = np.sqrt(sig_c + bg_c) # Calculate derivatives for uncertainty propagation polCaliFacFunc = lambda x: (x / sig_c) * (1 + mdr * TR_t) / (1 + mdr * TR_c) @@ -632,7 +665,7 @@ def depol_cali_mol(signal_t:np.ndarray, background_t:np.ndarray, signal_c:np.nda polCaliEta = polCaliFac * (1 + TR_c) / (1 + TR_t) polCaliEtaStd = polCaliFacStd * (1 + TR_c) / (1 + TR_t) - print(polCaliEta, polCaliEtaStd, polCaliFac, polCaliFacStd) results = {'eta': float(polCaliEta), 'eta_std': float(polCaliEtaStd), 'fac': float(polCaliFac), 'fac_std': float(polCaliFacStd), 'status': 1} + return results \ No newline at end of file diff --git a/ppcpy/calibration/rayleighfit.py b/ppcpy/calibration/rayleighfit.py index b6f604e..dece333 100644 --- a/ppcpy/calibration/rayleighfit.py +++ b/ppcpy/calibration/rayleighfit.py @@ -26,8 +26,8 @@ def getVal(array:np.ndarray, indices:list) -> list: out : list Values of the array at given indices. If indx is NaN value is also NaN. - """ + if indices[0] >= indices[1]: raise ValueError('Invalid index pair.') @@ -63,8 +63,8 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: with the best fit to the molecular signal. - Currently, only the FR reference heights are calculated, while the NR reference heights are taken from the config files. - """ + # ..TODO:: is data.distance0 and height the same? https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/preprocess/pollyPreprocess.m#L299 # --> data.distance0 equals range, not height. However, Picasso uses a constant 7.5 m height resolution while PicassoPy uses the height resolution form the raw data dict ~ 7.475 m height = data_cube.retrievals_highres['range'] @@ -76,12 +76,12 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: if not data_cube.polly_config_dict['flagUseManualRefH']: for i, cldFree in enumerate(data_cube.clFreeGrps): - print(i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") refH_cldFree = {} for wv, t, tel in [(532, 'total', 'FR'), (355, 'total', 'FR'), (1064, 'total', 'FR')]: if np.any(data_cube.gf(wv, t, tel)): - print(f'refH for {wv}') + logging.info(f"Channel: {wv} {t} {tel}.") # Extract signal rcs = np.squeeze(data_cube.retrievals_profile['RCS'][i, :, data_cube.gf(wv, t, tel)]) @@ -113,7 +113,7 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: window_size=config_dict[f'decomSmoothWin{wv}'], showDetails=collect_debug ) - print('DPInd:', DPInd) + logging.debug(f"DPInd: {DPInd}.") # Rayleigh fitting refInd = fit_profile( @@ -129,7 +129,7 @@ def rayleighfit(data_cube, collect_debug:bool=False) -> list: flagShowDetail=collect_debug, flagPicassoComparison=config_dict['flagPicassoComparison'] ) - print('refInd:', refInd) + logging.debug(f"refInd: {refInd}") refHeight = getVal(data_cube.retrievals_highres['height'], refInd) refRange = getVal(data_cube.retrievals_highres['range'], refInd) @@ -215,6 +215,7 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-04-17: Changed to ppcpy.misc.helper.moving_average to get values at edges. """ + # return uniform_filter1d(signal, window_len, mode='nearest') return moving_average(signal, window_len) @@ -240,7 +241,7 @@ def DouglasPeucker(signal:np.ndarray, height:np.ndarray, epsilon:float, heightBa window_size : int Size of the average smooth window. Default is 1. showDetails : bool - If true, print debug information. Default is False. + If true, collect debug information. Default is False. Returns ------- @@ -262,7 +263,8 @@ def DouglasPeucker(signal:np.ndarray, height:np.ndarray, epsilon:float, heightBa - 2024-12-20: Direct translated from matlab with ai """ - #print('height', height[:30], 'epsilon', epsilon, 'height', heightBase, heightTop, 'maxHThick', maxHThick, 'window_size', window_size) + + logging.debug(f"height {height[:30]}, epsilon {epsilon}, height range [{heightBase} - {heightTop}], maxHThick {maxHThick}, window_size {window_size}") # Input check if len(signal) != len(height): @@ -319,8 +321,8 @@ def DP_algorithm(pointList:list, epsilon:float, maxHThick:float, recursive_depth ------- sigIndx : list Indices of simplified points. - """ + if len(pointList) == 1: if showDetails: print(f'Recursion: {recursive_depth}, len(pointList) == 1, returning [0]') return [0] @@ -386,8 +388,8 @@ def my_dist(pointM:list, pointS:list, pointE:list, full:bool=True) -> float: ----- - If pointS and pointE are constant only the numerator is neede to calculate the extreme points with respect to pointM. - """ + num = abs(pointM[1] - pointS[1] + (pointS[1] - pointE[1]) / \ (pointS[0] - pointE[0]) * (pointS[0] - pointM[0])) if full: @@ -437,6 +439,7 @@ def chi2fit(x:np.ndarray, y:np.ndarray, measure_error:np.ndarray) -> tuple: -------- ``a, b, sigmaA, sigmaB, chi2, Q = chi2fit(x, y, measure_error)`` """ + if len(x) != len(y): raise ValueError("Array lengths of x and y must agree.") @@ -521,8 +524,8 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar residual calculations due to the subtraction and mean of very small numbers [-1e-24, 1e-24]. these numerical discrepancies may cause a different reference height to be chosen compared to Picasso. - """ + # if len([height, sig_aer, pc, bg, sig_mol, dpIndx]) < 6: # #if len([height, sig_aer, pc, bg, sig_mol, dpIndx]) < 6: # raise ValueError('Not enough inputs.') @@ -532,7 +535,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar raise ValueError('sig_aer and sig_mol must be 1-dimensional array') if dpIndx is None or len(dpIndx) == 0: - print('Warning: dpIndx is empty') + logging.warning('dpIndx is empty!') return np.nan, np.nan # parameter initialize @@ -551,7 +554,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar test1 = test2 = test3 = test4 = test5 = True iDpBIndx = dpIndx[iIndx] iDpTIndx = dpIndx[iIndx + 1] # + 1 # matlab slicing issue?? - #print(iIndx, iDpBIndx, iDpTIndx) + # print(iIndx, iDpBIndx, iDpTIndx) # check layer thickness if not ((height[iDpTIndx] - height[iDpBIndx]) > layerThickConstrain): @@ -566,11 +569,11 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar sig_factor = np.nanmean(sig_mol[iDpBIndx:iDpTIndx+1]) / \ np.nanmean(sig_aer[iDpBIndx:iDpTIndx+1]) - #print('sig factor ', sig_factor) + # print('sig factor ', sig_factor) sig_aer_norm = sig_aer * sig_factor std_aer_norm = sig_aer_norm / np.sqrt(pc + bg) - #print('sig_aer_norm ', sig_aer_norm.shape, sig_aer_norm[iDpBIndx:iDpTIndx+1]) - #print('std_aer_norm ', std_aer_norm.shape, std_aer_norm[iDpBIndx:iDpTIndx+1]) + # print('sig_aer_norm ', sig_aer_norm.shape, sig_aer_norm[iDpBIndx:iDpTIndx+1]) + # print('std_aer_norm ', std_aer_norm.shape, std_aer_norm[iDpBIndx:iDpTIndx+1]) # Quality test 2: near and far - range cross criteria winLen = int(layerThickConstrain / (height[1] - height[0])) @@ -596,15 +599,15 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar continue # Quality test 3: white-noise criterion - #print('white noise criterion ', iDpBIndx, iDpTIndx) + # print('white noise criterion ', iDpBIndx, iDpTIndx) residual = (sig_aer_norm[iDpBIndx:iDpTIndx+1] - sig_mol[iDpBIndx:iDpTIndx+1]) - #print(sig_aer_norm[iDpBIndx], sig_mol[iDpBIndx], sig_aer_norm[iDpBIndx]-sig_mol[iDpBIndx]) - #print(sig_aer_norm[iDpBIndx+1], sig_mol[iDpBIndx+1], sig_aer_norm[iDpBIndx+1]-sig_mol[iDpBIndx+1]) - #print(sig_aer_norm[iDpBIndx+2], sig_mol[iDpBIndx+2], sig_aer_norm[iDpBIndx+2]-sig_mol[iDpBIndx+2]) - #print(sig_aer_norm[iDpBIndx+3], sig_mol[iDpBIndx+3], sig_aer_norm[iDpBIndx+3]-sig_mol[iDpBIndx+3]) - #print('residual ', residual.shape, residual) + # print(sig_aer_norm[iDpBIndx], sig_mol[iDpBIndx], sig_aer_norm[iDpBIndx]-sig_mol[iDpBIndx]) + # print(sig_aer_norm[iDpBIndx+1], sig_mol[iDpBIndx+1], sig_aer_norm[iDpBIndx+1]-sig_mol[iDpBIndx+1]) + # print(sig_aer_norm[iDpBIndx+2], sig_mol[iDpBIndx+2], sig_aer_norm[iDpBIndx+2]-sig_mol[iDpBIndx+2]) + # print(sig_aer_norm[iDpBIndx+3], sig_mol[iDpBIndx+3], sig_aer_norm[iDpBIndx+3]-sig_mol[iDpBIndx+3]) + # print('residual ', residual.shape, residual) x = height[iDpBIndx:iDpTIndx+1] / 1e3 if len(residual) <= 10: @@ -614,10 +617,10 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue # Note: chi2fit implementation needed here - #print('white noise chi2fit input', np.nanmean(x), np.nanmean(residual), np.nanmean(std_aer_norm[iDpBIndx:iDpTIndx+1])) + # print('white noise chi2fit input', np.nanmean(x), np.nanmean(residual), np.nanmean(std_aer_norm[iDpBIndx:iDpTIndx+1])) thisIntersect, thisSlope, _, _, _, _ = chi2fit( x, residual, std_aer_norm[iDpBIndx:iDpTIndx+1]) - #print('white noise chi2fit ', thisIntersect, thisSlope) + # print('white noise chi2fit ', thisIntersect, thisSlope) residual_fit = thisIntersect + thisSlope * x et = residual - residual_fit @@ -628,21 +631,21 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar print(f'Region {iIndx}: {height[iDpBIndx]} - ' f'{height[iDpTIndx]} fails in white-noise criterion.') test3 = False - #continue + # continue # Quality test 4: SNR check sigsum = np.nansum(pc[dpIndx[iIndx]:dpIndx[iIndx+1]+1]) # adaption needed for the background not given as a profile bgsum = bg * (dpIndx[iIndx + 1] - dpIndx[iIndx]) SNR = sigsum / np.sqrt(sigsum + 2*bgsum) - #print('SNR', sigsum, '/', bgsum, '=', SNR) + # print('SNR', sigsum, '/', bgsum, '=', SNR) if SNR < SNRConstrain: if flagShowDetail: print(f'Region {iIndx}: {height[iDpBIndx]} - ' f'{height[iDpTIndx]} fails in SNR criterion.') test4 = False - #continue + # continue # Quality test 5: slope check x = height[iDpBIndx:iDpTIndx+1] @@ -667,9 +670,9 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue _, aerSlope, _, deltaAerSlope, _, _ = chi2fit(x, y_aer, std_y_aer) - #print('aer chi2fit', aerSlope, deltaAerSlope) + # print('aer chi2fit', aerSlope, deltaAerSlope) _, molSlope, _, deltaMolSlope, _, _ = chi2fit(x, y_mol, np.zeros_like(x)) - #print('mol chi2fit', molSlope, deltaMolSlope) + # print('mol chi2fit', molSlope, deltaMolSlope) slope_condition = (molSlope <= (aerSlope + (deltaAerSlope + deltaMolSlope) * slopeConstrain) and @@ -686,7 +689,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar #continue if not (test1 and test2 and test3 and test4 and test5): - print("one tests failed?") + logging.info("One tests failed") continue # save statistics @@ -730,7 +733,7 @@ def fit_profile(height:np.ndarray, sig_aer:np.ndarray, pc:np.ndarray, bg:np.ndar else: # QuicFix for all NaN arrays: if np.isnan(X_val).all(): - print("None valid clean region found.") + logging.warning("None valid clean region found.") return np.nan, np.nan indxBest_Int = np.nanargmin(X_val) diff --git a/ppcpy/calibration/select.py b/ppcpy/calibration/select.py index c513d1d..0e5c9e1 100644 --- a/ppcpy/calibration/select.py +++ b/ppcpy/calibration/select.py @@ -6,10 +6,9 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict: - """Select the best calibration constant + """Select the best calibration constant. - - generalization of lidarconstant.get_best_LC + Generalization of lidarconstant.get_best_LC Parameters ---------- @@ -22,27 +21,26 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict relative : bool If true, choose calibration constant based on the relative error. - Returns ------- best : dict Lidar constants/Etas with lowest standard deviation per channel. - - Notes - ----- - Since ``LC = LC_stable`` and ``LCStd = LC_stable * LC_Std`` so will any negative LC also have - a negative LCStd, and thus be chosen as the best LC. **History** - 2026-02-16: Added additional checks to hinder negative LCs to be chosen. - 2026-03-27: generalized to also hold for depolarization calibration + """ best = {} for k, l in d.items(): - val = np.array([e[name_val] for e in l if e[name_val] >= 0]) - min = np.array([e[name_min] for e in l if e[name_val] >= 0]) + val = np.array([e[name_val] for e in l if name_val in e and e[name_val] >= 0]) + min = np.array([e[name_min] for e in l if {name_min, name_val}.issubset(e.keys()) and e[name_val] >= 0]) + + if len(val) == 0 or len(min) == 0: + best[k] = np.nan + continue if relative: best[k] = val[np.argmin(min / val)] @@ -52,18 +50,24 @@ def single_best(d:dict, name_val:str, name_min:str, relative:bool=False) -> dict return best -def plot_cals(d, param, used=None): - """plot the calibration constants +def plot_cals(d:dict, param:str, used:dict=None) -> tuple: + """Plot the calibration constants. Parameters ---------- d : dict - the dict as in data_cube + The dict as in data_cube. param : str - the parameter to extract - used : dict - the LCused or etaused (will produce a dashed line in the plot) - + The parameter to extract. + used : dict, optional + The LCused or etaused (will produce a dashed line in the plot). + + Returns + ------- + fig : figure + Matplotlib figure object. + ax : axis + Matplotlib axis object. Examples -------- diff --git a/ppcpy/cloudmask/cloudscreen.py b/ppcpy/cloudmask/cloudscreen.py index aa0cb53..40f2b81 100644 --- a/ppcpy/cloudmask/cloudscreen.py +++ b/ppcpy/cloudmask/cloudscreen.py @@ -1,6 +1,7 @@ import numpy as np -from scipy.ndimage import label, uniform_filter1d -from ppcpy.misc.helper import uniform_filter, savgol_filter +from scipy.ndimage import label +from ppcpy.misc.helper import savgol_filter, moving_average +from ppcpy.retrievals.collection import calc_snr import matplotlib.pyplot as plt import logging @@ -28,10 +29,11 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter - 2026-03-17: Changed back to scipy.ndimage.uniform_filter1d due to NaN-related issues in Zhao's cloud screening algorithm. + - 2026-07-24: Changed to ppcpy.misc.helper.moving_average. + """ - # return uniform_filter(signal, window_len) - return uniform_filter1d(signal, window_len, mode='nearest') # <- Should switch to the incoming moving average filter here! + return moving_average(signal, window_len) def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: @@ -68,10 +70,10 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: - xxxx-xx-xx: First edition by ... - 2026-03-18: Updated to include necessary configurations for cloudScreen_Zhao. - 2026-04-09: Added 'maxCloudSearchHeight' config parameters. - - 2026-04-20: Added option for no cloud screening ie. 'cloudScreenMode' = 0 + - 2026-04-20: Added option for no cloud screening ie. 'cloudScreenMode' = 0. + """ - print('Starting cloud screen') config_dict = data_cube.polly_config_dict height = data_cube.retrievals_highres['range'] bg = np.squeeze(data_cube.retrievals_highres['BG'][:, data_cube.gf(wv, 'total', 'FR')]) @@ -97,7 +99,8 @@ def cloudscreen(data_cube, wv:int=532, collect_debug:bool=False) -> np.ndarray: 'bg': bg, 'minSNR': 2, 'heightFullOverlap': hFullOL, - 'showDetails': collect_debug + 'showDetails': collect_debug, + 'flagPicassoComparison': config_dict['flagPicassoComparison'] } else: raise ValueError(f"cloudScreenMode {config_dict['cloudScreenMode']} not properly defined.") @@ -162,6 +165,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea - 2021-05-18: First edition by Zhenping. - 2025-03-20: Translated into python. + """ if len(search_region) != 2 or search_region[1] <= height[0]: @@ -192,7 +196,7 @@ def cloudScreen_MSG(height:np.ndarray, signal:np.ndarray, slope_thres:float, sea def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search_region:list=[0, 10000], minDepth:float=100, heightFullOverlap:float=600, smoothWin:int=7, minSNR:float=1, - showDetails:bool=False) -> tuple: + showDetails:bool=False, flagPicassoComparison:bool=False) -> tuple: """Cloud layer detection based on Zhao's algorithm. Parameters @@ -231,6 +235,7 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search - 2021-05-18: First edition by Zhengping. - 2026-03-11: Translated into python. + """ if (signal.shape[1] != height.shape[0] or @@ -260,7 +265,8 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search minHeight=heightFullOverlap / 1e3, smoothWin=smoothWin, minSNR=minSNR, - showDetails=showDetails + showDetails=showDetails, + flagPicassoComparison=flagPicassoComparison ) for iLayer in range(len(layerInfo)): @@ -282,7 +288,7 @@ def cloudScreen_Zhao(height:np.ndarray, signal:np.ndarray, bg:np.ndarray, search def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float=0.2, minHeight:float=0.4, smoothWin:int=3, savgolSmoothWin:int=9, minSNR:int=5, - showDetails:bool=False) -> tuple: + showDetails:bool=False, flagPicassoComparison:bool=False) -> tuple: """Cloud layer detection with VDE method. This method only required elstic signal. Parameters @@ -342,6 +348,8 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= - 2021-06-13: First edition by Zhenping. - 2026-03-11: Translated into pyhton. - 2026-04-09: Enhanced robustness against NaN values. + - 2026-07-28: Fixed SNR / noise calculations. + """ if np.floor(minLayerDepth / (height[1] - height[0])) < 3: @@ -357,7 +365,9 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= # ---------------------------------------------------- # 1. Semi-Discretization Process (SDP) # ---------------------------------------------------- - noise = np.sqrt(P + BG)*3 + noise = np.sqrt(P + 2*BG)*3 + if flagPicassoComparison: + noise = np.sqrt(P + BG)*3 min_noise = np.ones_like(P)*np.sqrt(3)*3 noise_tresh = np.nanmax(np.vstack([noise, min_noise]), axis=0) Ps = smooth_signal(P, smoothWin) @@ -425,7 +435,9 @@ def VDE_cld(signal:np.ndarray, height:np.ndarray, BG:float, minLayerDepth:float= layerDepth = height[topIndex] - height[baseIndex] layerIndex = (L == iLayer) - layerSNR = np.nanmean(P[layerIndex]) / np.sqrt(np.nanmean(P[layerIndex] + BG)) + layerSNR = calc_snr(np.nansum(P[layerIndex], keepdims=True), BG*np.sum(layerIndex)) + if flagPicassoComparison: + layerSNR = np.nanmean(P[layerIndex]) / np.sqrt(np.nanmean(P[layerIndex] + BG)) if showDetails: print(f"layer: {iLayer} at height [{height[baseIndex]:.3f}, {height[topIndex]:.3f}] km, Depth: {layerDepth:.3f} >= {minLayerDepth}, SNR: {layerSNR:.3f} >= {minSNR}") diff --git a/ppcpy/cloudmask/profilesegment.py b/ppcpy/cloudmask/profilesegment.py index 34b7c72..1a06046 100644 --- a/ppcpy/cloudmask/profilesegment.py +++ b/ppcpy/cloudmask/profilesegment.py @@ -1,14 +1,15 @@ import numpy as np +import logging from scipy.ndimage import label def segment(data_cube) -> np.ndarray: - """Perform cloud free profile segmentation + """Perform cloud free profile segmentation. Parameters ---------- data_cube : object - Main PicassoProc object + Main PicassoProc object. Returns ------- @@ -19,34 +20,34 @@ def segment(data_cube) -> np.ndarray: - xxxx-xx-xx: First edition by ... - 2026-03-18: Updated flagValPrf to include 'shutterOnMask' and 'fogMask'. + """ config_dict = data_cube.polly_config_dict flagValPrf = data_cube.flagCloudFree & (~data_cube.retrievals_highres['depCalMask']) \ & (~data_cube.retrievals_highres['shutterOnMask']) & (~data_cube.retrievals_highres['fogMask']) - print('intNProfiles', config_dict['intNProfiles'], 'minIntNProfiles', config_dict['minIntNProfiles']) + logging.info(f"intNProfiles: {config_dict['intNProfiles']}, minIntNProfiles: {config_dict['minIntNProfiles']}") clFreeGrps = clFreeSeg(flagValPrf, config_dict['intNProfiles'], config_dict['minIntNProfiles']) return clFreeGrps - -def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): - """splits continuous cloud-free profiles into small sections. +def clFreeSeg(prfFlag:np.ndarray, nIntPrf:int, minNIntPrf:int) -> np.ndarray: + """Splits continuous cloud-free profiles into small sections. Parameters ---------- - prfFlag: array-like (boolean) + prfFlag : array-like (boolean) Cloud-free flags for each profile. - nIntPrf: int + nIntPrf : int Number of integral profiles. - minNIntPrf: int + minNIntPrf : int Minimum number of integral profiles. Returns ------- - clFreSegs: 2D numpy array + clFreSegs : 2D ndarray Start and stop indexes for each cloud-free section. [[start1, stop1], [start2, stop2], ...] @@ -57,15 +58,15 @@ def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): - 2021-05-22: First edition by Zhenping - 2025-03-20: Translated to python + """ # Label contiguous cloud-free segments clFreGrpTag, nClFreGrps = label(prfFlag.astype(int)) - clFreSegs = [] if nClFreGrps == 0: - print("No cloud-free segments were found.") + logging.warning("No cloud-free segments were found.") else: for iClFreGrp in range(1, nClFreGrps + 1): iClFreGrpInd = np.where(clFreGrpTag == iClFreGrp)[0] @@ -91,7 +92,5 @@ def clFreeSeg(prfFlag, nIntPrf, minNIntPrf): ]).T clFreSegs.extend(subClFreGrp.tolist()) + return np.array(clFreSegs, dtype=int) if clFreSegs else np.empty((0, 2), dtype=int) - - - diff --git a/ppcpy/config/json2nc-mapper_NR_att_bsc.json b/ppcpy/config/json2nc-mapper_NR_att_bsc.json index e24db75..1f14ff8 100644 --- a/ppcpy/config/json2nc-mapper_NR_att_bsc.json +++ b/ppcpy/config/json2nc-mapper_NR_att_bsc.json @@ -143,7 +143,7 @@ "unit": "", "long_name": "near-field quality mask for attenuated backscatter at 355 nm", "standard_name": "quality_mask_355", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_532nm": { @@ -158,7 +158,7 @@ "unit": "", "long_name": "near-field quality mask for attenuated backscatter at 532 nm", "standard_name": "quality_mask_532", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "SNR_355nm": { @@ -173,7 +173,7 @@ "unit": "", "long_name": "SNR at 355 nm", "standard_name": "near-field signal-noise-ratio 355 nm", - "comment": "" + "comment": "Near-field signal-noise-ratio at 355 nm used for quality mask" } }, "SNR_532nm": { @@ -188,7 +188,7 @@ "unit": "", "long_name": "SNR at 532 nm", "standard_name": "near-field signal-noise-ratio 532 nm", - "comment": "" + "comment": "Near-field signal-noise-ratio at 532 nm used for quality mask" } } } diff --git a/ppcpy/config/json2nc-mapper_att_bsc.json b/ppcpy/config/json2nc-mapper_att_bsc.json index d156f95..b0c5d72 100644 --- a/ppcpy/config/json2nc-mapper_att_bsc.json +++ b/ppcpy/config/json2nc-mapper_att_bsc.json @@ -164,7 +164,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 355 nm", "standard_name": "quality_mask_355", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 355 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_532nm": { @@ -179,7 +179,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 532 nm", "standard_name": "quality_mask_532", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 532 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "quality_mask_1064nm": { @@ -194,7 +194,7 @@ "unit": "", "long_name": "quality mask for attenuated backscatter at 1064 nm", "standard_name": "quality_mask_1064", - "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 1064 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog)" + "comment": "This variable can be used to filter noisy pixels of attenuated backscatter at 1064 nm. (0: good data; 1: low SNR; 2: depolarization calibration periods; 3: shutter on; 4: fog, 5: saturated)" } }, "SNR_355nm": { @@ -209,7 +209,7 @@ "unit": "", "long_name": "SNR at 355 nm", "standard_name": "signal-noise-ratio 355 nm", - "comment": "" + "comment": "Signal-noise-ratio at 355 nm used for quality mask." } }, "SNR_532nm": { @@ -224,7 +224,7 @@ "unit": "", "long_name": "SNR at 532 nm", "standard_name": "signal-noise-ratio 532 nm", - "comment": "" + "comment": "Signal-noise-ratio at 532 nm used for quality mask." } }, "SNR_1064nm": { @@ -239,7 +239,7 @@ "unit": "", "long_name": "SNR at 1064 nm", "standard_name": "signal-noise-ratio 1064 nm", - "comment": "" + "comment": "Signal-noise-ratio at 1064 nm used for the quality mask." } } } diff --git a/ppcpy/config/json2nc_translator.json b/ppcpy/config/json2nc_translator.json index 62eb65a..fb9fe09 100644 --- a/ppcpy/config/json2nc_translator.json +++ b/ppcpy/config/json2nc_translator.json @@ -710,13 +710,13 @@ "parameter":"attBsc_1064_total_FR" }, "SNR_355nm": { - "parameter":"SNR_FR_total_355nm" + "parameter":"flag_355_total_FR" }, "SNR_532nm": { - "parameter":"SNR_FR_total_532nm" + "parameter":"flag_532_total_FR" }, "SNR_1064nm": { - "parameter":"SNR_FR_total_1064nm" + "parameter":"flag_1064_total_FR" }, "quality_mask_355nm": { "parameter":"flag_355_total_FR" @@ -744,10 +744,10 @@ "parameter":"attBsc_532_total_NR" }, "SNR_355nm": { - "parameter":"SNR_NR_total_355nm" + "parameter":"flag_355_total_NR" }, "SNR_532nm": { - "parameter":"SNR_NR_total_532nm" + "parameter":"flag_532_total_NR" }, "quality_mask_355nm": { "parameter":"flag_355_total_NR" diff --git a/ppcpy/config/polly_global_config.json b/ppcpy/config/polly_global_config.json index 94ad0cc..bddcafb 100644 --- a/ppcpy/config/polly_global_config.json +++ b/ppcpy/config/polly_global_config.json @@ -220,6 +220,7 @@ "LCMeanMaxIndx": 1000, "LCCalibrationStatus": ["none", "Klett", "Raman", "Defaults", "History"], "flagUseRetrievedExt4LCCalc": true, + "flagUseImprovedSNR": true, "quasi_smooth_h": [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], "quasi_smooth_t": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10], "flagOnlyUseValidQuasiData": true, diff --git a/ppcpy/interface/picassoProc.py b/ppcpy/interface/picassoProc.py index e8abdc3..67169dd 100644 --- a/ppcpy/interface/picassoProc.py +++ b/ppcpy/interface/picassoProc.py @@ -11,7 +11,6 @@ import ppcpy.qc.overlapCor as overlapCor import ppcpy.qc.qualityMask as qualityMask - import ppcpy.calibration.select as select import ppcpy.calibration.polarization as polarization import ppcpy.cloudmask.cloudscreen as cloudscreen @@ -33,26 +32,62 @@ import ppcpy.io.sql_interaction as sql_db + class PicassoProc: + """Picasso Processor. + + This class is responsible for perfoming the processing of PollyXT data. + """ counter = 0 def __init__(self, rawdata_dict:dict, polly_config_dict:dict, picasso_config_dict:dict): - """Initialize the data_cube. + """Initialize the PicassoProc object. Parameters ---------- rawdata_dict : dict - The dict returned by readPollyRawData.readPollyRawData(filename=rawfile) + The dict returned by readPollyRawData.readPollyRawData(filename=rawfile). polly_config_dict : dict - The configuration specific to the specific polly loadConfigs.loadPollyConfig(polly_config_file_fullname, polly_default_config_file) + The configuration specific to the specific polly loadConfigs.loadPollyConfig(polly_config_file_fullname, polly_default_config_file). picasso_config_dict : dict - The general picasso config loadConfigs.loadPicassoConfig(args.picasso_config_file,picasso_default_config_file) + The general picasso config loadConfigs.loadPicassoConfig(args.picasso_config_file,picasso_default_config_file). + + Yields + ------ + self.rawfile : str + Path to level0-file. + self.rawdata_dict : dict + The input parameter `rawdata_dict`. + self.polly_config_dict : dict + The input parameter `polly_config_dict`. + self.picasso_config_dict : dict + The input parameter `picasso_config_dict`. + self.device : str + Name of pollyXT device. + self.location : ... + Location of PollyXT device during the measurement. + self.date : str + Measurement date. + self.num_of_channels : int + Number of measurment channels. + self.num_of_profiles : int + Number of profiles in the measurment (time-dimension). + self.retrievals_highres : dict + Dictionary to store high resolution time, height data. + self.retrievals_profile : dict + Dictionary to store profile date. + self.retrievals_profile['avail_optical_profiles'] : list + Availabele optical profiles. + self.pol_cali : dict + Dictionary to store polarization calibration constants. + self.LC : dict + Dictionary to store lidar calibration constants. Notes ----- - - The `polly_default_dict` is not longer available as a separate variable, but is included into the `polly_config_dict` - + - The `polly_default_dict` is not longer available as a separate variable, but is included into the `polly_config_dict`. """ + type(self).counter += 1 self.rawfile = rawdata_dict['filename_path'] self.rawdata_dict = rawdata_dict @@ -66,21 +101,28 @@ def __init__(self, rawdata_dict:dict, polly_config_dict:dict, picasso_config_dic self.retrievals_highres = {} self.retrievals_profile = {} self.retrievals_profile['avail_optical_profiles'] = [] - self.pol_cali = {} self.LC = {} - def mdate_filename(self): - """Get the date from filename in YYYYMMDD.""" + def mdate_filename(self) -> str: + """Get the date from filename in YYYYMMDD. + + Returns + ------- + str + Measurement date. + """ + filename = self.rawdata_dict['filename'] - mdate = re.split(r'_',filename)[0:3] + mdate = re.split(r'_', filename)[0:3] YYYY = mdate[0] MM = mdate[1] DD = mdate[2] return f"{YYYY}{MM}{DD}" - def gf(self, wavelength, meth, telescope): + + def gf(self, wavelength:float|int|str, meth:str, telescope:str) -> np.ndarray: """Get flag shorthand. i.e., the following two calls are equivalent @@ -94,44 +136,63 @@ def gf(self, wavelength, meth, telescope): Parameters ---------- - wavelength - wavelength tag - meth - method - telescope - telescope + wavelength : float or int or str + Wavelength tag. + meth : str + Method type. + telescope : str + Telescope type. Returns ------- - array - with bool flag - + ndarray + With bool flag """ + return getattr(self, f'flag_{wavelength}_{meth}_{telescope}', False) + # def msite(self): # #msite = f"measurement site: {self.rawdata_dict['global_attributes']['location']}" # msite = self.polly_config_dict['site'] # logging.info(f'measurement site: {msite}') # return msite # +# # def device(self): # device = self.polly_config_dict['name'] # logging.info(f'measurement device: {device}') # return device # +# # def mdate(self): # mdate = self.mdate_filename() # logging.info(f'measuremnt date: {mdate}') # return mdate - def mdate_infile(self): - """First date in file as string.""" + + def mdate_infile(self) -> str: + """First date in file as string. + + Returns + ------- + str + Measurement date. + """ + mdate_infilename = self.rawdata_dict['measurement_time']['var_data'][0][0] return f"{mdate_infilename}" - def check_for_correct_mshots(self): - """Check if mshots are more than 1.1 * laser_prf * deltatime or smaller 0.""" + + def check_for_correct_mshots(self) -> np.ndarray: + """Check if mshots are more than 1.1 * laser_prf * deltatime or smaller 0. + + Returns + ------- + condition_check_matrix : ndarray + Boolean array. True for elements where the condition above holds, otherwise False. + """ + laser_rep_rate = self.rawdata_dict['laser_rep_rate']['var_data'] mShotsPerPrf = laser_rep_rate * self.polly_config_dict['deltaT'] mShots = self.rawdata_dict['measurement_shots']['var_data'] @@ -141,27 +202,80 @@ def check_for_correct_mshots(self): return condition_check_matrix + def filter_or_correct_false_mshots(self): """Filter or correct the mshots (currently only logging). .. TODO:: that might be covered via the mcps conversion + --> How exactly??? + + Yields + ------ + self.rawdata_dict : dict + With filtered or corrected enteries for timestamps where `data_cube.check_for_correct_mshots()=True`. + + Notes + ----- + - Now includes a first try for the function. I Do not know if it actually is needed yet. + + .. TODO:: Not yet crosschecked with the matlab version. + + ** History ** + + - 2026-06-24: Translated to python """ - logging.info(f"flagFilterFalseMShots: {self.polly_config_dict['flagFilterFalseMShots']}") - logging.info(f"flagCorrectFalseMShots: {self.polly_config_dict['flagCorrectFalseMShots']}") + + ## Extract the necesary information + laser_rep_rate = self.rawdata_dict['laser_rep_rate']['var_data'] + mShotsPerPrf = laser_rep_rate * self.polly_config_dict['deltaT'] + mShots = self.rawdata_dict['measurement_shots']['var_data'] + mTime = self.rawdata_dict['measurement_time']['var_data'] + rawSig = self.rawdata_dict['raw_signal']['var_data'] + condition_check_matrix = self.check_for_correct_mshots() + condition_check_matrix = np.any(condition_check_matrix, axis=0) + + ## Filter out timesteps with faulty measurement shots if self.polly_config_dict['flagFilterFalseMShots']: logging.info('filtering false mshots') + + if np.sum(~condition_check_matrix) == 0: + logging.critical('No profile with mshots < 1e6 and mshots > 0 was found Please take a look inside level0 file.') + # raise ValueError('No profile with mshots < 1e6 and mshots > 0 was found Please take a look inside level0 file.') + return + + rawSig = rawSig[~condition_check_matrix] + mShots = mShots[~condition_check_matrix] + mTime = mTime[~condition_check_matrix] + if 'depol_cal_angle' in self.rawdata_dict: + self.rawdata_dict['depol_cal_angle']['var_data'] \ + = self.rawdata_dict['depol_cal_angle']['var_data'][~condition_check_matrix] + + ## Correct timesteps with faulty measurement shots elif self.polly_config_dict['flagCorrectFalseMShots']: logging.info('correcting false mshots') - condition_check_matrix = self.check_for_correct_mshots() - ##TODO - return self + mShots[condition_check_matrix] = mShotsPerPrf + # .. TODO:: In the matlab vesion this also does a fix to mTime. I do not know if this is necesary yet. + + ## Overwrite the filtered / corrected information + self.rawdata_dict['measurement_shots']['var_data'] = mShots + self.rawdata_dict['measurement_time']['var_data'] = mTime + self.rawdata_dict['raw_signal']['var_data'] = rawSig + def mdate_consistency(self) -> bool: - """Check mdate consistency.""" + """Check mdate consistency. + + Returns + ------- + bool + True, if measurement date of file name equals + that of in the file, otherwise False. + """ + if self.mdate_filename() == self.mdate_infile(): logging.info('... date in nc-file equals date of filename') return True @@ -169,8 +283,20 @@ def mdate_consistency(self) -> bool: logging.warning('... date in nc-file differs from date of filename') return False + def reset_date_infile(self): - """Correct the date in the file.""" + """Correct the date in the file. + + Yields + ------ + self.rawdata_dict['measurement_time']['var_data'] : ndarray + Updated .... + + Notes + ----- + .. TODO :: Finish docstring. + """ + logging.info('date consistency-check... ') if self.mdate_consistency() == False: logging.info('date in nc-file will be replaced with date of filename.') @@ -178,7 +304,7 @@ def reset_date_infile(self): mdate = self.mdate_filename() np_array[:, 0] = mdate ## assign new date value to the whole first column of the 2d-numpy-array self.rawdata_dict['measurement_time']['var_data'] = np_array - return self + def setChannelTags(self): """Set the channel tags. @@ -204,11 +330,61 @@ def setChannelTags(self): data_cube.flag_355_total_FR ``` - Returns - ------- - self - + Yields + ------ + self.retrievals_highres['channel'] : list + Updated channel tags. + self.polly_config_dict['channelTags'] : list + updated channel tags. + self.flag_355_total_FR : ndarray + True if the channel is `355nm total FR` else False. + self.flag_355_cross_FR : ndarray + True if the channel is `355nm cross FR` else False. + self.flag_355_parallel_FR : ndarray + True if the channel is `355nm parallel FR` else False. + self.flag_355_total_NR : ndarray + True if the channel is `355nm total NR` else False. + self.flag_387_total_FR : ndarray + True if the channel is `387nm total FR` else False. + self.flag_387_total_NR : ndarray + True if the channel is `387nm total NR` else False. + self.flag_407_total_FR : ndarray + True if the channel is `407nm total FR` else False. + self.flag_407_total_NR : ndarray + True if the channel is `407nm total NR` else False. + self.flag_532_total_FR : ndarray + True if the channel is `532nm total FR` else False. + self.flag_532_cross_FR : ndarray + True if the channel is `532nm cross FR` else False. + self.flag_532_parallel_FR : ndarray + True if the channel is `532nm parallel FR` else False. + self.flag_532_total_NR : ndarray + True if the channel is `532nm total NR` else False. + self.flag_532_cross_DFOV : ndarray + True if the channel is `532nm cross DFOV` else False. + self.flag_532_rr_FR : ndarray + True if the channel is `532nm rr FR` else False. + self.flag_607_total_FR : ndarray + True if the channel is `607nm total FR` else False. + self.flag_607_total_NR : ndarray + True if the channel is `607nm total NR` else False. + self.flag_1058_total_FR : ndarray + True if the channel is `1058nm total FR` else False. + self.flag_1064_total_FR : ndarray + True if the channel is `1064nm total FR` else False. + self.flag_1064_cross_FR : ndarray + True if the channel is `1064nm cross FR` else False. + self.flag_1064_total_NR : ndarray + True if the channel is `1064nm total NR` else False. + + Notes + ----- + .. TODO:: + - Several channels are still missing channelFlags. + - We are not consistant in the naming scheam of the RR-channels. + - Need to implement new config flags for fluorescence and high intensity channels. """ + ChannelTags = pollyChannelTags.pollyChannelTags( self.polly_config_dict['channelTag'], # TODO key: channelTags vs channelTag??? flagFarRangeChannel=self.polly_config_dict['isFR'], @@ -271,79 +447,156 @@ def setChannelTags(self): self.flag_1064_cross_FR = ChannelFlags[18] self.flag_1064_total_NR = ChannelFlags[19] - return self def preprocessing(self, collect_debug:bool=False): - """Preprocessing of Lidar data. - - Including in the followin processes in order: - Dead time correction - Background correction - Range correction - etc. + """Preprocessing of Lidar data. Includes the followin processes in order: + 1. Deadtime correction + 2. Background correction + 3. First-bin shift + 4. Mask for low-SNR + 5. Mask bins with laser shutter on + 6. Mask bins with fog + 7. Mask for depolarization-calibration process + 8. Range correction. Parameters ---------- - collect_debug : bool - If true, collect debug information. Default is False. + collect_debug : bool, optional + If true, collects debug information. Default is False. - Yeilds - ------ - - Background corrected signal including the background per channel. - - Range corrected signal. - - etc. - - .. TODO:: This is just a first draft for a docstring. Improve it. There is more processes and outputs of the function. + Yelds + ----- + self.retrievals_highres : dict + With preprocessed lidar data. + + Keys + ---- + mShots : ndarray + Number of the laser shots for each profile. + sigDTCor : ndarray + Dead time corrected signal [photon count]. + BG : ndarray + Background. + sigBGCor : ndarray + Backgound corrected signal [photon count]. + range : ndarray + Height above ground at zenith angle [m]. + height : ndarray + Height above ground [m]. + alt : ndarray + Altitude [m]. + time : list + Measurement time in unix time. + time64 : ndarray + Measurement time in numpy.datetime. + SNR : ndarray + Signal to noise ratio. + lowSNRMask : ndarray + True if SNR is less SNRmin. Otherwise False. + shutterOnMask : ndarray + True if at timesteps where the shutters was on. Otherwise False. + fogMask : ndarray + If it is foggy which means the signal will be very weak, + fogMask will be set True. Otherwise, False. + mask607Off : ndarray + Mask of PMT on/off status at 607 nm channel. + mask387Off : ndarray + Mask of PMT on/off status at 387 nm channel. + mask407Off : ndarray + Mask of PMT on/off status at 407 nm channel. + mask355RROff : ndarray + Mask of PMT on/off status at 355 nm rotational Raman channel. + mask532RROff : ndarray + Mask of PMT on/off status at 532 nm rotational Raman channel. + mask1064RROff : ndarray + Mask of PMT on/off status at 1064 nm rotational Raman channel. + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + Time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + Time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + Time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + RCS : ndarray + Range Corrected signal [MCPS]. + PCR_slice : ndarray + Background corrected signal in photon count rate [MCPS]. + + Notes + ----- + .. TODO:: + - This is just a first draft for a docstring. Improve it. There is more processes and outputs of the function. + - Also go over the inputs and see if all of them are actually needed. """ + + logging.info("Preprocessing ...") preproc_dict = pollyPreprocess.pollyPreprocess( self.rawdata_dict, deltaT=self.polly_config_dict['deltaT'], - flagForceMeasTime = self.polly_config_dict['flagForceMeasTime'], - maxHeightBin = self.polly_config_dict['max_height_bin'], - firstBinIndex = self.polly_config_dict['first_range_gate_indx'], - firstBinHeight = self.polly_config_dict['first_range_gate_height'], - pollyType = self.polly_config_dict['name'], - flagDeadTimeCorrection = self.polly_config_dict['flagDTCor'], - deadtimeCorrectionMode = self.polly_config_dict['dtCorMode'], - deadtimeParams = self.polly_config_dict['dt'], - flagSigTempCor = self.polly_config_dict['flagSigTempCor'], - tempCorFunc = self.polly_config_dict['tempCorFunc'], - meteorDataSource = self.polly_config_dict['meteorDataSource'], - gdas1Site = self.polly_config_dict['gdas1Site'], - gdas1_folder = self.picasso_config_dict['gdas1_folder'], - radiosondeSitenum = self.polly_config_dict['radiosondeSitenum'], - radiosondeFolder = self.polly_config_dict['radiosondeFolder'], - radiosondeType = self.polly_config_dict['radiosondeType'], - bgCorrectionIndexLow = self.polly_config_dict['bgCorRangeIndxLow'], - bgCorrectionIndexHigh = self.polly_config_dict['bgCorRangeIndxHigh'], - asl = self.polly_config_dict['asl'], - initialPolAngle = self.polly_config_dict['init_depAng'], - maskPolCalAngle = self.polly_config_dict['maskDepCalAng'], - minSNRThresh = self.polly_config_dict['mask_SNRmin'], - minPC_fog = self.polly_config_dict['minPC_fog'], - flagFarRangeChannel = self.polly_config_dict['isFR'], - flag532nmChannel = self.polly_config_dict['is532nm'], - flagTotalChannel = self.polly_config_dict['isTot'], - flag355nmChannel = self.polly_config_dict['is355nm'], - flag607nmChannel = self.polly_config_dict['is607nm'], - flag387nmChannel = self.polly_config_dict['is387nm'], - flag407nmChannel = self.polly_config_dict['is407nm'], - flag355nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag532nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - flag1064nmRotRaman = np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), - isUseLatestGDAS = self.polly_config_dict['flagUseLatestGDAS'], + flagForceMeasTime=self.polly_config_dict['flagForceMeasTime'], + maxHeightBin=self.polly_config_dict['max_height_bin'], + firstBinIndex=self.polly_config_dict['first_range_gate_indx'], + firstBinHeight=self.polly_config_dict['first_range_gate_height'], + pollyType=self.polly_config_dict['name'], + flagDeadTimeCorrection=self.polly_config_dict['flagDTCor'], + deadtimeCorrectionMode=self.polly_config_dict['dtCorMode'], + deadtimeParams=self.polly_config_dict['dt'], + flagSigTempCor=self.polly_config_dict['flagSigTempCor'], + tempCorFunc=self.polly_config_dict['tempCorFunc'], + meteorDataSource=self.polly_config_dict['meteorDataSource'], + gdas1Site=self.polly_config_dict['gdas1Site'], + gdas1_folder=self.picasso_config_dict['gdas1_folder'], + radiosondeSitenum=self.polly_config_dict['radiosondeSitenum'], + radiosondeFolder=self.polly_config_dict['radiosondeFolder'], + radiosondeType=self.polly_config_dict['radiosondeType'], + bgCorrectionIndexLow=self.polly_config_dict['bgCorRangeIndxLow'], + bgCorrectionIndexHigh=self.polly_config_dict['bgCorRangeIndxHigh'], + asl=self.polly_config_dict['asl'], + initialPolAngle=self.polly_config_dict['init_depAng'], + maskPolCalAngle=self.polly_config_dict['maskDepCalAng'], + minSNRThresh=self.polly_config_dict['mask_SNRmin'], + minPC_fog=self.polly_config_dict['minPC_fog'], + flagFarRangeChannel=self.polly_config_dict['isFR'], + flag532nmChannel=self.polly_config_dict['is532nm'], + flagTotalChannel=self.polly_config_dict['isTot'], + flag355nmChannel=self.polly_config_dict['is355nm'], + flag607nmChannel=self.polly_config_dict['is607nm'], + flag387nmChannel=self.polly_config_dict['is387nm'], + flag407nmChannel=self.polly_config_dict['is407nm'], + flag355nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is355nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask + flag532nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is532nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask + flag1064nmRotRaman=np.bitwise_and(np.array(self.polly_config_dict['is1064nm']), np.array(self.polly_config_dict['isRR'])).tolist(), # TODO: is this not rdundent as we already should have an RR mask. Also this will not work for the 1058nm channel. + isUseLatestGDAS=self.polly_config_dict['flagUseLatestGDAS'], collect_debug=collect_debug, + flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'], ) self.retrievals_highres.update(preproc_dict) - return self def SaturationDetect(self): - """Saturation Detection.""" + """Saturation Detection. + + Yields + ------ + self.flagSaturation : ndarray + 1 dimensional temporal boolean array per channel. + True if the channel is saturated, else False. + """ + logging.info("Saturation detection ...") self.flagSaturation = pollySaturationDetect.pollySaturationDetect( data_cube = self, - sigSaturateThresh = self.polly_config_dict['saturate_thresh']) + sigSaturateThresh = self.polly_config_dict['saturate_thresh'] + ) + def polarizationCaliD90(self, db_path:str=None): """Calibration with the Delta-90 method. @@ -354,10 +607,20 @@ def polarizationCaliD90(self, db_path:str=None): Path to database to read DC values from in case none are successfully retrieved. Default is None. - The stuff that starts here in the matlab version - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5efd7d35596c67ef8672f5948e47d1f9d46ab867/lib/interface/picassoProcV3.m#L442 + Yields + ------ + self.pol_cali['D90' & 'D90_db'] : dict + All retrieved and read delta-90 depol calibration constants and retrieval information. + self.etaused : dict + The used depol calibration constants per channel. + + Notes + ----- + - The stuff that starts here in the matlab version + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/5efd7d35596c67ef8672f5948e47d1f9d46ab867/lib/interface/picassoProcV3.m#L442 """ + logging.info("Delta 90 polarization calibration ...") polarization.loadGHK(self) self.pol_cali['D90'] = polarization.calibrateGHK(self) isUsable = [element['status'] for key, val in self.pol_cali['D90'].items() for element in val] @@ -377,94 +640,226 @@ def polarizationCaliD90(self, db_path:str=None): def cloudScreen(self, collect_debug:bool=False): - """Basic cloud screenting. + """Basic cloud screening. + + Parameters + ---------- + collect_debug : bool, optional + If true, collects debug information. Default is False. - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L663 + Yields + ------ + self.flagCloudFree : ndarray + 1 dimensional temporal boolean array. 0 = cloudy, 1 = cloud free. + + Notes + ----- + - Matlab equivalent code at: + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L663 """ + + logging.info("Cloud screening ...") self.flagCloudFree = cloudscreen.cloudscreen(self, collect_debug=collect_debug) def cloudFreeSeg(self): """Cloud free profile segmentation. + + Yields + ------ + self.clFreeGrps : nested list + List with start, stop index of each cloud free segement. - https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L707 - + Notes + ----- + - The matlab eqvivalent code starts here: + https://github.com/PollyNET/Pollynet_Processing_Chain/blob/b3b8ec7726b75d9db6287dcba29459587ca34491/lib/interface/picassoProcV3.m#L707 + .. TODO:: Write a more extensive example for the docstring. + + Example + ------- .. code-block:: python data_cube.clFreGrps = [ - [35,300], - [2500,2800] + [35, 300], + [2500, 2800] ] - """ + + logging.info("Segment cloud free groups ...") self.clFreeGrps = profilesegment.segment(self) - def aggregate_profiles(self, var:str=None): + + def aggregate_profiles(self, var:str|list=None, func=np.nansum): """Aggregate highres profiles over cloud free segments. Parameters ---------- - var : str - Name of variables to aggregate. - Default is `RCS`, `sigBGCor`, and `BG`. - + var : str or array_like + Name of variable to aggregate. Default is None. + func : function, optional + Function to do the aggregation (mean, sum, median, etc.). Default is np.nansum. + + Yields + ------ + self.retrievals_profiles[`var`] : ndarray + Aggregated profile. + Notes ----- - .. TODO:: Decide on a consistent way for doing the aggregation, do not mix mean and sum + - The variable(s) `var` need to be stored in the dictionary data_cube.retrievals_highres. + All aggregated profiles will be stored in the dictionary data_cube.retrievals_profiles under the + the same key. + - If var is None. The following variables will be aggregated: + `sigBGCor`, `BG`, `RCS`, `mShots`, `mask387Off`, `mask607Off`, `mask407Off` + - `func=np.nansum` is needed for correct SNR calculations. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - 2026-07-28: Added option to aggregate multiple variables in the same call. + And corrected the aggregation for PCR-signals like `RCS`. + """ - if var == None: - self.retrievals_profile['RCS'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'RCS', func=np.nanmean) - self.retrievals_profile['sigBGCor'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor') - self.retrievals_profile['BG'] = \ - preprocprofiles.aggregate_clFreeGrps(self, 'BG') - - # Remove empty dict keys (temporarly solution) - for v in ['RCS', 'sigBGCor', 'BG']: - if self.retrievals_profile[v] is None: - del self.retrievals_profile[v] + if var is None: + # Take care of the default scenario + self.aggregate_profiles(['sigBGCor', 'BG', 'RCS', 'mShots']) + self.aggregate_profiles(['mask387Off', 'mask607Off', 'mask407Off'], np.nanmean) + return + + if isinstance(var, str): + var = [var] + + for variable in var: + logging.info(f"Aggregating variable: {variable} ...") + if variable in self.retrievals_highres: + if variable == "RCS": + if self.polly_config_dict['flagPicassoComparison']: + self.retrievals_profile[variable] = \ + preprocprofiles.aggregate_clFreeGrps(self, variable, np.nanmean) + else: + self.retrievals_profile[variable] = pollyPreprocess.calculate_rcs( + signal=pollyPreprocess.photonCount2PCR( + signal=preprocprofiles.aggregate_clFreeGrps(self, 'sigBGCor', func), + mShots=preprocprofiles.aggregate_clFreeGrps(self, 'mShots', func), + hRes=self.rawdata_dict['measurement_height_resolution']['var_data'] + ), + ranges=self.retrievals_highres['range'] + ) + else: + self.retrievals_profile[variable] = \ + preprocprofiles.aggregate_clFreeGrps(self, variable, func) + else: + logging.critical(f"{variable} is NOT in data_cube.retrievals_highres") + raise ValueError(f"Could not locate variable '{variable}'.") - else: - self.retrievals_profile[var] = \ - preprocprofiles.aggregate_clFreeGrps(self, var) - - # Remove empty dict keys (temporarly solution) - if self.retrievals_profile[var] is None: - del self.retrievals_profile[var] def loadMeteo(self): - """Load meteorological data.""" + """Load meteorological data. + + Meteorological data like; + - Temperatue + - Pressure + - Relative Humidity + - Spesific Humidity + + is read form the cloudnet ECMWF file specified by the config variable; + - `meteorDataSource` + - `meteo_folder` + - `meteo_file` + + and saved in the dataFrame `self.met`. + + Yields + ------ + self.met : object + Object to handle meterological data. + + Notes + ----- + - Only `meteorDataSource = 'nc_cloudnet'` is currently suported. + """ + + logging.info("Loading meteorological data ...") self.met = readMeteo.Meteo( self.polly_config_dict['meteorDataSource'], self.polly_config_dict['meteo_folder'], self.polly_config_dict['meteo_file'] ) self.met.load( - datetime.datetime.timestamp(datetime.datetime.strptime(self.date, '%Y%m%d')), - self.retrievals_highres['height'], float(self.polly_config_dict['asl']), - self.polly_config_dict['flagPicassoComparison'] + times=datetime.datetime.timestamp(datetime.datetime.strptime(self.date, '%Y%m%d')), + heights=self.retrievals_highres['height'], asl=float(self.polly_config_dict['asl']), + flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'] ) def loadAOD(self): - """Load the AOD from a co-located fotometer - - .. TODO:: Not yet implemented! + """Load the AOD from a co-located fotometer. + + Notes + ----- + .. TODO:: Implement the option to load AOD from co-located fotometer. """ - # raise NotImplementedError - pass + + logging.critical('Function loadAOD is not yet implemented!') + raise NotImplementedError('Function loadAOD is not yet implemented!') def calcMolecular(self): """Calculate the molecular scattering for the cloud free periods with the strategy of first averaging the met data and then calculating the rayleigh scattering. + + Yields + ------ + self.mol_profiles : dict + Dictionary containing calculated molecular profiles for each channel. + + Keys + ---- + mBsc_355 : ndarray (channels, height) + Molecular backscatter at 355 nm [m^{-1}Sr^{-1}]. + mExt_355 : ndarray (channels, height) + Molecular extinction at 355 nm [m^{-1}]. + mBsc_387 : ndarray (channels, height) + Molecular backscatter at 387 nm [m^{-1}Sr^{-1}]. + mExt_387 : ndarray (channels, height) + Molecular extinction at 387 nm [m^{-1}]. + mBsc_407 : ndarray (channels, height) + Molecular backscatter at 407 nm [m^{-1}Sr^{-1}]. + mExt_407 : ndarray (channels, height) + Molecular extinction at 407 nm [m^{-1}]. + mBsc_532 : ndarray (channels, height) + Molecular backscatter at 532 nm [m^{-1}Sr^{-1}]. + mExt_532 : ndarray (channels, height) + Molecular extinction at 532 nm [m^{-1}]. + mBsc_607 : ndarray (channels, height) + Molecular backscatter at 607 nm [m^{-1}Sr^{-1}]. + mExt_607 : ndarray (channels, height) + Molecular extinction at 607 nm [m^{-1}]. + mBsc_1058 : ndarray (channels, height) + Molecular backscatter at 1058 nm [m^{-1}Sr^{-1}]. + mExt_1058 : ndarray (channels, height) + Molecular extinction at 1058 nm [m^{-1}]. + mBsc_1064 : ndarray (channels, height) + Molecular backscatter at 1064 nm [m^{-1}Sr^{-1}]. + mExt_1064 : ndarray (channels, height) + Molecular extinction at 1064 nm [m^{-1}]. + number_density : ndarray (channels, height) + Number density. + + Notes + ----- + .. TODO:: How are these molecular profiles aggregated (mean or sum??). + .. TODO:: Idea: As we already need the highres molecular signal for the WV-product. Would it not make more sens to + retrieve this first and then use the aggregate_profile() function to get the cloud free profiles? + --> This only makes sens if we can directly retrieve the highres molecular signal which I dout we can. """ + logging.info("Calculating molecular profiles ...") time_slices = [self.retrievals_highres['time64'][grp] for grp in self.clFreeGrps] - print('time slices of cloud free ', time_slices) + logging.info(f"time slices of cloud free {time_slices}") mean_profiles = self.met.get_mean_profiles(time_slices) self.mol_profiles = molecular.calc_profiles(mean_profiles, flagPicassoComparison=self.polly_config_dict['flagPicassoComparison']) @@ -474,14 +869,30 @@ def rayleighFit(self, collect_debug:bool=False): Parameters ---------- - collect_debug : bool + collect_debug : bool, optional If true, collects debug information. Default is False. + Yields + ------ + self.retrievals_profiles['refH'][idx][ch] : dict + Reference height information for cloud free segment `idx` and channel `ch`. + + Keys + ---- + DPInd : list + List of Douglas-Peckur indecies used as potensial reference index candidates. + refInd : list + Indices (start, stop) of the retrieved reference height. + refHeigt : list + Height above ground (start, stop) of the retrieved reference height. + refRange : list + Height above ground at zenith angle (start, stop) of the retrieved reference height. + Notes ----- - Direct translation from the matlab code. There might be noticeable numerical discrepancies (especially in the residual) seemed to work ok for 532, 1064, but with issues for 355. - - The Douglas Peucker algorithm works fine (gives the same result as the Matlab version). However, due the numerical discrepancies + - The Douglas Peucker algorithm works fine (gives the same result as the Matlab version). However, due to the numerical discrepancies In the residuals of the fit algorithm sometimes different refH segments are chosen. """ @@ -492,69 +903,199 @@ def rayleighFit(self, collect_debug:bool=False): def polarizationCaliMol(self): - """Calibration with molecular signal in reference height.""" + """Calibration with molecular signal in reference height. + + Yields + ------ + self.pol_cali['mol'][ch][idx] : dict + Molecular depol calibration constants and retrieval information for + channel `ch` and cloud free index `idx`. + + keys + ---- + eta : ndarray + Polarization calibration eta. + etaStd : ndarray + Uncertainty of polarization calibration eta. + fac : array + Polarization calibration factor. + facStd : ndarray + Uncertainty of polarization calibration factor. + status : int + Retrieval status + 0 : Bad + 1 : Good + time_start : int + Start time of the cloud free segment for the retrieval in unixtime. + end_time : int + End time of the cloud free segment for the retrieval in unixtime. + + Notes + ----- + - Calibration is only performed if the config variable `flagMolDepolCali = True`. + """ - logging.warning(f'not checked against the matlab code') if self.polly_config_dict['flagMolDepolCali']: + logging.info("Calibrating molecular signal ...") + logging.warning(f'not checked against the matlab code') self.pol_cali['mol'] = polarization.calibrateMol(self) else: logging.warning("'flagMolDepolCali' set to False") def transCor(self): - """GHK-Transmission correction + """Perform GHK-transmission correction on the signal. - .. TODO:: + Yields + ------ + self.retrievals_highres : dict + With corrected signal. - flagTransCor = True: - Fix the GHK - Transmission correction - flagTransCor = False: - Check if it is correct to use the BG corrected signal and find a better solution. - It is a bit confusing to overwrite the signal as it is called sigTCor but actually is sigBGCor - Like storing a dedicated signal dict to be used throuhot the processing, the dictionary could - have elements like signal (sig), background (bg), and name. which we could overwrite each time - a new correction is made. And by checking the name of the signal (TCor, BGCor) you can find out - which signal it is. + Keys + ---- + sigTCor : ndarray + GHK-Transmission corrected signal [MCPS]. + BGTCor : ndarray + GHK-Transmission corrected background. + + Notes + ----- + - if the config entry `flagTransCor = False`, no GHK transmission correction + will be done and the background corrected signal will replace the transmission + corrected signal for the rest of the processing steps. + + .. TODO:: Overwriting `sigTCor` with `sigBGCor` when `flagTransCor = False` can + cause confiusion among useres. It would be more intutive to not have + a `sigTCor` variable in this case. However, this requiers re... the + rest of the processing chain. """ if self.polly_config_dict['flagTransCor']: - logging.warning('transmission correction') + logging.info('GHK Transmission correction ...') self.retrievals_highres['sigTCor'], self.retrievals_highres['BGTCor'] = \ transCor.transCorGHK_cube(self) else: - logging.warning('NO transmission correction') + logging.warning('No GHK transmission correction done.') self.retrievals_highres['sigTCor'], self.retrievals_highres['BGTCor'] = \ self.retrievals_highres['sigBGCor'], self.retrievals_highres['BG'] - def retrievalKlett(self, oc=False, nr=False): - """Apply Klett retrieval.""" + def retrievalKlett(self, oc:bool=False, nr:bool=False): + """Apply Klett retrieval. + + Parameters + ---------- + oc : bool, optional + If true, apply the retrieval on the overlap corrected profiles. The outupt will then also be saved under + the key 'klett_OC' insted of 'klett'. Default is False. + nr : bool, optional + If true, apply the retrieval on the near range profiles as well as the far range profiles. Default is False. + + Yields + ------ + self.retrievals_profile['klett' or 'klett_OC'][idx][ch] : dict + Klett retrieved optical profiles and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + aerExt : ndarray + Aerosol extinction coefficient [m^{-1}]. + aerExtStd : ndarray + Uncertainty of aerosol extinction coefficient [m^{-1}]. + aerBsc : ndarray + Aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBscStd : ndarray + Uncertainty of aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBR : ndarray + Aerosol backscatter ratio. + aerBRStd : ndarray + Statistical uncertainty of aerosol backscatter ratio. + retrieval : str + Name of retrieval type, eg. 'klett'. + signal : str + Name of the signal used for the retrieval, eg. 'TCor'. + refBeta : float + Reference value used for the retrieval [m^{-1}Sr^{-1}]. + + Notes + ----- + - The retrieval is by default applied on the FR profiles. if `nr=True` the retrieval will be applied both on + the NR and FR profiles. + """ retrievalname = 'klett' kwargs = {} if oc: # Check if overlap corrected signal exist if "sigOLCor" not in self.retrievals_highres: # This should be done more general not only for OLCor but for other signals as well. + logging.warning("No overlap corrected signal found.") return retrievalname +='_OC' kwargs['signal'] = 'OLCor' if nr: kwargs['nr'] = True - print('retrievalname', retrievalname) + logging.info(f"Klett retrieval for FR {'& NR' if nr else None} {'overlap' if oc else 'GHK-transmission'} corrected signal ...") self.retrievals_profile[retrievalname] = klettfernald.run_cldFreeGrps(self, **kwargs) if retrievalname not in self.retrievals_profile['avail_optical_profiles']: self.retrievals_profile['avail_optical_profiles'].append(retrievalname) - def retrievalRaman(self, oc=False, nr=False, collect_debug=False): - """Apply Raman retrieval (nighttime only).""" + def retrievalRaman(self, oc:bool=False, nr:bool=False, collect_debug:bool=False): + """Apply raman retrieval (nighttime only). + + Parameters + ---------- + oc : bool, optional + If true, apply the retrieval on the overlap corrected profiles. The outupt will then also be saved under + the key 'raman_OC' insted of 'raman'. Default is False. + nr : bool, optional + If true, apply the retrieval on the near range profiles as well as the far range profiles. Default is False. + collect_debug : bool, optional + If true, collects debug information. Default is False. + + Yields + ------ + self.retrievals_profile['raman' or 'raman_OC'][idx][ch] : dict + Raman retrieved optical profiles and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + aerExt : ndarray + Aerosol extinction coefficient [m^{-1}]. + aerExtStd : ndarray + Uncertainty of aerosol extinction coefficient [m^{-1}]. + aerBsc : ndarray + Aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + aerBscStd : ndarray + Uncertainty of aerosol backscatter coefficient [m^{-1}Sr^{-1}]. + LR : ndarray + Aerosol Lidar ratio [sr]. + effRes : ndarray + Effective resolution of aerosol lidar ratio [m]. + LRStd : ndarray + Uncertainty of aerosol lidar ratio [sr]. + retrieval : str + Name of retrieval type eg. 'raman'. + signal : str + Name of the signal used for the retrieval eg. 'TCor'. + refBeta : float + Reference value used for the retrieval. + + Notes + ----- + - The retrieval is by default applied on the FR profiles. if `nr=True` the retrieval will be applied both on + the NR and FR profiles. + """ retrievalname = 'raman' kwargs = {} if oc: # Check if overlap corrected signal exist if "sigOLCor" not in self.retrievals_highres: # This should be done more general not only for OLCor but for other signals as well. + logging.warning("No overlap corrected signal found.") return retrievalname +='_OC' kwargs['signal'] = 'OLCor' @@ -568,6 +1109,7 @@ def retrievalRaman(self, oc=False, nr=False, collect_debug=False): kwargs['nr'] = True kwargs['collect_debug'] = collect_debug + logging.info(f"Raman retrieval for FR {'& NR' if nr else None} {'overlap' if oc else 'GHK-transmission'} corrected signal ...") self.retrievals_profile[retrievalname] = raman.run_cldFreeGrps(self, **kwargs) if retrievalname not in self.retrievals_profile['avail_optical_profiles']: self.retrievals_profile['avail_optical_profiles'].append(retrievalname) @@ -578,47 +1120,104 @@ def overlapCalc(self, collect_debug:bool=False): Parameters ---------- - collect_debug : bool + collect_debug : str, optional If true, collects debug information. Default is False. + Yields + ------ + self.retrievals_profile['overlap']['frnr'][idx][ch] : list of dicts + Far-range-Near-range retrieved overlap function and retrieval + information for cloud free segment `idx` and channel `ch`. + + Keys + ---- + olFunc : ndarray + Overlap function. + olFuncStd : ndarray + Standard deviation of overlap function. + sigRatio : float + Signal ratio between near-range and far-range signals. + normRange : list + Height index of the signal normalization range. + + self.retrievals_profile['overlap']['raman'][idx][ch] : list of dicts + Raman retrieved overlap function and retrieval information + for cloud free segment `idx` and channel `ch`. + + Keys + ---- + olFunc : ndarray + Overlap function. + olFunc_raw : ndarray + Overlap function with no smoothing. + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. + Notes ----- - Different to the matlab version, where an average over all cloud - free periods is taken, it is done here per cloud free segment - - .. TODO:: - The data structure of retrievals_profile['overlap'] differs from - retrievals_profile['raman']! - + free periods is taken. Here the overlap is applied per cloud free period. """ + if not self.polly_config_dict["flagOLCor"]: return + logging.info("calculating overlap functions ...") self.retrievals_profile['overlap'] = {} self.retrievals_profile['overlap']['frnr'] = overlapEst.run_frnr_cldFreeGrps(self, collect_debug=collect_debug) self.retrievals_profile['overlap']['raman'] = overlapEst.run_raman_cldFreeGrps(self, collect_debug=collect_debug) - + + def overlapFixLowestBins(self): - """The lowest bins are affected by stange near range effects.""" + """The lowest bins are affected by stange near range effects. + + Yields + ------ + self.retrievals_profile['overlap']['frnr'][idx][ch]['olFunc'] : ndarray + FRNR retrieved overlap func with subdued near-range effect. + self.retrievals_profile['overlap']['raman'][idx][ch]['olFunc'] : ndarray + Raman retrieved overlap func with subdued near-range effect. + """ + if not self.polly_config_dict["flagOLCor"]: return height = self.retrievals_highres['range'] for k in self.retrievals_profile['overlap']: - print(f"Fixing lower bins for {k} overlap functions") + logging.info(f"Fixing lower bins for {k} overlap functions") overlapCor.fixLowest(self.retrievals_profile['overlap'][k], np.where(height > 800)[0][0]) def overlapCor(self): - """Overlap correction + """Overlap correction. - the overlap correction is implemented differently to the matlab version - first a 2d (time, height) correction array is constructed then it is applied. - In future this will allow for time varing overlap functions + Yields + ------ + self.retrieval_highres : dict + With overlap corrected signal and retrieval information. + + Keys + ---- + overlap2d : ndarray + 2 dimensional (time, height) overlap function. + sigOLCor : ndarray + Overlap corrected signal [photon count]. + BGOLCor : ndarray + Overlap corrected background. + heightFullOverCor : ndarray + Retrieved height of full overlap [m]. + Notes + ----- + - the overlap correction is implemented differently to the matlab version + first a 2d (time, height) correction array is constructed then it is applied. + In future this will allow for time varing overlap functions """ + if self.polly_config_dict['overlapCorMode'] == 0 or not self.polly_config_dict["flagOLCor"]: - logging.info('no overlap Correction') - return self - logging.info('overlap Correction') + logging.info('no overlap correction applied') + return + logging.info('Apply overlap correction ...') if self.polly_config_dict['overlapCorMode'] == 1: logging.info('overlapCorMode 1 -> need file for overlapfunction') if not 'overlap' in self.retrievals_profile: @@ -632,35 +1231,124 @@ def overlapCor(self): def calcDepol(self): - """Calculate the volume depol and the particle depol.""" + """Calculate the volume depol and the particle depol. + + Yields + ------ + self.retrievals_profiles[sig][idx][ch] : dict + Volume and particle depolarization profiles and retrieval info + for signal type `sig` for cloud free segment `idx` and channel `ch`. + + Keys + ---- + vdr : ndarray + Volume depolarization ratio []. + vdrStd : ndarray + Uncertainty of the volume depolarization ratio. + mdr : ndarray + Molecular depolarization ratio []. + mdrStd : ndarray + Uncertainty of the molecular depolarization ratio. + pdr : ndarray + Particle depolarization ratio []. + pdrStd : ndarray + Uncertainty of the particle depolarization ratio. + + + Notes + ----- + - This retrieval is done for all avaiable optical profiles. + """ for ret_prof_name in self.retrievals_profile['avail_optical_profiles']: - print(ret_prof_name) + logging.info(f"Calculate volume and particle depolarization ratios for product {ret_prof_name} ...") self.retrievals_profile[ret_prof_name] = depolarization.voldepol_cldFreeGrps( self, ret_prof_name) self.retrievals_profile[ret_prof_name] = depolarization.pardepol_cldFreeGrps( self, ret_prof_name) + def estQualityMask(self): - """Estimate the quality mask.""" - logging.info("Estimate quality mask") + """Estimate the quality mask. + + Yields + ------ + self.retrievals_highres['quality_mask'] : ndarray + High resolution (time, height) quality masks per channel. + 0 : good data + 1 : low-SNR data + 2 : depolarization calibration periods + 3 : shutter on + 4 : fog + 5 : saturated (NEW) + + Notes + ----- + - If the config variables `flagUseImprovedSNR=True`, SNR and the lowSNRMask are + recalculated with quasi-smoothed signals to improve data quality. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - 2026-07-17: Added quasi-smoothed SNR and lowSNRMask. + + """ + + logging.info("Estimate quality masks ...") + if self.polly_config_dict['flagUseImprovedSNR']: + self.retrievals_highres['SNR_quasi'], self.retrievals_highres['lowSNRMask_quasi'] = qualityMask.improvedSNR(self) + self.retrievals_highres['quality_mask'] = qualityMask.qualityMask(self) def Angstroem(self): - """Calculate the angstrom exponent.""" + """Calculate the angstrom exponent. + + Yields + ------ + self.retrievals_profiles[sig][idx][ch] : dict + Volume and particle depolarization profiles and retrieval info + for signal type `sig` for cloud free segment `idx` and channel `ch`. + + Keys + ---- + AE_{prod}_{wv1}_{wv2} : ndarray + Angstroem Exponent for product `prod`, and wavelengths `wv1` and `wv2`. + AEStd_{prod}_{wv1}_{wv2} : ndarray + Uncertainty of Angstroem Exponent for product `prod`, and wavelengths `wv1` and `wv2`. + + Notes + ----- + - AE_{prod}_{wv1}_{wv2} = log(prod_wv1 / prod_wv2) / log(wv2 / wv1) + - This retrieval is done for all avaiable optical profiles. + """ + for ret_prof_name in self.retrievals_profile['avail_optical_profiles']: - print(ret_prof_name) + logging.info(f"Calculate Angstrom exponents for product {ret_prof_name} ...") self.retrievals_profile[ret_prof_name] = angstroem.ae_cldFreeGrps( self, ret_prof_name) + def LidarCalibration(self, db_path:str=None, collect_debug:bool=False): - """calculate the lidar constant + """Calculate the lidar constant. + Yields + ------ + self.LC['klett' & 'klett_db'] : dict + All retrieved and read Klett lidar calibration constants and retrieval information. + self.LC['raman' & 'raman_db'] : dict + All retrieved and read Raman lidar calibration constants and retrieval information. + self.LCused : dict + The lidar claibration constant used per channel. + + Notes + ----- .. TODO:: Find out how we prioritise raman, klett, and database retrieved LC... """ + + logging.info("Calculating lidar calibration constants ...") self.LC['klett'] = lidarconstant.lc_for_cldFreeGrps( self, retrieval='klett', @@ -693,19 +1381,44 @@ def LidarCalibration(self, db_path:str=None, collect_debug:bool=False): def attBsc_volDepol(self): - """highres attBsc and voldepol in 2D.""" + """Highres attBsc and voldepol in 2d. + + Yields + ------ + self.retrievals_highres : dict + With attenuated backscatter. + + Keys + ---- + attBsc_{wv}\_{t}\_{tel} : ndarray + High resolution (time, height) attenuated backscatter at chennel {wv}\_{t}\_{tel}. + attBsc_{wv}\_{t}\_OC : ndarray + High resolution (time, height) overlap corrected attenuated backscatter at channel {wv}\_{t}\_FR. + """ # for now try with mutable state in data_cube - logging.info('attBsc 2d retrieval') + logging.info("2D attenuated backscatter retrieval ...") highres.attbsc_2d(self) - logging.info('voldepol 2d retrieval') + logging.info("2D volume depolarization ratio retrieval ...") highres.voldepol_2d(self) def molecularHighres(self): - """calculate the molecular signal for the 2d high resolution.""" + """Calculate the molecular signal for the 2d high resolution. + + Yields + ------ + self.mol_2d : xr.Dataset + xarray dataset with dimensions time and height and variables molecular + backscatter (mBsc) and molecular extinction (mExt) per wavelength. + + Notes + ----- + - mol_2d's time dimension differe from the time dimension of the measurement. + """ + logging.info("2D molecular retrieval ...") self.mol_2d = molecular.calc_2d( self.met.ds, flagPicassoComparison=self.polly_config_dict['flagPicassoComparison'] @@ -713,7 +1426,44 @@ def molecularHighres(self): def quasiV1(self): - """QuasiV1 retrivals and target categorisation.""" + """QuasiV1 retrivals and target categorisation. + + Yields + ------ + self.retrieval_highres : dict + With high resolution Quasi version 1 products as well as Target categorization. + + Keys + ---- + quasiBscV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle backscatter at channel `{wv}_{t}_{tel}`. + quasiExtV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle extinction at channel `{wv}_{t}_{tel}`. + quasiVdrV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 volume depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiPdrV1_{wv}\_{t}\_{tel} : ndarray + Quasi version 1 particle depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiAEV1_{wv1}\_{wv2} : ndarray + Quasi version 1 Angstroem exponent between two wavelengets `wv1` and `wv2`. + tcMaskV1 : ndarray + Classification mask version 1 (time x height). + 0: No signal + 1: Clean atmosphere + 2: Non-typed particles/low conc. + 3: Aerosol: small + 4: Aerosol: large, spherical + 5: Aerosol: mixture, partly non-spherical + 6: Aerosol: large, non-spherical + 7: Cloud: non-typed + 8: Cloud: water droplets + 9: Cloud: likely water droplets + 10: Cloud: ice crystals + 11: Cloud: likely ice crystal + + Notes + ----- + - QuasiV1 products uses Klett-retrieved products. + """ logging.info('Calculating Quasi V1 particle backscatter coefficient') quasiV1.quasi_bsc(self) @@ -727,8 +1477,46 @@ def quasiV1(self): logging.info('Producing V1 target categorization') quasi.target_cat(self, version='V1') + def quasiV2(self): - """QuasiV2 retrivals and target categorisation.""" + """QuasiV2 retrivals and target categorisation. + + Yields + ------ + self.retrieval_highres : dict + With high resolution Quasi version 2 products as well as Target categorization. + + Keys + ---- + quasiBscV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle backscatter at channel `{wv}_{t}_{tel}`. + quasiExtV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle extinction at channel `{wv}_{t}_{tel}`. + quasiVdrV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 volume depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiPdrV2_{wv}\_{t}\_{tel} : ndarray + Quasi version 2 particle depolarization ratio at channel `{wv}_{t}_{tel}`. (usally `wv =532`). + quasiAEV2_{wv1}\_{wv2} : ndarray + Quasi version 2 Angstroem exponent between two wavelengets `wv1` and `wv2`. + tcMaskV2 : ndarray + Classification mask version 2 (time x height). + 0: No signal + 1: Clean atmosphere + 2: Non-typed particles/low conc. + 3: Aerosol: small + 4: Aerosol: large, spherical + 5: Aerosol: mixture, partly non-spherical + 6: Aerosol: large, non-spherical + 7: Cloud: non-typed + 8: Cloud: water droplets + 9: Cloud: likely water droplets + 10: Cloud: ice crystals + 11: Cloud: likely ice crystal + + Notes + ----- + - QuasiV2 products uses Raman-retrieved products. + """ logging.info('Calculating Quasi V2 particle backscatter coefficient') quasiV2.quasi_bsc(self) @@ -742,6 +1530,7 @@ def quasiV2(self): logging.info('Producing V2 target categorization') quasi.target_cat(self, version='V2') + def write_2_sql_db(self, parameter:str, db_path:str|None=None, method:str|None=None): """Write LC or eta to sqlite db table. @@ -749,12 +1538,11 @@ def write_2_sql_db(self, parameter:str, db_path:str|None=None, method:str|None=N ---------- parameter : str can be LC (Lidar-calibration-constant) or DC (Depol-calibration-constant) - method : str + method : str or NoneType 'raman' or 'klett' - db_path : str + db_path : str or NoneType location of the sqlite db-file - Notes ----- - The unique columns are needed that new entries overwrite old ones, @@ -795,10 +1583,21 @@ def read_calibration_db(self, db_path:str|None=None): Parameters ---------- - db_path : str - path to database of calibration values. + db_path : str or NoneType + path to database file... Default is None. + + Yields + ------ + self.LC : dict + Original `LC` with all additinal lidar calibration constant availabel + in the datafram `db_path` in the time period of the measurement +-24h. + self.pol_cali : dict + Original `pol_cali` with all additinal polarization calibration constant + availabel in the datafram `db_path` in the time period of the measurement +-24h. - Time interval includes 24h before and after the actual date + Notes + ----- + - Time interval includes 24h before start of the measurement and after the end of the measurement. """ if db_path == None: @@ -813,27 +1612,51 @@ def read_calibration_db(self, db_path:str|None=None): self.pol_cali.update(sql_db.get_from_sql_db(db_path, table_name, ts_interval)) - def adding_retrieving_infos_2_polly_config_dict(self): - """Some infos from the polly_config_dict should have there own keys, - e.g. reference_search_range. + """Some infos from the polly_config_dict should have there own keys, e.g. reference_search_range. + + Yields + ------ + self.polly_config_dict : dict + With additional info. + + Keys + ---- + reference_search_range_355_total_FR : list + ... + reference_search_range_532_total_FR : list + ... + reference_search_range_1064_total_FR : list + ... + + Notes + ----- + .. TODO :: + - Finish docstring. + - Is this function necessary? """ - + lower_overlap = np.array(self.polly_config_dict['heightFullOverlap']) - reference_search_range_355_total_FR = [int(lower_overlap[self.flag_355_total_FR][0]),self.polly_config_dict['maxDecomHeight355']] - reference_search_range_532_total_FR = [int(lower_overlap[self.flag_532_total_FR][0]),self.polly_config_dict['maxDecomHeight532']] - reference_search_range_1064_total_FR = [int(lower_overlap[self.flag_1064_total_FR][0]),self.polly_config_dict['maxDecomHeight1064']] + reference_search_range_355_total_FR = [int(lower_overlap[self.flag_355_total_FR][0]), self.polly_config_dict['maxDecomHeight355']] + reference_search_range_532_total_FR = [int(lower_overlap[self.flag_532_total_FR][0]), self.polly_config_dict['maxDecomHeight532']] + reference_search_range_1064_total_FR = [int(lower_overlap[self.flag_1064_total_FR][0]), self.polly_config_dict['maxDecomHeight1064']] self.polly_config_dict['reference_search_range_355_total_FR'] = reference_search_range_355_total_FR self.polly_config_dict['reference_search_range_532_total_FR'] = reference_search_range_532_total_FR self.polly_config_dict['reference_search_range_1064_total_FR'] = reference_search_range_1064_total_FR - - return self + # def __str__(self): # return f"{self.rawdata_dict}" + def __del__(self): type(self).counter -= 1 + + + def runProcessing(self): + """Idea: Include a run function in the object as an alternative to the default run script... + """ + raise NotImplementedError() diff --git a/ppcpy/io/loadConfigs.py b/ppcpy/io/loadConfigs.py index 79a1131..7293895 100644 --- a/ppcpy/io/loadConfigs.py +++ b/ppcpy/io/loadConfigs.py @@ -4,20 +4,30 @@ import numpy as np import traceback -def loadPicassoConfig(picasso_config_file, picasso_default_config_file): - """load the general Picasso config file + +configIdxKeys:list = [ + 'first_range_gate_indx', 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', + 'LCMeanMinIndx', 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', + 'depol_cal_minbin_1064' +] + + +def loadPicassoConfig(picasso_config_file:str, picasso_default_config_file:str) -> dict: + """Load the general Picasso config file. Parameters ---------- picasso_config_file : str or path - the specific config file + The specific config file. picasso_default_config_fil : str or path - the default (template) file + The default (template) file. Returns ------- - picasso_config_dict + picasso_config_dict : dict + Mereged config file containing all config variables. """ + picasso_default_config_file_path = Path(picasso_default_config_file) picasso_config_file_path = Path(picasso_config_file) picasso_config_dict = {} @@ -48,7 +58,7 @@ def loadPicassoConfig(picasso_config_file, picasso_default_config_file): picasso_config_dict[key] = picasso_config_file_dict[key] return picasso_config_dict except Exception: - logging.critical('picasso_default_config_file: {picasso_default_config_file} can not be read.', exc_info=True) + logging.critical(f'picasso_default_config_file: {picasso_default_config_file} can not be read.', exc_info=True) else: logging.warning(f'picasso config file: {picasso_config_file} does not exist') @@ -59,9 +69,24 @@ def loadPicassoConfig(picasso_config_file, picasso_default_config_file): return None -def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): - """ +def readPollyNetConfigLinkTable(polly_config_table_file:str, timestamp:str, device:str) -> pd.DataFrame: + """Read PollyNet processing chain config link table. + + Parameters + ---------- + polly_config_table_file : str + Path to the link tabele file. + timestamp : str + Date of measurement. + device : str + Name of polly device. + + Returns + ------- + config_array : pd.DataFrame + Row of intrest in the PollyNet processing chain config link table. """ + polly_config_table_file_path = Path(polly_config_table_file) if polly_config_table_file_path.is_file(): @@ -73,12 +98,12 @@ def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): before_end_date = excel_file_ds['Stoptime of config'] >= timestamp between_two_dates = after_start_date & before_end_date filtered_result = excel_file_ds.loc[between_two_dates] - # print(filtered_result) + # print(filtered_result) ## get config-file for timeperiod and instrument config_array = filtered_result.loc[(filtered_result['Instrument'] == device)] if len(config_array) > 0: - #polly_config_file = str(config_array['Config file'].to_string(index=False)).strip() ## get rid of whtiespaces - #return polly_config_file + # polly_config_file = str(config_array['Config file'].to_string(index=False)).strip() ## get rid of whtiespaces + # return polly_config_file return config_array else: logging.warning(f'no polly-config file could be found for {device}@{timestamp}.') @@ -88,9 +113,20 @@ def readPollyNetConfigLinkTable(polly_config_table_file, timestamp, device): return pd.DataFrame() -# def fix_indexing(config_dict, keys=['first_range_gate_indx', ]): -def fix_indexing(config_dict, keys=['first_range_gate_indx', 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx', ]): - """ +def fix_indexing(config_dict:dict, keys:list=configIdxKeys) -> dict: + """Fix indexing convention of config variables to 0based. + + Parameters + ---------- + config_dict : dict + A dictionary containing all config variables. + keys : list + Key(s) of `config_dict` variables to fix indexing convention. + + Returns + ------- + config_dict : dict + `config_dict` with 0based indexing. """ if not 'indexing_convention' in config_dict: @@ -108,38 +144,39 @@ def fix_indexing(config_dict, keys=['first_range_gate_indx', 'bgCorRangeIndx', ' return config_dict -def getPollyConfigfromArray(polly_config_array, picasso_config_dict): - """function to load the config for the time identified +def getPollyConfigfromArray(polly_config_array:pd.DataFrame, picasso_config_dict:dict) -> dict: + """Function to load the config for the time identified. - aim is to declutter the runscript + Aim is to declutter the runscript - Parameters ---------- polly_config_array : pandas dataframe - selected line form the links.xlsx + Selected line form the links.xlsx. picasso_config_dict : dict - general picasso config + General picasso config. Returns ------- polly_config_dict : dict - + A dictionary with the polly config / default parameters. """ + assert len(polly_config_array) == 1, 'given config array has more than one value' polly_config_file = Path( picasso_config_dict['polly_config_folder'], - polly_config_array['Config file'].item()) - #print(polly_config_file) + polly_config_array['Config file'].item() + ) + # print(polly_config_file) polly_default_config_file = Path( picasso_config_dict['path_config'], 'polly_global_config.json' ) - #print(polly_default_config_file) + # print(polly_default_config_file) polly_config_dict = loadPollyConfig( - polly_config_file, polly_default_config_file) - + polly_config_file, polly_default_config_file + ) polly_config_dict['name'] = polly_config_array['Instrument'].item() polly_config_dict['site'] = polly_config_array['Location'].item() polly_config_dict['asl'] = polly_config_array['asl.'].item() @@ -149,9 +186,28 @@ def getPollyConfigfromArray(polly_config_array, picasso_config_dict): return polly_config_dict -def loadPollyConfig(polly_config_file, polly_default_config_file): - """ +def loadPollyConfig(polly_config_file:str, polly_default_config_file:str) -> dict: + """Load the spesific and default config files and combine them into one dictionary. + + Parameters + ---------- + polly_config_file : str + Path to polly config file. + polly_default_config_file : str + path to polly default file. + + Returns + ------- + dict + A dictionary with the polly config / default parameters. + + Notes + ----- + .. TODO:: + - Maybe change `polly_default_config_file` to `polly_global_config_file` + as the defualt one is no longer used. """ + polly_default_config_file_path = Path(polly_default_config_file) polly_config_file_path = Path(polly_config_file) polly_config_dict = {} @@ -212,8 +268,10 @@ def loadPollyConfig(polly_config_file, polly_default_config_file): if 'first_range_gate_indx' in polly_default_config_file_dict.keys(): fix_indexing_keys = ['first_range_gate_indx'] elif 'LC' in polly_default_config_file_dict.keys(): - fix_indexing_keys = ['LC'] - return fix_indexing(polly_config_dict, keys=fix_indexing_keys + ['bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx']) + fix_indexing_keys = ['LC'] # TODO: Why are we subtracting one from LC??? + return fix_indexing(polly_config_dict, keys=fix_indexing_keys + [ + 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', + 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', 'depol_cal_minbin_1064']) except Exception: logging.warning(f'polly_config_file: {polly_config_file} can not be processed.', exc_info=True) @@ -224,21 +282,29 @@ def loadPollyConfig(polly_config_file, polly_default_config_file): if 'first_range_gate_indx' in polly_default_config_file_dict.keys(): fix_indexing_keys = ['first_range_gate_indx'] elif 'LC' in polly_default_config_file_dict.keys(): - fix_indexing_keys = ['LC'] - return fix_indexing(polly_default_config_file_dict, keys=fix_indexing_keys + ['bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', 'LCMeanMaxIndx']) - else: + fix_indexing_keys = ['LC'] # TODO: Why are we subtracting one from LC??? + return fix_indexing(polly_default_config_file_dict, keys=fix_indexing_keys + [ + 'bgCorRangeIndx', 'bgCorRangeIndxLow', 'bgCorRangeIndxHigh', 'LCMeanMinIndx', + 'LCMeanMaxIndx', 'depol_cal_minbin_355', 'depol_cal_minbin_532', 'depol_cal_minbin_1064']) + logging.critical(f'polly_default_config_file: {polly_default_config_file} can not be found. Aborting') return None + def checkPollyConfigDict(polly_config_dict:dict) -> dict: - """ - Check and potentially modify polly config dict + """Check and potentially modify polly config dict. - Parameters: - - polly_config_dict (dict): polly config dict to be checked - Output: - - new_polly_config_dict (dict): checked (and modified) polly config dict + Parameters + ---------- + polly_config_dict : dict + Polly config dict to be checked. + + Returns + ------- + new_polly_config_dict : dict + Checked (and modified) polly config dict. """ + logging.info(".. checking polly config dict") new_polly_config_dict = polly_config_dict.copy() diff --git a/ppcpy/io/readMeteo.py b/ppcpy/io/readMeteo.py index f09bc06..3d22312 100644 --- a/ppcpy/io/readMeteo.py +++ b/ppcpy/io/readMeteo.py @@ -102,8 +102,9 @@ def __init__(self, meteorDataSource:str, meteo_folder:str, meteo_file:str): Notes ----- - **History** + ** History ** - 2021-05-22: First edition by Zhenping. + """ assert meteorDataSource == 'nc_cloudnet', "Other meteo sources are not implemented yet" @@ -114,7 +115,6 @@ def __init__(self, meteorDataSource:str, meteo_folder:str, meteo_file:str): def load(self, times:float, heights:np.ndarray, asl:float, flagPicassoComparison:bool=False): """Load the data and resample to 15 minute intervals. - Parameters ---------- times : float @@ -149,7 +149,6 @@ def get_mean_profiles(self, time_slice:list) -> list: for t in time_slice: mean_profiles.append(self.ds.sel(time=slice(*t)).mean(dim='time')) - print(f"get_mean_profiles(time_slice: {type(time_slice)}) -> {type(mean_profiles)}") return mean_profiles @@ -178,8 +177,9 @@ def __init__(self, basepath, filepattern): Notes ----- - **History** + ** History ** - xxxx-xx-xx: First edition by ... + """ if not '/' == basepath[-1]: @@ -203,14 +203,14 @@ def find_path_for_time(self, time:float) -> str: """ candidates = glob.glob(self.basepath + "**", recursive=True) - #print('candidates ', candidates) + # print('candidates ', candidates) dt = datetime.datetime.fromtimestamp(time) regex = re.compile(self.filepattern.format(dt)) - #print('regex ', regex) + # print('regex ', regex) filename = [s for s in candidates if regex.search(s) ] - #print('filename ', filename) + # print('filename ', filename) assert len(filename) == 1, f"{os.getcwd()}, {self.basepath} found {candidates} reduced to filenames {filename}" diff --git a/ppcpy/io/readPollyRawData.py b/ppcpy/io/readPollyRawData.py index c2b07f1..5c443e2 100644 --- a/ppcpy/io/readPollyRawData.py +++ b/ppcpy/io/readPollyRawData.py @@ -4,77 +4,70 @@ #from pathlib import Path #import logging #import sys + def readPollyRawData(filename: str) -> dict: - """read the Polly raw file + """Read Polly raw data. Parameters ---------- filename : str - + Absolute path of the polly data. Returns ------- data_dict : dict - + rawSignal : ndarray (channel x height x time) + backscatter signal [Photon Count]. + mShots : ndarray + number of the laser shots for each profile. + flagValidProfile : ndarray + flag to represent the validity of each signal profile. + mTime : ndarray + datetime array for the measurement time of each profile. + depCalAng : ndarray + angle of the polarizer in the receiving channel. (>0 means + calibration process starts) [degree]. + zenithAng : numeric??? + zenith angle of the laser beam [degree]. + repRate : float + laser pulse repetition rate [s^-1]. + hRes : float + spatial resolution [m]. + mSite : str + measurement site. + deadtime : ndarray (channel x polynomial_orders) + deadtime correction parameters. + lat : float + latitude of measurement site [degree]. + lon : float + longitude of measurement site [degree]. + alt : float + altitude of measurement site [degree]. + filenameStartTime : datenum?????? + start time extracted from filename. + Notes + ----- + .. TODO:: + - Go over the Returns section in the docsting. Are these variables actuly returned by the function? + - Deleat the matlab code underneath.S + + ** HISTORY ** + + - 2024-03-21: First edition by Andi Klamt. + - xxxx-xx-xx: Translated to python. + + .. Authors: - klamt@tropos.de """ -#% READPOLLYRAWDATA Read polly raw data. -#% -#% USAGE: -#% data = readPollyRawData(file) -#% -#% INPUTS: -#% file: char -#% absolute path of the polly data. -#% -#% -#% OUTPUTS: -#% data: struct -#% rawSignal: matrix (channel x height x time) -#% backscatter signal. [Photon Count] -#% mShots: array -#% number of the laser shots for each profile. -#% flagValidProfile: array -#% flag to represent the validity of each signal profile. -#% mTime: array -#% datetime array for the measurement time of each profile. -#% depCalAng: array -#% angle of the polarizer in the receiving channel. (>0 means -#% calibration process starts). [degree] -#% zenithAng: numeric -#% zenith angle of the laser beam. [degree] -#% repRate: float -#% laser pulse repetition rate. [s^-1] -#% hRes: float -#% spatial resolution [m] -#% mSite: char -#% measurement site. -#% deadtime: matrix (channel x polynomial_orders) -#% deadtime correction parameters. -#% lat: float -#% latitude of measurement site. [degree] -#% lon: float -#% longitude of measurement site. [degree] -#% alt: float -#% altitude of measurement site. [degree] -#% filenameStartTime: datenum -#% start time extracted from filename. -#% -#% HISTORY: -#% - 2024-03-21: First edition by Andi Klamt. -#% -#% .. Authors: - klamt@tropos.de - - data_dict={} + data_dict = {} filename_path = Path(filename) if filename_path.is_file(): logging.info(f'reading nc-file: {filename}') else: -# print(f'{filename} does not exist. Aborting') logging.critical(f'{filename} does not exist.') sys.exit(1) - return None + return ## open nc-file as dataset nc_file_ds = netCDF4.Dataset(filename, "r") @@ -86,8 +79,8 @@ def readPollyRawData(filename: str) -> dict: data_dict['global_attributes'] = {} for nc_attr in nc_file_ds.ncattrs(): att_value = nc_file_ds.getncattr(nc_attr) - #global_attr[nc_attr] = att_value - #data_dict[f'global_attr__{nc_attr}'] = att_value + # global_attr[nc_attr] = att_value + # data_dict[f'global_attr__{nc_attr}'] = att_value data_dict['global_attributes'][nc_attr] = att_value var_ls = [] @@ -95,8 +88,8 @@ def readPollyRawData(filename: str) -> dict: var_ls.append(var) ## fill data_dict with variable-values - #for var_name in var_ls: - # data_dict[var_name] = nc_file_ds[var_name][:] + # for var_name in var_ls: + # data_dict[var_name] = nc_file_ds[var_name][:] ## fill data_dict with variable-value and get variable attributes from nc-file for var_name in var_ls: @@ -106,13 +99,13 @@ def readPollyRawData(filename: str) -> dict: for var_att in nc_file_ds.variables[var_name].ncattrs(): var_att_value = nc_file_ds.variables[var_name].getncattr(var_att) - #data_dict[f'{var_name}___{var_att}'] = var_att_value + # data_dict[f'{var_name}___{var_att}'] = var_att_value data_dict[var_name]['var_att'][var_att] = var_att_value - #print(var_name, type(data_dict[var_name]['var_data'])) + # print(var_name, type(data_dict[var_name]['var_data'])) if isinstance(data_dict[var_name]['var_data'], np.ma.MaskedArray): data_dict[var_name]['var_data'] = data_dict[var_name]['var_data'].data - #print(var_name, type(data_dict[var_name]['var_data'])) + # print(var_name, type(data_dict[var_name]['var_data'])) assert data_dict['measurement_height_resolution']['var_att']['units'] == 'ns' # measurement height resolution should be given in ns @@ -122,8 +115,10 @@ def readPollyRawData(filename: str) -> dict: nc_file_ds.close() return data_dict -# -#%% variables initialization + + +# ------ Matlab eqvialent code: +#% variables initialization #data = struct(); #data.rawSignal = []; #data.mShots = []; @@ -140,7 +135,7 @@ def readPollyRawData(filename: str) -> dict: #data.angle = []; # # -#%% read data +#% read data #try # rawSignal = double(ncread(file, 'raw_signal')); # if is_nc_variable(file, 'deadtime_polynomial') diff --git a/ppcpy/io/sql_interaction.py b/ppcpy/io/sql_interaction.py index 91cf7ee..6f47525 100644 --- a/ppcpy/io/sql_interaction.py +++ b/ppcpy/io/sql_interaction.py @@ -7,31 +7,34 @@ import ppcpy.misc.helper as helper from ppcpy.misc.helper import default_to_regular + mapping:dict = {'far_range': 'FR', 'near_range': 'NR', 'dfov': 'DFOV'} mapping_inverse:dict = {y: x for x, y in mapping.items()} + def string_to_ts(s): - """string of format %Y-%m-%d %H:%M:%S to timestamp (timezone-aware)""" + """String of format %Y-%m-%d %H:%M:%S to timestamp (timezone-aware)""" return datetime.strptime(s, "%Y-%m-%d %H:%M:%S").replace(tzinfo=timezone.utc).timestamp() + def get_from_sql_db(db_path:str, table_name:str, ts_interval:list[str]) -> dict: - """read lidar calibration constant or depol calibration from database + """Read lidar calibration constant or depol calibration from database. Parameters ---------- db_path : str - name of the specific sqlite db file. + Name of the specific sqlite db file. table_name : str - default 'lidar_calibration_constant' + Default 'lidar_calibration_constant'. ts_interval : str - the date or timestamp to look for - + The date or timestamp to look for. Returns ------- dict - in calibration storage format + In calibration storage format. """ + delta = timedelta(hours=24) start = ( datetime.fromtimestamp(ts_interval[0], timezone.utc) - delta @@ -82,21 +85,31 @@ def get_from_sql_db(db_path:str, table_name:str, ts_interval:list[str]) -> dict: return ret + def prepare_for_sql_db_writing(data_cube, parameter:str, method:str) -> list[tuple]: - """ - Collect all necessary variable and save it to a list of tuples for inserting into a SQLite table. + """Collect all necessary variable and save it to a list of + tuples for inserting into a SQLite table. Parameters ---------- data_cube : object + Main PicassoProc object parameter :str - LC or DC + Name of parameter to stoere eg. 'LC' or 'DC'. method : str - klett or raman + Name of retrieval method used to retrieve the parameter + eg. 'klett' or 'raman'. Returns ------- rows_to_insert : list of tuples + Rows to insert in the database. + + ** History ** + + - xxxx-xx-xx: First edition by + - xxxx-xx-xx: Translated to python. + """ rows_to_insert = [] @@ -139,7 +152,9 @@ def prepare_for_sql_db_writing(data_cube, parameter:str, method:str) -> list[tup wv, tel_db, str(data_cube.rawfile), data_cube.device)) return rows_to_insert -def setup_empty(db_path:str, table_name:str, column_names:list[str], data_types:list[str], unique:str=''): + +def setup_empty(db_path:str, table_name:str, column_names:list[str], + data_types:list[str], unique:str=''): """Create/Initialise an empty database. Parameters @@ -165,7 +180,8 @@ def setup_empty(db_path:str, table_name:str, column_names:list[str], data_types: conn.close() -def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], rows_to_insert:list[str]): +def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], + rows_to_insert:list[str]): """Insert multiple rows into a SQLite table. Parameters @@ -182,15 +198,15 @@ def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], ro Notes ----- - The IGNORE syntax somehow did not work. - With the UNIQUE colums defined and INSERT OR REPLACE at least the new values are updated. - Though they are given a new ID. + - The IGNORE syntax somehow did not work. + With the UNIQUE colums defined and INSERT OR REPLACE at least the new values are updated. + Though they are given a new ID. """ placeholders = ', '.join(['?'] * len(column_names)) columns = ', '.join(column_names) - #sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders}) ON CONFLICT(cali_start_time, cali_stop_time, wavelength, polly_type, telescope) DO UPDATE SET data = excluded.data" + # sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders}) ON CONFLICT(cali_start_time, cali_stop_time, wavelength, polly_type, telescope) DO UPDATE SET data = excluded.data" sql = f"INSERT OR REPLACE INTO {table_name} ({columns}) VALUES ({placeholders})" try: @@ -207,5 +223,3 @@ def write_rows_to_sql_db(db_path:str, table_name:str, column_names:list[str], ro logging.info(f"{inserted} rows inserted into '{table_name}'.") except sqlite3.Error as e: logging.warning(f"SQLite error: {e}") - - diff --git a/ppcpy/io/write2nc.py b/ppcpy/io/write2nc.py index 824d641..23a24d9 100644 --- a/ppcpy/io/write2nc.py +++ b/ppcpy/io/write2nc.py @@ -10,12 +10,32 @@ import ppcpy.misc.json2nc_mapping as json2nc_mapping from ppcpy._version import __version__ + ## getting root dir of PicassoPy root_dir0 = Path(__file__).resolve().parent.parent.parent root_dir = helper.detect_path_type(root_dir0) -def get_git_info(path="."): - """ """ + +def get_git_info(path:str=".") -> tuple: + """... + + Parameters + ---------- + path : str + Path to ... + + Returns + ------- + branch : ... or None + ... + commit : ... or None + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + try: repo = Repo(Path(path).resolve(), search_parent_directories=True) branch = repo.active_branch.name @@ -24,24 +44,76 @@ def get_git_info(path="."): except Exception: return None, None -def date_splitting(timestamp): - """ """ + +def date_splitting(timestamp:str) -> tuple: + """Split date into YYYY, MM, DD. + + Parameters + ---------- + timestamp : str + Date in format 'YYYYMMDD'. + + Returns + ------- + YYYY : str + Year. + MM : str + Month. + DD : str + Day. + """ + YYYY = timestamp[0:4] MM = timestamp[4:6] DD = timestamp[6:8] - return YYYY,MM,DD + return YYYY, MM, DD + +def adding_fixed_vars(data_cube, json_nc_mapping_dict:dict): + """Add fixed variables to mapping dict. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ -def adding_fixed_vars(data_cube, json_nc_mapping_dict): - """ """ ## adding fixed variables json_nc_mapping_dict['variables']['altitude']['data'] = data_cube.polly_config_dict['asl'] json_nc_mapping_dict['variables']['latitude']['data'] = data_cube.polly_config_dict['lat'] json_nc_mapping_dict['variables']['longitude']['data'] = data_cube.polly_config_dict['lon'] json_nc_mapping_dict['variables']['tilt_angle']['data'] = data_cube.rawdata_dict['zenithangle']['var_data'] -def adding_global_attr(data_cube, json_nc_mapping_dict): - """ """ + +def adding_global_attr(data_cube, json_nc_mapping_dict:dict): + """Add global attributes to mapping dict. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## adding global attributes json_nc_mapping_dict['global_attributes']['location'] = data_cube.polly_config_dict['site'] json_nc_mapping_dict['global_attributes']['source'] = data_cube.polly_config_dict['name'] @@ -56,15 +128,34 @@ def adding_global_attr(data_cube, json_nc_mapping_dict): json_nc_mapping_dict['global_attributes']['history'] = f'Last processing time at {now_utc} UTC, git branch: {gitbranch}, git commit: {gitcommit}' -def write_channelwise_2_nc_file(data_cube, root_dir=root_dir, prod_ls=[]): - """ """ +def write_channelwise_2_nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[]): + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + root_dir : str + Path to root directory. + prod_ls : list of str + List of product names. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## writes data from products, listed in prod_ls, to nc-file # available products: prod_ls = ["SNR", "BG", "RCS", "att_bsc", "vol_depol"] for prod in prod_ls: logging.info(f"saving product: {prod}") # json_nc_mapping_dict = {} # if prod in polly_config_dict["prodSaveList"]: - json_nc_mapping_dict = json2nc_mapping.read_json_to_dict(Path(root_dir,'ppcpy','config',f'json2nc-mapper_{prod}.json')) + json_nc_mapping_dict = json2nc_mapping.read_json_to_dict(Path(root_dir, 'ppcpy', 'config', f'json2nc-mapper_{prod}.json')) if prod == "SNR" or prod == "BG" or prod == "RCS": """ map channels to variables """ @@ -82,7 +173,7 @@ def write_channelwise_2_nc_file(data_cube, root_dir=root_dir, prod_ls=[]): ## adding dynamical variables for v in list(json_nc_mapping_dict['variables'].keys()): ## use list here to suppress RuntimeError: dictionary changed size during iteration - #for v in json_nc_mapping_dict['variables'].keys(): + # for v in json_nc_mapping_dict['variables'].keys(): if v in data_cube.retrievals_highres.keys(): json_nc_mapping_dict['variables'][v]['data'] = data_cube.retrievals_highres[v] ## update variable attribute @@ -98,14 +189,34 @@ def write_channelwise_2_nc_file(data_cube, root_dir=root_dir, prod_ls=[]): """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod) -def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): - """ """ + +def write2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[]): + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + root_dir : str + Path to root directory. + prod_ls : list of str + List of product names. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + ## writes data from products, listed in prod_ls, to nc-file for prod in prod_ls: logging.info(f"saving product: {prod}") @@ -122,60 +233,66 @@ def write2nc_file(data_cube, root_dir=root_dir, prod_ls=[]): adding_global_attr(data_cube, json_nc_mapping_dict) ## adding dynamical variables - json_nc_translator = json2nc_mapping.read_json_to_dict(Path(root_dir,'ppcpy','config', f'json2nc_translator.json')) + json_nc_translator = json2nc_mapping.read_json_to_dict(Path(root_dir, 'ppcpy', 'config', f'json2nc_translator.json')) for var in json_nc_translator[prod]['variables'].keys(): - #print(f'var: {var}') if var in json_nc_mapping_dict['variables'].keys(): pass else: logging.warning(f'variable {var} not in json2nc_mapper_file') continue parameter = json_nc_translator[prod]['variables'][var]['parameter'] - if "quality_mask" in var: + if "quality_mask" in var and 'quality_mask' in data_cube.retrievals_highres: ch = getattr(data_cube, parameter) - qm = np.squeeze(data_cube.retrievals_highres['quality_mask'][:,:,ch]) + qm = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, ch]) json_nc_mapping_dict['variables'][var]['data'] = qm + elif "SNR" in var: + ch = getattr(data_cube, parameter) + if data_cube.polly_config_dict['flagUseImprovedSNR'] and 'SNR_quasi' in data_cube.retrievals_highres: + snr = np.squeeze(data_cube.retrievals_highres['SNR_quasi'][:, :, ch]) + json_nc_mapping_dict['variables'][var]['data'] = snr + elif 'SNR' in data_cube.retrievals_highres: + snr = np.squeeze(data_cube.retrievals_highres['SNR'][:, :, ch]) + json_nc_mapping_dict['variables'][var]['data'] = snr else: pass - #print(f'para: {parameter}') - if parameter in data_cube.retrievals_highres.keys(): - #print(f'para in retrievals_highres: {parameter}') json_nc_mapping_dict['variables'][var]['data'] = data_cube.retrievals_highres[parameter] ### remove empty key-value-pairs for var in list(json_nc_mapping_dict['variables'].keys()): ## use list here to suppress RuntimeError: dictionary changed size during iteration if json_nc_mapping_dict['variables'][var]['data'] is None: - print(f'removing variable: {var}') json2nc_mapping.remove_variable_from_json_dict_mapper(data_dict=json_nc_mapping_dict, key_to_remove=var) """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod) def write_profile2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[], collect_debug:bool=False): - """ - Saving profile data to NetCDF4 files + """Saving profile data to NetCDF4 files. Parameters ---------- data_cube : object - Main PicassoProc object + Main PicassoProc object. root_dir : str + Path to root directory. prod_ls : list - List of product names + List of product names. + Notes + ----- .. TODO:: - Missing comment in variable attributes. - Not all retrievals / information needed for the profiles are in data_cube.retrivals_highres... - write docstring + - Missing comment in variable attributes. + - Not all retrievals / information needed for the profiles are in data_cube.retrivals_highres... + - write docstring """ + ## writes data from products, listed in prod_ls, to nc-file ## available products: prod_ls = ["profiles", "NR_profeils", "OC_profiles"] @@ -246,15 +363,34 @@ def write_profile2nc_file(data_cube, root_dir:str=root_dir, prod_ls:list=[], col #print(data_dict_copy['variables'].keys()) """ Create the NetCDF file """ - yyyy,mm,dd = date_splitting(data_cube.date) - output_path = Path(data_cube.picasso_config_dict["results_folder"],data_cube.device,yyyy,mm,dd) + yyyy, mm, dd = date_splitting(data_cube.date) + output_path = Path(data_cube.picasso_config_dict["results_folder"], data_cube.device, yyyy, mm, dd) output_path.mkdir(parents=True, exist_ok=True) output_filename = Path(output_path, f"{data_cube.date}_{data_cube.device}_{start}_{stop}_{prod}.nc") json2nc_mapping.create_netcdf_from_dict(output_filename, data_cube, json_nc_mapping_dict, compression_level=1, prod=prod, cldFreeIndx=n) def adding_mol_profiles(data_cube, json_nc_mapping_dict:dict, cldFreeGrp:int) -> dict: - """Temporarily quick fix for adding molecular profiles as variables to the NetCDF profile outputs""" + """Temporarily quick fix for adding molecular profiles as variables to the NetCDF profile outputs. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + json_nc_mapping_dict : dict + ... + cldFreeGrp : int + Index of floud free segment. + + Yeilds + ------ + ... + + Notes + ----- + .. TODO:: Finish docstring. + """ + # molExt and molBsc: for wv in [355, 387, 407, 532, 607, 1058, 1064]: for var_name in [f'mExt_{wv}', f'mBsc_{wv}']: diff --git a/ppcpy/misc/concat.py b/ppcpy/misc/concat.py index 17a3a87..6e02db0 100644 --- a/ppcpy/misc/concat.py +++ b/ppcpy/misc/concat.py @@ -15,23 +15,43 @@ from scipy.sparse import diags import tracemalloc # memory usage tracking + def os_name(): - """""" + """ """ return platform.system() def date_splitting(timestamp): + """ """ YYYY = timestamp[0:4] MM = timestamp[4:6] DD = timestamp[6:8] - return YYYY,MM,DD + return YYYY, MM, DD -def get_input_path(timestamp, device, base_dir): - """ - Checking for the correct subpath - TODO: using glob or similiar to be more flexible in terms of level0b or martha... + +def get_input_path(timestamp, device:str, base_dir:Path): + """Checking for the correct subpath. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of polly device. + base_dir : Path + Path to base directory. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: + - Using glob or similiar to be more flexible in terms of level0b or martha... """ - YYYY,MM,DD = date_splitting(timestamp) + + YYYY, MM, DD = date_splitting(timestamp) search_root_normal = Path(base_dir) / device search_root_special = Path(base_dir) search_pattern = f"**/*{YYYY}_{MM}_{DD}*.nc*" @@ -40,20 +60,36 @@ def get_input_path(timestamp, device, base_dir): return file_path.parent.resolve() for file_path in search_root_special.rglob(search_pattern): return file_path.parent.resolve() + return - return None +def get_pollyxt_zipfiles(timestamp, device:str, raw_folder:Path): + """This function locates multiple pollyxt level0 nc-zip files + from one day measurements, and returns a list of zip-files. -def get_pollyxt_zipfiles(timestamp, device, raw_folder): - """ - This function locates multiple pollyxt level0 nc-zip files from one day measurements, - and returns a list of zip-files + Parameters + ---------- + timestamp : ... + ... + device : str + Name of Polly device. + raw_floder : Path + Path to raw data folder. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + input_path = get_input_path(timestamp, device, raw_folder) if input_path: path_exist = Path(input_path) else: - return None + return if path_exist.exists() == True: @@ -71,21 +107,40 @@ def get_pollyxt_zipfiles(timestamp, device, raw_folder): #for file in polly_zip_files_list0: # polly_zip_files_list.append(str(file)) -def get_pollyxt_nc_files(timestamp, device, raw_folder): - """ - This function locates pollyxt level0 nc-files (i.e. already 24h-merged level0b files) from one day measurements, - and returns a list of nc-files + +def get_pollyxt_nc_files(timestamp, device:str, raw_folder:Path): + """This function locates pollyxt level0 nc-files (i.e. already 24h-merged level0b files) + from one day measurements, and returns a list of nc-files. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of polly device. + raw_folder : str + Path to raw data folder. + + Returns + ------- + polly_files_list : ... + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + input_path = get_input_path(timestamp, device, raw_folder) if input_path: path_exist = Path(input_path) else: - return None + return if path_exist.exists() == True: ## set the searchpattern for the zipped-nc-files: - YYYY,MM,DD = date_splitting(timestamp) + YYYY, MM, DD = date_splitting(timestamp) nc_searchpattern = str(YYYY)+'_'+str(MM)+'_'+str(DD)+'*_*[0-9].nc' @@ -93,16 +148,34 @@ def get_pollyxt_nc_files(timestamp, device, raw_folder): polly_files_list = [x for x in polly_files if x.is_file()] return polly_files_list else: - return None + return -def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): - """ - This function checks the size of the zip-files. - If smaller than threshold (e.g. 500000 Byte), the file will be skipped. - The files passing the filesize check will be unzipped - returns a list of unzipped files +def unzipping_pollyxt_files(polly_zip_files_list:list, timestamp, output_path:Path): + """This function checks the size of the zip-files. + If smaller than threshold (e.g. 500000 Byte), the file will be skipped. + The files passing the filesize check will be unzipped + returns a list of unzipped files. + + Parameters + ---------- + polly_zip_files_list : list?? + ... + timestamp : ... + ... + output_path : Path + Path to output folder. + + Returns + ------- + polly_files_list : list?? + ... + + Notes + ----- + .. TODO:: Finish docstring. """ + # Ensure the destination directory exists Path(output_path).mkdir(parents=True, exist_ok=True) @@ -123,14 +196,14 @@ def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): continue ## go to next file ## unzipping - YYYY,MM,DD = date_splitting(timestamp) + YYYY, MM, DD = date_splitting(timestamp) date_pattern = str(YYYY) + '_' + str(MM) + '_' + str(DD) if len(to_unzip_list) > 0: ## if working remotly on windows, copy zipped files first, than unzip if os_name().lower() == 'windows': logging.info("Copy zipped files to local drive...") for zip_file in to_unzip_list: - logging-info(zip_file) + logging.info(zip_file) shutil.copy2(Path(zip_file), Path(output_path) / Path(zip_file).name) logging.info(f"Unzipping to: {output_path}") for zip_file in Path(output_path).iterdir(): @@ -154,15 +227,34 @@ def unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path): return polly_files_list else: logging.warning('no files to unzip') - return None + return -def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): - """ - This function locates multiple pollyxt level0 nc-zip files from one day measurements, - unzipps the files to output_path - and returns a list of files to be merged +def get_pollyxt_files(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """This function locates multiple pollyxt level0 nc-zip files from one day measurements, + unzipps the files to output_path and returns a list of files to be merged. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + polly_files_list : list?? + ... + + Notes + ----- + .. TODO:: Finish docstring! """ + unzip = kwargs.get('unzipping', True) if str(unzip).lower() == 'true': @@ -175,7 +267,7 @@ def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): sys.exit() ## unzip files - polly_files_list = unzipping_pollyxt_files(polly_zip_files_list,timestamp,output_path) + polly_files_list = unzipping_pollyxt_files(polly_zip_files_list, timestamp, output_path) if polly_files_list: pass else: @@ -190,19 +282,39 @@ def get_pollyxt_files(timestamp, device, raw_folder, output_path, **kwargs): logging.error('No files found. Aborting') sys.exit() - ## sort lists polly_files_list.sort() return polly_files_list -def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): - """ - This function locates multiple pollyxt logbook-zip files from one day measurements, - unzipps the files to output_path - and merge them to one file +def get_pollyxt_logbook_files(timestamp, device:str, raw_folder:Path, output_path:Path) -> tuple: + """This function locates multiple pollyxt logbook-zip files from one day measurements, + unzipps the files to output_path and merge them to one file. + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + tuple + Empty tuple. + + Notes + ----- + .. TODO:: + - Finish docstring! + - Why are we returning an empty tuple? """ + input_path = get_input_path(timestamp, device, raw_folder) path_exist = Path(input_path) @@ -215,10 +327,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): zip_searchpattern = str(YYYY)+'_'+str(MM)+'_'+str(DD)+'*_*laserlogbook*.zip' - polly_laserlog_files = Path(r'{}'.format(input_path)).glob('{}'.format(zip_searchpattern)) + polly_laserlog_files = Path(r'{}'.format(input_path)).glob('{}'.format(zip_searchpattern)) polly_laserlog_zip_files_list0 = [x for x in polly_laserlog_files if x.is_file()] - ## convert type path to type string polly_laserlog_zip_files_list = [] for file in polly_laserlog_zip_files_list0: @@ -239,7 +350,6 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): to_unzip_list.append(zip_file) - ## unzipping if len(to_unzip_list) > 0: for zip_file in to_unzip_list: @@ -265,9 +375,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): laserlog_filename = polly_laserlog_files_list[0] laserlog_filename = Path(laserlog_filename).name - laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc',laserlog_filename)[0] + laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc', laserlog_filename)[0] laserlog_filename = f'{laserlog_filename_left}_00_00_01.nc.laserlogbook.txt' - destination_file = Path(output_path,laserlog_filename) + destination_file = Path(output_path, laserlog_filename) # Open the source file in binary mode and read its content with open(result_file, 'rb') as source: @@ -283,7 +393,7 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): def add_to_list(element, from_list, to_list): - """""" + """ """ if from_list[element] in to_list: pass else: @@ -291,17 +401,41 @@ def add_to_list(element, from_list, to_list): -def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): - """""" +def checking_vars(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """... + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_floder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + selected_var_nc_ls : ... + ... + + Notes + ----- + .. TODO:: + - Finish docstring! + - Variable `force` is not defined. + """ + ## select only those nc-files where the values of some specific variables haven't changed vars_of_interest = [ 'measurement_height_resolution', 'laser_rep_rate', 'laser_power', -# 'laser_flashlamp', + #'laser_flashlamp', 'location_height', 'neutral_density_filter', -# 'location_coordinates', + #'location_coordinates', 'pm_voltage', 'pinhole', 'polstate', @@ -327,22 +461,22 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): print('\n') logging.info('checking differences in selected variables ...') for ds in range(0,len(polly_file_ds_ls)-1): -# print('\n') -# print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) + # print('\n') + # print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) for var in vars_of_interest: if var in polly_file_ds_ls[ds].variables.keys(): ## check if var is available within the polly-datastructure (depending on polly-system) var_value_1=str(polly_file_ds_ls[ds].variables[var][:]) var_value_2=str(polly_file_ds_ls[ds+1].variables[var][:]) - # print(var + ": " + var_value_1) - # print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) if var_value_1 == var_value_2 and diff_var==0: # print('no difference found ...') add_to_list(ds, polly_files_list, selected_var_nc_ls) elif var_value_1 != var_value_2 and diff_var==0: logging.info('difference found in var:') logging.info(var) - #print(var + ": " + var_value_1) - #print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) diff_var=1 add_to_list(ds, polly_files_list, selected_var_nc_ls) if force == True else None elif var_value_1 == var_value_2 and diff_var != 0: @@ -357,7 +491,7 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): add_to_list(-1, polly_files_list, selected_var_nc_ls) logging.info('no differences found in selected variables!') elif diff_var != 0: -# add_to_list(-1,polly_files_list,selected_var_nc_ls) if force == True else None + # add_to_list(-1, polly_files_list, selected_var_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: add_to_list(-1, polly_files_list, selected_var_nc_ls) @@ -371,26 +505,31 @@ def checking_vars(timestamp, device, raw_folder, output_path, **kwargs): return selected_var_nc_ls -def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): +def checking_attr(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): """... Parameters ---------- timestamp : ... ... - device : ... - ... - raw_folder : ... - ... - output_path : ... - ... + device : str + Name of PollyXT device. + raw_folder : Path + Path to raw data folder. + output_path : Path + Path to output folder. Returns ------- ... - TODO: Variables 'force' and 'polly_files_list' are not defined anywhere + Notes + ----- + .. TODO:: + - Finish docstring! + - Variables 'force' and 'polly_files_list' are not defined anywhere. """ + ## select only those nc-files where the global attributes and the var-attributes haven't changed selected_var_nc_ls = checking_vars(timestamp, device, raw_folder, output_path, **kwargs) if len(selected_var_nc_ls) == 1: @@ -409,15 +548,15 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): logging.info('checking differences in attributes ...') for ds in range(0, len(polly_file_ds_ls) - 1): ## get global attributes as a list of strings -# print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) -# print('\nglobal attributes:') + # print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) + # print('\nglobal attributes:') for nc_attr in polly_file_ds_ls[0].ncattrs(): # att_value=repr(input_nc_file.getncattr(nc_attr)) att_value_1 = polly_file_ds_ls[ds].getncattr(nc_attr) att_value_2 = polly_file_ds_ls[ds+1].getncattr(nc_attr) -# print(nc_attr) -# print(" " + att_value_1) -# print(" " + att_value_2) + # print(nc_attr) + # print(" " + att_value_1) + # print(" " + att_value_2) if att_value_1 == att_value_2 and diff_att==0: add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) elif att_value_1 != att_value_2 and diff_att==0: @@ -433,15 +572,15 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): logging.info('difference found!') add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) if force == True else None -# print("\nvariable attributes:") + # print("\nvariable attributes:") for var in polly_file_ds_ls[0].variables.keys(): -# print(var) + # print(var) for var_att in polly_file_ds_ls[0].variables[var].ncattrs(): var_att_value_1 = polly_file_ds_ls[ds].variables[var].getncattr(var_att) var_att_value_2 = polly_file_ds_ls[ds+1].variables[var].getncattr(var_att) -# print(" " + var_att) -# print(" " + var_att_value_1) -# print(" " + var_att_value_2) + # print(" " + var_att) + # print(" " + var_att_value_1) + # print(" " + var_att_value_2) if var_att_value_1 == var_att_value_2 and diff_var_att == 0: pass elif var_att_value_1 != var_att_value_2 and diff_var_att == 0: @@ -458,12 +597,11 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): for ds in range(0, len(polly_file_ds_ls) - 1): polly_file_ds_ls[ds].close() - if diff_att == 0: add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) logging.info('no differences found in global attributes!') elif diff_att != 0: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) if force == True else None + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: @@ -477,22 +615,41 @@ def checking_attr(timestamp, device, raw_folder, output_path, **kwargs): if diff_var_att == 0: logging.info('no differences found in variable attributes!') -# elif diff_var_att != 0: -# ## if force==true, merge, but if force==false: the whole day will not be in list anymore -# if force == True: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) -# print('\ndifferences found in variable attributes! But will be force-merged.\n') -# else: -# print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') -# sys.exit() - + # elif diff_var_att != 0: + # ## if force==true, merge, but if force==false: the whole day will not be in list anymore + # if force == True: + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) + # print('\ndifferences found in variable attributes! But will be force-merged.\n') + # else: + # print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') + # sys.exit() logging.info(selected_att_nc_ls) return selected_att_nc_ls -def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): - """""" +def checking_timestamp(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs): + """... + + Parameters + ---------- + timestamp : ... + ... + device : str + Name of PollyXT device. + raw_folder : Path + Path to raw data folder. + output_path : Path + Path to output folder. + + Returns + ------- + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ selected_timestamp_nc_ls = checking_attr(timestamp, device, raw_folder, output_path, **kwargs) if len(selected_timestamp_nc_ls) == 1: return selected_timestamp_nc_ls @@ -503,8 +660,8 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): polly_file_ds = Dataset(files, "r") polly_file_ds_ls.append(polly_file_ds) - for elementNR,ds in enumerate(polly_file_ds_ls): - # print(selected_timestamp_nc_ls[elementNR]) + for elementNR, ds in enumerate(polly_file_ds_ls): + # print(selected_timestamp_nc_ls[elementNR]) timestamp_ds = ds.variables['measurement_time'][:] if 19700101 in timestamp_ds.T[0]: logging.info(f'The file: {selected_timestamp_nc_ls[elementNR]} contains incorrect timestamps!') @@ -512,9 +669,9 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): ## get correct timestamp_ds from filename timestamp_filename = selected_timestamp_nc_ls[elementNR] timestamp_filename = timestamp_filename.stem - timestamp_filename = re.split(r'_',str(timestamp_filename))[-3:] + timestamp_filename = re.split(r'_', str(timestamp_filename))[-3:] ## del. nc-file - #os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps + # os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps ## calc. the deltaT between measurementdatapoints laser_rep_rate = float(ds.variables['laser_rep_rate'][0]) measurement_shots = ds.variables['measurement_shots'][:] @@ -566,7 +723,6 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): else: new_var[:] = var[:] - ds.close() new_dataset.close() os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps @@ -580,7 +736,6 @@ def checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs): if ds.isopen(): ds.close() - logging.info('the following ' + str(len(selected_cor_timestamp_nc_ls)) + ' files can be merged:') logging.info(selected_cor_timestamp_nc_ls) return selected_cor_timestamp_nc_ls @@ -591,11 +746,27 @@ def get_memory_usage(): current, peak = tracemalloc.get_traced_memory() return current / 1024 / 1024, peak / 1024 / 1024 # MB + def write_netcdf_robust(ds: xr.Dataset, out_file: Path, comp_level: int = 1, max_retries: int = 3) -> bool: - """ - Robust NetCDF writing with retries and error handling + """Robust NetCDF writing with retries and error handling. + + Parameters + ---------- + ds : xr.Dataset + ... + out_file : Path + ... + comp_level : int, optional + ... Default is 1. + max_retries : int, optional + ... Default is 3. + + Returns + ------- + bool + True if ..., otherwise False. """ # Start tracing memory @@ -701,17 +872,27 @@ def write_netcdf_robust_old(ds: xr.Dataset, out_file: Path, comp_level: int = 1, max_retries: int = 3, timeout_seconds: int = 600) -> bool: - """ - Robust NetCDF writing with retries and error handling + """Robust NetCDF writing with retries and error handling. - Args: - ds: xr Dataset to write - out_file: Output file path - max_retries: Maximum number of retry attempts - timeout_seconds: Timeout in seconds + Parameters + ---------- + ds : xr.Dataset + Dataset to write. + out_file : Path + Output file path. + max_retries : int + Maximum number of retry attempts. + timeout_seconds : int + Timeout in seconds. - Returns: - bool: True if successful, False otherwise + Returns + ------- + bool + True if successful, False otherwise. + + Notes + ----- + - What is the difference between this function and the one above... """ for attempt in range(max_retries): @@ -784,11 +965,33 @@ def write_netcdf_robust_old(ds: xr.Dataset, out_file: Path, return False -def concat_files(timestamp, device, raw_folder, output_path, **kwargs): - """""" - ## merge selected files - sel_polly_files_list = checking_timestamp(timestamp,device,raw_folder,output_path, **kwargs) +def concat_files(timestamp, device:str, raw_folder:Path, output_path:Path, **kwargs) -> Path: + """... + + Parametrs + --------- + timestamp : ... + ... + device : str + Name of Polly device. + raw_folder : Path + Path to the raw folder. + output_path : Path + Path to the output folder. + + Returns + ------- + destination_file : Path + ... + + Notes + ----- + .. TODO:: Finish docstring! + """ + + ## merge selected files + sel_polly_files_list = checking_timestamp(timestamp, device, raw_folder, output_path, **kwargs) if len(sel_polly_files_list) == 0: logging.info('no files found for this day. no merging.') @@ -800,19 +1003,19 @@ def concat_files(timestamp, device, raw_folder, output_path, **kwargs): if len(sel_polly_files_list) == 1: logging.info("Only one file found. Nothing to merge!") - os.rename(sel_polly_files_list[0],Path(output_path,filestring)) + os.rename(sel_polly_files_list[0], Path(output_path, filestring)) return () else: ## parameters for controlling the merging process compat='override' ## Values of variable "laser_flashlamp" often changes, but those files will be merged anyway. This option picks the value from first dataset. coords='minimal' - ds = xr.open_mfdataset(sel_polly_files_list,combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) + ds = xr.open_mfdataset(sel_polly_files_list, combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) ## save to a single nc-file logging.info(f"merged nc-file '{filestring}' will be stored to '{output_path}'") logging.info("writing merged file ...") - merge_proc = write_netcdf_robust(ds=ds,out_file=Path(output_path,filestring_dummy)) + merge_proc = write_netcdf_robust(ds=ds, out_file=Path(output_path, filestring_dummy)) ds.close() @@ -820,16 +1023,15 @@ def concat_files(timestamp, device, raw_folder, output_path, **kwargs): pass else: logging.error('merging failed. Aborting') - return None + return logging.info("deleting individual .nc files ...") for el in sel_polly_files_list: logging.info(el) os.remove(el) - destination_file = Path(output_path,filestring) + destination_file = Path(output_path, filestring) if os.path.exists(destination_file): os.remove(destination_file) # Remove the existing destination file - os.rename(Path(output_path,filestring_dummy),destination_file) + os.rename(Path(output_path, filestring_dummy), destination_file) logging.info('done!') return destination_file - diff --git a/ppcpy/misc/helper.py b/ppcpy/misc/helper.py index db69f56..09aef48 100644 --- a/ppcpy/misc/helper.py +++ b/ppcpy/misc/helper.py @@ -13,7 +13,8 @@ from pathlib import Path import numpy as np from scipy.sparse import diags -from scipy.signal import savgol_coeffs +from scipy.signal import convolve, savgol_coeffs +from scipy.ndimage import uniform_filter as _uniform_filter def detect_path_type(fullpath:str): @@ -296,9 +297,9 @@ def get_pollyxt_logbook_files(timestamp, device, raw_folder, output_path): laserlog_filename = polly_laserlog_files_list[0] laserlog_filename = Path(laserlog_filename).name - laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc',laserlog_filename)[0] + laserlog_filename_left = re.split(r'_[0-9][0-9]_[0-9][0-9]_[0-9][0-9]\.nc', laserlog_filename)[0] laserlog_filename = f'{laserlog_filename_left}_00_00_01.nc.laserlogbook.txt' - destination_file = Path(output_path,laserlog_filename) + destination_file = Path(output_path, laserlog_filename) # Open the source file in binary mode and read its content with open(result_file, 'rb') as source: @@ -365,10 +366,10 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): 'measurement_height_resolution', 'laser_rep_rate', 'laser_power', -# 'laser_flashlamp', + # 'laser_flashlamp', 'location_height', 'neutral_density_filter', -# 'location_coordinates', + # 'location_coordinates', 'pm_voltage', 'pinhole', 'polstate', @@ -393,23 +394,23 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): diff_var=0 print('\n') print('checking differences in selected variables ...') - for ds in range(0,len(polly_file_ds_ls)-1): -# print('\n') -# print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) + for ds in range(0, len(polly_file_ds_ls)-1): + # print('\n') + # print(polly_files_list[ds] + ' vs. ' + polly_files_list[ds+1]) for var in vars_of_interest: if var in polly_file_ds_ls[ds].variables.keys(): ## check if var is available within the polly-datastructure (depending on polly-system) var_value_1=str(polly_file_ds_ls[ds].variables[var][:]) var_value_2=str(polly_file_ds_ls[ds+1].variables[var][:]) - # print(var + ": " + var_value_1) - # print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) if var_value_1 == var_value_2 and diff_var==0: # print('no difference found ...') add_to_list(ds, polly_files_list, selected_var_nc_ls) elif var_value_1 != var_value_2 and diff_var==0: print('difference found in var:') print(var) - #print(var + ": " + var_value_1) - #print(var + ": " + var_value_2) + # print(var + ": " + var_value_1) + # print(var + ": " + var_value_2) diff_var=1 add_to_list(ds, polly_files_list, selected_var_nc_ls) if force == True else None elif var_value_1 == var_value_2 and diff_var != 0: @@ -424,7 +425,7 @@ def checking_vars(timestamp, device, raw_folder, output_path:str): add_to_list(-1, polly_files_list, selected_var_nc_ls) print('\nno differences found in selected variables!\n') elif diff_var != 0: -# add_to_list(-1,polly_files_list,selected_var_nc_ls) if force == True else None + # add_to_list(-1, polly_files_list, selected_var_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: add_to_list(-1, polly_files_list, selected_var_nc_ls) @@ -480,15 +481,15 @@ def checking_attr(timestamp, device, raw_folder, output_path): print('checking differences in attributes ...') for ds in range(0, len(polly_file_ds_ls) - 1): ## get global attributes as a list of strings -# print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) -# print('\nglobal attributes:') + # print(selected_var_nc_ls[ds] + ' vs. ' + selected_var_nc_ls[ds+1]) + # print('\nglobal attributes:') for nc_attr in polly_file_ds_ls[0].ncattrs(): # att_value=repr(input_nc_file.getncattr(nc_attr)) att_value_1 = polly_file_ds_ls[ds].getncattr(nc_attr) att_value_2 = polly_file_ds_ls[ds+1].getncattr(nc_attr) -# print(nc_attr) -# print(" " + att_value_1) -# print(" " + att_value_2) + # print(nc_attr) + # print(" " + att_value_1) + # print(" " + att_value_2) if att_value_1 == att_value_2 and diff_att==0: add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) elif att_value_1 != att_value_2 and diff_att==0: @@ -504,15 +505,15 @@ def checking_attr(timestamp, device, raw_folder, output_path): print('difference found!') add_to_list(ds, selected_var_nc_ls, selected_att_nc_ls) if force == True else None -# print("\nvariable attributes:") + # print("\nvariable attributes:") for var in polly_file_ds_ls[0].variables.keys(): -# print(var) + # print(var) for var_att in polly_file_ds_ls[0].variables[var].ncattrs(): var_att_value_1 = polly_file_ds_ls[ds].variables[var].getncattr(var_att) var_att_value_2 = polly_file_ds_ls[ds+1].variables[var].getncattr(var_att) -# print(" " + var_att) -# print(" " + var_att_value_1) -# print(" " + var_att_value_2) + # print(" " + var_att) + # print(" " + var_att_value_1) + # print(" " + var_att_value_2) if var_att_value_1 == var_att_value_2 and diff_var_att == 0: pass elif var_att_value_1 != var_att_value_2 and diff_var_att == 0: @@ -534,7 +535,7 @@ def checking_attr(timestamp, device, raw_folder, output_path): add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) print('\nno differences found in global attributes!\n') elif diff_att != 0: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) if force == True else None + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) if force == True else None ## if force==true, merge, but if force==false: the whole day will not be in list anymore if force == True: @@ -548,14 +549,14 @@ def checking_attr(timestamp, device, raw_folder, output_path): if diff_var_att == 0: print('\nno differences found in variable attributes!\n') -# elif diff_var_att != 0: -# ## if force==true, merge, but if force==false: the whole day will not be in list anymore -# if force == True: -# add_to_list(-1,selected_var_nc_ls,selected_att_nc_ls) -# print('\ndifferences found in variable attributes! But will be force-merged.\n') -# else: -# print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') -# sys.exit() + # elif diff_var_att != 0: + # ## if force==true, merge, but if force==false: the whole day will not be in list anymore + # if force == True: + # add_to_list(-1, selected_var_nc_ls, selected_att_nc_ls) + # print('\ndifferences found in variable attributes! But will be force-merged.\n') + # else: + # print('\ndifferences found in variable attributes! Selected Date will be skipped.\n') + # sys.exit() print(selected_att_nc_ls) @@ -595,8 +596,8 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): polly_file_ds = Dataset(files, "r") polly_file_ds_ls.append(polly_file_ds) - for elementNR,ds in enumerate(polly_file_ds_ls): - # print(selected_timestamp_nc_ls[elementNR]) + for elementNR, ds in enumerate(polly_file_ds_ls): + # print(selected_timestamp_nc_ls[elementNR]) timestamp_ds = ds.variables['measurement_time'][:] if 19700101 in timestamp_ds.T[0]: print(f'The file: {selected_timestamp_nc_ls[elementNR]} contains incorrect timestamps!') @@ -604,9 +605,9 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): ## get correct timestamp_ds from filename timestamp_filename = selected_timestamp_nc_ls[elementNR] timestamp_filename = timestamp_filename.stem - timestamp_filename = re.split(r'_',str(timestamp_filename))[-3:] + timestamp_filename = re.split(r'_', str(timestamp_filename))[-3:] ## del. nc-file - #os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps + # os.remove(selected_timestamp_nc_ls[elementNR]) ### remove unzipped nc-file with incorrect timestamps ## calc. the deltaT between measurementdatapoints laser_rep_rate = float(ds.variables['laser_rep_rate'][0]) measurement_shots = ds.variables['measurement_shots'][:] @@ -633,7 +634,7 @@ def checking_timestamp(timestamp, device, raw_folder, output_path): ## check if seconds_ls does not contain seonds of day larger than 86400 ## TODO ## if so, remove file from list and del. file ## TODO t_check = any(value > 86400 for value in seconds_ls) - #t_check = False ## do not skip files which are longer than 24h, seconds_ls > 86400 + # t_check = False ## do not skip files which are longer than 24h, seconds_ls > 86400 if t_check == True: print('seconds of day exceeds 86400. file will be removed from merging list.') ds.close() @@ -703,7 +704,7 @@ def concat_files(timestamp, device, raw_folder, output_path:str): ## merge selected files -# concat='concat' + # concat='concat' sel_polly_files_list = checking_timestamp(timestamp, device, raw_folder, output_path) @@ -720,12 +721,12 @@ def concat_files(timestamp, device, raw_folder, output_path:str): os.rename(sel_polly_files_list[0], Path(output_path, filestring)) return () else: -# sel_polly_files_list = [ str(el) for el in sel_polly_files_list] + # sel_polly_files_list = [ str(el) for el in sel_polly_files_list] ## parameters for controlling the merging process compat='override' ## Values of variable "laser_flashlamp" often changes, but those files will be merged anyway. This option picks the value from first dataset. coords='minimal' - ds = xarray.open_mfdataset(sel_polly_files_list,combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) + ds = xarray.open_mfdataset(sel_polly_files_list, combine = 'nested', data_vars="minimal", concat_dim="time", compat=compat, coords=coords) ## save to a single nc-file print(f"\nmerged nc-file '{filestring}' will be stored to '{output_path}'") print("\nwriting merged file ...") @@ -741,8 +742,8 @@ def write_netcdf(ds: xarray.Dataset, out_file: Path) -> None: enc[k] = { "zlib": True, "complevel": 1, -# "fletcher32": True, -# "chunksizes": tuple(map(lambda x: x//2, ds[k].shape)) + # "fletcher32": True, + # "chunksizes": tuple(map(lambda x: x//2, ds[k].shape)) } ds.to_netcdf(out_file, format="NETCDF4", engine="netcdf4", encoding=enc) @@ -866,11 +867,16 @@ def uniform_filter(x:np.ndarray, win:int, fill_val:float=np.nan) -> np.ndarray: **History** - 2026-02-02: First edition by Buholdt + """ if isinstance(win, int): if len(x.shape) > 1: raise NotImplementedError('Support for smoothing of mulitdimensional arrays is not yet implemented') + + if len(x) < win: + logging.warning("`win` is bigger than `x`. No smoothig applied.") + return x f = np.ones(win)/win x_smoothed = np.convolve(x, f, mode='valid') @@ -931,12 +937,17 @@ def moving_average(x:np.ndarray, win:int) -> np.ndarray: - 2026-03-24: First edition by Buholdt """ + if isinstance(win, int): if len(x.shape) > 1: raise NotImplementedError('Support for smoothing of mulitdimensional arrays is not yet implemented') + if len(x) < win: + logging.warning("`win` is bigger than `x`. No smoothig applied.") + return x + if win % 2 == 0: - print("Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.") + logging.warning("Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.") win -= 1 f = np.ones(win)/win @@ -1003,8 +1014,12 @@ def savgol_filter(x:np.ndarray, window_length:int, polyorder:int=2, deriv:int=0, [3] Jianwen Luo, Kui Ying, and Jing Bai. 2005. Savitzky-Golay smoothing and differentiation filter for even number data. Signal Process. 85, 7 (July 2005), 1429-1434. - """ + + if len(x) < window_length: + logging.warning("`window_length` is bigger than `x`. No smoothig applied.") + return x + f = savgol_coeffs(window_length, polyorder, deriv=deriv, delta=delta) x_smooth = np.convolve(x, f, mode='valid') fill = np.full(int((window_length - 1)/2), fill_val) @@ -1018,6 +1033,169 @@ def savgol_filter(x:np.ndarray, window_length:int, polyorder:int=2, deriv:int=0, return out +def uniform_filter_2d(x:np.ndarray, Nr:int, Nc:int, fill_val=np.nan) -> np.ndarray: + """2 dimensional uniform smoothing filter. + + The regions affected by edge/padding effects are replaced with ´fill_val´. + + Parameters + ---------- + x : ndarray (time, height, channel, ) + 2 dimensional time-height signal(s) to be smooth. + Nr : int + Smoothing window size along x-axis (rows). + Nc : int + Smoothing window size along y-axis (columns). + fill_val : float, optional + Value to be used for filling edges, in order to recreate the input + dimension. Default is np.nan. + + Returns + ------- + out : ndarray (time, height, channel, ) + Smoothed 2 dimensional time-height signal(s). + + Notes + ----- + - Invalid pixels are masked out and replaced with 0 during + the convolution process to avoid propagation during smoothing + process. + - scipy.ndimage.uniform_filter is used instead of alternative convolution + based filters due to its increased efficiency. + + **History** + + - 2026-07-17: first edition by Buholdt + + """ + + # Copy and check input dimensions + x0 = x.copy() + if len(x0.shape) == 2: + filterShape = (Nr, Nc) + elif len(x0.shape) == 3: + filterShape = (Nr, Nc, 1) + else: + raise ValueError(f"´x´ must either be a 2D or 3D array (multiple 2D arrays). Not {len(x0.shape)}D.") + + # Smoothing process + invalid = np.isnan(x0) + x0[invalid] = 0 + out = _uniform_filter( + x0, + size=filterShape, + mode="constant", + cval=0.0, + ) + out[invalid] = np.nan + + # Mask edge affected pixels + top = Nr // 2 + bottom = (Nr - 1) // 2 + + if top: + out[:top] = fill_val + if bottom: + out[-bottom:] = fill_val + + left = Nc // 2 + right = (Nc - 1) // 2 + + if left: + out[:, :left] = fill_val + if right: + out[:, -right:] = fill_val + + return out + + +def moving_average_2d(x:np.ndarray, Nr:int, Nc:int, sum:bool=False) -> np.ndarray: + """2 dimensional uniform smoothing filter ala. 2d version of smooth(y, span, method='moving') + in Matlab. + + This function uses a centered moving filter with dynamic size reduction at edges. + + Examples + -------- + The output of the function for win = 5 in one dimension follows the following logic: + out[0] = x[0]/1 + out[1] = (x[0] + x[1] + x[2])/3 + out[2] = (x[0] + x[1] + x[2] + x[3] + x[4])/5 + out[3] = (x[1] + x[2] + x[3] + x[4] + x[5])/5 + ... + out[-4] = (x[-6] + x[-5] + x[-4] + x[-3] + x[-2])/5 + out[-3] = (x[-5] + x[-4] + x[-3] + x[-2] + x[-1])/5 + out[-2] = (x[-3] + x[-2] + x[-1])/3 + out[-1] = x[-1]/1 + + Parameters + ---------- + x : ndarray (time, height, channel, ) + 2 dimensional time-height signal(s) to be smooth. + Nr : int + Smoothing window size along x-axis (rows). + Nc : int + Smoothing window size along y-axis (columns). + sum : bool, optional + If True, return moving sum results. Otherwise, return + moving average results. Default is False. + + Returns + ------- + out : ndarray (time, height, channel, ) + Smoothed 2 dimensional time-height signal(s). + + Notes + ----- + - Invalid pixels are masked out and replaced with 0 during + the convolution process to avoid propagation during smoothing + process. However, unlike in ´uniform_filter_2d´ these pixels + are not counted in the averaging procedure if ´sum=False´. + - By using `sum=True` the function will return the moving sum result + instead of the moving average. + - scipy.ndimage.uniform_filter is used instead of alternative convolution + based filters due to its increased efficiency. + + ** History ** + + - 2026-07-17: first edition by Buholdt + + """ + + # Copy and check input dimensions + x0 = x.copy() + if len(x0.shape) == 2: + filterShape = (Nr, Nc) + elif len(x0.shape) == 3: + filterShape = (Nr, Nc, 1) + else: + raise ValueError(f"´x´ must either be a 2D or 3D array (multiple 2D arrays). Not {len(x0.shape)}D.") + + # Smoothing process + invalid = np.isnan(x0) + x0[invalid] = 0 + num = _uniform_filter( + x0, + size=filterShape, + mode="constant", + cval=0.0, + ) + if sum: + num *= Nr * Nc + den = 1 + else: + den = _uniform_filter( + (~invalid).astype(float), + size=filterShape, + mode="constant", + cval=0.0, + ) + out = num/den + out[invalid] = np.nan + + return out + + def mean_stable(x:np.ndarray, win:int, minBin:int=None, maxBin:int=None, minRelStd:float=None) -> tuple: """Calculate the mean value of x based on the least fluctuated segment of x. The searching is based on the std inside each window of x. diff --git a/ppcpy/misc/molecular.py b/ppcpy/misc/molecular.py index 5a83eaf..3b167a2 100644 --- a/ppcpy/misc/molecular.py +++ b/ppcpy/misc/molecular.py @@ -2,6 +2,7 @@ from collections import defaultdict import numpy as np import xarray as xr +import logging # Global variable ASSUME_AIR_IDEAL:bool = True @@ -816,10 +817,10 @@ def calc_profiles(met_profiles:list, wavelengths:list=[355, 387, 407, 532, 607, input wavelength. """ - print('len mean_profiles', len(met_profiles)) + logging.debug(f"len mean_profiles: {len(met_profiles)}") shp = (len(met_profiles), met_profiles[0].height.shape[0]) - print('shape of the molecular scattering', shp) - print('for the wavelengths ', wavelengths) + logging.debug(f"shape of the molecular scattering {shp} for the wavelengths {wavelengths}.") + m_p = defaultdict(lambda: np.zeros(shp)) for i, p in enumerate(met_profiles): diff --git a/ppcpy/preprocess/pollyPreprocess.py b/ppcpy/preprocess/pollyPreprocess.py index 437bba4..641184b 100644 --- a/ppcpy/preprocess/pollyPreprocess.py +++ b/ppcpy/preprocess/pollyPreprocess.py @@ -1,207 +1,719 @@ import numpy as np import logging import datetime -import time +# import time import itertools from scipy.ndimage import label -from multiprocessing import Pool, cpu_count -#from ppcpy.preprocess.optimized_polyval import process_signal -#from ppcpy.preprocess.compute_pcr import compute_pcr -#import numpy as np -#from multiprocessing import Pool, cpu_count +# from multiprocessing import Pool, cpu_count from ppcpy.retrievals.collection import calc_snr -def compute_channel_pcr(args): - """ - Computes PCR for a single channel. - Parameters: - args: Tuple containing (rawSignal, mShots, scale_factor, channel_index). +def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, flagPicassoComparison:bool=False, **param:dict) -> dict: + """Preprocessing of Lidar-data. + + Includes the following processes in order: + 1. Deadtime correction + 2. Background correction + 3. First-bin shift + 4. Mask for low-SNR + 5. Mask for depolarization-calibration process + 6. Range correction. + + Parameters + ---------- + rawdata_dict : dict + rawSignal : ndarray + Signal [Photon Count]. + mShots : ndarray + Number of the laser shots for each profile. + mTime : ndarray + Datetime array for the measurement time of each profile. + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + zenithAng : ndarray + Zenith angle of the laer beam. + repRate : float + Laser pulse repetition rate [s^-1]. + hRes : float + Spatial resolution [m]. + mSite : str + Measurement site. + collect_debug : bool + If true, collects debug information. Default is False. + flagPicassoComparison : bool + If true, use Picasso values and logic. + + Keyword arguments + ----------------- + deltaT : float + Integration time for single profile [s]. Default is 30. + flagForceMeasTime : bool + Flag to control whether to align measurement time with file creation + time, instead of taking the measurement time in the data file. + Default is False. + maxHeightBin : int + Number of range bins to read out from data file. Default is 3000. + firstBinIndex : int + Index of first bin to read out. Default is 1. + pollyType : str + Polly version. Default is 'arielle'. + flagDeadTimeCorrection : bool + Flag to control whether to apply deadtime correction. Default is False. + deadtimeCorrectionMode : int + Deadtime correction mode. Default is 2. + 1: polynomial correction with parameters saved in data file. + 2: non-paralyzable correction. + 3: polynomail correction with user defined parameters. + 4: disable deadtime correction. + deadtimeParams : list + Deadtime parameters. Default is []. + flagSigTempCor : bool + Flag to implement signal temperature correction. + tempCorFunc : list + Symbolic function for signal temperature correction. + "1": no correction + "exp(-0.001*T)": exponential correction function [K]. + meteorDataSource : str + Meteorological data type. + e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' + gdas1Site : str + The GDAS1 site for the current campaign. + meteo_folder : str + The main folder of the GDAS1 profiles. + radiosondeSitenum : int + Site number, which can be found in + doc/radiosonde-station-list.txt. + radiosondeFolder : str + The folder of the sonding files. + radiosondeType : int + File type of the radiosonde file. Default is 1. + - 1: radiosonde file for MOSAiC. + - 2: radiosonde file for MUA. + bgCorrectionIndexLow : list + Base indecis of bins for background estimation. + Defults is [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. + bgCorrectionIndexHigh : list + Top index of bins for background estimation. + Defults is [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]. + asl : float + Above sea level in meters. Default is 0. + initialPolAngle : float + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskPolCalAngle : list + Mask for positive and negative calibration angle of the polarizer, in + which 'p' stands for positive angle, while 'n' for negative angle. + Default is []. + minSNRThresh : list + Lower bound of signal-noise ratio. + minPC_fog : float + Minimun number of photon count after strong attenuation by fog. + flagFarRangeChannel : list + Flags of far-range channel. + flag532nmChannel : list + Flags of channels with central wavelength (CW) at 532 nm. + flagTotalChannel : list + Flags of channels receiving total elastic signal. + flag355nmChannel : list + Flags of channels with CW at 355 nm. + flag607nmChannel : list + Flags of channels with CW at 607 nm. + flag387nmChannel : list + Flags of channels with CW at 387 nm. + flag407nmChannel : list + Flags of channels with CW at 407 nm. + flag532nmRotRaman : list + Flags of rotational Raman channels with CW at 532 nm. + flag1064nmRotRaman : list + Flags of rotational Raman channels with CW at 1064 nm. + + Returns + ------- + data_dict : dict + mShots : ndarray + Number of the laser shots for each profile. + sigDTCor : ndarray + Dead time corrected signal [photon count]. + BG : ndarray + Background. + sigBGCor : ndarray + Backgound corrected signal [photon count]. + range : ndarray + Height above ground at zenith angle [m]. + height : ndarray + Height above ground [m]. + alt : ndarray + Altitude [m]. + time : list + Measurement time in unix time. + time64 : ndarray + Measurement time in numpy.datetime. + SNR : ndarray + Signal to noise ratio. + lowSNRMask : ndarray + True if SNR is less SNRmin. Otherwise False. + shutterOnMask : ndarray + True if at timesteps where the shutters was on. Otherwise False. + fogMask : ndarray + If it is foggy which means the signal will be very weak, + fogMask will be set True. Otherwise, False. + mask607Off : ndarray + Mask of PMT on/off status at 607 nm channel. + mask387Off : ndarray + Mask of PMT on/off status at 387 nm channel. + mask407Off : ndarray + Mask of PMT on/off status at 407 nm channel. + mask355RROff : ndarray + Mask of PMT on/off status at 355 nm rotational Raman channel. + mask532RROff : ndarray + Mask of PMT on/off status at 532 nm rotational Raman channel. + mask1064RROff : ndarray + Mask of PMT on/off status at 1064 nm rotational Raman channel. + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + Time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + Time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + Time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + RCS : ndarray + Range Corrected signal [MCPS]. + PCR_slice : ndarray + Background corrected signal in photon count rate [MCPS]. + + Notes + ----- + .. TODO:: + - Remove all unecesary comments. + - Why does this function not take data_cube as an input like the rest of the functions? + - Implement Temperature correction. + - Is using mShots_norm to convert back needed? + + + **History** + + - 2018-12-16: First edition by Zhenping. + - 2019-07-10: Add mask for laser shutter due to approaching airplanes. + - 2019-08-27: Add mask for turnoff of PMT at 607 and 387nm. + - 2021-01-19: Add keyword of 'flagForceMeasTime' to align measurement time. + - 2021-01-20: Re-sample the profiles into temporal resolution of 30-s. + - xxxx-xx-xx: Translated to Python by ... + - 2026-06-24: Cleaned and revamped by Buholdt. - Returns: - np.ndarray: Computed PCR for the given channel. """ - rawSignal, mShots, scale_factor, ch = args - return (rawSignal[:, :, ch] / mShots[:, np.newaxis, ch]) * scale_factor -def compute_pcr_parallel(rawSignal, mShots, scale_factor): + # ------------------------------------------------------------------------ + # Extract input data + # ------------------------------------------------------------------------ + + ## print all of the large arrays to screen, not only starts and ends of an array + np.set_printoptions(threshold=np.inf) + + ## Extracting data from rawdata_dict + rawSignal = rawdata_dict['raw_signal']['var_data'] + mShots = rawdata_dict['measurement_shots']['var_data'] + mTime = rawdata_dict['measurement_time']['var_data'] + depCalAng = rawdata_dict['depol_cal_angle']['var_data'] + zenithAng = rawdata_dict['zenithangle']['var_data'] + hRes = rawdata_dict['measurement_height_resolution']['var_data'] + # repRate = rawdata_dict['laser_rep_rate']['var_data'] # <-- Not used + # mSite = rawdata_dict['global_attributes']['location'] # <-- Not used + + data_dict = {} + + ## converting raw-mTime format from [YYYYMMDD seconds-of-day] to unixtimestamp-format + logging.info(f'... time conversion') + date_string = str(mTime[0][0]) + seconds_of_day = mTime[:, 1] + YYYY = int(date_string[:4]) + MM = int(date_string[4:6]) + DD = int(date_string[6:8]) + datetime_obj = datetime.datetime(YYYY, MM, DD) + mTime_obj = [ + datetime_obj.replace(tzinfo=datetime.timezone.utc) +\ + datetime.timedelta(seconds=int(s)) for s in seconds_of_day + ] + # mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] # <-- Not used + + # Convert to Unix timestamp + mTime_unixtimestamp = [int(datetime.datetime.timestamp(dt)) for dt in mTime_obj] + + ## Defining default values for param keys (key initialization), if not explictly defined when calling the function + # deltaT = param.get('deltaT', 30) # <-- Not used + # flagForceMeasTime = param.get('flagForceMeasTime', False) # <-- Not used + maxHeightBin = param.get('maxHeightBin', 3000) + firstBinIndex = param.get('firstBinIndex', False) + firstBinHeight = param.get('firstBinHeight', False) + pollyType = param.get('pollyType', False) + flagDeadTimeCorrection = param.get('flagDeadTimeCorrection', False) + deadtimeCorrectionMode = param.get('deadtimeCorrectionMode', 2) + deadtimeParams = param.get('deadtimeParams', False) + # flagSigTempCor = param.get('flagSigTempCor', False) # <-- Not used + # tempCorFunc = param.get('tempCorFunc', False) # <-- Not used + # meteorDataSource = param.get('meteorDataSource', False) # <-- Not used + # gdas1Site = param.get('gdas1Site', False) # <-- Not used + # gdas1_folder = param.get('gdas1_folder', False) # <-- Not used + # radiosondeSitenum = param.get('radiosondeSitenum', False) # <-- Not used + # radiosondeFolder = param.get('radiosondeFolder', False) # <-- Not used + # radiosondeType = param.get('radiosondeType', False) # <-- Not used + bgCorrectionIndexLow = param.get('bgCorrectionIndexLow', False) + bgCorrectionIndexHigh = param.get('bgCorrectionIndexHigh', False) + asl = param.get('asl', 10) + initialPolAngle = param.get('initialPolAngle', False) + maskPolCalAngle = param.get('maskPolCalAngle', False) + minSNRThresh = param.get('minSNRThresh', False) + minPC_fog = param.get('minPC_fog', False) + flagFarRangeChannel = param.get('flagFarRangeChannel', False) + flag532nmChannel = param.get('flag532nmChannel', False) + flagTotalChannel = param.get('flagTotalChannel', False) + flag355nmChannel = param.get('flag355nmChannel', False) + flag607nmChannel = param.get('flag607nmChannel', False) + flag387nmChannel = param.get('flag387nmChannel', False) + flag407nmChannel = param.get('flag407nmChannel', False) + flag355nmRotRaman = param.get('flag355nmRotRaman', False) + flag532nmRotRaman = param.get('flag532nmRotRaman', False) + flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) + # isUseLatestGDAS = param.get('isUseLatestGDAS', False) # <-- Not used + + # ## .. TODO:: What does this part do and why is it omited? --> I think it is shown in the mail form Martin!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + # logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): + # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') + # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') + # maxHeightBin = np.ones(rawSignal.shape[2]) + # logging.info(f'maxHeightBin: {maxHeightBin}') + # firstBinIndex = 251 + + # ## .. TODO:: This part is currently not used! Should we use it? + # mShotsPerPrf = deltaT * repRate + # if len(mTime) > 1: + # # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s + # else: + # nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) + + + # ------------------------------------------------------------------------ + # Check and store mesurment shots + # ------------------------------------------------------------------------ + if not np.all(mShots[:, 0] == mShots[0, 0]): + logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") + + # take mean/median value over time to recalculate photon count signal normalized to a common integration time + mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) + if flagPicassoComparison: + mShots_norm = np.repeat(np.median(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) + + # For now store mShots in data_cube.retrievals_highres + data_dict['mShots'] = mShots + + + # ------------------------------------------------------------------------ + # Deadtime correction + # ------------------------------------------------------------------------ + PCR = photonCount2PCR(rawSignal, mShots, hRes) + + logging.info('... calculate dead-time corrected signal') + PCRDTCor = pollyDTCor( + PCR=PCR, + polly_device=pollyType, + flagDeadTimeCorrection=flagDeadTimeCorrection, + DeadTimeCorrectionMode=deadtimeCorrectionMode, + deadtimeParams=deadtimeParams, + deadtime=rawdata_dict['deadtime_polynomial']['var_data'] + ) + + # .. TODO:: Is it correct to use mShots_norm here to convert back from PCR?? + # In the matlab version they use mShots_norm here but with median value + # insted of the mean... + # data_dict['preproSignal'] = PCR2PhotonCount(PCRDTCor, mShots hRes) + data_dict['preproSignal'] = PCR2PhotonCount(PCRDTCor, mShots_norm, hRes) + + + # ------------------------------------------------------------------------ + # Background Correction + # ------------------------------------------------------------------------ + logging.info('... calculate background corrected signal') + sigBGCor, bg = pollyRemoveBG( + rawSignal=data_dict['preproSignal'], + bgCorrectionIndexLow=bgCorrectionIndexLow, + bgCorrectionIndexHigh=bgCorrectionIndexHigh, + maxHeightBin=maxHeightBin, + firstBinIndex=firstBinIndex + ) + # Store the background and background corrected signal + data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix + data_dict['sigBGCor'] = sigBGCor + + + # ------------------------------------------------------------------------ + # Height and first bin height correction + # ------------------------------------------------------------------------ + logging.info('... height bin calculations') + # .. TODO:: first bin hight might change for different telescopes... --> should expand range to be channel spesific. + data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] + data_dict['height'] = data_dict['range'].copy() * np.cos(zenithAng*np.pi/180) + + # correction firstBinHight = range per channel ^ 2 / range first channel ^ 2 + # .. TODO:: write a better explenation for this correction. + correction_firstBinHight = ( + ((np.arange(0, sigBGCor.shape[1]) * hRes)[:, np.newaxis] + firstBinHeight)**2 + / data_dict['range'][:, np.newaxis]**2 + ) + + data_dict['sigBGCor'] = data_dict['sigBGCor'] * correction_firstBinHight[np.newaxis, :, :] + + data_dict['alt'] = data_dict['height'] + float(asl) ## geopotential height + data_dict['time'] = mTime_unixtimestamp + data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) + + + # ------------------------------------------------------------------------ + # Mask for bins with low SNR + # ------------------------------------------------------------------------ + logging.info('... mask bins with low SNR') + + SNR = calc_snr(data_dict['sigBGCor'], bg) + data_dict['SNR'] = SNR + + data_dict['lowSNRMask'] = np.zeros_like(sigBGCor, dtype=bool) + data_dict['lowSNRMask'][SNR < minSNRThresh] = True + + + # ------------------------------------------------------------------------ + # Mask for bins whit laser shutter on + # ------------------------------------------------------------------------ + logging.info('... mask bins with laser shutter on') + flag532FR = (np.array(flag532nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) + flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) + + if any(flag532FR): + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532FR])) + elif any(flag355FR): + data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355FR])) + else: + raise ValueError('No suitable channel to determine the shutter status.') + + + # ------------------------------------------------------------------------ + # Mask for bins with fog + # ------------------------------------------------------------------------ + logging.info('... mask bins with fog') + # .. TODO:: The original matlab code raises questions. Why 40:120 and why hard coded? + # When sum is used (as in matlab), minPC_fog is range resolution dependent + fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:, 39:120, flag532FR]), axis=1) + data_dict['fogMask'] = fogsum < minPC_fog + + + # ------------------------------------------------------------------------ + # Mask for bins where a single channel was off + # ------------------------------------------------------------------------ + logging.info('... mask bins where a single channel was off') + flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag607FR): + data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag607FR])) + + flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag387FR): + data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag387FR])) + + flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag407FR): + data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag407FR])) + + flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag355RRFR): + data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag355RRFR])) + + flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag532RRFR): + data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag532RRFR])) + + flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) + if any(flag1064RRFR): + data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:, :, flag1064RRFR])) + + + # ------------------------------------------------------------------------ + # Mask for polarization calibration + # ------------------------------------------------------------------------ + logging.info('... mask bins during polarization calibration periods') + (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], + data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], + data_dict['depCalMask']) = pollyPolCaliTime( + depCalAng=depCalAng, + mTime=mTime_unixtimestamp, + init_depAng=initialPolAngle, + maskDepCalAng=maskPolCalAngle + ) + + + # ------------------------------------------------------------------------ + # Range-corrected Signal calculation + # ------------------------------------------------------------------------ + logging.info('... calculate range-corrected Signal') + + # .. TODO:: Is it correct to use mShots_norm here for converting to PCR??? + # This is not done in the matlab version. + data_dict['PCR_slice'] = photonCount2PCR(data_dict['sigBGCor'].copy(), mShots_norm, hRes) + if flagPicassoComparison: + data_dict['PCR_slice'] = photonCount2PCR(data_dict['sigBGCor'].copy(), mShots, hRes) + + data_dict['RCS'] = calculate_rcs(data_dict['PCR_slice'].copy(), data_dict['range']) + + return data_dict + + +def photonCount2PCR(signal:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute PCR from photon counts. + + PCR = (c * signal)/(2 * hRes * mShots) + + Parameters + ---------- + signal : ndarray + Signal [Photon Count] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. + + Returns + ------- + PCR : ndarray + Photon count rate signal [MCPS]. + + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly get MCPS. + + ** History ** + + - 2026-04-22: First edition by Buholdt + """ - Computes PCR using multiprocessing for channel-wise parallelism. + + c = 3e2 # meter / microsecond + PCR = (c * signal)/(2 * hRes * mShots[:, np.newaxis, :]) + return PCR + + +def PCR2PhotonCount(PCR:np.ndarray, mShots:np.ndarray, hRes:float) -> np.ndarray: + """Compute photon counts from PCR. - Parameters: - rawSignal: 3D input array (shape: [M, N, P]). - mShots: 2D multiplicative factors array (shape: [M, P]). - scale_factor: Scaling factor for the computation. + photonCount = 2 * PCR * mShot * hRes / c + + Parameters + ---------- + PCR : ndarray + Photon count rate signal [MCPS] (shape: [M, N, P]). + mShots : ndarray + Mesurments shots [counts] (shape: [M, P]). + hRes : float + Reight or range resolution [m]. + + Returns + ------- + photonCount : ndarray + Signal [Photon Count] (shape: [M, N, P]). + + Note + ---- + - The speed of light `c` is given in meter per microsecond to directly translate form MCPS. + + ** History ** + + - 2026-04-22: First edition by Buholdt - Returns: - np.ndarray: 3D output array (PCR) with the same shape as rawSignal. """ - M, N, P = rawSignal.shape - PCR = np.zeros((M, N, P), dtype=np.float64) - # Prepare arguments for each channel - args = [(rawSignal, mShots, scale_factor, ch) for ch in range(P)] + c = 3e2 # meter / microsecond + photonCount = 2 * hRes * PCR * mShots[:, np.newaxis, :] / c + return photonCount - # Use a pool of workers to compute each channel in parallel - with Pool(processes=min(cpu_count(), P)) as pool: - results = pool.map(compute_channel_pcr, args) - # Collect the results - for ch, result in enumerate(results): - PCR[:, :, ch] = result +def faster_polyval(p:np.ndarray, x:float|np.ndarray) -> float|np.ndarray: + """Faster version of np.polyval(). - return PCR + If `p` is of length N, this function returns:: + + y = p[N]*x**(N-1) + p[N-1]*x**(N-2) + ... + p[1]*x + p[0] + + Parameters + ---------- + p : ArrayLike + Polynomial coefficient including coefficients equal to 0, from constant term to highest order term. + x : float or ArrayLike + Value(s) at which to evaluate the polynomial `p`. + + Returns + ------- + y : float or ArrayLike + Polynomial `p` evaluated at values `x`. + + Notes + ----- + - Using numba would provide 10% increase, but with different function. + + ** History ** -def faster_polyval(a, x): - """faster version than np.polyval(), using numba would provide 10% increase, but with different function""" - y = a[-1] - for ai in a[-2::-1]: + - xxxx-xx-xx: first edition by + + + Example + ------- + >>> faster_polyval([-1, 0, 3], 5) # 3*5**2 + 0*5**1 + (-1) + 76 + >>> faster_polycal([-1, 0, 3], [5, 2, -1]) + [76, 11, 2] + """ + + y = p[-1] + for pi in p[-2::-1]: y *= x - y += ai + y += pi return y -#@jit(nopython=True) -#def faster_polyval(p, x): -# y = np.zeros(x.shape, dtype=float) -# for i, v in enumerate(p): -# y *= x -# y += v -# return y + #@profile -def pollyDTCor(rawSignal:np.ndarray, mShots:np.ndarray, hRes:float, **varargin:dict) -> np.ndarray: - """ Dead Time Correction +def pollyDTCor(PCR:np.ndarray, **varargin:dict) -> np.ndarray: + """Dead Time Correction. Parameters ---------- - rawSignal (ndarray): Raw signal [Photon counts]. - mShots (ndarray): Measurment shots per ... []. - hRes (ndarray): Height resolution [m]. + PCR : ndarray + Photon count rate signal [MCPS]. - keyword arguments: - device (str or bool): Name of PollyXT device, default: False - flagDeadTimeCorrection (bool): ....., default: Flase - DeadTimeCorrectionMode (int): deadtime correction mode: - 1: use the parameters saved in the netcdf files, - 2: nonparalyzable correction with user define deadtime (default), - 3: paralyzable correction with user defined parameters, - 4: no deadtime correction, - deadtimeParams (list): ...., default: [] - deadtime (list): ...., default: [] + Keyword arguments + ----------------- + device : str or bool + Name of PollyXT device. Default is False. + flagDeadTimeCorrection : bool + If true, perform dead time correction. Otherwise, no dead time + correction is performed. Default is False. + DeadTimeCorrectionMode : int + Deadtime correction mode. Default is 2. + 1: use the parameters saved in the netcdf files, + 2: nonparalyzable correction with user define deadtime, + 3: paralyzable correction with user defined parameters, + 4: no deadtime correction, + deadtimeParams : list + Deadtime parameters from config-file at MCPS scale. Default is []. + deadtime : list + Deadtime parameters from level0 nc-file at MCPS scale. Default is []. Returns ------- - signalDTCor (ndarray): Dead time corrected signal [Photon counts]. + PCR_DTCor : ndarray + Dead time corrected photon count rate signal [MCPS]. + + Notes + ----- + + ** History ** + + - 2021-05-16: first edition by Zhenping + - 2025-02-19: edition by Cristofer Jimenez + - xxxx-xx-xx: translated to python + - 2026-06-24: Removed signal conversion (photon count to PCR) from function - .. TODO:: - - Finish docstring and remove all unnecessary comments - - Could think of moving the scale convertion to after the loops ie. form PCR to PC """ + ## Defining default values for param keys (key initialization), if not explictly defined when calling the function - polly_device = varargin.get('device', False) + # polly_device = varargin.get('device', False) # <-- TODO: is this needed?? flagDeadTimeCorrection = varargin.get('flagDeadTimeCorrection', False) DeadTimeCorrectionMode = varargin.get('DeadTimeCorrectionMode', 2) deadtimeParams = varargin.get('deadtimeParams', []) deadtime = varargin.get('deadtime', []) - # print('mShots', np.all(mShots[:,0] == mShots[0,0]), mShots[0,0], np.min(mShots[:,0]), np.max(mShots[:,0])) - if not np.all(mShots[:, 0] == mShots[0, 0]): - logging.warning(f"... mShots not constant min {np.min(mShots)} max {np.max(mShots)}") - mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - print('mShots_norm', mShots_norm.shape, 'mShots_norm', mShots_norm[0, :]) - - logging.info(f'... Deadtime-correction (Mode: {DeadTimeCorrectionMode})') - - Nchannels = mShots.shape[1] - - scale_factor = 150.0 / hRes + logging.info(f"... Deadtime-correction (Mode: {DeadTimeCorrectionMode})") - ## convert photon counts to Photon-Count-Rate PCR [MHz] - # start_time_command1 = time.time() - # compute_pcr(rawSignal.astype(np.float64), mShots.astype(np.float64), scale_factor, PCR) - # Compute PCR in parallel - # end_time_command1 = time.time() - # elapsed_time_command1 = end_time_command1 - start_time_command1 - # print(f"Time taken: {elapsed_time_command1:.4f} seconds") - PCR = rawSignal * (150.0 / hRes) / mShots[:, np.newaxis, :] - #PCR_Cor = np.zeros_like(PCR) - signalDTCor = np.zeros_like(PCR) + Nchannels = PCR.shape[-1] + PCR_DTCor = PCR.copy().astype(np.float64) ## Deadtime correction if flagDeadTimeCorrection: - ## polynomial correction with parameters saved in the level0 netcdf-file under variable 'deadtime_polynomial' if DeadTimeCorrectionMode == 1: for iCh in range(Nchannels): - # Extract polynomial coefficients for the channel and reverse their order - # coeffs = deadtime[:, iCh][::-1] # <-- Not used - # PCR_Cor[:, :, iCh] = np.polyval(deadtime[:, iCh][::-1], PCR[:, :, iCh]) - # PCR_Cor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / (150./hRes) - signalDTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor - + PCR_DTCor[:, :, iCh] = faster_polyval(deadtime[:, iCh], PCR[:, :, iCh]) ## nonparalyzable correction: PCR_cor = PCR / (1 - tau*PCR), with tau beeing the dead-time ## reading from polly-config file under key 'dT' (only the first value from each channel) elif DeadTimeCorrectionMode == 2: for iCh in range(Nchannels): - # PCR_Cor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / scale_factor - signalDTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor - + PCR_DTCor[:, :, iCh] = PCR[:, :, iCh] / (1.0 - deadtimeParams[iCh][0] * 10**(-3) * PCR[:, :, iCh]) ## user defined deadtime, reading from polly-config file under key 'dT' (the whole matrix, polynome) elif DeadTimeCorrectionMode == 3: if np.array(deadtimeParams).size != 0: - # deadtimeParams=np.array(deadtimeParams) - # signal_out = np.zeros_like(PCR) - # process_signal(PCR, mShots.astype(np.float64), deadtimeParams, Nchannels, scale_factor, signal_out) - # PCR_Cor = PCR - # Pre-extract polynomial coefficients for all channels and reverse their order coeffs_matrix = np.array([np.array(deadtimeParams[ch][::-1]) for ch in range(Nchannels)]) for iCh in range(Nchannels): - # Evaluate the polynomial for the current channel - # PCR_Cor[:, :, iCh] = np.polyval(coeffs_matrix[iCh], PCR[:, :, iCh]) - # PCR_Cor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) - # signalDTCor[:, :, iCh] = PCR_Cor[:, :, iCh] * mShots_norm[:, np.newaxis, iCh] / scale_factor - signalDTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) * mShots_norm[:, np.newaxis, iCh] / scale_factor + PCR_DTCor[:, :, iCh] = faster_polyval(coeffs_matrix[iCh][::-1], PCR[:, :, iCh]) else: - logging.warning(f'User defined deadtime parameters were not found in polly-config file.') - logging.warning(f'In order to continue the current processing, deadtime correction will not be implemented.') + logging.warning(f"User defined deadtime parameters were not found in polly-config file.") + logging.warning(f"In order to continue the current processing, deadtime correction will not be implemented.") ## No deadtime correction elif DeadTimeCorrectionMode == 4: - signalDTCor = rawSignal.astype(np.float64) - logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') + logging.warning(f"Deadtime correction was turned off. Be careful to check the signal strength.") else: - logging.error(f'Unknow deadtime correction setting! Please go back to check the configuration.') - logging.error(f'For deadtimeCorrectionMode, only 1-4 is allowed.') + logging.error(f"Unknow deadtime correction setting! Please go back to check the configuration.") + logging.error(f"For deadtimeCorrectionMode, only 1-4 is allowed.") - ## flagDeadTimeCorrection = False + ## flagDeadTimeCorrection equals False else: - signalDTCor = rawSignal.astype(np.float64) - logging.warning(f'Deadtime correction was turned off. Be careful to check the signal strength.') + logging.warning(f"Deadtime correction was turned off. Be careful to check the signal strength.") - # return PCR_Cor, signalDTCor - return signalDTCor + return PCR_DTCor -def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: + +def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionIndexHigh:list, + maxHeightBin:int=3000, firstBinIndex:list|None=None) -> tuple[np.ndarray, np.ndarray]: """Background correction. Remove mean background noise from signal. Parameters ---------- - rawSignal (np.ndarray): Lidar Signal to be processed - bgCorrectionIndexLow (list of int): lower index of background noise per channel - bgCorrectionIndexHigh (list of int): upper index of background noise per channel - maxHeightBin (int): maximum height bin index (default: 3000) - firstBinIndex (list of int): first height bin index per channel (default: 0 per chanel) + rawSignal : ndarray + Signal to be processed [MCPS or Photon counts]. + bgCorrectionIndexLow : list of int + Lower index of background noise per channel. + bgCorrectionIndexHigh : list of int + Upper index of background noise per channel. + maxHeightBin : int + Maximum height bin index. Default is 3000). + firstBinIndex : list of int + First height bin index per channel. Default is 0 per channel. Returns ------- - - signal_out (np.ndarray): Background corrected signal - - bg (np.ndarray): Removed background noise + signal_out : ndarray + Background corrected signal [MCPs or Photon counts]. + bg : ndarray + Removed background noise [MCPS or Photon counts]. + + Notes + ----- + + ** History ** + + - 2021-05-16: first edition by Zhenping + - xxxx-xx-xx: translated to pyhton + - 2025-08-13: allow channel dependent background range + """ + logging.info(f'... removing background from signal') if firstBinIndex is None: @@ -218,18 +730,29 @@ def pollyRemoveBG(rawSignal:np.ndarray, bgCorrectionIndexLow:list, bgCorrectionI signal_out = slicerange(rawSignal, maxHeightBin, firstBinIndex) - bg return signal_out, bg + def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.ndarray: """Slice a given array across the height/range dimension from firstBinIndex to maxHeightBin + firstBinIndex. Parameters ---------- - array (np.ndarray): array to be sliced - maxHeightBin (int): length of slice - firstBinIndex (list of int): start hight/range index of slice per channel + array : ndarray + Array to be sliced. + maxHeightBin : int + Length of slice. + firstBinIndex : list of int + Start hight/range index of slice per channel. Returns ------- - out (np.ndarray): sliced array + out : ndarray + Sliced array. + + ** History ** + + - xxxx-xx-xx: first edition by ... + - 2025-08-13: vectorized by Buholdt + """ assert len(firstBinIndex) == array.shape[2], f"first bin index and array do not match {len(firstBinIndex)}, {array.shape}" @@ -238,8 +761,55 @@ def slicerange(array:np.ndarray, maxHeightBin:int, firstBinIndex:list) -> np.nda out = array[:, heightBins, np.arange(array.shape[2])] return out -def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): - """ """ + +def pollyPolCaliTime(depCalAng:np.ndarray, mTime:list, init_depAng:float, maskDepCalAng:list) -> tuple: + """Retrieve the time for the polly depolarization calibration + period. depolarization calibration: 5 min (+45°) + 5 min (-45°) + 0.5 min. + + Parameters + ---------- + depCalAng : ndarray + Angle of the polarizer in the receiving channel + (>0 means calibration process starts). + mTime : list + Datetime ndarray for the measurement time of each profile. + init_depAng : float + Initial polarization angle of the polarizer for polarization + calibration. Default is 0. + maskDepCalAng : list + Mask for positive and negative calibration angle of the polarizer, in + which 'p' stands for positive angle, while 'n' for negative angle. + Default is {}. + + Returns + ------- + depCal_P_Ang_time_start : list + Time for the first profile with valid positive angle depolarization + calibration. + depCal_P_Ang_time_end : list + time for the last profile with valid positive angle depolarization + calibration. + depCal_N_Ang_time_start : list + time for the first profile with valid negative angle depolarization + calibration. + depCal_N_Ang_time_end : list + time for the last profile with valid negative angle depolarization + calibration. + maskDepCal : ndarray + If polly was doing polarization calibration, depCalMask is set + True. Otherwise, False. + + Notes + ----- + .. TODO:: Clean comments of the function. + + **History** + + - 2021-04-21: First edition by Zhenping + - xxxx-xx-xx: Translated to Python by ... + + """ + depCal_P_Ang_time_start = [] depCal_P_Ang_time_end = [] depCal_N_Ang_time_start = [] @@ -257,55 +827,52 @@ def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): ## invalid profiles with different ## depol_cal_angle - flagPDepCal = np.zeros(len(maskDepCalAng),dtype=bool) - flagNDepCal = np.zeros(len(maskDepCalAng),dtype=bool) - for iProf in range(0,len(maskDepCalAng)): + flagPDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + flagNDepCal = np.zeros(len(maskDepCalAng), dtype=bool) + for iProf in range(0, len(maskDepCalAng)): if maskDepCalAng[iProf] == 'p': flagPDepCal[iProf] = True elif maskDepCalAng[iProf] == 'n': flagNDepCal[iProf] = True + flagDepCal = (np.abs(depCalAng - init_depAng) > 0.0) ## the profile will be treated as depol cali profile if it has different ## depol_cal_ang than the init_depAng - #print(init_depAng) - #print(depCalAng) - #print(flagDepCal) + maskDepCal = flagDepCal ## search the calibration periods valuesFlagDepCal = flagDepCal.astype(int) - print('flagNDepCal', flagNDepCal) - print('flagPDepCal', flagPDepCal) + # print('flagNDepCal', flagNDepCal) + # print('flagPDepCal', flagPDepCal) ## label connected components in the matrix; 0 will stay 0 ## connected 1s will be numbered consecutively depCalPeriods, nDepCalPeriods = label(valuesFlagDepCal) - print('depCalPeriods', depCalPeriods) + # print('depCalPeriods', depCalPeriods) - if nDepCalPeriods >= 1: - pass - else: + if nDepCalPeriods < 1: logging.info(f'No Depolarization Calibration phase found.') return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal - for iDepCalPeriod in range(1,nDepCalPeriods+1): - #flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. + for iDepCalPeriod in range(1, nDepCalPeriods+1): + # flagIDepCal = (depCalPeriods == iDepCalPeriod) # flag for the ith calibration period. flagIDepCal = depCalPeriods[depCalPeriods == iDepCalPeriod] # flag for the ith calibration period. indices = np.where(depCalPeriods == flagIDepCal[0])[0] - print('flagIDepCal', flagIDepCal) - print(len(flagIDepCal), len(maskDepCalAng)) + # print('flagIDepCal', flagIDepCal) + # print(len(flagIDepCal), len(maskDepCalAng)) if len(flagIDepCal) != len(maskDepCalAng): logging.warning(f"Depolarization Calibration from Timestamp " - f"{mTime[indices[0]]} - {mTime[indices[-1]]} " - f"does not match the maskDepCalAng pattern in the polly-config file.\n" - f"This calibration phase will be skipped.") + f"{mTime[indices[0]]} - {mTime[indices[-1]]} " + f"does not match the maskDepCalAng pattern in the polly-config file.\n" + f"This calibration phase will be skipped." + ) continue - else: - pass + tIDepCal = mTime[indices[0]:indices[-1]+1] - + t_all_p_depCal = list(itertools.compress(tIDepCal, flagPDepCal)) t_all_n_depCal = list(itertools.compress(tIDepCal, flagNDepCal)) depCal_P_Ang_time_start.append(t_all_p_depCal[0]) @@ -313,454 +880,51 @@ def pollyPolCaliTime(depCalAng, mTime, init_depAng, maskDepCalAng): depCal_N_Ang_time_start.append(t_all_n_depCal[0]) depCal_N_Ang_time_end.append(t_all_n_depCal[-1]) - return depCal_P_Ang_time_start, depCal_P_Ang_time_end, depCal_N_Ang_time_start, depCal_N_Ang_time_end, maskDepCal -def calculate_rcs(datasignal, ranges) -> np.ndarray: - """ - Function for calculating RCS. + +def calculate_rcs(signal:np.ndarray, ranges:np.ndarray) -> np.ndarray: + """Function for calculating RCS. Parameters ---------- - datasignal: - signal to range correct - ranges: - ranges that are squared + signal : ndarray + Signal to range correct [PCR]. + ranges : ndarray + Ranges dimension [m]. Returns ------- - np.ndarray: Computed RCS array. - """ - - print(datasignal.shape) - ranges_squared = ranges**2 - ranges2d = np.repeat(ranges_squared[np.newaxis, :], datasignal.shape[0], axis=0) - print('ranges2d', ranges2d.shape) - - # Perform the computation - #RCS = ( - # datasignal / mShots_broadcasted * 150 / float(hRes) * height_squared_broadcasted - #) - RCS = ( - datasignal * ranges2d[:, :, np.newaxis] - ) + RCS : ndarray + Range corrected signal [PCR]. - return RCS - - -def pollyPreprocess(rawdata_dict:dict, collect_debug:bool=False, **param:dict): - """Deadtime correction, background correction, first-bin shift, mask for low-SNR and mask for depolarization-calibration process. - - Parameters - ---------- - data: struct - rawSignal: array - signal. [Photon Count] - mShots: array - number of the laser shots for each profile. - mTime: array - datetime array for the measurement time of each profile. - depCalAng: array - angle of the polarizer in the receiving channel. (>0 means - calibration process starts) - zenithAng: array - zenith angle of the laer beam. - repRate: float - laser pulse repetition rate. [s^-1] - hRes: float - spatial resolution [m] - mSite: string - measurement site. - - deltaT: numeric - integration time (in seconds) for single profile. (default: 30) - flagForceMeasTime: logical - flag to control whether to align measurement time with file creation - time, instead of taking the measurement time in the data file. - (default: false) - maxHeightBin: numeric - number of range bins to read out from data file. (default: 3000) - firstBinIndex: numeric - index of first bin to read out. (default: 1) - pollyType: char - polly version. (default: 'arielle') - flagDeadTimeCorrection: logical - flag to control whether to apply deadtime correction. (default: false) - deadtimeCorrectionMode: numeric - deadtime correction mode. (default: 2) - 1: polynomial correction with parameters saved in data file. - 2: non-paralyzable correction - 3: polynomail correction with user defined parameters - 4: disable deadtime correction - deadtimeParams: numeric - deadtime parameters. (default: []) - flagSigTempCor: logical - flag to implement signal temperature correction. - tempCorFunc: cell - symbolic function for signal temperature correction. - "1": no correction - "exp(-0.001*T)": exponential correction function. (Unit: Kelvin) - meteorDataSource: str - meteorological data type. - e.g., 'gdas1'(default), 'standard_atmosphere', 'websonde', 'radiosonde' - gdas1Site: str - the GDAS1 site for the current campaign. - meteo_folder: str - the main folder of the GDAS1 profiles. - radiosondeSitenum: integer - site number, which can be found in - doc/radiosonde-station-list.txt. - radiosondeFolder: str - the folder of the sonding files. - radiosondeType: integer - file type of the radiosonde file. - - 1: radiosonde file for MOSAiC (default) - - 2: radiosonde file for MUA - bgCorrectionIndexLow: 1-dim. array - base indecis of bins for background estimation. - (defults: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]) - bgCorrectionIndexHigh: 1-dim. array - top index of bins for background estimation. - (defults: [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]) - asl: numeric - above sea level in meters. (default: 0) - initialPolAngle: numeric - initial polarization angle of the polarizer for polarization - calibration. (default: 0) - maskPolCalAngle: cell - mask for positive and negative calibration angle of the polarizer, in - which 'p' stands for positive angle, while 'n' for negative angle. - (default: {}) - minSNRThresh: numeric - lower bound of signal-noise ratio. - minPC_fog: numeric - minimun number of photon count after strong attenuation by fog. - flagFarRangeChannel: logical - flags of far-range channel. - flag532nmChannel: logical - flags of channels with central wavelength (CW) at 532 nm. - flagTotalChannel: logical - flags of channels receiving total elastic signal. - flag355nmChannel: logical - flags of channels with CW at 355 nm. - flag607nmChannel: logical - flags of channels with CW at 607 nm. - flag387nmChannel: logical - flags of channels with CW at 387 nm. - flag407nmChannel: logical - flags of channels with CW at 407 nm. - flag532nmRotRaman: logical - flags of rotational Raman channels with CW at 532 nm. - flag1064nmRotRaman: logical - flags of rotational Raman channels with CW at 1064 nm. - - Returns - ------- - data: struct - rawSignal: array - signal. [Photon Count] - mShots: array - number of the laser shots for each profile. - mTime: array - datetime array for the measurement time of each profile. - depCalAng: array - angle of the polarizer in the receiving channel. (>0 means - calibration process starts) - zenithAng: array - zenith angle of the laer beam. - repRate: float - laser pulse repetition rate. [s^-1] - hRes: float - spatial resolution [m] - mSite: string - measurement site. - deadtime: matrix (channel x polynomial_orders) - deadtime correction parameters. - signal: array - Background removed signal - bg: array - background - height: array - height. [m] - lowSNRMask: logical - If SNR less SNRmin, mask is set true. Otherwise, false - depCalMask: logical - If polly was doing polarization calibration, depCalMask is set - true. Otherwise, false. - fogMask: logical - If it is foggy which means the signal will be very weak, - fogMask will be set true. Otherwise, false - mask607Off: logical - mask of PMT on/off status at 607 nm channel. - mask387Off: logical - mask of PMT on/off status at 387 nm channel. - mask407Off: logical - mask of PMT on/off status at 407 nm channel. - mask355RROff: logical - mask of PMT on/off status at 355 nm rotational Raman channel. - mask532RROff: logical - mask of PMT on/off status at 532 nm rotational Raman channel. - mask1064RROff: logical - mask of PMT on/off status at 1064 nm rotational Raman channel. + Notes + ----- - """ - logging.info('starting data preprocessing...') - rawSignal = rawdata_dict['raw_signal']['var_data'] - mShots = rawdata_dict['measurement_shots']['var_data'] - mTime = rawdata_dict['measurement_time']['var_data'] - depCalAng = rawdata_dict['depol_cal_angle']['var_data'] - zenithAng = rawdata_dict['zenithangle']['var_data'] - repRate = rawdata_dict['laser_rep_rate']['var_data'] - hRes = rawdata_dict['measurement_height_resolution']['var_data'] - mSite = rawdata_dict['global_attributes']['location'] + ** History ** - data_dict = {} - - ## print all of the large arrays to screen, not only starts and ends of an array - np.set_printoptions(threshold=np.inf) + - xxxx-xx-xx: First edition by ... - ## converting raw-mTime format from [YYYYMMDD seconds-of-day] to unixtimestamp-format - logging.info(f'... time conversion') - date_string = str(mTime[0][0]) - seconds_of_day = mTime[:,1] - YYYY = int(date_string[:4]) - MM = int(date_string[4:6]) - DD = int(date_string[6:8]) - datetime_obj = datetime.datetime(YYYY, MM, DD) - mTime_obj = [ - datetime_obj.replace(tzinfo=datetime.timezone.utc) + datetime.timedelta(seconds=int(s)) for s in seconds_of_day] - mTime_str = [dt.strftime('%Y%m%d %H:%M:%S') for dt in mTime_obj] - # Convert to Unix timestamp - mTime_unixtimestamp = [int(datetime.datetime.timestamp(dt)) for dt in mTime_obj] - - ## Defining default values for param keys (key initialization), if not explictly defined when calling the function - deltaT = param.get('deltaT', 30) - flagForceMeasTime = param.get('flagForceMeasTime', False) - maxHeightBin = param.get('maxHeightBin', 3000) - firstBinIndex = param.get('firstBinIndex', False) - firstBinHeight = param.get('firstBinHeight', False) - pollyType = param.get('pollyType', False) - flagDeadTimeCorrection = param.get('flagDeadTimeCorrection', False) - deadtimeCorrectionMode = param.get('deadtimeCorrectionMode', 2) - deadtimeParams = param.get('deadtimeParams', False) - flagSigTempCor = param.get('flagSigTempCor', False) - tempCorFunc = param.get('tempCorFunc', False) - meteorDataSource = param.get('meteorDataSource', False) - gdas1Site = param.get('gdas1Site', False) - gdas1_folder = param.get('gdas1_folder', False) - radiosondeSitenum = param.get('radiosondeSitenum', False) - radiosondeFolder = param.get('radiosondeFolder', False) - radiosondeType = param.get('radiosondeType', False) - bgCorrectionIndexLow = param.get('bgCorrectionIndexLow', False) - bgCorrectionIndexHigh = param.get('bgCorrectionIndexHigh', False) - asl = param.get('asl', 10) - initialPolAngle = param.get('initialPolAngle', False) - maskPolCalAngle = param.get('maskPolCalAngle', False) - minSNRThresh = param.get('minSNRThresh', False) - minPC_fog = param.get('minPC_fog', False) - flagFarRangeChannel = param.get('flagFarRangeChannel', False) - flag532nmChannel = param.get('flag532nmChannel', False) - flagTotalChannel = param.get('flagTotalChannel', False) - flag355nmChannel = param.get('flag355nmChannel', False) - flag607nmChannel = param.get('flag607nmChannel', False) - flag387nmChannel = param.get('flag387nmChannel', False) - flag407nmChannel = param.get('flag407nmChannel', False) - flag355nmRotRaman = param.get('flag355nmRotRaman', False) - flag532nmRotRaman = param.get('flag532nmRotRaman', False) - flag1064nmRotRaman = param.get('flag1064nmRotRaman', False) - isUseLatestGDAS = param.get('isUseLatestGDAS', False) - - - # print(flagFarRangeChannel) - # print(flag1064nmRotRaman) - - -#%% Determine whether number of range bins is out of range -#if (max(config.maxHeightBin + config.firstBinIndex - 1) > size(data.rawSignal, 2)) -# warning('maxHeightBin or firstBinIndex is out of range.\nTotal number of range bin is %d.\nmaxHeightBin is -# %d\nfirstBinIndex is %d\n', size(data.rawSignal, 2), config.maxHeightBin, config.firstBinIndex); -# fprintf('Set maxHeightBin and firstBinIndex to default values.\n'); -# config.maxHeightBin = ones(1, size(data.rawSignal, 1)); -# config.firstBinIndex = 251; -#end -# logging.info(f'Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - #if (maxHeightBin + np.max(firstBinIndex) -1) > len(rawSignal[0]): - # logging.warning(f'maxHeightBin or firstBinIndex is out of range. Total number of range bin is: {len(rawSignal[0])}\nmaxHeightBin is: {maxHeightBin}\nfirstBinIndex is {firstBinIndex}.') - # logging.info(f'Set maxHeightBin and firstBinIndex to default values.') - # maxHeightBin = np.ones(rawSignal.shape[2]) - # logging.info(f'maxHeightBin: {maxHeightBin}') - # firstBinIndex = 251 - - mShotsPerPrf = deltaT * repRate -# print(mShotsPerPrf) -# print(mShots) -# print(mTime) -# print(deltaT) -# print(np.nanmean(np.diff(np.array(mTime[:,1])))) -# print(np.array(mTime[:,1])) -# print(len(mTime)) -# print(np.diff(mTime)) - if len(mTime) > 1: - # nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))) * 24 * 3600)) ## number of profiles to be integrated. Usually, 600 shots per 30 s - nInt = np.round(deltaT / (np.nanmean(np.diff(np.array(mTime[:, 1]))))) ## number of profiles to be integrated. Usually, 600 shots per 30 s - else: - nInt = np.round(mShotsPerPrf / np.nanmean(np.array(mShots[0, :]))) - - - ## Deadtime correction - # PCR_Cor, preproSignal = pollyDTCor(rawSignal = rawSignal, - preproSignal = pollyDTCor( - rawSignal = rawSignal, - mShots = mShots, - hRes = hRes, - polly_device = pollyType, - flagDeadTimeCorrection = flagDeadTimeCorrection, - DeadTimeCorrectionMode = deadtimeCorrectionMode, - deadtimeParams = deadtimeParams, - deadtime = rawdata_dict['deadtime_polynomial']['var_data'] - ) - # most likely the preprocesssed deadtime corrected signal can be omitted - if collect_debug: - # data_dict['PCR_cor'] = PCR_Cor - data_dict['preproSignal'] = preproSignal - # data_dict['PCR_slice'] = slicerange(PCR_Cor, maxHeightBin, firstBinIndex) - - ## Background Substraction - sigBGCor, bg = pollyRemoveBG( - rawSignal = preproSignal, - bgCorrectionIndexLow = bgCorrectionIndexLow, - bgCorrectionIndexHigh = bgCorrectionIndexHigh, - maxHeightBin = maxHeightBin, - firstBinIndex = firstBinIndex - ) - data_dict['BG'] = bg[:, 1, :] ## reshaping the3-dim. BG-matrix to 2-dim matrix - # Store the background corrected signal - data_dict['sigBGCor'] = sigBGCor - - ## Height and first bin height correction - logging.info('... height bin calculations') - # TODO first bin hight might change for different telescopes... - data_dict['range'] = np.arange(0, sigBGCor.shape[1]) * hRes + firstBinHeight[0] - data_dict['height'] = data_dict['range'].copy() * np.cos(zenithAng*np.pi/180) - - correction_firstBinHight = (( - (np.arange(0, sigBGCor.shape[1]) * hRes)[:,np.newaxis] + firstBinHeight)**2 - / data_dict['range'][:,np.newaxis]**2) - data_dict['sigBGCor'] = data_dict['sigBGCor'] * correction_firstBinHight[np.newaxis, :, :] - - data_dict['alt'] = data_dict['height'] + float(asl) ## geopotential height - data_dict['time'] = mTime_unixtimestamp - data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj]) - - - ## Mask for bins with low SNR - logging.info('... mask bins with low SNR') - SNR = calc_snr(sigBGCor, bg) - data_dict['SNR'] = SNR - #print(SNR) - ## create mask and mask every entry, where SNR < minSNRThresh - #data_dict['lowSNRMask'] = np.ma.array(np.zeros(sigBGCor.shape, dtype=bool), mask=np.ones(sigBGCor.shape, dtype=bool)) - # a plain bool mask should be faster. Let's give it a try - data_dict['lowSNRMask'] = np.zeros_like(sigBGCor).astype(bool) - #print(data_dict['lowSNRMask']) - for iCh in range(0, sigBGCor.shape[2]): - #data_dict['lowSNRMask'][:,:,iCh].mask = SNR[:,:,iCh].data < minSNRThresh[iCh] - #data_dict['lowSNRMask'][:,:,iCh] = np.ma.masked_where(SNR[:,:,iCh].data < minSNRThresh[iCh], SNR[:,:,iCh]) - data_dict['lowSNRMask'][:,:,iCh][SNR[:,:,iCh] < minSNRThresh[iCh]] = True - # TODO check the low SNR mask - - # TODO mask for laser shutter? - flag532FR = (np.array(flag532nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) - flag355FR = (np.array(flag355nmChannel) & np.array(flagFarRangeChannel) & np.array(flagTotalChannel)).astype(bool) - print('flag 532 FR', flag532FR) - print('flag 355 FR', flag355FR) - if any(flag532FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532FR])) - elif any(flag355FR): - data_dict['shutterOnMask'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag355FR])) - else: - raise ValueError('No suitable channel to determine the shutter status') - - # TODO mask for fog? - # the original matlab code raises questions. Why 40:120 and why hard coded? - # When sum is used (as in matlab), minPC_fog is range resolution dependent - fogsum = np.sum(np.squeeze(data_dict['sigBGCor'][:,39:120,flag532FR]), axis=1) - data_dict['fogMask'] = fogsum < minPC_fog - - # TODO mask for single channels on 607, 387, 407, 355RR 532RR 1064RR - flag607FR = (np.array(flag607nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - print('flag 607 FR', flag607FR) - if any(flag607FR): - data_dict['mask607Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag607FR])) - flag387FR = (np.array(flag387nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag387FR): - data_dict['mask387Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag387FR])) - flag407FR = (np.array(flag407nmChannel) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag407FR): - data_dict['mask407Off'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag407FR])) - flag355RRFR = (np.array(flag355nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag355RRFR): - data_dict['mask355_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag355RRFR])) - flag532RRFR = (np.array(flag532nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag532RRFR): - data_dict['mask532_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag532RRFR])) - flag1064RRFR = (np.array(flag1064nmRotRaman) & np.array(flagFarRangeChannel)).astype(bool) - if any(flag1064RRFR): - data_dict['mask1064_RROff'] = any_signal(np.squeeze(data_dict['sigBGCor'][:,:,flag1064RRFR])) - - ## Mask for polarization calibration - logging.info('... mask for polarization calibration') - (data_dict['depol_cal_ang_p_time_start'], data_dict['depol_cal_ang_p_time_end'], - data_dict['depol_cal_ang_n_time_start'], data_dict['depol_cal_ang_n_time_end'], - data_dict['depCalMask']) = pollyPolCaliTime( - depCalAng=depCalAng, - mTime=mTime_unixtimestamp, - init_depAng=initialPolAngle, - maskDepCalAng=maskPolCalAngle - ) - -# print(data_dict['depol_cal_ang_p_time_start']) -# print(data_dict['depol_cal_ang_p_time_end']) -# print(data_dict['depol_cal_ang_n_time_start']) -# print(data_dict['depol_cal_ang_n_time_end']) -# print(data_dict['depCalMask']) - -#%% Mask for polarization calibration -#[data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... -# data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... -# depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... -# config.initialPolAngle, config.maskPolCalAngle); -#data.depCalMask = transpose(depCalMask); - - ## Range-corrected Signal calculation - logging.info('... calculate range-corrected Signal') - # mask = data_dict['lowSNRMask'].mask - # mask = data_dict['lowSNRMask'] - # masked arry might be slow - # RCS_masked = np.ma.masked_array(sigBGCor+bg, mask=mask) - # data_dict['RCS'] = calculate_rcs(datasignal=preproSignal, data_dict=data_dict, mShots=mShots, hRes=hRes) - mShots_norm = np.repeat(np.mean(mShots, axis=0)[np.newaxis, :], mShots.shape[0], axis=0) - data_dict['PCR_slice'] = data_dict['sigBGCor']*(150/hRes)/mShots_norm[:, np.newaxis, :] - data_dict['RCS'] = calculate_rcs(data_dict['PCR_slice'], data_dict['range']) - - - logging.info('finished data preprocessing.') + """ - return data_dict + ranges_squared = ranges**2 + ranges2d = np.repeat(ranges_squared[np.newaxis, :], signal.shape[0], axis=0) + RCS = signal * ranges2d[:, :, np.newaxis] + return RCS -def any_signal(sig: np.ndarray) -> np.ndarray: - """check if there is any signal +def any_signal(sig:np.ndarray) -> np.ndarray: + """Check if there is any signal POLLYISLASERSHUTTERON determine whether the laser shutter is on due to the flying object. Parameters ---------- - sig: np.ndarray + sig: ndarray BGCor signal with shape [height, time]. Returns ------- - flag: np.ndarray + flag: ndarray Boolean array of shape [time,] where True indicates the laser shutter is turned on. Notes @@ -777,92 +941,125 @@ def any_signal(sig: np.ndarray) -> np.ndarray: std_sig = np.std(sig, axis=1, ddof=0) # Detect when both mean and std dev are below threshold - # for some reason had to set the thresholds higher than in matlab version + # for some reason had to set the thresholds higher than in matlab version .. TODO:: <-- Check this!! flag = (mean_sig <= 0.02) & (std_sig <= 0.9) return flag -# -#%% Temperature effect correction (for Raman signal) -#if config.flagSigTempCor -# temperature = loadMeteor(mean(data.mTime), data.alt, ... -# 'meteorDataSource', config.meteorDataSource, ... -# 'gdas1Site', config.gdas1Site, ... -# 'meteo_folder', config.meteo_folder, ... -# 'radiosondeSitenum', config.radiosondeSitenum, ... -# 'radiosondeFolder', config.radiosondeFolder, ... -# 'radiosondeType', config.radiosondeType, ... -# 'method', 'linear', ... -# 'isUseLatestGDAS', config.flagUseLatestGDAS); -# absTemp = temperature + 273.17; -# -# for iCh = 1:size(data.signal, 1) -# leadingChar = config.tempCorFunc{iCh}(1); -# if (leadingChar == '@') -# % valid matlab anonymous function -# tempCorFunc = config.tempCorFunc{iCh}; -# else -# tempCorFunc = vectorize(['@(T) ', '(', config.tempCorFunc{iCh}, ') .* ones(size(T))']); -# % fprintf('%s is not a valid matlab anonymous function. Redefine it as %s\n', config.tempCorFunc{iCh}, tempCorFunc); -# end -# -# corFunc = str2func(tempCorFunc); -# corFac = corFunc(absTemp); -# data.signal(iCh, :, :) = data.signal(iCh, :, :) ./ repmat(reshape(corFac, 1, [], 1), 1, 1, size(data.signal, 3)); -# end -#end -# -# -#%% Mask for polarization calibration -#[data.depol_cal_ang_p_time_start, data.depol_cal_ang_p_time_end, ... -# data.depol_cal_ang_n_time_start, data.depol_cal_ang_n_time_end, ... -# depCalMask] = pollyPolCaliTime(data.depCalAng, data.mTime, ... -# config.initialPolAngle, config.maskPolCalAngle); -#data.depCalMask = transpose(depCalMask); -# -#%% Mask for laser shutter -#flagChannel532FR = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -#flagChannel355FR = config.flagFarRangeChannel & config.flag355nmChannel & config.flagTotalChannel; -#if any(flagChannel532FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel532FR, :, :))); -#elseif any(flagChannel355FR) -# data.shutterOnMask = pollyIsLaserShutterOn(... -# squeeze(data.signal(flagChannel355FR, :, :))); -#else -# warning('No suitable channel to determine the shutter status'); -# data.shutterOnMask = false(size(data.mTime)); -#end -# -#%% Mask for fog -#data.fogMask = false(1, size(data.signal, 3)); -#is_channel_532_FR_Tot = config.flagFarRangeChannel & config.flag532nmChannel & config.flagTotalChannel; -#data.fogMask(transpose(squeeze(sum(data.signal(is_channel_532_FR_Tot, 40:120, :), 2)) <= config.minPC_fog) & (~ data.shutterOnMask)) = true; -# -#%% Mask for PMT on/off status of 607 nm channel -#flagChannel607 = config.flagFarRangeChannel & config.flag607nmChannel; -#data.mask607Off = pollyIs607Off(squeeze(data.signal(flagChannel607, :, :))); -# -#%% Mask for PMT of 387 nm channel -#flagChannel387 = config.flagFarRangeChannel & config.flag387nmChannel; -#data.mask387Off = pollyIs387Off(squeeze(data.signal(flagChannel387, :, :))); -# -#%% Mask for PMT of 407 nm channel -#flagChannel407 = config.flagFarRangeChannel & config.flag407nmChannel; -#data.mask407Off = pollyIs407Off(squeeze(data.signal(flagChannel407, :, :))); -# -#%% Mask for PMT of 355 nm rotation Raman channel -#flagChannel355RR = config.flagFarRangeChannel & config.flag355nmRotRaman; -#data.mask355RROff = pollyIs607Off(squeeze(data.signal(flagChannel355RR, :, :))); -# -#%% Mask for PMT of 532 nm rotation Raman channel -#flagChannel532RR = config.flagFarRangeChannel & config.flag532nmRotRaman; -#data.mask532RROff = pollyIs607Off(squeeze(data.signal(flagChannel532RR, :, :))); -# -#%% Mask for 1064 nm rotation Raman channel -#flagChannel1064RR = config.flag1064nmRotRaman; -#data.mask1064RROff = pollyIs607Off(squeeze(data.signal(flagChannel1064RR, :, :))); -# -#end + + + + +# # The functions below are old functions currently not in use. +# # They are only saved for ... resons. + + +# def compute_channel_photon_count(args:tuple) -> np.ndarray: +# """Computes Photon count from PCR for a single channel. + +# Photons = 2 * PCR * mShot * hRes / c + +# Parameters +# ---------- +# args : tuple +# PCR : ndarray +# Photon Count Rate signal [MCPS] (shape: [M, N, P]). +# mShots : ndarray[time, channels] +# Mesurments shots [counts] (shape: [M, P]). +# scale_factor : flaot +# Scaling factor for the computation: c / (2 * hRes). +# channel_index : int +# Index of the channel to compute the PCR for. + +# Returns +# ------- +# ndarray +# Computed PCR for the given channel (shape: [M, N, P]). + +# Notes +# ----- +# .. TODO:: change scale factor with hRes and include c/2 as a part +# of the function. Could even add an flag option for MCPS or CPS. +# """ + +# PCR, mShots, scale_factor, ch = args +# return PCR[:, :, ch] * mShots[:, np.newaxis, ch] / scale_factor + + +# def compute_channel_pcr(args:tuple) -> np.ndarray: +# """Computes PCR for a single channel. + +# PCR = (signal * c)/(mshots * 2 * hRes) + +# Parameters +# ---------- +# args : tuple +# rawSignal : ndarray +# 3D Raw signal [Photon count] (shape: [M, N, P]). +# mShots : ndarray[time, channels] +# Mesurments shots [counts] (shape: [M, P]). +# scale_factor : flaot +# Scaling factor for the computation: c / (2 * hRes). +# channel_index : int +# Index of the channel to compute the PCR for. + +# Returns +# ------- +# ndarray +# Computed PCR for the given channel (shape: [M, N, P]). + +# Notes +# ----- +# .. TODO:: change scale factor with hRes and include c/2 as a part +# of the function. Could even add an flag option for MCPS or CPS. +# """ + +# rawSignal, mShots, scale_factor, ch = args +# return (rawSignal[:, :, ch] / mShots[:, np.newaxis, ch]) * scale_factor + + +# def compute_pcr_parallel(rawSignal:np.ndarray, mShots:np.ndarray, scale_factor:float) -> np.ndarray: +# """Computes PCR using multiprocessing for channel-wise parallelism. + +# Parameters +# ---------- +# rawSignal : ndarray +# 3D input array (shape: [M, N, P]). +# mShots : ndarray +# 2D multiplicative factors array (shape: [M, P]). +# scale_factor : float +# Scaling factor for the computation. + +# Returns +# ------- +# PCR : ndarray +# 3D output array (shape: [M, N, P]). +# """ + +# M, N, P = rawSignal.shape +# PCR = np.zeros((M, N, P), dtype=np.float64) + +# # Prepare arguments for each channel +# args = [(rawSignal, mShots, scale_factor, ch) for ch in range(P)] + +# # Use a pool of workers to compute each channel in parallel +# with Pool(processes=min(cpu_count(), P)) as pool: +# results = pool.map(compute_channel_pcr, args) + +# # Collect the results +# for ch, result in enumerate(results): +# PCR[:, :, ch] = result + +# return PCR + + +# @jit(nopython=True) +# def faster_polyval(p, x): +# """Numba verison of faster_polyval().""" +# y = np.zeros(x.shape, dtype=float) +# for i, v in enumerate(p): +# y *= x +# y += v +# return y diff --git a/ppcpy/preprocess/profiles.py b/ppcpy/preprocess/profiles.py index da0a44d..a6dd661 100644 --- a/ppcpy/preprocess/profiles.py +++ b/ppcpy/preprocess/profiles.py @@ -1,23 +1,29 @@ - import numpy as np -def aggregate_clFreeGrps(data_cube, var:str, func=np.nansum): - """ - Aggregate the highres signal over the periods of the cloud free signal. - Input: - - data_cube (object): Main PicassoProc object. - - var (string): name of variable to be aggregated. - - func (function): function to do the aggregateion (mean, sum, median, etc), defult: np.nansum. - - Output: - - out (np.ndarray): Aggregated highres signal for each cloud free segment. - """ - # Check if variable exists, if not return. - if var not in data_cube.retrievals_highres: - print(f"Retrieval {var} do not exist.") - return +def aggregate_clFreeGrps(data_cube, var:str, func=np.nanmean) -> np.ndarray: + """Aggregate the highres signal over the periods of the cloud free signal. + Paremeters + ---------- + data_cube : object + Main PicassoProc object. + var : str + Name of variable to be aggregated. + func : function + Function to do the aggregation (mean, sum, median, etc.). Default is np.nanmean. + + Returns + ------- + out : ndarray + Aggregated highres signal for each cloud free segment. + + Notes + ----- + .. TODO:: This function could easily be separated from the data_cube object + + """ + shp = list(data_cube.retrievals_highres[var].shape) shp[0] = len(data_cube.clFreeGrps) out = np.empty(shp) diff --git a/ppcpy/qc/overlapCor.py b/ppcpy/qc/overlapCor.py index f3b4359..eb1946a 100644 --- a/ppcpy/qc/overlapCor.py +++ b/ppcpy/qc/overlapCor.py @@ -7,142 +7,219 @@ def spread(data_cube): - """select the correct overlap method, spread the profiles to 2d for each wavelength + """Select the correct overlap method, spread the profiles to 2d for each wavelength. design decision for now: drop the signal glue option (overlapCorMode == 3) - in the matlab version any olFunc is additionally smoothed with - `olSm = smooth(olFuncDeft, p.Results.overlapSmWin, 'sgolay', 2);` - (e.g. here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/pollyOLCor.m#L106) - This should probably be done more explicitly + Parameters + ---------- + data_cube : object + Main PicassoProc object. + + Returns + ------- + dict + 2d overlap function per channel. + + Notes + ----- + - in the matlab version any olFunc is additionally smoothed with + `olSm = smooth(olFuncDeft, p.Results.overlapSmWin, 'sgolay', 2);` + (e.g. here https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/pollyOLCor.m#L106) + This should probably be done more explicitly. + - Also the 'glueing' in function [sigCor] = olCor(sigFR, overlap, height, normRange) + seems wired (https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/olCor.m#L34). + + .. TODO:: Using different overlap function at different periods of the signal introduces harsh transitions as long as the overlap functions + are not stable. Look into options for smoothing the overlap function in the time dimension to ease the transition from + one period to another. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: Translated to python. - Also the 'glueing' in function [sigCor] = olCor(sigFR, overlap, height, normRange) - seems wired - (https://github.com/PollyNET/Pollynet_Processing_Chain/blob/e413f9254094ff2c0a18fcdac4e9bebb5385d526/lib/qc/olCor.m#L34) """ + + logging.info("Computing 2d overlap function.") config_dict = data_cube.polly_config_dict height = data_cube.retrievals_highres['range'] time = data_cube.retrievals_highres['time64'] - print('overlapCorMode ', config_dict['overlapCorMode'], - ' overlapCalMode ', config_dict['overlapCalMode']) - overlap = data_cube.retrievals_profile['overlap'] - print(overlap.keys()) + + ## Get overlap mode if config_dict['overlapCorMode'] == 1: - k = 'file' + logging.info("overlapCorMode = 1 --> using overlap function form file.") + olMode = 'file' elif config_dict['overlapCorMode'] == 2: if config_dict['overlapCalMode'] == 1: - k = 'frnr' - print("Warning: The frnr overlap calulations are very unstable.") + logging.info("overlapCorMode = 2, overlapCalMode = 1 --> using overlap function calculated thorugh the FRNR-method.") + logging.warning("FRNR calculated overlap functions may be unstable.") + olMode = 'frnr' + logging.warning("The frnr overlap calulations are very unstable.") elif config_dict['overlapCalMode'] == 2: - k = 'raman' + logging.info("overlapCorMode = 2, overlapCalMode = 2 --> using overlap function calculated thorugh the Raman-method.") + olMode = 'raman' elif config_dict['overlapCorMode'] == 3: + logging.critical('overlapCorMode 3 not implemented, see docstring for further information') raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') - print('overlap correction source', k) - ol_profiles = overlap[k] - # TODO: add code to select only one profile - #ol_profiles = [overlap[k][0]] + + ol_profiles = overlap[olMode] + # .. TODO:: Add code to select only one profile. + # ol_profiles = [overlap[olMode][0]] - # get the channel information for all the cloud free profiles - # and convert into a plain list + ## get the channel information for all the cloud free profiles and convert into a plain list channel_per_profile = list(itertools.chain(*[list(e.keys()) for e in ol_profiles])) clFreeGrps = data_cube.clFreeGrps time_slices = [time[grp] for grp in clFreeGrps] - print(time_slices, np.ravel(time_slices)) - print(clFreeGrps) ret = {} for channel in set(channel_per_profile): + logging.info(f"channel: {channel.replace('_', ' ')}") olFuncs = [o[channel] for o in ol_profiles if channel in o.keys()] - time_slices_this_channel = [t for i,t in enumerate(time_slices) if channel in ol_profiles[i].keys()] - print(channel, 'len(olFuncs)', len(olFuncs), len(time_slices), len(time_slices_this_channel)) + time_slices_this_channel = [t for i, t in enumerate(time_slices) if channel in ol_profiles[i].keys()] + logging.debug(f"# of olFuncs: {len(olFuncs)}, # of time slices: {len(time_slices)}, # of time slices for this channel: {len(time_slices_this_channel)}") + logging.debug(f"time slices for this channel: {time_slices_this_channel}") if len(olFuncs) > 1: - logging.debug('overlap function set to time varying') + ## Use different overlap function for different times, centered around theire cloud free period. + logging.info(f'Using time-varying ovrlap function for channel for channel: {channel.replace('_', ' ')}.') olFunc_2d = np.zeros((2*len(olFuncs), height.shape[0])) - print(olFunc_2d.shape) - # set the estimated overlap profiles to the beginning and - # end of the profile + + ## Set the estimated overlap profiles to the beginning and end of the profile for i, f in enumerate(olFuncs): - olFunc_2d[[2*i, 2*i+1],:] = f['olFunc'] - #print(f.keys(), f['normRange']) + olFunc_2d[[2*i, 2*i+1], :] = f['olFunc'] + finterp = interp1d( - np.ravel(time_slices_this_channel).astype(float), - olFunc_2d, axis=0, - fill_value='extrapolate', kind='nearest') + x=np.ravel(time_slices_this_channel).astype(float), + y=olFunc_2d, + axis=0, + fill_value='extrapolate', + kind='nearest' + ) olFunc_2d = finterp(time.astype(float)) - print(olFunc_2d.shape) + else: - #print('only one overlap function') - # then just use that function for the whole time period + ## Use same olFunc function for all timestamps. + logging.info(f'Using time-constant overlap function for channel: {channel.replace('_', ' ')}.') ol = olFuncs[0]['olFunc'] olFunc_2d = np.repeat(ol[np.newaxis, :], time.shape[0], axis=0) - print(olFunc_2d.shape) + ret[channel] = olFunc_2d return ret def apply_cube(data_cube): - """ + """Apply overlap function to 2d (time, height) signal. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + + Returns + ------- + sigOLCor : ndarray + Overlap corrected signal (time, height). + BGOLCor : ndarray + Overlap corrected background (time, ). + heightFullOverlapCor : ndarray + Height of full overlap [m?]. + + Notes + ----- + - Shoud we apply the overlap correction on the GHK-Transmission corrected signal or the Background Corrrected signal?? + Currentlly we are using a wired mix here. sigOLCor is inintialized with the sigTCor but the correction is applied + based on the sigBGCor. The correction to the BG is alwaysed based on the BGTCor. + - Also, in highres the GHK-Transmission correction is again applied on sigOLCor before the OC_attBsc is calculated. + + .. TODO:: At times the retrieved overlap function is outside the range [0, 1], the values of the function should + be checked and corrected before applying. Currnetly only values less then 0.07 are corrected (set to NaN). + + .. TODO:: This function could be easaly vectorized by cunstructing all the 2d overlap function into a 3d Matrix / Tensor + Channels that do not have overlap function can be replaced by ones. This alongside the use of alternative overlap + functions (using 355 insted of 386 etc.) can be handled in the construction of the 3d Matrix / Tensor. + Then the correction itself can be applied like this: sigOLCor = sigTCor / olFunc_3d + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: Translated to python. """ + height = data_cube.retrievals_highres['range'] config_dict = data_cube.polly_config_dict - BGOLCor = data_cube.retrievals_highres['BGTCor'].copy() - heightFullOverlapCor = np.repeat( - np.array(config_dict['heightFullOverlap'])[np.newaxis, :], - BGOLCor.shape[0], axis=0) + # .. TODO:: why are we using sigTCor as the basis when we update it with the sigBGCor is used to apply the correction?? sigOLCor = data_cube.retrievals_highres['sigTCor'].copy() + BGOLCor = data_cube.retrievals_highres['BGTCor'].copy() overlap2d = data_cube.retrievals_highres['overlap2d'] + heightFullOverlapCor = np.repeat( + np.array(config_dict['heightFullOverlap'])[np.newaxis, :], + BGOLCor.shape[0], axis=0) + alt_wv = {607: 532, 387: 355, 1064: 532} for wv in [355, 387, 532, 607, 1064]: flag = data_cube.gf(wv, 'total', 'FR') indxt = np.where(flag)[0] - # TODO fix that error, that is for now required for debugging - #sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigTCor'][:, :, flag]) + # .. TODO:: fix that error, that is for now required for debugging + # .. TODO:: should the we use sigBGCor or sigTCor here??? + # sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigTCor'][:, :, flag]) sigBGCor_total = np.squeeze(data_cube.retrievals_highres['sigBGCor'][:, :, flag]) - bg_total = np.squeeze(data_cube.retrievals_highres['BGTCor'][:, flag]) + bg_total = np.squeeze(data_cube.retrievals_highres['BGTCor'][:, flag]) # TODO: This is still the GHK-transmission corrected background... if config_dict['overlapCorMode'] in [1, 2]: - print('correct overlap', wv) + logging.info(f'Applying overlap correction for channel: {wv} total FR.') + + ## Extract overlap function if f"{wv}_total_FR" in overlap2d.keys(): olFunc = overlap2d[f"{wv}_total_FR"] - elif f"{alt_wv[wv]}_total_FR" in overlap2d.keys(): - print(f'using {alt_wv[wv]} instead of {wv}') + elif wv in alt_wv and f"{alt_wv[wv]}_total_FR" in overlap2d.keys(): + logging.info(f'Using overlap function for channel {alt_wv[wv]} total FR.') olFunc = overlap2d[f"{alt_wv[wv]}_total_FR"] else: - logging.warning(f"no overlap correction function for {wv}") + logging.warning(f"No overlap function found for channel {wv} total FR. Using O(R) = 1.") olFunc = 1 + ## Check and correct low / negative values idxOL = np.argmax(olFunc > 0.07, axis=1) olFunc[olFunc < 0.07] = np.nan - sigOLCor[:, :, indxt] = np.expand_dims( - sigBGCor_total / olFunc, -1) + + ## Aplly overlap correction + sigOLCor[:, :, indxt] = np.expand_dims(sigBGCor_total / olFunc, -1) BGOLCor[:, indxt] = np.expand_dims(bg_total, -1) - #print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) heightFullOverlapCor[:, indxt] = np.expand_dims(np.take(height, idxOL), -1) - #print(np.ravel(heightFullOverlapCor[:, indxt])[:5]) elif config_dict['overlapCorMode'] == 3: + logging.critical('overlapCorMode 3 not implemented, see docstring for further information.') raise ValueError('overlapCorMode 3 not implemented, see docstring for further information') return sigOLCor, BGOLCor, heightFullOverlapCor def fixLowest(overlap:np.ndarray, indexsearchmax:int, thres:float=0.05): - """very rough fix for exploding values in the very near range of the overlap function + """Very rough fix for exploding values in the very near range of the overlap function. in the lowest heights (below indexsearchmax, e.g. 800m) - search for chunks, where the overlap function is smaller than 0.05 + search for chunks, where the overlap function is smaller than a treshold (thres e.g. 0.05) in that chunk take the miniumum and fill heights below + Parameters + ---------- + overlap : list + List of dicts including ovelap functions per cloud free period. + indexsearchmax :int + Maximum height index for the applying the fix. + thres : float + Threshold for searching for a stable low overlap value. Default is 0.05. """ + for i, grp in enumerate(overlap): for channel, vals in grp.items(): var = vals['olFunc'][:indexsearchmax] @@ -151,7 +228,7 @@ def fixLowest(overlap:np.ndarray, indexsearchmax:int, thres:float=0.05): np.split(lt, np.where(np.diff(lt) != 1)[0] + 1), key=len, reverse=True)[0] if len(longestrun) == 0: - print(f"Warning: Fix not applied for channel {channel} in cloud free group {i}.") + logging.warning(f"Fix not applied for channel {channel} in cloud free group {i}.") continue idx = np.argmin(var[longestrun]) + longestrun[0] vals['olFunc'][:idx] = vals['olFunc'][idx] diff --git a/ppcpy/qc/overlapEst.py b/ppcpy/qc/overlapEst.py index 0ab3d2a..55e1ae4 100644 --- a/ppcpy/qc/overlapEst.py +++ b/ppcpy/qc/overlapEst.py @@ -20,8 +20,6 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: Returns ------- - overlap : list of dicts - Per channel per cloud free period: overlap : ndarray Overlap function. overlapStd : ndarray @@ -31,14 +29,13 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: normRange : list Height index of the signal normalization range. - Notes ----- - The matlab version preforms some convertio from PC to PCR after calculating the overlap function (this might just be relevent at all) - The matlab version also uses calculates 1 overlap function for each channel while we calculate 1 per clFreeGrp per channel... - The frnr overlap calculations are very unstable and frequently results in values outside the range [0,1]. - """ + height = data_cube.retrievals_highres['range'] logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') logging.warning(f'at 10km height this is a difference of about 4 indices') @@ -46,26 +43,32 @@ def run_frnr_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: overlap = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Overlap retrieval') + logging.info('Starting Overlap retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree', i, cldFree) + logging.info(f"Cloud free segment: {i}. Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) for wv in [355, 387, 532, 607]: if np.any(data_cube.gf(wv, 'total', 'FR')) and np.any(data_cube.gf(wv, 'total', 'NR')): - print(wv, 'both telescopes available') + logging.info(f"Channels: {wv} total FR | {wv} total NR.") sigFR = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, 'total', 'FR')]) bgFR = np.squeeze(data_cube.retrievals_profile['BGTCor'][i, data_cube.gf(wv, 'total', 'FR')]) sigNR = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, 'total', 'NR')]) bgNR = np.squeeze(data_cube.retrievals_profile['BGTCor'][i, data_cube.gf(wv, 'total', 'NR')]) hFullOverlap = np.array(config_dict['heightFullOverlap'])[data_cube.gf(wv, 'total', 'FR')][0] - ol = overlapCalc(height=height, sigFR=sigFR, bgFR=bgFR, sigNR=sigNR, bgNR=bgNR, hFullOverlap=hFullOverlap, collect_debug=collect_debug) + ol = overlapCalc( + height=height, + sigFR=sigFR, bgFR=bgFR, + sigNR=sigNR, bgNR=bgNR, + hFullOverlap=hFullOverlap, + collect_debug=collect_debug + ) overlap[i][f"{wv}_total_FR"] = ol return overlap -def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.ndarray, bgNR:np.ndarray, hFullOverlap:float=600, collect_debug=False) -> dict: +def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.ndarray, + bgNR:np.ndarray, hFullOverlap:float=600, collect_debug=False) -> dict: """Calculate overlap function. Parameters @@ -88,9 +91,9 @@ def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.n Returns ------- - overlap : ndarray + olFunc : ndarray Overlap function. - overlapStd : ndarray + olFuncStd : ndarray Standard deviation of overlap function. sigRatio : float Signal ratio between near-range and far-range signals. @@ -99,42 +102,59 @@ def overlapCalc(height:np.ndarray, sigFR:np.ndarray, bgFR:np.ndarray, sigNR:np.n Notes ----- + .. TODO:: Define the Default values of the output ei. olFunc = 1 & olFuncStd = 0. + + .. TODO:: Is the first if-statment needed? + + .. TODO:: are bgFR and bgNR actually ndarrays of floats?? **History** - 2021-05-18: First edition by Zhenping """ - if sigNR.shape[0] > 0 and sigFR.shape[0] > 0: - # Find the height index with full overlap - full_overlap_index = np.where(height >= hFullOverlap)[0] - if full_overlap_index.size == 0: - raise ValueError("The index with full overlap cannot be found.") - full_overlap_index = full_overlap_index[0] - - # Calculate the channel ratio of near and far range total signals - sigRatio, normRange, _ = mean_stable(sigNR / sigFR , 40, full_overlap_index, len(sigNR), 0.1) - - # print('is normRange index or slice?', normRange) # -> is list of indices - # Calculate the overlap of the far-range channel - if normRange is not None and normRange.shape[0] > 0: - if collect_debug: - print("bgFR.shape", bgFR.shape) - print("bgNR.shape", bgNR.shape) - print("normRange.shape", normRange.shape) - print("normRange", normRange) - print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) - print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) - SNRnormRangeFR = calc_snr(np.sum(sigFR[normRange], keepdims=True), bgFR*normRange.shape[0]) - SNRnormRangeNR = calc_snr(np.sum(sigNR[normRange], keepdims=True), bgNR*normRange.shape[0]) - sigRatioStd = sigRatio * np.sqrt(1 / (SNRnormRangeFR**2) + 1 / (SNRnormRangeNR**2)) - overlap = (sigFR / (sigNR + 1e-6)) * sigRatio - overlapStd = overlap * np.sqrt((sigRatioStd / sigRatio)**2 + 1 / (sigFR + 1e-6)**2 + 1 / (sigNR + 1e-6)**2) - + + if not (sigNR.shape[0] > 0 and sigFR.shape[0] > 0): + logging.critical("sigNR and/or sigFR can not be empty.") + raise ValueError("sigNR and/or sigFR can not be empty.") # TODO: This check seams unecescery. Might be something leftover from the matlab version. + + # Find the height index with full overlap + full_overlap_index = np.where(height >= hFullOverlap)[0] + if full_overlap_index.size == 0: + raise ValueError("The index with full overlap cannot be found.") + full_overlap_index = full_overlap_index[0] + + # Calculate the channel ratio of near and far range total signals + sigRatio, normRange, _ = mean_stable(sigNR / sigFR , 40, full_overlap_index, len(sigNR), 0.1) + + # print('is normRange index or slice?', normRange) # -> is list of indices + # Calculate the overlap of the far-range channel + if normRange is not None and normRange.shape[0] > 0: + if collect_debug: + print("bgFR.shape", bgFR.shape) + print("bgNR.shape", bgNR.shape) + print("normRange.shape", normRange.shape) + print("normRange", normRange) + print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) + print("bgFR*normRange.shape[0]", bgFR*normRange.shape[0]) + SNRnormRangeFR = calc_snr(np.sum(sigFR[normRange], keepdims=True), bgFR*normRange.shape[0]) + SNRnormRangeNR = calc_snr(np.sum(sigNR[normRange], keepdims=True), bgNR*normRange.shape[0]) + sigRatioStd = sigRatio * np.sqrt(1 / (SNRnormRangeFR**2) + 1 / (SNRnormRangeNR**2)) + overlap = (sigFR / (sigNR + 1e-6)) * sigRatio + overlapStd = overlap * np.sqrt((sigRatioStd / sigRatio)**2 + 1 / (sigFR + 1e-6)**2 + 1 / (sigNR + 1e-6)**2) + + else: + # .. TODO:: Implement default values for the overlap function and its Std for when mean_stable fails. + # What shape do the ovelap function have. Does it go only up to height full overlap or does + # it spann the whole height grid? + # overlap = np.ones(...) + # overlapStd = np.zeros(...) + raise NotImplementedError('Default overlap values are not yet implemented') + ret = {'olFunc': overlap, 'olFuncStd': overlapStd, 'sigRatio': sigRatio, 'normRange': normRange} - return ret + return ret def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: @@ -149,16 +169,16 @@ def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: Returns ------- - overlap : list of dicts - Per channel per cloud free period: olFunc : ndarray Overlap function. - olStd : float - Standard deviation of overlap function. - olFunc0 : ndarray + olFunc_raw : ndarray Overlap function with no smoothing. - + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. """ + height = data_cube.retrievals_highres['range'] hres = data_cube.rawdata_dict['measurement_height_resolution']['var_data'] logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') @@ -167,29 +187,24 @@ def run_raman_cldFreeGrps(data_cube, collect_debug:bool=True) -> list: overlap = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Raman Overlap retrieval') + logging.info('Starting Raman Overlap retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment: {i}. Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) channels = [((532, 'total', 'FR'), (607, 'total', 'FR')),((355, 'total', 'FR'), (387, 'total', 'FR'))] for (wv, t, tel), (wv_r, t_r, tel_r) in channels: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv_r, t_r, tel_r)): - print(wv, wv_r, 'both wavelengths available') + logging.info(f"Channels: {wv} {t} {tel} | {wv_r} {t_r} {tel_r}.") sig = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv, t, tel)]) # bg = np.nansum(np.squeeze(data_cube.retrievals_highres['BGTCor'][slice(*cldFree), data_cube.gf(wv, t, tel)]), axis=0) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] + # molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :] - if f"{wv}_{t}_{tel}" in data_cube.retrievals_profile['raman'][i].keys(): - pass - else: + if f"{wv}_{t}_{tel}" not in data_cube.retrievals_profile['raman'][i] or \ + 'aerBsc' not in data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"]: + logging.info(f"Skipping channel pair {wv} {t} {tel} and {wv_r} {t_r} {tel_r}.") continue - if 'aerBsc' in data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"].keys(): - pass - else: - continue aerBsc = data_cube.retrievals_profile['raman'][i][f"{wv}_{t}_{tel}"]['aerBsc'] sig_r = np.squeeze(data_cube.retrievals_profile['sigTCor'][i, :, data_cube.gf(wv_r, t, tel)]) @@ -271,14 +286,23 @@ def overlapCalcRaman( ------- olFunc : ndarray Overlap function. - olStd : float - Standard deviation of overlap function. - olFunc0 : ndarray + olFunc_raw : ndarray Overlap function with no smoothing. + LR : float + Optimal Lidar Ratio for ... + normRange : list + Height index of the signal normalization range. + + References + ---------- + Wandinger, U. and Ansmann, A. (2002) ‘Experimental determination of the lidar overlap profile with + Raman lidar’, Applied Optics, 41(3), pp. 511–514. Available at: https://doi.org/10.1364/ao.41.000511. + Notes ----- - .. TODO:: What is returned by the function and what is described in the docstring does not corresponed. + .. TODO:: + Finish docsting! (LR output...) .. TODO:: This function uses a mix of ppcpy.misc.helper.unifrom_filter and scipy.ndimage.uniform_filter1d @@ -293,112 +317,118 @@ def overlapCalcRaman( be considered. Optimally, should we designe our own filter for this purpuse that do not have the issue with propagating NaN values, Like what is used in the rest of the modules. However, without reducing dimension or filling in NaN values. + + .. TODO:: + Is the if statment that privusly checked if len(aerBsc) > 0, now len(aerBsc) > 6 to be consistant + with the action of the statment actually needed. I believe it might be a leftover from the Matlab + version where they often used staments like this to check if the aerBsc existed or not... ** History ** - 2023-06-06: First edition by Cristofer + - xxxx-xx-xx: Translated to python """ - if len(aerBsc) > 0: + aerBsc = aerBsc.copy() + aerBsc = np.array([1, 2, 3]) - sigFRRa0 = sigFRRa.copy() - for _ in range(5): - sigFRRa = uniform_filter1d(sigFRRa, smoothbins) - sigFRel = uniform_filter1d(sigFRel, smoothbins) + sigFRRa0 = sigFRRa.copy() + for _ in range(5): + sigFRRa = uniform_filter1d(sigFRRa, smoothbins) + sigFRel = uniform_filter1d(sigFRel, smoothbins) - aerBsc = aerBsc.copy() - if len(aerBsc) > 0: - aerBsc[:5] = aerBsc[5] - else: - aerBsc = 0 + aerBsc = aerBsc.copy() + if len(aerBsc) > 6: # TODO: is this even necessary?.... + aerBsc[:5] = aerBsc[5] + else: + aerBsc = np.zeros_like(height) - aerBsc0 = aerBsc.copy() - # Use ppcpy.misc.helper.unifrom_filter here to avoid propagating the NaN-values in aerBse througout the smoothed array. - aerBsc = uniform_filter(aerBsc, smoothbins) + aerBsc0 = aerBsc.copy() + # Use ppcpy.misc.helper.unifrom_filter here to avoid propagating the NaN-values in aerBse througout the smoothed array. + aerBsc = uniform_filter(aerBsc, smoothbins) - LR0 = np.arange(30, 82, 2) # LR array to search best LR. + LR0 = np.arange(30, 82, 2) # LR array to search best LR. - diff_norm = [] + diff_norm = [] - for ii in range(len(LR0) + 1): - if ii == len(LR0): - indx_min = np.argmin(diff_norm) - LR = LR0[indx_min] - else: - LR = LR0[ii] + for ii in range(len(LR0) + 1): + if ii == len(LR0): + indx_min = np.argmin(diff_norm) + LR = LR0[indx_min] + else: + LR = LR0[ii] - # Overlap calculation (direct version) - transRa = np.exp(-np.nancumsum((molExt_r + LR * aerBsc * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) - transel = np.exp(-np.nancumsum((molExt + LR * aerBsc) * np.concatenate(([height[0]], np.diff(height))))) - transRa0 = np.exp(-np.nancumsum((molExt_r + LR * aerBsc0 * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) - transel0 = np.exp(-np.nancumsum((molExt + LR * aerBsc0) * np.concatenate(([height[0]], np.diff(height))))) + # Overlap calculation (direct version) + transRa = np.exp(-np.nancumsum((molExt_r + LR * aerBsc * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) + transel = np.exp(-np.nancumsum((molExt + LR * aerBsc) * np.concatenate(([height[0]], np.diff(height))))) + transRa0 = np.exp(-np.nancumsum((molExt_r + LR * aerBsc0 * (Lambda_el / Lambda_Ra) ** AE) * np.concatenate(([height[0]], np.diff(height))))) + transel0 = np.exp(-np.nancumsum((molExt + LR * aerBsc0) * np.concatenate(([height[0]], np.diff(height))))) - if sigFRRa.shape[0] > 0 and sigFRel.shape[0] > 0: - fullOverlapIndx = np.searchsorted(height, hFullOverlap) - if fullOverlapIndx == len(height): - raise ValueError('The index with full overlap cannot be found.') + if sigFRRa.shape[0] > 0 and sigFRel.shape[0] > 0: + fullOverlapIndx = np.searchsorted(height, hFullOverlap) + if fullOverlapIndx == len(height): + raise ValueError('The index with full overlap cannot be found.') - olFunc = sigFRRa * height ** 2 / molBsc_r / transel / transRa - olFunc0 = sigFRRa0 * height ** 2 / molBsc_r / transel0 / transRa0 + olFunc = sigFRRa * height ** 2 / molBsc_r / transel / transRa + olFunc0 = sigFRRa0 * height ** 2 / molBsc_r / transel0 / transRa0 - for _ in range(5): - olFunc = uniform_filter1d(olFunc, 3) + for _ in range(5): + olFunc = uniform_filter1d(olFunc, 3) - ovl_norm, normRange, _ = mean_stable(olFunc, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) - ovl_norm0, normRange0, _ = mean_stable(olFunc0, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) + ovl_norm, normRange, _ = mean_stable(olFunc, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) + ovl_norm0, normRange0, _ = mean_stable(olFunc0, 40, fullOverlapIndx - round(37.5 / hres), fullOverlapIndx + round(2250 / hres), 0.1) - if ovl_norm is not None and ovl_norm.size == 1: - olFunc /= ovl_norm - else: - olFunc /= np.nanmean(olFunc[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) + if ovl_norm is not None and ovl_norm.size == 1: + olFunc /= ovl_norm + else: + olFunc /= np.nanmean(olFunc[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) - if ovl_norm0 is not None and ovl_norm0.size == 1: - olFunc0 /= ovl_norm0 - else: - olFunc0 /= np.nanmean(olFunc0[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) + if ovl_norm0 is not None and ovl_norm0.size == 1: + olFunc0 /= ovl_norm0 + else: + olFunc0 /= np.nanmean(olFunc0[fullOverlapIndx + round(150 / hres):fullOverlapIndx + round(1500 / hres)]) - # first bin to start searching full overlap height. % Please replace the 180 by a paramter in future. - bin_ini = int(np.ceil(180 / hres)) + # first bin to start searching full overlap height. % Please replace the 180 by a paramter in future. + bin_ini = int(np.ceil(180 / hres)) - full_ovl_indx = np.argmax(np.diff(olFunc[bin_ini:]) <= 0) + bin_ini + full_ovl_indx = np.argmax(np.diff(olFunc[bin_ini:]) <= 0) + bin_ini - if full_ovl_indx == 0: - full_ovl_indx = fullOverlapIndx + if full_ovl_indx == 0: + full_ovl_indx = fullOverlapIndx - diff_norm.append(np.nansum(np.abs(1 - olFunc[full_ovl_indx:full_ovl_indx + round(1500 / hres)]))) + diff_norm.append(np.nansum(np.abs(1 - olFunc[full_ovl_indx:full_ovl_indx + round(1500 / hres)]))) - olFunc[full_ovl_indx:] = olFunc[full_ovl_indx] - olFunc /= olFunc[full_ovl_indx] # renormalization + olFunc[full_ovl_indx:] = olFunc[full_ovl_indx] + olFunc /= olFunc[full_ovl_indx] # renormalization - if (full_ovl_indx - bin_ini) < 1: - full_ovl_indx = bin_ini + 1 + if (full_ovl_indx - bin_ini) < 1: + full_ovl_indx = bin_ini + 1 - norm_index0 = np.argmax(olFunc[full_ovl_indx - bin_ini:full_ovl_indx + bin_ini * 3]) - norm_index = norm_index0 + full_ovl_indx - bin_ini - 1 - olFunc /= np.mean(olFunc[norm_index - 1:norm_index + 2]) + norm_index0 = np.argmax(olFunc[full_ovl_indx - bin_ini:full_ovl_indx + bin_ini * 3]) + norm_index = norm_index0 + full_ovl_indx - bin_ini - 1 + olFunc /= np.mean(olFunc[norm_index - 1:norm_index + 2]) - half_ovl_indx = np.argmax(olFunc >= 0.95) + half_ovl_indx = np.argmax(olFunc >= 0.95) - if half_ovl_indx == 0: - half_ovl_indx = full_ovl_indx - int(np.floor(180 / hres)) - # smoothing before full overlap to avoid oscilations on that part. - for _ in range(6): - # smoothing before full overlap to avoid S-shape near to the - # full overlap. - olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)] = uniform_filter1d(olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)], 5) + if half_ovl_indx == 0: + half_ovl_indx = full_ovl_indx - int(np.floor(180 / hres)) + # smoothing before full overlap to avoid oscilations on that part. + for _ in range(6): + # smoothing before full overlap to avoid S-shape near to the + # full overlap. + olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)] = uniform_filter1d(olFunc[half_ovl_indx:norm_index + round(bin_ini / 2)], 5) - olFunc[olFunc < 1e-5] = 1e-5 + olFunc[olFunc < 1e-5] = 1e-5 - #olFunc = olFunc.T - #olFunc0 = olFunc0.T + # olFunc = olFunc.T + # olFunc0 = olFunc0.T ret = {'olFunc': olFunc, 'olFunc_raw': olFunc0, 'LR': LR, 'normRange': normRange} return ret - def load(data_cube) -> list: - """read the overlap function from files into a structure similar to the others. + """Read the overlap function from files into a structure similar to the others. Parameters ---------- @@ -408,34 +438,35 @@ def load(data_cube) -> list: Returns ------- overlap : list of dicts - + ... """ - print(data_cube.picasso_config_dict['defaultFile_folder']) - print(data_cube.polly_config_dict) - print(data_cube.polly_default_dict) + + # print(data_cube.picasso_config_dict['defaultFile_folder']) + # print(data_cube.polly_config_dict) + # print(data_cube.polly_default_dict) ovl_files_for = [k for k in data_cube.polly_default_dict.keys() if 'overlapFile_' in k] - print(ovl_files_for) + # print(ovl_files_for) height = data_cube.retrievals_highres['range'] polly_default = data_cube.polly_default_dict folder = Path(data_cube.picasso_config_dict['defaultFile_folder']) - #data_cube.retrievals_profile['overlap']['raman'][i][f'{wv}_total_NR']['olFunc'] + # data_cube.retrievals_profile['overlap']['raman'][i][f'{wv}_total_NR']['olFunc'] overlap = [{}] for k in ovl_files_for: - print(k) + # print(k) full_f = folder.joinpath(polly_default[k]) - print(full_f) + # print(full_f) if polly_default[k] == "": - print('empty string') + # print('empty string') continue dat = np.loadtxt(full_f, skiprows=1, delimiter=',') - print(dat.shape) + # print(dat.shape) h_ovl = dat[:, 0] ovl = dat[:, 1] - print(h_ovl.shape, h_ovl[:10], h_ovl[-10:]) - print(ovl.shape, ovl[:20]) + # print(h_ovl.shape, h_ovl[:10], h_ovl[-10:]) + # print(ovl.shape, ovl[:20]) if height.shape != h_ovl.shape or not np.all(np.isclose(height, h_ovl)): logging.warning('Heights not equal, need interpolating') ovl = np.interp(height, h_ovl, ovl, left=0, right=1) diff --git a/ppcpy/qc/pollySaturationDetect.py b/ppcpy/qc/pollySaturationDetect.py index f12edde..8a23e4d 100644 --- a/ppcpy/qc/pollySaturationDetect.py +++ b/ppcpy/qc/pollySaturationDetect.py @@ -3,27 +3,30 @@ from scipy import ndimage import numpy as np -def pollySaturationDetect(data_cube, rfill=250, sigSaturateThresh=500): - """detect the bins which are fully saturated by the clouds. - - INPUTS: - data: dict - Data dictionary. See documentation for format. - - KEYWORDS: - hfill: float - Minimum range gap to fill (m). Default: 250 - sigSaturateThresh: float - Threshold of saturated signal (photon count). Default: 500 - - OUTPUTS: - flag: boolean ndarray - True indicates current range bin is saturated by clouds. - - HISTORY: - - 2018-12-21: First Edition by Zhenping - - 2019-07-08: Fix the bug of converting signal to PCR. - - 2025-05-14: translated and changed the algorithm to use scipy.ndimage + +def pollySaturationDetect(data_cube, rfill:float=250, sigSaturateThresh:float=500) -> np.ndarray: + """Detect the bins which are fully saturated by the clouds. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + rfill : float, optional + Minimum range gap to fill [m]. Default is 250. + sigSaturateThresh : float, optional + Threshold of saturated signal [photon count]. Default is 500. + + Returns + ------- + flagSaturation : boolean ndarray + True indicates current range bin is saturated by clouds. + + ** History ** + + - 2018-12-21: First Edition by Zhenping + - 2019-07-08: Fix the bug of converting signal to PCR. + - 2025-05-14: translated and changed the algorithm to use scipy.ndimage + """ logging.info('Saturation detection') @@ -37,10 +40,10 @@ def pollySaturationDetect(data_cube, rfill=250, sigSaturateThresh=500): flagSaturation = np.full(PCR.shape, False, dtype=bool) for iChannel in range(nChannels): - flag = PCR[:,:,iChannel] > sigSaturateThresh + flag = PCR[:, :, iChannel] > sigSaturateThresh # manually unmask the first 3 range gates as they might only be affected by straylight - flag[:,:3] = False - flag = ndimage.binary_closing(flag, structure=np.ones((2,hfill_bins))) - flagSaturation[:,:,iChannel] = flag + flag[:, :3] = False + flag = ndimage.binary_closing(flag, structure=np.ones((2, hfill_bins))) + flagSaturation[:, :, iChannel] = flag return flagSaturation diff --git a/ppcpy/qc/qualityMask.py b/ppcpy/qc/qualityMask.py index 46aee88..c0e974c 100644 --- a/ppcpy/qc/qualityMask.py +++ b/ppcpy/qc/qualityMask.py @@ -1,11 +1,15 @@ import numpy as np import logging +from ppcpy.retrievals.collection import calc_snr +from ppcpy.misc.helper import uniform_filter_2d, moving_average_2d -def qualityMask(data_cube): + +def qualityMask(data_cube) -> np.ndarray: """Estimate quality mask. - Categories: + Categories + ---------- 0 : good data 1 : low-SNR data 2 : depolarization calibration periods @@ -13,31 +17,121 @@ def qualityMask(data_cube): 4 : fog 5 : saturated (NEW) + Parameters + ---------- + data_cube : object + Main picassoProc object. + useQuasiSNR : bool + If True, use `lowSNRMask_quasi`. Otherwise use `lowSNRMask`. + + Returns + ------- + quality_mask : ndarray + Quality mask with categories listed above. + + ** History ** + + - xxxx-xx-xx: First edition by ... + - xxxx-xx-xx: translated to python + - 2026-07-03: vectorized and added option to use `lowSNRMask_quasi` + - 2026-07-03: added resistance toward missing mask-variables + + """ + + quality_mask = np.zeros_like(data_cube.retrievals_highres['sigBGCor']).astype(int) + + # Flagg pixels with low SNR + if data_cube.polly_config_dict['flagUseImprovedSNR'] and 'lowSNRMask_quasi' in data_cube.retrievals_highres: + logging.info("Using improved SNR in quality mask estimation.") + quality_mask[data_cube.retrievals_highres['lowSNRMask_quasi']] = 1 + elif 'lowSNRMask' in data_cube.retrievals_highres: + logging.info("Using raw SNR in quality mask estimation.") + quality_mask[data_cube.retrievals_highres['lowSNRMask']] = 1 + else: + logging.warning("Missing SNR information!") + + # Flagg pixels during depol. calibration periods + if 'depCalMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['depCalMask'], :, :] = 2 + else: + logging.warning("Missing depol. calibration period information!") + + # Flagg pixels where the shutter is on + if 'shutterOnMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['shutterOnMask'], :, :] = 3 + else: + logging.warning("Missing shutter position information!") + + # Flagg pixels affected by fog + if 'fogMask' in data_cube.retrievals_highres: + quality_mask[data_cube.retrievals_highres['fogMask'], :, :] = 4 + else: + logging.warning("Missing fog information!") + + # Flagg saturated pixels + if hasattr(data_cube, "flagSaturation"): + quality_mask[data_cube.flagSaturation] = 5 + else: + logging.warning("Missing saturation information!") + + return quality_mask + + +def improvedSNR(data_cube) -> tuple: + """Artificially improve SNR by smoothing the signal and background. + Parameters ---------- data_cube : object Main picassoProc object. + Returns + ------- + SNR : ndarray + Improved signal to noise ratio + lowSNRMask : ndarray + True if SNR is lower than the config variable 'mask_SNRmin'. Otherwise False. + Notes ----- - - The lowSNRMask is actually calculated twice, once in pollyPreprocess.m - and then again when the quality mask is evaluated in picassoProcV3.m. - Also the original processing chain has a quality_mask_vdr, which should be a composite - of cross and total, maybe this can be handled more logically here. - + - Could also be in the collection retrieval together with ´calc_snr´. + ** History ** - xxxx-xx-xx: First edition by ... + - 2026-07-03: translated to python + - 2026-07-12: vectorized for better efficiency + """ - quality_mask = np.zeros_like(data_cube.retrievals_highres['sigBGCor']).astype(int) + logging.info("Calculating SNR from smoothed signal to improve data quality...") + signal = data_cube.retrievals_highres['sigBGCor'].copy() + BG = np.repeat(data_cube.retrievals_highres['BG'].copy()[:, np.newaxis, :], data_cube.retrievals_highres['range'].shape[0], axis=1) + + quasi_smooth_t = np.asarray(data_cube.polly_config_dict['quasi_smooth_t']) + quasi_smooth_h = np.asarray(data_cube.polly_config_dict['quasi_smooth_h']) + + # Group based vectorization + windows = np.stack((quasi_smooth_t, quasi_smooth_h), axis=1) + unique_windows, order = np.unique(windows, axis=0, return_inverse=True) - for ich, ch in enumerate(data_cube.retrievals_highres['channel']): - logging.info(f"channel {ich}, {ch}") - quality_mask[:, :, ich][data_cube.retrievals_highres['lowSNRMask'][:, :, ich]] = 1 - quality_mask[data_cube.retrievals_highres['depCalMask'], :, ich] = 2 - quality_mask[data_cube.retrievals_highres['shutterOnMask'], :, ich] = 3 - quality_mask[data_cube.retrievals_highres['fogMask'], :, ich] = 4 - quality_mask[:, :, ich][data_cube.flagSaturation[:, :, ich]] = 5 + smoothFunc = uniform_filter_2d + if data_cube.polly_config_dict['flagPicassoComparison']: + smoothFunc = moving_average_2d + + for grp_idx, (Nr, Nc) in enumerate(unique_windows): + logging.info(f"Vectorized group {grp_idx}, Nr = {Nr}, Nc = {Nc} ...") + grp = np.where(order == grp_idx)[0] + signal[:, :, grp] = smoothFunc(signal[:, :, grp], Nr, Nc) + BG[:, :, grp] = smoothFunc(BG[:, :, grp], Nr, Nc) + + # Multiply with smoothing window size to get back to photon counts + signal *= quasi_smooth_t * quasi_smooth_h + BG *= quasi_smooth_t * quasi_smooth_h + + SNR = calc_snr(signal, BG) + + lowSNRMask = np.zeros_like(signal, dtype=bool) + lowSNRMask[SNR < data_cube.polly_config_dict['mask_SNRmin']] = True - return quality_mask \ No newline at end of file + return SNR, lowSNRMask \ No newline at end of file diff --git a/ppcpy/qc/transCor.py b/ppcpy/qc/transCor.py index 46ccf1d..f1c2207 100644 --- a/ppcpy/qc/transCor.py +++ b/ppcpy/qc/transCor.py @@ -5,69 +5,91 @@ import ppcpy.retrievals.depolarization as depolarization -def transCorGHK_cube(data_cube, signal='BGCor'): - """ """ +def transCorGHK_cube(data_cube, signal:str='BGCor', collect_debug:bool=False) -> tuple: + """Perform GHK transmission correction. + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + signal : str + Signal type. Default is 'BGCor'. + collect_debug : bool + If True, collects debug information. Default is False. + + Returns + ------- + sigTCor : ndarray + Transmission corrected signal [Photon counts]. + BGTCor : ndarray + Transmission corrected background. + """ config_dict = data_cube.polly_config_dict + # Use the background corrected signal as a default BGTCor = data_cube.retrievals_highres['BG'].copy() - # Store the background corrected signal sigTCor = data_cube.retrievals_highres[f'sig{signal}'].copy() - tel = 'FR' + tel = 'FR' # only done for the far-range signals for wv in [355, 532, 1064]: flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) indxt = np.where(flagt)[0] - #print(flagt, indxt) + if np.any(flagt) and np.any(flagc): - logging.info(f'and even a {wv} channel') + logging.info(f"Channel: {wv} total FR | {wv} cross FR") - sigBGCor_total = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:,:,flagt]) - bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:,flagt]) - sigBGCor_cross = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:,:,flagc]) - bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:,flagc]) + sigBGCor_total = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:, :, flagt]).copy() + bg_total = np.squeeze(data_cube.retrievals_highres['BG'][:, flagt]).copy() + sigBGCor_cross = np.squeeze(data_cube.retrievals_highres[f'sig{signal}'][:, :, flagc]).copy() + # bg_cross = np.squeeze(data_cube.retrievals_highres['BG'][:, flagc]) # TODO: Not used! - print('G', config_dict['G'][flagt], config_dict['G'][flagc]) - print('H', config_dict['H'][flagt], config_dict['H'][flagc]) - print('polCaliEta', data_cube.etaused[f'{wv}_{tel}']) + if collect_debug: + print('G', config_dict['G'][flagt], config_dict['G'][flagc]) + print('H', config_dict['H'][flagt], config_dict['H'][flagc]) + print('polCaliEta', data_cube.etaused[f'{wv}_{tel}']) # similar to voldepol_2d vdr, vdrStd = depolarization.calc_profile_vdr( - sigBGCor_total, sigBGCor_cross, - config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigBGCor_total, sigc=sigBGCor_cross, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], ) sigTCor_total, bgTCor_total = transCor_E16_channel( - sigBGCor_total, bg_total, vdr, - config_dict['H'][flagt], + sigT=sigBGCor_total, bgT=bg_total, + voldepol=vdr, + HT=config_dict['H'][flagt], ) - sigTCor[:,:,indxt] = np.expand_dims(sigTCor_total, -1) - BGTCor[:,indxt] = np.expand_dims(bgTCor_total, -1) + + sigTCor[:, :, indxt] = np.expand_dims(sigTCor_total, -1) + BGTCor[:, indxt] = np.expand_dims(bgTCor_total, -1) return sigTCor, BGTCor -def transCor_E16_channel(sigT, bgT, voldepol, HT): - """ transmission correction for the total channel using the Mattis 2009/Engelmann 2016 method + +def transCor_E16_channel(sigT:np.ndarray, bgT:np.ndarray, voldepol:np.ndarray, HT:float) -> tuple: + """Transmission correction for the total channel using the Mattis 2009/Engelmann 2016 method Parameters ---------- - sigT : array + sigT : ndarray Signal in total channel (background-corrected) - bgT : array + bgT : ndarray Background in total channel - voldepol : array + voldepol : ndarray Volume depolarization ratio HT : float Transmission ratio of total channel in GHK notation Returns ------- - sigTCor : array + sigTCor : ndarray Signal in total channel corrected for polarization induced transmission effects - bgTCor : array + bgTCor : ndarray Background of total signal @@ -92,60 +114,74 @@ def transCor_E16_channel(sigT, bgT, voldepol, HT): """ R_t = (1 - HT) / (1 + HT) - print('calculated R_t', R_t) + # print('calculated R_t', R_t) sigTCor = sigT * (1 + R_t*voldepol) / (1+voldepol) bgTCor = bgT return sigTCor, bgTCor -def transCorGHK_channel(sigT, bgT, sigC, bgC, transGT=1, transGR=1, transHT=0, transHR=-1, polCaliEta=1, polCaliEtaStd=0): +def transCorGHK_channel(sigT:np.ndarray, bgT:np.ndarray, sigC:np.ndarray, bgC:np.ndarray, transGT:float=1, transGR:float=1, + transHT:float=0, transHR:float=-1, polCaliEta:float=1, polCaliEtaStd:float=0) -> tuple: """Corrects the effect of different polarization-dependent transmission inside the total and depol channel. + Follows the matlab code at: https://github.com/PollyNET/Pollynet_Processing_Chain/blob/master/lib/qc/transCorGHK.m - INPUTS: - sigT: array - Signal in total channel. - bgT: array - Background in total channel. - sigC: array - Signal in cross channel. - bgC: array - Background in cross channel. - transGT: float - G parameter in total channel. - transGR: float - G parameter in cross channel. - transHT: float - H parameter in total channel. - transHR: float - H parameter in cross channel. - polCaliEta: float - Depolarization calibration constant (eta). - polCaliEtaStd: float - Uncertainty of the depolarization calibration constant. - - OUTPUTS: - sigTCor: array - Transmission corrected elastic signal. - bgTCor: array - Background of transmission corrected elastic signal. - - REFERENCES: - Mattis, I., Tesche, M., Grein, M., Freudenthaler, V., and Müller, D.: - Systematic error of lidar profiles caused by a polarization-dependent receiver transmission: - Quantification and error correction scheme, Appl. Opt., 48, 2742-2751, 2009. - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. - Atmos. Meas. Tech., 9, 4181–4255 (2016). - - HISTORY: - - 2021-05-27: First edition by Zhenping. - - 2024-08-14: Change to GHK parameterization by Moritz. - - 2024-12-28: AI translation - - Authors: - zhenping@tropos.de, haarig@tropos.de + Parameters + ---------- + sigT : ndarray + Signal in total channel. + bgT : ndarray + Background in total channel. + sigC : ndarray + Signal in cross channel. + bgC : ndarray + Background in cross channel. + transGT : float + G parameter in total channel. + transGR : float + G parameter in cross channel. + transHT : float + H parameter in total channel. + transHR : float + H parameter in cross channel. + polCaliEta : float + Depolarization calibration constant (eta). + polCaliEtaStd : float + Uncertainty of the depolarization calibration constant. + + Returns + ------- + sigTCor : ndarray + Transmission corrected elastic signal. + bgTCor : ndarray + Background of transmission corrected elastic signal. + + Notes + ----- + .. TODO:: Input parameter ´polCaliEtaStd´ is not used. + + References + ---------- + - Mattis, I., Tesche, M., Grein, M., Freudenthaler, V., and Müller, D.: + Systematic error of lidar profiles caused by a polarization-dependent receiver transmission: + Quantification and error correction scheme, Appl. Opt., 48, 2742-2751, 2009. + - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. + Atmos. Meas. Tech., 9, 4181–4255 (2016). + + ** History ** + + - 2021-05-27: First edition by Zhenping. + - 2024-08-14: Change to GHK parameterization by Moritz. + - 2024-12-28: AI translation + + + Authors + ------- + - zhenping@tropos.de, haarig@tropos.de """ + if sigT.shape != sigC.shape: raise ValueError("Input signals have different sizes.") diff --git a/ppcpy/retrievals/angstroem.py b/ppcpy/retrievals/angstroem.py index 18d270b..ce53992 100644 --- a/ppcpy/retrievals/angstroem.py +++ b/ppcpy/retrievals/angstroem.py @@ -27,6 +27,7 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter. """ + return uniform_filter(signal, window_len) @@ -61,8 +62,9 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di the higher of the two wavelengths, and discriminates between FR and NR. """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] + + config_dict = data_cube.polly_config_dict.copy() + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() for i, cldFree in enumerate(data_cube.clFreeGrps): cldFree = cldFree[0], cldFree[1] + 1 @@ -77,10 +79,10 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di flag = (f'aer{prod}' in opt_profiles[i][ch1]) and (f'aer{prod}' in opt_profiles[i][ch2]) else: flag = False - #print(prod, wv1, wv2, tel, opt_profiles[i].keys(), ' -> ', flag) + # print(prod, wv1, wv2, tel, opt_profiles[i].keys(), ' -> ', flag) if flag: - print('channels available', ch1, ch2, prod) + logging.info(f"Channels: {ch1}, {ch2}. Product: {prod}.") retrieval = opt_profiles[i][ch1]['retrieval'] ae, aeStd = calc_ae( @@ -89,7 +91,8 @@ def ae_cldFreeGrps(data_cube, ret_prof_name:str, collect_debug:bool=False) -> di param2=opt_profiles[i][ch2][f'aer{prod}'], param2_std=opt_profiles[i][ch2][f'aer{prod}Std'], wavelength1=wv1, wavelength2=wv2, - smooth_window=config_dict[f"smoothWin_{retrieval}{'_'+tel if tel=='NR' else ''}_{wv2}"] + # smooth_window=config_dict[f"smoothWin_{retrieval}{'_'+tel if tel=='NR' else ''}_{wv2}"] + smooth_window=1 # Hard Coded! ) opt_profiles[i][ch1][f'AE_{prod}_{wv1}_{wv2}'] = ae @@ -102,7 +105,6 @@ def calc_ae(param1:np.ndarray, param1_std:np.ndarray, param2:np.ndarray, param2_ wavelength1:float, wavelength2:float, smooth_window:int=17) -> tuple: """Calculates the Ångström exponent and its uncertainty. - Parameters ---------- param1 : array @@ -127,28 +129,36 @@ def calc_ae(param1:np.ndarray, param1_std:np.ndarray, param2:np.ndarray, param2_ angexpStd : array Uncertainty of Ångström exponent. - Notes ----- - Should the ratio be smoothed or not?? If we do not smooth the ratio then why are we + .. TODO:: Should the ratio be smoothed or not?? If we do not smooth the ratio then why are we considering the smoothing window in the error calculations?? **History** - 2021-05-31: first edition by Zhenping + Examples -------- - angexp, angexpStd = pollyAE(param1, param1_std, param2, param2_std, wavelength1, wavelength2) + angexp, angexpStd = calc_ae(param1, param1_std, param2, param2_std, wavelength1, wavelength2) """ + + param1 = param1.copy() + param2 = param2.copy() + # Replace non-positive and 0 values with NaN param1 = np.where(param1 > 0, param1, np.nan) param2 = np.where(param2 > 0, param2, np.nan) # Compute smoothed ratio - #ratio = smooth_signal(param1, smooth_window) / smooth_signal(param2, smooth_window) - ratio = param1 / param2 + if smooth_window > 1: + ratio = smooth_signal(param1, smooth_window) / smooth_signal(param2, smooth_window) + elif smooth_window == 1: + ratio = param1 / param2 + else: + raise ValueError("Unsupported smoothing window size") # Compute Ångström exponent angexp = np.log(ratio) / np.log(wavelength2 / wavelength1) diff --git a/ppcpy/retrievals/collection.py b/ppcpy/retrievals/collection.py index a537dfe..ae848c3 100644 --- a/ppcpy/retrievals/collection.py +++ b/ppcpy/retrievals/collection.py @@ -1,8 +1,6 @@ - - import numpy as np -def calc_snr(signal, bg): +def calc_snr(signal:np.ndarray, bg:np.ndarray) -> np.ndarray: """Calculate signal-to-noise ratio (SNR). .. TODO:: @@ -11,15 +9,15 @@ def calc_snr(signal, bg): Parameters ---------- - signal : numpy.ndarray + signal : ndarray Signal strength. - bg : numpy.ndarray - Background noise. - + bg : ndarray + Background noise. + Returns ------- - SNR : numpy.ndarray - Signal-to-noise ratio. For negative signal values, the SNR is set to 0. + SNR : ndarray + Signal-to-noise ratio. For negative signal values the SNR is set to 0. References ---------- @@ -30,12 +28,22 @@ def calc_snr(signal, bg): Notes ----- + - `signal` and `background` must be in Photon counts! **History** - 2021-04-21: First edition by Zhenping - 2024-12-10: Translated with AI, moved to own function + + Example + ------- + >>> # SNR for time-height array + >>> SNR_array = calc_snr(signal_array, bg_array) + + >>> # SNR for aggregated signal + >>> SNR = calc_snr(np.sum(signal_array, keepdims=True), np.sum(bg_array, keepdims=True)) """ + tot = signal + 2 * bg tot[tot <= 0] = np.nan @@ -43,6 +51,4 @@ def calc_snr(signal, bg): SNR[SNR <= 0] = 0 SNR[np.isnan(SNR)] = 0 - return SNR - - + return SNR \ No newline at end of file diff --git a/ppcpy/retrievals/depolarization.py b/ppcpy/retrievals/depolarization.py index fbff880..903c1e0 100644 --- a/ppcpy/retrievals/depolarization.py +++ b/ppcpy/retrievals/depolarization.py @@ -21,30 +21,46 @@ def smooth_signal(signal:np.ndarray, window_len:int) -> np.ndarray: Notes ----- + .. TODO:: Check which smoothing shoule be used here! + **History** - + - 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter """ + return uniform_filter(signal, window_len) -def voldepol_cldFreeGrps(data_cube, ret_prof_name): - """ +def voldepol_cldFreeGrps(data_cube, ret_prof_name:str) -> dict: + """... + + Parameters + ---------- + ret_prof_name : str + ... + + Returns + ------- + opt_profiles : dict + ... + + Notes + ----- + .. TODO:: Finish the docstring. .. TODO:: Should the GHK-Transmission corrected (TCor) or the Background corrected (BGCor) signal be used for calculating the volume depolarisation ratio? - - - With the GHK formula the BGCor signal has to be used (in the current implementation, the voldepol is even - needed to perform the polarization-transmission-correction + - With the GHK formula the BGCor signal has to be used (in the current implementation, + the voldepol is even needed to perform the polarization-transmission-correction. + .. TODO:: Is the return in this function actually needed?? """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] - print('no_profiles ', len(opt_profiles)) - print(opt_profiles[0].keys()) + config_dict = data_cube.polly_config_dict.copy() + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() + logging.debug(f"no_profiles: {len(opt_profiles)}") + logging.debug(f"{opt_profiles[0].keys()}") - signal = 'TCor' + # signal = 'TCor' signal = 'BGCor' for i, cldFree in enumerate(data_cube.clFreeGrps): @@ -53,61 +69,67 @@ def voldepol_cldFreeGrps(data_cube, ret_prof_name): wv, t, tel = channel.split('_') flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) - #indxt = np.where(flagt)[0] + # indxt = np.where(flagt)[0] retrieval = opt_profiles[i][channel]['retrieval'] if np.any(flagt) and np.any(flagc): logging.info(f'voldepol at channel {wv} cldFree {i} {cldFree}') - sigt = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i,:,flagt]) - #bgt = np.nansum(np.squeeze( - # data_cube.retrievals_highres[f'BG{signal}'][slice(*cldFree),data_cube.gf(wv, 'total', tel)]), axis=0) - sigc = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i,:,flagc]) + sigt = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, flagt]) + # bgt = np.nansum(np.squeeze( + # data_cube.retrievals_highres[f'BG{signal}'][slice(*cldFree),data_cube.gf(wv, 'total', tel)]), axis=0) + sigc = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, flagc]) - print(channel, data_cube.etaused[f'{wv}_{tel}']) + logging.debug(f"{channel} {data_cube.etaused[f'{wv}_{tel}']}") vdr, vdrStd = calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], window=config_dict[f'smoothWin_{retrieval}_{wv}'] - ) + ) opt_profiles[i][channel]['vdr'] = vdr opt_profiles[i][channel]['vdrStd'] = vdrStd if np.isnan(data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd']).any(): + logging.warning("No valid refHInd found, skipping retrieval for this channel.") continue + mdr, mdrStd, flgaDeftMdr = get_MDR( - vdr, vdrStd, data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'], + vdr=vdr, vdrStd=vdrStd, + refHInd=data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'] ) if config_dict["flagUseTheoreticalMDR"]: - logging.info("use the theoretical MDR value") + logging.info("Use the theoretical MDR value.") mdr = data_cube.polly_config_dict[f"molDepol{wv}"] - print(f"est. mdr {channel} {mdr} {mdrStd}") + + logging.debug(f"est. mdr {channel} {mdr} {mdrStd}") opt_profiles[i][channel]['mdr'] = mdr opt_profiles[i][channel]['mdrStd'] = mdrStd # experimental code to calculate the mdr without smoothing(?) vdr, vdrStd = calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], window=1 - ) + ) mdr, mdrStd, flgaDeftMdr = get_MDR( - vdr, vdrStd, data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'], + vdr=vdr, vdrStd=vdrStd, + refHInd=data_cube.retrievals_profile['refH'][i][f"{wv}_{t}_{tel}"]['refInd'] ) - print(f"est. mdr {channel} {mdr} {mdrStd} (smooth1)") + logging.debug(f"est. mdr {channel} {mdr} {mdrStd} (smooth1)") - print(opt_profiles[i][channel].keys()) + logging.debug(f"{opt_profiles[i][channel].keys()}") return opt_profiles -def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, - voldepol_error, window=1, flag_smooth_before=True): -#def polly_vdr_ghk(sig_tot, sig_cross, GT, GR, HT, HR, eta, -# voldepol_error_a0, voldepol_error_a1, voldepol_error_a2, -# smooth_window=1, flag_smooth_before=True): +def calc_profile_vdr(sigt:np.ndarray, sigc:np.ndarray, Gt:float, Gr:float, Ht:float, Hr:float, eta:float, + voldepol_error:float, window:int=1, flag_smooth_before:bool=True) -> tuple: """Calculate volume depolarization ratio using GHK parameters. Parameters @@ -118,11 +140,11 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, Signal strength of the cross channel [photon count]. Gt : float G parameter in the total channel. - Gc : float + Gr : float G parameter in the cross channel. Ht : float H parameter in the total channel. - Hc : float + Hr : float H parameter in the cross channel. eta : float Depolarization calibration constant. @@ -151,9 +173,14 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, - Freudenthaler, V. About the effects of polarising optics on lidar signals and the Delta90 calibration. Atmos. Meas. Tech., 9, 4181–4255 (2016). + Notes ----- - + - Be awere: `sigT = 0` will cause `vol_depol` and `vol_depol_std` to be NaN! + .. TODO:: Should consider implementing something to avoid NaN values in this function, + as these will also harm the qulity of the retrievals that depend on the + transmission corrected signal (ei. introduce more NaN's). + **History** - 2018-09-02: First edition by Zhenping @@ -163,7 +190,7 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, """ - print(f"G {Gt} {Gr} H {Ht} {Hr} Eta {eta} error {voldepol_error} Window {window} ") + logging.info(f"G {Gt} {Gr} H {Ht} {Hr} Eta {eta} error {voldepol_error} Window {window} ") # Smooth signals before or after ratio calculation sig_ratio = sigc / sigt if window > 1: # smoothing with a window size of 1 equals no smoothing @@ -184,60 +211,105 @@ def calc_profile_vdr(sigt, sigc, Gt, Gr, Ht, Hr, eta, return vol_depol, vol_depol_std -def get_MDR(vdr, vdrStd, refHInd): - """get the vdr at reference height - in the matlab pollynet processing chain this is done by recalculating vdr for the - reference height chunk - (Pollynet_Processing_Chain/lib/calibration/pollyMDRGHK.m) +def get_MDR(vdr:np.ndarray, vdrStd:np.ndarray, refHInd:list) -> float: + """Get the vdr at reference height. - Assuming that it is more efficient to use the precalculated vdr + Parameters + ---------- + vdr : ndarray + Volume depolarization ratio. + vdrStd : ndarray + Uncertainty of the volume depolarization ratio. + refHInd : list + Reference height indcies [bottum, top]. + + Returns + ------- + mdr : ndarray + Mean volume depolarization ratio at reference height. + mdrStd : ndarray + Mean uncertainty of the volume depolarization ratio at reference height. + bool + False. + + Notes + ----- + - in the matlab pollynet processing chain this is done by recalculating vdr for the + reference height chunk (Pollynet_Processing_Chain/lib/calibration/pollyMDRGHK.m) + - Assuming that it is more efficient to use the precalculated vdr + - The snr criterion is missing for this very first version. + + ** History ** - The snr criterion is missing for this very first version """ + mdr = np.mean(vdr[slice(*refHInd)]) mdrStd = np.mean(vdrStd[slice(*refHInd)]) return mdr, mdrStd, False -def pardepol_cldFreeGrps(data_cube, ret_prof_name): - """ +def pardepol_cldFreeGrps(data_cube, ret_prof_name:str) -> dict: + """... + + Parameters + ---------- + data_cube : object + Main PicassoProc object. + ret_prof_name : str + ... + + Returns + ------- + opt_porfiles : dict + ... + + Notes + ----- + .. TODO:: + - Finish docsting. + - Is the return here necessary? + - Cleare unused variables. """ - config_dict = data_cube.polly_config_dict - opt_profiles = data_cube.retrievals_profile[ret_prof_name] - print('no_profiles ', len(opt_profiles)) - print(opt_profiles[0].keys()) - signal = 'BGCor' + # config_dict = data_cube.polly_config_dict # <-- Not used! + opt_profiles = data_cube.retrievals_profile[ret_prof_name].copy() + logging.debug(f"no_profiles {len(opt_profiles)}") + logging.debug(f"{opt_profiles[0].keys()}") + # signal = 'BGCor' # <-- Not used! for i, cldFree in enumerate(data_cube.clFreeGrps): cldFree = cldFree[0], cldFree[1] + 1 for channel in opt_profiles[i]: wv, t, tel = channel.split('_') - print(f"=== {channel } ==============================================================") + logging.info(f"Channel: {channel}.") flagt = data_cube.gf(wv, 'total', tel) flagc = data_cube.gf(wv, 'cross', tel) - #indxt = np.where(flagt)[0] - retrieval = opt_profiles[i][channel]['retrieval'] + # indxt = np.where(flagt)[0] # <-- Not used! + # retrieval = opt_profiles[i][channel]['retrieval'] # <-- Not used! if np.any(flagt) and np.any(flagc) and 'aerBsc' in opt_profiles[i][channel]: - logging.info(f'pardepol at channel {wv} cldFree {i} {cldFree}') + logging.info(f"pardepol at channel {wv} cldFree {i} {cldFree}") pdr, pdrStd = calc_pdr( - opt_profiles[i][channel]['vdr'], opt_profiles[i][channel]['vdrStd'], - opt_profiles[i][channel]['aerBsc'], np.ones_like(opt_profiles[i][channel]['aerBscStd'])*1e-7, - data_cube.mol_profiles[f'mBsc_{wv}'][i,:], opt_profiles[i][channel]['mdr'], - opt_profiles[i][channel]['mdrStd'], + vol_depol=opt_profiles[i][channel]['vdr'], + vol_depol_std=opt_profiles[i][channel]['vdrStd'], + aer_bsc=opt_profiles[i][channel]['aerBsc'], + aer_bsc_std=np.ones_like(opt_profiles[i][channel]['aerBscStd'])*1e-7, # TODO: Hard coded! + mol_bsc=data_cube.mol_profiles[f'mBsc_{wv}'][i, :], + mol_depol=opt_profiles[i][channel]['mdr'], + mol_depol_std=opt_profiles[i][channel]['mdrStd'] ) opt_profiles[i][channel]['pdr'] = pdr opt_profiles[i][channel]['pdrStd'] = pdrStd - #print('after ', opt_profiles[i][channel].keys()) + # print('after ', opt_profiles[i][channel].keys()) return opt_profiles -def calc_pdr(vol_depol, vol_depol_std, aer_bsc, aer_bsc_std, mol_bsc, mol_depol, mol_depol_std): +def calc_pdr(vol_depol:np.ndarray, vol_depol_std:np.ndarray, aer_bsc:np.ndarray, aer_bsc_std:np.ndarray, + mol_bsc:np.ndarray, mol_depol:float, mol_depol_std:float) -> tuple: """Calculate the particle depolarization ratio and estimate its standard deviation. Parameters @@ -266,7 +338,8 @@ def calc_pdr(vol_depol, vol_depol_std, aer_bsc, aer_bsc_std, mol_bsc, mol_depol, References ---------- - - Freudenthaler, V., et al., Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006, Tellus B, 61, 165-179, 2009. + - Freudenthaler, V., et al., Depolarization ratio profiling at several wavelengths in pure Saharan dust during SAMUM 2006, + Tellus B, 61, 165-179, 2009. Notes ----- diff --git a/ppcpy/retrievals/highres.py b/ppcpy/retrievals/highres.py index 42d83be..78930d0 100644 --- a/ppcpy/retrievals/highres.py +++ b/ppcpy/retrievals/highres.py @@ -8,7 +8,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): - """Attenuated Backscatter + """Attenuated Backscatter. Parameters ---------- @@ -18,7 +18,22 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): If Ture, calculate the attbsc for FR and NR channels. Default is True. collect_debug : bool, optional If True, collects debug information. Default is False. + + Yeilds + ------ + data_cube.retrievals_highres[f"attBsc_{channel}"] : np.ndarray + Attenuated backscatter per channel. + Notes + ----- + - If ´nr=True´ also yeild the attenuated backscatter of the near-range + channels. + - In addition to the standard far-range channels the function also yeilds + the attenuated backscatter of the overlap corrected far-range channels. + + ..TODO:: is it correct to transmission correct the overlap corrected signals + before calculating their attenuated backscatter? Are not the OL + signals already transmission corrected? """ rgs = data_cube.retrievals_highres['range'] @@ -39,11 +54,10 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): sig = np.squeeze( data_cube.retrievals_highres[f'sigTCor'][:, :, data_cube.gf(wv, t, tel)]) - if channel in data_cube.LCused.keys(): - pass - else: + if channel not in data_cube.LCused.keys(): logging.info(f'{channel} skipped at attbsc_2d') continue + attBsc = sig * ranges2d / data_cube.LCused[channel] attBsc[data_cube.retrievals_highres['depCalMask'], :] = np.nan @@ -52,8 +66,8 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): # experimental, the calibration constant requires the OL corrected signal if 'sigOLCor' in data_cube.retrievals_highres: - print(f"Exprimental, attenuated backscatter solution for {channel}") - sigOLTCor, _ = transCor.transCorGHK_cube(data_cube, signal='OLCor') + logging.warning(f"Exprimental, attenuated backscatter solution for {channel}") + sigOLTCor, _ = transCor.transCorGHK_cube(data_cube, signal='OLCor') channels = [(355, 'total', 'FR'), (532, 'total', 'FR'), (1064, 'total', 'FR')] for wv, t, tel in channels: channel = f"{wv}_{t}_{tel}" @@ -62,9 +76,7 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): # data_cube.retrievals_highres[f'sigOLCor'][:, :, data_cube.gf(wv, t, tel)]) sig = np.squeeze(sigOLTCor[:, :, data_cube.gf(wv, t, tel)]) - if channel in data_cube.LCused.keys(): - pass - else: + if channel not in data_cube.LCused.keys(): logging.info(f'{channel} skipped at attbsc_2d OL') continue @@ -75,22 +87,27 @@ def attbsc_2d(data_cube, nr:bool=True, collect_debug:bool=False): def voldepol_2d(data_cube): - """Calculate the volume depolarisation ratio + """Calculate the volume depolarisation ratio. Parameters ---------- data_cube : object Main PicassoProc object + Yeilds + ------ + data_cube.retrievals_highres[f"voldepol_{wv}_total_{tel}"] : np.ndarray + Time-hight volume depolarization ratio at wavelengths: + 353 nm, 532 nm, 1064 nm, and 532 nm DFOV. """ config_dict = data_cube.polly_config_dict channels = [ (532, 'FR'), (355, 'FR'), (1064, 'FR')] - if '532_DFOV' in data_cube.pol_cali: + if '532_DFOV' in data_cube.etaused: channels += [(532, 'DFOV')] - print('voldepol also for DFOV') + logging.info("voldepol also for DFOV") for wv, tel in channels: if tel == 'DFOV': @@ -107,9 +124,12 @@ def voldepol_2d(data_cube): vdr, vdrStd = depolarization.calc_profile_vdr( - sigt, sigc, config_dict['G'][flagt], config_dict['G'][flagc], - config_dict['H'][flagt], config_dict['H'][flagc], - data_cube.etaused[f'{wv}_{tel}'], config_dict[f'voldepol_error_{wv}'], - window=1) + sigt=sigt, sigc=sigc, + Gt=config_dict['G'][flagt], Gr=config_dict['G'][flagc], + Ht=config_dict['H'][flagt], Hr=config_dict['H'][flagc], + eta=data_cube.etaused[f'{wv}_{tel}'], + voldepol_error=config_dict[f'voldepol_error_{wv}'], + window=1 + ) vdr[data_cube.retrievals_highres['depCalMask'], :] = np.nan data_cube.retrievals_highres[f"voldepol_{wv}_total_{tel}"] = vdr diff --git a/ppcpy/retrievals/klettfernald.py b/ppcpy/retrievals/klettfernald.py index 82722ff..bcdda36 100644 --- a/ppcpy/retrievals/klettfernald.py +++ b/ppcpy/retrievals/klettfernald.py @@ -38,33 +38,33 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b retrieval : str Name of retrieval type, eg. 'klett'. signal : str - Name of the signal used for the retrievals, eg. 'TCor'. + Name of the signal used for the retrieval, eg. 'TCor'. + refBeta : float + Reference value used for the retrieval [m^{-1}Sr^{-1}]. Notes ----- + .. TODO:: Should sigBGCor, sigTCor or RCS be used for the Klett retrievals? **History** - xxxx-xx-xx: TODO: First edition by ... - 2026-02-09: Modified and cleaned by Buholdt - - .. TODO:: Should sigBGCor, sigTCor or RCS be used for the Klett retrievals? """ - height = data_cube.retrievals_highres['range'] - config_dict = data_cube.polly_config_dict + height = data_cube.retrievals_highres['range'].copy() + config_dict = data_cube.polly_config_dict.copy() logging.warning(f'rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!') logging.warning(f'at 10km height this is a difference of about 4 indices') opt_profiles = [{} for i in range(len(data_cube.clFreeGrps))] - print('Starting Klett retrieval') + # print('Starting Klett retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) # Define channels to run the retrieval for channels = [(532, 'total', 'FR'), (355, 'total', 'FR'), (1064, 'total', 'FR')] @@ -73,7 +73,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b for wv, t, tel in channels: if np.any(data_cube.gf(wv, t, tel)): - print(f'== {wv}, {t}, {tel} klett =================================') + logging.info(f"Channel: {wv}, {t}, {tel} klett.") # Telescope type dependent configurations if tel == 'NR': @@ -81,8 +81,6 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b keyminSNR = 'minRefSNR_NR_' key_LR = 'LR_NR_' refBeta = config_dict[f"refBeta_NR_{wv}"] if f"refBeta_NR_{wv}" in config_dict else None - # TODO seperate klett and raman refBeta in config file? - # refBeta = config_dict[f"refBeta_NR_klett_{wv}"] if f"refBeta_NR_klett_{wv}" in config_dict else None else: key_smooth = 'smoothWin_klett_' keyminSNR = 'minRefSNR' @@ -90,18 +88,18 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b refBeta = config_dict[f'refBeta{wv}'] # Elastic signals - sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]) - bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] + sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]).copy() + bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]).copy() + molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() if np.isnan(sig).any(): # Current temporary version of fernald() does not support NaN values in the signal. - print(f'NaN-values detected in signal {signal}, skipping Klett retrieval for this channel.') + logging.warning(f"NaN-values detected in signal {signal}, skipping Klett retrieval for this channel.") continue # Reference height refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] if np.isnan(refHInd).any(): - print('No valid refHInd found, skipping Klett retrieval for this channel.') + logging.warning("No valid refHInd found, skipping Klett retrieval for this channel.") continue # Calculate SNR in the reference height @@ -112,7 +110,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b # Checking SNR treshold if SNRRef < config_dict[f'{keyminSNR}{wv}']: - print('Signal is too noisy at the reference height, skipping Klett retrival for this channel.', SNRRef, config_dict[f'{keyminSNR}{wv}']) + logging.warning("Signal is too noisy at the reference height, skipping Klett retrival for this channel.") continue if refBeta is None and tel == 'NR': @@ -121,10 +119,10 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', nr:bool=False, collect_debug:b if 'aerBsc' in opt_profiles[i][f'{wv}_{t}_FR']: refBeta = np.nanmean(opt_profiles[i][f'{wv}_{t}_FR']['aerBsc'][refHInd[0]:refHInd[1] + 1]) else: - print('No valid refBeta found, skipping Klett retrieval for this channel') + logging.warning("No valid refBeta found, skipping Klett retrieval for this channel.") continue else: - print('No valid refBeta found, skipping Klett retrieval for this channel') + logging.warning("No valid refBeta found, skipping Klett retrieval for this channel.") continue # Print debug info @@ -227,6 +225,7 @@ def fernald( propagating NaN-values in the backward and farward retrievals. """ + # Convert units height = height / 1e3 # Convert to km molBsc = np.array(molBsc) * 1e3 # Convert to km^-1 sr^-1 diff --git a/ppcpy/retrievals/quasi.py b/ppcpy/retrievals/quasi.py index 0c0de04..4ff7154 100644 --- a/ppcpy/retrievals/quasi.py +++ b/ppcpy/retrievals/quasi.py @@ -27,6 +27,7 @@ def quasi_pdr(data_cube, wvs:list=[532], version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-27: Added flag to use only valid data + """ rgs = data_cube.retrievals_highres['range'] @@ -108,6 +109,7 @@ def quasi_angstrom(data_cube, version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-21: Fixed calculation + """ t = 'total' @@ -140,6 +142,7 @@ def target_cat(data_cube, version:str='V1'): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python - 2026-05-21: Added dependency on Quality mask + """ config_dict = data_cube.polly_config_dict @@ -295,6 +298,7 @@ def target_classify(height:np.ndarray, attBeta532:np.ndarray, quasiBsc1064:np.nd - 2021-06-05: First edition by Zhenping - 2025-03-25: AI based translation to python - 2026-05-21: Fixed dimension issue + """ # Default parameter values @@ -413,10 +417,11 @@ def detect_liquid_bits(height:np.ndarray, bsc1064:np.ndarray, cloudThresBsc1064: - 2021-06-05: First edition by Zhenping - 2025-03-25: AI based translation to python - 2026-05-21: Fixed dimension issue + """ logging.warning("Still in testing phase, may show strange classifications.") - # bsc1064 = np.nan_to_num(bsc1064) # Replace NaN 0 and inf with large positive or negative numbers + # bsc1064 = np.nan_to_num(bsc1064) # Replace NaN with 0 and inf with large positive or negative numbers bsc1064[~np.isfinite(bsc1064)] = 0 # Replace NaN and inf with 0 flagLiquid = np.zeros_like(bsc1064, dtype=bool) diff --git a/ppcpy/retrievals/quasiV1.py b/ppcpy/retrievals/quasiV1.py index 06aaab4..41f16b6 100644 --- a/ppcpy/retrievals/quasiV1.py +++ b/ppcpy/retrievals/quasiV1.py @@ -20,6 +20,7 @@ def quasi_bsc(data_cube): - xxxx-xx-xx: First edition by ... - xxxx-xx-xx: AI based translation to python + """ rgs = data_cube.retrievals_highres['range'] @@ -115,6 +116,7 @@ def quasi_retrieval(height:np.ndarray, att_beta:np.ndarray, molExt:np.ndarray, - 2018-12-25: First edition by Zhenping - 2019-03-31: Added the keyword 'nIters' to control iteration times. - 2025-03-21: AI based translation to python and debugging + """ # Compute differential heights diff --git a/ppcpy/retrievals/quasiV2.py b/ppcpy/retrievals/quasiV2.py index 53f0f51..a19aef2 100644 --- a/ppcpy/retrievals/quasiV2.py +++ b/ppcpy/retrievals/quasiV2.py @@ -44,7 +44,7 @@ def quasi_bsc(data_cube): if config_dict['flagOnlyUseValidQuasiData']: quality_mask = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, data_cube.gf(wv, t, tel)]) att_beta_qsi[quality_mask != 0] = np.nan - # TODO check if halving the window is needed: Yes at the moment they need to be halved. + # TODO check if halving the window is needed: Yes, at the moment they need to be halved. smooth_t = int(np.array(config_dict['quasi_smooth_t'])[data_cube.gf(wv, t, tel)][0] / 2) smooth_h = int(np.array(config_dict['quasi_smooth_h'])[data_cube.gf(wv, t, tel)][0] / 2) att_beta_qsi = helper.smooth2a(att_beta_qsi, smooth_t, smooth_h) @@ -54,7 +54,7 @@ def quasi_bsc(data_cube): if config_dict['flagOnlyUseValidQuasiData']: quality_mask_r = np.squeeze(data_cube.retrievals_highres['quality_mask'][:, :, data_cube.gf(wv_r, t_r, tel_r)]) att_beta_r_qsi[quality_mask_r != 0] = np.nan - # TODO check if halving the window is needed: Yes at the moment they need to be halved. + # TODO check if halving the window is needed: Yes, at the moment they need to be halved. smooth_t_r = int(np.array(config_dict['quasi_smooth_t'])[data_cube.gf(wv_r, t_r, tel_r)][0] / 2) smooth_h_r = int(np.array(config_dict['quasi_smooth_h'])[data_cube.gf(wv_r, t_r, tel_r)][0] / 2) att_beta_r_qsi = helper.smooth2a(att_beta_r_qsi, smooth_t_r, smooth_h_r) diff --git a/ppcpy/retrievals/raman.py b/ppcpy/retrievals/raman.py index 5f92c80..5660d26 100644 --- a/ppcpy/retrievals/raman.py +++ b/ppcpy/retrievals/raman.py @@ -45,26 +45,30 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n retrieval : str Name of retrieval type eg. 'raman'. signal : str - Name of the signal used for the retrievals, eg. 'TCor'. + Name of the signal used for the retrieval eg. 'TCor'. + refBeta : float + Reference value used for the retrieval. Notes ----- + .. TODO:: + - sigma_angstroem and MC_count are hardcoded. Can this be automated? + - in raman_ext calulations we use a different hard coded MC_count than the global parameter. + - Should sigBGCor, sigTCor or RCS be used for the Raman retrievals? RCS dampens the effect form + the wrong first bin and makes the profile more straight, insted of s-shaped, in the lower bins. + - use flag367Off and flag607Off to give warnings or error if the channels are on less then X%of the + time during a cloud free segment. + - Add flag for nighttime measurements. **History** - xxxx-xx-xx: TODO: First edition by ... - 2026-02-04: Modified and cleaned by Buholdt - 2026-02-27: Added ext_aer_mod and ext_mol_mod to backscatter calculations. - - - .. TODO:: - - sigma_angstroem and MC_count are hardcoded. Can this be automated? - - in raman_ext calulations we use a different hard coded MC_count than the global parameter. - - Should sigBGCor, sigTCor or RCS be used for the Raman retrievals? RCS dampens the effect form - the wrong first bin and makes the profile more straight (insted of the s-shape) in the lower bins. """ - height = data_cube.retrievals_highres['range'] + + height = data_cube.retrievals_highres['range'].copy() hres = data_cube.rawdata_dict['measurement_height_resolution']['var_data'] config_dict = data_cube.polly_config_dict @@ -73,15 +77,12 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n opt_profiles = [{} for i in range(len(data_cube.clFreeGrps))] - if not heightFullOverlap: + if not heightFullOverlap: heightFullOverlap = [np.array(config_dict['heightFullOverlap']) for i in data_cube.clFreeGrps] - print(heightFullOverlap) - print('Starting Raman retrieval') for i, cldFree in enumerate(data_cube.clFreeGrps): - print('cldFree ', i, cldFree) + logging.info(f"Cloud free segment {i}, Time: {data_cube.retrievals_highres['time64'][cldFree[0]]} - {data_cube.retrievals_highres['time64'][cldFree[1]]}.") cldFree = cldFree[0], cldFree[1] + 1 - print('cldFree mod', cldFree) # Define channels to run the retrieval for channels = [((355, 'total', 'FR'), (387, 'total', 'FR')), @@ -93,8 +94,17 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n for (wv, t, tel), (wv_r, t_r, tel_r) in channels: if np.any(data_cube.gf(wv, t, tel)) and np.any(data_cube.gf(wv_r, t_r, tel_r)): - print(f'== {wv}, {t}, {tel} | {wv_r}, {t_r}, {tel_r} raman ========') - # TODO add flag for nighttime measurements? + logging.info(f"Channels: {wv}, {t}, {tel} | {wv_r}, {t_r}, {tel_r} raman.") + + # Check availability of Raman channel during retireval priod + if data_cube.retrievals_profile[f'mask{wv_r}Off'][i] > 0: + logging.warning(f'Channel {wv_r} {t_r} {tel_r} was not opertional {data_cube.retrievals_profile[f'mask{wv_r}Off'][i]*100:.2f}% of the profile retrieval period.') + # .. TODO:: add this information to a quality flag for the profile based on how long wv_r was unoperational. + if data_cube.retrievals_profile[f'mask{wv_r}Off'][i] > 0.5: # <-- .. TODO:: decide on a treshold for skipping the channel processing. + logging.warning('Skipping Raman retrival for this channel.') + continue + + # .. TODO:: add flag for nighttime measurements? # Telescope type dependent configurations if tel == 'NR': @@ -102,8 +112,6 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n keyminSNR = 'minRamanRefSNR_NR_' angstrexp = config_dict['angstrexp_NR'] refBeta = config_dict[f'refBeta_NR_{wv}'] if f'refBeta_NR_{wv}' in config_dict else None - # TODO seperate klett and raman refBeta in config file? - # refBeta = config_dict[f'refBeta_NR_raman_{wv}'] if f'refBeta_NR_raman_{wv}' in config_dict else None else: key_smooth = 'smoothWin_raman_' keyminSNR = 'minRamanRefSNR' @@ -117,22 +125,22 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n print('angstrexp', angstrexp) # Elastic signals - sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]) - bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]) - molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :] - molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :] + sig = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv, t, tel)]).copy() + bg = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv, t, tel)]).copy() + molBsc = data_cube.mol_profiles[f'mBsc_{wv}'][i, :].copy() + molExt = data_cube.mol_profiles[f'mExt_{wv}'][i, :].copy() # Inelastic signals - sig_r = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv_r, t, tel)]) - bg_r = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv_r, t, tel)]) - molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :] - molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :] + sig_r = np.squeeze(data_cube.retrievals_profile[f'sig{signal}'][i, :, data_cube.gf(wv_r, t, tel)]).copy() + bg_r = np.squeeze(data_cube.retrievals_profile[f'BG{signal}'][i, data_cube.gf(wv_r, t, tel)]).copy() + molBsc_r = data_cube.mol_profiles[f'mBsc_{wv_r}'][i, :].copy() + molExt_r = data_cube.mol_profiles[f'mExt_{wv_r}'][i, :].copy() - number_density = data_cube.mol_profiles[f'number_density'][i, :] + number_density = data_cube.mol_profiles[f'number_density'][i, :].copy() if wv == 1064 and wv_r == 607: # calculate the extinction based on the 532nm molecular profiles and a correction afterwards - molExt_mod = data_cube.mol_profiles[f'mExt_532'][i, :] + molExt_mod = data_cube.mol_profiles[f'mExt_532'][i, :].copy() wv_mod = 532 else: # calculate normally @@ -165,14 +173,12 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n refHInd = data_cube.retrievals_profile['refH'][i][f'{wv}_{t}_{tel}']['refInd'] if np.isnan(refHInd).any(): - print('No valid refHInd found, skipping Raman retrieval for this channel.') + logging.warning("No valid refHInd found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue hFullOverlap = heightFullOverlap[i][data_cube.gf(wv, t, tel)][0] hBaseInd = np.argmax(height >= (hFullOverlap + config_dict[f'{key_smooth}{wv}'] / 2 * hres)) - print(hFullOverlap, config_dict[f'{key_smooth}{wv}'] / 2 * hres) - print('refHInd', refHInd, 'refH', height[np.array(refHInd)], 'hBaseInd', hBaseInd, 'hBase', height[hBaseInd]) # Calculate SNR in the reference height SNRRef = calc_snr( @@ -186,7 +192,7 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n # Checking SNR treshold if SNRRef < config_dict[f'{keyminSNR}{wv}'] and SNRRef_r < config_dict[f'{keyminSNR}{wv_r}']: - print('Signal is too noisy at the reference height, skipping Raman retrival for this channel.', SNRRef, config_dict[f'{keyminSNR}{wv}'], SNRRef_r, config_dict[f'{keyminSNR}{wv_r}']) + logging.warning(f"Signal is too noisy at the reference height, skipping Raman retrival for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue @@ -196,18 +202,18 @@ def run_cldFreeGrps(data_cube, signal:str='TCor', heightFullOverlap:list=None, n if 'aerBsc' in opt_profiles[i][f'{wv}_{t}_FR']: refBeta = np.nanmean(opt_profiles[i][f'{wv}_{t}_FR']['aerBsc'][refHInd[0]:refHInd[1] + 1]) else: - print('No valid refBeta found, skipping Raman retrieval for this channel.') + logging.warning("No valid refBeta found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue else: - print('No valid refBeta found, skipping Raman retrieval for this channel.') + logging.warning("No valid refBeta found, skipping Raman retrieval for this channel.") opt_profiles[i][f'{wv}_{t}_{tel}'] = prof continue aerExt_tmp = prof['aerExt'].copy() aerExt_tmp[:hBaseInd + 1] = aerExt_tmp[hBaseInd] aerExt_mod[:hBaseInd + 1] = aerExt_mod[hBaseInd] # should the hBaseInd for 1064 or 532 be used here for the 1064nm channel?? - print(f'filling aerExt below overlap with {aerExt_tmp[hBaseInd]} for calculating the backscatter') + logging.info(f"Filling aerExt below overlap with {aerExt_tmp[hBaseInd]} for calculating the backscatter") # Change equation back for comparison with Picasso. This is only a temporary addition that will be removed # once picasso is updated or the comparison to picasso is completed. @@ -320,21 +326,20 @@ def raman_ext( in cirrus clouds by using a combined Raman elastic-backscatter lidar. Applied Optics Vol. 31, Issue 33, pp. 7113-7131 (1992). + Notes ----- - + .. TODO:: `moving_smooth_varied_win` and `moving_linfit_varied_win` functions are not yet implemented. + Investigate what smothing function is used, Savitzky-Golay or something else? + **History** - 2021-05-31: First edition by Zhenping - 2025-01-05: AI supported translation - 2026-02-04: Cleaned by Buholdt - .. TODO:: - - moving_smooth_varied_win function is not yet implemented. - - moving_linfit_varied_win function is not yet implemented. - - Investigate what smothing function is used, Savitzky-Golay or something else? - """ + # Prepare variables temp = number_density / (sig * height**2) temp[temp <= 0] = np.nan @@ -487,12 +492,13 @@ def raman_bsc( - 2026-02-27: Added ext_aer_mod and ext_mol_mod for better consistancy with the 1064nm channel. """ + if isinstance(MC_count, int): MC_count = np.ones(4, dtype=int) * MC_count if np.prod(MC_count) > 1e5: - print('Warning: Too large sampling for Monte-Carlo simulation.') - return np.nan * np.ones_like(sigElastic), None, None + logging.warning("Too large sampling for Monte-Carlo simulation.") + return {'aerBsc': np.nan * np.ones_like(sigElastic), 'aerBscStd': None, 'LR': None} # Calculate beta_aer: beta_aer, LR, ODs, signalratio = calc_raman_bsc( @@ -633,7 +639,8 @@ def calc_raman_bsc( References ---------- - Ansmann, A., et al. (1992). "Independent measurement of extinction and backscatter profiles in cirrus clouds by using a combined Raman elastic-backscatter lidar." Applied optics 31(33): 7113-7131. + Ansmann, A., et al. (1992). "Independent measurement of extinction and backscatter profiles in cirrus clouds by + using a combined Raman elastic-backscatter lidar." Applied optics 31(33): 7113-7131. Notes @@ -648,8 +655,11 @@ def calc_raman_bsc( - 2024-11-12: Modified by HB for consistency in 2024. - 2026-02-04: Cleaned by Buholdt - 2026-02-27: Added ext_aer_el_raman and ext_mol_el_raman for better consistancy with the 1064nm channel. + - 2026-07-28: Added dependencies on `mask387Off` and `mask607Off`. """ + + ext_aer = ext_aer.copy() ext_aer[~np.isfinite(ext_aer)] = 0 if height[HRefInd[0]] >= height[-1] or height[HRefInd[1]] <= height[0]: @@ -753,15 +763,17 @@ def lidarratio( **History** - 2021-07-20: First edition by Zhenping (translated to Python) - 2026-02-04: Changed from scipy.signal.savgol_filter to ppcpy.retrievals.ramanhelpers.savgol_filter + - 2021-07-20: First edition by Zhenping (translated to Python) + - 2026-02-04: Changed from scipy.signal.savgol_filter to ppcpy.retrievals.ramanhelpers.savgol_filter + """ + # Adjust smoothing window for backscatter to match extinction resolution if smoothWinExt >= smoothWinBsc: smoothWinBsc2 = round(0.625 * smoothWinExt + 0.23) # Eq (6) in reference smoothWinBsc2 = max(smoothWinBsc2, 3) # Ensure minimum value of 3 else: - print("Warning: Smoothing for backscatter is larger than smoothing for extinction.") + logging.warning("Smoothing for backscatter is larger than smoothing for extinction.") smoothWinBsc2 = 3 # Smooth the backscatter using Savitzky-Golay filter diff --git a/ppcpy/retrievals/ramanhelpers.py b/ppcpy/retrievals/ramanhelpers.py index 2158c67..d8c9a9b 100644 --- a/ppcpy/retrievals/ramanhelpers.py +++ b/ppcpy/retrievals/ramanhelpers.py @@ -3,7 +3,8 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: - """calculates the slope of the signal with a moving slope. + """Calculates the slope of the signal with a moving slope. + This is a wrapper for the `movingslope` function to make it compatible with height-independent smoothing windows. @@ -32,6 +33,7 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndar - 2018-08-03: First edition by Zhenping. """ + if winWidth is None: raise ValueError("Not enough inputs. `winWidth` must be specified.") @@ -52,16 +54,19 @@ def movingslope_variedWin(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndar return slope -def moving_smooth_varied_win(signal, winWidth): - """ """ + +def moving_smooth_varied_win(signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: + """.. TODO:: Implement function!""" raise NotImplementedError -def moving_linfit_varied_win(height, signal, winWidth): - """ """ + +def moving_linfit_varied_win(height:np.ndarray, signal:np.ndarray, winWidth:int|np.ndarray) -> np.ndarray: + """.. TODO:: Implement function!""" raise NotImplementedError + def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float=1) -> np.ndarray: - """estimates the local slope of a sequence of points using a sliding window. + """Estimates the local slope of a sequence of points using a sliding window. Parameters ---------- @@ -86,7 +91,9 @@ def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float= **History** - Original MATLAB implementation by John D'Errico (woodchips@rochester.rr.com) + """ + vec = np.asarray(vec) n = len(vec) @@ -130,6 +137,7 @@ def movingslope(vec:np.ndarray, supportlength:int=3, modelorder:int=1, dt:float= # Scale by spacing return Dvec / dt + def _getcoef(t:np.ndarray, supportlength:int, modelorder:int) -> np.ndarray: """Helper function to compute the filter coefficients. @@ -147,13 +155,14 @@ def _getcoef(t:np.ndarray, supportlength:int, modelorder:int) -> np.ndarray: coef : ndarray Filter coefficients for slope estimation. """ + A = np.vander(t.flatten(), modelorder + 1, increasing=True) pinvA = np.linalg.pinv(A) return pinvA[1] # Only the linear term def sigGenWithNoise(signal:np.ndarray, noise:np.ndarray=None, nProfile:int=1, method:str='norm') -> np.ndarray: - """SIGGENWITHNOISE generate noise-containing signal with a certain noise-adding algorithm. + """Generate noise-containing signal with a certain noise-adding algorithm. Parameters ---------- @@ -180,7 +189,9 @@ def sigGenWithNoise(signal:np.ndarray, noise:np.ndarray=None, nProfile:int=1, me - 2021-06-13: First edition by Zhenping. - 2026-02-04: Modifications to reduce computational time, Buholdt + """ + if noise is None: noise = np.sqrt(signal) diff --git a/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb b/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb index de995df..dc20cdc 100644 --- a/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb +++ b/tests/test_case_cpv_2024-08-21_PicassoPy_workshop_plotExamples.ipynb @@ -23,7 +23,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 55, +======= "execution_count": 1, +>>>>>>> origin/main "id": "d9357e59-6b1c-48ef-887f-92bc1bfdad3f", "metadata": {}, "outputs": [], @@ -62,7 +66,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 56, +======= "execution_count": 2, +>>>>>>> origin/main "id": "757db1a3-8f88-4d02-bb42-235dee8f1ff6", "metadata": {}, "outputs": [], @@ -89,7 +97,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 57, +======= "execution_count": 3, +>>>>>>> origin/main "id": "4fcd069d", "metadata": {}, "outputs": [ @@ -125,7 +137,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 58, +======= "execution_count": 4, +>>>>>>> origin/main "id": "648de474-fe46-4f39-82ee-d1731db90ec8", "metadata": {}, "outputs": [ @@ -133,6 +149,16 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:50,062 - INFO - picasso_default_config_file: c:\\Users\\buholdt\\Documents\\PicassoPy\\ppcpy\\config\\pollynet_processing_chain_config.json\n", + "2026-05-11 19:23:50,063 - INFO - picasso_config_file: test_case_config/pollynet_processing_chain_config_test.json\n", + "2026-05-11 19:23:50,065 - INFO - pollynet_config_link_file: test_case_config/pollynet_processing_chain_config_links.xlsx\n", + "2026-05-11 19:23:50,109 - INFO - polly_default_config_file: c:\\Users\\buholdt\\Documents\\PicassoPy\\ppcpy\\config\\polly_global_config.json\n", + "2026-05-11 19:23:50,111 - INFO - polly_config_file: test_case_config\\pollyxt_cpv_config_20230927.json\n", + "2026-05-11 19:23:50,112 - INFO - keys default/template file, but not in specific file {'molDepol1064', 'minSNR_4_sigNorm', 'refH_FR_532', 'clear_thres_par_beta_1064', 'zLim_VolDepol_355', 'depol_cal_time_fixed_m_start', 'overlapFile_532_total_FR', 'flagUseManualRefH', 'yLim_Profi_WV_RH', 'depol_cal_time_fixed_p_end', 'indexing_convention', 'volDepolerror355', 'large_thres_ang', 'zLim_quasi_ANG', 'ice_thres_par_depol', 'zLim_FR_RCS_607', 'xLim_Profi_LR', 'yLim_all_profiles_low_range', 'flagSigTempCor', 'polCaliEtaStd355', 'turbid_thres_par_beta_532', 'depol_cali_mode', 'tTwilight', 'zLim_NR_RCS_607', 'overlapFile_355_total_FR', 'xLim_Profi_RH', 'polCaliEta355', 'bgCorRangeIndxHigh', 'bgCorRangeIndxLow', 'droplet_thres_par_depol', 'isParallel', 'yLim_beta_532_Poliphon', 'volDepolerror1064', 'cloud_thres_par_beta_1064', 'zLim_VolDepol_532', 'yLim_all_profiles_high_range', 'zLim_VolDepol_1064', 'depol_cal_time_fixed_p_start', 'polCaliEtaStd532', 'flagMolDepolCali', 'depol_cal_time_fixed_m_end', 'polCaliEta1064', 'spheroid_thres_par_depol', 'prodSaveList', 'xLim_beta_532_Poliphon', 'flagPicassoComparison', 'ice_thres_vol_depol', 'turbid_thres_par_beta_1064', 'zLim_NR_RCS_407', 'min_atten_par_beta_1064', 'yLim_cloudinfo', 'search_cloud_below', 'xLim_Profi_AE', 'polCaliEta532', 'maxCloudSearchHeight', 'deltaT', 'tempCorFunc', 'zLim_FR_RCS_407', 'logbookFileName', 'molDepolStd1064', 'radiosondeFolder', 'flagUseTheoreticalMDR', 'radiosondeType', 'refH_FR_355', 'smoothWin_klett_NR_532', 'logbookPath', 'zLim_FR_RCS_387', 'maxCloudSearchHeight_NR', 'volDepolerror532', 'small_thres_ang', 'refH_FR_1064', 'colormap_basic', 'imgFormat', 'zLim_NR_RCS_387', 'polCaliEtaStd1064', 'search_cloud_above', 'smoothWin_klett_NR_355', 'unspheroid_thres_par_depol', 'flagUseRetrievedExt4LCCalc'}\n", + "2026-05-11 19:23:50,113 - INFO - keys specific file, but not in default/template file {'meteo_file', 'bgCorRangeIndx', 'overlapFile355', 'overlapFile532'}\n", + "2026-05-11 19:23:50,114 - INFO - reading nc-file: test_case_data\\2024_08_21_Wed_CPV_00_00_01.nc\n" +======= "2026-05-11 10:57:24,990 - INFO - picasso_default_config_file: c:\\Users\\buholdt\\Documents\\PicassoPy\\ppcpy\\config\\pollynet_processing_chain_config.json\n", "2026-05-11 10:57:24,992 - INFO - picasso_config_file: test_case_config/pollynet_processing_chain_config_test.json\n", "2026-05-11 10:57:24,993 - INFO - pollynet_config_link_file: test_case_config/pollynet_processing_chain_config_links.xlsx\n", @@ -141,6 +167,7 @@ "2026-05-11 10:57:25,243 - INFO - keys default/template file, but not in specific file {'flagUseTheoreticalMDR', 'depol_cal_time_fixed_p_end', 'indexing_convention', 'polCaliEtaStd355', 'bgCorRangeIndxHigh', 'molDepol1064', 'zLim_VolDepol_1064', 'zLim_VolDepol_355', 'turbid_thres_par_beta_1064', 'flagMolDepolCali', 'xLim_beta_532_Poliphon', 'yLim_Profi_WV_RH', 'polCaliEta355', 'zLim_NR_RCS_607', 'zLim_FR_RCS_387', 'imgFormat', 'zLim_FR_RCS_407', 'isParallel', 'logbookPath', 'droplet_thres_par_depol', 'xLim_Profi_LR', 'overlapFile_355_total_FR', 'depol_cal_time_fixed_p_start', 'polCaliEta1064', 'zLim_NR_RCS_407', 'refH_FR_1064', 'deltaT', 'flagSigTempCor', 'clear_thres_par_beta_1064', 'xLim_Profi_AE', 'radiosondeFolder', 'flagUseManualRefH', 'volDepolerror355', 'maxCloudSearchHeight', 'yLim_all_profiles_high_range', 'volDepolerror532', 'ice_thres_par_depol', 'polCaliEtaStd1064', 'large_thres_ang', 'zLim_NR_RCS_387', 'cloud_thres_par_beta_1064', 'min_atten_par_beta_1064', 'small_thres_ang', 'polCaliEta532', 'smoothWin_klett_NR_532', 'volDepolerror1064', 'yLim_all_profiles_low_range', 'zLim_FR_RCS_607', 'depol_cal_time_fixed_m_end', 'colormap_basic', 'zLim_quasi_ANG', 'radiosondeType', 'ice_thres_vol_depol', 'depol_cali_mode', 'unspheroid_thres_par_depol', 'overlapFile_532_total_FR', 'yLim_cloudinfo', 'search_cloud_above', 'molDepolStd1064', 'tempCorFunc', 'spheroid_thres_par_depol', 'tTwilight', 'maxCloudSearchHeight_NR', 'turbid_thres_par_beta_532', 'smoothWin_klett_NR_355', 'zLim_VolDepol_532', 'minSNR_4_sigNorm', 'search_cloud_below', 'polCaliEtaStd532', 'bgCorRangeIndxLow', 'depol_cal_time_fixed_m_start', 'refH_FR_355', 'yLim_beta_532_Poliphon', 'xLim_Profi_RH', 'flagUseRetrievedExt4LCCalc', 'refH_FR_532', 'logbookFileName', 'prodSaveList', 'flagPicassoComparison'}\n", "2026-05-11 10:57:25,243 - INFO - keys specific file, but not in default/template file {'overlapFile355', 'overlapFile532', 'bgCorRangeIndx', 'meteo_file'}\n", "2026-05-11 10:57:25,245 - INFO - reading nc-file: test_case_data\\2024_08_21_Wed_CPV_00_00_01.nc\n" +>>>>>>> origin/main ] } ], @@ -176,7 +203,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 59, +======= "execution_count": 5, +>>>>>>> origin/main "id": "1141175f-1f22-47ae-96dd-d9226e854e18", "metadata": {}, "outputs": [], @@ -187,7 +218,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 60, +======= "execution_count": 6, +>>>>>>> origin/main "id": "70b3264d-9b4d-4994-8d88-557cba209d84", "metadata": {}, "outputs": [ @@ -195,12 +230,21 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:50,392 - INFO - date consistency-check... \n", + "2026-05-11 19:23:50,394 - INFO - ... date in nc-file equals date of filename\n", + "2026-05-11 19:23:50,395 - INFO - ChannelLabels: ['FR-total-355 nm', 'FR-cross-355 nm', 'FR-387 nm', 'FR-407 nm', 'FR-total-532 nm', 'FR-cross-532 nm', 'FR-607 nm', 'FR-total-1064 nm', 'NR-total-532 nm', 'NR-607 nm', 'NR-total-355 nm', 'NR-387 nm', 'DFOV', '1058', '1064s', 'none']\n", + "2026-05-11 19:23:50,395 - WARNING - removed none tag from channel list [15]\n", + "2026-05-11 19:23:50,396 - INFO - date consistency-check... \n", + "2026-05-11 19:23:50,397 - INFO - ... date in nc-file equals date of filename\n" +======= "2026-05-11 10:57:25,553 - INFO - date consistency-check... \n", "2026-05-11 10:57:25,554 - INFO - ... date in nc-file equals date of filename\n", "2026-05-11 10:57:25,555 - INFO - ChannelLabels: ['FR-total-355 nm', 'FR-cross-355 nm', 'FR-387 nm', 'FR-407 nm', 'FR-total-532 nm', 'FR-cross-532 nm', 'FR-607 nm', 'FR-total-1064 nm', 'NR-total-532 nm', 'NR-607 nm', 'NR-total-355 nm', 'NR-387 nm', 'DFOV', '1058', '1064s', 'none']\n", "2026-05-11 10:57:25,556 - WARNING - removed none tag from channel list [15]\n", "2026-05-11 10:57:25,557 - INFO - date consistency-check... \n", "2026-05-11 10:57:25,558 - INFO - ... date in nc-file equals date of filename\n" +>>>>>>> origin/main ] } ], @@ -235,7 +279,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 61, +======= "execution_count": 7, +>>>>>>> origin/main "id": "75e45529-0a19-4580-9bc5-269eadba869b", "metadata": {}, "outputs": [ @@ -243,10 +291,17 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:50,403 - INFO - starting data preprocessing...\n", + "2026-05-11 19:23:50,404 - INFO - ... time conversion\n", + "2026-05-11 19:23:50,409 - WARNING - ... mShots not constant min 2993 max 2999\n", + "2026-05-11 19:23:50,410 - INFO - ... Deadtime-correction (Mode: 1)\n" +======= "2026-05-11 10:57:25,568 - INFO - starting data preprocessing...\n", "2026-05-11 10:57:25,570 - INFO - ... time conversion\n", "2026-05-11 10:57:25,576 - WARNING - ... mShots not constant min 2993 max 2999\n", "2026-05-11 10:57:25,578 - INFO - ... Deadtime-correction (Mode: 1)\n" +>>>>>>> origin/main ] }, { @@ -262,6 +317,15 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:53,477 - INFO - ... removing background from signal\n", + "2026-05-11 19:23:53,864 - INFO - ... height bin calculations\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\preprocess\\pollyPreprocess.py:653: UserWarning: no explicit representation of timezones available for np.datetime64\n", + " data_dict['time64'] = np.array([np.datetime64(t) for t in mTime_obj])\n", + "2026-05-11 19:23:53,926 - INFO - ... mask bins with low SNR\n", + "2026-05-11 19:23:55,099 - INFO - ... mask for polarization calibration\n", + "2026-05-11 19:23:55,102 - INFO - ... calculate range-corrected Signal\n" +======= "2026-05-11 10:57:29,378 - INFO - ... removing background from signal\n", "2026-05-11 10:57:29,956 - INFO - ... height bin calculations\n", "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\preprocess\\pollyPreprocess.py:653: UserWarning: no explicit representation of timezones available for np.datetime64\n", @@ -269,6 +333,7 @@ "2026-05-11 10:57:30,046 - INFO - ... mask bins with low SNR\n", "2026-05-11 10:57:31,515 - INFO - ... mask for polarization calibration\n", "2026-05-11 10:57:31,518 - INFO - ... calculate range-corrected Signal\n" +>>>>>>> origin/main ] }, { @@ -313,7 +378,11 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:55,339 - INFO - finished data preprocessing.\n" +======= "2026-05-11 10:57:31,796 - INFO - finished data preprocessing.\n" +>>>>>>> origin/main ] }, { @@ -327,10 +396,17 @@ { "data": { "text/plain": [ +<<<<<<< HEAD + "" + ] + }, + "execution_count": 61, +======= "" ] }, "execution_count": 7, +>>>>>>> origin/main "metadata": {}, "output_type": "execute_result" } @@ -342,7 +418,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 62, +======= "execution_count": 8, +>>>>>>> origin/main "id": "774aa365", "metadata": {}, "outputs": [ @@ -350,12 +430,21 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:23:55,363 - INFO - saving product: SNR\n", + "2026-05-11 19:23:55,480 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_SNR.nc\n", + "2026-05-11 19:23:57,541 - INFO - saving product: BG\n", + "2026-05-11 19:23:57,636 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_BG.nc\n", + "2026-05-11 19:23:57,675 - INFO - saving product: RCS\n", + "2026-05-11 19:23:57,767 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_RCS.nc\n" +======= "2026-05-11 10:57:31,822 - INFO - saving product: SNR\n", "2026-05-11 10:57:31,942 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_SNR.nc\n", "2026-05-11 10:57:34,303 - INFO - saving product: BG\n", "2026-05-11 10:57:34,410 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_BG.nc\n", "2026-05-11 10:57:34,454 - INFO - saving product: RCS\n", "2026-05-11 10:57:34,563 - INFO - writing to file: pollyxt_cpv\\2024\\08\\21\\20240821_pollyxt_cpv_RCS.nc\n" +>>>>>>> origin/main ] } ], @@ -366,7 +455,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 63, +======= "execution_count": 9, +>>>>>>> origin/main "id": "0df6f7b6-f5a2-4cc2-9257-64e0d5fe8a88", "metadata": {}, "outputs": [ @@ -390,7 +483,11 @@ " 14: '1064s'}" ] }, +<<<<<<< HEAD + "execution_count": 63, +======= "execution_count": 9, +>>>>>>> origin/main "metadata": {}, "output_type": "execute_result" } @@ -402,7 +499,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 64, +======= "execution_count": 10, +>>>>>>> origin/main "id": "3dfe7541-d789-4a4a-be43-b7a6d08d4dd5", "metadata": {}, "outputs": [ @@ -410,7 +511,11 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:00,651 - INFO - Saturation detection\n" +======= "2026-05-11 10:57:39,343 - INFO - Saturation detection\n" +>>>>>>> origin/main ] } ], @@ -431,7 +536,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 65, +======= "execution_count": 11, +>>>>>>> origin/main "id": "72ef8fc9", "metadata": {}, "outputs": [ @@ -484,7 +593,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 66, +======= "execution_count": 12, +>>>>>>> origin/main "id": "8fa26092", "metadata": {}, "outputs": [ @@ -532,7 +645,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 67, +======= "execution_count": 13, +>>>>>>> origin/main "id": "2bf29575-306c-4e4b-84e9-16e80cc0d559", "metadata": {}, "outputs": [ @@ -540,18 +657,31 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:06,509 - INFO - and even a 355 channel\n", +======= "2026-05-11 10:57:47,702 - INFO - and even a 355 channel\n", +>>>>>>> origin/main "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:314: RuntimeWarning: divide by zero encountered in divide\n", " dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win)\n", "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:314: RuntimeWarning: invalid value encountered in divide\n", " dplus = smooth_signal(sig_x_p, smooth_win) / smooth_signal(sig_t_p, smooth_win)\n", "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\polarization.py:315: RuntimeWarning: divide by zero encountered in divide\n", " dminus = smooth_signal(sig_x_m, smooth_win) / smooth_signal(sig_t_m, smooth_win)\n", +<<<<<<< HEAD + "2026-05-11 19:24:06,559 - INFO - pol_cali_355 [{'eta': 47.742418057789656, 'eta_std': 1.0396617649540707, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,560 - INFO - and even a 532 channel\n", + "2026-05-11 19:24:06,613 - INFO - pol_cali_532 [{'eta': 12.380587648929659, 'eta_std': 0.13602867159593543, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,614 - INFO - and even a 1064 channel\n", + "2026-05-11 19:24:06,667 - INFO - pol_cali_1064 [{'eta': 0.14153155793311498, 'eta_std': 0.008380733632373237, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", + "2026-05-11 19:24:06,669 - INFO - Using retieved polarization calibration constants.\n" +======= "2026-05-11 10:57:47,773 - INFO - pol_cali_355 [{'eta': 47.742418057789656, 'eta_std': 1.0396617649540707, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", "2026-05-11 10:57:47,774 - INFO - and even a 532 channel\n", "2026-05-11 10:57:47,842 - INFO - pol_cali_532 [{'eta': 12.380587648929659, 'eta_std': 0.13602867159593543, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n", "2026-05-11 10:57:47,843 - INFO - and even a 1064 channel\n", "2026-05-11 10:57:47,902 - INFO - pol_cali_1064 [{'eta': 0.14153155793311498, 'eta_std': 0.008380733632373237, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n" +>>>>>>> origin/main ] }, { @@ -573,6 +703,8 @@ "pol_cali_nang_start_time [1724207790]\n", "[{'eta': 0.14153155793311498, 'eta_std': 0.008380733632373237, 'time_start': 1724207790, 'time_end': 1724208000, 'status': 1}]\n" ] +<<<<<<< HEAD +======= }, { "name": "stderr", @@ -580,6 +712,7 @@ "text": [ "2026-05-11 10:57:47,905 - INFO - Using retieved polarization calibration constants.\n" ] +>>>>>>> origin/main } ], "source": [ @@ -589,7 +722,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 68, +======= "execution_count": 14, +>>>>>>> origin/main "id": "e7bae91c", "metadata": {}, "outputs": [ @@ -601,7 +738,11 @@ " '1064_FR': np.float64(0.14153155793311498)}" ] }, +<<<<<<< HEAD + "execution_count": 68, +======= "execution_count": 14, +>>>>>>> origin/main "metadata": {}, "output_type": "execute_result" } @@ -629,7 +770,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 69, +======= "execution_count": 15, +>>>>>>> origin/main "id": "9279e9d1-b4d9-4f81-a50d-e970ccbf4c48", "metadata": {}, "outputs": [ @@ -637,7 +782,11 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:06,684 - INFO - cloud screen mode 1: MSG method.\n" +======= "2026-05-11 10:57:47,924 - INFO - cloud screen mode 1: MSG method.\n" +>>>>>>> origin/main ] }, { @@ -655,7 +804,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 70, +======= "execution_count": 16, +>>>>>>> origin/main "id": "cb1ecc63-0bb1-40da-9a2d-d03a7e3a2202", "metadata": {}, "outputs": [ @@ -674,7 +827,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 71, +======= "execution_count": 17, +>>>>>>> origin/main "id": "2a502a15", "metadata": {}, "outputs": [ @@ -685,7 +842,11 @@ " [144, 244]])" ] }, +<<<<<<< HEAD + "execution_count": 71, +======= "execution_count": 17, +>>>>>>> origin/main "metadata": {}, "output_type": "execute_result" } @@ -705,7 +866,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 72, +======= "execution_count": 18, +>>>>>>> origin/main "id": "685a5a0f", "metadata": {}, "outputs": [ @@ -749,7 +914,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 73, +======= "execution_count": 19, +>>>>>>> origin/main "id": "623aeeb1", "metadata": {}, "outputs": [], @@ -768,13 +937,21 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 74, +======= "execution_count": 20, +>>>>>>> origin/main "id": "ee64aec2", "metadata": {}, "outputs": [ { "data": { +<<<<<<< HEAD + "image/png": 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", +======= "image/png": 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", +>>>>>>> origin/main "text/plain": [ "
" ] @@ -793,7 +970,11 @@ "ax[0].grid()\n", "ax[0].set_ylim(0, 18)\n", "ax[0].legend(loc='upper right')\n", +<<<<<<< HEAD + "ax[0].set_ylabel('Height [km]')\n", +======= "ax[0].set_ylabel('Height [m]')\n", +>>>>>>> origin/main "ax[0].set_xlabel('RCS [MCPS]')\n", "ax[0].set_title('cldFreeGrp 0')\n", "\n", @@ -812,6 +993,105 @@ ] }, { +<<<<<<< HEAD + "cell_type": "code", + "execution_count": 75, + "id": "9152280a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['RCS'][0, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 5)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('RCS [MCPS]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "80e83b5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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++ukn+vbti81m47zzzquS0H7atGn06tWL9957j9atWxMeHs7tt9+Oz+fjmWeeISkpiYSEBJ588slTtr36pgGJiYmYzeYax5OTk/nDH/7ALbfcwooVK3A6nac895kIoWsVReasbgplZbDrxMnz0TTU4mIID4eGXLmelgZhdU+efzIZGRlkZWUxcuRI/3MWi4XBgwezbNkybrnlFnJycli5ciXXXnst5513Hrt376ZTp048+eSTnH/++f7XHTlyhFtuuYWvv/4am812wvecOnUqzz33HPHx8dx2223cdNNN/Prrr/7ju3fvZu7cucybN4/du3dz+eWXk5GRQXp6OkuWLGHZsmXcdNNNDBs2jAEDBjTI7yErK4vZs2djMBhqbLfU0EIoEIHGyVNtSg1g1y4YdeLk+QoQ3gjJ8/nhB+het+T5p1KZcD4xMbHK84mJiezbtw/QtwoCvbfy7LPP0qtXL/7zn/8wbNgwNm3aRIcOHRBCcMMNN3DbbbfRt29f/2tq8+STTzJ48GAAHnzwQcaMGUN5ebk/tYamabz33ntERET4k+lv376dOXPmoKoqHTt25F//+heLFy8+aSDauHFjlVzYHTt25Pfff69xXNM0/1jX5MmTsdvtdfztnZ6QCkTIQNT40tL0oHACQtMoLi4mPDwcpaF7RA2serA8fveRyvzRt956KzfeeCMA55xzDj/99BPvvfceM2bM4JVXXsHpdPLQQw+d8r1OlGC/devWALRp04aIiAh/mcTERAwGQ5XxqLok2O/YsSPffvutvw1ut7vW4y6Xi2+++YYvvviiTpd8ZyqkApG8NGsCYWEn75loGprTCZGRDXtp1oAqt5/Oysqqksc6Ozvb30uqLNOlS5cqr+3cuTP79+8HYOHChaxYsaLGmFHfvn259tpr+eCDD/zPNVWCfbPZ7E+gX5kY7UTHu3btys6dO7n99tv58MMPT3reMxWcfwmNRCgyMZp0am3btiUpKYkFCxb4n3O73SxZsoTzzjsPgNatW5OcnFxjl9QdO3b4U86+/PLLrF+/nnXr1rFu3TrmzJkDwGeffdYkvYyG8Mgjj/DJJ59U2aaoMYRUj8ggE6NJQHFxMbuOG1DPyMhg3bp1xMTE0Lp1axRF4d577+Wpp56iQ4cOdOjQgaeeegqbzcY111wD6L2PKVOmMG3aNHr27EmvXr344IMP2LZtG19++SWA/7KqUuXYTPv27WnVqlUTtfbMtGvXjnHjxvHoo4/y3XffNdr7hFQgUoRc4iHBqlWrGDJkiP/nyk0VJ02axMyZMwF93k9ZWRl33HEHR48epX///syfP5+IiAj/5c8999yDy+XivvvuIz8/n549e7JgwYIqmyWeDf76178yaNAgVq5cSf/+/RvlPUIqeT4Li/nL5fcEPPH86ZLJ84PD2dAGkMnzm5wQQuZEk6QgFhKBqPIuhE+R+5pJUjAKiUB0jOwWSVIwCrFAJElSMJKBSJKkgAupQCTkEg9JCkohFYg0ObNakoJSSAUik2iYNBGSJDWskApE8p6ZJAWnkApEkiQFp5AKRAZNjhFJ8MYbb9CjRw8iIyOJjIxk4MCBzJ07t0qZG264oUqieUVRaiQcGzp0aI0yV199dVM2xW/mzJkoisLo0aOrPF9QUICiKCxevNj/3PHJ81u1asU555zjX2MXKCEViEya3HJaglatWvH000+zatUqVq1axdChQxk3blyNXTpGjx7tTzSfmZnpT+NxvFtuuaVKmTfffLOpmlGD0Wjkp59+YtGiRacs+/7773Po0CF+/vlnrrzySm688UZ+OElCu8YWGoGoYl1vsbmB05NKzdLYsWO5+OKLSU9PJz09nSeffJLw8HBWrFhRpZzFYqmSaD4mJqbGuWw2W5UyDofDf2zv3r0oisLs2bMZMmQINpuNnj17snz5cn+ZmTNnEhUVxXfffUfHjh2x2WxcfvnllJSU8MEHH9CmTRuio6O5++678flO3qO32+3ceOONPPjgg6f8HURFRZGUlETbtm156KGHiImJYf78+ad8XWMJjUBUSWmeq+6bE58mKHR5Tvoo9minLFPfh087vVsRPp+PTz/9lJKSEgYOHFjl2OLFi0lISCA9PZ1bbrml1jSsH330EXFxcXTt2pUpU6ZU2W6o0tSpU5kyZQrr1q0jPT2diRMn4vUe652Xlpby8ssv8+mnnzJv3jwWL17MhAkTmDNnDnPmzOHDDz/krbfe8uc5Oplp06axcePGOpWtbP/nn39Ofn5+QDM6hFQ+Ig15adbYij1efj2Qf9IymtBQCxt2AfKglBgclrp/kDZu3MjAgQMpLy8nPDycr776qkra14suuogrrriC1NRUMjIyeOSRRxg6dCirV6/2f2AnTpxI+/btSUpKYtOmTTz00EOsX7++SmZHgClTpjBmzBgApk+fTteuXdm1axedOnUC9PQub7zxhj+P0eWXX86HH37IkSNHCA8P9yfLX7RoEVddddVJ25WcnMw999zD1KlTGT9+/AnLTZw4EYPBQHl5OT6fj5iYGG6++eY6//4aWkgFIrnBYuMLNxkZlFLzEqaSpmmUFJdgD7c3aC6fcFP9/pQ7duzIunXrKCgoYNasWUyaNIklS5b4g9HxH/hu3brRt29fUlNT+f777/0f8FtuucXfhm7dutGhQwf69u3LmjVr6N27t//1J0qMXxmIbDZblWRqiYmJtGnTpspuG3VJjF/p73//O2+++SbvvffeCTdHfOGFFxg6dCjbt2/n0Ucf5b777vPnqg6EkApEGjINSGMzqMpJeyaapqG4VCItpoAmFTs+SXzfvn35/fffeemll0442NyiRQtSU1PZuXPnCc/Zu3dvTCYTO3furBKImioxfqWoqCgeeughpk+fziWXXFJrmaSkJNLS0khISOCzzz6jT58+9O3bt8ZmAE0lpLoIBmE5dSEpJAkhcLlcJzyel5fHgQMHquzqUd3mzZvxeDwnLdNU7r77blRV5aWXXjpl2bS0NC677LI6bXvUWEKjR1TxXyGLV/aIJHj44Ye56KKLSElJoaioiE8//ZTFixczb948QE+uP23aNC677DJatGjB3r17efjhh4mLi+OPf/wjoCfc//bbbxkzZgxxcXFs2bKFv/71r5xzzjkMGjQokM0DwGq1Mn36dO688846lf/rX/9Kz549WbVqFX379m3k2tUUUj0is08GIknfBvr666+nY8eODBs2jJUrVzJv3jxGjBgBgMFgYOPGjYwbN4709HQmTZpEeno6y5cv929yaDKZWLhwIaNGjaJjx45MnjyZkSNH8uOPPzb69sx1NWnSJNq1a1enst27d2f48OE8+uijjVyr2oVG8nwheO3Im7h+zuHu8Q8GPPH86ZLJ84PD2dAGkMnzA0ZTmm3MlaSzWkgFIrmvmSQFp9AIRBWD1XIekSQFp5D6ZMqVZpIUnEImECkAQoYiSQpGIROIdDIQSVIwCqlApMhAJElBKbQCkYxDkhSUQicQCfDJRa+SFJRCJxABBprnjGpJOtuFVCDycuLV1VLomDZtWo2k90lJSTXKdOrUCbvdTnR0NMOHD2flypX+40ePHmXy5Mn+9K6tW7dm8uTJFBYWNnVzAJk8v5mRg0SSrmvXrlWS3m/cuLHK8fT0dF599VU2btzIL7/8Qps2bRg5ciQ5OTkAZGZmcvjwYZ599lk2btzIzJkzmTdvHn/+858D0RxAJs9vEDNmzEBRFO69995Gew8ZhqRKRqOxStL7+Pj4KsevueYahg8fTrt27ejatSvPP/88TqeTDRs2ANClSxe+/PJLxo4dS/v27Rk6dChPPvkk//vf//z5qGXy/LoLinxEv//+O2+99VaVlJoNT0FT3I14fgnAK7wUaAUnPK5pGiWiBLfPjSoa7r+DUWoURqXuf847d+4kOTkZi8VC//79eeqpp06YMsPtdvPWW2/hcDjo2bPnCc9ZucrcaKxaj6lTp/Lss8/SoUMHpk6dysSJE9m1a5e/3PHJ84uKipgwYQITJkwgKiqKOXPmsGfPHi677DLOP//8U+asnjZtGmlpaXz55Zdcfvnlp/w9yOT5FYqLi7n22mt5++23eeKJJxr1vRQR8Oae9Qq0Ar4u+vqkZTQ01JKG7YyPjxhPnCGuTmX79+/Pf/7zH9LT0zly5AhPPPEE5513Hps3byY2NtZf7rvvvuPqq6+mtLSUFi1asGDBAuLi4mpN2ZqXl8c///lPbr311hrHZPL8Uwv4J/POO+9kzJgxDB8+/JSByOVyVUnn6XQ6Af0f0uM59a15A8Y6lQtWlXUPhjZ4PB6EEGiaVuWDGUkkl9ovPeHrhBCUlpRis9n8+ZsbQiSRdc7pPGrUKP/3Xbt2pX///nTo0IGZM2dy3333+Y8NHjyYNWvWkJubyzvvvMOVV17J8uXL/Zdxle13Op2MGTOGzp0788gjj/jrUfm1W7du/u8TExMByMrKIj09HU3TsNlstG3b1l8mISGBNm3aYLPZqjx35MiRE7bx+Pf829/+xptvvumvc+Xzx7/2ueeeY9iwYezYsYNHH32Ue+65h3bt2tX5d3j8+woh8Hg8NRLC1efvNKCB6NNPP2XNmjX8/vvvdSo/Y8YMpk+fXuP5+fPnY7PZTv7icwGUGlu9NEfB0IbKMZbi4mLc7qqXvGbMJ32tRbFAacPWp/QMT9i5c2c2b97s/49bpYSEBBISEnj++efp06cPr7/+Ovfffz8ARUVFFBUVcdlll2Gz2Zg5cyZlZWWUlZUBem8f9Eu7yvNWPldcXIzT6aS8vByj0Vjlfd1uN6qqVnnO6/VWOU915eXlCCFwOp2oqsq9997L448/zuDBg/XfT2lpldc6HA4SExNJTEzk3XffZfDgwXTq1MnfS6srt9tNWVkZS5curbJXW+V71lXAAtGBAwe45557mD9/fp0z/D300EP+PwLQe0QpKSmMHDnylBng3s59D03xMmLEiIBnNzxdHo+HBQsWBEUbysvLOXDgAOHh4fXK0CiEoKioiIiIiAbtEZ0Jl8vFzp07ufDCC0/6d1R52zsiIoKioiKEEFx55ZXYbDa+++67Gv8xrNwOyG63+89b2eOw2WxERkZitVpRFKXK+1osFgwGQ5XnTCYTRqPxhPWrfp4pU6bw9ttv8/7771d5v0phYWH+dvTs2ZMJEybw1FNP8fXXX9f11wbofwdhYWFccMEFtWZorKuABaLVq1eTnZ1Nnz59/M/5fD6WLl3Kq6++isvlqtHVs1gsWCw1d+IwmUx1+mAq9SgbzIKhDT6fD0VRUFW1XmlGKz+Ila8NhClTpjB27Fhat25NdnY2TzzxBE6nkxtuuAFVVSkpKeHJJ5/k0ksvpUWLFuTl5fH6669z8OBBrrzyShRFoaioiCuuuILS0lL++9//Ulxc7O/txMfHYzAY/O07/ndU/bnqz8OxLYeqP3ey31n189hstirJ86v/O6mq6n8fRVGYMmUKPXv2ZM2aNfVKnl95ntr+JuvzNxqwQDRs2LAaczduvPFGOnXqxN///vdGSEAukDfwJYCDBw8yceJEcnNziY+PZ8CAAaxYsYLU1FRAT56/bds2PvjgA3Jzc4mNjaVfv378/PPPdO3aFU3TWL9+vX+CY/WNCTMyMmjTpk1TN6uGSZMm8dxzz7Fly5ZTlj0+ef6cOXOaoHZVBVXy/AsvvJBevXrx4osv1ql8fZJzv575b8p+zmXyH/8e8N7E6ZLJ84PD2dAGkMnzA0RBVYMm5kqSdJyA374/3vHrYRpH/W5NSpLUNEKoRwReRS56laRgFFKBSBUnn98iSVJghFQgMmnhga7CWae+M3Gls0tD/fsH1RhRY/MqDTydN4SZzWZUVeXw4cPEx8djNpvrNEFR0zTcbjfl5eXN9o7T2dAGOLN2CCFwu93k5OSgqipm85ldbYRUIEKR//VuKKqq0rZtW39enroSQlBWVkZYWFjQzKyur7OhDdAw7ahMCnemATmkApEBueV0QzKbzbRu3Rqv13vKXDmVPB4PS5cu5YILLgj4XKjTdTa0Ac68HQaDAaPR2CDBOKQCkdaA+W8k3Ymm95+IwWDA6/VitVqb7Yf4bGgDBFc7QuqT6cN76kKSJDW5kApERrmLhyQFpZAKRGrwLKuTJOk4IRWIzD5510ySglFIBaIyc93u7EiS1LRCKhAh5O17SQpGIRWIhCJ7RJIUjEIqEBnkoldJCkohFYhkPiJJCk4hFYg0udZMkoJS6AQiAQI5RiRJwSh0AhGghFZzJanZCKlPpqqE1BpfSWo2QioQycFqSQpOIRaIQqy5ktRMhM4nUwFFk6vvJSkYhU4gEuBVZc5qSQpGoROIFJmyWpKCVcgEIgFYZBoQSQpKIROIFADFGuhqSJJUi5AJRAhwNd+dXyTprBY6gQgwCnlpJknBKHQCkQJGudZMkoJS6AQiAV5V9ogkKRiFTiAChNzpVZKCUmgFIkVuJyRJwSikApEmc1ZLUlAKqUCkyEszSQpKIRaILIGugiRJtQipQIQm75pJUjAKqUBk0soDXQVJkmoRUoFIk6liJSkohUwgEgrY3Z5AV0OSpFqETCBSBJTLBI2SFJRCJhABeFR5aSZJwSikApGcVy1JwSnEApE30FWQJKkWIRaIZJ9IkoJRSAUikwgLdBUkSapFSAUiudJMkoJTaAUiTa6+l6RgFEKBSEHgCnQlJEmqRQgFIoEPOaNRkoJRyAQiRUCpRe4nJEnBKGQCkVDA7JVpQCQpGIVMIFIEeFU3Qsi5RJIUbEImEAlFwSQ7RJIUlEImEAEYhQFFkeNEkhRsQioQCbnTqyQFpZAKRIoWUs2VpGYjpD6ZRiEXeUhSMAqpQCQ3WJSk4BRagSi0mitJzUZIfTLNQiZGk6RgFDKBSAAGOY9IkoJSyAQiAFUGIkkKSiEViDQ1pJorSc1GyHwyFcAru0SSFJRCJhAhFISiUeopDXRNJEmqJnQCEQKfquDV5J0zSQo2IROIhKIQ4RJYjdZAV0WSpGpCJhAhwKcK3D53oGsiSVI1IROIFPS5RHlleYGuiiRJ1YRMIBIKmHwKyeHJga6KJEnVhEwgUoSgzKxgUOUKfEkKNiETiADKTODxeQJdDUmSqgmhQKQQ7hZYjJZAV0SSpGpCJhBpikJsqUZZoRyslqRgEzKBSBWCIrOCU5G37yUp2IRMIAJwGxRi7HGBroYkSdWETCASikKYV6AJufBVkoJNyAQiRQhKTAqqEjJNlqRmI6Q+lQahYDKYAl0NSZKqCZlAJBSwyIX3khSUQiYQAShoOF3OQFdDkqRqQiYQKQLKTVDmKQt0VSRJqiZkAhGAUVNJsCcEuhqSJFUTMoFIEYJsG/L2vSQFoZAJREJRiHAjV99LUhAKmUAEYPIJDhUdCnQ1JEmqJqQCkVAgpzQn0NWQJKma0ApEQKlbbickScEmhAKRglAUVh5eGeiKSJJUTQgFIjBqgtHtRwe6GpIkVRMygUhTvRjdGjHWmEBXRZKkakImEAkFHOWCSFegayJJUnUBDURvvPEGPXr0IDIyksjISAYOHMjcuXMb5b0UzYDPrKJZZc5qSQo2AQ1ErVq14umnn2bVqlWsWrWKoUOHMm7cODZv3tzwb6YIPAjKFLkEX5KCTUAD0dixY7n44otJT08nPT2dJ598kvDwcFasWNEI7yZwq1BQXtAI55Yk6UwYA12BSj6fjy+++IKSkhIGDhxYaxmXy4XLdWyQx+nUU3p4PB48nlPvV2bQwKwZ6lQ2GFXWu7nWH2Qbgkljt6M+51WEEKJRalFHGzduZODAgZSXlxMeHs7HH3/MxRdfXGvZadOmMX369BrPf/zxx9hstpO+z/7+mbT/aidHwloTE92mIaouSdJJlJaWcs0111BYWEhkZORJywY8ELndbvbv309BQQGzZs3inXfeYcmSJXTp0qVG2dp6RCkpKeTm5p60oUII3jz6DlHfZdDnohvpEN+hUdrS2DweDwsWLGDEiBGYTM0z5a1sQ/Bo7HY4nU7i4uLqFIgCfmlmNptJS0sDoG/fvvz++++89NJLvPnmmzXKWiwWLJaad71MJtMpf5EKoCoWOsR3aNZ/PFC39gY72Ybg0VjtqM85g24ekRCiSq+noSiaijNM5iKSpGAU0B7Rww8/zEUXXURKSgpFRUV8+umnLF68mHnz5jX4eylCRTVqHHQepG1s2wY/vyRJpy+ggejIkSNcf/31ZGZm4nA46NGjB/PmzWPEiBEN/l6a6sPsEYSbwxv83JIknZmABqJ333236d5MKFh84NXkhEZJCjZBN0bUWBShohigyF0U6KpIklRNyAQiofoQbk1uOS1JQShkPpWqz0h5hJHDRYcDXRVJkqoJmUAEoGiwOnN1oKshSVI1oROIFAE+wQ09bwh0TSRJqiZ0AhEapUbILskOdEUkSaomZAKRUED1KbSKbBXoqkiSVE2d5hFNmDCh3if+97//TUJC8OwzLxSB2athVAO+vE6SpGrq9Kn8+uuvufLKKwkLC6vTST/++GOKi4uDKhChgKNMI78snxbmFoGujSRJx6lz9+Dll1+uc2D58ssvT7tCjUEIgUAgVIVIy8nTEUiS1PTqNEa0aNEiYmLqvg3P3Llzadmy5WlXqqEpioLqNVISaWBf4b5AV0eSpGrqFIgGDx6M0Vj3sZXzzz+/1rxBAaVqlAtBZlFmoGsiSVI1pz1ym52dTXZ2NppWNcdPjx49zrhSjUEoApMPzm15bqCrIklSNfUORKtXr2bSpEls3bqVyiyziqIghEBRFHw+X4NXskEIBYvPiNVoDXRNJEmqpt6B6MYbbyQ9PZ13332XxMREFEVpjHo1OIPPQJktoOm5JUk6gXoHooyMDGbPnu3PM91cCAXsbhmIJCkY1Xtm9bBhw1i/fn1j1KVR+Qxeop0yKZokBaN694jeeecdJk2axKZNm+jWrVuNTP2XXnppg1WuIRm8RpxRQXYnT5Ik4DQC0bJly/jll1+YO3dujWNBPVgNlKg+nC4nsabYQFdFkqTj1PvSbPLkyf6E95qmVXkEcxBCgTCPwONr3tsES9LZqN6BKC8vj/vuu4/ExMTGqE+jEYqPckzE2mRvSJKCTb0D0YQJE1i0aFFj1KVxKQIf8q6ZJAWjeo8Rpaen89BDD/HLL7/QvXv3GoPVkydPbrDKNRQhBEIVCM0tx4gkKQid1l2z8PBwlixZwpIlS6ocUxQlKAORoiggFAyKEbvJHujqSJJUzWlNaGyOVE3BYxbsPrqbrkldA10dSZKOU+8xog0bNpzw2Ndff30mdWlUiqbiMyrklOYEuiqSJFVT70A0atQo9uzZU+P5WbNmce211zZIpRqDoikIo0KppzTQVZEkqZp6B6Lbb7+dYcOGkZl5LK/PZ599xp/+9CdmzpzZkHVrUD6jRmSRl5WHVga6KpIkVVPvMaJHH32UvLw8hg8fzs8//8y8efO4+eab+fDDD7nssssao44NQqASXS747siJLy0lSQqM00qM9tJLL3H99dczYMAADh06xCeffMK4ceMaum4NTlFU3hzzZqCrIUlSNXUKRN9++22N58aPH8+SJUuYOHEiiqL4ywTroleheinXfLg1ucRDkoJNnQLR+PHjT3jsvffe47333gOCe9GrqimUWFW25W6jZVTwJPaXJKmOgah6XurmSNEMGA0KeWV5ga6KJEnVhMyW0wgjVkWVOaslKQjVKRC9/PLLlJeX1/mk//73vykqKjrtSjUGVRgQBgOzts0KdFUkSaqmToHovvvuq1dgeeCBB8jJCa4ZzD7Vh6XcS5uI1oGuiiRJ1dRpjEgIwbBhw+q8yWJZWdkZVaoxKGj4FAWny+nf+kiSpOBQp8jy2GOP1euk48aNq9cW1U3BZ/QRViLIXPxftp17G53jOwe6SpIkVWiUQBSMVGGjPMxCm1wvYaawQFdHkqTjhM5dM01BVQSKAE00/+kIknQ2CZlApAozhQ4bA/a48WpyfzNJCiYhE4iE6qG8yEqHbB/zlv830NWRJOk4IROIjFg4FJ5Kq3Ir2z5/jd35uwNdJUmSKtQ7ED3++OOUltZMLlZWVsbjjz/eIJVqDKqq4TVYyW13DpN+KSK/LD/QVZIkqUK9A9H06dMpLi6u8XxpaSnTp09vkEo1Bq9HxRzmo7xXO+KLNBxWR6CrJElShXoHohNNBly/fn3QzR06nk8tR5SZSf1xPj92luvNJCmY1DkxWnR0NIqioCgK6enpVYKRz+ejuLiY2267rVEq2RAMrkjs7TIpj45k2NYjePPyQG5vJklBoc6B6MUXX0QIwU033cT06dNxOI5d2pjNZtq0acPAgQMbpZJnSggB9kKKd9nY0WUISQd2kqsV0zHQFZMkCahHIJo0aRIAbdu25bzzzquxw2swUxQFizsBU3g2nfft5ee2NjIKtzKIEYGumiRJnEbO6sGDB6NpGjt27CA7O7tG0rQLLrigwSrXoDQFX7kJe9ZujvZvzZacLYGukSRJFeodiFasWME111zDvn379Eue4wRzqlhhdOMttZBvTMB+9CAmtfn06CTpbFfvu2a33XYbffv2ZdOmTeTn53P06FH/Iz8/eOfmCHyoioGS9HS6bDoi15tJUhCpd49o586dfPnll6SlpTVGfRqN1wNGo8ASmU+pz0OkKlfgS1KwqHePqH///uzatasx6tKoDEYVL26M54wjpdzCyp2LAl0lSZIq1KlHtGHDsd1R7777bv7617+SlZVF9+7da9w969GjR8PWsIEIxQ0+C7YFH1PQoyPbvVmUuEuwm+2Brpokhbw6BaJevXqhKEqVwembbrrJ/33lsWAerMZrxWAuw9K9A2E/bUEIgaqEzJpfSQpqdQpEGRkZjV2PRqeiohh95ER1ID7zKAlOeHzJ48wYPiPQVZOkkFenQJSamtrY9Wh0mqEMUWYnctFHqFYrWKHQVRjoakmSxGncNavc4746RVGwWq2kpaXRtm3bM65Yg/NYUW3FWK+4hNKpC0k9bOFvt/0t0LWSJInTCETjx4+vMV4EVceJzj//fL7++muio6MbrKJnzFSOt8RGXlEJYcCRGAsRlohA10qSJE7j9v2CBQvo168fCxYsoLCwkMLCQhYsWMC5557Ld999x9KlS8nLy2PKlCmNUd/TpmJCtZRjXvkLh5PDMaakYjfJO2aSFAzq3SO65557eOuttzjvvPP8zw0bNgyr1cpf/vIXNm/ezIsvvljlrlowUFQNTRPs6DiWnku/Ysh3m/m0z6fceM6Nga6aJIW8eveIdu/eTWRkZI3nIyMj2bNnDwAdOnQgNzf3zGvXgCw+B/bEQgr7DCbPYWbk5nJ6JAbnnCdJCjX1DkR9+vThb3/7W5W97XNycnjggQfo168foC8DadWqVcPVsgGUi2LKc6PolFhAi1KVJR0tfLP9m0BXS5IkTiMQvfvuu2RkZNCqVSvS0tLo0KEDrVq1Yu/evbzzzjsAFBcX88gjjzR4Zc+EWdgJSzzKwX1llJQ7yXQYSItpXuvlJOlsVe8xoo4dO7J161Z++OEHduzYgRCCTp06MWLECFRVj2vjx49v6HqeMaF68TqtFPkchBnDMDnCeXHFi/RL7kfn+M6Brp4khbR6ByLQb9WPHj2a0aNHN3R9Go1HuHEXm0ntGIbJZOGOtGv4vPg9FuxZIAORJAVYnQLRyy+/zF/+8hesVisvv/zySctOnjy5QSrW0MxGI6pRsPmHw3T2+dhuPIpdsXNrn1sDXTVJCnl1CkQvvPAC1157LVarlRdeeOGE5RRFCdpA5PEKvC6V1oPbwpLutPvPd5RM9GBUT6tTKElSA6r3otfmugDWrJow2Tys/91MnzapGDcuxuyN4bXfX2Ny/+AMnpIUKk47D4bb7Wb79u14vd6GrE+jUhSBO8eJYe48YiISMHsFS/YtqbFcRZKkplXvQFRaWsqf//xnbDYbXbt2Zf/+/YA+NvT00083eAUbiqa68XlMmMLNkJCAs2cniq0qTwx5otadayVJajr1DkQPPfQQ69evZ/HixVitx7ZuHj58OJ999lmDVq4haV4Fg6KS3sMKZjOZVn18KMGeEOiqSVLIq/dI7ddff81nn33GgAEDqvQkunTpwu7duxu0cg3K6EFVVCwFR2DfPr7sonBL71uJtcl9pyUp0OrdI8rJySEhoWYvoqSkJKgvcXzCR2mhEVu4AkYjLZyCo+VHA10tSZI4jUDUr18/vv/+e//PlcHn7bffZuDAgQ1XswZmUIxYHGXkqvFw0UVcs93Mp5s+ZWvO1kBXTZJCXr0vzWbMmMHo0aPZsmULXq+Xl156ic2bN7N8+XKWLFnSGHVsECbNhiXmMCWHNcjMxNmuJbCH/LLg3RRSkkJFvXtE5513Hr/++iulpaW0b9+e+fPnk5iYyPLly+nTp09j1LFBeJVy3EcjEEUu2LqVuE69Adh9NIjHtSQpRJzWtOLu3bvzwQcfNHRdGpVZMWMwCzQNREkJn+QvpW2HtkzsNjHQVZOkkFfnQOR0OutUrrakacGgWCui+EgEg3e+i9vr4nv7If455DNMBtOpXyxJUqOqcyCKioo66V2xYN9g0YcHrdxG1JvPsDU1nG0pPpmPSJKCRJ0D0aJFx/aKF0Jw8cUX884779CyZctGqViDU3208h6i0OVkXoyRl0fPpLWjdaBrJUkS9QhEgwcPrvKzwWBgwIABtGvXrsEr1SgUUFweMJl4boSN1a0GBLpGkiRVCJkcGAbNhNVewtyOZoa0HUJSeFKgqyRJUoXTXn3f3CgKmEwamx0uxnccH9SzwCUp1JxRIGpWH2ZVQ3OppJSH8/DCh/H4PIGukSRJFep8aTZhwoQqP5eXl3Pbbbdht1fdLXX27NkNU7MGpmJAMUOpInBYHIGujiRJx6lzIHI4qn54r7vuugavTGPyeQygeukpHLxalo/T5ZQr7yUpSNQ5EL3//vuNWY9GZzAo5AoHI3/fR+sreskgJElBJGQGq32Km0h3CesTBe0SOwW6OpIkHSdkApHmCkNEQAdzPPN2zeO/G/4b6CpJklQhdAKRsQxDvkZqnpeLjkTyw+4fAl0lSZIqhEwgUlSNLFpS7vaSV5rLJR0uCXSVJEmqEDKBSDW7SDSUUFruxWlV+HDDh2SXZAe6WpIkEeBANGPGDPr160dERAQJCQmMHz+e7du3N8p7Gbw2DkXHYjFYufVAAmsy17Ajb0ejvJckSfUT0EC0ZMkS7rzzTlasWMGCBQvwer2MHDmSkpKSBn8vxR2GyVKO1QKHWoRzQeoFnN/6/AZ/H0mS6i+gi17nzZtX5ef333+fhIQEVq9ezQUXXFCjvMvlwuVy+X+uTNbm8XjweE6+ZEOzlFCY4yAvogUxv28i/dJRp3xNsKmsb3Or9/FkG4JHY7ejPucNqtX3hYWFAMTExNR6fMaMGUyfPr3G8/Pnz8dms5303N7eXlSzgtddTmR4S9797V26FXbDolrOvOJNbMGCBYGuwhmTbQgejdWO0tLSOpdVRJBs/C6EYNy4cRw9epSff/651jK19YhSUlLIzc09ZYra/+Z+x4EMhSlj7mDmzR35V/IeNty6AZvp5AEsmHg8HhYsWMCIESMwmZpnilvZhuDR2O1wOp3ExcVRWFh4ys9n0PSI7rrrLjZs2MAvv/xywjIWiwWLpWYPxmQynfoXqQiEMOILC6e3sRVesYOvd3zNpF6TzrTqTa5O7Q1ysg3Bo7HaUZ9zBsXt+7vvvptvv/2WRYsW0apVq0Z5DwNGzGopamkx/XpdglE1ogmtUd5LkqT6CWggEkJw1113MXv2bBYuXEjbtm0b770Ao8Glbyc0bw4en4cyb1mjvZ8kSXUX0EuzO++8k48//phvvvmGiIgIsrKyAD3lSFhYWIO+l0kxg91EiRLO3uwtALSKbJzelyRJ9RPQHtEbb7xBYWEhF154IS1atPA/PvvsswZ/L82n4vEJyiITSY5JJcISwbqsdQ3+PpIk1V9Ae0RNecNOUzS8bgNGxUtMcnsiLfso9dT99qIkSY0nKAarm4TixaiqbIr5A2LpUg4XHaZjbMdA10qSJEIpEAnwuc3EJplRDQYGthrI/D3zA10rSZIIoUBkViwY7WXEbF9BSYc27D66G4uh+c2qlqSzUcgEIg0fitdAeMFBclo4OFJ8hAtSa65nkySp6YVMIPIqHjSPgUx7Gm32HuWKzpfz9C9PU+aRc4kkKdBCJhCZfOEQXsyBIdfC4sUMKYqj2F1Moasw0FWTpJAXMoFI1SwYrG4iEs1oCN7c+iHntjyXpPCkQFdNkkJeyASiclM23vw4onYs43drPusjShjZfmSgqyVJEiEUiMxYMFpd7CprT9tiIyavoFtCt0BXS5IkQigQGYQZg8kD+w4Sb4qiJREsO7As0NWSJIkQCkRutRSPM5wentXkdEllr7GYP7T+Q6CrJUkSIRSIXJTjKzVhKneyx+CkXXQ7BrUeFOhqSZJECAUiG3YiEwpxHN1HTus49hfux+1zB7pakiQRQoFIaCrFZWa2tR5B+uKNhJnCOFx0ONDVkiSJEApEigImxUe37MWkDp1AubechRkLA10tSZIIoUCkoSFQKVXsFGxejSY0yr3lga6WJEkE0S4ejU1TPChCxeoq4LVkgU9TuK7HdYGuliRJhFCPSCAQPkA10KVtf+xmO2HGhs2LLUnS6QmZQIQwEuvJJM9u5MWITVyafikmQ/Pfk0qSzgYhc2kmVBcGJYz5bbLZZXUw+w8PBbpKkiRVCJkekZsyTFmltOJcAH7eV/u21pIkNb2QCUSgoJrA5StGURTCTHJ8SJKCRQgFIh8Wn5tsUzhCCHom9gx0hSRJqhAygUjFRLnZTETxbi5IvUAmRJOkIBIygUjRwKh62JiURkF5AYqiBLpKkiRVCJlAZMRKud3IgP15lHllwnxJCiYhE4jKDUWYCzUOaCbsxohAV0eSpOOETCAyajYweXBaTAifIdDVkSTpOCETiDxKOZajpbhEDHuduwNdHUmSjhMygShMi8SZ5sAZkUWkJTLQ1ZEk6TghE4hUoxstLJHWYgfdkroEujqSJB0nZNaalVNKRI6PAm9bduTuDHR1JEk6Tsj0iMxaGD5jKblKd3Yf3cmKgysCXSVJkiqETCACFTRof9iA2w3zd88PdIUkSaoQMoHIixelxIsjZgNmM9w/8P5AV0mSpAohM0akaWAud7ImMZ5OcTGEm8MDXSVJkiqETI8orFSlKDWK8mQ3qqLi8roCXSVJkiqETCDy2jRMHiuDliSxOXsLa7PWBrpKkiRVCJlAJIQRQ3EhYzJ/Ibxc46DzYKCrJElShdAJRKrAaU3AoJYSWSZo7Wgd6CpJklQhZAKRT/Fg8XopF3b22VUKygsCXSVJkiqETCAyCAtopRRZvZiN0C+5X6CrJElShZAJRBavCTXCy0stx6EpCu+seSfQVZIkqULIBCJ9xpSRYfs8xBpacbT8aKBrJElShZAJRCpG3BYz57sXItzFMhWIJAWRkAlEoGDQPCyz/IFMbwFtotoEukKSJFUImUDk87kwFbtYa2+P0QjxtvhAV0mSpAohE4i87lKMLi+f269D0xS25W4LdJUkSaoQMoFINVjxhil0cR0hUqTw5dYvA10lSZIqhEwgcpvKMZX5ULs/TL5vP2M6jAl0lSRJqhAygQh8GLxWbMVWYiyJTDlvSqArJElShZAJRLajBopaKKwxXUN2yRHyy/IDXSVJkiqETCDSRDk+zOQVtcZuh8LywkBXSZKkCiETiLzhKvYiI3H5cQjNwPKDywNdJUmSKoRMIEIxoamlpMQkQ3kUOSU5ga6RJEkVQiYQ2crDKY8U7Dwcjq/MRqmnNNBVkiSpQsgEItUHPoOCT/XhsRwh3i5nVktSsAiZQOQN82L0WkiN+RivcMt8RJIUREImEHlUF6YyQWnEQcwW2J63PdBVkiSpQsgEIpsShStC4cUjGXg2j+GBb2eQVZwV6GpJkkQIBaIyswdziY9OXQyEr5lKuVbK/T/I3V4lKRiETCAyaXbKHQZ8Py7mylalXNfnj2zK3sQh56FAV02SQl7IBCJV9eK1WjliSeWxFaN57KdyWhQrXPjBhTy//HmOlsnUsZIUKKETiIQJzeAh85NZvGh6AMPnPzD31QIez+/FK7+9wsB3B7K3YG+gqylJISlkAlFLXyc01YerRSbv2+/ikVG/oYz/IxPf/JVNzutJKTVxwfsXcOUXV/L+2vfx+DyBrrIkhQxjoCvQVIrQF7memx7D3/8O//pXBKNnvsCI1NbYX3iBeV4PpQaNZ+4q5NGDj/Ll1i+5ossVDEoZRFpMGoqiBLgFknT2CpkeUYLVjqIoZGmZXHqp/tyhwwrcfz9s3IihfRoRmpF/dr2Lr6/6GpNq4uGfHmbwzMG0fL4luaW5gW2AJJ3FQiYQmbwOLOWxHNIOkpAAXbvCq69CSQkQFQVPPglmMzz0EH2e+5iv0x+lR2IPABRFwWFxBLT+knQ2C5lAFGY0oKk+BGC3w3vvQV4evPhiRYHBg+GHH2DiRPj1V7jkEv6zrTOP7Uhm3JpS3n/iMpZ9OINtP3xE3rrlaJmHobgYNC2ArZKks0PIjBFZDSr20hSywjZz2HuYlJRkLrkEFi+GqVMrCnXsqP8waRLccAMJ3y/mFqeTo/lleLU5CL4HwAPkoKAqKqpqwG0z47FZ8dnD8EZGUNa2Fa4O7RAdO2Lo2o3w+JY4rA4cFgdWo1WON0lSNSETiGKsRqxlcWgigjnFc7gi4grOP9/BrFnwxBPwt7+BxVJRuFUr+PFHQO8yxvp8iOJinLmHyM7cRV7OPgqyD+DMO0RZfjaiyAlFRcQdPEqvLZlErtuKQPjf+6EJDn7oZgXgxl438uSwJ5u49ZIU3EImEHk1gapYSFd6sIllKChceSUsWgSvvw7p6XDllSd4scGA4nDgcDhwtO9Ch+rHNQ3WrYPrrsOlusmK8pGZaGN9eDF7ohVWtjMDYDFauK7HdY3YSklqnkInEAm9h1JIHgAePCgKREbqx/v0qecJnU5YsgR++gkWLoTcXEhK4oYHO7FEywDgzn5/p09sB66KSaN7QndMBlNDNUeSziohE4isBhWERpY4QGdrZ2INsQD8/e/w3//q49Pt25/iJHv26APaP/0Ev/0GXi906gRXXcWRAd2ZG5mN2Pcj7Mvg/Nbn8/AfHpbjQZJUByETiHwCUFRQBBZFHwxyueCzz/Tjn38Of/rTSU6wZg1ccol+i/8Pf4DHH4fhw3HGRzJt8TQ+W387BsVA/1b9eWrYU1zf43oZhCSpjkImEJX7NDR8eHFjQr9E+tvf4Msv9bGh228/xQm2bdO/tmgBd9wBAwcC8MHKV/h006c8OfRJLu9yORGWiEZshSSdnUJoHpGKZijHi5c4QxwAR47ox3r00O/cn1TFXTQOHYLduwFYdmAZr/3+GqPTRnPjOTfKICRJpylkApEQoGr63SuXcAHw0Uf6ILV/UuOJlJTAgQP6914vxMezt2AvV35xJe2i2/Hi6FOdQJKkkwmZS7NCtxdFGAD9jhlARgasXq0PWNcqLw+ee06/fisu1rtOI0fCwIG0tIfRLaEb+wv343Q5ibRENlFLJOnsEzKB6Gi5B4EH9bjxY5MJFAVKS8HnA4PhuBcIoc+w3rMHbrkFrr4aUlIAWJSxiGmzp7EzbydG1UjG0QxaRbZq2gZJ0lkkZC7NYq0misP3IwQ4VH0Ba5s2MHYsvPKKvgi/Co9Hv1N2yy36qHZFEALwaB4yjupzhRLsCZzb8twmaoUknZ1CJhCFmw0YfWH6olfVDuiXZt9+q/eMxo2r9gKzGa66Sh9AmjWryqEjxUfwal4AJnabKCcqStIZCplAVO7VMHrtKIBX6EEkouIm1wsvwNChtbzonnv0qdfPP1/l6fGdxtMysiUAPRJ7oCoh82uUpEYRMp8gtyYos2ZjUkzYVBug34kHfWJjDe+9B+edpw9Y33ef/+mdeTsZ+p+hHHIeokdiDwamDGyC2kvS2S1kAlGEyYBQvKiaxT+zunNnOPdcfQho06ZqL2jb9tj3kyfrd86ADzd8yCHnIfok9+GFUS8Qbg5vohZI0tkrZAKRqigoQkUoPkTFAlizGb74Qr9ztnx5tRcMGQK7dsE11+g/V1ye3dL7Fm7ufTMHCg8w9pOxFLmKmrAVknR2CplA5PLpY0QuyvDh8z+/fbt+675792ov8HqhXz/4+GN9JLtiUVqKI4XHhzzOF1d8QamnlA83fNiErZCks1PIBCJVAaM3AgXY4d7hf/677/SvPXpUe4Gm6YmtAb75Rk9cVHlIaNzwzQ0A8tJMkhpAyAQiq0HF4nbg0BI46D0I6CmFXn5ZP37vvbB163EvMJvxb/cB8PDDcNNN8M9/on70MTe2nYDFaOEfC//BhM8m8NKKlzjoPNhk7ZGks0nIzKz2CgGKikU14xP6pVlkJHzyiZ4v/7vv9Njz6qvHvejKK/VCR47A3r36xKO5c+HAAW5OTGTcux/zvdjOkn1LeO3313h+xfNc3+N6bul9C6lRqQFppyQ1RyETiCrGp4kgmgzvJo54j5BoTOScc/RYEx0Nzz5b7UXVe0UlJXpitHffhfXriS/yccP5N3BDrxsocZfw9pq3+feqf/Pe2vd4+A8Pc9e5dzVZ+ySpOQuZSzOPpkeiYgoxK2bClDBAD0JPPw379umzrE9qzBi46y5YuxYeegjOP99/yG62c++Ae3ls8GMAfLzxY4rdxY3SFkk624RMIAozqghPOUe0w6io/kmNcCwv0aBBpzjJU0/pl2t2u74h47p1NYqMSR+DUTWyt2Avn2/+vOEaIElnsYAGoqVLlzJ27FiSk5NRFIWvv/660d5LVRQUFCJFLG7clIky/7EhQ/Svv/9+ipOcd55+qVa5TH/XrhpFwoxh/gRp/1j4Dx5Y8EBDVF+SzmoBDUQlJSX07NmTV6uMEDcizYcXHwmGBCLUY9kU09P1q6z//rcO5/jXv6BlS/j5Z7j88hqHTQYTm27fxB9S/wBAfll+Q9Veks5aAR2svuiii7jooovqXN7lcuE6bmGY0+kEwOPx4PF4Tvpaj8eDovkI98aR782oUf6881SeeUblX//SuO8+jRPlvVf79EGdORNGj0Z07Iho3x7RtSvi+uvBqP86i93F/LzvZwDu7HPnKetWV5XnaajzBYJsQ/Bo7HbU57zN6q7ZjBkzmD59eo3n58+fj81mq+UV1cSn4Cwqx4mTWetmEVYY5j+UnGygvHw4//oXJCcvIjLSXfs5+vYl1m4nYv9+Onz1FeqyZQAsNplwxcQAsLN0J+Xl5QA89s1j/Lnln+vZ0pNbsGBBg54vEGQbgkdjtaO0tLTOZRVRufAqwBRF4auvvmL8+PEnLFNbjyglJYXc3FwiI0+eqtXj8fDDpt3YWljYH7GE/ub+dDZ19h8/dAjOO8/I44/7mDTp1L8S9a23UJ98EjFiBL6pU6sskp27ay63zbnN//PHf/yYQSmnGgk/NY/Hw4IFCxgxYgQmU/PMgSTbEDwaux1Op5O4uDgKCwtP+flsVj0ii8WCxb9B/TEmk6lOv0hhMKJYilFQiDHFVHlNy5b6Xonffmvk5ptPcSKPR1+NP3w4ysyZqMddx3l8HlyaiwmdJzB762wA4sLjGvQfuq7tDWayDcGjsdpRn3OGzO17AIRGtK8l4Wo4P5f9XOWQyQR33w2rVsHs2ac4z+LF+mrZu+6i+mDSj3t+5J559zB762wUReGl0S/RLaFbw7ZDks4yIRWIFE0jU91DsVZMgiGhxvFx42DCBH1ZWcUQT+0qxoJqm0c0Om00/5v4P+7pfw9CCBbtXVSjjCRJVQU0EBUXF7Nu3TrWVXygMzIyWLduHfv372+cN/R6KBL6nbYhtiE1Dqsq1GXMmz599G2FKjddPI6iKPRJ7sM9A+7BqBr5etvXLN67+AwrLklnt4AGolWrVnHOOedwzjnnAHD//fdzzjnn8OijjzbK+wlbBIqxjHhDfK15pp99Vp9L9NBDYLWe4mQxMfo21Pv21Xr4w/Uf+hPsd0+onuxIkqTjBTQQXXjhhQghajxmzpzZKO8nvC4KycegGP0J9Cv5fPDOO5CWBtddd4oTaRq0awe5ubX2igD+2PmP/u/3HN1zplWXpLNaSI0RqSVFJJT0JcubycLShVWOGQzw73/rqzZOOdH7uuv0dWdJSfCnP9VaxGww09rRGoDxn41nyvwpDdEESTorhVQgwmon3KTPWAhXa2ZWrNxe6Lvv9E7PCQ0frn/NyoIT9N4iLZGsuHkFD57/IEIIksKTzqDiknR2C6lAJExmIkQMCkqVtWaVevSAO+6AzZth4cJaTlBpzBi44Qb9+3btTvqeM9fNpGtCV24656bTr7gkneVCKhAp7nLKvPqsaZNSc7KVyQSXXaZ/f8Lb93ffDeeco/eELrvslLlDLut8GVtyttDz3z2Z9PUk/wC2JEnHhFQgEmYrEWYDAoFaS9N9Pj3dEOh36Gt1/H5ns2bpPaLffjvhe97Q6wbCzeF6ChJzJAonWE0rSSEspAKRUlpETqkHo2KkWKuZPdFggNsqloi9/voJTnL//fDcc8dyEp17rr5T4wlsydlCkauIZ0c+yysXv4JBNZxhKyTp7BNSgQhAmAvxCi8pxpRaj48bp3/9/HMoK6u1CLzyip7yY8MG+PrrY6PctbiwzYUMazeMh356iK05W09YTpJCWWgFIiEwanrqj+3u7bUWadVKn0ukafra1hp8Pn1cyOWCvLxTvyWCnXk7URWVMu+JIpskhbbQCkSajyPmrQgEcYa4WosUFsLu3fpUoVozF7z4Inz0EVx1FXTocMq3VBWVIyVHuG/AffRu0fvM6i9JZ6mQCkSK14PN1RJNgEu4ai0TGakvI/vppxOcZMMG/ev11+uL005BVVQizBEyZawknURIBSJhteE1FmFWjHQyd6q1jKJAz56QmQnFte0G9MIL+tc6LkMRQuD2uSnzyMsySTqRkApESmEOmuJG85mwqDUTrFW68kp9oHrGjFpmWBcW6l+//BKys0/5ngbVwODUwfx2+Dc0cbLp2pIUukIqEOHz4sOHRy2jSCs6YbHkZH0I6P33Yc2a4w643XDxxfrS/ClTIK72cabqJnafyObszfxt/t/OsAGSdHYKrUCkacR70kHAXs/ekxbNrxjS+b//O65XVFioByO7HW68sU5jRACHnIcA+DHjRzy+5r3zgyQ1htAKRIBQyxEIYg2xJyzz7bf6Fvegxx2/+Hj9YF6ePn+ojvYX6oneOsd15qeMn3D7TrBDiCSFqNAKRGERuL0Ve4/5Trwvvc+nf3U4oHt3WLnyuINdu+qzHqdN06/d6uDB8x/kn0P+ydqstdz0zU1MWzzt9OovSWepZrWLxxkrK8ZgcuETAouomQak0vjx+sTpxYv1ZGnvvQcHDhyXJ//ll+F//4OpU2HixBOmcyxyFfH88udZd2QdW3O2UuTSx6WGtKmZplaSQllo9YgQIFQUFKyGE6/5UhQYO1ZfUnb11foYUUnJcQU8Hv0yDfQEaSdQUF7AvN3zWHlwJU6Xnis7LSaNNZlrWJSxyP+cJIW60OoRmSyUi3IUhTqtgv/mG/j0U30lvn87taIi6NcPKra7Jjr6hK9PcaSw/M/LyS/LZ+ORjWzM3sjazLV8tPEjXlr5EgbVwDtj32FU2qgGaJwkNV+hFYi8bqK0RApFGJvcmxhqGnrS4gMG6Hfo9+zRcxUBEB4Ojz0Gf/2r/vPOnad825iwGAa3GczgNoMBfZLj3oK9PPTTQzy2+DFSHCl0juuMosgUIVJoCqlLM0UIku02fJqoNVVsdYmJcMstcPQo/OUvsH49+nXbxInwx4rk+KWl8PHHsGnTCVbJ1lIPRaFtdFueGPoETpeT4f8ZTu+3evPiihdPv3GS1IyFVCASRjP7ikoRBjdmxVyn19x1F7z0EixYABddpG81BOiLX6dMgZ9/1r+OHKkv2//Pf+pcn7SYNNbdto7Pr/ic3km9eebXZyh2n/huniSdrUIqEOHzYjcZMfsiyPadenkG6B0gRdGzfrRvD0Mrr+ZMJmjdumrSoi5doHf9VtibDWbOb30+sbZYWka2JMwYVq/XS9LZIKQCkSI04qxmwnyxOLW637GqTBvbpg2cf/5xBy65BJ5/Xr9Mi4+HLVtg69ZjE5HqQQjBIechdh/djRCi3q+XpOYstAargQKXhwiLgyzfXvZ59pFqSj3la9q2hU8+gT//Wc9T9PnnFZlirVb9/v7VV+vjQ5Mnwz336BuknSJzY433iNZzYV8480JaRLSgZ2JP2kW3Iy0mjXbR7Wgf3Z4IY93PJ0nNScgFIlCIcrfDad3FXs/eOgUigMGD4cMP9Y07LrsMrrlG7wj576aZTPDGG3Dttfry/Ysv1nMWXXHFSW/xV7qj3x3c2OtGVh5aydJ9S9mau5X/7fgfB50H/T2k2/vcTg96nG7DJSlohVwgSo+28fsRJ4laG3a6NzMobBBGpW6/hoED4W9/0xfC/vab3iG69NJqhc4/H95+Wx/lnjYN9u2DJ5+s0/nDTGFc2OZCLmxzof85l9fFoPcGcbjoMG+sfoNulm7krM9h0jmTMBlqbokkSc1RSI0RAURZjNiMBhSvHYACraBer69cia8o+oaMtRozRr+LBvocgJ074TTHfSxGCz/96SfeufQdbu19K0XeIqYvnc7Vs66WY0nSWSPkApEQAq8mKFNOb3nF/ffrSRoTEuCCC/R1aLX6y1/0BbLPPKNf13XvDv/8Jxw6VO/3dFgdXNzhYh4+/2EebPsgt/e9neUHlvPt9m9Pqw2SFGxCLhABeJQyDirbADDW8+pUUfSkacuW6btO/+MfJwhGffrok4+2bdPXiZxzjj6GdNVVp907qnR99+sBuP372/lpz08y86PU7IXcGBFAmTkTDY14QxwO1XFa5wgLg+nT9VX6//iHvg1R5dVYFeHhetfp00/1n1u1gsOHoWXL066/2XBsMub1X12PxWihVWQrWjtakxadxgODHsButp/2+SWpqYVcIBKAw90OzVhCvroHDQ0Dp7f7qqIcyxZ7+PApCj/7rD4R6Z139N1hb7wRnnjitN43JiyGnXfvZH/hfg44D7C/cD/7C/ezdN9SFmUsol10Oyb1mnRa55akQAi5QKQqCu2j7KwoCgMz5PvyiTfGn/b5Nm3Sv7Zrp19xnXDdqs0GDzwAd94Jr72mLxHZtEkfPxo1Sp+VXQ92s53O8Z3pHH9su2uPz8PVs67m4YUP8+XWLxnWdhjD2g6ja0JXVCUkr8KlZiLk/jq9miCrxEWktxVxhnjmlczDpdW+x1ldPPCA3sG5+mp96tDPP5/iBXa7vjbthRcgNlaf/Dh8uD7/aPXq064HgMlg4vPLP+eFUS+QZE/i9d9fZ9R/R9H7zd7cN+8+vtvxncyBJAWlkOsRlXl95Je7iQ0Lp6ttOJ8Xf8YW9xbOsZ5zWudr0wa++goWLtTnL151lb7zR1LSSV6kqnrBq67SZ2TPnasvFRk7Vn+88UadE/NXZ1ANXNn1Sq7seiUen4ffDv3GwoyF/JTxE59t/gyjaqRfy36M6TCGMR3GkBieeFrvI0kNKeQCUYTZSK9EB+uPFHKwwEj7sPasLl9NkjGJFsYWp3VORYFhw+CRR+Bf/9LXvb77rr5a/5RMJn1W5Jgx+uj3O+/oA04DBui3/7t101fbnkauIpPBxKDWgxjUehCPDH6EA4UHWJixkAV7FjBt8TQeWfQIbaPa0jKyJa0iWtEqUn+0jGxJh5gOxNtP/5JVkuoj5AIRQHK4lfwyN0dKXBRZ9DzSgjOfHHj77foVVqdO+ti0w6FnBomPr0McMRj06zyHQ9/W+quv9LEk0Ocg3X67HqzOQIojhUm9JjGp1yQKygv4YdcPbM3dyiHnIbbkbmH+nvnkleYBes6kA/cdkGNLUpMIyUAE+liRxycYEDaAr4u+Zqd7J8nG5DM+b2Qk/Pe/+trXyy8/9lyHDnqAevBBfWioVuHhxzI/AhQU6GtJ7rsP7rgDtm8/4/pVirJGcVW3q2o8X+4tp+9bffFqXtZmrqV3i94yc6TU6EL2P3cun4aigAN9QWqpVopXeBvk3EOHwuOPH/vZ6YR162DVqmOpruskKkqfnHTzzfrPubkNUr+TsRqtfDD+A8JMYYz9ZCwD3h3Arf+7lWeXPcvX275mc/Zmyr3ljV4PKbSEbI+oZbiVDdlOPJog2ZjMIe8hFpYuZKS9tlmJ9ec9Lqb96U9w772nGMA+mU8+0btRpqZZ5NonuQ+rblnFioMr+GD9B/xvx/+qHI+0RLLutnVYjbVvoyRJ9RWyPaLDxeUYVQWh+DjiO0KkGklPS88GO/9ll+l36C+4AD76SB97vvtuOHLkNE521VWQl4exf3/Of+ghDDfcALNmNVhda2NQDQxqPajGbidG1cjotNHklubKRbdSgwnZHlHnuAhWHDrK2iNH8dl99ArrRaKx4W5lH3+HPi8PPvsMXn0VsrP1O2tt2tTjZH/9K1x8Mb41a/A+/zzKokX6ZMjLLmuw+p7I86Oe59oe17Iuax3rs9az7sg6Pt/8OZ9v/pxYWyy9knrRO6k3/Vr2o1dSL8LNp96UQJKqC9lAZFIVfEJgxEK8IZ7lZctpZ2pX59xE9REbq481t26tD1YPGqTfsf/73+sRkDp3hs2bcWRk6ImQ0tP1u2pt2ujTujt1Oq1b/KdiN9u5IPUCLki9wP9cdkk267PWs/7IetZmreXN1W/yf8v+D1VR6d2iN6PTRjOh8wSSwk/3WlQKNSEbiMwGlXCTAQ2NYlGMR3jY4NpAL0uvRrtlfckl+iTqL77QL9tGjoT58+sejESfPmQOGEBqcjLKwYP61tdF+vQD3n77jG/v11WCPYER7Ucwov0IADShsWTvEq6dfS2rDq9i1eFVvLn6Tdbftr5J6iM1fyE7RqQqCtFWM26vYKRNH6BeU76GQq2wUd/XatVnYC9eDDExMHo0zJ5dxxe3asWGW2/F98YbMG+enmJkxQr92Pvvw8GDjVXtk1IVlbVZa6s8l1OSQ+fXOjPkgyFc9eVV3DvvXrblbgtI/aTgF7I9IoB4m5l9haUUFdmJMERgUAxEqpFN8t6RkfDDD3oKkbvu0p+bMKGeJ1EU/bqvTx89QdK55+rdq9dfh169GrjGJ3ffgPv48zl/Jqs4i+yS7Bpff9n/C4v2LuLcFudSllVGwaYC2se2p7WjNckRyRjVkP5TDHkh/a+fVaIvdo20mBlqHso3xd+w3b2dLpb6rYQ/XQ6HfnVVUACPPqrvKDt2rJ79sc7sdvjf//STLF+un/DWW+Gnn/QJkk1EURQcVgcOq4OOcR1rHN9fuJ9Xf3uVPfl72ODcwOJFi/0J3UwGE+2i29EhpoP+iO3AhW0uJMoa1WT1lwIrpANRpEVvfn65m/bWGAyKgRxfTpPWQVH0ZPwzZsBjj+nr1Vq3hn799OwgF19cx/WvUVH64rYWLfRo1rmzvjRk0CB9mncTBqXatHa05pkRz+DxeJgzZw4jRo0guzybvQV7yTiawc78nezM38lHGz8iuySbP/X8E08PfzqgdZaaTkgHotTIMLyaYEdeMUJAD3sP1pavpa+1L3a16TIcJiXp21o//LC+ouP33/UrrVmz9AT9n3+uX8rVSa9eei6SX3/V5wu89po+jjRuHKSm6lGudWs9xWQAmQwm2kS1oU1Umyq7lgBM+GwCH2/8mHBzOCmRKbR2tKa1ozWtIlthMVoCU2GpUYV0IFIUhbRoOwedZZR5faSZ0lhbvpavir7i6sirG+VW/skkJh7LBFI5xLNhA+zapae8rrM2bfTH4MH6fIHKLY1cx+VdGjNGv9MWhO7odwdvrX6L73d+zyHnIbzasWnqrR2t6d2iN32T+9K7RW9940mL3HiyuQvpQFQp0mKkyO3FYYjFptpQK/4XSA89pF+y5ebqt/2joqBnTwPl5V3o37+Oy0VatdJX4IK+D1JODuzfr/eOvv9enyj5l79Ax5pjOoE0vN1whrcbDoBX85JVnKWnxS08wJacLazJWsP3O7/H4/MA+i4nrR2taR3ZmhRHCqmOVMZ1GifHmJqRkA9EHk0jq8SFuWIgpp+1H0tKlzC7eDaj7KOIUAPzX9vKWdllZfrl2nPPwZIlCuXlKdx7r4HJk/XhoKSkOl5lqare5UpM1C/dvvhCf3z+uR71WrXSHykpeiLuIFlxb1SN/jxJpBx73uV1sSVnC/sK93Gg8IA/f/e8XfM4VHSI135/jQ//+GGtA+dS8An5QKRpYDGouLwaa7MKaRGnBx6n5sQlXEQQ2G5/WJh+hZWXp6/eB1i6VGHp0qrlEhL0Ff510r69fsk2ZAhMnaoPUFVOjAR9slNqqj7jcsIEfRZ3kASmShajhXNanMM5LWpesx5yHuJPX/+JcZ+Oo0t8F2LCYoi2RhMTFqN/Hxbtfy7WFkucLU4uTQmwkA9EFqNKbJiZw0XlHC330Frog6FxZ7DVUGOYMAHGjvUyZ84PDB58MZ9/bmLatGPHs7P1we0uXfTcR8a6/Mv27w8//qh/73TCgQPHHlu3wgcfwCuv6IGpZctjvabKnlP//me0LVJjaRnZks8v/5w3Vr1BVnEWR8uPcijnEPll+RwtO0qxu7hKeYvRwnMjn6NHYg9iw2JxWB0yIVwTC/lABJAWbcfl08grdVNWZme0fTQ/lPzAFtcWelobbkV+Q7Hb9dQi+fn6sE9mpp4z7e679eNhYXqW2cpEbDExdThpZKT+oq5djz339NP6ZdyePXpwOngQ1q/Xx5cKCvSBq48+qudIetOItcXyjwv+Uesxt8/N0bKj5Jflk1+WzzPLnuGuOXf5jxtUAzFhMcTZ4ogNi/V/3z66PZd3uRyrKtOfNDQZiIDwijzWC/fm4NUEbUytSDQmstq1mhhDDCmmlFOfpIlZrXqQOZ7TCZs363fapk/XL+V279bT16al6Vdk9vrMSjCb9WTcw4bVPJabq5/4kkv0yU5jx+oZ4QI8X6kuzAYzieGJ/o0Dvmz5JfsK95FXmkdeWR65pbnklVZ8LcsjvyyfHXk7+HDDh/zr138xKGUQRZlF7F+9n1aOViSFJ9EiogVJ4UkyR9NpkoGogsWgYlAVSr0+AEbbR/Nt8bcsL1selIGoNpGRMHCg/ujSRZ+XtGfPsSUkoGcB+EftHYX6iYvTe0affAL/+Q/cdpueuG3gQD0gDR162kn/m5rJYCItJo20mLSTljtSfIR3177Lusx1bCnewvrf11PkLqpSJjE8kf4t+zOw1UD6texHgj2BKGuUXMJyCvK3c5wku5X9zjI6xYajolKgFaAJjbXla+lh6YFBOb0dYQPhD384tsfajh36GFN+vj6kk58P0dENECOMRn0F7/XX69MCfvhB31fpqadg2jR9HGnIEH2aeKtWkJx8koTdwS8xPJGH//Cwf3b4xRdfjFu4ySrOIrM4k6ziLHbk7WD5weU8suiRKvOfHFaH/zIv1hZLjLXia1gM56WcR4/EHgFsWeDJQHScNo4wDhaVsSarkHZRNsbax7LVvZW1rrU4NWeTz7huKBs36sEH9JtkU6fqnZeEhGN39Nu107NI9utXj1ncx2vdGm65RX+UlelTwxcuhEWL9B5TBaOiMNhiwfDWW3pw6tdPv7RLPvONCwLBbrbTPqY97WPaV3m+xF3Cttxt5JXlkVeqX95VXubll+WzLW8b+Qfz2VewD4BpF04j1ZFKalQqqY5UwkyBnfne1GQgOk6kxUTvJAdbcopZcegodrOBQa3+QJwhjt/Kf2OXexftzO3oY+3TZKv0G8Jll+lDOdnZ+uPIkZqPL744tnvRRRfp+7KdtrCwqmNLJSX6Xm2HD+Pbt4+DixbROTJSH/z+5z/1Fb9duhwbYe/cWX/Ex5/2RpOBZjfb6ZPc55TlvtvxHc8ue5anf3m6yqYECfYE4mxxRFoi/Y8IcwQOq4MIc4T+syUCh8VBhCXCfzwmLAaToWlymzckGYiqSbJbibWaWZCRQ4nbh6pAF0sX0sxp7HTvZHnZcvZ79nN95PXN6havxaJfKaWcYLhLCH0lyOWX6xvPdulybMJkUpL+vc2mx4rBg+v55na7PqegQweEx8Nuh4OOF1+MwWTSR9gXLNB7UNu2wXff6T0q0LttlW+elKT3mlq00L8OHRrw9XIN4ZL0S7gk/RKEEGSXZLOvcB/7Cvaxr3Af+WX5OF1OnC4n+wv3U+QuwulyUuQqoshddMKc4VHWKOLt8XowC4sjwZ5AvD2eeFs8SeFJdIjtQIvw09tMtLHIQFSL3DK3//tVmQUkR1hpFRFGklFfVxFtiG5WQaguFEVfnjZvnn41lZWlPzIz9fHo4+3dq99QaxCRkXqXrTL/tqbp4007dsChQ3oFKiuycaP+tbxcn2Q5daoeWVu1ahZ3605GURT/nbxzW557yvKa0Ch2F1PkqghO7iIKywvJL8snpzSHnJIcskuyyS3LZUvuFrJLsiksP5b0L9wcToeYDhgKDWT8nkFKVArJEcn+R1P3qmQgqkWS3cKgVjHklrnJLnWx4YgTFYVyi/4Pme3N5teyXxloHXjWBaS4OLjiiqrPvfIKPPMM+HwQEaGnLLn+en1cqcGp6rFFu7XRND0/yubNMGnSsedtNigthTff1KcSnOVURfVfsrWkbpNK3T43h4sOszNvJzvydrAtZxu/HvmVN1a/UePuX7w9nuSIZHom9mRImyEMaj2oUWefy0BUCz3JlwmH1URBub6wstDloXNEO1oYW7DDvYNV5avI9maTZk4j3ZSORT1701PcfTfccIOeTWTxYn152ptv6mtl+/bVH+ef30STrFVVv5Tz+fTZnJUTLe+8Uz9+1116b6pVK/1WYRPtBdccmA1mf+qVEe1H6Hf/vMfu/h0uOlzlcdB5kKX7lvKf9f/BZDBxbstzGdJmCKPaj6oxOH+mZCA6hVSHjSK3l4yCUuJsZuJtYfS09iTOEMfckrn6BDhfLkNsQwJd1UYVEQEjRuiPRx/VL+F+/VWfNPnRR3qZqVOPxYNGZzDo40Ymk16JLl1gyxbwePQVwqD3kG68sYkq1LzZzXY6xOrZMavbW7CXRRmL+GzzZzyx9AmeWPoEO+7e0aA9JBmITiHOZmZw61h+PZjPltwi0qIF8TYzccY4f5nD3sP8VvYbcYY44gxxRKgRZ/V+8RaLnklk5Eh44gnIyNBTHb3/vj5hskmb/uWX+p030HMstWmjL9iNjNRv/0ln5L8b/ssDCx4gwhJBkUu/fIuwRFSZI9UQZCCqA0VR6JXoYH22k/VHCkGB1EgbN8TdwAHvAfZ69rLbs5sNrg3+11wcfjHJxuY5N6aupkyBr77Sx42feEJfA9vk8bd3b33k3O3WB7mDNNlbc+HVvOzK38Xegr3sObqHJ5Y+AUCRq4h3Ln2HTnGdaO1o3eAzxWUgqqNws5FBrWIo9/o44CxjZ34JXk2jU1wqbW1tASjTylhYupBMbybLy5bTx9qHVGPqWdc7KimBb77R5yuCPiSzZYs+TtTkMjP1IAT67rc7duiRUTotjy16jPfXvV/j+Yf/8DAXd7i40d737Lrl0wSsRgNp0Xa6J0SSXepmxaF8/4B2mBrGcNtw+lr7oqLyY8mPbHFvCXCNG9533+m9ocLjtoC7uPH+Rk9u5Ei47z59EEuImiuBpTrxal4OOg/Sr2W/Widi/qXPXxr1/WWP6DQoikJKZBgxYSZWZRaw7GA+8TYzfVtEYVEt9LL2ope1F8vLlrO8bDmHvIeIMcTgUB1EqVE4DA7MSkNNxGl6AwdW/TkpCbwNO2RwagUFepfshx/0iU/FxfrY0IwZTVyR5m1Gxgxuf/n2Gs8n2BPo3aI3I9qNYEjbIZgNjfv3KgPRGbCbjFyQEsvc3dnklLrZnFtEjNVMnM2M2aAywDqACDWC/Z797HTvpEQr8b82wZhAN3M3Uk2pzWoxLVSdzDh5sp76uknvkm/demz5SMuW+gj5qFH68pCz7DK4sUUZo9jv21/luU13bCImrC5JrBqODERnSFEU+iVHcbionLwyN/sLyzAZFDrFRtAywko3Sze6WboB4BZuCn2FHNWOstO9k4WlC7EoFtqZ29HL0qvZLKhNStJv3T/5pL6f4+zZetrrP/6xiSrQrh1ceil8+62+3cm99zbRGzc/327/ls83f06qIxWny8nR8qP6o+woeaV55DhzsFqP5VC6sdeNRFujm7yeMhA1gHibhXibPqGx3OtjW14xG7Od7D5aQqrDRotwC1ajAbNiJt4YTzzxpJvTyffls8u9ix3uHWR7s+ll7UVLY8tmcdk2Z46e60hR9PmEr7/ehIHIYtFv2S9apM8fkk7onnn34PK6qjwXaYmkX8t+/LHjH8nalcWQAUOIC4+jbVRbf7K4piYDUQOzGg30SnTQNsrG7qMlbMsrZmteEVEWE/E2M/E2Cw6LEUVRiDHEcG7YuaSaUllatpSfSn5CVVRi1ViiDFFEqVFEG6KJMkQRoQTP3l0LFui9oUsv1Xe37tOnkZZ7nIjbDaNH65OXmiz6NU+77t7Fmsw1zN89nxWHVrArfxeF5YX8tOcnftrzE5pb459t/onNagtoPWUgaiQOi4neSVF4fBpHSlwcKXWRUVDKzvwSTAaVuDAz8TYzSeFWEo2JXBFxBU6fkwPeA+T6cinwFZDhycAr9FFgg2IgUomksH0hG9wbaKm09C/CbUpC6DtYt2qlD82kpOipq5t0aMZk0t80M1MfLzrRujQJg2qgX8t+9GvZDwAhBFnFWfR5S78z5tbc3PzdzTw36jlaRARuRb4MRI3MZFBpFRlGq8gwNCEoKPeQU+omt8zNhhwn2/KKaRVpJcJsxG4KI93Uma4WfVaFEIISUUKBr4Cj2lHyPHnkWfPY5NnEWs9aelt7E6lGYlft2BU7NtXW6LvTKoq+nOuJJ/ROCejZOFq2PLbRx4ABem+pUQawhdCTKm3dqv/85z/rKURkMDohIQTPLnuW5QeXoygKmUWZVY4v3reYf6/6N9OHTA9QDWUgalKqohATZiYmzExHoNTjY09BCYeKynF5NX85s0El3GwkNsxEnM1MsqUlrZRWeFQPhZsLGXXRKBZ7FrPJtQm3cFd5D4tiwa7acagOYgwx/ke4Et5gEyvvuAOuvFJf2rF6tf5YtQqWLNGPf/SRvlD255/1tNUN4vg7ZdXNm6cvcI2N1degSX45JTksP7icF1a8AECf5D5clHYRSeFJxIXFsWf9HsaPGE+bmDYBracMRAFkMxnoFh9Jt3jwaholHh8lbh8lHi9Ot5eMQv1SzqjqASzaYkCYw9A0hVG2USiKgkd4KNFKKBEllGqllGglFGvFFGgFbHRt9Acqg2LArtgJV8Oxq3Yi1Ah/T6ryOZNy8i6Mpumr73/5RX9s3qx3UCpZrfodNadTTydSrx1DTiU8XO+O1ZYM7PHH9QfA778H5V5rTc3j8/DfDf9l6sKpgL53W4/EHrwz9h3i7fF6GY+HOTvn0NrRGoMa2AAuA1GQMKoqDouKw3IsGGhCUOjyklfqJrfMxfZ8F1pKRxYdLMCoKliNBsKMasVXB2HGGKKNBqxGlTCjAVWBUlFKvi+fAq1AD1iafql3yHuIUq20Sh0qe1OVgSlcCSdCjcBhcBCpRjJ/rolbbjlW/qGHoHv3Y0kUIyMbcayoVSt97+1t22DNGn16986dx45HRem9oqSmHzcLhN35u5m/e/6xnNjlei7syvzYTpfTX7Z7Ynd+uO6HANb21GQgCmKqohBtNRFtNZGGnTKXmx8WLaHHgIF4UCj3apR5fRS5veSU+qpc3oE+PqUHqgjCzdG0thiJNBuxmwwoioJP+CgRJf5elP+rKCHLm0VOcQlFLjeaT0//Uxxt448vRVKUbcdVZCFpmIXY9mZUxcJRxUypz4JFsWBWzJgVMyZMp3c56PXqQWb9ev2xebO+g2TlttiRkfrSjn/8A847r4G7XoElhKDIXeRPsn+07Ni8n6Pl+qaQGQUZ/LzvZ2wmGwn2BP9uIGnRafRv2V/fKaRix5DosGjSY4N/7Z0MRM2IUVVQXKUk2S2YahkJ1oSgzOuj3KtR7vVRVvG11OPjcFEZe47qgUpVFSLMRiJMRsoLjRTlxpJ7OJHDBxUyDytkZuq57rduhX6Dyul5fiHxbZ20aunEcm4hBnsJijkfN25Wl7v8d/aqU1CwKBbC1DDC1XDC1XBsJaBp+8n5/QccuSVYMnMxZOWiZGfric5ycvRV9GVlevcqLQ26ddMDT8eOelL9li2bzQxqIQSlnlKK3EXkluYeS+FamutP6ZpTqj9yS3MpKC/Ap/lqnCfMFEa0Ndq/6+wLo17gj53/2OhLL5qKDERnEVVRsJuM2E8w1OP2aThdXnKKPOQ4vfyywYPZXo7BKKAFxLQEh0eho1dFRSXCptC+jYrZEIbZYMektsRkUIkJMxFmPDam4BM+3MKNW7hxCRduzYUr9zCuA7txHz1CqauQYqWMrDCN4jgb7s4m5ijrIBbopKIKExZXMmZvKyyaEbPRijkyDktsCyyWcMyK+bieloJFyzv2M+ZGz25Q7i3391Dyy/LJKc7h5/yf2b96PyXeEn/eaKfbWSWHtNPlpNhdXGtgibRE+hPcx9viSY9NJ84Wp/dirNFEh0UTGxZLdFg00dZoLMazNwMoyEB0VtI0PcH9+vV6ZoxDhyq3EVI5csRMaemx/4qqBsH3P3qJS/KhKQK3T8OjaXh8Arem4fJpFHs0/XmfQBMChCAWL7FlxYQVFRKWn0dYbjaRWVkoO7brYzg5OfobxMTosx0rHp6UViw9uJdulw7DFR+Oy6gdC2DHfS0VLgpEPi53Jm7hxiM8tbZVQcGkmPyByaJYqnxvU22EKWEYhRGfx4fH7aHEVUKxu9i/Q0blzhhOl1MPNuX5VQJPmaesxvu6XW5ii2P17X0qtvOJtESSHJFMp7hOVbb8qdzqJ9YWS7wtnnh7/FnTk2koMhA1Y0LoW9Dv2HHssX27PqRSOZzSurU+6bBFC31ZVuWGigkJkJSgkWQvwuY6Crvy9F0Yjx7Vv57ge4/bQ1af/hy+YCh7W7fFHemAmFYQm4KSrhHW9wKsf1IxR0RgSYjDHG7HbFCxGFTMBhVVaBT9tIDoFp0x12ErECEE5d5yCsoLKPAUcNR9FKfbidPrpMRXQqmvlAKtAJdw4cGDFy9CFQhVYDAaMJvNKKqC4NjdNq/XS2lZKWWuMtxeN0ITKEYFg8GAOdxMqppKR7UjdqOdCGMEkaZIoixRRJujibPGEWmIZNlPy7hkzCW1XiJL9ScDUZDRND3PT06OHmRyc499f+SIyrp15/DWWwb/8+UVe/KZTPqcnfR0fcuvXl09dE914lCL9PvpR47oG5ft3Qu/7te/7t9/LKnY8cLD9Z5MdLT+tVUr/fZYTAymmBhSoqNJiYmBmBi80dGURTgoQ6XM6/OPUbm8PpxeLyX5RWhCIIRAINCEhqtNZ77auY6jZWuqbofjKqxxaVPkKjppWtJwc3iVXsfxGxDazDYiLBFEWCOwh9mxWCyYzWaMdiM4AAOggBcvHjx4hMffKzt+3Ku04n+HOQyA8Ak8fT0n3FdMqj8ZiJqAx6N3KiqDR21BpvJrXh74PBp2UUy4KCJccxJjdNIywkkLewGjyvbTIVElNrKEqCgnUap+3K45UYucsLII5hcei1DHs1j0nD2pqWiDL8CdnERZrIPyyDBKI6wUhZsptpkpUbyUel2Ue9yUe12Ue924NS9unwe3z4nHV4DHtRPfYYH3oIYQCgbViskQhtlgw2IIx2aOxG6KRK2W4sSneckvzeLLLc9Q5D6CI8xBVFgUEdYIIq2RtAxvSSdLJ+xmO3aLHZvZhs1sw2qyEmYKw2qyYjaasZgsGFUjQhFoaPiEDx8+//fVn9OEhg8fLuGilNJj5UTNcnVhz7GjJDaPAfPmQAaiM1RUpI/BHDx47GvWfjfFh52UZztx5zrRCpxEiIqHVki4KCLWVEhns5NBRidRBieRFGLTnFg9hZi1cjSLCSxmNKsRzWxBM5nwmsyURRsQMdG4w+2UR9jJslvZFxFNud2KK8yMy2bGZTXhMZvwWIx4TCoek6HioaIoKqqqoqgqRtWIQTViUA0YVAOqCkJxAQJMKsJkAcWMqgisFQ8AFAGKQL/g0RCKD4H+QHGjKHkoai4eRaBUPtSK1wFhrnJu6XotCnX7IFf2SI5n8BowYEBVVAwc+15FxaBU/Hzc92bFjEExnPB49dee9HxeA0v2L4FuDfiHFOJkIKqF5tXI3ZZL/q58nIeKKD5USOmRItw5hXjynPgKChFFeSilOZg9BdjtZZhTrbR3qHSOUFEjzHjb2fH2sOANt+G1W/DaLPjCLLjtFjz2MDzWSEosJpxWI5rJgDCqCIOKMIBQNYRS+RD+7/UAoOnjHYoABCj6F6qMggBowLHLLnPFAwV/APD/vwI+FDSo+PAZMChqxQdX/77yfwZFrfjwq6gVH1AFpcoHurYPeeVzaLBp+ybO6XkOZqP5hOWqP+cPIhXPBZJHq33gXDp9oRuIfD58W3ew77uN5K49gG//IQyZB7EdPYCj7CAmygg3Q9mg1liTIlBiLChd4nG3bYErPhJPVAre8PZoVj2AnOg/7uK4/0cogAJCQREKitBQ8KLiq/yYY6z4sBkVM0bFiEkxYVZNmFQTJgzs272Prp26EGa0VgQL1f8hVY8LGCf8/iTHFJRGvxXu8XjYm72XDqYOcqBX8gu5QKTMmoXr09kULlkHxcXYhA9bmImsaJXDMT4Oti8lr0cLtD69ie3Xk7DIKAyqCQNhGIQVg2ZGFUZMwogdE1bNgl0JI9JoI9ykP8IMFsyqEYtqxKgYq/zX/Ux4PB6KDh/lnF495IdYOquEVCBKWLMGw7vvslIbyI+Oq9l4+Vx+SzmIOcrKhZ2HkJ7aiYioMCINCkZhxeZuiaO8HYmWGGIsZhxWE1EWIxajXOEtSQ0pZAKRsmwZvV55hU0tR/FH60RsVz5Ix5YpPP2H+3CH+yjRyjD67NjdLYkWLUg0xZNgt5KUaMVskLsuSVJjCplABFBebmRywV9x/eVSBrcfwujBoykQpdjLUuiqdqBbREscsSZMMvBIUpMK+Cfu9ddfp23btlitVvr06cPPP//cOG909Cg+r0r8hXnYbXB572sooxSTLwKHyUpsVCkl5iyKKKiRbEySpMYV0B7RZ599xr333svrr7/OoEGDePPNN7nooovYsmULrVu3btD3EqoBn6bw4rfXkLe4mG6fP8620b357boBZIXv4VDhZiwlFesiKm6BXf3UOsKLfHrWP6MRVFX/evz3BsOx45Xfn+i56uVPdb5qzylA1K5dsHatnoXs+PLh4SGTi0c6+wQ0ED3//PP8+c9/5uabbwbgxRdf5IcffuCNN95gRgPu2OksLeHrkj2Y37kVX5INb1wkq6JseKwCr9ENwoWh3KVPga7QetlerAt+Bk/NldOBYgD6l5djPG4fqip++klPkyFJzUzAApHb7Wb16tU8WG2v8pEjR7Js2bJaX+NyuXC5ju3RVFix+Xp+fj4ez4knmeUcLeVIPx+qLxJTqUaY8yhhhw7jyCki6lAOjoPZhDndODKLsRe4/b2R4qTkmj0ZVa3Ze6l8vuI5cXyvRlFq7xWpas2eT8Vz4rjvjy/rUxQ2bt5Mt1699GCkKMfe02aD+Hh9jUgQ83g8lJaWkpeX12ynIJwNbYDGb0dRxcrruqzJC1ggys3NxefzkZhYdUO3xMREsrKyan3NjBkzmD695k4Dbdu2bZQ6SpJ05oqKinA4HCctE/C7ZtVn8gohTji796GHHuL+++/3/6xpGvn5+cTGxp5yRrDT6SQlJYUDBw4QGRl55hUPANmG4HA2tAEavx1CCIqKikhOTj5l2YAFori4OAwGQ43eT3Z2do1eUiWLxYLFUjVTXVRUVL3eNzIysln/8YBsQ7A4G9oAjduOU/WEKgXs9r3ZbKZPnz4sWLCgyvMLFizgvPPOC1CtJEkKhIBemt1///1cf/319O3bl4EDB/LWW2+xf/9+brvttkBWS5KkJhbQQHTVVVeRl5fH448/TmZmJt26dWPOnDmkpqY2+HtZLBYee+yxGpd2zYlsQ3A4G9oAwdUORch8l5IkBVjAl3hIkiTJQCRJUsDJQCRJUsDJQCRJUsCFRCBqslQjdbB06VLGjh1LcnIyiqLw9ddfVzkuhGDatGkkJycTFhbGhRdeyObNm6uUcblc3H333cTFxWG327n00ks5ePBglTJHjx7l+uuvx+Fw4HA4uP766ykoKDjj+s+YMYN+/foRERFBQkIC48ePZ/v27c2qDQBvvPEGPXr08E/mGzhwIHPnzm1WbTjejBkzUBSFe++9t3m2QZzlPv30U2EymcTbb78ttmzZIu655x5ht9vFvn37AlKfOXPmiKlTp4pZs2YJQHz11VdVjj/99NMiIiJCzJo1S2zcuFFcddVVokWLFsLpdPrL3HbbbaJly5ZiwYIFYs2aNWLIkCGiZ8+ewuv1+suMHj1adOvWTSxbtkwsW7ZMdOvWTVxyySVnXP9Ro0aJ999/X2zatEmsW7dOjBkzRrRu3VoUFxc3mzYIIcS3334rvv/+e7F9+3axfft28fDDDwuTySQ2bdrUbNpQ6bfffhNt2rQRPXr0EPfcc4//+ebUhrM+EJ177rnitttuq/Jcp06dxIMPPhigGh1TPRBpmiaSkpLE008/7X+uvLxcOBwO8e9//1sIIURBQYEwmUzi008/9Zc5dOiQUFVVzJs3TwghxJYtWwQgVqxY4S+zfPlyAYht27Y1aBuys7MFIJYsWdJs21ApOjpavPPOO82qDUVFRaJDhw5iwYIFYvDgwf5A1JzaIIQQZ/WlWWWqkZEjR1Z5/mSpRgIpIyODrKysKvW1WCwMHjzYX9/Vq1fj8XiqlElOTqZbt27+MsuXL8fhcNC/f39/mQEDBuBwOBq83ZWpWGJiYpptG3w+H59++iklJSUMHDiwWbXhzjvvZMyYMQwfPrzK882pDRAEq+8b0+mkGgmkyjrVVt99+/b5y5jNZqKjo2uUqXx9VlYWCQkJNc6fkJDQoO0WQnD//fdz/vnn061bt2bXho0bNzJw4EDKy8sJDw/nq6++okuXLv4PWLC34dNPP2XNmjX8/vvvNY41p38HOMsDUaX6pBoJBqdT3+plaivf0O2+66672LBhA7/88kuNY82hDR07dmTdunUUFBQwa9YsJk2axJIlS074/sHUhgMHDnDPPfcwf/58rCfK2FnL+wdTG453Vl+anU6qkUBKqsg5fbL6JiUl4Xa7OXr06EnLHDlypMb5c3JyGqzdd999N99++y2LFi2iVatWzbINZrOZtLQ0+vbty4wZM+jZsycvvfRSs2jD6tWryc7Opk+fPhiNRoxGI0uWLOHll1/GaDT6zx/MbTjeWR2ImluqkbZt25KUlFSlvm63myVLlvjr26dPH0wmU5UymZmZbNq0yV9m4MCBFBYW8ttvv/nLrFy5ksLCwjNutxCCu+66i9mzZ7Nw4cIa2TGbQxtO1jaXy9Us2jBs2DA2btzIunXr/I++ffty7bXXsm7dOtq1axf0baiiwYa9g1Tl7ft3331XbNmyRdx7773CbreLvXv3BqQ+RUVFYu3atWLt2rUCEM8//7xYu3atfzrB008/LRwOh5g9e7bYuHGjmDhxYq23XFu1aiV+/PFHsWbNGjF06NBab7n26NFDLF++XCxfvlx07969QW653n777cLhcIjFixeLzMxM/6O0tNRfJtjbIIQQDz30kFi6dKnIyMgQGzZsEA8//LBQVVXMnz+/2bShuuPvmjW3Npz1gUgIIV577TWRmpoqzGaz6N27t/9WcyAsWrRIADUekyZNEkLot10fe+wxkZSUJCwWi7jgggvExo0bq5yjrKxM3HXXXSImJkaEhYWJSy65ROzfv79Kmby8PHHttdeKiIgIERERIa699lpx9OjRM65/bXUHxPvvv+8vE+xtEEKIm266yf83ER8fL4YNG+YPQs2lDdVVD0TNqQ0yDYgkSQF3Vo8RSZLUPMhAJElSwMlAJElSwMlAJElSwMlAJElSwMlAJElSwMlAJElSwMlAJElSwMlA1AhuuOEGxo8fH+hqNKmZM2eiKEqNdKV1eV1UVFSj1StU3XDDDf5/j+rpiIORDESN4KWXXmLmzJn1es2FF17o/8NRVZXExESuuOIKf+6YSm63m//7v/+jd+/e2O12HA4HPXv25B//+AeHDx+uUjYrK4u7776bdu3aYbFYSElJYezYsfz0009n2sRaRUZGkpmZyT//+c9a22WxWEhPT+epp57C5/M12Pvu3bsXRVFYt25dg53zVNauXcsVV1xBYmIiVquV9PR0brnlFnbs2NFkdQBYvHgxiqLUyCH90ksvkZmZ2aR1ORMyEDUCh8NxWv+Vv+WWW8jMzOTQoUN88803HDhwgOuuu85/3OVyMWLECJ566iluuOEGli5dyurVq3nmmWfIy8vjlVde8Zfdu3cvffr0YeHChTzzzDNs3LiRefPmMWTIEO68887TbpvH4znhMUVRSEpKIiIiotZ2bd++ncmTJ/OPf/yDZ5999rTrEGjfffcdAwYMwOVy8dFHH7F161Y+/PBDHA4HjzzySKCrB+h/g5XpTJqFBl25FkK++OIL0a1bN2G1WkVMTIwYNmyYP4H8pEmTxLhx4/xlnU6nuOaaa4TNZhNJSUni+eefr7FAsfrPQgjxn//8R9hsNv/PM2bMEKqqijVr1tRaJ03T/N9fdNFFomXLllWS2lc6fsHivn37xKWXXirsdruIiIgQV1xxhcjKyvIff+yxx0TPnj3Fu+++K9q2bSsURanyPpXef/994XA4ajxfW7uGDx8uBgwYUOV18+bNE506dRJ2u12MGjVKHD582F/e5/OJ6dOni5YtWwqz2Sx69uwp5s6d6z9OtQW4gwcPrtPrMjIyBCBmzZolLrzwQhEWFiZ69Oghli1bVuvvVwghSkpKRFxcnBg/fnytx4//3S5evFj069dPmM1mkZSUJP7+978Lj8fjP56amipeeOGFKq/v2bOneOyxx6q07e233xbjx48XYWFhIi0tTXzzzTdV6k8ti6ePf331DRqCkewRnYbMzEwmTpzITTfdxNatW1m8eDETJkxAnGD98P3338+vv/7Kt99+y4IFC/j5559Zs2bNSd8jPz+fL774okqu4E8++YQRI0Zwzjnn1Pqayox5+fn5zJs3jzvvvBO73V6jXGVvTQjB+PHjyc/PZ8mSJSxYsIDdu3dz1VVXVSm/a9cuPv/8c2bNmtUglz9hYWFVelalpaU8++yzfPjhhyxdupT9+/czZcoU//GXXnqJ5557jmeffZYNGzYwatQoLr30Unbu3Angz5Xz448/kpmZyezZs+v0ukpTp05lypQprFu3jvT0dCZOnIjX66217j/88AO5ubk88MADtR6v/N0eOnSIiy++mH79+rF+/XreeOMN3n33XZ544ol6/76mT5/OlVdeyYYNG7j44ou59tpryc/PJyUlhVmzZgGwfft2MjMzeemll+p9/qAQ6EjYHK1evVoAJ8xpdHyPyOl0CpPJJL744gv/8YKCAmGz2Wr0iEwmk7Db7cJmswlApKeni4yMDH8Zq9UqJk+eXOW9xo8fL+x2u7Db7WLgwIFCCCFWrlwpADF79uyTtmP+/PnCYDBUSfuwefNmAYjffvtNCKH3iEwmk8jOzj7puerSI/L5fGLu3LnCbDaLBx54wP86QOzatcv/mtdee00kJib6f05OThZPPvlklfP269dP3HHHHUKIYz2DtWvXVilT19e98847Ndq/devWWtv5r3/9SwAiPz//JL8NIR5++GHRsWPHKr3H1157TYSHhwufzyeEqHuP6B//+If/5+LiYqEoir9nV5lW5kRpOZA9orNXz549GTZsGN27d+eKK67g7bffrpFus9KePXvweDyce+65/uccDgcdO3asUbYyu9769ev55ZdfSEtLY+TIkRQVFfnLVM8T/Prrr7Nu3TpuuukmSktLAfw9s1PlFN66dSspKSmkpKT4n+vSpQtRUVFs3brV/1xqairx8fEnPdfJvP7664SHh2O1Wrn00ku57rrreOyxx/zHbTYb7du39//cokULsrOzAXA6nRw+fJhBgwZVOeegQYOq1LG6+ryuR48eVd4b8L9/daKOWXO2bt3KwIEDq/wbDBo0iOLi4hobGJ7K8fWz2+1EREScsH7NlQxEp8FgMLBgwQLmzp1Lly5deOWVV+jYsSMZGRk1yp4oKNT2B+1wOEhLSyMtLY1Bgwbx7rvvsnPnTj777DMAOnTowLZt26q8pkWLFqSlpfm386kspyjKST+olXWoS2L02i7v6qMywO7evZuysjLeffddbDab/7jJZKpSXlGUGr+f090AoS6vO/79K49pmlbr+dLT0wFq/DtUV9v7VP9bUFW1RjtruxlQ2+/nRPVrrmQgOk2KojBo0CCmT5/O2rVrMZvNfPXVVzXKtW/fHpPJVCXnr9PprDFOURuDwQBAWVkZABMnTmTBggWsXbv2pK+LiYlh1KhRvPbaa5SUlNQ4Xnmrt0uXLuzfv58DBw74j23ZsoXCwkI6d+58yvrVVWWATUlJ8bepriIjI0lOTq6xU8iyZcv8dTSbzQBVpgTU5XWnY+TIkcTFxfHMM8/Uevz43+2yZcuqBJply5YRERFBy5YtAYiPj69yi93pdNb6H7OTqa3tzZEMRKdh5cqVPPXUU6xatYr9+/cze/ZscnJyav0Dj4iIYNKkSfztb39j0aJFbN68mZtuuglVVWv8F7O0tJSsrCyysrJYv349d9xxB1ar1b8B3n333cfAgQMZOnQoL730EmvWrCEjI4MffviBuXPnVvmQv/766/h8Ps4991xmzZrFzp072bp1Ky+//DIDBw4EYPjw4fTo0YNrr72WNWvW8Ntvv/GnP/2JwYMH07dv30b8DdbP3/72N/71r3/x2WefsX37dh588EHWrVvHPffcA+h7bIWFhTFv3jyOHDni3/TxVK87HXa7nXfeeYfvv/+eSy+9lB9//JG9e/eyatUqHnjgAW677TYA7rjjDg4cOMDdd9/Ntm3b+Oabb3jssce4//77UVX9Yzd06FA+/PBDfv75ZzZt2sSkSZPqHahTU1NRFIXvvvuOnJwciouLT7ttARWQkalmbsuWLWLUqFEiPj5eWCwWkZ6eLl555RX/8brcvj/33HOrbHs9ePDgKrdho6OjxeDBg8XChQurvHd5ebl4+umnRc+ePUVYWJiwWCyiU6dO4r777quRa/jw4cPizjvv9Odmbtmypbj00kvFokWL/GXqevv+VOpz+/5Ur/vqq6/E8X+ax9+GN5lMNW7DCyHE22+/LVJSUoSqqrXevq/tdbUNch89elQAVX5Htfn999/FhAkT/H8DaWlp4i9/+YvYuXOnv8ypbt8XFhaKK6+8UkRGRoqUlBQxc+bMWgerqw82OxyOKjnCH3/8cZGUlCQURWm2t+9lzuoAKCkpoWXLljz33HP8+c9/DnR1GsTMmTO59957a8zwlQJLURS++uqroF9yJC/NmsDatWv55JNP2L17N2vWrOHaa68FYNy4cQGuWcMqLCwkPDycv//974GuSsi77bbbCA8PD3Q16kz2iJrA2rVrufnmm9m+fbt/08fnn3+e7t27B7pqDaaoqMi/I2hUVBRxcXEBrlFoy87Oxul0Avqd1TO989nYZCCSJCng5KWZJEkBJwORJEkBJwORJEkBJwORJEkBJwORJEkBJwORJEkBJwORJEkBJwORJEkB9/9oVgUNpN9qswAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(355, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(532, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(1064, 'total', 'FR')]), data_cube.retrievals_highres['height']/1000, color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(355, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['sigBGCor'][0, :, data_cube.gf(532, 'total', 'NR')]), data_cube.retrievals_highres['height']/1000, color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 5)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('sigBGCor [Photon Count]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "7c86b4b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3, 6), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(355, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='blue', alpha=0.9, linewidth=1, label='355nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(532, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='green', alpha=0.9, linewidth=1, label='532nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(1064, 'total', 'FR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='red', alpha=0.9, linewidth=1, label='1064nm FR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(355, 'total', 'NR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='lightblue', alpha=0.9, linewidth=1, label='355nm NR')\n", + "ax.plot(np.squeeze(data_cube.retrievals_highres['preproSignal'][0, :, data_cube.gf(532, 'total', 'NR')]), np.arange(data_cube.retrievals_highres['preproSignal'].shape[1]), color='lightgreen', alpha=0.9, linewidth=1, label='532nm NR')\n", + "ax.grid()\n", + "ax.set_ylim(0, 650)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Range Index')\n", + "ax.set_xlabel('DTCorSig [Photon Count]')\n", + "fig.tight_layout()\n" + ] + }, + { +======= +>>>>>>> origin/main "cell_type": "markdown", "id": "4e8eb3be-3dcc-4518-9117-4c490044d3af", "metadata": {}, @@ -823,7 +1103,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 78, +======= "execution_count": 21, +>>>>>>> origin/main "id": "48fc41a3-bab6-40eb-bd05-38f23fa98582", "metadata": {}, "outputs": [ @@ -835,31 +1119,53 @@ "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n", "To opt-in to future behavior, set `decode_timedelta=False`.\n", " ds = xr.load_dataset(filename)\n", +<<<<<<< HEAD + "2026-05-11 19:24:19,646 - INFO - Performing model height correction.\n", + "2026-05-11 19:24:19,647 - INFO - Uncorrected model height[:,0]:\n", +======= "2026-05-11 10:57:52,011 - INFO - Performing model height correction.\n", "2026-05-11 10:57:52,013 - INFO - Uncorrected model height[:,0]:\n", +>>>>>>> origin/main "[10.466079 10.4652 10.460253 10.455192 10.449221 10.447984\n", " 10.453651 10.460172 10.464973 10.472917 10.479133 10.485509\n", " 10.4861145 10.477216 10.487477 10.491782 10.485256 10.485559\n", " 10.488882 10.489608 10.490549 10.499391 10.501605 10.50699\n", " 10.502246 ]\n", +<<<<<<< HEAD + "2026-05-11 19:24:19,649 - INFO - Geopotenital surface height:\n", +======= "2026-05-11 10:57:52,015 - INFO - Geopotenital surface height:\n", +>>>>>>> origin/main "[-44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", " -44.945835 -44.945835 -44.945835 -44.945835 -44.945835 -44.945835\n", " -44.945835]\n", +<<<<<<< HEAD + "2026-05-11 19:24:19,650 - INFO - Model surface altitude:\n", +======= "2026-05-11 10:57:52,016 - INFO - Model surface altitude:\n", +>>>>>>> origin/main "[-44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", " -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", " -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552 -44.94552\n", " -44.94552 -44.94552 -44.94552 -44.94552]\n", +<<<<<<< HEAD + "2026-05-11 19:24:19,651 - INFO - Station altitude: 10.0\n", + "2026-05-11 19:24:19,652 - INFO - Heigth shift:\n", +======= "2026-05-11 10:57:52,017 - INFO - Station altitude: 10.0\n", "2026-05-11 10:57:52,019 - INFO - Heigth shift:\n", +>>>>>>> origin/main "[-54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", " -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", " -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552 -54.94552\n", " -54.94552 -54.94552 -54.94552 -54.94552]\n", +<<<<<<< HEAD + "2026-05-11 19:24:19,653 - INFO - Corrected model height[:,0]:\n", +======= "2026-05-11 10:57:52,020 - INFO - Corrected model height[:,0]:\n", +>>>>>>> origin/main "[-44.47944 -44.480316 -44.485268 -44.490326 -44.4963 -44.497536\n", " -44.491867 -44.485348 -44.480545 -44.472603 -44.466385 -44.46001\n", " -44.459404 -44.468304 -44.458042 -44.453735 -44.460262 -44.45996\n", @@ -905,7 +1211,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 79, +======= "execution_count": 22, +>>>>>>> origin/main "id": "69098193", "metadata": {}, "outputs": [ @@ -962,7 +1272,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 80, +======= "execution_count": 23, +>>>>>>> origin/main "id": "5535caf2-eaaf-45d4-823c-f2b6cfc2b7c0", "metadata": {}, "outputs": [ @@ -970,9 +1284,18 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:20,796 - WARNING - Potential for differences to matlab code due to numerical issues (subtraction of two small values)\n", + "2026-05-11 19:24:20,797 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 19:24:20,797 - WARNING - at 10km height this is a difference of about 4 indices\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\calibration\\rayleighfit.py:571: RuntimeWarning: divide by zero encountered in divide\n", + " std_aer_norm = sig_aer_norm / np.sqrt(pc + bg)\n", + "2026-05-11 19:24:20,997 - INFO - Using Config values for NR refH.\n" +======= "2026-05-11 10:57:53,733 - WARNING - Potential for differences to matlab code due to numerical issues (subtraction of two small values)\n", "2026-05-11 10:57:53,734 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", "2026-05-11 10:57:53,735 - WARNING - at 10km height this is a difference of about 4 indices\n" +>>>>>>> origin/main ] }, { @@ -1010,6 +1333,9 @@ "DPInd: [ 120 922 923 1725 1726 2407]\n", "refInd: (1726, 2407)\n", "refH for 1064\n", +<<<<<<< HEAD + "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n", +======= "Warning: Odd smoothinglengs not allowd, will preform smoothing with length win - 1.\n" ] }, @@ -1026,6 +1352,7 @@ "name": "stdout", "output_type": "stream", "text": [ +>>>>>>> origin/main "DPInd: [ 107 374 375 642 643 693 960 961 1228 1229 1496 1497 1764 1765\n", " 1938 2069 2099 2121 2122 2164 2179 2201 2338 2377 2475 2531 2602 2637\n", " 2644 2650 2675 2719 2730 2784 2816 2840 2878 2918 2932 2959 2966 2973\n", @@ -1045,7 +1372,121 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": null, + "id": "2f5fd81a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DPind: [ 107 508 509 910 911 1312 1313 1714 1715 2116 2117 2408]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wv, t, tel = 532, 'total', 'FR'\n", + "grpIdx = 0\n", + "\n", + "mSig = (\n", + " data_cube.mol_profiles[f'mBsc_{wv}'][grpIdx, :] * \\\n", + " np.exp(-2 * np.cumsum(data_cube.mol_profiles[f'mExt_{wv}'][grpIdx, :] * np.concatenate(([data_cube.retrievals_highres['range'][0]], np.diff(data_cube.retrievals_highres['range'])))))\n", + ")\n", + "print('DPind:', data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])\n", + "colorList = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n", + "\n", + "fig, ax = plt.subplots(figsize=(3, 8), sharey=True)\n", + "ax.plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(wv, t, tel)]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label=f'{wv}nm {tel}')\n", + "# ax.plot(data_cube.mol_profiles['mBsc_532'][grpIdx]*7e11, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "# ax.plot(mSig, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "ax.axhspan(*np.array(data_cube.retrievals_profile['refH'][grpIdx]['532_total_FR']['refHeight'])/1000, color='green', alpha=0.2)\n", + "\n", + "# for i in range(len(data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])-1):\n", + "# ax.axhspan(\n", + "# data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + "# data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + "# color=colorList[i], alpha=0.3\n", + "# )\n", + "\n", + "ax.grid()\n", + "ax.set_ylim(0, 20)\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_xlabel('RCS [MCPS]')\n", + "ax.set_title('cldFreeGrp 0')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "7d37bfc5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(5, 8), sharey=True)\n", + "ax[0].plot(\n", + " mSig,\n", + " data_cube.retrievals_highres['height']/1000,\n", + " color='green',\n", + " alpha=0.9,\n", + " linewidth=1,\n", + " label=f'MolSig {wv}nm')\n", + "ax[1].plot(np.squeeze(data_cube.retrievals_profile['RCS'][0, :, data_cube.gf(wv, t, tel)]), data_cube.retrievals_highres['height']/1000, color='blue', alpha=0.9, linewidth=1, label=f'{wv}nm {tel}')\n", + "# ax.plot(data_cube.mol_profiles['mBsc_532'][grpIdx]*7e11, data_cube.retrievals_highres['height']/1000, color='green', alpha=0.9, linewidth=1, label='532nm')\n", + "\n", + "for i in range(len(data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'])-1):\n", + " ax[0].axhspan(\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + " color=colorList[i], alpha=0.3\n", + " )\n", + " ax[1].axhspan(\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i]]/1000,\n", + " data_cube.retrievals_highres['height'][data_cube.retrievals_profile['refH'][grpIdx][f\"{wv}_{t}_{tel}\"]['DPInd'][i+1]-1]/1000, \n", + " color=colorList[i], alpha=0.3\n", + " )\n", + "\n", + "ax[0].grid()\n", + "ax[1].grid()\n", + "ax[0].set_ylim(0, 20)\n", + "ax[1].set_ylim(0, 20)\n", + "ax[0].legend(loc='upper right')\n", + "ax[1].legend(loc='upper right')\n", + "ax[0].set_ylabel('Height [km]')\n", + "ax[0].set_xlabel('RCS [MCPS]')\n", + "ax[1].set_xlabel('molSig [MCPS]')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, +======= "execution_count": 24, +>>>>>>> origin/main "id": "9cd3a4d9", "metadata": {}, "outputs": [ @@ -1083,7 +1524,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 82, +======= "execution_count": 25, +>>>>>>> origin/main "id": "e6c7b77c", "metadata": {}, "outputs": [ @@ -1091,8 +1536,13 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:21,018 - WARNING - not checked against the matlab code\n", + "2026-05-11 19:24:21,019 - WARNING - 'flagMolDepolCali' set to False\n" +======= "2026-05-11 10:57:54,097 - WARNING - not checked against the matlab code\n", "2026-05-11 10:57:54,098 - WARNING - 'flagMolDepolCali' set to False\n" +>>>>>>> origin/main ] } ], @@ -1103,7 +1553,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 83, +======= "execution_count": 26, +>>>>>>> origin/main "id": "640e277b-a513-4b55-978d-596f56d51725", "metadata": {}, "outputs": [ @@ -1111,6 +1565,10 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:21,028 - WARNING - transmission correction\n", + "2026-05-11 19:24:21,082 - INFO - and even a 355 channel\n" +======= "2026-05-11 10:57:54,158 - WARNING - transmission correction\n", "2026-05-11 10:57:54,223 - INFO - and even a 355 channel\n", "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:168: RuntimeWarning: divide by zero encountered in divide\n", @@ -1120,6 +1578,7 @@ "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:178: RuntimeWarning: invalid value encountered in divide\n", " vol_depol = (sig_ratio / eta * (Gt + Ht) - (Gr + Hr)) / ((Gr - Hr) - sig_ratio / eta * (Gt - Ht))\n", "2026-05-11 10:57:54,456 - INFO - and even a 532 channel\n" +>>>>>>> origin/main ] }, { @@ -1137,7 +1596,17 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:168: RuntimeWarning: divide by zero encountered in divide\n", + " sig_ratio = sigc / sigt\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:168: RuntimeWarning: invalid value encountered in divide\n", + " sig_ratio = sigc / sigt\n", + "c:\\Users\\buholdt\\Documents\\PicassoPy\\tests\\..\\ppcpy\\retrievals\\depolarization.py:178: RuntimeWarning: invalid value encountered in divide\n", + " vol_depol = (sig_ratio / eta * (Gt + Ht) - (Gr + Hr)) / ((Gr - Hr) - sig_ratio / eta * (Gt - Ht))\n", + "2026-05-11 19:24:21,222 - INFO - and even a 532 channel\n" +======= "2026-05-11 10:57:54,654 - INFO - and even a 1064 channel\n" +>>>>>>> origin/main ] }, { @@ -1148,7 +1617,24 @@ "H [-0.01477] [-0.9987]\n", "polCaliEta 12.380587648929659\n", "G [1.] [1.] H [-0.01477] [-0.9987] Eta 12.380587648929659 error [ 0.00027 0.02024 -0.00463] Window 1 \n", +<<<<<<< HEAD + "calculated R_t [1.02998285]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-05-11 19:24:21,369 - INFO - and even a 1064 channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ +======= "calculated R_t [1.02998285]\n", +>>>>>>> origin/main "G [1.] [1.]\n", "H [-0.02439] [-0.996]\n", "polCaliEta 0.14153155793311498\n", @@ -1164,7 +1650,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 84, +======= "execution_count": 27, +>>>>>>> origin/main "id": "a0fd215d-efa5-47d8-aa7d-4ef6e44953a6", "metadata": {}, "outputs": [], @@ -1190,7 +1680,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 85, +======= "execution_count": 28, +>>>>>>> origin/main "id": "18872d17-27d1-49e8-b58d-d7df1d220f62", "metadata": {}, "outputs": [ @@ -1198,8 +1692,13 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD + "2026-05-11 19:24:21,585 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", + "2026-05-11 19:24:21,586 - WARNING - at 10km height this is a difference of about 4 indices\n" +======= "2026-05-11 10:57:54,922 - WARNING - rayleighfit seems to use range in matlab, but the met data should be in height >> RECHECK!\n", "2026-05-11 10:57:54,923 - WARNING - at 10km height this is a difference of about 4 indices\n" +>>>>>>> origin/main ] }, { @@ -1248,7 +1747,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 86, +======= "execution_count": 29, +>>>>>>> origin/main "id": "32a8ce62", "metadata": {}, "outputs": [ @@ -2544,13 +3047,21 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 60, +======= "execution_count": 44, +>>>>>>> origin/main "id": "34383cc9", "metadata": {}, "outputs": [ { "data": { +<<<<<<< HEAD + "image/png": 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Q2jNe0znF8VlqPopgbAyne4/By5FxzGm72O9Jr4fF9Qe1X9x2LjUfxVLz0dT73THntZL+AMfyx7HUNKDEzB87/+Q5F9cf1DZ4+ZxeK2634Y+WtK/YDhcku+wo2+XlcwL0zDXdvp6/7p0ShBkbQ9xqy7wy/2eb2L/H8scRXHUYs+WbFQwQiDIIEYzX9OfZ8s06xsGhKacvejp3juWPm33CjrXbfo6jazKe0qa42dL1zzZzLNi2gzSu27jVNizjqO5jSa+HpeajO/YXd885lj8OL8wh2tzUdRS32ql2c43Nlm82187By4Xab9FGHf5oCX7Jzg0vTM+bpNd35ov0MeeqF+Z0LDl+LohnOy9ku3038Brca9kGWcd2brEfLnaPi/1+eJ3tZuw7N1jirnF33w+ussX4vHwewaEp+KMlRMur8Mujqe/G/Zo7Hy/0TJcCpbvZ8JoXxUXuIp/ILLPMnkujH5ZZZs+WPa+g8xd+4RfwhS98AY8//jj+9E//FD/+4z+Ora0t3Hzzpb+Qhy246rCCQ37JEtzNX/02zF/9NsyN34ak18Ox/HFEG3UAAqai5VUAdFR7KeAwW74ZC+fvw/zVb0t9sfpjNXXA+RmCCtdBAQAvFyLerMPL5TA3fpt+jkCDdrr3mAJgRuu9vDh2dHLFoc2r4w/ggl/U7nMQeADmC96ADgJDF+z6pVLKQfFHRfY2N34bvHxeAan83jo+/Pdwmwj8+B46vMfyx1NODPtitnwz/FIJwaEpLJy/b8fzLZy/T5zV0ZJl03K51PuWmo8iPHok9VwEFHwmgpu4ZUGwP1ZTEOc6dq5TTGeJgIjjFByaQjLo6zxZaj5qghU9ae/UBGDU7P5oSYEkAZnMT7vJc2wWzt8nz1weFYBUsw72sfxxbb8LrONmS4MVx/LH4Y+W9NlP9x5DcGhKnVovnwOSJAXg6czzWficvM8wyKJZENbXMfHCnK4JPmfcbuu1k15/x5cbFQq8z9z4bdr2073HgHZH/p8kGpjwchboLTUflfEgsN/c0vEYdvrjVlsBK4NLQa0m4HCz7vRBXv8dHJpKgRPOB4Kfpeaj+m+OgdtHBLpucIi2G0v2bFtqnAwIdv/G/p4bv03nKQMJbF8ykH7iWBNs80UQzf3MH6tJYMqsAQbJUu0a9HWc/FJJ5wznMOf2bPlmnTPH8sd1r3fXI8dzr+a+l88Z1GrynKYPuEaP5Y/rXn2haw3fm/sGf+8G7GjD+yH3aAIyBiW0v0z/RmfO6d+TXg/R8ioWzt9n1kAvNacuZ24NqwWGjevpUuDUNXfNH8sf37U/Mssss8yeT4uRXNYrs93teQWdTz31FH7yJ38Sr3jFK/CmN70J+Xwef/Inf4IXvehF+75WdOYc4lZbgSXBHQAkrba8+v0029Nu7wBXXi6XcrQJVJJWOwW0ouVVcbJyOf2sP1pStmeYfaRzQoCw1HwU8zN3qXNNQJo4bI06paFlVufGb4NXKu5w/L1cDv5YTZ2dYfCnjlguh/mZu1JsbNxsafuRJPpMCiKNQxm32srQJL2+Oup0fAjkAOgzKVtbKtk+MCwS2zY3fpu+2EaOGxlMkSXX1PmJ6nVE9br2fdxqq1M6P3MXAGDw9Fm5T2idai+XEwbAOLuu/Do4NIVoedWMV7pimQJm9hMAr1pO/Y3Bi2EHLOn1BTh+4wNY3HhIxnLjIUQbdXHM+30Eh6awuPGQXtsdDzrdi+sPKkDyR0sK5AEo2xOM1zA3fpvORXfeAeLozc/cJevBAWhsF80NCMTNFvyxmt6HgIJAgHNrB0vat/OC9znde0zaXyopa+6PllKslcuq6hw17ec8WTh/n3ze/N4fLSGq11PXWHjqXv28Ox4Elu6/h0F7VK/rXHH7gcA7Wl5NMUBJrw8kiYLr2fLNGKysal+47/XyOQ0knO49hsX1B/V5XVbroI19pesxF2qAhPMh6Zt1nrPrfAeYyueQ9C2IdYEY98C58dsQLa/uAGnuXsu5TMA3rHLgXsw9gnMIgO4dLph2g0x77U/2iTtXAWgf8OfTvccQLa8iODqja9VdM7sxmS5ITnp9xO32jnYN9y1B7nCe+G6BRn53cT9zr+0Gzi53brlzc/gavOfw989+zJ077vUvBHSfizWSWWaZfXtbfJn/Zba7Pa+g89SpU3j66afR6/Vw5swZfPrTn8Z3fud3Xta16HAo62Mc3mGGYTjXh1+QBFdJ30poFWRMTaTuFRydUceS96FzNjd+m34BezkLoNTZdhguOjIESQQUbKcrP1SgU6uIs0P2IZR7LG48BJi2p5yNnI2Os38Wzt+H2AHR7BuCuKRvwcxS81FEy6sqQQ3Ga/IMR2cQjNeUsTrdewyLGw+pU3gsfxyLGw9ZRzsnnyXzFRya0r5Z3Hgodc/FjYckINBqp5yZaKO+I9rOMeDf58ZvUwkkHTWX8eC9+Nml5qOYv/ptMt6ttvare1+2c3i+xavrykboOJXSoNGVMHNshmXBABBv1hVEeXkZTzq4NIIxzpVweirlpHF8FzceUtDAZ3fzC6ONus5FOstsh5XbppnjpCXjTCeYACnp9y3jnbP9rQyzAYOcZ2TQ2O7g0JQ+J5lX/ttlLlVWaMaOawYQ5jvaqKf61XWyXfDiggF3D3DBKmBZKJWAmmu4nyHQ4f8XNx7SQJE7f9w9x8vn4E9N6LOy792xHW7/QZjLOgPQMWQQZHjfjM3a4PMo2HbApipNTPCO1/VHSzvWtysjd9/nhel5yra6ANANQnBvcwEng08uALsUu7cbkCLw5R7L4JALlKOnz+uac2036a5ru7GO7v3ZHjeo4uVyClzdYA7bkjj7P/fqhafu3REI2Ivt1hdu2y8VHLnU81/Khvtlt/sctBogs8wyyyxKkst6Zba7vaByOi/XvHyojgYABNNy3Io6QMYJdp3H+avuVlCiTIcBga6MieACAPxaFXGrjejp8ynHkteQtuSsY93vq1PG33mlooIitmXh/H3qvLP9CjLzOWUijuWPA/2BgD2HueLfCFhcptRlD8iMERiTIWRbXXkT5YG0aKMO/8iMvjdeXbfFeAz7lGJ9AAWgBCcE1Mw14jO7uZzaBzWbw8hr7Tr2DiD08paJ4PMNMwqU4rpOk8v6UeqpuYvjt9lAxqCvDCvlhK7xXsNtocM4DMboFLtBChpBCOcPrznM1qVyQQ2T7wJcjo8rmQRg51BxRAEpr+86s+5cJijm8/C6ixsPIRn0UyCZa8qdXwSKdIh1zTkBDjcfktdl3/CZyfT7tSqSgTyXy65wPgXTkzoObtsYuBh2Zvn7ufHbFGQA0ACAG0TgenPl2bPlm4EwTIF1l73kMy584wOpZ91N9svfHaRx/XthTtfnsbzkDAfjtR1F2NycTeZi0tjP8zN3aWDHnW8uizz/4p+Xue0AKP7f3T/5O3+0pICW88AftRWeGWjiHs9g17CMmnYpAMPPpmSsfQmqRMurute5wRlXkn4xRpGM/m42HKBwwR3ncHCR6o+Ubbt9CqQDT/z5UjbcH8PPNRy8vRhI3YtRrj0sM9+tPZlllllmz5Vl8tpn164I0Jn0Bog26pifuQv+aAkLZz5k2ULjTNHp5BdwtLKmTjadU9f5Pd17DPNX3Q0URyyDavLIhlmg4dwht6AJAI30A9D7AhZs0lTS1u6Ik1MqCkNYszlVcX3LPov5G5mDpeaj8GtV+DWpMMaiQgQJZMZ4z7iVzqujE0cnTxlD09747PlUDtbc+G0pJ8jtAzqGZNJcUE4ZJOVqKuMNQyuR7qcj82RX6fj7JXF63fcNM3T8mxemwR7NBWtkUry89ClZahes+KVSKqeTTJEyIi47vOHkAtYqyogDEihY3HhI5dUMAtBp5nPMz9y1g6FwJcluGwgOCASVVXOkx5S1Ut4JQAMo7hi5feoySsqYO8EUdWTDtGPrBk0orY6NxJ3X0/7JpWXWNDfPa3HjIQWX/BmDgf6bYInP7OVziFbW7NwamiMXAx4uy+zmsnGuun3r5g/61xxFXN+ycz1Ms3nuc/E1zDTzngwcHKRx/Q/vVTS37SppHbUFiOJ2W/cajuHC+ft0PqoKxAFD81fdrUE8V4mwcP4+AatOoIL9GrfaQGjyrJ29i+9xzZ2XLrvv2sVAn5s7PFu+WVl6NyBE1pbpBPzbMFjd7T4XK8wxPN5uISEGqVwWmu1w15w7pjbdJL2+98tCDj+PC4ZplyOnHTa/VEq1TYPITm50ZpllltlzaTESRPt8ZaDzwnZFgE4AKWdo/qq7LcPm5GvRaWTOm7JuK2spZ59AIlpZw8I3PpCKFLtsHtkoOh1xu50CG665gIgsGSWEdAiGnduk3RFWp7/TWaBT58pk58ZvE8Da7li5mgPA1BnOpeWdLqNJFinaqIuMeEgm57/iulR/RvX6DiaJ/eTKON3nZoVKOkjBuIA8nntKVoH9FRydQbRRx8I3PmCdSke6zOdwJYJusRp1Dh3Zqgv2XOc62rC5opQUBuM1Zc+1AIxhaTW/0vQvJZ5kP5aajyJeXd8Bfvj8DAKwYBUBsOt8D88jts11vOdf/PO278y9OY467wyD6OVs/qULoNzcS46xK1XcLYAAWAA8/+Kf1zFwHXM+O6/LdrKf+ByUBROkuGyuW9hm4cyHMD9zlzI/bmAk6Un+rMus8RrDMmP+neZKXdlurkmXyXLHgX2a9PrA+qbe61j+eIq5ca/Nz7ng3xadSrN/B2VcG1r1uW3HwJVtuwG1pN9PyUi9MIfB2XN2vE2qgMvwM0DBVALutS6j7ioj3L5h5Vh+zmWZ3WCGa+4cdaXnezU+n1vRG0iDXO4xroR9eC+6mF3o78Osobs36Z7UtsEQ97vN3V8YrOIewjXMfWE/5gbYLiRzZVufiTFotJsx9/kgLQO1mWWW2W727cR0fuYzn8F3fdd3Heg9rgjQycPA6STHdTm8niym63DSGeaXMWDzBTWnsm+dXhcYkjUEbPVR5hTOz9wluYpDOYGABQIuYOB1h8EaQWAwXoNXq4j80bxPJZfGESEwI/hxr0UA4zKbfFbXQVeAOV6Tg8gN8PPyOSx84wPyrEZWvHD+PsR//7gClNO9x7TKp+vQ+LVqykF3GQw6mwSBlMYR4GnukiM3jlfX0wV2atVUISEyMCl5pZELE6y44+jlJa+OOah8Xv4/qNVSx3AAwlDTCXYdOjfXTNjbUB1mYIh5dHMUTZv5zCzMo2eUmve4gHG4ne5Y61gN5dO540dWyg1AEHS4jO+wMQgzLN9eOH+fPc5i0EdSb8iZj8zjc5ze4eJRbtv5+fmr7hZ1AWwFVWV8DeChbJOMu1t5mM/sAru58dsQHJ1Jyb/nxm9TCTvBoAtcycZzjrvFaobzRFUObuTKw/03DE5dBm63vE8GN/j7gzK/WLQBNMO+kVGKNuq6Zy01H5WARpJYVpP7h8PauaCH+yKQDg65e6+bIgAgxXBzHw+PHE5VeCYAdYH+8HU4L9z9Z7/9OCx7d9f5MKtJ+f3ixkOpPNK9ALTh4kC7tZV9Gk5PyXoId8p59butt5MFduXGfKb9ACyO8aX60gWl3GP3Y6d7j6UKQw1f+6Atk+9mlllmu9mVltP54IMP4s1vfjOOHz+OP/3TPwUAfP7zn8f111+Pn/qpn8JrXvOaA73/FQE6k94AXqloHFA5Y84vFuWPvT5mR09gdvSEzcOLE63UCBhwWqsiqFXVsdFDzovFVFXRxY2HgCQRlm+jLvdJEs0hQyyTbW78NvjFIvxiUR0kAiaABX56ClSUeTXvXzh/H+KVNaCzbRidKoLpSQM2Q5EEV8opZieYnoQXhvDCUNvuj0oFVLZ5dvRESiLr5ULLrDmRZr9YxOzoCQAwx72E+rP0eQ/z03dqfiNfSa+PaGUN89N32qIz5nPB4UOmj3vmGsbZrVUFmPDswjAUdsP0r/SfVCSOmy1pj5G5cpxZIdh9Xj5z3Grrv71cKOCx3hBnJuFc6Ms4FItIej0ZyyTRnD0vFyKctICA/SbzKhTmmv2TJDovlDkZPWEZoGIRXhimzlR15bL2yJZemslwQKseO2HGZXb0hL5vceMhDVR4uRALZz4kn213AOwsknS6J8cL+RPj1mnMhbpGkl5Pf+az+YcP6X3jVlv7TcZF+pPriePNNrJCspcLLdiq1RBv1nWuSaCiZ890TRJZU7p+Qh17zWsNc+bZwxToZbEXt9+SdkeBgtvHWkgptPJEnU/G5q+62zxraANdzRaQJJgdPaGg1jWXyUx6fcxP34n56Tv1/XR6o5U1y6466+3ZNr9UsjmyZh25+wLn7+zoCdmHIPuA9ocxKbIj/b1w/j7EzVZq3ZK95vV0Dpt9MqhVzbm7Zm6NlhDUqnKtzbrZ23p27VKOb9ZI3Gw5c9Uy2pybzxSwSBBH1pj0V0/bDNigx25jdTEgQyZ8t99rfu/oiR1Flbi/+bWqzh0qNjQA4+xpu6klTvce2xXwXqyte7GU7HafThf34mfKmGaWWWaZZba7/dqv/Rre+ta34vHHH8fv/u7v4l/+y3+J973vffiJn/gJvPGNb8QTTzyBj33sYwfahisCdAIwjnUIL58XR6ndRnD4EABgqXUSS62TFri0mduWN3mTYepacauN+ek75ff5dGGL2dET8Mzh5FZ2NkDS64kcd+V+ZYUWVu7Hwsr96rh4YQgYh8krFeHl8/qFHrfaCKYnEdW3kPR64qD3ByKxNUCJn/XHakCvj4UnPqiFVHgWaFTfQmSY3sjkf3r5vObGefm8sjx8bhc4AIAXGmDWH1xA4tmTtpmcOgITHrdwuveYAQs9m9uXC5E0mkCtAr9U0v5d3HhIn5ntc3NLk8FAx5X3Zf/zGQWkhrtWfPVyoZz91zppGIE8/GJRgfxS6ySS/sDkSw30uQnQ5qfvFJCYz9t7haEeaL/UOmn7pT9A3OnIWZ+mjXRWPXPGo0oKTd+x/zifgloVSd/+jb8n06JzyVw/6Q90Lge1quSKTt+ZYvI5puyP+ek7leVjIOJY/jiSRlPnBNcMn8/9NwBET5wxObXS1rjT0b8nA1kPCyv3O/fPpZ5Lx9IB2DZPdyAAs1RS1pmftUy8jIeylcbp5/+9MNRAEucS7+XOk2GwwCAC+92yRnmdM5JzbZ7bBLrcsVjceAjRyprNxXbANX+O223dh9wCPa65/f1s22BNAkPsIy+fV/CoLO5YTZ5pckKfzx0LnmHJvtHgUq2qe5BrbmXnZDAwhbm2EHc6ss7NGreseF7bYfN/7V6dDPqyVvsDXd/DQHN4b9+LudewhbAGmJ++M7VH8PpeLkTSH6TYTXftaVucfPBLMWv8O/ePoFbVNbzUOgn0bJGrxY2HdN7qXtkf6JzfbW5dKLf0QrnOFwKeF2RzL2Puxp3Orr/PWMjMMsvs+bL4Ml8vRPvN3/xN3H///fjSl76E3/u930On08HnP/95fO1rX8Mv//IvY2pq6sDbcEWAzqBa0aIjdIqWWicRnVtGVN/aIXUisInqdQF1+TySweCCOSUEVIA4H15JwNnwIfd06IPDh1IMFE3uuSUO88qaMkj8bLSyZp1w4zTQOefn6bjF7bZxGo38rGQr37rVb4PpSQXArqPBfqKDwmuoQ2juD6QBhysrXNx4KFVIaKl1El6pqMCcoF7v0+sB9Yb2tZfPY376TmGqCFjM8zJXdHHjISy1TkqlUONYJr0egmuvQnDtVSrdpNMXTE+qI8r7EkzPjp4wEs2BAmMagftS66SyVATWyWCQYknjdlvBsEjJLAgGhEVj/7JddMrjVhtxp5MCkHz2ufHbZH6QqTPjwnnEZ2eQg+OwsHK/HjMz/9L/TwIu5hkYhEkGA1NwKm//nQtToIx9HNSqyqDMjp5QgK2VbM2zs62AONRyn74+Oxk8f0zAibJaZm5oEMA8K/uabdGgCe9xaEr6LAx1DQLQ6zC44OXzWFi5H3Fb5r3mSZv+nL/qbilIZQI77nU4Zvw7r83xWli530quDbDiPb1ciIWV+/VzlOhyvtG45mSep8EZgcNBSwr9YlHnMoMp7hqM220snPmQ9EmjKcEUw0KyrxSQhyH8l12n48o9kUzx3PhtWFi5X4NLBOpkSckquwEy5jHOX/sOgOkETvBsODCjjF/rZLpY2D7BD/euYZB7uveYBlF4Tc4Njr1rbu6rK2NOBs6xJpeQ13JezY6eQFTfMn1YN32zhaXWSatycPpjceMhVcIM95Hbnt3sYgBvN/C52/svh6U/3Xsspa5x7WJS5cwyy+y5sf2oI64k228RIb5eiPbNb34Tr3vd6wAAr33ta5HL5fArv/IrGBsbe87acEWAzk8//kEpCOQ4cJQppdgeskOGVQlqNSORs6ASsI6GPzmBhTMfks+4rIwp1OPeSx3JgQVwLljjvYPpSZXVEhRpRN84qAQ+ZEMlX1HeE7fbiDfr4mQUR9SBI5hlISHKwNzCHYA4JMoKG7maPpcBVy4YdqVqLtgBLEvkPme0sqYOUNLrqVySlVORN/JLOklO4Sb2OSAsSjA9mTpHkO3wx2qInjiD6IkzCpTI0EQrawq62dcEtwRCdPzINBPkJP2BAMPNuvaLVykrw+Xl8wIYOx29bmDyJS3Dlkv1H4GlCyL9YlGl0GwTAKco0UBZYrIorlPpOriUUnN8oifOKFMbt9rwxsdSn41b7VRgICWpNn3s2lLrpLL1BOwu4HX7nuPmSg/np+9E0u4gPHI4xYx7oQRolOF2GSzzO8qu1anvbAOAAL/NugJQXieo1YwzHyoAYd+QLVpqnXQKVg10j+CzK1sUWtWEFowJQwWswfSkvpf353zStW4qS7uOuc0NDFWizd/r85vxchUWz7YNB0oAYP7ad+izLrVOYv7adzjspWGczRi6smgAgJEwsx8YxOHeylxdAKrWIFPJ3wHQvYz54uhsp+cflQ+5UOcalSO2WmsvFfC5ZF8MOVOp84VNP3GPCoyslX0w/N3h9g0tFXBgleddGNgd1WuduafzM8zZXGPuySZIQsUDIFLo6Nyy/M7pW5XOX6YDeSFQOrx37Bfs7xa8owWmRsBun7mUfbs6ypll9mzbxSpvX8kWJZf3eiHa9vY2RkZG9Od8Po/p6enntA1XBOj8sZf921QxF3+0hODwIXs2Jh2TkYKCFEb3CdYo6/NyIp2M223AFC1RBsD83Y3WA855d/xybzQB2Agtge1S6yTiTcnNkeNGqjbPr1RSSeDs6AmRz575kM2pC0P41Yq+93TvMZXXJv0BgsOHUtF1ysAAKxELajUrpTRSwKBWhV+taK6llwsFVPWtc+mynC6gJCPJ55y/9h0IJsdTbVAGdnJcmKx2J8WQRStr8EYKFmw5Es9oZU0dSVfmtnDmQynpJJlEjgf7WP/fMlJGSn3DXOp5+X9l/ZzzB6Nzy/KPiTH5m8nTleuE6oQTgAaT46nPL248BH9yQu+hrFy7Yx3o0IJ/14F1JajDrKoGUAYDGUPjXLpSPH+0hPj8sl6XoIr94sqVuTb8akUZKTqpx/LH4Y0UZM5efRSuse8J2hlcIGBmICjerMMbH0sFWch207xKWfKUKXc2QRBeM15b13aQfQQs45hiRUcKmD/yVgWtZIcoLXbfy3tQak0GUgMWR96qbVw48yFhlU0fck0utU7CGynIWI2PmfxVO5YEv+r0O/1A4Mr5HUyOp5jEgzCyitr/YYDo3LIGZ2ZHTyjIZ/+c7j2ma9W9DmDGLhfCe+mL5NojBZkHM4fgzxxSFYdfrcg82awjnDmk8ncag2yqCmhbICmgy8pHo3pd+3/hzIdUGeICnuGKsLsZ7z87egK+Uc3oPDJsIedLVN+y3xNUfZQsm83vH/d+7r95r6XWyR3A0wVW3KM5193Pc10CjrzXyNlTUm8TiN2tP55tB3K43/cbMBlWI7jmyvRpDCpfsl3fpo5yZpll9uzYcyGv/eIXv4g3vOENOHr0KDzPw+c+97k9f/aP//iPEYYhvud7vmdP73/ooYdw77334t5778VgMMDHP/5x/Zmvg7QrAnQm3S6AIeet0UzlCXr5PBbOfgSAdQIIfuhY0ImgE5tsd1MsFtlJsjsqqzO5NnGnowCT+WaUfzL6z2g0WQM6DFF9C8lgoE5NMrA5RIDNAaRDPH/krSqvJQukUX7DpPqTE0gGfduOVhv+5ISCI5cZjupbyjL6RZvnSkkV5bEEZuzvoFaz1UobTWAQAYDKNxX4bjV2MAK8RrS2oXmZFvjUBcC5VWurFRnja9+hn3cLMfF6BPAqsTUSVgIryiDpALOPF1bu3yF5VMdwfTN9TUo4jTNOeStgjzWgxDppNMUBr1bUmU56PZW7ktnx8nl1Rt08NTLJ6gibsWB/USYJCFvFuZT0euL0G6fYdej4O/bbwtmPyH23u2kmlYGU7a4EW84vy/yfnLAMIJmnyXHN01O20gGpycamjoH7HDovOtsKdMgiuSy8l88j3mqk8lMBwH/ZdemxYnu3u/Z5DQiO222dm2RCaWS9h0FVtLYhQYqBgADuMQCAXA4IA8wfeaveb+Effk0+2+mkpdF9G6Dgc2uxrnZbgwLcpw60eq0BaCySFq1taDs4/7hG2B/z03ci3mqkAHswOS7zb03O3sQTZ+Vvpl/Q6QCdDoLJcR0/DTCZ/pKKrHlJSzA500l/oAExzpel1km5F5niMAcMIq1S7aYicBw5vherusogUtIfIFrbkL3TgNnBU2dkXzf7Cq/rzxzSdnHtMUAzvM8NM518724Aa/gzWgjNWHj1VTb46eZI9+33w9x3///k304BL/bfcB76Xu1Sc9G95rH88VQAdK92IYC43yJNmWWWWWbPlsXwEO3zFcPb1z1arRa++7u/Gx/+8If39bl6vY4TJ07gh37oh/b0/muvvRYPPvggPvCBD+ADH/gADh8+jE984hP68wc+8AF88IMf3Fcb9mtXBOj0yqMIrj6q7B6dUkAYD7fYgv7eOMlePo/o3LIWvqERuDA6D0Aj4GTe6GAsrNxvmDdzXIrJi9NzB+lshmE6Gu1Epd2fh4tQUNIYrW1oXlW81dA2J/0BEAapL/q58dsQr60LKB0p6HXjtXXJ1zIgI6pvAYNInBjD0ALC4CxuPJSSfrpyTIIiV57MPDzAKejiFLwh2KbDv7jxUCpHleMSbzUQTk6qI8y+idY24JdKWHjigzv6h2OmLBUZQOPgHssfF8BinOVj+ePan/zd7OiJVN6a68CyQFMwOZ5ic3ktt/AT+0+l1dOT2udu/5K50fxCw2CwT1jd1M0NJmBN+lK0KJgct7mrpRKic8tWNp3PA42mAgmXqU7lOjrgR+ebA1IJULx8XpkgggzNeex0kGx3U/Jav1RC9NTTkp9rJMkwR8VwTP1qRceN4NmV1bK4iBeGupaY66kBgqfPCxB25pICglZbP+tPTmgeHINAqWMnXBWD4wATcPJ6ZF6S/gDRuWUsnP2IgjGyhEutkwgnbX4xmRktCGWKJnEukL2juZLzA7E4liMqzLyjZNNlP2dHT2jgTcG/A5wBAAawcZx0bq6tY+HsRxCtbSBa29BgAfPug8lxBZeUznJdcw4kR6d3rFn/qiO6JzOgR3NTIGgXKoLjFvTROZazFanjrQYAVprmETEDraqcbGyq5J6fVdZxKId3N6ZzOPXjYhaYI6IAUV64knM3tYHyXzxxVqXklN3y7wddFRmwwbb9Wjg5uavs+CALamWWWWaZXczi5PJe+7H5+Xm8973vxZve9KZ9fe5nf/Zncfz48T0fc/KNb3wDjz/++EVf//AP/7C/xu/TrgjQGZ1fRWKcYDqvzNtM2h09noBg1C+OCJAcREAcq8SRclCXfaRRtghAZXSAOMMs9IA41gI/UX3LOapFpL383W4Rar84oiDXZUsBWNmneQ8LCqWu005X/rNOeV2YxJGCSqCWWicxV7tVnMDiiDjpprgN+4FOF9kmPoPbZlYJpgPtHiXAwiT8jAIh83xztVtVtugWEuK1ovoW/OKIAnfX3FwqF4xZ8F/V/mb7/eKIOKVmDgRXH5VnN89MaSXiGIO1NVPYYkSlr35RdPDxVkNZ27naranxAcQx9asVDM4v69+T8ysAoHPQL46oE07mjX1K5ghAav6xbzTfMGeOcDEBA8SxLUJUsqCO0mwvDHWus3rw7OgJO++YZxvH2g4FtAYsJL0eknZHAcPs6AnMX/N2WWumIjDiOOUkBpPjCK86Krlo1QoSI2t3AzxRfUslwSp5N/NVjvMY0XXFcfCLI9JGE9xx+8sLDetqmHnOk4QKBvOc/P1c7VY7VgYwsL0amIlj7T+uHRcMcVwBKLAl2KdxzpNxZYBs4exHdG0ocD5AaS1gi6kNB7wWNx4SQGrmhcs2L6zcr8/IvuC+y6NyyMj7pRLmarfKXuvsW1F9C8HMNGDGOZgcB+J4B0iJ6ltIvvZNnXt+qSTz4MxZabPZuzXwEMcqM2f/uSkXwO7gj20HoAEDN+DisoqaE2mCGBw/7sUuAOR8cu/v3ouFknYDxKkquKa/w8lJDWZquoUpuKTHaF19VPYOM7fnr3m7/pv7GWAl4Xs1N4d1uH3D7wEuDyRyvVyM/b2QZUWGMssss4Oy/bKcfB20PfLII/j617+OX/7lXz7wez2bdkWATr9YQNzZFsBh5HyUh3mlYuoYEclZ3FamMO5s27zDWhWolPVLk/l58VYDc7VbBQhVKwI6dvlyjDvbmhO3I2I7iNRZ57XZHraJgEWlicapjtttlfLGbZGfkg2Q5x9BMhhoTpJfrYij57TBbfOx/HEs1h9G3NmGVyrCnzmkZ1DGnW11Eo/lj4vjbcCAe40U8zFSAEYKCkDJDB3LH9fjJZL+wAIcVgs2wHZ29EQqKBC32/pMCo4mxjUYENSqMoZGiknHlrLHuN0WGZ8Bmnyu4TFJBoNU31nAnLNg1swrfl6fZWJc77+wcr/m47Kvw8lJLNYf1nsRTLky6WQwUIACGGbQSI35XOx7AgC/VErNMy2awgJWEzKvgplprSbsTU7YvjIOZ+rYh3wei/WHddxO9x5TcO3lQsn3q29ZafhWQ+8XnV+RuUG2qFRUULawcj+itQ0kWw1xiLcaO3NS2x2d44v1h7XPorUNlViSBWTV4KBWleq1RsrNOaSAPJ8XMK6y923DDG9jrnar5ou64+AeYcNrMZDD91NKnwxsBWtX/jtcZMeVSPM9fC6+FusPG4nnSEraTdbtoEyPjXLmtAu+4862Pi/zyV0gpcyvmVfBzLSu+aXWSSAMtJ+8UlHWGnOdt7vAdtcGcsw1lHUtWbWBVyqm0g8AA8R4fQbEzN4Yt9PHz7j/vlAxGi0sNsRSshoz1xRgc9Pnr3m7DZ6YICSDm6d7j6XWvmvJoC/rI2fPmR02l32nsQr7YG3NHmnV66WUFwv/8Gt2fubziNc3hJ2tP4z5a96u/cx9d6/GfeJi83H4+3AvwNMt8sN5drkFgzL79rFsPmT2rWJbW1upV7fbvfSH9mBf/epX8e///b/HJz/5SYThTnXIhezaa6/F2tqa/vzhD38YW1s7jzc7SLsiQCedj7jdRrK2nmIhyAQN56mlWMJeT3PgkrV1PWKCVWDpmPrFEZV8qgRzMND7JYO+gAYnr5AR6ai+pZ915YeuzI7yzMX6wwpACECP5Y8DIwXNO1I20IAQOtgLZz+ChbMfUacnqAjIYBvnj7wVfnFEWa5obQPRU0+n+oZOImCYPQOI3bYrewYBHtH5Fa106l4LsKwUwc0ww+J+zkpHt1M/R+dX9DPigPbtOX0DeX/kMFleGCJqNJT98IsjCsL8khTY8UslHT+Ca54fyH8HtaoyPKk5s9VIHUSvvzcOPJk5ACmAnPTFESegnKvdagtDmfu4Z8cu1h+GV63Aq1b0GSlBdue0f2hK2vrkb8g91zeEgV/bQLK2bqXkaxspkByb6qAstOMfmkqxPQw28F4u+5r0pejNYv1hy0iSITcAhA4x1yVgjw7i+zgfCHroePrFEQzMBulKCRlUIMiYq916QYaEAQwFMVW7HlzZO38elpGyXW7QwAtDXctu0Si203WcCdRSbL5pT1CrKvPJ9erlwsvOu9uPkbkGkJKLu2DaBXoYRLoPpPY3A/Qjw+YHk+M67wCrDGBxNnceJQNhLLkWABMgMECOahQvn99RoCtpd1J7LLA7g+ey0bvZ8BEl7lEpsm8UkZg57Y57vL6hUmT+nfPR3Q9SR3WZczoZBBm+//D73doDQa1qgms2/9SjWod5rbVbVWbP4CQl1NwX+NlnYsOAGEirPZhucClz2WbOf5dN3U97Mvv2sWy8M3su7Zkwnddccw1qtZq+3v/+9z/z9kQRjh8/jnvuuQcvf/nL9/XZp556ClFkg/X/4T/8B6yurj7jNu3HrgjQ+dlzDwj7ZBzzxfrD4ixO3SERcTILAyuvBKDyOWU9DHjxiraksMuAIBfCr5a1+q1fHJH3KriyDkHS68GbGIM3MWYLFrkOrSM3SxIjaTRM03DlS0CYhHh9A361rEWAgslxJEmsTAxls3O1W/VLX4sVDSyAgHEU4862gBED2Ck7JqMpMkMrTXWlwDTmxgaT45bhSWLrAJp7D85L3uz8kbcqc+CFoTI9AFLglm1RZsv0/WL9YbnfhLw4BpTQaiGUwQBBRdhAMtpzU3cg3mooW+ACn6Tf1zYFlYoAqeIIFlbulyIxg3S+mMtqaRXbsaq2wyuOKKAe7i8NPhgQjjBQRy3ubAPjNe3r+e/8D1h48jew8ORvmMIqZSuB7nRkDGemEZ05p2dEJr0e/KOHLbtsquXSjuWPq3OfCgzMTCNeXrVgwpk3ZFc06FAckXnnXFfY846d4xDQ4ZWKFnQncQrcDRcFApBinf1iMVUgZv6at2PYXECv92ARMQYSkhh+taxgnOMpOX02KECZNdUH2i7mgBtmXC0M7Fx18jfdPuHPmudslAoEzy4IYH+w0u1BWapKcr+vY80gE2DXYNzZlv5jWgJMRdEc56Fl0RkQGw7sUSXCeaTBCI7TxJiuYwLfpN1Rlj1a25A9akKCHBxTZdzrWylmmvPXPVNzL+bmZzMY4pWKuvek1BJhIDnABkQO50Lvmktq9ocLtcfNQeV7VFKbz8MrjiDubMs5sOdXpA8433M2v57fYW5Ax92Hnm0jE8t27xfYHssfh3fIHkw+fMxQZpntxTIWNLNn2+LEu6wXADz55JOo1+v6ete73vWM29NoNPClL30JP/dzP4cwDBGGId7znvfgL//yLxGGIT7/+c/v+VpJ8tyf7XJFgM43Tv4MgqsOq6RyrnYr/Ko9GJuR2aQ/UMAFQPPqUoxHLkTS2YZ/9DC8UtHmKFUrSDrbSDrb1inOhYi3msAQIKMDlzSaSBpNGz03DgNgIuWaJ9gUYJfE+juyYQKEO0iMM7+4+oA4dtUyknYHXi6nwEcZ2sFAHDheKwzhHz2s4Hhx9QGV0aakVkZS6OZssi0qYwsdVqZUVOaVVR+P5Y8jMfJlAIDjfPiHpsQxNA7/Yv1hzevUoySMVDZqNFRqquznVlNYrXZHrmscwsT0KRxAtNQ6afvaVJ/k+7wwZ59Jc1ibKu/kOZboOwWIHCbMzUv1zZgm/QESA9joCC/WH7bSWc6Lvn32JIkxV7sV8VZD8tzM+6KvfcO0exvJmXMaSIjbbfusAMKXXCfOu+nPuak7rFx0eVX+ZhzRuLOtgQUXwFM2mQwGSDodAatG5q0gobOtDqEGZEzf8BnJWlAy5zq4Mk8pa2/aYI0xWRedlLx2qXXSzn3zfLOjJzSI5FcrqWv41bLkANcfhle0TL0EFAZIOttYXH0gxWBy3tFc1posLsdB1nhf2j8xLuvPzH0+I9ugz2XyP2lk0rj2PcPwUXnAPiIrepDOtioXiiOIt5qy95k28dkoB/a4z0EAqhZjyuX0eBcAOo81j9jsm361IgEyJ0Dh/jvubCNeXpX9tT/AwtmPIOl04BHEmjZ5YYh4fUOZZ/fzDLwRYO0WALiYuQwn58ZS66Tus/FWU/dZzruk3zdS9/LOdIpd+7yvgSLacPtY7Ed/b+TtVARwHCT1wX7HIRfCy+W0P1L7rtmjXDZ9P7aXeeimc7hBo4tZSl6bCzH4+uOZvDazZ2RZgCKzZ9ueCdNZrVZTr0Kh8IzbU61W8dd//df4yle+oq8777wTr3jFK/CVr3wF3/d93/eM73GQdkWATgCIzpyzDuVAAIk4MX112v3iiDrtWrXQRJ79Stk6qP0B4qfPpaqNwpFxqaTLMEmL9Yctu+WwqYurD2Bx9QGtZgkDOIKJMQFFxoEIZqatRJbP0O6kGNSksw2/Usb84beYnLumvs8FPjb/UsCwSmC/+RSi9Y1UDiFyoTjQ5mzEeKupMmIXKCf9fkrGNle71bAcfQUwflXaluofWKffC3OIl1cRd7YxMMdusEokP882SF/kLNtEBsSANTJTsQNuyWi4/epKHVlp1zfnCSJJNAgRzEwrWJir3ap5dwLKxNmFAzb5M/vQZQUV7JjgBJkgb0j6Nzd1BxZXHxDprHGmVcZdLSOYGENwlQQKFAwb4JsMBggmxjD4+uNaGdQ/ehhw8tHIWrmSYc5xLxQAfix/XKoYm7YlnW1E33xK5gurjprgRLK86jjbJsBSLSvQDQ9Na26irkHz3tjMXQAIJsZk7jiS8MXVBxC+5DotUKKgxrDO7rqLl0UKIhVjm/ArZV0PUUNyrxPDHiaDAfxDUxrMYDDKzR2T4zHK+iz8XTLoI95qWKaawajiCOL1Dbm3AZme2QO4ftz5RnbMZS7Z/4mZwwSfBJp874EyncwDZiClOCKg3AAWznU3QCBzqqmgLlrbQNLu6Lh7xREkBlRpTmMYAqHsFcO5swRwwcy0VT/kQsxf907Tjr5lGCtlmUcT4/Y6JmeWoB2QOZlioodsL33KOTw7ekLGyVFfLNYftoCOAaTVBwDslJ0O/0x5rat4GXaS3Xl5LH9c7jViHRUGpxgI8IpFeMWizD3nOwOABhDJ2Lss7qXMztXcJd4pxv0JQKqg0l5tqXUSQaWyQ157sfM4MzCaWWaZHbRF8C/rtR9rNpsKIAHg8ccfx1e+8hU88cQTAIB3vetdOHHCfMf5Pl71qlelXocOHcLIyAhe9apXYXR09KL3ys7pfBYtmBgDAPjGQSGIUwbFYTlpZLjiRhNxp6PRY684ok4oACw8+RupgiQuk0I2I3KAg3sfrzgiX5Amip4YqSedG+ZD0cFxGVDAOmdxo4mFcx+1TG5O8hbJDmq0f2AdSWX/cnKuHdtKxyde30C0vql9p89knj1a3xTHxzC76kwapjEx/0ffOpQKkhzpK6VufnEE4aFpBagEmgTwfG+qCFCjqYBrbuoOzZ/VnE6yG51tzB9+i7DdU3co++sXTXGbiTF5HtPXdNxYcONY/rj2pW/GjKxwvNXUsY8Na8b5lPQHOwAZ/03gm3Q69tqHpoC+yKh5bwXDnAedbSTrmwoOtZqlmZ9xowm/WLQFaL75lC2IxFziShkL5z5qfs4hWt9MrQ0+Y8LjLCplnQfBxBiCiTEFXOpU9gcKBDSwYQDZ6d5j8IwkWgGLCUpo3nWjmWJhYoLz5VUde5cd5HwERJZKEMm+GCyv6Fryi8UUi+RXygDX7FZTgSOAlPPN4FC81bTH3IQ5nW/CAg+x6Q77F61v2gJF5ppkj+em7lDJrioBHMkyA2GuDJeg+GCZTpu3DKSDJMlgoIy39qXDgrKPVX7NvcbsA1SbEITK3C/aoA/BOPe5dSk2xeBXdOactinpbKuk2jfX8w3rrrJqs2f6lbKOCQMJFwJ1rrkVZfV5q2VnLoSqOtF16KwjPivXqctU7lYxNxX4u4id7j0m8439bQIe3AP84ojm07N93Ne49/vOvkQWdy8yWxu86u94jt2Mqof92PC5nCxi5t6ffbnb/Q9ifWRANrPMMnMtuQxpbZLsr3rtl770JVx//fW4/vrrAQDvfOc7cf311+OXfumXAABnz55VAPpMLDun81kyvyLI3gUU+oUN2HMOHWdXJFo5ZRa84oiwKoaRibea6vAP550FE2PWOatYiVNQqVh5lnGeRT5pJLbG8Uw5sQAC40jHnW1xUA2wYU6l66S6QDgxjCBzUXngOB3fhXMfFSDsFDtSZsVhfbUdzKsyTCmdX7e9zDsku7Pbgd7K6jhMa9zp2MIYBkwNS+SGGQplUcfHJHfRgDVxnIrwi7bgDh1P9l3iOGqUiCYGXA9H+slKAQbgGoeNDg/lfYCtlDs3dYcC7YDS0zDUPuR7CXzhMMlwcgj98TENRLAtKpEdDOCPjyE4NIXg0JSyO36lrA46+yyYGEsVY1msPyzBDeMQs/2JkQMy15b/5rMjHAK+zpjE5hzXlMScZuZ7stXYkffIuURjzrVbKdlVGRDgepVyCswnWw2dU1wHQaUiDB3ZKPM8fnFE9oPhZ6/I3xkAoEPrggUBmv10Dq+zrpK+MKj+kGPPNhEAkNEEdpdvst9ipz/npu7QuXGQDnBQqShoctvDIEb89DkdCwAaeCGrx34gkATMGpwY07m7uPoAvPExeONjEljLhbL2mbfe2cbCuY/qfklTMGECVclWIwVEozPn9J7zh99ilR1haGW4W02VxO5mLoPnnp1JWT7BK9vhmfXGn6P1TcuAm/2RQNd9hmGmMxn0VfmymwR1mOXWQGQYpl6u2oBz3g2AaaDOYWkT5/uH9Qf2MseG2cZhEMjUiGd6/qc/PnbBPNjnyjJ5ZmaZZebac3Fkymtf+1okSbLj9fGPfxwA8PGPfxx/9Ed/dMHPv/vd71aW9GKWndP5LJnmDznOrjr6xtH0K+VUJHax/jCCibEdX+6JAa40glf3CzVa37QFXTRv0II5AOoIU65KSSrliARCp3uPIVpehT8+pu32iiMIDk2JQ0U2oNFUeRtzSdUh79iCJwRFfkXkgkGlos+uzqGR0C21Tgq7dWhKr590ti0IIWhxHR7mfDn5ka7cza+W1aGnQ0aZZCrfjW0ybBiBm7KGuVCZi2h5VcbB5DYB4qCwz/S6rvNsmIh4qyngxbQlbjSVCWDUPyWr7A8UXPFoA5dBUCa5PKrMWbItAM0zQId9owzM+JgyQhxLtjEx7SHo0rMzTfuS7W19cd7yOQjY/OKIHFlj2hBMjGH+pf+fgBdzbTJQUaOh81ONILc/wMJT9+rctEcsNJWZJlBh+/gz5yXf78rVvVyoYNYrjmD+xT8vct8hKSTBNVmTaHlVgwBk2KLlVblP387V+avfZkEt2XGOgRkP9imdcTiAiqzisHO91Dqp/UTWl2MbnTmnwN4br2kfUz5MCbYL6FRm79ji6gOpyrYMmNGRPyhzlRoAEDUaCri4t/nVsmVfczb3lOtNwTzXVRgiWd9U1nH+xT+PZGMTyYbZL0dGLAtn7Fj+OIKjMzoHlSU1e6sGFxwlhl8tpwKMLkPGfZH7IO+x8/l3BssoP3Ur3jIgFG9spvap1L5gpNFkei9UgVXY5Zzmmrr94JrLcqcCc07Qh8/msvRc+/xctL6ZSnMgMGQRMt5rL+bKw931cqFr7Ddgcix/PFX/wLXdZMiZZZZZZs+FRYl/Wa/MdrcrrmdSstbxmjirjSbiRlOAo8N+Kjhw5ZHG8eGXt+QdNvV9gCnMYFirZOCwAY2m5nrRwVxqndR8JLJddFz84gjmD79F2LdG00ont5qIN8RZpuPAFxlTvo8MF51EtomFUwiMjuWPCxNgHELAVDFtNJFsb4sTQibHAKyUDMtII8mAsYCM6xgrQ+D0p8puCfooa93YVIcy6Q8Al+U07Rgsr6Scx2SjDgDaX0mjqQVnyPoRyBNc+lUBdrSkL+whmQqyopTODQNYlQgPveKVNQWai6sPSEVjc013PsUNM5a8XuhI3MjA8m9DIMVl6BLDgpNh1TExwCt6+rzmlyWDAaInzsg9+ulAQjAxrnl5lER65mxaLWhj5n8wIcDerxqZbnEEwbVXpSXbyjT3gf4AUaOh4MwLxUn3Qhlv9mv09HlhWkw/kdGNNzYRG4BC84oj8EZG1NHm2kMulKADgTlVB47UNzg6Y6W5FQmU+ONjGmAhyKQSYpitoczXn56UvMSBPXeT4HipdRJJs2XZdGd9sM3DzJC7Zo7lj2P+8FtS950dPXFZFUD3Yyp7L7LYWE5lypwvGszqW4DqFuhShpqBBwbuDIMZr9jzwAjcAFgFRE6UAYMnJI/Ym5zY0U6VRQ/SReBO9x5LSUdZZIprn0GTYYB0MePadYs4+cUR+EdmbG68KXjEgBd/z4rQrrz7UkApxaayn4bG3Jue1MCGZ4JG7AsGMTXlwMy9VAqJ2ceXWie1wNp+ghlUA7ny8N2ej0Eo9oUExHL7Ap9k/IdtLzmdmSw2s8wyOwiL4SGGv8/X/pjObye7IkDnp7/2X+UfOev4AEDSbNnKjJQkkZ1xovXqnBvpIh1ULXRhJJmUwfLoFMDma2rhjWo5lRsGQKPkdPQiU1DHq5Q1T9OrlNUZI+AlAKYjs1h/GPOH36JsBCWEqTxL3rPDI0KsYzN4+qwAEsNG8D4CXse1X9yCTGTrFlcf2OFYAGkniSCP/afAnLleFWmzPz2pv/Mc9tW/+qj2r/scmvc3sNIxto1sBFk/LxcqK+dffTQl3dSov8MuqkPm5G2lpKCG6SKYpmyPMlj+fW7qDmXjUgWHAJ0XzGXTXN7lVc07o8xWrT9IXYN97hYxcnMC6TDPTd2BpLON4OiMBXScW4Y5DowEkkWbouVVm6s2PYlofVNfILtuxih64kzKsSXbyfmjsk2qDpziMQQmDJQQpC21TgpwdCSs6nw67Cvzsgk8441Nm0to2sfc29nRE4iePm/vawBPYhg5FiAadnLJrvM8xbnarUg26lh46l74lbICSM4hLfZi5NiUBitL5rBwCo5ggxlsN2Xg7vo6UFkh55rZ16hecOX1VEJo1VNH1ny695iyjf74GLyKlZZ6YYj5V/x7ZQkpa/bCEP70pFMEjIXLihI4efyJdB66E2TgOuO8ZXDErdCdNFsaCLgcNo5Fn4aDBNETZ1QSDqSDZKouuPYqveelwKZWU3fY1mEAxVd89ryspY06vBFJASGrysCiq1rwTDALcNhhZ7/gvNxrgSBXesz/75Zj6X7PaDBv0N8XQ+nm8bu2l5zOjAnNLLPMMnvh2xUBOt84+TMW2DjnGNLcoxiSgZHAGUeROYB0OGLDoCWDgQLBhXMflS9SOq5Ozo8rdQKgTCelWm5hF0rQWPSH15ur3Ypke1sdLh4dQMechVvmareqo+dWmmXREs2FC+3RL57JR2KhlcRIABfOfVQdfgKXZNC3/ycLZZwblxnmZ3gf2nCEnb9jHpGCxY26lVY6wDpZW1dZXWIADEFZ4jjJgM2xU7YtZwDd+JjNSzy3rG1wpdcuu8j8MrbZzYukrFodqpytVMtqyHNTd2Cudqs4fi7ob0iBKlc2qdcz/2aw41j+uDqxgZMTR4aez+o78lDOJ8ACGL9atuxhs2XzCnk9Mk1OnzAnTKvKGjZZWUUDrijLs1VKt9OydkpxXWmxM+dpwdEZfS8B/3C+dWpeDga2KE/tVstAD0uVjYTZ/Zs+q7mPO+YL5z66o5In5zpZNCoS7LEw6flGUMRjRzgXdmsHr6nSZErvzTpwKy6nwM0BmTJyuVDl4YnZ6wiU3YJODHZokMVIzwEJnoCMJ/ewtXXNx06tyWbLkeX2LUtqgiFUU7jVgLUfzdx284AXzn3USufNcTzMy52buuOiYGQ4TxGwx6S4/RRMjGH+6rfpegmOzth5Z8YyPnt+TxWHuZbkeyGne48L6nYAV67pbavIcIsYzdVuVTC/cO6jquzQNWmUDbGRrw4D3t3sYizipYD1cEGhvbKQTIMY/uwO1U1mmWWW2XNkz0VO57eTXRGg06+MWlDhsAsEXVpICPb8v8X6w5JjZBxnN8eGeaCROZ7BLZ6Tku86AEWr1ho52g7ZqXESmJPHYwdciZo6ZnRu2R4T2WfhFQAWXDtMEnPhCHa0+ivBsvk5Wt9UqTGraQJAeGga/pEZhEePpBg0fpbPO1xdVEEQ2QvjOAJQoEUmjw43GSm9BvP0CJQc9kplf5Q3myqrbvEcSogXnrpXJJpG/oahMSKY8kzOqMuWuJJp18k53XvMgniCLiPlZfXj+avfpuPjj48h6fUwO3pCACkDHp1tkWrCOoQEHszRjBtNYY4IJPkcsPl3LtjUKrKABlK8Eaf6cc5WWdXcyIaVLKpjSum0A8zdQjNecURy72Dz5pQBc9ZFSp6cC1MVQBlwGDZlqwjqwxDzL/556U/jTLtrx80D5RxbeOre1P31jMUh5pRga6526478SjlGaVudegaftOKzG1TpD1JjyGq2ZOP1mZ0cP1eRwMDKsDqBnz3o6rVIYm2LMvImmKL7RmcbcUPANINIrNJL5s8fH7OqAVaPpYphZERl8PHGpqYYkF0NKhVbkM0NqJhgDhUmXnHE5hRvNeEdPqRrj1WqVc6fs6oCL9x7cRuXwWPhNwYKksFA5wL6A2AQAYCmKXB9uewqg47DFnc6KuEFsGMODrdJx8KsT11nrAg9XoM3XgMGUSoHF4AGh4KJMZ17ewHGw7nN/PeFPjf8/uG0i73O43jD5qC6DDW/Wy5078wyyyyzg7Isp/PZtSuiZ7xyKSXpo+R1mClgzhr6A8wffguS7W3rPFXLiOtbtrS8YRjp8PLLD7D5UPYcTQEySa+nlVYp2dTPGseMRyi4MktAQF3kHJDOtgeHplQqqc/hROLplDFXhxH+Y/nj4rT1elbqmMQK1tw8QsnT6mjEng4W87fIDqdYBzI9LBgzsOd78jgEF7BG55dTuX4EfmRu3UqLKrk1baAkmoxptL6pLGDsOGIMHrAgDtk9lbzmLABloQ0gXYzEM/JfF1DREfdNfpXL1PEZmYsYt9tItrfhF4vqULJ4FADEK2vWKXYBGiwgipZXtXpyvLGpBXa0n3o9fQYyAWTsASjod4uveC99kckR3rCyRwY9nIAKgalbMTe49iph/L/xgRTwdQGKx4JU7baypgyWJL1+apz4fh3vEQuwFWRvd+W5Nuq6dlxm0h8fs1WHDbBLHWMxdQe8sZrMW9Mv2odmPdA55npzWSeufV6T4AhASnKqQIrPNRSY4ppQZ585sM4a555DaT5ZxoN0qP1SSaXRKVmt2ffmX/zz9pnCEHF9S39mURnfsG+cx6mjl4YYKy0oBTP229uS/1stp/Y2AiXABlcAWMnooSnETz2dkvK64IqsKfcHYO/ARNnoodzeFAs/MPt0X/YhVT8U07m7ZPGHzQuCS4IwXsM9VktTRAb2+8kLQ8Qra4hX1lSCrgXZzD7pV2zRN7ZrL/3gtvdiEmX+3i2e5M7tvRq/X93iXrTdAOel2pRZZpll9myY5HTu//VCs62trT2/DtKuCNCZNFrwRwpIWh0gTpC0OgimJ4Ekhl8eBeJEXkmCpNcDkgRxvQH0+pibuB3+SAGIE/jFolyn1wOCAIurDyAYryFutzE3cTviegNJr4+k1YEXBPb/vT7idlsa0+/DL49ifuYuIAiAIMBc9RZ4QQDECbwgwPzMXZir3oKk1weCAEmvh2C8BiSxaV8sAKvdQXR+Wc5pixNhJxJ5Pp4fiSRG0upgtngTvEIB6PWBXh/+SAHR2rq0ic8PIK43EK2twy+PIq5vwSsUcCx3g/Qj+6/XF2cjdwP88ii8QkEOHo8TuUeSIK5vIVpbR1xvwAsCeT7zmdniTUCvD29E2uMFgVy/J32T9Pry7HymIBAQXh6FFwSIzi/DCwL45VHMFm9C0usjmJ7E3MTt5v7ST+w3vZbpXy8IMH/tOxB3OgJMTB8nvR7idtuMcV8+a8bQKxTkXq0OBk88JePH5zXXTLaaeh3OI3d86ZgnrQ7ijowJP+uPFIR9K49KPweBjkPS68MrFPT5+ZK/9bDw1L3C5MVyv2Bi3PRBX+cukkSfg3MqaXeQtDrynsef0nFAEsu/Td9zbsbtNhAnUoxoqyHHnxQKUtE5ScxYyPzkPWUeNRCvbSBe24BfqSCuN2TtTE/Kc1TLCMZrwsq22zJeZi3OX3W3tLPdAfp9oC9zQ9ZaT9vkFWz/IU4Qr22YZzF9cvVRJO2Oji3iBMnyKpJ2x9yzJ2vJrBevIOt8qXXSrCu79jgPkMR6TT5z0ushWluXNRMn4vQm0j5/pIC4viVy/JGCVAo2/UYWbHH1AR03f6Qg897Mjbi+hWC8hmO5G3SeHpR55v6ne48hbrclGGHWatLqID4vFaOTXg9xvQG/WNS2J72efo5rLq5v6Z7jzUwDvb7MIbPW3X0VfRkfvzCi63ax/jAQJ7omECfwZw4hmJ6UsTDzIjq/rGsoGK/JHmfmftLuAKUSkMRaxE3H92J9YdbFsdwN+nwEjHG7LW02c51zhGuaa4fzhXu3fh8MWRJFmJu4Xe/LvTf9Jmnv4uoD8Mx89kbsuuZ6SHp9+GM1+GM1aWvdrNmRAvyxmuyl6xJ0m5u43a6nS/THcHv98uju7Rxus/PaLxicq94CBIHsmY4dy91g9yrHMrCZWWaZPRcWw0e0z1f8AoRWY2NjGB8fv+iL7zlIe+H1zGUY894AaNQ5Wl6BX6lIfljnE1jqfMLIK4uW2ex0JJcuDBE3GhpN9ysVxI0GZos3meMfKlhcf1DzYJQdcX4ODk0jODStRSai9Q1byXLrEcSdjuZ+ResbwuxMjCFuNLSdXpiDF+bgF4uYn7kLi1uPIDg0LfKtgW27so1JrOet6VEtW4/oi5Y6b3DQT90vbjRwun8KQVmYA79aRtwVqW8wMS79MhhgcesRuY7JddVzMosj9n6DAU73T8lzGhkZAPgTY+AZdXBYoKXOJ8z4NcTRHqrmGK1v6HuSRhNxo2Ekv0V9ResbqWdTNnNlDUG5LM4MTBXG/ikZ204nXXUV0HkCAOH0lF534cyH5JlZCIpjVKk40t2O/i2YGMdS5xOpQh3KpBVvshJj0/+uZNctbuXm9s1N3C6AuzgifdCXfua1fcNc+4bV43xc3HpE+qQoxUWS/gDB2FhqvnCsRG5csUfhOAW14kbDMJN9BIemU5+Hw3771TKizU0kg76M5/qmYYQ29Pk57ipNdZi2hfP3YeH8fYaJlHu5Z7HOFm/S3FcvF8o9Oh3EjQbis+f1GAjPYRMXtx6RuVKp6Lh5uRCL6w/q+OhcNnNEf2/myuLWI/q5YEI25ODQtMgZTRs0J/fIYQko9QfiLIchkkEfs8Wb1HF32dX5a98BQJzrYGJc593p/qnUGj4I08AIgPDIYfAYG2GWZQzZ/0l/IIEYpM+4BKD7qv68uq7zjeZVyvq5hfP32TaY/WV+5i6dg9zvks064vXNVH66Xyymqon7xSJAtrMvnzndP4VjuRv05Y7pxex0/5TuBxwvL8wh2tyU5+lsI7juRfI8OcuwJoO+rnkvzGGueou2wTWC29gcWxR3t1Nt4/v5u7nqLakK6xgMZN9xzQT7AFtBduH8fdqu4OqjEhhqSOBgr33hWrIL0zhsvO7p/qldn/1Cpn1i9iDuwe51L5R/utd7ZJZZZpldrl0p8to//MM/xOc///mLvvieg7QXXs88A2MBnLjRUMAUdzopp93mNW6IA9PpOHKuhpW1Toxr/h0GA8sGOl+Ap/ungDDE4tYjiJZXxMl2vqCTQV+dNwKMpc4nFJy6wAMw4IygaauJ2eJNAnoAcaxMDiKfCZAva79aFqfXyXVzv5AJcvlcUlG0byrgFhWYxV1zrmUQqPzSBVe8X7S8kpJQzc/cpQ7p3MTtqT6wUlkBEbGpnOvlQmFnjSNKp5WVFUWeWxSgZsaV71ncekRBWjAxbsG/OQNvqfMJHevFrUcEREOcuLjRMJVJOxZ4mz6cq95izgCUuYHBQPqZz+/0b9xo6Msdw6Qvc0XBHqABC4JGz5HkETQljSbi9U0FTrwfr20lvUV10gnw4nX5f9JoakGXeH1TQQNgHcdoc1Mdd79YRLS8Io61ATtecQTHcjcgWl5BtLySuqf+v1LR4EPc6cAbq8G/5qiA2nIZwcS4jivBeLS5CX9iDKf7p3QtemFO+4L9dix3gwYWRDLcUYd/OOgTTIwjiSIBk9WyAGInxxcQ8OBKNnlNl1GJOx0jL5frc/4wIESwu3D+Pp0b0fJKKgCia+PsOXsN0wen+6f0ZwWpxpJmS4MIrJrrVyq6ng7KtI/N2ZHx+qYCh6hp28GACoBUgEfX7sCRahdHBJwXR2SeHJnRgF+0vKJjPT9zFxbXH9T9B4CCVK4PtnGp8wkJeJjrM5CSDPoaDInXN1Pzd656iwJA9vlupnt6FO3699P9U4i72wjGxmygYHVd/x6tb8jeYYAgn5XBggsBPP7eZVj5e/77WO4G2V/d+RzatA/uRQtPfBALT3xQ9vCB3Y8ZAFz4h19TkMt2uQGCvQA3rzhySbDqXmevQN8LglTf8ztvN9vtepcDoDPLLLPM9mP7Py7lhcl0/uAP/uCeXwdpL7yeuQzTwjCGeWO0nOyGMo7rD6rj4xeLytglg76yYKmzETfrQ/cpikNtmBhGoucmbtd7e7lQHDWHDVA2MheKnNCpLugyUTHzG5kzCuiXcNxoKAhQoGacHRbl8SplzBZvEjmfy9g4/ULnhSyh5HV1lAWTQi45ZaMIrOi8k4ml7TjWgaygYVEBiIMeRdp+sst0Hr0wp0wqWRZ1ssjMmn4iaxStbyibTBaVbTmWu0GO+uhuC4MSBApCCXRSz0DQpUecSP8SEHJMpFBMX+eLC6wUtJDxMowgAJ0fCvyMg7y4/qCCFn6en3Or87p9S0s62yJPBBSgy7mfHWUlyYxH6xvKIvgFmWt+tSyBDPadGS8FUpWK9jtg2SnJl2uYHOQVafvKGuInn9YgT7zVtGPJfMpyWYM3SWdbQQhBIFlEMolkr/isOn+NIy75wBvwC3atDM6e0z4iY8czNtlPnJucywy4cJ1oAIXKArNGF7cewWzxJlsB+pqrLZtv+l3XuVmHbDvnJPt5mCn0cqG2g2Aq3moeKJPDdgcT4/CPSIEo7mdeEKT2Sc577o/uOtV+JevHsS2OAK0W5qq3YK56i+yblbLOfVU2EFQ1minQSiXIXPUWxOv27Na5idvTfVgowJ+eTO9ZVB6Y/eJCx4MMgxbum6rKgKgeVJlgGHL3c8GRw8BgoPsuGVIXPGqfG4DFFAQCYvd6BJ76O7Of+tOTMieNmoEgnP07PLZJf6CBST7TXPUWC2Yv0Ae7GZUotN2ejwznfmwY7EtxpJ2s5l6Pd8nsyrKMyc7shWBR4l3W61vB2u02/u7v/g5/9Vd/lXodpF0RoPMzZ+5TR0OPCXHYDZpGUcPQgjzjeDFSHG814Y3VUnJdghWXFSWrQkck6Q+EeTKMXLp6bUfbROYyNkU0AOtUA/acNS0ucmTGOnzFYgoESfGMcb3XwpkPpQrm+MUikigSYDnEwiIMxRlxAJj797nqLRacGyedVUvpECaDvha8UUeIrIRx7hfOfEjAejld/EiZOyNbnJu4XVhRp12UhBEIResbCrSHwdrp/imdA+r8G3kjn23YSSaoZVtVFm1ABoMMcaOhoItgjHLAlETZONbBxHiadTQgjUDQC3NAGNrcLiM9JBNMZpoMb2oeGfDMfweHprXvAShr5YU5mceO0802E9C784TSRpURE3QYoDdXvUXA6pEZBaMEYIAtbsUgDh1f7V8zf4Y/y/XC809VYm36Ljg0rTJ0rzyaCuxwLixuPSLvLZe1bwhuXIkmBgPtV5U4Oqzj4tYjKk0c/ttc9ZbU/InPnrefW39Q9wftfwPKljqf0H2H6wiwlXqHC4cNA5CDNIJbDWa4ktgw1ECMFL7K2WCRURDwfQxW6HibII77bO48TslxBwOVMHNM9DOs0szKwJ1tq9KYnpQ5tLqG6Ow53UP4LCrbNmB0Nxt2apc6n5B9zym2M1hZTe1tx3I36Hv8YlHmlyMNXep8IgXA3DEks7lrkMxpk0rnKf03suEdfcIgjwk0AbDnrZq5NVe9RdUibM+OQOEl7HT/VCpIN/y34We4nHl7un9qh0w7s29vy5jszDI7GFtZWcGP/MiPoFKp4JWvfCWuv/761Osg7YoAnW+cuEWllQCU2fGrZZFn0YF2QV7OAkbmkRHAJKtrAJACnwDUAY+3mspKIgxVlukXRlL5jIzIM89RnXdj9siFvgJGBXAm/zFZXbMOHyxrqdcgI1YcEWkrc8WuuhtxpyPspcOc0pGmQ8JnSAZ9+Iend1SZpHPJPlPH0ADBVFuM1FPZXofBiZpNZYkV7JjcWebE0ak8lrtBnSiyduxPAnQXsABQSR1zc8k8EYAtdT6hLJrm1Tq5f/w/GW+CrHiridP9U5i/6m7JFd7cRLS5mQbgBoTyuBg3N1UB1qBv79+V54wbDQVMdBYB6HMyb5DsKttOEODlQmGRHSkv7wlIXqsLdNhmv1pWibQ6oGGoAIBVbqP1DQVqGnBptVL5xW67uAYVvDJHj+wnAyWHplOyTc3FdPJaAQFzlK37lQrilTVtWzAxrsqFuYnbLSBngMYwZu4zsh0aZHGYxxTraYCVGzzi/GM/ufLZ+Zm7hEU2zw1A80Dnr7o7BXoW1x/U/HK3ai2AlOT8QkDp2TJXGeIqOnQ+OEwvAGXGVa0Aq7Rw1R8uO66BEzM/o+UVK+/vDzR9wa9UMH/dO1P3W1x/0CpAOD8qDju/WYc3VrPrvNORPF2j4uB9L3Ye5bBTy1xcytpnizepmsDtE29S1rHuQSYFgazjhRgaMnsumHQBqvs5sq5xpyNpFybQp5J2w9Trtd3zfKcnZf5N2L2fQZHLsdniTVjcemRXVnO3Z3WZ0EuZFjcDdP8ZtkudKZpZZpkdvKXW6reR7beIEF8vZHvHO96BjY0N/Mmf/AmKxSIWFxfx6KOP4mUvexn+x//4Hwd67xd2z+zDKMmkQ85I+/zMXRoNppyLDp1Xq0rk2uTtMRdNpVmb9ZTjohHkXb4EF87fp2wKIA4XI99zE7crEGGOGh3YYGLc5i4a8LO4/qACMgV9ZLIqIlMk2IgbDc2hSskvG00FJmQofOcIDYJaOiV+pYKk3tACS5RPzhZv0mMN6GAl/QG80RIAKLubctINW0mHMekPEE5PpVgUf2pCAWxqHI1zz0JQzDlkG/yrj6Sec3HrEb2XmwsGCNDl+M1N3J5iEBlZBxzWyUh3tb8NqzJ/zduVKQ7KZWHUeLC96V8GChi4oPPkMrMMblDiGhya1nxBN7ix1PmE5mi6eYPB4RnLLjssKGXTWjzk0HTqmeiMk7XXfGYysCaXjnmSuxnZGUq5VQ5smHZXdqmfMXNL2XhzJqnL1rBvCLqTzrYWRpqbuF3BoztXyHqrJJVA1gBflXUb1ppMkD6DYSRVHj2UV7q4/iDi7rY+pwJYSl87nbRs0kjsveIIvFoVgJGB5kJVNtBJdwsKAfbcRl2PTh8eqLzM5KKrcsIB15yzbJfmFDt722zxJpXec/6wL6gQQBhqAICf5R7sFUeAbhfe5Lhcv96wsm+TS+2ysDpvXFZ2s54CXrPFmxQQ6n33YS7g4Rp011G81ZT9t20l5JTUL64/mCrgthvw0srUJhAZd7dTOYwukHMLOfF54/VNDM6eg1+pYOH8ffAnxlI5xRyb6Ow5uYZTYM+13X53KbvQXNytEBKwd0msKznW78U9XGu4PZkUM7PMDtYulPt+pVuc+Jf1eiHb5z//eXzgAx/A937v98L3fbzoRS/CT/3UT+FXf/VX8f73v/9A7/3C7pk9mufkphHcMfocrW8gmJpEMCVR9aSzjfkjbzWOzpYApUJev6CZK0YJolw/l2KE6Jir3Ks/EGBCoGHAF3N8tHhMIQ/ARvGTzjaQzyu7Q2BFqW9weMZK8wyYo8MeTE0KcIoiab+T66ZMpblH1BRnjYyRW7mTrJ8+S2dbGRsytCnGi46mOR4knDmk13LbMH/N29UBBWDvYWzhyd9I5w668juyg51trV6oMtU1W8wEEOeeeYIErGSOgjGpmhtMTSpLQeeY0lYAKu8DAH+savtvtCTA99x5C6xMQMJlDtyCH5TPBlOTyr4QsHMs/Rdfbft6rJpml41Dz7nmMn/o9VKFlyivVobUYakUkJqABq/N3xF48XnJVjHP1zWyy27f+sUigqlJZdkVvLIwUt+CDJVX8gxFSmN9X8Gea64cloxwilVnZWVKCwlorz5i5eIDy1oHU5O2gJUpkJMCFI4UGDDgJQhS7DSlmgRULptLoJk0mkjqW1jqfEKlq9wDAAtkdrPT/VO6Lpk/epDmFfKao8rxY5AlJbF2FCQAdA15uVCO03DYzuDqowCggQ0Fqo5KA0AKrKLd0d8B0PnhjZZSMmq3GJqA+4oWAHML3Sx89Vf1Wdzg1IXsWO4GmxvtBCUIYqKmKBd0HhglA/uMz7zbeA0zqUkUqePmzolhaSpBKfO0F858SIKIuVByTM3YwfdtqojpC+ZZkyXlXsBA2+Wam8O+mw3n+u+XqU99x2Jn32WgMrPMMns+7EpkOlutFg4dEt99YmICKyuiYHr1q1+NL3/5ywd67xd2z+zR6Py5rEvcaGBx/UEEU5OIVtcQra5ZxrLVVgcZYYik21PZHGCZF/f6rlOqTrgDCKJz5wXQbT2y47gAShHjTQFq8zN3KQCNV9dT0kNWREz6A8Sr6+oEkk3Q4irdnp5hlnR7ciOX6XTyJr0gEKfElM6fLd6k7AMlcfyM+3mCXzqeZERdkBitrsGfmoA/NaF9Fa1vIF5dh1ttks4tHVZW8UyxxwbYugwU2S4Aml+ZynM01WMBR4aVz6euEW9uqURUq3I6lWdZQMYvFhFvbtmcPjNefP6ksy2Onu9bKWF/oPON/eYfnsbg/LLIBo+8VSWx/Hv0tceFKR7InFC5pQPK6dxrsRVTuVJZeAP29PgIw5QFh2dsIZ9a1QJXA3I573Q8NrfgzaSrKRNsuMDaZQ8BWwlT2VLKMIv2KB1tW2db5geZMiMfjlbXFKwPH32jAQwD/JP+IBWs4fuDsTFVMSRrGzaAEIaYP/JWUQWsrqVUCJr3Z/YA7h+UDbry7+FgiSomHNlxdO48vJotgDNbvEmrKlPiTxsuuMK56BaEOZa74cDltVwTTBfwctLXnOsMPvjVckr9AEDnRThzKJ0SsGbTGnRNEtA7Ml0vzFmZ7VDevVa07fWsfH4wSBWYSjrbQLtjFS1O2oLmLTvqi4sBeDKOQFrGSeAXlOUIqaSzDf/qI1aKb1QFSV3mP8Gjm5O5WzEhLwhUAcO/DQMsPSbK9BtVMHrvVlv2tdV1BdfsC79YhFeravEywEqpybLvV67K9XCxOelW7b0cSwZ93Vd3s+E+utTPmWWWWWbPhsXYfzGhvZ+E/PzYK17xCvz93/89AOB7vud78LGPfQxnzpzB/fffjyNHjhzova8I0Jn0B/AKBQVsLFgyN3E7klbbyhOd0vf+WFWizgNznp57HIbrWBJwGTBEXTsdeIIxnn84V71FAYVfHpWDzgH9fzA2JoeJd3ua6+TlQpFdzUwrO+flQvhjVQWsfnnUtmW0pAAhmJoUx7w8KhVgR0vyMtd1HUUeL6G5maFp98AB1A7jgTCEV7AH1OuxBAa4MH8qXl1HvLouDI9TLEYBronYYzCQIIBxjvxiEf7UhEjFWJTG9JsCSzIWhrX2i0XtSxrzdgETIKhvaf5l0pcqttr2nG2HOy7xVhNeoQB/rKoAney0ssdhKAGLVlvbFjcawsKEOTu/6g2R4Ha2dZw1T3dgwbrmvZpjZrziiBw6b9hPMuYKkAsFnX/+4enUnFUgbY50iDsdYLsrjBAlkJ2OALBCQQ6XJ+A/v6JVMjnu7nWZv8gcSEr/MIiERXSCIVpBNwhkHJlb12rLfGG/mjVDmTavywCFP1ZVYBo3GvDLo9pP/tSEsuRJFNmz/AybrUqGVlvXlKoWnDWhz9O356gC5kges9b9YhH+4WkNqrh7DuJE+qhYRPTU07KuyV6b9blw/r5UHree/0iW1Izn4tYj2g49UuMAq3amiikZ0BWtrtlibIYFJ/BJokhAhxnD0/1TwCAC4kSORGk2rVTYmcsc07jTkb0kTmQt5fO2r3OhsOYmmMC+VSBsPudKfDUIZkDp8FE57N+407kkgOcYuGyeK7WnHH7hq79qWeCh7wO3qu4wK5cqJmT+Tob1YoVzvOKI7k8q7zaBHK9QgD81oWtZ21MoIKlv2WJhzv59oerJl7KLgUk+67BM+HKY+sH55V0ZzoMOwGSWWWaZXciulCNTXHvHO96Bs2fPAgB++Zd/GYuLi7j22mtx77334n3ve9+B3vuF3TN7NK84grjZSh/zwBwi5nOSsWHBkVZbWZKFlftTUkk6sV6hkKoy6I9VETdbluXZ3BL2sNNB3GzZ6o4GlFBOFXc6SLpdcXi7XSTdLgCo7DPudAQgnV+R9zkMqsrMmi0LXshsQqLe8eo64mZLnPJuL/V3wDqvWrjIyFDjRgNxswWvVrUMF0G5YdoWVu7fIQtMuj0BIoV8CqDMFm8SUBEECKYmtaAHACvzMv2ueYH1Lct6GsATN1sCGpviYC1uPaJySRcwkG1koQvmyZIVZmEnLwiEtTD3RRgKQ1ssylgYRzra3ETS7cn7ul1pU6tt55AB6nxmzfejNGykYJzrvJx1yFxbzjvjFKqs2Kn+mgz6hrWWueFPTcCrVVN5ZtK+rjjnTz0t4zQ1IRJZA5C84gj8qQnJ/+x2ge2uBkTs+HVlvUxN6PN4hYIFus6RGd5oCbPFm6RQDqvcshBPFAnwdK6vkurQnsFHQMFz+bzJcRmjZksZcXWazRi6LDPbrCDZANjF9QcRbW6qwz9chCTpDxB94wkNLrn5k8HYmDq4w8wTK9XG5gzNZG1DAxkuSIibLb0P5xLnuFeras4e20UHmtJHrvvT/VOpI1Mul5Haj/lly0ay6u/p/qlUleek1VbZejhzSM+5TbpdWbPNluw71Vu0OrVXHIE3WlJAq8GlSgULK/fbYjqtNrxCQQB4oaDjHRyekVzYlfvlOptbWFi5X/uZ48fK1F6hoPuxyoQdc8++vJAlUZQ6h5lreqnzCQ0MMS3DGy1pkJL7ORUcbv7/hY4Q4Txn6sIwoHPZUc5vf2pC0x74fZN0u0jqWzoGyrw3W+mq6mY/AaDPtN95NczYuvmq7hpy37Pfe5zun0oXbXJ+76ZCZJbZC8ky2feVb1HiX9brhWw33ngjfvqnfxoAcP311+Mb3/gG/uzP/gxPPvkk/s2/+TcHeu8Xds/s1Zwy+W4knEcZ8JV0u+I0Jol+zgtzmJ++UxwwI31iJdTEATpeEKisaX76TgWicbMlbONIATDOCFkpsnS+ceoQBPBqJoevkE851gQk0vhEpZHx5pY4PiMFZQbJBtJSBWOSRD4fBNIex7xcqE6HXx5VRik6Z45/cN7PtsxVb8H89J3iFJTLQCKMw/z0ndrnZHTVUe92VcLM/EEFlcbkM2XAgCX5nSn3PyJ9RyfkWO4GrZ4owDRAcNURBFcdUSBN9og/UyaZdLYRvOgakagZ4B9tbmo7kv4ASBJx7gxrQjABmCqk5TKCsTELyIJAfi6XlQmlsxuMjWHh7EcQ1GrKMAo7W7ZBBzM3FSSZeaYgZ1vAYnTuvID3clnbkPQH8GemzP1HhdXY7iJhHmeSIDp3HtG583otPvfp/ikBo8xdPndeGftoc9MWk3Ec1aS+JWxs9ZZU4MY3524mg4GA2EYD3kjBXmO7a+ZGWWS0QAow873JoK9zyt40UdDLeerVqrafk0SKzVRvQTg5aWXiZq5Fq2uiEpiZgl+20mcvzMl9zV4wN35bKveawDq1Dly1gGH4NeBh1Af6HgPWASBptlIMGu1Y7gbMjd9mZcimSirXOBlftvegLKrX5X4jBVVKEBRrAMrsAf7UhIJE7pMAdM2n9pkkEQbUmDJxhbwAVcrjzbwJxsaQDAayv81MYXBGginz03fq/ngsdwMwWpS2aR/ZnM3Z4k2y15vxYQAl6Q92la/uZgSnDMa5Ra4AmD0pkv242ZJnDgLpoyRRltRlEncrJBQ3GgrQos3NC7Ki/H8wNqb58+4+RXUPx5Drgnsn+8UryPeSy/j6zvFVlzIWPhpu226Vf3crirRXmy3eBG+ksKskeficUNcypz+z59MyWfeVbzG8y3q9kO0973kP2u22/lwqlfBP/sk/wejoKN7znvcc6L2vDNAJK7FM+gOR7Y0UxKk0zq3rGCbbXeswwDBIgz7iZlMApykMgiiS6PZIQZ16r1AQ0GIAqsg1I8TNJmDOxAQAEIQ6DhqiSCSPRqqpQNUxb2JMQUdi2M3gKtFYJ/0BsN21zChz34xjIGA5hBeG4pRFEfyxqr32aMkC7FDaFq2uCdgb9OW+ME6mAeTeaAkLK/cLyIwiecZBH4nJR4y728reBlOTqTbxnq4zplF6A2x4T/Y3x4HAKOmLFHaueouCr6TVllyqumFTxqp6bQIZtxpwfOactMWMdzg5mWIZPQfAU8oMiNM/f83btY1Rs6nMQsLghmFCASjzcCx3g/S9ezapuUbSFwdbWGGb55mSXIbC9ARjY+LsG8dS2e71TWUrEQQ691wJt8ojt7vinNaqkl+6up6eEyMFuYcJeujcN3OXsk9eV/PsOB+jCF6tqs/iTYxJP0eRaXNTgxspULbdtczqSEHbos/a6cCrVeV4mk4HC0/+hm2vWQOSLzyANyEBAIJh5uYiipFsd1Ps5OLGQ6k27CYdjJtNA/ZHJSgyNaGsN+eFTKRA2+pPTSA2ElNZj8II09GnQ366f0qfnUw1YHJ4m01t2+n+qQOVFfqFEVln213JYzbBsbnx2xB3t9PqDDPP3GAEgyRemLP7aZiTPWKkoGqBxAQmFs5+REE2+wyQQAQBcLK+iaBW07kOQINtPJKE8wqAtolBEQYFF1bu10DYXphOwBQUKpct6C5YAJT0B1IVd7SogRAAus8m210F7PxOcMGuVsR2Cgkdy92QOrt4t/bMFm+y/ZALZV6OjSnY5B7EvyfbXXhhqN9tysQ6VWEBWQOX6gtaYvYVspsXlM0aKfXlFgDSivK7BFouxBrzb5lllllmme3d7rnnHjSbzR2/b7fbuOeeew703lcE6Ez6A8yN36ZObdxsYXHjIXGGoshG241Dr07nwOZKMlJNYKfvabXFSXTAkV8sItnuqiQubrZSACOYOSS5dQRp211hkgzoWdx6RO5hHGjAVLbsdhGdOavOAh32ZH1TnseAPTrWekRJFKWYV5epI1vgX3XYOs0jRs4WWWmkggbD0HojBZFybW4Jy+VICYOpSe3HoFzW/qVkFYA8u+k/dRIBK8U04J8OKHNT6XTE3W352bTVKxR0HBa3HtF/U27pMl3KaJlncI1SaK9qz1RM6lsWUBkJNWDyP8094mZLnpXAyIB7IO0oKYAywIv5qX55VMef7fbM+ah0rDUo0pW5FW1uwqtWUs/qSlnJUgVjY9YBHy0pa8mxTwamKBXn9SCywQ6zPvi+hSd/w/RRE3GzKUEKExwhI6X3Nv+Ozp3XeZ7w/Fo+p2HGtC0E+mQ6DRvrT47rc+o8ctac5kVGEfxJOWboWO4GyQltdeQa5bLMOz7bwKkKPFqCP1ZVKTeNDAsDAYBZ36229GMYKtPkSjf9YlEYXDPPF578DZtjPVJQsEwW1a2Mq0fiDPrAdlcLSbGvdssLfLbNlUnH3W3dqxBFu+YaJttdeNUK/MlxeGGoqgO/PAp/ZkpAtgHpw2oBALI/Azrf3UJD4cwhB9ALuGIbuT8kaxu28FMY6t4OmP2UQOvwIblXIS8v7E1iKyC6ac8VNrJnBCawcOYc0JKAAkKT1z8YCHA3YIvFoNy8XCBdodYLAu0L3pe2o4qtyesnWxmMjcm8arb0e0ODjGbvUhVHmFa6kNHfyxmaw0COfbFbIESB+XZX+9k9FmivoH+uekvqO/ZC/ZJZZpll9lzalSivTZIEnreTjf3Lv/xLTExM7PKJZ89e2D2zR/NGCur0Mgdybvw2+DNTAORLUxmTlfuFjTGOC4Hn/PSdQOBbIMKCPX3HcWV0mUVSCBIMS0ggk2w11FnSY1QISlnuHqaCpHF456fvVPCFIDBAxTJh80featjC0D5nwUb8PecMTgDKBunPy6sKHLTfCgVhIwhqRgqWgaQTaIABJVyaU1erqGzLZS5ZjIcFQpL+QCPrS51PpNg5gm6CJgQB4u62fe7trrI/0eYmvJGCjYiTFazaczDp7LnzIRkM4E+OG0ff9GngIzaVTtm/iQEolIKqlLTZSh3fwEJNlJVy/O28kWIdscMOJYO+MpPqdBdkLgXXvcg6jQZ4sS/8chkLT91rx6dg51/cEaAVr20YR7Sp4+YzEGD6K242ZW6b+YwwUJDmFRxWKQyVgQ3GxlT6iCiyYMCAVAYtvEJBJLQ8E5XXNwBN1klfx1rAtjDo7DtEEWJT+ZRgSMfEVBd2GUmyy6f7p0zAwZGbk2E3DC4AmSODSJ1aze01RoCt424ATDBzSPub16RMMe50VIrrF4sWrKyuybo27Sbz7rI1LmvvSix1rIwd5LEpSRxb+WxhROc7gSX7AXDUCGacuIeIsmGAZF2AEAGPzMFR/Sz3lsWtR4yEPdJ7SP59ZGWgZE/ds26LRakObBjUuNnSfOMd/dTrCRA0Bb+GK8peyqiW0GJOzrx190adSzOHtCiZm0awm2yWOc26Vs0cHM6PpDG4BkA/Q/acwUaEAoL9a45axUKtktoz3CDa5TCDF/uM+zeuAbLFe/k8zc1t/1YEmV4QXPpNmWWW2becXUlHpoyPj2NiYgKe5+HlL385JiYm9FWr1fD6178eP/ETP3GgbXhh9sw+zQtC46j7IhUzOZNJXXIr6UADEnGPzpwVx2XEMjysWLq49YgFHJtb8A24Eoe6j8WNh6xjGPhI+n3jnPcRb9ZFHks54jVXI7jmam1n3GwhNudMxs2WBVCjJWmPU1AjJTMdkbwhf3IcCGTIyJ7SMYzOnEUSx8ICTY4rm0C2T/KcnGJAxqmLOx34xqEDxBlFkiiw4fUJHJO+yBmTtQ2Re01OWKmgAXRgNN+wBGR65g+/xVyDOWuhZYU264jOLyOcFBbVL4woUxZ3OgjGxrC48RBmyzeLU2KA2cJT9yKJY+2nY7kbFHCrFHOzrvf1Cnkgik0hJIJ2X6WRClphgWe0umadIuPMKgjs9zWHbFgqnWx3xbk3DBarR8ZGpgsAC//3v5h+9oFuT8ChCSzwef0jM/CPzKiUL242DWgZBQLfOnXO/YXxbqqcOul2de4JGO/r+9x1wPsvrNwvhV/6fSRxrAxyEscGUPsKghc3HkJ49IjK2jmHpV9b8HKUYPoyvwAB/Y5ckH9TMGTGhEB2bvw2/Tv7frZ8s/Q/gwD9vpGu9qUPzbXizboA2EJe2SKuAdfsURV9nZOuyoHtnRu/TVmwpC85jV6paJi/smXYTfCI1TxnyzfLc3U60tZ+X4E/kC7CcpD5nABEeuzkUjLwE69tYPD02ZRsVlnZJJHcccozk0T3KO5LrOgt/V0w+4Hsy3Pjt8GbkOJt0flllbHqeDA4YQo9MYc87nRkf+v37f4XxxJIiGPZtzcekvucXzWKlb7uM3sFWgwMMH/8WO4Gm3dfKJj5lBfwTGDXaut73MDCbvd0D1fXisu7vJds4Wz5Zv2uCWYO6RqZn75T1wIGUswrWVmzQYJ6Q+bWzJQGZOJOB0kcyxzE/kHSXgIgSRwjGBtLMft7NVdeO9wfB74WngVzxzazzDK7cixOvMt6vRDtgx/8IP7rf/2vSJIE99xzDz7wgQ/o6/7778f/+T//Bx/5yEcOtA1XBOiMtzvWEQp8+QKLxLkly+k6NgDEaTAg0jqVLVNcJKcOPfPC+MU3f/gtVpJaLMp9Ah/BdS+S+52RMsTeaAlot4F22ziRoQBCR+5JpznerFtwOCJFN+iIMi8u6fbsUR2Br443AJV5LjUfTYEivm+wtmYYqzjl7MpncyrXSvpGolpvmHt2U23V/j6/anMUnUq5njnz1AV5bIewqj3pd1NARI406aern5ocMDqrdDIB2MPsCwUr/7z6bSIrHSlIvp5ztIdXyEsf8DmiWCvVSjEoWxl12JiD6tcq1nkG0iCk05E+NffXaznt5j3cvCqCczKLiGJ4uZwWYZL3jGL+xT8v19zYlJdhjILJCVs8qNu1jqSZB1wLfrmsEkMBvnIOHsGXzINRdea5NuJOB/OH34L5w2/Rqqxa7KlY1GcjeJKiL3XtH5Hj+pqvyesm3a48q5nrfrmslUldY04eC9e4+cZeLqfr0svlTP5mXf+mAYNmyxS6Gci62O46jG5OgY3rHCvj2HzUVhmumTNiJyeUNWJbFjceknYU8jZo5DCVSb+vwZalziew1HwUS81HxamOYt2j+H+/XFaZ/3NxTISyu6b6NccunJxUljzZ7trAWRRr4SP2he5BuZwAI7O+5NkHMiZmP/XCUI/00bzEblfSERy23SsUrMS7botA+cUiFs59VK7f7QKFvM7L2fLNuhfrsTvF4p4Bpys9ZUEhQKTHyaAPFPK6RufGb0Ncb2hQa6nzCQWJXIvutWjD1Vl3AypkZRWEcc6Zis2ITNDHzBndL+HkjXN/4vFJZpyWmo/KXlUo7BskXWw+HsvdoOtkYeX+XXM7dzMX+FLmvhtY3e1334psaGaZZfatZ/FlsJwv1CNTbr75Zvz0T/80/vAP/xB33XUXbr75Zn395E/+JF7zmtcceBtemD2zX4tirU7qT4wLKzIYwKsIsGIEWiqwjlqHKZezuUZDxW0Sc+af5sgYR5df+lpMJvAFpD19ThwvOrLdHqK1dURr6/YAe8M0adVH4zh4uZytvBiGiOsN+GM1rfoYXHO1AZl5+dvRw/rcnsld8grCKDLHh843nQG3eITIF/vWGTTOupfLKetGJ5vXoLOvgGJyXIq3NJvKrmq+VVcArDKKBhzw2YB0HmzcbMEfq4mc0bBUBGt0dBNToTTa3LTOOqBVNZUVZJGPkQIGyyv682z5ZmWjo81NkVkX8ilww0rGCthNNV4FloWCso7+5LjOC7JCixsPIZicsIDICTBwHml+q2GaCFC98TEDxsp63fj86o7cOmVJDRNI51grekLYfAWTg4HOAcA4sXU5Codt8ItF+EcP2/Y6czxutnT+at+6Y2PmjLbTtEvHxwAZzeMLfKMWaMELRQav+cJmDdKpJ4AnQPGLRVMIyBz90LfzgUVUWDhICy/1+xKYcB1XM2bR5qYeHeK+3Jy7uC5HYURr6/DHaup8zx9+ixYqi9bWpU/MXCVrzv2Ec55skcsaqfrAgH6yoZfDFu3XvEJB90hbrKopwaDtrm1nr5fKWfdGBMBw/L0Rc51uD9H5ZQWbZKGViTa/98dquh+QiWaQAoW8KQ5kAjST4wiOHDbrURhvzY/v9oycdaB9iCjWIkda1GsPNlzIhn3vl0oiiV3b0KCOy7iyYB1g5y/ZuuECOHF3OwW0+O/hXE7+To90MsFDBlcAu697lbKOoe7PsAGexY2HtF4A7zOc534pG87THAbTp/unEK2tp/blvfS7C3yHpeWuMdDhWlZAKLPMLs+ygM3+LE78y3q9kO0Hf/AH4fs+Pv3pT+O9730vfuVXfgWf/exnET0Hio0Xds/s0egwBddcjaThOLymJPDc1B2Ym7pDqgGaCDVZP4ILwLAZlbJltRyJmJfLGamtAQmjFgwQlNEJdlkAr1AAzLmbNo9poGwLIE5MXG8YB1dAAgvwLJz7KJKNTflsswVvtIj46XN6vWhtXZjSXE4dMzdvM+n24AWBSlsBR1I5JPXT/szl5HN8D+WUDtiIN+ta+Cc6v4zo/LLcL4lNQZdYnWeRSToMJqCOlBS/KUquWKttwZi5j/YRmS4DQIKZQ4YhadpS+1Gs4C5uNhHUavoZHV8DfkS6ZosucTxV6grr2HgjBZXdxmfPIz57XtvnjRR0Xsxf/TYBGHQSTd/6k+MCRs3cieriPC5uPKRsT/TkUyqdjTsdATlHZlLHu8i9qtYhZ2EjMzfJVif9vgVyg4G2CVEM/8iM5BBf+w4FOslggPjpczLmTnCB468suSkQ4wJJnS9mbJOuyRU1YJRsEBkhAAqsyHQPgyuvkJc+mzmEuN1WZ5YsEwGRC2S9URMgCnwkSaz9hSgWwFAwhbFMsEmdcyM1TgZ9eOZYjrjZRNxuSzCkVhEpc62qOYK6hgp5W8Ao8HUecP4LU17d8Xy8H/N7XTbfDX4dpHmjRqURG+B4eFqepVZF8OJrhS2cnoT38hcDvp+Wtvb7FhSbORSvrOk+x34FrBzdtXhtA0kSKwh1xxeDgYJMr1DAwlP3Il7fsHPRtBFASn2AKJaAXK1ySSnobsdyaLvN+cwug+sXRmR9JRZUKeMOwDs8nSqWlgz6KWVIqt8NqNXjTLC7vNatDsz56s4LDd6trMlL80RlHnuFArzRorKvruw3brZ2PRPzombGg9cYbvPlVFselvj65rxo1y733M/MMstsd8sCNvuzCN5lvV7I9rWvfQ3/+B//Y5w4cQKf+cxn8Du/8zv4qZ/6Kbzyla/E17/+9QO99xUBOuEbALO1ZSWdgC2kUN9CbCpQasVY4zAsrj6guUWD5RUBqgYweUZSRQcjWltXOWlc37LyNOOACrtlmQM6kEm7DX9iXGW0dOA0pxCAPzEGb1QYyKTbg1cSEDR/1d3wKmXEm3Vhr7YtC6CRbzI9uRwWNx7S/Cb2gbbRgBbKFRXIOQ6TgoR+H0mrg6TVSd2D15bcvXrKYeb9CDDI6Go+qeZ9FXZ8Rpk70z/e1IQ6TwCUnRLg0ES8WTd9Uk49B/tfck2lL8lQaxABPMImts9S37L/NsEKSvbiZtMELGzhp6TVlvEysk2/PKpVdF0GG4CA8rV1vWd45LBK8tgfkh9pcwK9QgHRk08pqFL5qxkHssksEsTAgI6jARVxfcsAJ8OuN5rmOpEdV+bYGhAFIDX2HE+y8X6tAq9UUhDA98T1BoLJCZPnx5zRpgYP1BGPYvgT46aitD3XkP0Q1xuS87lZF+fYkVoTpLKfFbgBCkbi+laKQaRFa+vwaxVbWImAlef4tjpGLZDTgAVzY11TMEtg74AeK3vs6zrTCp6Bb9j4oaN0oliYw6G1cZDOQVzfkvlrAGH0tcftH8NA9ol2G975NQnkRTEWzn1UgxwAtM/8WlXm4MqaUUjkDfgetUWaOHbO/KL5tYquRWlMpGt2/tp36PqUAmpF3etUZmpSKrzRogbLFNRf4AgO9/80r1DYIX3W41zMvq/7+nYXi6sPyBp88mkJLtWq8GtVAWBmPIeN4Gk3gDZcUMhln/ld5puCcSxO5tcq8GsVBIemVV2RbHdTTDSiOFVMKel2pWLxHu10/5QypxdiSTRnGbvnZe7aF05UXa879H1ysRzZzK58y1i5zJ5vuxKZzre97W14yUtegieffBJf/vKX8Rd/8Rd44okncN111+Ftb3vbgd77hd0zezVTnESdZFP0h84hnVl/Ykwllf7EOLxcDvOH3yLgJpeDXyrZaxqncnHjIZV8AlYuyKgs/x3XGyqB1fMB9VzJjjhwuZw464CVXxIQmS91tSBQ0JA0mlhqPqrghuBMZLmFFFsyN36byL2iWNpoAIxfq4qDtL6pbBlz6+L6FrxcThi4KFbWz70ugaEbwVeG+dA0gkPT8CfG5VWrYG7qDo3U01zATIaTIIsMGfsmWV1H0uoYFq2veZNxvaFMmsu8+uWyOrVa5dUARulPeY6FMx+CViHmONe3LLNRKCCYGLdjAijLSbkzX26eYbS2btiuqvQfZaambX65rI5rtLyCuak7bAAE0LEksNQjVigvLRSsYx74CI4cTsnBpd+rhqXu6jgylzZaW1dAB8CA0Wqawd6luAjn0NzUHSo1jZutVHCG7AqAHWPOM2BZKVkZRjOn+dmk24U/PSnsmgENS81H1Tl2QQpBItvnjiPllfreXE6uafo3WlvXINMOM6qHpc4nBGyMFnXcOU6UlO/4HBnbwIdXKlpguYvDz3nGdUlFAP/OnM6DdLj4TG6Aimx49LXHZb+LYp3XCHxhmQ2TTpAHAPH6pg2wrK3bfareSAH6peajGtjhHp30+xoMUFm3CRD605NIGk3ZB0Ir42VhKnf9Mzc62e7CKxUveMSHa7v1r8uSemFOC5rxPlr1mbLayLLqDG5ejEkF7DEiw4y2K609lrtB9vmK7B38jkraHZ2Tsgf05NU2xe/M+p6bukPHluCbx/fspzAP2Uj2Jds+/GwqucblV6CN61s7+oTnn7r9ktm3j2XBhswye/btC1/4An71V381dTzK5OQk/vN//s/4whe+cKD3vjJAJ2wE2WUbCZQIhpJ2xxTJaQG9nrIzSaujLA6ZITKXbt6WyOyccvSsjkoGzETaAQewGdmdnoXYbqdkqypTZDGRWlUi20YmzPyb+WvfIZ/v9tRhTFWj7Vq2UBlYw8b65VEk7Y469IurD2gOXLQmxSaSfh9BrSa/r5SdAkbGSXblxARlrY5hCM2ZcFFkjixoWbbAyYXVPMPIBgkI6AmKyfCq7LHft2eoEqBVygr8AKTyCF2mUecEgU6ro4xT0pKjL/zpSZG+mrkT85xJk9fqT4xJPuh21xQw6emLTAJZDmVjSiUFOXwWshWsdss+t4VvmgZ4j5qfzTmHUYxoeQXR8oplhKIY0dlzttBOt4vo7Dm9P9lGNQYOzPEtlNgm7Y6MJaR4TrS8ksoLA6D9kbQ6lhVUpq9pcxkLBW0fgwRkUemkB4emlVHWnEFlY6tINreQbHLOljFbvlmcXoLbbjclV1Xm17SNMldKrRNzfifXEgMbyUCCL+66499P909hbvw2WQutjighnPe5clr2J8fJC3OpYIXb1tniTdYpd9ZttLxi22D6kU77QVbt5BjPjd+muZUcUwZIJEBX1uCPG6Bg/rj2nZnLwZHDKbBKJg6Bj/mr7pYgBtetmctemFPgyTWQbHdFVlsqKrsX17cQr6zBKxXTgTHA7tlmXg8DlN3Ayg6H1uSgE1AvdT5hzgsuKoBz8yFVyprLyXeAAdgEZhdymOem7pA9rVDYIQXm5/jZeH0jFeACbAGrxKgW3H3TXbsAUkXwyP7uRwZLNvKS1WuDZ+ZKMACzm2zXZYAzEJJZZpk9lxbhciS2L2wrFApoNBo7ft9sNpHP5w/03i8Y0Pn+978fnufhHe94x/4/7PsIDk0DgJWGku3J5ZC025rfadm9hjITCHygOKLFYlKMT+Ajrm8hOCQ5TyyYwGg770NQoeDHgDo537BlJX/GGdZiOYGTLxX4VvZoroG8SNXidXvcBdm0peajCny9QsGAZ5uPxAqh2i8NAaRkKxVIm+M3FAT1LGPoAidW11144oNy/VpFAM76hnltWtBdKsK/+ogtIjNiz4tzK8Iyf3Rx9QF13tgO5jNpdUTKX1fWbD6mAbVuZViCBFYDJeOozF+ro2xs0miqJE8PpTcVOCXPdktZ4cHySnpsDHjiGKi8c30DiSkkw5xPMlrW0bbMudxT8v+itXV1KnW+GtkeWQwksUpTya7y3mQig8mJlDyW88AvlxF94wn9fXT2nLBYU3cog8xcVQZSlC3JWRmsC54JwCi/DCYnbP6sAeUKLstlB5RVlO0GkK6sbPoqmJxQCa3kSkY6d/yJcSCOLdg1xbFS7D8lwMoY5xU4q8yeedqGDeI69kYKKqt2TfcXINUHBEKUXbpG5tKtIkpWk/3rSoX5mYMygkytMmuON/JrVZm75vfsm7jZlDVaKmpggefOKgA3QJF9Lc/Z01zhpN2R12BgWfYRy+ZLDm3V7hMaKJRARXD1UeeszE6KZU+6PbjH5tAudAbmsB3L3aBg32Uwg1pNmVQvDMGjd6hosQGwggLWCwFOPaeTbOhgkBrj4c8xiKHFzAyLO1u8SfqsYmXa8fqmjA331G2pau3mZkt6Qnnf57+yCBv70lUaKJi/iPR2T2b2xN2u4YW5Hb/PGM/MMsvsubDnQl77xS9+EW94wxtw9OhReJ6Hz33ucxd9/2c+8xm8/vWvx/T0NKrVKl7zmtdgaWlpz/f7kR/5Edxxxx340z/9UyRJgiRJ8Cd/8ie488478aM/+qP7avt+7QUBOv/sz/4MDzzwAL7ru77rsj6fdLtImi341bJW6vOCQHMkvSAQxiTwkXQ66iypNG8wQPTU0/BLJfjjY8BgIGf0dTpAX/IAE1PEJ7j6qFy304GXF0muN1oUSaZx3pDEymSqAzVaAoxT5OWl8MrixkNAfwB/tCSfM2c9nu6fwuLGQ/CrZcTLKwIazHs0wl8sYn7mLmkfWcjA12fVKqfm3E0vL45IYJitZCD9knQ65rWNuN2Wf5uCHq55QSBOXbOF+eveiaTTwcK5j0pe52AgVVKrZQWM0fKKHPPRbMm9Ox19n7J4dBL7fczP3KXVVnWszJmAlMvy9/7haX3OpNnS/lbgGMdyvzjG7MiNyh7BnOXpT4xJfm0QyHv7fZk/xhFOOttaLVfaK4xCUKkok+zlc5bdbrbgj5awuPqA9MNoCQhDCVSYoj7JtpmT+Rz8qw6nKrYCkD4ybZir3Wqlumbcku0uvHxOHXR/elLG1oyTK03zR0vmGcwYm/xPjrfLOAYT43J9U+mVY8VxSdyCWHEMJDHitXW5R1fkmRyH2fLN8llTNCpVoCo2QLZUtAA8Sex9zXjqnDRrNBkMJKDUH+japRw7Xt9AEkVIOtsyjt2u9knS6aTWTFzfknXBPOQ41mNMdB9xglBeoaBnb3rmrErNBQ0CkZMbqfxS81G5dn0LXj6HudqtNhhknsl1kmdHbrTrKp9mM5kDeuDGM1m5N+ZCmztYHpVndvO+gwDzM3chXlvXfU/3WdMfvB5tbuqOFKjXddm3udVeqWjbYO7jj5ZkjS6vyPsB+ONjSNZN1VqzNjmmgO1Hf7SUKjoz3PcXMrfabNxs6n25PyLw5TvBVC1PzH4Xr5jjqJz9cliCqsVwogie2d8BIO71Uu/ZIV1N4tTe7k+MaS4uBgPEyysWVCY2TUBztIMAPMYn6cr+wbNv/X1EsznONIJr1+JmU9vvPuOewWEixelSKS78fBLvyoBmlllmmR20RYl/Wa/9WKvVwnd/93fjwx/+8J7e/8UvfhGvf/3r8b/+1//Cn//5n+Nf/It/gTe84Q34i7/4iz19/t5778VLXvISvOY1r8HIyAhGRkbwAz/wA3jpS1+KD37wg/tq+37teQedzWYTN954Ix588EGMj49f9nWSbhdJFEmhDlPmXv8WRfIyDlXS7QogYOXSQkG/7JJORz4bhkAYIm63JfofRUiaLcRnz8MrFuVnshXGQfNrVfjTU5oH54+WFGxG6xuAOcrEK4+Kkz5yozgM+TxQKWvb5qbukOqq9S3401MaIUcuFGDqPJc3UlAw5RYv8oJAHKFcqJ8lGPICKRQCI2vV3E/+3hgdDT6/FwTSxvVNeOVRAUdhCH9yAr45OxIAPHPcileQZ0UobVja/qSOlToxBKKaxzWlz+mXSgJGuyJh5hiibfP7OE4YDCwLPFLQMffKowJknbPp1Dljn7I/WeTEvI/tp4Mct9sSmOAxCkbOCnNUx2z5Zukfw4DTKfanJy1DE0WIn3raFo4xTvZi/WF7TdPXiWHIFYyZ4EoSRcLQmnlKeSolewBkvM1Y0lHnmHPeLzUfVWk45wL7jXOXcxLmTFC9RhTBr1Utm2KACqXfXrFo2bPpSUSNhi2AtN2FPz6GuNXW8SFoTrrSn4AcV4H+QICOGVNvxASAzLX5f7YniSL4kxNY2v6k9Ge7Lf3DXGpHHnksd4OuQebJadBgMADyOTsHzBhwLOO1dWWy52q36pyO220kUaQBCK9Q0Hl/un9KwQnzoBU0mHFy58VBWmQqwrJoFoMLAKyCA5BnGCnAn55KrRtdC7Vqeh80c4UgfHH1AWF+udeWR2VPYn7vYCD9VX9YPtNqI261Ad/mMvujJUTnzuu81Cq7h6btvlzfksCEUWiwf5e2P3lJgDLc15zHx3I36P7m7o1anK3V1vnslUdT53S6UtAUe+mAYBf4EbAFE+M2/5ffWWS/fV+vwfmO/kBeYajrmcEW7ie8vu5rQYC4Z89X3otxDg+fP+o+m55zu/3JVD/sxdi2i+W5ZpZZZpk915bAQ7zPV7LP6rXz8/N473vfize96U17ev8HP/hB/OIv/iK+93u/Fy972cvwvve9Dy972cvwP//n/9zT58fGxvC7v/u7+L//9//id37nd/Dbv/3b+Pu//3t89rOfxdjY2L7avl973kHnW9/6VvzwD/8wXve6113yvd1uF1tbW6kXkHYavJECkAuVwaFDq9Vr3ah6oQB/akIBJAABcAQq5n0LZz4kTsdhOfwcsS1OpEDIgLt4Y1Pv5TIFwVVH1Emg8+bXqkCljHhjU6qKkvUyZx56hQLijc108ZEZkREjjm0OYXlUnQ+vPKpAj8/D9sStNhbOfEiuZ4CqAmzjABF0EFgQ/NCJUelaXwBt3G4LEGq24I3XpL+/+WSq4mRkZKlztVvlGYxzSmBAICDS2VVxHo1jG61vmDywgjrsGkQw40Yn0WX91Bmms2vGknOCTBR/ZkCAwErbyeNlHODK+/IeBGS8PgMV/mhJAWIwMZ4C2y5YS7a7Kr0Mrjqi13PvlXRlnP3JCQFFzlwmOJubukPBNx1+9qtXHhVnHgKeg0PT8n4zlpqTSokj10x/kArOEBwyAMGAi/5s1k507rz2f3z2PMIjh7VtSbeLeGVVnsM4zO64EgTrXASAvEgYvWIxNbeTKALy+RTrFte3MDtyo4ATEyRw+zp1Jq8BLUmzpb+LW23EvZ4EmKhAMKA8XlnTQAevByC95iBOctzrgecjnu6fwuzIjanAiwZTHNbbdehdRvQgbHH1AQV6LrD2RgrwTZ6zX6vKGG5san+5e6POURMM4eeQkzzRudqtAso5j7fN3mPWluYMGvDvj4/JtXs9uz/l87ImzVxDTgInDEYQGHLPYL9yHlzMjuVukGDlyI2YHblRfyYY17XY7WLh/H12fkKCNtouqkPMHnQhkBT3eungkGOn+6ckOGk+r+8xgcZ4ZVXHi2sUuVACTFMTIpGvVRGtb0j7BwNpY6kkKgSTs7xrEa09mMsG7wYGU2zoBar3Xsj4fbq0/ckdubhaVXjIMiCaWWaZHbQ9E6ZzGK90D+g4tDiO0Wg0UoWBLmbvec970G638dKXvhRveMMb8KM/+qN46Utfik6ng/e85z0H0kba8wo6T506hS9/+ct4//vfv6f3v//970etVtPXNddcAwDpCL3zokOiQJMSqNCJtJtzLL1CAV61ooCLeWHBxLg4sA6AUVDpOq7OPekQkMXwa1VEZ84q80hGJK5vIdmswx8tieOXy0k7WdSo3VaAoc7y+obcfzBQIKPXdYCUP2pZCI2IAwo0lCE1/RJvbFqWyrBvlN0lUSR9YwBY3G4rQ+gbhz/pdoHOtpVi9gfqnCo76Cw4OqQEMnTYyaz5oyV1Nhi951gqUCQQdMaBAEEZLTKhvK+ZE8jn1aF02a9gajIFQth3BFP+1IS8jDOuTDGddqfa6a5su8kbXmo+alkKw0r6lTLic8sCTMIQfqWcYh+TbcOw8brG4XTBv8tKz8/cJfd2QZUDzOP6lj67yzK6klqvZI6tCUMtVuICVLYtrm9Z9s8EVMgSuowLx94FmCpHvu5FCK57UZppozFQ0OnAN7ls7Ifo3Hn4pVL6HmYeuay3MkWO070bo+hXyggqFQW+7J/YnJ3rss5uAIP7hTdSsGDXBC9cB9kFugpggwDB1GQKzBGgHoT5pZJKz2kEbJRbM9DEgI7OcY5puw30ZR/SPZjPxPU/xORqUS/Tr4urD8CvlOXzvo/o3HndW9jfSaeTUnu4/cbAikr8TZ+7gYW9gBO3r/l9oEDHyIF53iUDEbPlm1PKk+G9Zlhe6wUB/HxeAWASRRcEU3pMCeeX2ae8akXvz7mlwTHunc6eND9zV0q54fb9Xo1tvBiA57X38t5dP+8E49w+YT/sqRBUZpllltmzbHHiXdYLAK655poUZtkr1tmv/fqv/zparRZ+4id+Yk/vv+eee9AcrsIPoN1u45577nm2m5ey5w10Pvnkk3j729+O//bf/htGRvZ2UPW73vUu1Ot1fT355JMAgHirIRHyfF5lgYx2U1ZIWWDcaotzX99C0h8gpkwR4rx6+Tzip562hUYAC0KLRb120pUqpW4up5fPY3H1AWWUkM8LC7PdRTA1qcAScJxdIwkj25Zsd+X+lF6NjlqZJGW8oyVpO4/68H0FxDQ+H2Ckc6ViilkiYxXXt1Qmpu0KAmk3QZxhrgg2/FIJiBMrZy2V4JVKpjBLYCWahw9Zp904r+y3JIq0YFNweAZ+pSyOOOWq/QHidhtztVsFXPdtsRPXPDJglEcCAqJX1+FXKuqMLtYfFpBpwBfPJ/RLJfu7ZgvR6po61F6hIGMZBOIc16pItpryarbEUe0PpAiVYTgRJ7ZtZk6wsAedc69UVPaHY8WCSoBhQfN5KbpiPsd5RoeejBGZSL8iZ2cmdPYNCIvbbWXpAMA/ekTeGwRyzf7ABhScwIwFyT3bTlMFFn25vs45J59NJcrNll6XwRE69gxEJNsC5JSJf+IpxE88BS+ft7nHBMLtjvRtnOjxEABSUkK3CFjSbAkQ5dzuyxxWlo4BDPM51yFPuj1EjUYKkPqViuwNjmJiafuT8AoFZaekiJmMSdxuy/vbbQtoGFgx92fwC4MBlrY/KWwi33OZbNReLcUqm7Zrv5h2S46szOe417Nzuz/A6f4p2QcMs6wssXluvU9/oPsQP7tYf1iuFYYCwNodCcI1mgqo2KfKiJJRrlbtsVP5POaPvFXmn5Hjuoy3AtWLmAuSOJ6uagIAvPGaBoxmyzfLM0WRrO1qBaDSYui6w/JaVYuwYN0l2nS6f0rXtF+pyHdHr6cS7qTblUCK7wNxgvjcciroiv7AfheZ/gKggcz9mMvSD7eVx7+4f99vwETn25A9G2zmQUvVM8sssyvXIviX9QIE57iY5V3vetez3r5PfepTePe7343f+q3fwqFDh/b0mSRJ4Hk7JcB/+Zd/uWe29HLteQOdf/7nf47l5WX803/6TxGGIcIwxBe+8AXce++9CMMQ0S7OQqFQQLVaTb0AAGGAaHVNK7/SMQwmxsX5NnlC/viYeb84nGDuWqWsjkPS65njOwZSSKRvJVPxxqbmDAGAlwsVeCVRhKTdxvzMXXqfZKshL7fAS7sj9zJAjeZPT8kxLszVoRPd6SjgcgEn2xm323JNA1i9fB5ePi8OVC4UMF4oCGDwfcCwmHR6/aNHFIB4BmgmvR6SlnEeHSmyOqYAkl5P2LpcDt5oCR4Z2V5PwP/UBJItnpEZqNRUAIRIv5JeT37fMg4nc+oMk0VGSUCZZXKVmTbj5Y0I8MZgYEHWaAlxowHPFIeaHblR+rxQMA5caEEK2XATRPDyeZsXbNo5O3Kj9LuphOyVSpibuB1eqYj43LKMYaUibKBpR1zfQtxoSPVOylBHjGTaOK2AAN9odU3ea1hKMG/OdRBN7ioDCAqOSyWZi70eFtcfTOVyeoWCjOt2V0B4HMGrlmVuEyBuNSz4KpUUDCmD7OTpAcIWJ72eBZF0oA1gYWAh3tjUYEQwNakAgSyPypIddggA4oZUDSYDpnOu3UZictHYf8yB9SusWhylQA/bt1h/WKTLZj6yjQCwWH9Ycgp5ryhSQJUqNNTr2Vxgp9KmX6nIPsIK2gZUxI1GigHajbEJDs8AAOYmbrcKjKH7HoRpkZn6lo6ZSlujCH6lIvL3dlvk2JUK4Mqd3WuVSpLLPiW5j0mvJ+sgDOGN1eCN1eBXKgp0Z0du1HHkfGaQITV3zTgySLXUfFSKC7Xbdo1QoWECEdyLaX6ppPPnQgCGAQTaYv1hyUs0ewY6clYs9wYvF+q+wOAV55QLnIZlolzbysSadl4UWEWR3A8S9IlbbbtPjo/BHt9k2Ez2Gf9v5hLHBUAKiO5mwyzlhQAnjewvP3e5snBvSA3Da+9m+wGj+2V2M8sss8xoz4TpHMYrhWc5mPxbv/Vb+Jmf+Rn89//+3/eUojg+Po6JiQl4noeXv/zlmJiY0FetVsPrX//6PbOll2vhpd9yMPZDP/RD+Ou//uvU72655RZ8x3d8B/7dv/t3CPYZndRiL622OueAAMPYnEcTr6wKy2fykwBoRB2ALRDjfCknbQtWPZNH6Y+PIQaU2YtbbQFZG3UBJq22yDQdRkaODQiVQQMMCAXgFfJIthoiZaTDHARA11TjhMnDm5KKpYmJdGO0JECCDjwA30gg569+mzCg3Z5lIRpN6Rc6dFEEtFpWauzkSKaOjSFIBxTgLJ6/z56jaP6WyoPq9SzTVxDWjkVDtK9ZhKNUVIfWr1UFgJLBzNvzU3n/uNGQ5wawVH9YnnfmLnEQDaCN61sIJsaF6TJ9zn4LpiYtg0fw0u3Co3yv11OnjnPFN+OpgQvTnwSvCnxNfwUG0HpRBK+QF0a924XngB0e44MwRDBzCAtP3SvggyyUAarKQhsghDjWORpMjIsscXUNXhBgrnYrFusPmwq4AfzxmrC+BrwKAyznMPJs1cQBWdpHBgTErbZlaIyzH7ekjwkivdESokYDfhgq0EoM+OecUpBnZMNJuyNzn+uAxwMBMg5BoGAdSDPliZFRglJ4QMEHHOeaz+ePmgBBIS/AzoBHQJxaFj3ZUeDKyGKDiXEpvMNgR6mIaHXNSsbNNWEAMPM3/UpFx3h25EZ7L5NrB0ieMyCANul29ZqXcvSfqRFkxhubEoQgmDfP48Ew7oUC/JGCzrek2wWM1BQAYMATo5d+rQrkclKJdLSE+Omz8jkDZBmo09/VqhqIkwuYwFilbOaOnBfqlYqqAAD3LsACdbMGOTe5z3B/AC4MYIbBH9dQEsm5wwwyAbDr5fAhxOeW4Zm1lPR6uiftZqf7p/B6/82pgMLr/TdfdIwof47rW7L/OEBdUid69lzjdscGTcdqiFfXdCzc/NDZkRsvCcKG510SRZc8eoaBncsFnp7Ze4avfzGGNbMryy52tm1mmWWWtk996lO49dZb8alPfQo//MM/vKfPfPCDH0SSJLj11ltxzz33oFar6d/y+Txe/OIX4zWvec1BNRnA8wg6K5UKXvWqV6V+Nzo6isnJyR2/v5R5+Ty8OLCSJzo2uRzQ7yMw1Zhic2yHMlwGFPHAb5WrBgG8sRrip89aMLux6RS0sV/acaMh0tlzyxrN9/J5ZXkAA9QKeWAgjpcyfAZw8b0EW8wH9KcmASO5DGYOAd0eos1Nid6b9vrXXIVkeVXvRQkkwaAXBEiCAPA8OTqlVFJACFiQziM8yPgB0HZ6vg8U8kg26gIguz0BeeYedIZcWZqXz+t9FOSbc0vdwjsAxIGvVKTel2+OcSjkBcAYB1PHLIoQjI1pIIH5UcpSUbrZ68nxGoWCAEoIIyVzILJtNs4OWV0NOIShcXjlvjxihRJYglUGHjhHvFIJ/sQ44uUVKTzVkrMJPSNZ9sfHEJ1fVjBN4Ituz+bYOU43BlYCGZN9jhNpy8BKF11poTrngBS+MXMFQSBnDTIYwjli2uGXikh6PQQO2NacQzOPASiIh2GIZkduRDhzSJ7fea5gfAzR6pqCfMDkRg4GiHs9AY6hBSHK6LTbMlaNhrI5nmkv5y9g8nO3u6CgORWwMfOP0ktdGwZM8hrDhVCSKMKSAe1ePg8f0IJEBLzJVkMDIl4+r/sK5/ds+WbZB0xeJo+RmC3fDCSJglkdtyjC6e1PYnb0BBIjtWVRmwNzwoJAgQoGhl2PY5mfq2vwKhUJegWBAO58XtauWWeL9YcxO3oCABRc+5WKDVJ0u4jNswJAYNQjXJOIIvjFopzNWa3onIy3GnqGbbS6huDIYSRksw2YSswewP1lsfmogJwgkGs6Qam9GHNu3QAEx8yVwiZ9Z+5sNSVX3+xdUu04zbBTeurKa3lMyun+KWGPgR05jIAFbWTV49U1c5xXJHuf71vGF3L0TNJowq+UEZ1f1rFQmbD53ovNHrEf9o/PcTEbDpTM1W4F9lkhl59bdAIFbmGhDJBc2ZaNb2YvRIvhI96nKHS/7282m/ja176mPz/++OP4yle+gomJCVx77bV417vehTNnzuDkyZMABHCeOHECv/Ebv4Hv//7vx7lz5wAAxWIxBSSH7eab5fvtuuuuww/8wA8gDJ97CPi8V699Voz5gYOBRKL7A3FAuwI8kp5EopEkNqcmn5cvbeNIUTYrhVPyQK8n0WWCKCMT9CYnpNBPkojM0TBEjDLD+TLnIeKecfDiRkOlpdH6hrS5WLSyoiSR9wwG0lZ+ptu1R2QAek4cggDxk2fks4YZ8ytlYZIGg5QckZJRGOeNoMXLS/viTkf6IggQbW5KOxPJ20zaHZHKBoE6qDHPqzOOjxZr4ovsa7ttQFRF+ozSYgf4B1OTO2SR8camOLNdkytJFq7bFTA8PiavYhHBmOTzcqy0Dw07xz7TypYGzLKgk8pDo0jbqP/nOO5yrh3bgyiy+azdLuLlFal+em5Z7mna5Y+PiWNoisz4lYq8GAgoyHmkHO94q4Foc1OL/viHplWCG7fb8E1REc4dBejm2SgTTXrCavom/ywxkkbACU7k84i3GjrvKM31i0XNEQUBsplP8eoa5mq3wp+aFKBC2Xk+b+SkBmi2O4btbstYlUoIZw4586Eoz2zy/wgsBcx0dAzjRgOL6w8KUxQEkt9p1opKmqlqaDSk7UGAhbMfSRVR8quSn6k/m/FgfxzL3WBzkwk0u12rKoBU50x6PSAM9D72nN5EgiiVsoBXA86Xmo9iqXUSS62TClipBjiWu0GvrVLwfao99mVRpHtI0miq3Drealiw5cgdCQQpfWd73SI1lDkngwGCsTE7z00OOO+rwaooErbVpCD4xaI9uqrRFMDZbEnfJgmSRjMVVOL+MX/krRYYmhSFxfUHsbj+oDLJFzOVtAOpObG0/Ul5jkpZ56A3OW7fGyc6Jyin1mCYAeTaV455+TxmR25E1GjskOC6x44A0D09FQg8NC3jZFQfcaOhfYNcLiWxZTqBq1zZr9x0duTG1FxMnSU61I98v6pe9mj6fLvkdXpBkAGSzDLL7HmxKPEu67Uf+9KXvoTrr78e119/PQDgne98J66//nr80i/9EgDg7NmzeOKJJ/T9H/vYxzAYDPDWt74VR44c0dfb3/72Pd3vB3/wB58XwAk8j0znbvZHf/RHl/9hsmEwYAA7i3F4hYJKylwAB6cIjzJZMGDDMF4A4FXKcrRJrwcvDKWAUbWCeHXNFpMwjrkrzVRn3DgJBGhxoyEyYubTBQFQrdj7BYEAYgMyvSAQ1rYggFqcXpt/R6DlWmIi3F6pqKypyBKF9WLUm2fGeWEoTjTMOYlhoCxE0m4Dbh6qeZ7EceZZDZhsg8pEnag3JZx+qYTF9QcxP32n9C3E2Uy63ZST5Do86gg7zJlfrcAL7VE4zClTBjWKlFH2SyWTdxkhNnJGMsIKqCploN3R/FQF2g5AZRv9qrAVygwaRzicOSSOoXFGAQCGhU5MYaP56TuF0TXzKGl39P8cN1cKmpjD0wnemMvlHz2MZG3DKYxjihAxl7hUsqAyimQebjWE1Zw5BC+KrDyP88g1A/gZTPBKJaA4YiWZ7Y7eg0Zn3y+VRILIIkShMKvsGw1KTE2mZO9+qSTz3Dy/l8/D63alANNgIP0+iDRQ4pWKSNodLK4/aFkiw6bOjp7QwFTS66XXJYMagLJ5yn6bvGspsGMYe9MHcxO3p6W+gLJrXKvxqrDMrjyREl1dC5Qdc90advSgj0shk6wAsN2x42f6NKU0KBWlwJKRFbv9p4CsVNK5ANhzbXfct92WI06aLblvGFr5ahgAPbO+6luybrcakve8ualBK8q9kyiC183r/hmbIJjKlofn8gUsiSJhmrlfOYWRfFPV3AMQP33OjpUTOFtcfzB1vaXWyR1MdeKMMfe3YTBFVnE4xSFpt2UcKmXEyyu6Z3lG7h9vbEpQyxTQQijfJVjfRNxoKIglI3w5LORu544ClhVWCwKcHjr65FK2uP4g5l/x7xE/9fSF++0KsExCmllm31rm5mju5zP7sde+9rVIkuSCf//4xz+e+vkZYaXn2a4IplNBWj4Przhii6LQiSyOyO+7XS3HnziRWG9iDF6tmiq+QFmom+c1DCwASF7l1KTJk+s6eY22KuZi/WF1RP2SFBxhNUwA8IpSvTfa3HSAgUgdXZaMRxUsPHUvUBxRZo3tEbBVSr9MXh26LujrqETYjUgrq2vy7vheLTQyNWlZVRZKMayDMg/mmv5YTX5v2KdEC190LVsxGIgTVJaiMwRbZA+SblcdUjevynMiNF4YCkNsxobMMAZDzClBTrcr93KZRYchS3o9YX4CkRCyUqQ+E1lmc2+2220bJaxaEInSRGecmFulwJTnsQ4ima+UIFYqOn+9UnFXVj1+8kxqPjOPl8+nrJJKsBvKgETnl6UNbGuhkAIKrmTVKxTgjxnpRs/mCpOtI6vL4jMIAnjFEZFEmtw/Bnx8534ywX1lATmf4o1N7e9oc1Pv7RUKUjiM8zeKFNS6x4B4+byOK9n1YGwM/pEZO98Ni3K6f0rnHOe1OycwiHQ9KTMW2IJPwtz78rcw1AJPZJFdI1uvbG7XnslK6a1nmLaDMg0KDYEPrziizK7unybwwj7lM+neVKko6FusPyzX5Bx27se+9MJQgBMAL59TFQKAtER7MNB1pDmhhQKizc1U3jP3A7dSNm34+fQ5d2GRl1oiXVpcf1D/Ddjvl7jRSK8NfqdE5pzP0RPKcDKHdxh4+cWiFGjq9TS4R3PluHxPMhhoWgYA2cdN26PNTVtIiOqMUhEIJC8WzhFCgLCvS9uflDW2DyC3tP3J1FwcZjpP90+ljn9JjIT4UvcYZk+Tc8tIzDmmFzr/81vdMsCZWWbfWpYkPuJ9vpLkioBWB2IvKKbzsi3wbQ5hcUQYuHYbHvNYmOdYkPMj/UpZ8idZbXNlTYGfOjMTY/ADmwvmT05oJJ3X9ytloN5QJ94zR6vIZxrqJM9P3ymOFp1ZE6mWvL1YXsaULR0M4NWqSJZX5DOGTV1qncT84bcgMRUVCSwERJT198mAz9lVR80vFk3Rnlj7KtmsS85Qr4dwUopVuMwUK9p6hYLkFVUq4lz3+spUqZPYaKaZBUo31XEdqKOrcsVCHvHyquQrdXt6NqKbw0mgqgCC8lFzj6TXTwHDuNE0bexZBjyKJB+NrFIQwMvnbFsJoAx4CvJ5zI6eMG3Pwcub4ijG+VP20jjg0eamdcTNHAoI1itl6S+HbdX3GSdcc4EJYMjMGacWMMEJIwGOzdmFnvNMKnWsVcSZ72zrZ5NuV/MofTOmKSe6awumxJSdwwJU/beRis5f/TYdXwI/BRmFgrCnhgXjHNJ+DwIgii2T1u3Cy+dsjiDBmrPE/UoZ0dq6/t3L54FCXo9/idttHUNXgumP1eRepo0oFLD4+P0ytoUCFtcftCykmceBmUOax9nrwRti32MjXWfRIFEPFJFs1nX8dI4A9n1AiskkGxsZcLTUOikBiXb7YIsJmeAH1yvXWGLk28ramj7l/JYfZCzjXg+BUTMwsDE7ekKPIWI/AUix2oCVU8IJDCadbVm/m3XglS+F9/jT0v9m3WnOvhvscAr8AA7gvARQ2Q0QHcvdYKXOxlLKAq5X7k1OlWt/alKZbZel3gEyokgl127wbDfTYJXzM4MVwdgYUClLXwE7vue8METS2dbvNbKRO1jJPdgwwNwNOLmBFbbzUuaOAeXmMPPlSmU6M8vsubQsH/qZWwQPEfbHXO73/d9OdmXB8SRBvFnX/CDK5JhXlLTbxrnuWOfdsHBJry/OdnFEvjTrxmHM5+EfmlLwQ4aGziwgDtRi/WEreTLOUNxo2qql+bx8xgAvb8iB9YojCCYn4JVHJX+p11cpo+a5RBHmr32HOBdJIpFtApHBIPX7pNeT4jemuA/vt3Duo+IIJgkWzn1U89bCI4cFBJRHhW0ojsh1KN3tdkUS1+1K2+A4qsYRYv8IE9cXUGM+Ezca1vlnQSPDOvqVsml/Q8FgRMYySeeCse8IBL1AcumCsTED4vo2qJDPp9impNtVJiduNPQ5yEB75dGUfJbsbbS2LmM9JPNlTiIBtH42ihBMTui1tUiKw5SrFDCfT+VjArDFbgzLTocs6fW1v9UJr1W1MFPcaMi/1zbknkmi/a8WBEA+J3PC3Dsx1T69UkkLjpBhTMw6UcYuCDA/faew0r2eBa4EfiY44Eor405HmR23jySntWzGN1ZlgOSVtm3+aT6vzrVfLMpzMRADm5PnVyrwJydsoaog0M/pmAUB5iZu3yH7BATw+WbdplQGxaJl1TgegUjdmf+JJEGy1dB+YN706f4pzeFLjHSWbdacUV7bBDr0bwfsbLtKCwaFvHweweSECQjkUu8lC65qBTN/9D0TYwIaTd4616fmj1OqbAB50u3anE2y5Y2mjONKXec/2WD2G+eFrkcnx93L52W8eLTRHgEW36f5tubnZDCAf/VRzQf3yqPyCgJla5NuV8aedpF7xqa2wNL2J3fkPdJBTDGJZh+IGw05D9h8f/CoLH9yQgKiphie9seW7OWubPpyAxiar7+L7SqhvZx5y+DiBe7/bJzXmVlm3252oSBRZns3OR58v0emPN+tfuHaFQM69dwz46To0QhuBNbJRfLycnyCVyqpgwhAv+SFYYrFmaw3tNgGGRqRt0r3efkc5qq3GLDgI263sbj+oDrRnpGHpRhNOtswsq5eX5wKwzwm3a5lMw2I9ScnEBu2JzH5PcjlrLTWsA3KCJCpIXg1uWjsg7mJ2xW8xJt1kY+SKSWT2etJ+5x8Rzp0LIKjBXGGClYEh2cQr62rsx+7TqApZqPVYRtNdUqTdkcLkSAM9R5k5wAgWltHtLau53Im3a60v9027HGk7IoL2pPOtjJlBIwiU+ti4cyH4JljaLxKWaseB2NjlkU10tGUE+Ye0WOK7zDY4J5BqYCcxZCG5HV07Dmf1fF1AYjjbPvFouTFufPeXROGkSKY0jb3+paJcI/DCQJhvZ28Y1UQ5HNyDVbJzecERObzzjrYKSP1x2oIDk3Ls1IGaT4PwB6XYYAwHADMNaQMrjO/MVJIFR9RoNbZtuvbXEulmwxksE9Nxc1gbAzB2Bjmqrek2HQAqlbgnqLHwGgQqmKYbJuH7QYaUmMBqAwz5cgTMAVOJej/P3v/HmtJdl6H4Wvvqjrv1330a4Yv0fopL4cWbSKA8pIlZaaHig0jCpKhSZA0CckQHUSGX3HkIE7sRJGQOEFs5xcRCiVKZERwQNMRHNicnjEi0VEkx2BsRrJjRZZFieRMT3ffx3k/qk7t/ftjfd9XdW53z3QPp0f8tc8GLqbn3nPPqdq1q+5e31rfWmLG9KiGHw3NCVsLCHUWz/f7xizfWHyqklXXzkWHa7dYNLp5iyC/LOX5S3bfddpc+7pu+30kV6/wfpW1EE7Pdop6WtAJqxXQ61Trsr4Opeix8/5lucOyKdB6NdCi7P1FQyctBMWzsR1XefsOytt3dpQsvt+jwZAVh/J7Fgxc7Rpf737I1lidjahvEvW+c5JdGk7PTOFghkur9c7fCaAq8gB8Xuvv6DndyxTt1cbFc6mDYlujZYlvVBJ70YPhfp9/r7EHpfuxH/vxKMbDSmv165t5rNdr/Df/zX+D7/3e78V73vMe/N7f+3t3vh7l+OaemQccvt0xKaVV4i9IOXUTX++7vGgYAQFf9Z4zFEWNbQu20Yyn51ZxD7N5ZdZTBhpZDD5ibJxFZuiQ7wEAWk289+ofI5i5fVL1Sgp74gY9Y38gzJzv97i5Wa4Q54sKHJR0pvWjofWz2UZIexKFdTWmYbMxcAWALNK1K3Ddjr32vZd+kD9SM5gL7IOBBZWCqsQ2BG6OhJkyeeXhqNpIqCsluHlVtkzfT/stw3JpIEdBfCxL+MPKTVJZLWMQBXT40ZD/PTrc6d/UoZvb690PIZydkwE/PbM5+sKdj3N95cXOps1kwNsql5QS1baxpAqUsN0CrYp1du32TtSMSWWlV5dFg3bFNIpDqzGOwj7qNbix+NQue74ShqMMdJOUHj5lnXZYImPK6a5sbqy1jWpcroztAdjnZjLgLKsAU4x2nWj2srJ7Ki6XXFfCwitjpu9fByhxucQzg49U9267Dddu29q3+zJG+54yveV4bHObPHnN4mcU5Ot1UnBgDqB5XvXSSbFF1QLKuut9pNdfnw0Wx6PzJevz6ex91h9o8yr3UCU1bVfrqQYIHqWZUBhPjP234263jUHTogLKEu+9+seAbmUaZk7Ycuxxudo9dgXUdam9PDutr/rOScVCl8EKPexP9Ah3TuAOWQwov/4y1QxPXquUIwldg18oPisu39Wzyf6bJDaHrwaCYlmaRPqF4rNwsk5uLD5lcSjKGLpGg0WUft+OPy5XVBfIulHgqOBMAZHOkcl0k3ubCdXl3mE8AdZc//7aFaSXL/H6LZc7BUB4j7BaschzdFi1AYwntu6vdz/Ee2+1WxC5OC6CR+3Z1HE/9kTP+cb6Zx+epU8SbE9P7wmI9+61+7Ef+7Efb9z46Ec/iv/6v/6v8fa3vx1/4A/8AfyhP/SHdr4e5XgsejpjLiyXbFgAyKbDm0sjIOxirefOtZrwjczMhRRIWc8OxJQlz02yeWPxKfYtjYbW22TSzWYT6PeYHxjCDttRRa+0EU7PBACt2McoILfunGth4+sNWZ2kDSyq9wvzOXyvx56fTrvKkZTPdM0GknQk/Z4Ny3HT4dptuEtHCC/dZE9skti54vQcUUBLnf3QDaayW67ZJJDSHqtms9rMLpdQkxDOowCMXhfxbGzHqqAQs7mBuvoc3JD8wh3jlvHE3HaDZAiqdPF66wNWeHCdNjzIEOgm0TWYq5jI/Nc3f4AAhhVlazqnzxx8vwAGD6e3TOLhLx0hTmbGJMTtlht3MSLS89Z+RH8GM/Qpx2NeP7l2ytD4S8fVMUkBI8x53RI5Jn90KHI0MvlhPsfT2fuseKDnrcDL9/tm1uLFDMcN+xZu71pNXpMaS+PrMTIAnp9+EoBEQQjAjLJ5jWJYoj1oEYDvCNurrHua8hgkKsj6vgS416WGYT636+3FNVV/vzw9Eyl14HW6ehnhzmlNFjo3OSyShDL5stwB0NqfqPmkVkQQgyctFDwz+IhdQ2XROCehYl1X1RryvR7vm3abrrtyDrqJD6uV9XbWCxdalNLrpa95lPJaXecQxt/3+yjHYyRHh5zjlIUhW8/qFi3XzAo7cr28FFF0PUa9pzIy2uXpLfh2G1+483E8M/gI/NXL1bVpZFzPej82G1zj600V69Rpo3zpZvXMk+fPe5/8DwEA/tIRwZmyk+Vuj+drzaUa4WjPo2u3q6xOkctbT7f+vZD7UH+//hl1JlD/7RtVpNFFRrb+e/o7poZIPNzBETCd7f6NkBYCAMyjfutbEDW6KPF8nkpBzVjmXg/Pn3/iVZnBe/VTviboS5KdY7di20OMpNe7NyC+R8HgogvsHpTux37sx6MYAQ7hIXs0H/b1b/b4m3/zb+Jv/a2/hX/tX/vX3vTPfiyYTpVnAqw6u36PRjIb6Tm7dAx/6bgybqn3zwHGFsbtlnKk2UzyAdn7SZfCHP7wgBum0bDKRXvrExWo22yAUBJwzuYIc37VnUbjJueGaza346HL64rHHLmh9UeHiDGQEZovEF65zZ8JK+R7Pbhel463p2e2abXPrLmsxu2WAKrfg79yuTomCXIvT88qCWNDskXV/EWZsbIUCWybG+3lCjEGMk6yuS7HY/gnrlXsogDqmOdwksUZVytzYTV30/GE7G8M1WasQdnwM8d/tJq7uly6xhTppp/scsNyKBX0+EvHBBribOzbBHpxtYIX12LfI7Md5nOTG7tGg8cBlWNWrHmYzblpFhMr9oNWm0p/6RhxtUJYkelLrl6x+Y3bLTfpq9VOv69rtxEnUwMrMQZesx6/wmzO1yxXVmDROVETovpGVk1EdF6BSmJqRi/jCc+jkQGNrHovYf+UtX1m8JEqr0+VABc2lsY2yvc0M1ZBSqxtfrWowevsbe7DfM7eZplTAPb7SDznQRhXf3RI6Xuewx0fmnmWzbPcDyozjmVZFWtqx27uy7quUEl5LyoU7L5UwF1WBklIPK+PAE9lRF8oPrvTs6mFjyCvU+ZLGSI1ELoov34jh+u0d5+DtcKTrlErqiWVfF+lqOZWnWUG8vVaJ9/6Lbyfy5Ly7/nCepzf++R/yPnuVlmb5sCtz1r9bCscMvvTDwfyO94igZQhj2djYaUbu73192ET7zXqYEnXqut1eX7aWzybsdCkMmItFmhxRSS6ygbW2epY0nzJtVuvKvs1kNrr2d+GqMWaJkG9FW1qz0Eoe6/9/Ynndda1LIWGhy1oXG994IFZdzUDe5D3r99bMc/pIn8BYCr4vjj2IHM/9mM/3ozxZuR0vtnjySefRP9CO9abNR4L0Kkbb93k1zfVrtdFnEz5pTJXlVrWLOVtYyobewAGilTWGucLuOGgAkmbDVxeyfzwjifZTyjg1bfbZmjkDkcivy2B1XpHHuqvXraez+dPfoLHNpvDDaoYF390SHay0yY46HURVyskV6/w+4cjgpfhgEBKpHGukVG+dumI/aCLBTfdws5CZGoAbJMZN7mBLd/rGgNXuai24a5ckomLdEsd9pH8f94JbDbwhwecI7Hud0kiDqVkVerusq7ZhBcDn4v9ey5JxIGxXbEhF5lJ2cyH1aoCoQKKzZG4xhyqa68NubYQoKwAz8v8ul7XNnmukZEZ7PPnytL5XpfvIwZOKEsysO22XYs4X9g5OAHUTn4vrlZmXgPAXgeQpTXTJDWNKkvO33xhwMF6tnpd24hqr5euZQAEw3UwJfdDrGfV1jZ5nEcWCfyl44rlFEbd67UB8Pz5JyrZsAAYZNmuo6ich67fOF9UDpwKeMcThNUK5Su3qg21AsQaE6JFId9uA2LkouZHYT6362yS6LIkaBUgqvemvV+9KFVb+/We6TCeAHlh0lNXO764WlsRAoAx3mYipDEcSc0cqtZ/98zxH7X1/Kh71FTyDGH1dkyydE0J6Dejo14Xrt1iIUjPeTI1QGMy6lfucO0fHlTrVSXxom7Ay7fkXDMyc0P+AXRJwntCCmU8gGjrU2OLAMg9z+KiOxwZCH7+/BO7hYSHGLYuet1d4x1xyNZnsBb/4D2fzXXvgCRhfMoFcKfvbX3zF4DZxd5Ofe6YTF+k6iyUZSzwadFyu+U8CDNqLsBSHKr3hz9IT2c9V7ZugHVRMmznLD3INxafwgvFZ62I91pzvTPqzysZasT1ase4H/uxH/vxqMbj2NP53/63/y3+7J/9s/jt3/7tN/2zv7ln5gGH7/fx/Pkn+O+29FW229xc1v6QxbKEOz6yPk8ABIgXNg3GtiUe5dl5xWaUEmWim9ksA4oCYb4ABn24Vc7Qbd2Q6OfmOdkk+d0wX8AP+tzUTqY0qhgw5uKZg+/nBixJECWMXl0Iw9n5XQ5/uhkrX7q5U+EPd07IACuY0Cw3AS6u1+U8CWBw7RazSg9HlFvqxmi1JmN0dg4/HADe83fUrbFBp984mSHePiGwzRQUryk1ThKT9ur7KSgO4wnCfIE4m6NUN8g8R5hMK9nqamXgU9nKi5tKZQWQJIxfSRKCMoli0A2Oboa8bKhdt8PrKWYm9bXCiQ1Ijo/s2LV/NYiBT1yuqn/nBedIGMMwF8mwflaSsKexDhpls+wajSpPVtesyI3VLCSu1jxOmQ8vBiOu3eZ5JAncYECmPc8NOOucunaL8laAa3A4sHPX9axzanJB7Utecf3VwYjOoa5B7WPWnlwFs3a/6LWYL2TtcUOtx2HsVK8L3+shOTyo+n3FMdT1upwrWadoyDF7ygnDZLrbv1mTMvoD/o7O2/PTTxrw2ulfFeD5hVs/ztfqa7zfAeu8ZpKdqnOpRYzE70gvgYoFsrUggM8AqDi5Goh7jd67b2Q4Laglntd2POF9U2Nud6SZcj/FvODaqxUXAMAdX3BQxW6BAHov5jmS46PqubBa00To5m2+V7cL95Zr1foogz3D4nwhhYZQgae84PqczAhy85zgXXqAgVfvja2DK6ByKkZe8PfEMVqLelaY1CLQdgt4v/M3os5cX+x/1KLU9dYHXhWY2XGpQkSeGf7tb+XfHYAFxEGffzuyrPYspxrCDweIt+7sFEZeKD77UGBcZcf1tXuvnk691ta/+gBr9yKryaLNgxsQPawh0n7sx37sx8OOgId1rn14Oe6bPd7znvdgvV7jne98J/r9Pg4PD3e+HuV4LHo6kSR47zv+BDdvVy4RrJWBG8a8MFbOTWeI52PbjNgfX90oAtxkJ5IR2G4habWqCBHvEdc0y3HXrgDFlj00jQbirTuVRMyLm6fKG8uS8r8TghrbxK03BCFFQaCwWnOTIT2VfjjgJn0yteOyiAEI8yrgTpmX8oybdD13fzBCXK/5We2WsaxYrWoMHje8LssQ7ozh3/ok3JqukjEvkBweIMwXCJMpXz/oA0UB1yKI8W95wqbPJQmwWu2ACpsDZbrKkuC89jtqfBPzvLpucm11LhWw1jdrsSztdfXXKCBwSYKYBEpqRVoZJlMDL7EoBBiJRFDzLeU9Od/eftfA69Ehe1mbTUA2oSbXXK2N1dW5RlLbmOaFMH5t628FUG2w9feEWbFRlnBZhuTwgGtxsTQQHs6lJ3M6NUY3li2Re64r5rgs4S9fQhSwb2xbWXKN1NcTKqbDP3EN8eS0klleucQ1b6BUihSTqX0mQFAfF5KRqRFDwvC6a1eA2yeI9ftPr6tuWuWe0sgaXf/hpZtInrzGPr5eF3G9MSZJmUmftLhWQyBYn844X/Ie11sf2JFA+na7AnoCXuJqzXvofIwwncEP+gjn46pXVcAn8hyu1UI5mRIsCzN2MT7CGFP5HJV3K0ity3ofpes6jbFyY+PDfMHnEOS5lWUI52MkVy7b9fPHR1xz9ftUnnWYzqq+a0ivdbsF1xWX1dp7x8US7sql6s+yFJb88RGfpwNhPQWYumEfcULn2jBf7BZTAECfmwsBpxcKja82Xig+i6f8v2evC/X3BXiMJR1sAcCNhlxPAAtu0ynjSdotFjyw28cJYIfxdFIUK09OATEdqh/LPVlEObd4+RDx5h2uySuXuWYUkC+WNtdm4JRlcN5z7gUIawsCXgV4XuyXVLm33i/3GnVZ8YPKd3ek+bXn/IMOUw7sx37sx348ohFfR09n/CYHnX/4D/9hvPTSS/iv/qv/CleuXIFzb97xPhZMZ1yvDUyFr70EgBsWJ66l5W99FeVvfZUbQZUjKQOY1GIKjg6q91ytudEoCuZ0ajyJVp0XS25yZNMNkYrtyKmEndJNa1yt4I4Oqh6gfg+xKOBkk+oGfSAGSraGA4tDASDsbYub1CRhZbvdZhSK9EBGkQ/S0GQNNxpW2ZCycY+rNSvjnQ7lv4M+N5QHo4qhvHWHGxrvzVhGWab6RjqcnMI/eY2s53SGeHrGOV5vOP/9HjesiYcfiJOwBJZHAcT1TfaNxad4fnLexpApSEkSJFcuV6xeUrn26nsr2xDmc2OqlbkhiF/ZfAIVcDeppMhB9XVuNEAUVtDATxnE/bRgxI2aV5UBYTKlPFo20QDs2JEX/H0Baa7bMebStZoVG6cbL9mM6zl5BVd5wXUz6HPdtKrihjkct9tcI90Of083vb0uokorAWFsquiToD2lMkplRl++acfE/3/FQL71zin7pAAbVTapHw64JoS9cu0Wyt/8rarwU9+AZpmxGOYCXGNx/NEhfK8n7L7KjDPrEdX3DJMpiwo673ItkCTG0to51SS0dhx6rgK2/aDPohMqxtzW24TgIzk8qAyGtHCQVO6b2peo0vs4X1TKAnm23Fh8Cs9PP/lI+9ZMcizzZPOv2Zx1GaYw3lR1sBf9xuJTPGcpwrHYU0Xx2PVbb+Q1gc9cKbjE8zELbesNJeu9LsHboM9nBoDy5BRxOhOTKxbAFBDXQT+SBGjpein4fBPVQL2v8+Kogzuda9dgzmdyeGDryXUl61eKbXGxZC7ndgs3Gtrn6DWtv6+6uQJSXMtzFojkmO4CmLW1YkUkYfLdZAF3eMC1u93yb57Or/4N6naArjiFF1VbiKkSas/z+416IaZuhnVj/bN3nZuOi+/5sP3I2s8cai0v9c/YS2n34/WO/drZj29kPHxGJ7++mccv/dIv4XOf+xz+7J/9s/gjf+SP4MMf/vDO16McjwfoXCwrdqnXJQM3pdzTD/pIDg/IDimDkOeU8AmQMFOGmnTLNtfzBeJ4ivD1l1nhlkp1XK8BiUgBYJsif3TITcVoUG1m19yY+7c8gXj7hCCn1bRMyfDSTf7+ek3r/fVGQG5aARo5FgOiCnLFRdIAV+LNFAQhEEzqSDw3J90Of6/NeVLWFag2GSiDfQ95wc2M9rbKphBJQtdZGfxdD9dqcv5ncwKNLCNIqoG7MJ8TEJSl9fg9c/D9cC1hQtTAJ88rgFiWvNbr9a4kUmR7dSDh220DUJQ0zyv2W4+zy7zOMJmajDmcU+qs8xZPzw3oW69lWcK1WnaMyCmxth4sgGtE2B8WOoodWa3vdRFOTqFOqAgEYuF8XDHKdYfTmmzPNTL2mE1nZBKnM5PnGnhqtxDXG76ffp66zA76uz2zCgZRgSI1FkoOD8iYy7x5ZaEaDa53ed9Ylpwnybw1maqs0zBfACGYEy+yDMm1q7z2UuxRcx1VAOjnuEZmjFrMcwJ9PVYt6tQAnlcprt4XYPFAwZPKt5XttLlWiWWNSdf38e22rUtdxzHPBfjnVUEmq1jfZFhFq1xvfaD63AuDhYTcWN3r3Q/dk/F6Q4cCFO1BrYMQUXQ4Xe8qtdVCXSOjPFTluPJ+Mc/Joktrgj4P+Bkyb7V7Bs0m2UxtGdD7WY5LZdDs0V2waJdliNMZr31Zci03Mkr7pfgALfDo80qO8eJ8XmQkVaL//PSTfC95n7je7Mil7Vg3G/5dAMGumUPVgOPFwoEaMQF8bt0lU5W1wuPhOo7rNf9ezOaIr9zmZ52e8b6/dAR36ciuG8qS7H+3A3S4ZnlNhGmW58T9mMh6vIv9V+6ri0znxWNXUHpj/bPmdv2gw+7De4Dxi8Wg+x3zfuzHvcbedGo/vpHxOPZ0/vP//D+P1SNs33m18c09Mw84vDgjKisVF0tjjBSQaj+ca7eQvP2tACoZlmtJPIQwQ8gL6xfzvS5NGw5G1s8YF0tufk7PucnJZFOdeAEPbf6s1bT3DvMFAVojs98l81Vlt0Er+QCwYG4nykBw1GrCH4wIVEB2VzeBrsGIAX98ZEycu3zMqrwCH2W2+j0eW5YBaoIj8tE4m1MuWmMz/cGIG2tleI4ODRD4o0MCx8XSgGiU6jtA5rj+/QqMFNaPBQCu0xFp6ZwMAoDtnRMDpAaCAGOVbMiGKDk+2jFGiXlOVlRAkYE/ASm6mVT2OK5W9rownRGwiylPkF425AVBlcTlhPlip+/OwEoIJsHTYzLmVBhqnVstKthabLf5PrJZV1bO97oEqwcjW8u6DvQzlLGy620gtXIiRs7rRefU1k4BQ89fGWjOWWH9qrr2nz//hLHJBpDlM4M6IQsIVVZX58v36HAa12uCjbygPFmugx8OTHLuGll1b6qkEZQ4OpXBC2CP0ldLdl7ky5JXi7LkvSQgO7ly2UDSjfXPmmuuXS+g6k9U9UIjg7t8zDlWtnQ4YEFJr+egb/eb9ixrT5warWjPoPUka5FA5tAYddzd8/aGjsQby1j1I1IJEc7H0uu5piR5NLBf872eydHDdFbJUdVA6/Ydu7d37lMB4yaj7HbM+br+/TidcU47nWqepQDkhwMWs2qKCQjQ12JDlGKMFk0A3BcA1r/3dPY+c6x9ZvCRqh83z/kc7+zKhNUcideLcnbrdUySezJ0ys4+nb2PCpyz83sCYe0FDZMpwW9LlQ/elDW+163msKCSIs7mLEq2mjzOgnm85c1XzBSNsurivmtLgfi9YlN2jJWw2yvrGg37/4tutw8KCrUIePF47Pl1Yehn7EHFfuzHfuzHg48f+7Efw5/6U38Kv/ALv4DT01NMp9Odr0c5HgvQWd6+w43cbG5/WBWcaRi9MmaxLA3w6WYSQMUynY8R8xxfuPn/tU13mC8IpIqKrQIAd/nYNtR+0BfAtbbPRKspGZuJycJcllmPmRv0q89XJi3LyPwIq+QaWbXhVjZJNuLcdFVmQHHYhzqdYjYnSB70q76rvGDup4Bjy9eUPkHXahI0CFOm7Ksyrm7QN5YJ3pOpRcVQodmsmOQkIbjtdsgeChNUr5wrqNf3VDDg2i1mttWknrZJamQGXEzmK4UGGtSU1g+7ffmmOby6XlccgFvc2KoEc74wAwuTPipr1Wxyg6fXIUkQTs84R+uNgWhlN9QkKE5nZi5iVXrdVBdFxXiFIIUGMaPJMmNUwmRK2aFIkaMYEml/sUqQbfMNVCZG3Y6ZJyWHB/KeodrQCYsU84LgYrFkAUJ7iIuCm9csMxZU2VokiRi1tCqGsLaGvfSEGjs1X1Diq72LINOqAAeAgVpjp0qRnqt8UFgxk2aPJwRENaMolQgbWE0SBDGm0n5of0AzGC2E+OFgh+msrpWvFA+oOSlrjI4y5YcHArqz6mGUVHJ96zEFwcvT2fsqBkjWhLLqO3MMbqQfpC/uGxmuIcUvLdYomFETMH12np5Xxj0inVaAlhweGPCku22bxb+ytAJJXK0JjJR5zmuFKn0Ge1+BqlaryhROvAB7WeODfsXK5lynVliszZdGtVwESvcEQM7bfN9Y/yyCPD84Rw0BcIVljvK4EhYGpdjo2i1jp/X6XzTf0WejuiZflKAqWNthG1XdMWaxw0kkEDqyrsZT/izLWBzotqvXZSnc0UFVZHnAXte72NkLoE/Psz63Kie3glcN1D4QKEyS6u/rxd+5z7Hej8Hej/3Yj/14o8bjKK995pln8Mu//Mv4nu/5Hly+fBkHBwc4ODjAaDTCwcHBa7/BNzAeCyMhdRJUJoh9Q42KcdHeOrCSrtIyXRax4GbXj4aWl/nMwffvMEUA+HutJmW4AGVMYiziGg34a1cI9spdA5goLIK+zsnGCkVB8NASCVUZyKIWBTdhIRBsqEFEQedId3QApz9rNY1Zc8W2kiEWBVyzKZEblWwJWVYZ9BRkI7hhz4xF0L40ZRrC7RMzUrLNRJIYK2nvHQLc1cvAnVMyewoYFXBq/yQgUSFrMrISNRFnc8bQtBif4gSkqmlPHTTpxs73ujQS6XaAdzyJ+Otf4eucQ3rlsuX5GXuUZYjCAOgxuiQB0pTr5/KlymlX5M8atM6eWTl+5/g75+OK4YwRLk0Rlkt458jeCfviALjhgDEgziO5dMRon7KES1O4LLV16NotuLJEuHmrOj5wY+cPRozi8N6YezNPSiqXUQC2cQZ2Y1AwpwwSrSbinVOen3y2MvHai2mmMZePKyAAygn181y7jXI6R9LtWEwIAJEjB4mSKYxN1Wvojg4Qlc3v96z4EBdL9kzr65KEcvBuh7EQgz7CyWllIKSmNo0GHXaTpMqCBcyEyNaA9rypO7B+X+XMsjn3l495r7cTA+g6l67RQDw7N3MpZch3nhfO7cglAeCZg+/HC8VnyaYBZBLF6VjNwC72zj2KERcrIEt57mkqz03px3QeEbDew1iW8GkbYTGj4kIMl6ylQNk+jfARBYEaXXHOpJjQbrGAMJ5Y/6YpEOo942XJ6y09o0gSK3r4QZ9gczzh+7RbiGV1j+70o14Y9wRAUVokhOXUnviwWiF5+1ulb1vWYKdjxTo1VbKWAL1PyxJwHjcWn9q9jq5aPwCLdS+GzxmzqCDuqeRZO9ZnDr6f90C/B6w3fBbrWq4XHNdrxJMF/9b1u4h3Tvk6+TsCwJh3327BaevEg6yVYksGtPF+vJB/5lVBpBoO0bTuweVbVpC6wMA+lTyLZNDHDXGnr49XY7D3Yz/2Yz/eiBFeh5HQN7t77c///M//jn324wE60xQIEb7fB9KUm72iANYBftgHongR5gWQptILubL4Elazt7T1F1kq1huEyYzgR6SNaLfI3iUJ4npVVf6LLXPRXrktG8gFkieuAHNunF2zyQ1ZU8yNTs+EcZTK97UrcDEaK+CaVeSKU8leuwU4x830trRzdAcjYLlkMPpsbs6P2OQob74COIara6VbTVewySl3HA0JiKdz+4ywWFYbmhBwY/EpPHP4A3TRFYAWzsec82EfYULwWd4+gWtkuDH/Gbz3HX8CcTrf6TmMaQonGy9lEbFaIxbbyj0rTRHGUwKvZpMOwdozqACukdXkbluE9Qau2MItV3CdDq9pCBVL2mxW8lMBfOUdMuFJv4Pt2TnSwwOg2US4c8pzmi/gm11utBsZ577ZNOlwLEuE1RqJAruyRBDDE9+h8UhcrBCVFU8SbkybTbLn6oYpEsGwWnOtOo/tyRl8o4FQbOGdhzuU3raTM5Qnp0iODsmwCNvq+z0zcakAQQNhOjfQ5judipFqNlG+chte5LUuTRFmAnoSD2xLngNAtn4auGEutnbPGauhRiZy7epRGuwLFDl1s2FrByGgnM7gVbLd76F85TYSdUGWIkGYzBDXG8vLdGlqMk4/Glby8k6HUUCLFXxHlQ0FXy8MW5gvkKQpXLOBcHoO3+viC3c+juvtD9rGXV+n5xfPxtarGqdzhDxH0uuySNBsWB95zHOu4TSV65tzoy/3UCnXKYYIOIenkmc59wDv4/UGcHyOuEaG670PG0B5ZEOBjD7/pIAUlkvLzN0BAKJAYOzOmMe7YIHOuQbCYonk0hGLWGkKLFcI6w2fcwDPN01Rnp3DpRn84QjYbq04Rpl05Ne25HOu2QA2OaIU1fzlS8BsjlKuX5TvRY06SlM+L8rSgNb19gcR8hwvls/ddypcKgym83h++kn7Hd9ucX7SFJBzx5hO4tYvfnxoffgxRHsf64kMlQexFSdq9xFQgabr7Q8CAF4sn8PTjfcb6+6HhzRTarfgLx3xHDWKSNjXeHIG/9YngfUG8dYJ1TLLlUmXdW0hRJTnE8Tt3Y7R952fJMFTybP3nEP9fii21XpJ03v2Lr/aCIslkt4lxOn87p/N7/7efuzHfuzHmzFeD3P5zc50fud3fufv2Gc/FvJaraJrWDhEuuQlcLxuKgGR8qmMTSVO/likl6fniNP5DrNR3qGNfzwVJ0+t4IrMVSWF/viwktplWSU7VXlku2U9T67XJbBpt4HxlJuDgTpihgo8Cpsa8wIQQKW9UCiDsJXcMEKAjckH+30yaiJpU9YhzBdAl+wmDkc8DzH78BIl4g9HcEeHlFMOP1pJR6Xnyw8HVdalOkXK71/vfZibB5GaunaLMuOaMQ66bW5ey6p3UoexIyq/1B7QMqAU4KtSXTWqAQSgr9bcLJZBzD3WKMUUJIZovZFVD1iORE1JxGSDvZrCdh2OdtaYSjFVIvf82f9UsYiJr/VA5ibzVLBcSv9jcjiyjESbD1lPYbVCevWyrM0W17D2bTUyJEeHttYI8CrZnzJFxsbW3t+MloT1U8BZN2oKq1VlFqNDWKvtb3+tkvrqvMt1970uksOR9YpqH6wZdMm6qfebpuIOqtcjeftbUH79ZZRff9mkszzHyoFU3zOs1oCsP9dooJxMEU7OaEJUlnAHQ1M/KHuYHI4qRu34EEg8nkqe3TFpCvMFQk1ars8RgsusAopyD9Wl9hCpNQA7R5Ok6rquSRzrrqomB85zPD/5qR3jrEc5tPCi6zKWJQs0In2Oec77rW7kpL3rctwmFdXe6UaDbQpyHybDAZLhgM8SsODhD0fVPDQacJfYj12qVLnFYwgnZzZPvt1GHE9QTqZcEyLvjqdnvGc1TmnYvwvovVg+Z+zhvYYCsBurT+PpxvsR5H6+Mf8Zc+UNX78Jc55WkyEA4eatar3IqIP1HaAmjGpVcOA86rGFPMeN1afxVPIsXsg/U8mzVWKeJIBnX7b9TRBg7w9HNFM6OTPZdHnn1Hqp68fmGlkFtB9g3Fh9+r6gXb+fiNnaU8mz/Pv1EO+vxxVO2A9ev1Yvls8x1uZVrt9+7Md+7MejGo+jvBYAxuMxXnjhBfzP//P/jE996lM7X49yPBZMJwBz0oznjIMIeUEWqtUEsFvV1U2CbmKhlfEsg6ttwP1wgPLOKd93OuNnXDmGk0ozeyMTAGSZnLCjyRNX6Hp6LCGrZYAXiWYdYCWXjirJH4B4PiEzMpnC5wQF5WQKNxdgtVxWgELBReIJjBQMNC78sReQrfJjY6ikohxfesXcTMNkZv1+ABBPzwx8QeVvGo0gWYcu8dyYAkh6XZRnYwNzvt2ujGSEiXO9Llziq4p2I2N/kvSNAaiyHeW6AqiMoJIE6LRRvnIbAJBeOuJG6vIlbH/7a/A1YETA0qpkgAqCoG7DjChRiZ9mqbpGA6kAND1fOxa5duVkCt9o4Hrvw3ZNwtkYXjbAXhxOjWEWoEUWbvcS+Xabc6jMoMiNzdjFNugZ8wrbLa4VjUEQYF8Ke5pob2ZZsuggrEioAQZlrhTgIfGVEUttHWlhJL16WeSomfWXfuGV/xHPDD9qm3Rd6wbkRWpaTqZIDw+oHBAQSclyjriSOTo9r1hj6Uu2aInEw/W6eP6lv4pnhh9Fcjiq8glXa/h6JqrIDk3+K+darQndxOfV79QOX98LjcbOOtT7R9nQyl25MLOqKNdSPydI7mrS66KcL6q1caFHTgGqrie6EwMv5J/BoxpBGTCZJ8u/zHO4a1cQJT81CoNvfcByL+t7+Bp4VtMz9mnCihQAAJH5B2H5uY6kl/6rL7GP+3CEMJnZc801GiyOiamR63Xhy9Lm2w8H0scdqkiQeoQPACThviydDefxdOP99my8OE8Kcsuvv4zkLU8wZxjgPSQybZ1PgODRNxr3/FwW7sqdNVAHxfXf2bkH5dmLwwHcjIZrsdbGYSZg8vfFdTMkgx6fEyLX178n6RNXsX3p5VddH/XjUNZVGWM91vq5WREGuPffodcYQe5H327h+clP7XzG0433w7db9/zc/diP/diPRzkeR6bzf/1f/1d84AMfwGKxQL/f38npdM7hQx/60Kv89jc2Hgum019lULYyHb7d5ga227Y+yLhaY3vn1Hq7Yl7AHx9CYw/MbGdUsV7wXlizGuOw3pD5bLeAYsvN5mrNzWwmsrFiy99ZrPhVFOyfK+m0aMyr9DWGSWVs4wbCTioT2+sS6AozCqDqUUu8fRbjA3oS3bEwAyRk2Q5QAGrMZA0Au8vH3Cgo6D4jW5EcjpBcOiI7d3RgbCYgIGi1RnrpCKkcswIeQBhhBQIQBkAzOJME/spxZTIiG2BKGWf2/n7Y5ybuynHVI3n7BEmvi6TOkIbSQEsUZkulq+XZuGaO1KgB92CAM6zWlANLz61eE5VQ67VQU6r06mX4YZ+gqs7YgkyGMmUqwdTf1XnQHjY1VHLKXKNikPyQbIZvt+Q9eSwK8uvmKTEv7DqpM20yHJihUZjM5D0H0CgMLZzE+ia63aqMiOQY9TpaH+2QRkDXex+2+80YSI3KGA1s/hOJCFLDFes9lfss1JgjACbN2zFayQu89+ofszkNk6kVmrxcazM20riPxPO9Bz3r+/zCnY/XnhMt2+jqNXPtFsrJlNdZezS1IKNrWkCwzVvtGgRxCVXlgH6fv1sZuRiDJ0Bf162OWJZ4uvF+PKqR9LpILh3Z/WzsvEhInbp2Nxrwb7nGdXA23ilcaOFG+zvVpMsdHcpaSOx+CfWiEWq5jnkh6zy34pjOGQ3hznYNowb9ilGVIou5AYvRlBWe8gI3Vp9+oPlQgP9C/hkytL0unkqerUzgyhLJlUuA5vOu1uaUrOtXTdxeLJ/bLcTURwwG4OoSVwOaMj9PJc8iSF+pXh/XyBC/8nWbH38wsvm1yCRAevE94oBKADfoVYUrYRRfS75twM55vJB/xuZRwaj+XIGg73Xh2y37fn2tP8igKdXIlAH1z9C/DfXv1T97P/ZjP/bjUY3Hken8U3/qT+GjH/0oZrMZxuMxzs/P7evs7OyRfvZjATrDK7e5oZYNnG4qw8mZgS3X6yIVZlE3CXHKPrY4nQFpgng0BJarqlIuuZyu14UTmSXkj2OYL2wTpVJJc1AF4A6GVe6gbCq1rw/dduXUWAM1YbU2R0f/1idEzpnxPJRlETYSeQGoY66yFbdObE5ct1NtMHTIJl/ZCgXMWG+A8dRAgzIRBoRySnjD12/aeQA1+as6VIrBTx28Wt+buqzWDSwUHBRbY4+C9NGZrG2+IHiS1wRhWO0rS/n+5xM7NmVuSu3JE9YyiLutFQAUGNSOFYABDIveEIAfhFVBGUwaDIDSTWWQZbMbJuwF9ceH1XqT4gC6BJQKhuuSRWXq7LPENdgN+nACctNLR7Y5o2y0qEBkq8liijrICluoBZYohQh1jNUCjRZZ4mptLIpeBy0WaH9meTa2zbC/dAQcMEImPTywe0SZMjORkigRBa8GHFYrkzdvz86xPTu366fDXDc1PkjkzQB4L0kxx0lMhp5bmC9YtLh1YjLeZ4YfFSBNBtlk1XJuO7EeNVaWm+rKdXRHYllj21ySVGtDwIUyRLY283xnfnXDroZj9wKhb/Qw4FQD++bUXYp7crvF9SvSUn/lmH2bF5xide602BXHE7xYPlepIlR1IkUCdd2OMg8og0VcOemnrlQoweZYn4XqAgvAjr8qpoRK4i5SzXsyY3XQFYMBzGeGH4VLEpPk67GW0kPNeRKX4aKQqJ8+i0HzBcr5YoeRuwsYuaoYkYxGdxUWFPy6JCHgPxjVCidSKC3FkCkEW7smS0480OugfPkW3LnEy5xPbM7sd2PAqw077hjwdOP9O33ITyXP3nVecbXGjfnP2DlcZIxfa+gzent2fs+fP0rWfz/2Yz/2434jojITetCv+Jrv+js7XnrpJfzQD/0QOurd8SaOxwJ0KjuhAMWqzvJfBUU7rJPItNTePU5mcC/dRhxRFghhqih1K8xhFc5V4OTOabXhr1d2kwTly7fg+z34fo9/8AUUQcGwsDWAhIB32hXQSxJEAcwogzFJfG9fOfMuVzsyJpNH5gWjX2SDoTb7Kr+0yIckMdMcM2WpsS/aE6vyLj1O7Zu9aI2vcRJmOCKbUwWiYTKjOYh8rxTmxEx1FJipg6tu+gVQKUCyfkkF5HWA1O8R7I2Gd60Tve4KzjU2RNkRzSa01w/7VeGhxmSY7M0YQK4BpLU+x9EA6vDrjwjGrDhwcobybGyVfQNRKinUWAplWqWnM05mNSY735FFmpTROWCxMnbGt9t0hhUzJ3c4QnnrjqypzFhTM0sS1tBAmTqCStTOjdWnkVy9TPnofIE4mSHeugPXaUu/XRtOJHIALBNQwZ1KlG2jXJtHYxx7XZpfAShnsyqrFeBxpCI7H/apJACMEVMjLhsCBrUvWAs3ibLSwrSG+cL66IL0uSrw0eJFPZPS7i3rg87s+QJQfq0SbJVa6jybjBiweJJyvoDXntBG9ro27g8zwmZthaDtGfOG/dVLdvy+3QacQ3nrjqyHFWXJIhNWAGJ92iKp1mfGey/94A6LrOtZwaOqBExKvt6wCKbFGnnW+NEAfjTcVQK0W2KOVnODlevLtoi+nadvt8yg59XGi+Vz9uxRdYEWCbZnlH7Xc5GNVUwSmliVwfpX64ycykPro5Lr5zvgvQ6OdX2FkzNeC1nnCNGef1rgBCAGXXSJxpxtGHGxrN6n1ruNsnxwphO1Ql6tIPNq45nhRx+sYFI/BikC6j1QB7U217WhxYQ927kf+7Ef+/Fw4/r16/jSl770O/LZjwfo9EkFNNLEqvhhteZGWphOJAk3T+I+uSuBywkazsbV5r3VJMjMc8A5MhzLlfWI+X4PftCD67CPy3W44UaeI7l8bFmHkE2wmawow6AMW93U6GBIti/PuanypOmNFRkNzd0TnbY5ZOoGxjbu1mcl+aVN9qqVk6k5g9oxJN7mwlxQe10gRmO04Bn/EJcrfmZt02jVdmWIta9PqtcaX+HaLcTlytgUNYJB4uGPKmOZ5OgALkspjdXevRp74S8dYXvnFNs7p/AiBVVQGMaTnR5I12jQaVRkvjfmP1MB9e12h0UDRCY26NkGFK2mHW8sS4Iyle5CWZ7c5iau1jz2bVmxeeNJBZSlCKES2zBfAN5he3ZeSQrnCyQHQ1uXYTITJ2XOsboRb095/loM8aMBwit3qnNX9qwQ4yzvEM/GSC4f2zEnb3uS98xqbbmh9Q209ZbKcTxz+AMId06BppgpSV4gRgMrCJR3TvHea/+BSBCdgW0ABIrKZK/WVlSpg0I4hzCeAt4hvXTMKJp+D+h1bJ5dkvBcvdvJKo2rNeImt3WpxSiNwMCwylw1gClDe9fSy8fwauoF2XTL3Lt+ryarDdVXknDdyPslwwGdbhsZgXqvixfyzxDIy3mjFFfWQc8Y1RurT9uaf5SgUx1Wgxjz+H4PKLZ236HZwPb2yU5vruXFqtIhL/jsW63hu3RHtvWfEozZs6HZYNyKtAkoE1/1ief2Oh3l7RNgK9dbihrKPCIEgqAshZp06Xt84ZX/0Z6DDyPz9DXViTLSAJBeu8Jz0rxbmaOYF/IsDqaECDWmU4exdM5bUc+rHL02nkqe3XGwLcW1G80G4B3nvSg415MZ7712i1+bHEFUAkirbGMtDqVilAeI9PU1mM76CDVTtBfyz9wFqgE+b/S8reD0WqN2DLq2LrLTTyXPIswXdwH3i5//poDPR+0ovR/7sR/fdONxlNf+2//2v40/82f+DP7z//w/x+c//3n8jb/xN3a+HuV4PIyE2k24WGVU+kEP8N7ME2LNvl1lUQrYwnwB3+0QlIYAbPKKmRIGx3Xa/Jl3QF7CtZrcrNf6/ny3gyA5g67RoCFRT2I9NDdxNOT7axV90EN5PiETsGFvDuYL+Lc9ifjKbW5wZ/Md5s8yJPMCODmrJLOlmFPoiWqfmco+CwLb9PiQFXH5eTmZIjkYMrYhk2q5SmJV7leWiPM1XLeNuJI+pLMcyFL40QCluPr6doub0E3O/FCx6df4FyQJN2xeYiLkv7qZQprApQniho6nrtWEW60pbT4bm4so5suKLcrYu4cslf7WgOTJqwTaDW7Wos55o4Fnjv8oj7vRsBgBbiIzuFYTYbEkkAMM2CQHQ6DfQ3jpFURlZRWMJgmv/XQOd3gATKaI6w03xGpWJHmDxogmCVyD74EkAZpNMvTNBrNZp3PG1uhaUpMUjUkIBPKJd4yYaGYGxPxIzLM0rqfT5lzUeuWga6qRUZp+6QhuWzJ2Rzf+suGOeVGtDxlO3ExjWdIwa9BjLmCnA+cdPEAAXpaME7p0xHswFaCoBQ5hN8JqBddtW5yOxhnFYmtzQuBeILl6uQqRLwNfcziiVLvT5rlPZpTdhojkYMhr2u9xrchmPYyntias1xbg8cj81N1qseX9onMU8xwxhio2pMHYEAUVyor6bsekm/faGMdNDpelBhCutz9IQ6EHYOe+kZFePsb29gl8IucYgj2D/LUrCDdvce6EmYx5zmgeKeY5AdkoCvjRAHG5svsG3htg1eEg+aaHI+Y3ZilclvEY9Pklz2B9ZiVXL0tWLo3ekqMDhNkcmsNq97DMOdIEcblir3GtpeGe/ZUXQNdTybPGsnl9LkvRQotUfjREGE94P8sxhsUSPsRKiQLAlQmutz94Vz+pSxLEbSVh1u/pqLOiTyXPVvNXFHxOtppcw1KoiJvc0uC2Z+dILx/z3umNGJdS+9ywWNqaNnOnBxnOG0B2SWJZnRfnTgtpevy+3UJYLh/8c/TjGhlemH9mp3f0eu/DFUt7n/GmmAs9BFDfj/3Yj8djPI5GQj/wAz8AAPiLf/Ev3vUz5xzKR9ja83iU7jY5wcVkapvd0vI01ztVVGUw4myOuFwSCDabiMslXytMSjg9Z3i6xpMIoHXaD7hYEiRJzIXGVgAwY584m3PDn6XcEKeJMZdoNiTHUNivjPjfX7uCeErJaBjTidd12/w9YY0AEBgLExijMqbV5soJU6b9SWE6t2NnrynZBWXUdFOhzFU5mcJpn5x3uLH6dCUxbTUrkBdCJTuVTZKrSVtjWbLy7YW19R4IUYDm1mSU25MzXruiYK5qrYAQXrnDDdOgx8/wrjIo8h7+8MAyJNXhlUyS3DgxWs8ZAOsP1eOLxRbIUmxPzng9p3PZnEmI+3SO7Ve+KtfB25eTntKwWBKQz+aA9zRgOjpgMaPFbE93dGibanjHgoL2iC2XxvAo2EQpOaPekU3ZEKS7XpefMZ5UP5f1ZVE+S+kljgRrNDwRSWirab27O5vPZgP+2hWuMWHzVV4eZZ4VfBAoN+CGfQKOvGB24HLJbFDUih25MDBajGi3bNPO69WwtaCmKID0Ao+Gsia28N0OWTG5x12WcT30upS5FwXvF+0JlqJGmM7ZB7fJDdDEgmspKNNcW6tksjK7L8NqVd3zco/G1ZrfW6wqlrIseV55gbjd2rNiKz3VdeMi69+V9wqLpTFlahhzY/Xp+5vRvAEjzOYmew6rtRRF2KsYvn5Tnp0bHo/co7oebE0AshanvC87bZTSRpAMBxVrWpaIMfC5IUoJFFsC00EfrttGIoU+eF/1JM8XfFZIMUgLK/pcriTNGfsYT8+tJcHUBGX5QIDErksZUIqL+I3Vp6sIKVHRuFaTP281CXwPD3huWVr1e0sf7119j9sKCL9YPgffaBiA09fq/79YPlfFMXlvzxkUBcLpOf/tnd2n6bUrvL+ODqlEAKrnpJ6jKFkeZNR7Ou1ervXJXow10fNSifprgcS7RuIr9hgXQGQZHtgQaj/2Yz/2440cjyPTGUK479ejBJzAYwI6dxg5lbddPmY2XBngLx3R8KQM7KGSjQnKICBINilZRsAjGz/XasG1WjT3aQvgyPh9tCW8PMvgskwMgAKBxsFQ/uDLZ3S7FcAoS4Ik3XRnGcGpBshPp9y4q7mGyhIzDbAX0OdcxW6uN9wo93s8/hh5rgIwkoMhnRdDIPhMEiQHQzIXecFg7oOhHOfaNshhPKXUr9jimYPvJ7jq9yoWbtBHXG/gR0P40dDOA6sVWa/hAC6TsPsgc7/eGFh2raYxsIn2i5aBrwmhYo5GA4Kb9YbMR7dLoN7vSSB8wU2g5KZCgAUAxMWKMQ4QBlyNWhJv86SFAM33DHnO9/beAEd6fIgwnSMWhb23upj6fo+f08gQCzKDBnBEdh3PxwS/ypAoGFytCZhlkw+ATr0CcF2rcuJ0ScLiiJwvugSg8WzMDXEmrHKzyc2wsshAteblPF2W2XzFsSgBFIB5b5J0ZWkJlj1/r9Pma5309qp8t9hWBQmA5yBrN8zmAv5zA6au1bTCgev3KlWB3H/xfCyMGCWULkv5O94baI6zOe+TLCPTpnJbAR3+6ADh1gkB3Wxua4xh9N0dNtMk2nIvRwFmCFRExMWKa/2IRQ41dVEwbw6j640BKwA7jp56XFaoaTXtWmhhJ+YFYyJq7NkbPZSpdK0mj+NgCH94UEnPhYl0jayah6Ko5NQA52i54nyEYKqAuFxZgczMs4ots4sXS0rpO22kT1y1Y4nrDWXwRUE1yKB6lvHaZzwmAfsuq7HteYF4NoYf9Ewaby0D92GYdyeDDq36XKibi1lxqyytL1/vHwA2N2E6r9aC3HP36znULM46MNXXqmmP/tu1mnzGlYEFOgHpvtvhMUi/N7xnsW/LQp4T8zU36FdrXgB6XTr+IEMLDMo86lc95kX7dvV7r8cES1sPLo57Afh9L+d+7Md+vBnjcQSdv5PjsQCdrtUiQJGNp24IsBBXUOl78UcHgLAUAB1eETQ2QwxOuh1ufvs9AoNCHDjPx3ztbM5NyGpFE5X1mv2N3Q7ZrvWGzGuxhbt0xODz87H0sgUBBktu4gGThrpW0zZXftAjyJovjFGM6w2lfGrTL6xcHWzFxZKvW2+YgbjeIHniCjdEwm4CIDu63mB7+6Tq2VtvGLSu7GaWGqh1nXYFmtdrYyejuATH9Zrz0GpyTpsNbhg3GwPE8J6b/8WSbIaayihD2e8RmAAVsAAqADqe8v2ThEzwYsmv0zNuVDOp4isLKZtUnRdlS3RTrWtEs1Q17851OwSNCiQEuIXpHMmVS5wfkbi5hAZUJndbb+Dk8xXcl+cTY7RteE+QLowbN9Jku3y/RzOVJBFg2amusfd2HVyWIU6F2c8Lc8B1gz7C2TlZfAFyOyMhE2sMtTI8zgHa+wyQUZxMGQM0GooUWNjONZkxNPlv3+1wvQroQhkIyBaUXAaZfyS+YihlTsOK909cS09pCBXgb2Rw7bZJCxWsImgfpRQFVisCnUYDriUmTsIMxRkZ/uRgaOsBgLGtCuZdkuDG6tOIeUETnwvsppfnR9T37LR5Dw4GVbHDnkdNA9y+3bJeUYAb6DrbFBYsIihDeK+ewkcxXKsJ127Dtdvwb32CGb7LJQF8t8113u1U4CtLd9afb7es+BJOzxFOz6v7RdecXJ9KGcFiSZzNgTQVc6usMnUTsB5EMVBOZ7ZujB1Wky45B1UTaHFMC1U6lOl8EJBCqX1mz4br7Q9Wz5Ci4HNa33vQN/MbK2Jd+Ezg/rJPNZjSoXJS7ZnU90GxBbZbuUc2gLpA+90/3eHOKSXxacr5CwHuYIg4nSHM5oydUnO3xeqhIlMA7ID3OtOpx61/W5TBfdicTgCUag96d10r8wq48L392I/92I9HPWJ0r+vrm3l8/etfx3w+v+v7RVHg7/ydv/NIP/uxAJ1xNmOPZeLJ/gDCTlLOFm+fIN4+MaYJ4CZC40mUXYlriSyRHjIn0RJ16ayxc8LcoQyU9E1n3BTEyPfwnhJcARMotgR+42klWxOWI4ozoR/02B+Zs4cHibdeQJXg6THYudd67WJRcMPYbZtZSTwbw4skzHc78K0mK/W6Ec/5OyqhBADnPVyzSUMfAdIuS+EETCEEbrazjFEFzSZcs0kwoOxhRumbZozqSA6G1XmIAZPNGYByMqnORTerjYb0gtK510mfXlgsWclvt604YOyZzI0/HBk7qcdvjFniLVw+atj8Ygk3GBiodTXWLpyPUU5nKKczk1iqkUpcrbkpPRxRuthtk9Vp0EAFtbzXUjbpACrgIxtepGklJ1wsgfGkKiQoAFUg1uuyP/X4kOfebFIm2+1U1yMEsq8SFwQwZoVy79SAPPKc0kddX1nKwoPeE4AVPVQxgLNxxTgVzFjFcMCfbSgxjMK0w9QFpbkvJwdDJEcHdg1MupxWEtQ4X3B9FAX8O95iLCwA3idy3REjCy1y78XFisUROTakAh6mM66HsuR6yHM716cb74fLUrz3ysesgKDg3+TuwvZpISOenAJloIxWzwGUZCOyq05lhztAQ4sjwnb6dst6194MKSHX7Ipg885ppcrYbslMj6fsXxz2TaqvhRHnGQ/ivGeBTkC23rOmvMgyezYogwsI677dWmHLJQk/t9elTFnuw0SUFPY+mTCQaWr3hN7b6fEhEKMZa9nn11jHVxuaixmLwu6TG6tPc90qwCuD5XNCwBwAY/qVvfaNxo7xTR2k+WbLfnYRnNZfZxE7vS6PyUvrQ4w8f/kbpMMfjvjMOh9z3cfIAmunbfPgan/HHrY/MYhbdn1czM18If+MMbgPO9TpPEzndxUJ7uXkvI9Q2Y/92I83YzxsXIp+fTOOmzdv4l/5V/4VvP3tb8doNMKHP/zhHfB5dnaG7/qu73qkx/BYgE7X6wEzSlKxIfBzzQZlhyLZ0+q7OzzgBlZYnbjeIG42lILWe9iAyklxs6nZ8xeV8+j5GE6r7oejKvqk0+bnb+S9m032I6UpNwd5wQ1zXhCg1OWyrSbjWmQzphu65HBkgCoulvBPXuXGWvJDXbNRbZLFZZMbQUo362APqFWP9fXas7dYAo2M8QUiM6RsdAs0Mh4fgHD7jkmR41KYpn6PG8nVClgs2Lc4mTHOQjfpavDRatLdVTauyhik167Kxk82wXnB90sSGrysVpQ2ipQunI8Rzsecv/mCjKjGupQBcbOpjIEOR4xTaFRyaAD8bJWPNhvAcsn3uNhvOxxUwEH6ycLZWCSmZGzDrRNeM5GmOuk/i5Iv6ppNJJeOjAnT+Q2LJTeTq1UFHDVqQiW98wWcyol7XWOJkeeWxxpeuV1tjJ2jq+3xIdDt2PkgUwloaesOTno5pb+vDhZ2ih0HQ67t1coKEGg24NptRgBNmXEK75EeHyKKpFvnWY/XXzri/Ml96xqZgeRwcsbrrjLJRsb3OJ+I4c3Gvu8ODxijUmNWNIpIZbgmOxY1AwCRSm8JbBYrK/yE9YaO11nKNTedmwLBZRmZ7Gajep5I1FHS7VT9c2WJ5HDE6y3HoFJJ12pyU67yT+/hUhZ09N643vvwzr36KEYstpWrt0aRNDLew95zfYrRWpwv+Awrtizw1PKJ44L3ioHz1RpuNKjW7JZMXb1oFkSFYX3LjQbQ7fB5qvLq4cAAflB1iWeRKE6mfO+3XGGfNGBKFZM612TwdVOm+8k0rS8xk2dd4quYm16Xkt5mwzKZEaOBOS+ZyDoUdCmINHAmrKGvSakv9kbq+1xvf9BM2FySsLdys6HZ02zOtZymVVyUOpSLxD/O5jTLS1OLYIIoVx5uodzbcOni+7hGZoy+/j15mKFRSfW50PHM4COvS667H/uxH/vxjY7HSV77H//H/zGSJMH/+X/+n3j++efx//w//w9+/+///Tg/r/KRY3y0KaOPBehkRmLKzcFsThC0EcOT5crAH8qSLptJQpdLiLnCYiWb/SaZtH6PQEUmPy5X3HSt1nBHBwRQwhZivjSpXdzkiNstvzZVtTfO5gakdBMRQzDZYMxz/u5iJYBVgN1iCdehM2rcbrnxFkAZT88JKsWYI8zmPMbaJkTD2AGQ0ZSNo0bCaN6jvm/dVMVlGTTPE2lKsFYGuKuXyeC1aQyDLIMbDQi+hbFyTQGXEvbuv+WtBlah/bd5zogYwFjRZDiQCId2JceUPktjVJpNcWOlNNBlGfzVy4yAAQyEa0+rk42ZazUphQXokirmL4D0fbaaBLDzhV1H1+kIuypZrQBZ0iwlsJO8QYhBlev3qmzTO6dVwUN6slynTVCusTMCSNCnxNZko3L9tTjhu51qPba4RsP5mBt6Gf7wgPMt7Kvmi0JzFM/OCRiWK/aAJZ5rar6oGcVwc7qTc+kc12OaEpTcOavciAGylvMFokbrJOIWLXPrD0YCAuRYGxkdd+dLuCZ7jl2vS4Cs5yIAGXlhsmff7QC9Lo1lnK+Y0ymzS7WIo9de40Bcs1nJPRsZiwHSK+267SrupizNRbaUQgISz17wmrwzBlE2bLfsTT4bC6BKqr7UZtMcmIGqlzPm/F6ibsbrDedru7XIFe3DvjH/mUfqyOlaTcSzMSXCTToWh8US8eycz8v1Ruan9tzqtq244UXubGun02Yfd7sFLFeUC6uBW15U4LrTQXr1cmUcs1pzvUxmZDg3Odd5LaKKxTay5MbolwHh//0Kj6XdrtasFKrqbQd15q0+pxbNEYOxj9qvXs+kjOo2q3+MpSDmmg0pFDEaSHtQ1Y33IhOIGJiPWmwNCN8lG5UCl8mwZY6wWkshqME1WGylMCgFk0bGvzs1E584m9PVudmEV1WGgreHiP/wYval56RGSbvHnVX9uwBKNUR7tVE/hhj5XLhQbHkqeRblYnlPZnPf17kf+7Efj3q8GfLav/N3/g7+4B/8g3jiiSfgnMPP/dzPvebvfPGLX8Tv+32/D61WC+985zvx8Y9//DV/52//7b+Nv/yX/zLe85734N/6t/4t/OIv/iLe8pa34Lu/+7txdsa/7c49WsD8eIDOtjCFusm0zV9DAAcNgbQPjBEgArD6PUZGpCkdZSdTsj2rNTf36sY5m3MjPJkxv1I2urp59z2a2+hm2MlmJK7WlcEPQLZz2K8Zo7TgOx32hOrmSp12xQU2ESClEkmNTVEZLtmWBl0UnedXs4lysaSzbVkCjQzlnVO4LGX23nZr768uk8p8Ii+4Qez3eP4qu01ThK+9XIFFladuS4uUsI1Z4hFu3iKYmC/hRmTI0MjEMGRLySlAd1P5NzeeGwNqVqkX4I+84HUQFk8/Z8cpc8seWDWEMcCYplKQ2MB1O7Yx8r0u50zkwihLi++AcwYYlbV2zSaLDs5XeXnOIbxyB9s7pwKWWihfeqVyMpXoEmxpdlUvLkDAsDJ46l6s6xj6laYS9UMQjUYDTvIWo0ZJiOOrRrPE2ZyMqPZ9hkhALXJTf+kI8fScEvK8INBWGaxuYMuS1ypNCWwHBNb+cIS4Wtm10nOFMDRhvuCmV2S8KNmvGTcbK86kl44IgsXgKeaMM3L9HtzlY0B7mQGUX32pimFoSFROknDN6CFHMGsAAQAASURBVHxppqEY4YTxhNdtNACkFzrOF1wv6gTdkk25gHYvEtG4WO1IGFGWBDj9Hue92CI5HPF+SDyLS5sNzztRBl+AS1kirDe8J21eA5zkzIaFOP8KG6WGMo9sJB7uMg2rwoRSUT8amkGXMlXJcMDrm/CZou6tVnBJEq7bYotwemb3Zfq2J60XXMF1kP5nALLW5B6QOSrvnFqRCMIGE6iHym1aM0xF/YFGQwqKgde4Bnx01IHdvebUN1s7ESYqOXdZaveoOhPzeVDa+q1/Xiy2/Cpf3TFX5dqxLHfA1FPJs7ix+BQAgmMrhgxY8IPMt2+34a+yv9xM8kBvAzxxmfM66EvxY8O5ThKL53mY4ZutqsWgJlWu52M+lTxrzxM97gcqmNRzOoutsccXr5FvNXG9+6G7fv1NiUnZj/3Yj/14xGOxWOD3/J7fg//hf/gfHuj1X/nKV/C93/u9+Df+jX8D/+Af/AP8uT/35/BDP/RD+PznP/+qvzeZTHBwcGD/32w28df+2l/DO97xDnzXd30Xbt++/Q2dx4OMxwN0zlfAoMcNgmw8IAHwrtk0oxs0G1IFFxCqctXxBBgOgPGUDoDC1tERNK1y/JrNuwwjNIgeZeBmVja+cbOpwu7zAr5D45IwXwApgR+SRNxGG8IkdGpsE5m3OJuTxex0jK10g75FV7gWgVhYLLmJFtOkuNlQ8hcE7DpPWWdT+gvzgs6TByP+wU9T65Vyo+GOxFdZ3DBfEGiUZQVwBajHyZTnM50ZaPMHIwFDHlitgU4HbkA5slc5YppyjiQ7lKywADUBicamOEfQESLieMKv1drOwc4NYpSSpUCvI9ekyU1iStkkYkT69reajBaAyU2RprJZy3lN5Xfiao2wXPIYygBcOqxcigG4d75NAuaTar7NdInFB7p9HrJIsiGwiTJfzvmKFU2rAPo4mUkMTMn+5NWaPxdZqWs2gSeuAJMpv6dDCx7OGegPyyUBtcy5MbCS/XnX6HW4xqXXOJyecXOYErQ554HDEdmyVovXq85gqFy9LPk70rvl0pQgQYsLia8Yt9snXMuTGYsvYtSVXL3MuRsN7PPjas3XhojyFT4wfadDAJWm8EeHPJ7V2sCNSYqTBKEmJVbQB5GO+l6Xvbch8lz7PWMl43Zr1z5ut7wmWvASh9Uwm9tm3Ek2rG81ydZK7m55+8R+rpv7WJZ3STbf8JEXXB9pWklUlys6GYsSwIoivY4841gw8VJA2N455bzmhWT2DqvCgWQP63m60YCFJXUH7/eq697vAc0Gi2rtFu9veT7pMTh5rgHgM1QNh+RZicQDcwH0cr1Mog3cM/dUeweVtfOtJq+pc3zuraWAIH3/rtvhNe33WCRsNgnWO3zOqiJBr9u9jHd8s1W5f8v1rkt8r7c/WL221eSzcZPz3M/GPKfNxhQ2Fss1GACbHO5sakUfZd8NsOc5biw+xc99jZ5OPYaQ5wak7zUUgF40bKrnjd53XGRbiwLldLYDJl8snzMwf79j3I/92I/9eFTjzZDXvve978V/+V/+l/i+7/u+B3r9xz/+cbztbW/Df//f//f4F/6FfwHf//3fj49+9KP4S3/pL73q773zne/Er/zKr+x8L01TfO5zn8M73/lO/IE/8Ace6rhfz3gsQGcMJTCd8w/zal1J9ZS1kw09xIkV2y173LwnE3N0wA17mrCS3hfbfXVqHPSl+s4NlT86hJdKsktT+IMR+7yaDZM0stIsm+j5Atuzc5pMSJC6bbK1vxBicrLJK6lYYG+q7/cQTs9E+kl3XErMuJkvJ1PK3cS1Vl0M+aYRYb3B9uy82rCs1ta7Fqcz+GtXbF78aMDcQ9lM6ubb+pnyouoD67FX0mRVdXnjdisANJjcETHwS2MqZHPmRgOpxG8o5a319sQ8t2sazscWBq8g+cbiUzzuq5fYj1Zsea2dI9A/PadcVYFPh5LKMJ4YUxZncwP6kCKC5Uo2GzyX5YqMtPbCOgecjfkZAmbd7VOybGnKY3GOa0ijepYruhjnuUlU/due5BrYbMRsKFQxCUlCZ+SjA7ijA7LjzldscrvF6+Qc3JIgJy5XdLAVwAYAcdjnHOqc1lgdADxWZXFUCbARybeAXddq8nqHqo/XxtmYy/V8zHNvt5hheDCqAG+nzd8X4yn0OpLtuapiL+QzvTqDNhsmIY7DPu/NPEc8ObM4DieyQtdqkvnStSbAMs7mO5tuJwUZ12gYq7gTdSEy2DBf2PtiuzVjJtdqVoyezqVzVBrUnhkA+L2aBNrY/F63ikzJUvhBn+s4VEZnpbqUPqJhRlPaSy7FEdds8B419py90b7T2fn9mOeUCQv7j9XamF6XJIg3b/OZoX2j8xpDLWwhmg0WP/K8KrDJs8c1MmPUARZteIBOsmFzrsUQqiIQKOdWiTsAu74aU3K/8WL5HOe/2FZyWYDxLtJaAcg1XYm5Vl7cEwwpiFUQVmcG1ZDHSR94HZjWf/fpxvv5jC5L3iurNe8PfdYmEvWlf3dmcxpwlSWi9Nii3WIxVgyb9H3xGuvKZMcAJcGvYQz0VPIsn1+oQOgDGTjVgO8L+WcQNzmS2jV/rbFnOvdjP/bjUY9vRF47nU53vjabzWt82oONX/7lX8bTTz+9873r16/jS1/6EgqN9LvHeO9734uf+ImfuOv7Cjy//du//Q05vlcbjwXodG06i8I7brCbDW5yxORE/1CHW7fJbgpgwNmYstD50ja8rtcFViLnOz23zZYf9Mmw9Lr8nMWSBhnLpZllYEvZrkl3BRi5dgvpYUVpu9GQzOFMTEpazXuyTNpHqZmdyvgg8bYZd4M+K/RJAi8ZpOqkqBvi5HDEDaJunhPPareyNud039W4E/ZONqrokybjTeq9fq7bsXxHq0R3KetCSlDmBn0zvkGMZLDmS0aCpKnFjhi4PxgZyEMqG6NAt0bNn0uODnbm/JnDH6Bsr9gCoazMXGLVHxRPTsmydjsob5/AD/oEiydnPIYrl8juilTaZSmPv9sh8FPWUeYLiThJNhrVZwh48peOuAFbSqTLJufxqnlKrSjiuh0WOxRkCgsIzX1drWnE9PWbCF+/yXOW64M0JcBtNmiwcnrOzayw5v7aFR5HCCh/7TeMTfea24dalt4mF9DbNoDs0pRfzQav9XpT5XA25HsKHFpN9qUKyLJw+naLTGGacB2v1oi3T3jMZeCaU5OvGKs+vK322kp/3WwOvHyrkqgP+zRw6XRMIRC1iLKh3DycnvP1T1wxObvv97iOBNTGsmSBQ+9f7yTChGtAWUvXaSMs5Rmx3vBzNxsgRDx/9j9V94SZ2uTCgpaVbF0UB8qeqfmWH/RRnp7jvVf/GI+n03loCeTrGWEp8UryHFODNJSBz05d4wCVIAALAv2eFV+QpuKCLNLNZpORP9utXWM1h7IhgC0uV5xz7V0MEe5wxHlvt3jNNznB57DPZ9tkJhL0hPdwrwtsSxYQ0sQKcsryIWHsjG80doFUbdRlon7YF5dsZwyryvEBUPadpnafheWSxyBsrg6XJPeUgwIEwfDurjxPBai+0ahMjfQazJdU3XQ7VRRJmrL/tdGw55BGZrnFkvflZoP4yh0zOrPCyGuMi/OkpkH6uxcjUy4yuwDuihF6rfHM4CNUEqx3N2VPJc/e85gvugPvx37sx348ihFfB8upoPOtb30rhsOhff3oj/7oG3JMr7zyCq5cubLzvStXrmC73eLk5OS+v/cjP/Ij+NznPnfPn6Vpir/+1/86fvM3f/MNOcb7jUe/u3kzRiEbpsUKrgzsA3vyMtzphJsq2awqgxaERXCDPtk4oGIu5gsDVyqtdRAANiYbiiaZTGxyGiOXBLdwYsKitvva5/jkZYTf/FolN10sxWxHNvvKhkgfqOUniuEOAG5es7TaeIsjazg5lT68avMFwDaBKEsgSuam9vjIxknBnH32cgUn7635hsqGmsFMmlDWmufwx0cMcldmUnrvdENl7wEQ3KUpN0/6PlPmHZpDY1EA3S6c9IjaZkMrN2rUcTCyS69Zf9aDuC35GS3Gh7hel3E5Ytbke13OTaeNuFxWEl7tvVtvmOeqRQhlcwZ9vk424srqOc2k856gZDqvjHdq1ypOppYviG0Jd+UYOCNzG1Wi5x1cq8P1rBJfAP6aPFzynHOhG9xtSTfX+aKSH25LuONDYDanuVCMIoespMPh7NzcR9HIuI6HA8RBB266BIoCX7jDpvRnBh/h79XXXZoCmw3coEeH6DSFG/R4zHkOf+UY4c4pvGa+hkAQlmV8zWwO16nWMDa5FYgAAEnJY0pTROfgRepu6/RsLNcl532ocSrzpfW9el03kyraQoGmExAUiy3CbIYgbJWXfudyMhXH0CjFKOkx1qKNPEvieoP3XvsPKlAynki2ZUkDsPnC+kVVWu8FVJXisJsO+vDtFrZ3TqlWmM0IqrL00eZ0qjQfYpSWpTSVkjUdcwK+MF/Av+1J4OSc/btSzHp++km6igJ8hsi9jW4HrmZsVt68xfO8doXPSjFYU4OwMF/w+ZGliOcTrqWM6ylO53De8V4sS86/GICpUZtrNuDShOtH+/KlyKPjfkZCgICXGOAbDZRnY0b5aF+5mo2J3DbeulMBN1FGuCQhuNtsdljPsN7c/XmOET0+S/FC/hk8M/woXrzAwMayNID8zOAjPKdYiGtzDjcawsszWe9DQHoineM13eR8NhSOhZDtFm5V608O4aGMhOrKiIvgXf/93isf2wGf/kFAp/PGdob1BkmMxjDXGeLr7Q/eBTzrvbB7xnM/9mM/HtWIqMRlD/M7APC1r30NA2kpA9hD+UaNi4Y/6jr7akZAaZruHM/FkSQJ3v72t78xB3if8VgwnbT4lw3slowbvvJ1bnKVuWs1aZayyQn+dDMhm+i42ezEY7huhxX9fo8ASTauYTrjRrvVBIbSW9lqAkXBn0lIfZ1hdeczggr94615ip0O/JXL3Dw4Z/ELURgv3cxafyIg2XSVO65/4hpf02DlHcpQygbNddoMVB/0gWHfgKhtmJSNVXZlW1ZAPERuALVPKakxGsdHEvKeVICvWTEOUUGwvKeTXkeEwPlZreEORwS1yyUlzetNBTBFNuafvMrqvZqGrNbcVMmXazYrl+HFsjKayXMyqOsNDWnSxObDdciM+0GfDJ2Yz6j8EhteU5Ucu+GgkgTK5/pBn2vj0qExRmo+gq6YBgVxZFzTuAhqwOSdbOLXcFcvcdPtHc9fJdqtJo1ehgNgseBXo0FguRHJcVFU8SDS+2WxJ+IsjKKojEYuHVXASXMrb58A/S7B+61TYLFAODvHe9/6x/Het/7xSuZ7OKIkfZPzWAJzAJGmBLl6Dx2ObH50XUIlrjFWbqTb0nJy4R3wtmuI0zmixquklLma66uw+ZCMW5OzlyU33JKV65LEpJ4AaLwE9nkiRrKUmpMrsShJt8P+Z/ks327xM2O0+yhuNggSr6R9g65Fl1qVSgIC4BoNAlfvyGzKl8mdl0sDo+F8bOZFvtOpGLYQH+lmWh2zAaolEFjM0XvWH4ysnxGzBZDSsAkAsN0SrMk9YTmcwprWmcL0+JAZmnrueW4u2nKiPIbaH+O4lhYC6aPWfnJrCajFtGBb8rmr8y//1n56BUIX40l0vJB/hmBQ2G2A10edwENNlq5FPddkbrAVG2p5oDrqDOpdQ45FpdcKrl4sn8ML+WeM9YxlWeU/a1bxgiy5AuHyfEJjJ+lFtlYNkR/H6axyssaDAbR7GfkAeFWDoLje4PnpJzlPCtYfYjhPI0AtYO58jncsPuzHfuzHfrzJ4xvJ6RwMBjtfbxTovHr1Kl555ZWd792+fRtpmuLo6OhVf/cf/+N/jE9+8pP4tV/7NQDAr/3ar+FjH/sYPvrRj+J/+9/+tzfk+F5tPB6gUzMPAWG60kom22wAB0N+aRahboq3WzJw7RYNSgTwbIUZQyrsWwjcsHsHf/nYepg0fFs3Bv7yMcGF9CFa/1+tgusaDZP4xrJknmWSVBuaFs1IKHnLCSYnU2482i1KQWsySizoxBmWS26s9bi6ZM7iJmd243KF8PWXzUAFqQAb2UCh3bINp5mhbDZk8BZLHq9kwfFFwgwvV9a3pVE1FyWCYbms5IbtFq+LAM84mXLuFwu4q5cJKnTDL71KzO4LNCzSzUzKc7D/D6G2qV+Z3BIZmTGVXiJLK2YxBEoHdc0cDNlz2WoKk5DYext72m3zKwQCm9Wam0fnyBrqHPa6dJs8GFLKeOmAfVeh6ml13Q5w5wzotG2jbNmZiyVfv+Y1gPcEe7qGR0MCH5nruFha8YWy3g3nOU0pPW7JmpUsUtduVcWa2aKK+fF0KTXHXGWXFitguapkmeLSyt+f8zq95RrBpMYFbfm5cbWGv3ws/aY9Fg7ShIDae/7/b79c3cPCWDuRIrpOmzEl01llnLRYGdMLgBvvGG2jznvOI3z16/CdDo+7LLkWL5QtLWao0LijjfX3BlmPvt8nA5bn5ooNycUF6PIa1mIe1u3QBKbZZP+md1V+YafD14h8GWCuqLKuZm7k3Y6E8I0evt830Ba1cFFIz3eLsURe4kkMuASZ0/WGTO92iyDmWjHP+dwqtlassvtdGPi4XMEdDDm/ImP1MqdIEz7b2i0+n7zn98TtODk6sCIBGb7cDMBcs1kpNpzjsWnBz7u7HFXroKpu4hOLLV4sn4Pv9xE3G7xYPsfPrUXuRClIATCZtfaEv5B/xhi4ZwYfwdON9++AJy+RPE6YbOBumWj9/52aOSUJwp0TzsloWMn8W8z8TbSopIVXkT1bVNR6Y4WTZ4YfFTn7qxsJ1UeQ368bBd01YsT17ocot31QefiFY3DdTlVEq7+s2JoaQce9jmMvs92P/diPN3q8GZEpDzu+4zu+Ay+++OLO91544QW85z3vQZZl9/kt4Pnnn8e3f/u340//6T+Nd7/73Xj++efxb/6b/yZ+4zd+A1/96ldx/fr1Rw48Hw/QOVvIhlk2/hJvEgs6NOJ8wi+AoEP65iD5gdonhEEfSFOkb3uSrFohRhGjIeKdU/7RzwuCEe0DgzCRgz43wS3ZsDeb/HerWfV4ZpkZacTJlMBSjSHaLb5ONuFuyP7RuNkQ1LWalJ9J5IJJCzWHst1CjKHaQEv/GUCg6wZ960d1Wcpzm84IUIotwiu3OX/aG9VsVq6uco7KVMWyZK6pjlrfVtxwk66RKHoMxnSdjYEsQ3nrDuevkcm12iLevFV9ZkL5bV2y5kZDOmTGUIW/lyVNdgrGgDgxsVHptMWpbMXReCugW/qqCKwdnWnvnFaSaAWrgGQJzvjf8wm/ii03b7IhgzIfeS4b70Dm8eSMTNCYrHBcrVG+fItrIwT+d7myucd6I+xq3zbeyDL2eRbcbLpBH/H0jJty2WBrUSPWYmCgphxJwviaEExejRD4npucfY5veYKv9Z7veXrGz1CwnpJFsdzLRsbN/WpVgcw5+1hV+mp9eynn34kZkBMTLz1WNxxULqftlvXRsVghgFtZqNWa6zcGWQe8f5ODIQ2L5Dq4bgdhOoO/fMkYbP0yeTlQReBIlmEstsboxVLyVxVcCEsHz57ouFzxeyLH9L0uAZi4robVmr8XogGScjIlY6e9wQkZRN9uWQHhxfI5xBAfTKL4eod3tvbdk1cp95VM2nDnhGtgzWePsrRhuUQsmP0bl2T2/aBnUTPaKmA5n71u1eMumb3xXCTI2idYbHltvQcm04qhDsHWjxdpuz88qHpvlyyshNls57Tqxk/Knl4Ef3f9OwbrwXy68X6y1Mo4q4IlBpsve0Y0MpG/0oDo6cb7DTQ+P/2kAVDtgYxiFFUulgj3cGRVphNAZVYmz0N/MOKz+pXbdGZut/j/Gu+lfej9HosEovKxQpwyzJsNwnR3zi6Oe4FLlf3eb2j81Ivlcxb78tAjrSJq6p/la/2y9WN8kO/tx37sx358I+PNcK+dz+f48pe/jC9/+csAGIny5S9/GV/96lcBAD/8wz+MD32o8gn4wR/8Qfz2b/82/uSf/JP4x//4H+Onfuqn8JM/+ZP403/6T7/q5/zFv/gX8Wf+zJ/B6ekpPvnJT+L9738/fuAHfgAvvvgi/vbf/tv4j/6j/wg/9mM/9nAT9JDj8QCdOjSzzTnLJoMEtcdNXgWYZ6m5MsI7ggSAm/+ylIiKqW1Mw0s3aYQzm9smOpYlwaUYqyAEmhmtNxI5QAdVOEdGBzAJFCSnEY2MrEAj4/fS1IAUAB6XbpC1X6dJqa07OqDro7KgIZJpUEmnGN+4LCWoE+lhXK2reBHpLzPp23pjLGGUaJKgTCNgzKFKgN3hSIBxbhlwKkujqcfG+sZcu8Xjk/5LP+hVkrizsYHFqMxhSZDiGpnJl7V/ySUJwnSOMJ1XTEOe2wZLe/hcrwt/6ZjfT8kylbfuVK8tmFUaiy3n0zmE41Fl+uOcAQxcu1zFrQhoRVsMcNotMUaKlVOuSFvr/aaxKOBGQyQHQ0pjF0vEw4FFiGhfrbKocbniZ8hcAiKHBfj6GE0K6NIUYSKRP95xbidTrtHES8Zm29ZS3NDh2I2GPNbxRHrlxDGz2PJL1g9Cld1pPZIiX3RZyhgI/V3ADE90XcTpjAWgEPl5Mq/bV25Xc7Gp5KvaExeXqyq2ppHRWXYhsTnrDR2he13rOzXQGBh7FCdTnkOxtcxdAvyUgDXPEZdLxOWy+kxhPOEdQVOeC8tVAamwWIpknz2yKle2nElgh/F5ZvhRXG99gOCyke18Zh0YbE/O8HTj/byHwoOzUQ89nENUdcB8yTUZo7HnyjiF8cRygvmNaPJZzdl07XZljCNAybVbXI8aR3N2XhlMhWDFLjNaErYf4uiLEAjm1xszY1NVCqTAhBDhpWDB52zfeht1fhXw32vs9FJKLIiTolw5meJ678P8DGF8AVhfelgspUgo52vZsNU1VwBqsSLbC7mwjQwv5J/ZOQ4FrnVprwJMJ73dlv/cyExlorEz2G7ZTqDPGzW90qFMenr/avjF4dstYzoVkF5ki+v9nNe7H6LPwGuNi32ltWd4fcSyNGa4/pn7sR/7sR+Pw/jSl76Ed7/73Xj3u98NAPiTf/JP4t3vfjf+/J//8wCAmzdvGgAFgG/5lm/B3/pbfwu/8Au/gG//9m/Hf/Ff/Bf4K3/lr+Df/Xf/3Vf9nH/0j/4R/sgf+SMAgH//3//3MZvNdn7nD//hP3xXpMobPR4P0NntcPMcAv/IaiZft8tNjTIop2fchA76dFJVEDMcENg0BUB6x41MllV9lccSwp1Symg5gZMZ0O9KWPqA4EjZLh31zWOeV0Yw4Obccu3UxVFAlut1xR0y5SY4yygHzTKEr71kfZOu1bQQdTci42MxCM5xg77dErC2GfYdV2tmlWpkwre8jcxLr2t9Na7dgh8Nq2NVI6bxhJX3s7EZHKkRCQBojmN5PtmN5xAXTzqMdlh973QICpQ1VvmkyHkBEICpsytgrqNeN1yXj3eYK689mElC85NOB1iu2Es3oMuqk7ko7zDmBMsV3+83v0bZaKu5C8h/62sSFVOZiDACJlaflYshTqdDsLJaE5hpTEerxV5fCULHdss+SsCOFbpxB+CuXNrJhYV3cMdHXEN6vq0WXZTnCwLo8YQ5tMoYbwkeXaOBcPsOr5EYLEFdgNUEpbaxM8m0nocWKbS/S11IxY3VCUuo5lMXJayuBlzifGG9d8lwwKzDXpcFoU4H/vCAx+9ohBJr7qr+8MCuje91eb8O+nY8dWdel6U85oQy7ORgWEne9ec1R2Iv9wYAhNXKij46h3rsUDm8Yx+uPxjZc0bXurL9Yb0hwzlfIIZI9jMvEEQymFy9TJZ9tQa2nA8AO7ETj2Js75ya9FLbA+h8LcZJrSbc4YhutBKFpHJgfZ4EyclV05643lQZsmJUVAGkqrc+LCi3d41MwFKT0UZa7EhTPutqsl9AYlO6XbijQ65VVYnoZ83mLBRst7Z+6yzZfSWZAn5cklDG6RwSMeDZMQgK0VQPuqbo6EyWWl289Tl1EVDCeUpspTCl81o/FmXEXyyf47FL/IsbDRFfuU1VgALzNK0ispxnQSrL+KxbreEORiz8aR+RzJlvNQ0A32/UWcOwWu+ci91D93it9XM+SA/mBXntRUdfnRO9Xy4e36OUn+/HfuzHfgDmX/nQXw8zfv/v//2IMd719dM//dMAgJ/+6Z/GL/zCL+z8znd+53fi7//9v4/NZoOvfOUr+MEf/MGH+kzvPVqtFkajkX2v3+9jMpk83ME/5Hg8QOd2CzQz/oEuS25UZQNirp3OcXPQaiLePmEfIcBQ7bIkKyQ9ngAs48/3ugQxiwU3RNutMX10jM2A6dziQ3Aw5EZY+zj1j69Km+YLgrUkIWCAODmu1gQCMQJHBxUI1n5BoOq5ihH+7W+lnLTThhsMKCMcCpBYLglANrn1qPnRkOBiteaGTvsmAbgsA84nxhjUAak5eEofHwAz73H9XmV6JGxd3Mgm3Hsk3/I2AmFlAfPcAK9tVBVYliW/X4uPiSpDlA2HEzCHMhgrBoAZrd7Dfau4bmXpDjtYd6B0Kq2dU5KdSI9u3Gwsl1GZS+tzlPWjzK1rNS0TVo87jCfmFguVLbZb4mwp4DpGQNx2Y1nSjEkBzXZrYDFuWLSIt+5wA6kxOAqA5HgsAzIlGAzjiWxGE/5Mpbsp+4r9aMhChABwMlvB8i5ds1lFFkgvnj8+kgJLbiw2AIIRlX43GI/hBj1uegHK+/o9k2mGO6dcR3VQo/EUkjcazs4Rzs7ZO5xIBNByRRZX2EXkObNKNTtxtQbUnEijjNqtSh4q69glCQF3llk/JsoSMcRqPoWpjZsNkuGQ6yhGnveWDCrzZskwowxwWSZxTZTeu36Pa7HVhPMOvtU06ab2FTrvkAz6zCTU+RRwpEZGAHYZqjd4JL0ulQJ5YZJqxAjfbpv7rkanIEZRhfjKNEwNhAY9Fh06HesTjiIrNja004bm15rkX5h9xMhrriZe3jMHVXoSdb4tYzllxBMgbQ3Cosa8qAokzlX5wNutMXMX5ZeWnxmrfnDfalZO1iGaogBgi4ATQx8twMF7u26uZnhzr75RzbyMy6WZt13M6dS1YlEhZ+Oq1cFTsRDmC6Arf+M6HX4tl/QdSBMqJLxDbGUVA99uWzHgQUx+duJPspTspcxLvXf14ngh/wyLK6oqwIOzki5Nd4yb6uOixFbnZ8947sd+7MejHN+MPZ2vd7zjHe/Ab/zGb9j///Iv/zLe9ra32f9/7Wtfw7Vr1x7pMTweoBNAzBLgfMKNx3JFmepiaaYPAKrKuMi30O8hTqf8IyxMKWKEGwy4iZINJCSIHgDBk8RNmIHMdgvX7yFcPbTYEAhrqfl8BMAB/slrZo6h4CAeDblZ6jGDM770CuATun6OJxI9IsYvaUrgNZ1xU7Ves/culAj/71dM6hrGEwGlnSpnci3RD40GN5cSDg/ApJ1xsYQ7GHJOpL/TSeSDE0YglswjxSZHqRtOzTPt9/jZzQYwmVamNcrYhVhln2ogfa9rwDKcnNGF9nxMoCyb4iiZfvCULqs7JYqtSPUKxF/7p3z9esNrmxd83xpDSgZMDUho6rSzuQ+R55kwskZf4w8P+PsCADFhX5RzvgKpy6U5RbqWrA91OZ1MeWxHB/Z7cb3m7zeYgxrXa4tpAcB12m5VAfANjeNhvyhKMTOSOBpeU3HLFbOVuN5QnjqsAE6Q3EWEaO+DRoOvSxJeW/3abo1tNyZU2F0/6BvQsp7jsqziKrZbytQbDQLyEOjU2ulwDrMM7vCAa8dXj6KqT1LcVTtt+G7HmDN024inZ2RvJQoiaEah3O/hzimv15ogJAjbFmdzU0WQscxwY/4zuDH/GYJLARpODJjMhVfky4gRYbGk2cx2KzEiYq4lfa/ldGZ93K5BZls37M8MP4ob659FWCxNUmy/r2BnsyGL8wiZTngP324Zs8q+dk9DsuMjSn9XklEZQiVrBiojGwBoNjlHRUFwL+tEn1NxNpeIHPb5htW6KojN5mTe2u3qnjwYwff72P7T36pUFgo4YmSBodvh6zWXNUkkU7ZpcxlXK36JOVAdcNbZzfpw3lUGaTuSVLZKuEZmipE4X3BNAzyH48NKen9h7DCBaUaJaw1c1Y9PWVXK1rOKaQ/R3LRZOJLe/umUf8M6HT5fFiu2JXQ6cDMWYKKY0oXxxPrCXysyZYfpVMf0+4C8p5JnGa9jHgLZDkh80F7LMF+Y0qBu8OSyFDfmP3PX8V28rvuxH/uxH2/0eJxA58c+9jGUtaLj7/7dvxupKpgAfOELX8B3f/d3P9JjeCCbue/7vu976Df++Mc/jsuXLz/0772u0cjgbp4A3TYB09mYm/RuhwDE4kC2FXCazeGEFQVARsiJmYvKJsXpFEBlZqKypvmCkqfTczON8ZEbfiSeDqd11kgkYFAL/BCMKcGdc/beFVu4dhvos5rvmg2yhklSSSjb7NHTaAanJjPzBeWBVy/xM1++hRgDGVxAmChx7J3NgWaDwK7fI8gRSRoKAW2tJsLJmfRpeW501EBDXhu3W8rQBFAqOHGtJvv+nIPLaOjkWk2+/4IOqPp+Lk2rz9GR8PPIVvO9/eEBN7FFAZc0K5mnZFq6JuNE4tmYv7PJ+Xki//T9HmV50xl/R+SREHbEXz7mPAsD6kaDKs/xfGLyUJMYqmOymuiAbALWG8pFR0N+/ia3vs+4yeHOxsbAOI0g0fNIOEfhfAx/fMjzvXNqnx1u3uJ6UOfhYmvA0rWaiJHyamy3dAEuCgMAkOzTMJvB/a53ALfuECTJtQmnZ5RMr0UeWcq5b7f2+2FMCbK6kaLZ4GfnZNQtFkcKMbZeU/Yxa29lVIDpHOLZuX2W5a+uN+JS2yCrk4i50Yo5qXEy41prNuBEouf7PW7mJT/Vq3zbecuhdCkNiMLNW1xfAr6fOf6jnN/VmsAjI8vt2i2bQxRbxoicj+HbLYTVyiKHXEr3aJVHp4cHVXRPTVpfdxBNpPgAAOX0nLJv7wlohfl9pCOEXaAvoNwP+gTqgGXaxu0W/omrZBhL9lXGuUT4LFemhkDiWUSRvGF4z15pgAXAxFvuJsAiEDZ5ZU603cLlfJ/kYFjl/G5LizFCjIjDPtx0XvX6dtrsPU8YyeT7PbJ9IJB5uvH+u5m5e7i3hmILL2xbMhxge3Ze/Ww8IQhWx/NGgwWVy8d8Zi5WFWvdahoA0/FU8ixcKr2f7Ra8c/Dt9l2vU2Dns5TrPWUEE9s2CiCp3KH9waiKmFoSYLKvs6xMycqVqTZe6/wvHq8COpMS426TIQV+7736xyqGOC+Axqu+fTX073GISNrt6r65MK63PoAb6599wDfdj/3Yj/14Y0aIDu4hQeTDGgm9WeO1JLg/8iM/8siP4YGYzp/7uZ9Do9HAcDh8oK+/+Tf/Jubz+Wu/8Rs1nAMOR/xjmxeUuGYZnV7X690KdLNhG+SomwWAf7xTRgggRvl/kSY1GnCjAUGQys2SBJjMWFU+GBJArDf2x90cawGybZucILjZgDs6EJauqI4tRm7qEo/YacON2JcYi4JM3lY2veIW6g7o4qqbf0glOr58C/HlW9K/EyowkCZV/mazYbEhcbs1hg2tpv2O5plaxEuS2KZSwUFcrcnaCpsRi8Jcba3nDiCzsd7Q9XW7hb90xM9rNkRO2DZpHQCGxmsuqrAr4c6JSDIzvte65pApG2NsS3PSJFNVAL0OXUVnc5FQl9ZbiSQxcBHunAKTGY8rBPZHxShmHG1KDQGy0b0OWb31xtxwXbtlMjyvYHMhkT16HVrNCkRJxiS6BGUuy4wd8u02QbOwqJgvgfkS/tIxoyfEXAW60e917bzi6XnFgoq8NG5yGgCtVpTLak+ygP643hjYcAr8JO/VNckmBpFsax9jjKEC9s4R3IkBlG6EXat512ZXXZSdmJ5YD2AIlSN0s4Hwym1hPrJKVlxsDUwwLmjF8ywKy/10aVoxh94TnEtvc5TP850OQYo6lNaMWWKglDSuVsbcsbDD+fKXjnf6RgHYObp2G67RwPbsvGLcaudff304H/NrNkdydEBXY5Fe62svupu+kcNJPm3cbq1wo46xznmZg7U5QmO15rVoNQnA+/3qzeR+s/tKpdWaOav9x+0210tKZtIfH5L5ljVnBZMy8LUdkYQ6Zy7gcTaHOz3n2pR4o6onuM0Czbaq5LokwQv5Z3b6/+5ix2o9nQDjb6Lmh/Z7cFlW3dOqMlAFR4zs60883WnFxK1ufKMgLW4Lvma1FmO6GXy7tWPMo33HN9Y/y/dSEBkjP0PXWrez4yAeJbs3Sg8oxCgMvY61TSBJCPofQF5bn6MX8s/c00l5p+9yQ9n89dYH+MOLIPd+Q+b5hfwzBJz3YFPvdR/sZbX7sR/78WaMN6On85+l8YCBWsBf+St/5YGZy7/21/7a6z6g1z28I1s4mxOU9brc/GQZgShg5hTu6mUa9WxLgojxlADk9JzgUeznMZ5WBikiI4y3T7g5Kgr2zUxm3IillRQSW75e2QrXbvEY1OBI410OR8DpOTfH7RbcnHmYbsp4DYRAwOoJ9rDeEJDIxs8cAsvAjXx9PvICSD37fCy03SOMpwRmapgUPDAaUEopOYia52jzKmBanRvR5jz7ToebVAX0jcx68HDpkH2iKZ1LnfdkqRZLbo5UAuo9N+PaTynxKVoU0M2O63TYO+e8XBO5ppMZpc13Tg34mAyudMZo+i4lne6IsQvhzimLCceHzJhs0ojEHR8BAuxjXvB6hlCBh20l1aXEb1sVKDQ7UJxP4T3PVSI84mIJf/kS+4PVEXi5ojmKbviF5XIpc1YRI68PwPiVhoDT03Mx7QiIOeMrsMkJWrKsAu3jCY+z1YSXzWs8G8MNBwjzMYFEpy0ZrGTi0WxawSWcnBIUiOOoazXh2232wCYJ3Dap2DyRcFv/rPaPSnHAxWjmMO7oANuvvoSkS3dNV5Pjxems6o+V9ytPz+FFdhyLAs77Kv6lDEBDGFZZT1rMCOdjk2KryYkZlui6rW3CXSNDOZ1RdipFANdq8v5LyaSiDLYGfbezY5QTNznSy8dc52VJ9ls+M+l2LLrCFBarNcJ4usM8xbwgOHiILMWHHkVBY7EkgQsS7zRfsmA3m8P1RCWSpnAhIJyew8s6hDC2rtk0VQQaGdQcy2TgANc3YGoMvZ8g6xSrNRk6MTxDlgGdjM8ZcXB2x7zuZmx16YARROsNCyl96SXudaQ1wVdKiPmCLKN3O5JNHfqzGCLnWxzNw7kUTUvJgu20WdiYL8R9eMW/A3lhpkB18y41vql/1tPZ+2ru0RuL1am/5nrrAxXbqUZmQQoATa7DUEgebG1nQ5fsANy8TcVHTYEBLTzGiHI6s7zQ+42L8/RU8iy8FFHqDKj++8XyOTr9QtjiLDUgf69e2uqgva3xp5JneX+U5V3X56Jz7cV53Y/92I/9eFSDIPLhmMtvVtCZ5zkaWpQH8E//6T/FX/2rfxX/5J/8E1y7dg0f+9jH8Pt+3+97pMfwQEznz//8z+Pw8PCB3/QLX/gCnnzyydd9UA891htumFZrsjejITdCvY7EXEiEiRroLJasTmcZwtdetnw6QMxrNG+zFveBRoY4ngooETdP3TyOBiKhJTDUPkQ37EsvXagcQBNvPTpYrREvH/IY1xvr+0QmeZLDAav30vtYz/00xmc2FwfDgszRcsWfNTIe12TKTUtBMOavXuKGQzPmGhniyRnnQCJLnMh5bRO/2RBwb7dkucYTMbZwNjf65Y4PORfnE76/AvI05eZaekqd5KXG9UaMSsoqdy7Phb3aVlEFmmOo18R7frWawGoNf3xIyWOryf6n40NucNUISavl8wWQF4xaaDYQ75zS6Eaz+WZzRotoLuQm5/sJ86xukb7f47+34kLZbFYmHhD2TGJ6uEmXjMeFZLGOp1yz4iobi63lV7p2q4ol8Z4bdwXzIYrTMV2QY1nyPFPZRHYpkfaXKbNWMIT1hvcDhFFay8ZVXDqZXynyuPlCJOaB7+N9tdYBMlXDgW2Iw3QujFktAkMcgeF9tRkH+zWRMHs0vXq5WtudtuWRArAewbBaAVs6zwKomMc0td5XlXM6cdet+uESy3Z02h/b78l9Oag5JjcqdlSkvi5JWKDScz4YklG7fAn+2hW40YDKg8VS4lcKXmOJ9kCawn/L2yxCxff7ku3aMVku0pSgaUSAS5ARdh2LH9GIxZbXWZ1N58vKtKfd5vNE+yYPB/BXL1m2sZP1F7dbKUK1qOLQZ0qrSaObvKhMuLSVodflegqBxT6953QNrzeI7SYLZVnGIlEiebVpwvv6FXF89iwm6VxqUU+fAQjRwNILxWdftQfQvh8F4NVMgTRnFhJ3g9p9g9Ua7sqlnagUjV65aBIEgEZC2ocqa30nuqVeAPG+eoakKdDvAcK8YrEwpYC6ZKPVrEA92LPustRigZSBdZ3OPaNJ7jd0Hi7O3058yrZy7Q3FFkH6ul8VHNaKKr7REEVCBVb1912j8VDHux/78Y2MPYu+H/XxOPV0tttt3L59GwDw5S9/Ge9617vwxS9+EU8++SR+5Vd+Bf/qv/qv4u/9vb/3SI/hgXY33/md37nTbPpa41//1/91NHUz82YM2RzBORrInJwi3jkli7iR3EznWcFXM5FuB1it4J+4KtmZBQ0qJKpB+zFjLr00MZo0TDP8EOnIiqkAv6XIKTU3cL3hV4yUfRUFj8PLJsp5uNtnZE+aDW4qvEM8kU2VRnCo9BewvETX6xK0FhXgMddMrfKfnnMzogyXgHIFRyoXdsM+50DAICTvM0hPkzscWYVfowgAEDCpQY4Y+ISbt7j5A8j2bLdwA0rtNLhd59ZlKfygt8s0dTvsv/RisCPAQHMDFcBoDI71L223BEjah3lyRkA4EWa327GeL3gC/3g4EtMNyiI1IzKej7nBVLfbNDEwAi/XT8CmMaDCulkPmwB37T+s9zvRzVbcOSdTso3tVlXwEOOdmItsVAsJ0CiPgoz3ak3gvN7IcTkWBEIAVtrzm9qchDunQLuF8uYrVU8qCJDRbtl1d1lqQBertbE5ypYrC+7E4RYQs5/jQ7hDkYAryDfG9KwCXb1uxb7nBQGgmqbUwCRWazpHa56tyHVdr0vwqtdUP0evS1lyA6vy+Ka4Des5rDdcm9KrZmtJNrzp8SGLOyEwC3c2R/jay2R8VyvO8XLFNdftsPe626liYbq8P+KtE1sf5YRspmZ+YrXi8+fwgOesRZR2qzLVeZRGQtpTnCYGFJ30NOvP6RqbwZ3JPb5hcS+cjTlXZQm023zWTqaV2+14Sqfng1Hl4K3r8+SMn6fsuBRn4uk511q3TUZ8uWKRZFvy/bSwOJ5ynW63nOc0JXu+4b3D/vTS3K1DnsPXXGLvNWKIuN76QJUxLGvXt9tyT4qruWaLApwbMVhDIeZi4pjr0sxyLZ/O3rcDvHyjAZdmCIWYUHlnP386ex9eKD5rIM8+K+NzF7M5156THN5ut3oerzdVvjDEnE2LH9Im4ZS9X61f1UjoIlB0aQbEsAOi647AL5bPmdz6xfI5AtsHiUypX4Ntwb9HAkTrx/BaMvM9SNiPN3LsWfRXH/v77f9/R6xRsP/pf/qf4nu/93vx9//+38dP/MRP4O/9vb+HD3zgA/jP/rP/7JEew4MjyQvj9u3buH37NsKFAPN3vetd3/BBPfRYLIHhof3htb6eokCYTOGfFAvgRgZnLqrsG/Tanyl5b8gyAsBcehzFAAMhUl6YZcLwMG4CgRskN+wTaGp/2KBXyffWG4KLwxE3Bqt1tUFV98flCnE6N9AS5wtzTFRjGn5+ajJVNxrCD/t8//WmcjitDw1r9w6xcCY4d502N3bq0phllA4LaHedNvuYYpReuiUdReW44Cmh9JcvmSFLlE10nM0tWkaBgOkNGhl7k4otAPaEuk7bHFHDbM4N/PFRFY2gLPRqDYBsg4HQXhfYMBfO1YxRlCXzhwcEWNtSTKWETYsRbrYQ1roBXLsMN5lxA1lsKUEd9CvJoDJ4ut77PYSvfR3Jk9c4J9stHZAlzsC5miGGxsRIP5VJaAc9A6VxNrcIGswXnBctgNT2XE5ZIu/MRdN1RMJZiPnNbM73zEVmLsUL3+3wvNLMCgPGgq/WXH+aKarOodstwskZfELWxXJlyxLuyauUUHfbsHgdkZtrFqEyln7Y533S71nhhH1msiZDoAsnBCRKYSfO5nCXj7lWt1vei3lO45nxhKB10Oe8aKRPI+N9pvmSjYxu0OWGmZ7nY85fAJLDA4t3cK0qPzFOpvDHR0CMZmIVzse8J7ZbqgLEfRiprPFQrRV1tFYjMp+lZlRjma2gUiGuNwT6mvVaBpm/RyevdZ22gW97FoXAAlYo+d88r2Sy4Obf9brwcv+5w5HkQDakYJBwXTUy/v6ixqTKs9EN+9W6HfZZ3NF4jRZZTzOKKqoIK4QAHB9Uz5KmPOfyAnjrE8DJmR2z9mkDgM/zVzWgebF8Dk/5fw9wngZC3Q5iCHBZk8+z0dBijVy7VbUreE+5ux5nWbKABuALt34cT2fvA0CG1eYvRMq+a6xo/ecvFJ/F9d6HEUOkZLX9QWjMksp3nXdVm4A4aAMwWb8xnfpcFza0XCzh85zZsIM+/ENIt+uZnveSy6oE1gprr5OVjBLfdK/PiOH+erU9SNiP/Xjzxj9r91uUr4f9nW/28eUvfxmf/exn4VxVIPzjf/yP4/r164/0cx9ax/V//V//F3737/7duHbtGt71rnfh27/92/Hud7/b/vsw48d//Mfxrne9C4PBAIPBAN/xHd+BL3zhCw97SECnJUYfUp2XzV8sttw03LyFePMWv7fdmuzLHx8KE7itKsAhcOMkci036HEzlJOdwmZj/yVT6IU1XVushDscWQadGkC40VBeI3Er40nVL6RSUjWrUAdQNaZRV1HtNRU3VCyXdGvdbkWGJqAoMEpDN15xuWK1XPqu4iZnFp6yw87x/RsZz09+LxiYXFcZhU4ktSFyc356Bnc4YpB8t8M5EbCsI05ndi6uRXbMJMvLFcJkhjCZIubskUNtflyzUWWiShYjAPijQ2athmiyZGUD0W1zgybXDduSvYoSfcDM1YI/a2QIp+fAy7eEfXa85o2MRYuzMVkycTTVyJQ4nZIJmc6lfzYlE7Ja89jbLcaQCFtIg6MW2TOgYkHF5Ias7dYk2Qbka98rx2OuPZWgSmZr1CxQNRGRrE/f7RhbrRLgmOfwRwdAR8ybkgR+2CeIaDDOwSXsp8NsTrZRHGvNnfjogPMzlf7p0RBxOrN4EtehG3KYzHj+aiQzHFhBQs+NF9OT6dL1otdR5d/C7rhmg3PdEJATxBV4sbTsw6jFI2U5tXASBEyenot7NO/NL9z6cRZyMpHIyvXxwwENtuR6uizlsyQvpB+RER3wrgLDC+aA0ixKomfS1DbMSbcjRYNopkmu3yMgV+AyGiJuC4TVemez/yhG3GwsSkfnWM+BBleS66ju25223E9y3TQ/Uo3XtgJWs5Tvp9JpoFZoAosqwz5ivwPMFlUkijyHXadNSWmMgN6L3jOzUpn3Yos4pjFZbKg6I5gxUiy21huuAPBe4+nsfdZb6CXeRlsLYlnyeJKEBaLlCphMudbni0piW1NjhMn0VZkA553Jb+H8zmufzt63E9US5G9OzAt+vv7tKkuEV24ThIuxGcrSJOpR/q3HHwPPzTUaBLwhWM/p/YZtLJ2n1FzW8I6ktvbaugRWmd6HHtutxWnpNXsqefbR9jbvx3484NgzfP9sjsdJXuucM5CZJAkGg8HOzweDASaTySM9hocGnR/5yEfwbd/2bfilX/ol/OZv/ia+8pWv7Pz3YcZb3vIW/NiP/Ri+9KUv4Utf+hK++7u/G3/oD/0h/KN/9I8e7qBiNBkispQb7V6XIE36z5zk0blmkxtUgBuYEGwT77LUAKDJTzc5gYUyXpIZGZQVUunhoF+5387miGdjk3gBkD5IOpmaaUaxtXgCZFkl2RXQpgyCMqw7MiPrR5PIFAGfKsV0LUoKdXOkzrVxvgDeepWyRf1cySTUHkGIK6M5kx4fmmwL2+2OIYhrNdkXKb2RSCijK8/OYRmmhyNhtZhNGSczYZoEkHfbtoGLq3V1LiEQKIRAMOAckJO9jvMFv1RSlvhqszmdS/h6VknxxL1UgSUA/qzThheZbTyXm02vj4JnlUmHYJmZriWS3cNRtUkWFtqJ6QgCY2piIec1nZkDrBv0pYeTgF83i3EyY7+lmKu4RNij1RrJ5UuVO3JZmuMqQFlyPBtbjIRduyRBmMx4vSSaAmVJeajIR5FlVVyJzqdKZ70n6F9vLFcx3j7henGOn3vnlOejvbAxMgdSpMuafxsXS4khqbnWtluUR4qaQPMItcBhOaCHI0pAhwNea+15FWdpc7gFwVScznisS2GlpnMrXnhl1xOP6+0P8tiFLXWtJtShFyHyWl+5xFgOAZ8IsXJsLkteu9Wa57VeV87Y0qv8YvncjmzTDXpVwSfx1o8aC5pSqTTTpVXh5g0fWrgSJYNLEt6bxZZM8PkE8aBvZljwnq8VN9mwWgPjCZ+7qzXvpVbTTLU0hsn66ZerirkvGd+Dl2h6g3kNeKbSbz6jUZAZDelzWlsbspSy/W4Hfi4y8BgN7LhGJqx8bt+7V4/lC8VnKXsuS/YhRgFk0k+uz+d4PrFYJnd0wPWoBSQtgsjwjQZeKD67I0O1aVdgliSI22LXJbb4rDGfZnAkbQ3Y5MC1S1QRtFvsYf/ayyyinJ4TkE5mYsjWqozGJN7FZSlKzVnNi1cFcjuba3Xx9c4Y2nv1xsY8t3O+sfr0q8p37zW8OD+rkZDOw4vlc4Dz/8yxK/vxzTf2a/Cf0RFf59c34Ygx4tu+7dtweHiIl19+Gb/6q7+68/N/8k/+Ca5evfpIj+Gh5bVf+cpX8Nf/+l/Ht37rt37DH/4H/+Af3Pn/H/mRH8GP//iP4+/+3b+Lf+lf+pce/I28OHd2O4ycOBhxI3zlEjfXKveRzaI/PpK4iq5V7inJqvrcHAC4hhnaQExpFLD4EfvQXBDAez6m1E7NXRpZtZFai1wxrxxR2R+awm3ySvZZViyYycdKSkfjYkkAUzOqAUB5bSEOimr+A0CdP+NqzQ22uBu6o0OEX/+KyXDjYllFG6jbr8Ry6ObZnU+4yXMeGA0RJ1POi/TEGWMl/YDOeSRXLgs7yw1T3G4RNZ7GO16j5QphMkVy9QqPFaAUuMX+uziVTZT3vJbjKW31na+ky2o2FOKuvBZgIWC5qvJSQ+TvZhmD42/eIluZ0wDGSch9nMw4b91O1Te6ppmSraVNXvW+zhd0N64XCZLEjJ+cxtGoVFGvkfeAT7juioLRJRo4X3ttkIKBf/KaOP+S7XHHR+KUTNMO12lb7A0EpLvDEWV4nQ7C7Ts0zNHr1WC0BebLKiKiFg8CgP3JsuacGE3FYgt/7Qr7EiUuJq7X3OyWJZw6mqrxUozVPVEGcRd20rO6ZX7qbI64lQLNZkNQm4oCYTaHGwxgeY0JnZBpeJMDBVlVf3TA65MXFmti/d7iDOxEFovlisyrzqeM8nxC06Kt/NXYlgRFW5VXC2PvPHD1MsGZ9Iqj2bD70186IhgQie8LxWdxvfshgro7p5TUA2TBG4wOcirDBiitfJTDOT6fVGLb7ZjDqV5/d877zyTp2tsIZlFi0Ofxj4ZVXq2YlMWzsfQVyjrqtFlMunzINRblft3kQKdj84ptKcZu8nyazqtn6XxJUKrPkdGAvz+bU+kQJWpFzXXAImNYLk2yaf/1/x4AAViOcSfOM1bEA2Z4FSZTmo6pIdjZmMeu8nOds+12B2jVJaLGtMYAlyRIRiM+MxrJa+dPamas83TsVTfwEMmK672ZZVUf+XpNd+qzcxpZSa+6usPiVaSqwP0313qcF6XD6jxbn2N/sc3jNYb2VmtPsfa3ApT31v9fx73ciPfjwceeuduP/XiA8XqYy29SpvOTn/zkzv//rt/1u3b+/+/+3b+Lf+ff+Xce6TE8NOj8nu/5Hvzf//f//YaAzvooyxKf+9znsFgs8B3f8R0P98vLFZDQJdXFyI1JRrkjhIkAwM1Cv2cbVwASFSCA0zlWrGdzM9hwKo/Tzbxm2sVY9S8BcGLaEJdLfoawQzriZCoumVVUQFguCZhigFPAKWxAGE/hjw9tQwjnq0gMAS11p0wIuDQmbLUW9qllmY9IE8TTM5o+aJ+kSDONwVJjIUDy9Njb5DodnsMFZ03nvEW30FRja5JldRkNJ2c0mRHn2rhccrMkfaHhzgmPSed7RjdUrz2OYk4SS+YkhpMzk9kqwxXL0qRZnNcG4s1blTtqr8NIGpVEO9nYJsLglAHIICAqMWZQTpLrIUa6dAICRLZk6UoyoBAnXSeZpjsGUEVR9ZrFWMVKxMCNf1nCX73Mzbpm88nweg4i04267oR15iESnDopvpjcfL4EnryMePMO30cYUQPP2xK4dAiXF2KUUtg1AEDzLQFIcTojMEkSskOLJY9DXUrbDq7d5usSL73OA66jWmwR1hsrbiBNJK+2Wleu3UbEykyDUIp6QIGr5DZiubR/mzzZeRYDGhnPPQPvybprqt47El+iAAUhUgLrPGL9/pL1E19ZSNyQyD2l39s1GjXJLa+d9XIO+3cDyFrPW1yK6VIMgPPwowGen37yvlERb9SIeWEyG+uzzYRhLMsqMilJKqa/kVVzBfC1vS6LGi0xMxPzK6cqCy285QXwjieBl26ZC64V4dotynLnS4JvzUE9HsGVJY9ntSYrrOsbqJ5X6pydF9U9K0WpMJtZjyRwd1wK34jyUzNHAgxAeilMuE6Hz/52SwqELG7EzQbuyas0SJLCzY31z97Fpj6VPGvGZNuzc7rjiqHQvYb+HPrzS0c0bEoS6e9s7MRVhemM79/tmHzbdySipyEeBWnKNd9oAJt7fuw9x431z+Lp7H337ec0oCmGTfr/DzVE+aPzUe/h1NzSe33ufrz+US++7Md+7Me9x+vJ3fxmjUz58Ic//Ko///N//s8/8mN4aHntJz7xCfzUT/0U/sJf+Av4/Oc/j7/xN/7GztfDjl/91V9Fr9dDs9nED/7gD+J/+V/+F/yL/+K/eM/XbjYbTKfTnS8A3GBrNArATc9AIh00YmS+qDYLKVm9eD4meJCYAus3Gw3IojQb3ED3utxgLJfVaup1uXHywrp5LwZEBTf1ScL/VwCRSZSAZgXGAD9iEDiKLY9T7PSRpTRnUYdJ3fDFUEn/pJdPDZNwYYNKiSdlYmEpZkiykQzLpUStFJID6ioHzxir/jrvyTaqkZBkXmq8hOt2uKlTGbGwJFF7Z9eUObouTUvCZMbNY7GFGw3hmk340VB65Wq9YlnK61QG/ts7e399PyesrPaGOo2sSaQHScG6APE4m3OOY0DUGAbp/3NPXmVmqmxYeQ0yMjC6ZmQ+MF/wq9/jJrTXZXFgueJ8CsMZhH1BGaroHee5TtWsRRyOLadSTWgU2DaySg7sXAWAYiQwmkz572YD8coRvBYZtlto7E9YLuFOxsATl1kUaTKvUq9hXK0YbwMQ9NdBmK4jwCS/UcxK4njKeXce7toVboIFiDuJ/nCHI75/XrA/WPqXo/bZprxOloMq4IFgpc35Kgpze3biDB3GU64j56p518JB4q2nmfeb3wWcecFCj1yjMJtZZIrvdS3jk/cvsziZPUq5MJIEYTzhM0Wl82LUFMe8HlwbW5vLem+myyh9Lk9OUZ6cyj0g0v9GhlgU3MC3Hq37tzsc8RkH6aHtdtinHgMjhKSnOi5XO723CpxcmiJeOmBfZS5FvFaTc6zFNu8qZ+skgbt9xmvqvbUZmFMxQOmqXKtYFIxGUTdqYVw1qxMF2WzXbPC5AvAZrscqOwUv53hR5grs9i3eWP+sxXwwOqYW5zEaWqGMESUrOuMupehSbLk2ZFzs09TPioGMv280EENE2DCapd5zWgdTN1aftkJVPD2nU20jo8Rbe2nlmecPD6SdoRT2ntnKcbPh9XHe2NgHMfqxc3CevaY14F6XAev36r2q11sfQCLX9UHBZxC1SaLO6BfGxb7cPeB87bFnMvdjP77x8Tj1dH4zjIcupf/SL/0SfvEXf/Gehj/OOZQP6Vz3z/1z/xy+/OUvYzwe4/Of/zw+/OEP44tf/OI9geeP/uiP4i/8hb9w1/fjdAYctcUqPyfbud4A3TZ7cfQPmW7eM5qTqJwSqchzpf8s3j6BOxgCs1UlWUw83ELyB1XepdmHZbAoBq2Eh/NxJaFrNsTIJpG8yglZue2WkrBGg2zicCCsT4Jweg7X8xV4ylIyE2KoE4tC+jwLYBtYiV+tTcLqehJroT2sskGn22JhWYFxuYTvMBrADfvGfpisWMAsN4uyyffeGGR/+RKjOACaAHnPvtpXblcZmwLKbXOdthFPz/jvRoNgWgBxVPlku00w3B8CszlCWfUvogyI2DVZMUCnPbFyTsZMA0C/y7Vx+5Ts3nzJc7pzanJngGZHtj6UqQS4AVbmZjwFjg8QX7ljUTI0zfE0h+l0eF4az6NyszpTpKB3NOR1L7Zwl4/5726b60zjJgCun6Iwps01e9KHV8CNmTtrwHS7BbbcMCMv4KYLrs9IB013MITTXr1uh2yVyBVdr1sVMYotWZJmgz8biqQyZTFA4y+ixIE4iTeBd3JPkSH3R4d276hzsusOKWPvtKkUUAbr9JzAU4o02o/sel3E03PKaDc0gdJ72w37lHHL2qa0M1aMcU0aD8deOd/pIGo2JkDJo0YOhVBJtxuZOetitaY0fLE04y89TifGWE7zbbdb3qvzBfRPUFitkDSbSN7yRLWWNQ80L+CcJ+B8xLmENEfKaMJUlpSk6vNBjhuAgfY4mcG5yvwLAHDzDu877WGe1Qp7eg1kzsMrd+juKgw9xBTHdWRtbzYiDWX8jxsNyFR3O8BiScl9mlAW3e1w/uV+9YMegS3Aop/07QJAOD2z6BJlHF8sn4NvNHakttdbHzCg75xH3PJaOOf5vB70Gcc1m/OeiJH90SGShZfCBCAATJi6uiT0YoyIb7dxY/Ep+3/t49TxdPY+gmbtg3WOz0fnKqM4Wf/x9JxFOVW6iCOyHw4Q5guE9Yay4W3xQL3COzEv7TZisb1L4lqXt6o8+YXis7je/iCjnfDg4NDJ3xbte67/Xii2Dy3X3Y83Dpi/WeB1z17vxzfliO7h5bJ70Hnf8dBM5w/90A/hgx/8IG7evIkQws7XwwJOAGg0GvjWb/1WvOc978GP/uiP4vf8nt+Dv/yX//I9X/vDP/zDmEwm9vW1r30NAOD6fW4ENrlIxar+IN0QWixKs2GxFcaQNhvcJC5X7IeRXEp9j3h2TpAhxg5IE0q7pMoN6ZkBIDIhATrbsmIi1ZE2S/nZqcikBiI/dN6y5uJ0Jsft2ce5yRFunVSytxDINBSyMcoyY3n90QH80QFZmGajkp1qTMp6YyxDGE/ELIMmOXEyI4u0JcsY82rjhUZmkRwaeB/XG27eh32LxIjLFeLZmGBHsjaRpszi2+QI0xk3TmpM0mwQbKUMMXfDvrBRjH+Jt094HOoGmnj2xOpxFVuej5gwmXGSMGrO+araf+dMmDPHDa0adXQ6lYlQwbw49+RVA4LKHMb5opIajgbAyTnXm/c85tGAm+Ocn+GcOBvHWEUYbHL2EHZrLOpGeiDVfXmTVxEUacqNpebuaayPsn+jIT9zWxKoao6m9rxmaZV3eOmQ66/fE6ZR3Ekns6owoqy5mProiOs153CxYt9yJACPszmjI9ptgrG8qO4nWVdQM6RAFjAqe7nJgV4X8dYd/r/MrWtkVXZsStdUd/nYejXjbM7P77RFzslIC97bNIXBas37crG0HNe4WAoDz/7TsFxa3IWCvCjxQ7rRjxveL64jmaDCwutcmOlSRgbbjYacV3WmzjJumMVgy2Il1IhI421WMr+pGuE0HoiRer3DtVpijNWEv3RUmRoteX3R6wLNBkG3yvpXa86HZt2WwZxt43rNuS4KPkuWS14DyXv1l474wbky1zQbirM5P1PiMtyVS3AHI2PSTbpbFJVLtM7tsM9r3mjwnm5kNBZSUywASBI8lTy7474KYEfW6pKE/y8GYnFbmFS/lGimMJ0h3D7hfOizHDBFR1guEfIcIc8JwGrmRfqlYE+Z7yCMcB282WtCrP693lQmVWmKcr6w56+6TLsmczhds0GjoV7Xnu2u2aR7batJtvVhXJFjYO7utrirp3IHmApgfyp5VmTwrw4SLwIZl2aMqmlkd7HSLkloTrQfvyPjzQKCe8C5H9+MQ+W1D/u1H/ceDw06T09P8Sf+xJ/AlStXHsXxIMaITc02vj6azabFq+iXje2WmzZlkpoNbuCGg2oV6Oa0kUnot2w2VS6qoKHYGtAKqxX/fTAkS3PIaBLX73FjJn2gVokXAyDXaJiRjDqhRjENcgMek9P+UZFCKgBW59K4yaEOmPDMsDQwLFb+BjRklLdPUN4+4Xuog2aryWr3eFK54paU9yJGhrgDzHLsddn/qLJKkWiabLKWkec67UpWG2PFwLZbVQC8SOycgn7AgKtTZ1lw8+bSlHN85RLBS86qvjsY8b/tdmXFr6HzQMWuiXFOlJxLfzDiBqjfo0RNgcXxIYGeXGdjmiTk3TUaiC/f0kUn8ScZ3MHQwDOKgjJK7ZXyjrLqNKmuR7NB1ncyrUx1eh2yuBrNsxW34EzW5Can7HEyMzMgiy9R0xLHjE4aBkVhXwoyTeLQijThsU7nBNibHOGrL/FYpc8XIPgxGW+I/GwFiyJH5cKiEy+85C06b33EAPg76ngMsOBT3/xfkjnfyPqX9Yb1xpylLX5i0CfIXyz5vgqIfeWG7HpdnmurSWXBeFIBVul7s7UBGPvtmg2uM1U9yM80IsM1sqpnWyXeqzUdsRsNi1rxByNxYJae3MmMjL/0crtm0+ToQWTzTmSV5XhcOfWqTB8EGgRgkc+dR8nuFAWPodmg66lzvCclv1Nzg419FFDqWi3rTdfoKC2yOSk2mOmSOhULWx6lGIOa07BrNq1A55pN9kaGku/tfdUy0W5ZccDcucWNunzlVuUi7qkOMVmvyELv2tCKfFYBqeZfQnop2SLACCc/GlbX8mxMRluLblJA8b0e+zC9o/tq/pkdMPli+RzitjAw6VtNYzWViX0qedYkvgAEwLLAaL3crSaSQd/Ar0Y4hdkcyHOUZ+OqJ3a7tWJrEFdq32g8ENO5A/qazbu+d/G1sdhWEtwYKtO2+4y73G+3/BsRVuu7+25j2EtF92M/9mM/HoPx0KDz+77v+/DzP//zb8iH/7k/9+fwv//v/zt+67d+C7/6q7+K/+Q/+U/wC7/wC/jABz7wcG/kXSVfBLjZWbDSHk/OUN66g/LWHdmol9UGWnq3ogaLqzumbnYcXVbRbACn59afGdWgaLE0tirO5mSBdDPX71VgN/EStSD9SeMJv5+K4czBkJtalVE1m9a/g7KEG/Th+z34bqdicjWXbbHisYu0Mzk6QHIkDqgqtxTjFq8ukXXTIumzc41GZfoBVBK6GCuZcJIwgxDg+zQy9hIpK+a9SbHC6XmVKekdWcdBn5JGMRFSJsg1GraxgnfAeMperfkCrtOm66oA2lhUOavGNuQF5VybXKIZyJ4a6Ba2zTUyxCtHcJOZ5SIi8QQ9kj0KgMD/6mWExZJrxAvQXayqa7re8DqmqYC0SBdWL0ykSEixLeEPD3jMnQ4BoBZA2i3pC06q3sUYaZAjESJRwWajQZnxeCKbamHFxLgqFlug361FnVDe6IZ9y/LzhwfV8XsHDHpVD12/w8/UyJNCTFmG/coB1jmuOdmERsnMZAGmY+6mYTyRe2VrrtK25qSA4prcxGuUCrZbFnYOhuLmnFZxQBKBAyf3+XzJ79WzbPXc+j2uKWXvJFuUL/LcpEvRQGXf9hhR1k6KHZDiCHodFmPqxlLiFE2jJM6Zf/Iq76km3WjDasXnDmBrHDEgUYdiea94NhbnWIkIKsu7WKU3ekRlhouCAE7vFUDcZIPEqGx4/aSYEM7HjBeZLyo5pK7bBlUk5pAq194Jq+9aTd6r5+PqWecdTZq2W5R3Tjlvd04JsHItugUqEZxDeTauzmHKZ25y9cqOrJzuyOwrx4WeyfqogxtlQpntqetdJP/CQhN80oHZdztiqBURFssqUgoESk9n77Pom7vmvixxY/EpY1MB3N0jmSQEsZmci4DRcC5GY6W6KQdTvgCyhjdce87W4RpeDLDidte0635j57gjAeRF9lZf92L5HHuVhVV+ofjsXX2xrzmcZzFSPuOi8dO9DIxe7f/3Yz/2480dj+09GF/n1zfpKIoC3/Vd34Vf//Vf/x35/Ifu6fy2b/s2/PAP/zB+8Rd/Ef/yv/wvI8t2q6Y/9EM/9MDvdevWLZPqDodDvOtd78Lzzz+Pp5566uEOyjsAzlw2dzImnTMQFpdLiV7YVj1Zec4/4qsVN2Fd6U8SW/+dIHSt6mvvIoA4nZK50jy5NGV8ymJh/SlxI8yH9k52O9UmXlxw/aDPz+p0jOEDuLF3WcYN3WYjUkEx2hAWE5o7WgfearojZkfmxtrt8piThIAmS9l/qvEReQ4XA2JREHDneZUPWmz5vbJkH5W4+WqfYzg7J3u6XouxT8JNksgP42olrIZEU1y5xHPtyvyJdC5Mp5SIDfrWuwrn4I8PLVIhiuzNYk7ynExVq8PzGQ2sN82cgOcLuFvsP3USb2E5kgvGhjhhccNLN+EPRjynJ66aUZQ7HHEuyhJuvuB16veAxYLnIoHsanSCPCcwOzrgumiJ0U+jAYRt1bvXbMBdZSwMpNfODXoEInJ9XacDF4P1EsI5xKMDuJMzyfIrLHcTGY2xuPEseNzLVcUaqsOyMLBuJmBYQUS9N7QogGuXyb7q+7eacE32UVqchrgW+8uXuMZaLSvewMs8KWur7qbKkIVAQA4IIBG5JcB52Eo0jwKZopD82rRyIdalv17DFQXNoTZSBOp0hKmjXBQF8z9tfQIiPS/hux0C0w0NgjALcFlGh9BGg33jWcp1OJsbSAmv3La1ar3TLUoc42aDUsyFNOMXANexPEt8p4Mg7sdPN97/SOW1UQFdCFXfdpYhTKbWz2lyYilAaK9klF5Xl6UozyfwWljbbCifvnmLa7Tdtl5YZY9dpw1XNmyNQx2vnSNgEhMhVTEgTU25ENdrJJePKU/XZ1qMfK5cPqbM24pTLBDcWHzq3s6rAii1p/PF8DkCxSaBMV2tg5lc+V63kmcL4KtfH9dqIkwrlvJeRQPL6Gy07P8vgjc9zhfyz+B678OSNSr5ugAsQknvW3WWni8QpnMWPmZzOmFLn6fvdhDmc7hej+DuYrTUawydw4vA+O4T5DPj6cb7Hwpw6hxYMRK7/X0xRFxvf3BHYnvxGOpztx/7sR9v/nhc773XYwz0zWwklGUZ/uE//Ifm3/Jmj4cGnZ/4xCfQ6/XwxS9+EV/84hd3fuaceyjQ+ZM/+ZMP+/H3Hs6xR+58wv8qGycOoxqZ4tKUrqFFAXd0wNcp25H4Ki+zKBAWS2401ExEpJc0uWkxPy5LKyOQ0ZCbo25HMi6reAHXbnHTL5b+cTqrJKhlaeY5CBEu8tjJ6OUEPLM5N4ci71PpIGLkZ6sUzTtjlBSUqespe9tSuOWSG+NWE5jMKjmkDnFc9V2yHbHYIq7WYgIihkejobFYAKxf0fe6BHpJAqRkg7y+Vvq4IH07PhFpKoBwckYmSCTF6sAalXlRgL5ai/lIu9rwaXg6wOuhbJS6FRcFAWgIlexM5YDDQZUjuRH33CZle/5ghLhaEfienBlI1cxKeEfzJs01FPldXK+5qev3KFEU4GQGPaVcx8WCnyXgCasVixfC6DkFAiJTBMDr50VWqgzezdv8fDlXOCf9bznwxBXg5Ixg9vTczi0slvDOSX9oWjGCAIsvo0FllrXJKVH3qJhGlRarOZb08WkfpwPYgzlfEhRLjIbGAZGBj7yvxK03vHIbToob7ugAsdui/HCTA2mTJlDek1EV1ssN+xVQ1oza2VzMmGjsZfLl9QZYyXEDdDI+Pd9l/kW6bDE94nCsfdAqCweAMJF7uCwRc1Suos6RBZRCk91TjiDGeY+w2QC5mM7os6nZQJjN7R6CuIE+quHb7cptFiyMsU2gcs2ldDM3NtylzBGNmw2lkJsNewel9zZucrhNThMfMX2y99PPEQMcK/oVXBuxLJmbOptbsUsZZdelGRWShNm+8wULifJ8AoB4647JtONsbvOqzqv1Ua/Ia04nQHDj1Mm89oz1nQ7nJlTPI3OcVtUFUPXrCpi8FxBiLnAlWb+4UXM10OWlNcP1unzuSqau5roiS6vnvEr8TyWb82xMIK9GYo0GSolVicVupuhrjevdD90T+NXn0LdbgPN4sfwMCyYyVw8CBFVejDRFWCzv7vf07p6fe9HY6HHd9O7HfuzH7/D4JmYuX8/40Ic+hJ/8yZ/Ej/3Yj73pn/3QoPMrX/nKoziOb2jE2QJodLlRXIhTa1HAadagbt403LuRwZmMNjG7/zidE0isN9w4if28awpb2GoCMaJ8+RY3me0WXIgIwmg4AXd+2AfaLXiV9TWbwHxhId/otAlWxUTESXyDPzyopLoAj6FWtXdFUZnmqKS13r9Wy3aE9qa1mgh3Trn5kDlAUXBzcjhCuHVCUKZ5eTqnxZabuNlcWMOSMl0nsSFiDuSSxKIjLLO006nmXHNEZW7ILEZzznTdDueu2RRjkwYZLdlEuTS12JF4es7jzwuLQkC7RQfefo9AoNW08HkzHnIeMRYmn3WdtuRDbis34dGQ0kEJg0cIdK5UIADpberyc8PJGZk8AJhMCcbUuGi5ohurXA+Vjzq57jpXmunqVmuuB50fiYeIUzKVBu4XlFDHsoRbb4AsiGvukp/nKnYIWUqwNxwAZ+NKYlrUCgHK3kvxpM68W+9wpCmKm0n8UKcNdzZGPDvnPOfFToapa7cI0ldrxMMB3GLN9XIwNDY7Dnq8/1Yl+zfznCy2MvUxwk2XlTmQzA+KgmyrF3OoO6f8PS2czAnko3Nw2y1/T9nVrZiIxVhl8YoRmN5fMc9NHu71Hk0S/lulwBLP4rptAWMJ3UslFxebnFE+nbb1HwLACzk347qOdE3FTU6QULuXNJbkUcamhOWSc+ScnbfKxAHphXV0JA6LpTllu1bTwJVTeelqZWA5nI85h2q0pjFN+kw7PLD8zrhc8lnQyGj6pKZKi6XJWGnkVEoPIx2Bk6MDvkZBfeRzENstwmy+w+RpbE0dANbzCeuZkl7PrfZMRZLwuS33HcqSz8YW39e1W/DOGwOqo/55Bryct/iSp7P3vWrPrm81EdZcC+o+DkCKgmUVS6Rz0G3DrcVZWNUDMdrfkigOsHFbwGVNYLO+zyffDRRjsd2ZJ5Ur15nPZwYfQRCw7RsN+G4H5WTy4EBQDN/UAbc+Yoh3Acz659/rmPdjP/ZjP96I8bgxnQCQ5zk+8YlP4MUXX8R73vMedLu75NN/99/9d4/ssx+6p/NXfuVX7vuzn/u5n/tGjuV1j7jZ0AGzLLmBDMEMPzTPMm42FXsQBJB4j3B6xo3zGWV2cTxhj9FyBQz7ZLpW653sw+RgSEAoTp2+3bbPh3fW60MGKiWr5Rw3QMIoWP6iBND7fo/HNOhV0rvlysLB1RxFAZ4b9ChxbDUJ3BT8Sf+eStY04kGjCsLpmQHBeD6BH4ibqAa7QxhD7+jaqy68AihjnstxFZWZjzCRrt8jKFXAJwxXmExtY285jSGwcq/XcLWmbEw23AasS4njOKVTrOu0Lc8R4qjpsoz9n2LA5BQkOEdgWHeSLcuqT2y1JuAcSlRIs8nNd15YDIgfDatNqAIgkUDG9VrmpKgARrfNuIdW01yJXZbShViYTzPEabVYBJH+XpeldAEVoKJSX3OkbWScHyeZsJsNwp1T9pJqxqeaUm3Lyj00y2z9W89mGcj6a1FAnXl7XZo5aR7jpaOq39R5FjYkzxMAQfiAjGM8OUNsEtzG2Rz4ytfJhkp+rZqaKKMJzVaVtRpbmX1Rlpzx2A6GFYBpZObA64cDxGHPHIDj0ZBMp7CccavOxpIh2Wmz+DOdsu9SYiUsJkXZn8Tznh/2Kdtcrem8PF9UvciacSt5oIwmWsk18jvRIiHPcb31ATjvENYbrpe0yhYOqxWzH3MBntIPqn18j2KomZJLEivg2OeVZSW1TxKTrPpuR9y7pYAhuaJ+ODCQ5ocDFkWaZOmYa7mGa8m9pc6/CgxVndHI+GyxQmGwZ4trtcxFGCFW8x2DZM5mlaEVBGjmuXxVTq0KSu7FpAHiJuscyukMvtUkqJGeSetxl2sWJRLEZTSbQwhIel0kva4xdxf7ETVKhYy4txiSe/UnxjyHz+gS7tKUyhO5RvY3rGAvu/azB1XaaN6oqnQAIIpJnPM0J1JDtnuMe7Gv13sfvmdO58XXvVg+B3jO4b3m+n7jYpxMfS6SbudV2ep7HfN+7Md+7McbMh6znk4A+If/8B/i9/7e34vBYIBf//Vfxz/4B//Avr785S8/0s9+aKbz+vXr+D/+j/8D73znO3e+//nPfx4f+tCHsFgs3rCDe9DhW224Lh1h4/mY1vGaQ6lRBABjHUZDAx5xtaJ0So0vAAKT5Yqb8/mi2rQ4ByBaxAYNM3JW1VtNAp4QyHau1nBlZplpzDQUqV4IZBiAKv+u0yb4abeBGXtBbbPQajJbcbXmZkVkl3EqvWRaLW+3dqRxbtCvog0kpsOl6Y5ULEr/oQOkV9LBtbtVJIvGj2w2POZWE0575xoNuNWaPV3KkM6XCMsl/OGIMs1+D1guCeq2W6AtxiVHh9IDKUBusaxMJDQEXj4/lmV1zpsNN6BrcU8FZIPbqJhPMV+KqzU0fsSAOmCRFxqvYK/1mu0p772VXrOWfK7OtbLOwjTE5coMnwjsdW2tJSsysE9Oe4yHff7efEFGTB2Qld0ttpUUWxkcdWBVsNZqkvXsdcjCKUBPPeADwiu3OefO8bXag3Y2JtDX76sJksZYDHqUrnpnskU3mQLdruUoYsONeZhMWczIMmA8YdbmtkS8eYfrNOXGmhFCYpo0nROgjifAaEhVghhQAYCbC/tSVEABatCim2TnETtNuPGU63SxNrDtlhuJ2eAxuzTl8SrDrGui2AKOsupysUTiWOXzgx6ltDEaCxXvnFY9j60m14b0aSIv+O8VQTo2W4KyLCUrjwrIxRDF5IXnU94+4Xs2Mvhel0xgXU77EPLH1zNcu7UDyOJmA3d0QFWEAhx9LgbOB9sGGmTekgQOmUUZacSLFfdUfqoGPZsNYpoCq9L6I210RSUyXzAuZUkg5cQlPC5XVe/0dIo4mZHd73UJPIM39tRre4MC47LEC/k9+v+E6QQqJs1nKeJmA98iaH0qeRbJcGDsM7ZbthbMIaZUOZUtve6OwdHT2fvs+tUZunqUCgQwx/JuMKz//3T2PhqvbTbASvphe10aF+kx2fXLuWZDsMzXOF8AnQ77b/OcLuQAn0/h4XZFYbXeYW5tHpUllr8BTyXPviqgve9I00o2XBsvls/heu/Dd718DzL3Yz/2480ZTr4e9ne+eccbZQb7esZD72w+9rGP4Xu+53tw8+ZN+95zzz2HD33oQ/jpn/7pN/LYHnw0MusBsqr7sM+oCu8qJqNPCVjc5FVcQeLJBGnfYYyUbGqepXdV76aYuJj5h8RH0OGU/XxhIYYsIKgkaMuF5Qk8DpVYNglWo0rCJP7EdaTfypjXbpWxKRmZtrFfrREnU24EiwLxbEz2DCBDt9lYTyAt9MWhVOdku/3/sffmwbZmd1nws4Z32tMZ+3Z3BkJUgmCACBI1gqjQnWDKAGqqY1IJJmCkVGIYqywLYkBBKhaEaMlQQhE+EhODFKQwkjQlGpHCASnRiAiS0CQ93XvPtId3XGt9fzy/td597r3dfW+nbxOas6p23XPP2We/495nPeuZpJtO2KblKv2r9nbGc1xwcs1AFiPJv5peTwHDoW3phXzoEan/aFnbsVxx8tl13E7XJaCHvqeXKzJT4otMvYmR4YgTmb6HP1uNTGcIiXFMQ7yKalIl1ilJf4WRCrF+JqakAontjGmRCSiWZVrQSCPL5Hop7vdkQkAiqZwqz9K51DsL9mnOpqnXldfPjD2TAH/vjgMCVwE09GgOYxpvniPEICCRaOP4NPUlQjr6UBQMPrl8lecvy5JXNnow3QOf4L0afa0PX5bk0gC1mEEtZryfl6uxOqUgg6tjFVFkn6J/+WCfIFV8bwA4QX/wkTHxeTodfdex13FnvI+QZZQUx9Cok1MCyaoE1muoq8fJq5yqMaQrFVWZ0nURJa5XjpKXOnbEBgk6sYf7abv+bEWgU5VkGi9fTSD15678MPsY6zqpKMIwwD38SOqtZa0PK5dC11EeWRTQmcWHuncTeEmHocqsPOj9jp2NIaoQbjX98xZH9J2Glh2TwXuEkzPuTzyfwMigRyAefYuRlY01JcGnz72YDp4qUrRKwUPKGALsWLOUceEjVspgGBL7HJxjErJz/By9clUW8BT0fD7K28HPWh8/u7bA+3ZYz2OybsGPiasxJdkYaFF8nFNQxEVMUR2EyArH6xUoef1Q924A5yWgKiptnEPw4VxX6LUjeU1jPY8w56FuknxYyd+PWGXFPuI+9RLHSix3ciI7wEqXJNV9vG2PO43gHO5374UuC4JKqYYBxgCk6OWPIUjx54/FYF53CaKMWhJwz+3LMNz061yMi3ExLsZTOp6BTGccv/Vbv4UPfvCDqEW5k3JUbuO4ZdD57d/+7XjFK16BL/uyL8PR0RHe/e534/Wvfz1+/Md/HK985Suf+AVuxxiGMWI/hmHUDdmvfkjhOOHhy/QnWUupXNuRqRBGD7En0csfeitST6kHiamh6tIhPUVVxUmuUmQsjYGez8gmZRnC8WmK+o/pj2qxGPvTWrIlam+HE4dWekYllAZdx20cnUi9h8gTY0VG3yewrKqKk7g4CWmYtBsTOaPcDYCwTz3PmchV4zlRZfRWFpw8R0+r+BUBSmHptxL/2mIxdi9K8Iee0WOrLh0mL2ToB+5nlH1t1Z5Eb6uLbJzzUHs7vA7iV4zXQuVZAhtKwjaibDVNzGRy7E+XZA9jpykwlqZHEAgQyFpzjtWCcwmYpkmn1JfESh5VVVCTCQGpeINj76IqC4I4AP7hR9P5ScPRMxcXJJDnZNmV4r5IQm2qTFGKoGtvh0yyc/RVxkCjGIYlqcvq0iFDdbQiMIidtCJTNXccSPKrYbhVnoG1Hez3DGcrMkfDMIIKWaSA97z3NxvuK8DjjnUrITCsy2jeO2UhrG2OcHIqdTJyLpSCqjve50cn/H35Pfr8JvSmSuotg400/LMu8Xk7czLmsYZFK6DIUlBVDMtSOwv2dBZ5qh1yx6cJ1MeewNgrG5OK1WKOL/9D3wxdVQK8xAvuPczuLr3EElyTpNGTCVSewdcNPti8K/k5obiopKwd+0JDgJlORranbQl4biPbqSTMKchxxx5dJTVKcdEt9MMINOV66bmkhPtAACTPD97zPFrL94dSqacz9lr65RJ6MqF3W+qlQuxDVezK9GdcpEre0TxP4U6U0Us/sHy+hfg+lRCoZEWIdgGtrmPpgPPg6t7sVSL/9XJM8lkpjKHfbPiakmQLqVzSB/usispzXi/FEKIbyWYju/jB5l1QWvGab+3L/e69CXRF2TAi8PYhLUh4YXL9cgm/3vBvWlQ0xP7mEPjZpxTPtxybj5+FNzuCT0ywb9rHrC7ZZk7vMfelLtCbrf6J+wxrx2AhIHlhL8bv7/GMrdO4GBfj9+G4evUqvvRLvxQveMEL8Bf/4l9MJOLXfu3X4pu+6Ztu67af1Kf593//9+PzP//z8af+1J/C3/gbfwP/8l/+S/yVv/JXnup9u+kR2hZqb4eTgrpJAS7IpQNyUqVgD11V9GHu7/KP3LqmBy/KeizlYuh7eskkaAMyoQKQPH3ousSwphoV7/m92H2Zx0mu1HG0Uije9QQzWhEoxKAS5wgoohTQmgQaVPSJrTbJtxh9kTH4JU62Qttytf6uS4lxU5lNYDd0kuAroDQ4B/3cZyHVKOzMR/9jlJZaw30DWPExnTBs6eQU4eR09FXFOpnVmlJKqaFRO3MBFCZ5uWJnIiR4xzznWZzoOQYXIXaTAmSAYiKukcRhL8EaESxOJ8J+8hj1HQdp0uyvHCVAp3cWDEYSIGn2d3nfCKAKEjKUkiCl9iX1Vz77EtlGud6pd1IWLRITJ8xYBOFYMGQKsVJDpHCp8iPWrgB8vjXivXTjAsfZiuc4ypo30nMpgUsJeEUvctNyYUVpggFjOJGLyZtVCXV4QAZSwL7a5SMu4iil032Ntk2ybeztIOzvymsqMpl9zxTpTT2CZWA8V4vZ2KNpNPzhLn8+n/FR5OkcqehHPj3jeW3a9F7VD19hzcrVYx671JyE41OER67wGJ1P/aGxjiHEYCacZ2L0YpYCXBi4JSyfJ4MFS9ATPbxK9lUf7BGkxY5PmUDHrsXt7eiqHKXf0XMuya4JXAm7dHuZzhXDkKpSQnrI8KbE7b1dApbFjN+LQNR7+OVy9F3H+yN6gMuC7GCeJSCbHiGMgUBtx/eEHbt/QyvS5rKQBZAwLsxIsrDe36PSxNpRjQIQ+EkoGYBzPt0P9e+5LtgH2JKHynPcegMvDDWCHxlTpQm0nZMqlSCKlAlZ+MiuCtMZr9uNJKDRH6mseFivkblGVjGeD1UUBMECzP1ySaZRPhP1bEq5ctMSjG9VXAXnuCDb9fi543/BULW4sHIL99a14T7bns64r0a8vNvHcSsjsb6ysBFf/x5zH7yEL12M37/jQg59MX7fjmcg0/kN3/ANyLIMDzzwACZbfdH33Xcffu7nfu62bvumPsnf//73X/f4yq/8SjRNg7/21/4alFLp+79no+3GYBujrwmqcOcBVKxXmM/4R/rqMVfYow9RmEj/wCcAYOz/dA6oqjRZHusf/Oi/25YuRdZrNuXkWwCBnk2lzkVYgE3N5yiVmIYk+z1dchvOjRJJa1LVSthI0qPE/EeArXYWSW6bjhkQlitPUjGADIaaCKOaUTaWvHQSRpSOERjBpch1E3skr8X9H+sD6I/sx/TZmM4qfkyEQBBmdPK8qYwAMwjLm3yTceU/BrFEOfJ8liZZKVhEpIFqMUuMTpD6mVA3ZO8i4yyANqzWUPu74+/H8B1gXMwAoJpe0j59mhCnUJ6YhtpvgR1J8MXpGUHR6XLrXMm9GpNVJS05egMTY960lPEuZqn+BPOZSLG5qJKumywmYCBDHgNG1EKCd7qOgVNty4mz0WOwUNvRcysBWKgqLhxMqnQPhNWaku6zFdTpkvfY2TLVooSjY14P8YDyfPTjPm7qxGDry8fpugNIYU1Qmvsg0lXsLsbnGE1gK5LFMK9SkFe6P9frtGBz7vrJAoyqqvMT5bpB8FIXFFldJez56ZL+3QgoK3qLw3rD74tUXUttR2Q5VVHg3vzV+GDzLvh+oJxVJJpBJOrxXkmAz5hb9tzd6tBVNVY7DUOq0okhQf7Ry2IZaKEP9kRNUaaqpw8275IOYQF/wqQH5+DPVvxaQm+CqDRCP1B223ap2zQ0rF1JvbBSU+NPlwwOE5ktAH7mxYoQgNL4zI7VNTXtDaETBlTA47VeyeuGADCl1Vh7Er2o3vP4Rb6KfqBtY/t9L4BT2Yxg0gd8qH/PYwcW1Q3C0N/wGkdWFoE1O365TJ+l+tl3jaFPm5qdr6dLnqudefq7p6qSabVlIQuvGe7NXw2lNY8xLgg8xrguSMjy97V8Dt5j7rvu2HxcjASelKfzfvfecTHsmu8rY5Jc+WJcjItxMZ7WEdSTe3wKjw996EP4nu/5HjznOc859/3P+IzPwO/8zu/c1m3fVJDQV37lVz7mz370R38UP/qjPwoAUErB3eIK51Mx1HwGeFCSFWVbXZ+KtdOwlqBDvJAxwIZBLi5NiNV0QvYnJs16D0y5qq1aARqx8sMYTm4yJ9UcIk2NdRQAk1erEnAyAc4ykSVSChWAlEYI6YmjBLThvkRwG49hU6ei+9C0nMxNKgSMnXFoWrKRUp+BwYlPbiCIWK44SbGWiaQi4wx1PdaZLOZjYM5qzclEDE452Ee4epTqHgCyjAogGDhlemEM9AjN2FsYfUnJzyr9c5xc9nINBSQOLYJUp0RvLMNCYgeqVCwMbpQ4ipdMFZJYulzxPDb0OqEqoVQ7+nhlHxlashklqsJCxvCksFyN8tuY4pmJjNOYEdzIQoTamZOJM1sTKa3HflDgfDJw7L1sW7Io8dzEEVn0OInf1InxDsen9FMGBuaE2Jcq3Z0hhmO1HSf9W2BfRcYo1qYMbgxfkvOHnTncA5+AefZdZADj+8p7srdHJwS/q80YamUtcHKWWFrV9+O2h4HMahB2ViTf8TXDMEAFz+sn3k44kfQ6z/fipUPpLtUIH/0477uBCdbRFx2OTtivGLsX48Rfwm8gdRfpXpUFESh6dVVVwp8uoXfmcFeXlNOu1gTwsZqmKHifiFwyMobbvY0vnb6OoMMYwHVQ1XhdldYIzicgEqs7bqePjVL3MlWZKGuhq4pMWlWxmzemnTYNP29SMFuPl1avTQBU7dDOEO9zldmRRY+LFBtWnPgrR1CZhV91YzjRtILKy5Q2rHYWvFc2IiEXBYnKWaeULANVyYWr6MUMgddElA4AAdCH+nedq9S4rjJFvzKF/wTnKJUNHsEBCAHukUcprZ5M0oLkdg9rkAqnCLyUVkkeGj2QH+rfkzywMajosSpW0u8ZnK+FkcUdpaJPdmsR6HQpbCeTfvVsym3UzRgkJl5SdU29y82MxwJ9EXya2RTeh9QvGj3K2897PLbrpdVrofd3U+rtuRE87s1ffQE8L8bFuBhP+4iipFv9nU/lsV6vzzGccVy5cgVF/Jtzm8ZNMZ3e+5t6/F4ATgCJvVKzKfzZEqosE9uhxGOp5jNORnqyV0mW2nacoBR58mmFvqd3U1ir0LYixZyMAK7vk1w1lcYbDbVYkIkbBk6Cpc4EXU9AE713lgAARU72STyTKXlQkloBiPcz+vvov2T9ChmD5MOU4Iko0439kFjX5/yf2AgQcrJaH2WafS/psG1iSmN9iDKGzGDXcdJ/9YjgI9BjqfKMYC3LRtAjARdoOwK67bAVAflK/Hnnrue2/HExH7s6o0Q5hJSqGSQsCkAKglJVOW6v5f6ik3NdcuIb6macoMYKGO9TaBCcJ9hqW7LNTct9if63KK+ODHrbjdUdUscTogdRACJiN6yAKhhDeWoEeGUxMkdRiuhYxRI2dZpIQ+sU3BElv2o2BTYbhJMzMlBSMxHaLvV/qoKeSxXv4a5noNKmpkT69CwxSeruO6HuvpPX+q4DwAeYZ91JUGlN8iSGvke4esR7o++B3QWPu27IfGqVKiUwOKg7D4WV5TUJRydkWqOPU/ySnOC7sb6mbvg88W6r+Yz/l8AlNZ+N5zlKbSPbK6oBJb2c9MtxcUpldjyWjqAvhuQwEKhn767zTH4GoKaVsO+5LIa0I6MfK4KkJiR0PXSesyplKwk2Hqsqy6RwUHlOmWJMvL2Nn6fnejJFMeAlETa937se7uSEC1vrOslgU6VS1yXGm0muNcKaDFz0cUb5J+Q8qWmVwnqUtcJyhuQdV9aOiy+iJgnec38FcKo8k/eGHz8v5LMltG2qM4nncLu+5IbnwhjKOYMn4IyyYZFLx3CpuA09mUDP53wPx9AjT+9jfCQ/qgDOe8x9Un+SpXtBZ/bcft3v3ot781cngJaSu40ea7TqhvdP9NRH64j3SdoMIN1Den9vVJ30DORRtzqpCB73Zq9K+7Y97nfv5eKI+Frvd+/FB9c/zvOI6xN5H3czq3VaaNneTgqs+iTHha/wYlyMi3HL4xkor/2zf/bP4sd//MfT/5VS8N7jbW97G/78n//zt3XbzwijhMpG35i+85JM0NkPF0NNohQqBc80TZKEqjwjE2PZkwgfkhzVrzdQuztjyfbx6RjIohQnbrPpGASjFX2EEnSBXmoVhoFMnADa5Gc7PkEqvgcIEHYW3JdNTWCzmBNoSO9iCCxNVxOm3YaerFEIo69NFZzYBueYxLkVEpK8f5FxknPkV1tSxrgaHlkygKBWi5x2MhnPQxxG0xNbN0l2Frqe+xW9qcMYLIKqpDzxbJmCgJSkyaoiT4nAIXoejUlMS0q/LAqGZEQZZQgEU0cnnByLjNafrVLfpL50x1jM3rR87UmVgGNK+RWvlopVKF2XfIdhueJkvG44Ya/rkSkUX2wKx9AaamfO8xCBt1TR4PiUAEtY+dC07IAFJ5jb55fnRJ+TcIfTZfIYwlqovd0xFEaRDU8dlzEZ1tMrHAIXKpTct6pg/yGsGUN9ug546DKwXtNLWpXS1WgI5MqS90UMgjk+kW3mQJbBrzec+MYu2NWGPtfZBP7qMe/16DVtuLgTmi3Po6Qip+ON0uy2HeWxW1JdYJtdbxJYQUeGN9XviOcacWIv39exwsYyBCuBGemWDLLoond3CLSGgfeWLCbFWgqVZ6N0VMBM8uptp5ZK1UVkW8Nmkxi3mw1heVIjBIYdVaV8hvRpcSfWnrDOZTYulElib6qgKovEQAfn2MW5M2dXrUg4twN9tFxriIw1OEf5KCDPI/unqhL+6lF6b+mqSsnWkd2MvmcVlSixCineA1EKrPTjSpUjGLw3e1XyqYauS59/WipHADBlexjIdMuiQ1jX4zajr9WY5FHc9j4G5+C7DjrPYaLHe2vcm70qdV2m/QGoPImLpZKuHb3sfrXm57YxYxcywOAj5/j3Ra6VnjJ5PKkgHuecJIAWw+N8eEymMZ1fkSm/tHxNAok36+UL6XpmN/z5jVj/WwWRF77Ci/EHfVwsvDyJ8TTJa//5P//neP7zn4+yLPEFX/AF+I//8T8+7vPf9a534fM+7/MwmUxw99134/Wvfz2uXr16U9t629vehh/6oR/Cl3/5l6PrOnzrt34rXvjCF+LDH/4wvud7vueW9/1Wxk2Bzne84x1orq2leJzxgz/4g1gubyCTuV0jyhedIzslkxo1qTjRtCYlbCagGXvcRLoarh6ff44xwGzKCXOU4nUd/X5FniSVqiwpe6pKsomPXqEPUWpHYjWHkj/4sBb++ISTbJm8ou9TMiJC4M/ynGCnKFLnH2Yi+y3ZaRk2dQI0PtaUSIpn6HuCCGMIdrt+lPAWOSf4EjIR/VR6fy+d0jSBicxp8JxQDyJla1tJBxYZXUxZjSydeDJT9cnZSipqDNTBXpLgxXoElWepPgLWjqE+8pw0sZvPZPKVj8D69IxgKkpJh4EJr1onibHe3x3rbGKXrEiZz3VJZlkKIYpSTpQMP8FizqCc9YYs13yWWAa9lUQZGVRMp2PYVAhkH7ue1zemSM5nZEdj2JNU4/jjk8SuJCY51ptYMybk5hkBd2Rblyu5T5hKzHL7kNJ2YTTCs+4QCeqE98J8xkWOGJ7ifXrPxIRiaD2muVo7Mr2W4EHNZ2QyY72MMKgR0KYaIqNT6I6KlUCyyBCGYZwQKyULErxvVJHz37JEWMy4T0b8vPF8e0/VgGV1DHzg/otPVlmbkmxTwJDSyf8X2T8myHYE0DmZdbW/m+T6Ks/glyt+bzZlbZBS8MsV9O5O8oQG5xIITcBGFlUiKwgj8t7tDkcZt3WCEBdHxLeZFkfyjOCwKkd5azwHW2mycI7HENnkKG0VllnleUpUjRJZv6LHNgUpacUFIzk/6TkCiLVcq9CSaQ11w8UGrcd0aVnY8hKupff3eE3rJnlknxBsSOqsl6obZbMkRU2qkX6AP+LnsJ5NE/Ood+aj2iSO4BN4vKGvUyuG+Th37ueptkW+RvC8R4YxEAjAqK7QOoXVYRjGQKcsg9nfS+cvWkCCJF5fK+29dpw7X/KZsB2IlWpStkZMEQYYCnQz0vBzADP46xQvgAQJ9cN1CzBPJNe9GBfjYlw/Lt4ztz5UeHKPWxnvfe978eY3vxl//+//ffzqr/4qvviLvxhf/uVfjgceeOCGz//FX/xFvO51r8PXfM3X4CMf+Qje97734b/+1/+Kr/3ar72p7X32Z382fu3Xfg0vfvGLcc8992C9XuMv/+W/jF/91V/FH/7Df/jWdv4Wx02Bzm/4hm+4JRD5rd/6rbh8+fKT3qlbHnGy05FNiX2JoWEfYjg5YwedTNwjA0WpknxvMRtX7dcbPh69cr5TcmfBbcTAlxhsUwsjqfXog5SJfKpGkPCfmJyqRDYVmccYBqSmk5HZifJXqQKJASthveEkOk5Spf9TaZ32XYlUGEXBCfuUbJra25UeTIbeqN0dvkbbkZ2NEtZ+SPLOKEvU0msammbs1dxK4UQIBDHejwxd6sfMKVPtegKjqaS2Sn3JOf+X+E1D8KlgPh6Tv3pMsBS3l+cI3sMLMPenS+7/uk7sdpTh6Ut3jJ6wIifI2k4SlXRW+reqsZIlvtZyRTlpBO6yQBC2UmDR9+eTbrNslC8Lk6oiK5Pno7c3yxLgVAd7BPRR0hdDk+I5WtdkG+O5tZbAObLXmzolzIamHe93YRLVlRMBkhHYiaR2sZDAoS0mOSYmS19mOD1L6azQmlVBk4oM3XwmEseO/l5j+P0YkDOd8B4Aks9ZLeaUz0owiioKMqRHxylUJnk6Ab6PHnyE12CQUClAUm+LdF+HzWZMmJX70NeUOPsYvgUQIMSk07hAIrJpZS1VAsOAcPU4VVNEiWM4OmHAEwiMlLVj8jKYVBsXt5SEpPia95LeXUDvLsiaibT4HAMKMAjmNo3UhVk3vB/F9+pXa94n/ZBCoVJomjHpXosKAL2YjenQUWbbtAnMJvknMMqTjZFuZZFOdx10VbLGZ2eRlBZJih5VENMqLXAxhKxIwMpcOuR1a9vrANW1wC91YEImYdtJtQA/65VOMnqVZax6qSoeg9F8/4n0Pf0tEZbzQ/17EHzAS8vXJOAZ9yGqEHRVIvjwmJPAWDvChQ6R0c6m/HsWWX1DmX0MvFJRCRI/d+L3+oH3eZ7fdGXK9n5FyfA95r4bsu/bz00VKlvn87EWT8KwlbegRj/wtf5bndnkkb0Yf7DGBTN3MX7Px9Mgr/3e7/1efM3XfA2+9mu/Fp/1WZ+Ft7/97Xjuc5+LH/iBH7jh83/5l38Zn/7pn443velNeP7zn48v+qIvwt/8m38T/+2//beb2t4DDzyAO++8E29961vxsz/7s/jABz6Af/gP/yHuvvvuxwS6T9W4KdAZQsCXfumX4vM///Nv6lFveWqejhFEPpp6ArnT/MMsq/cqlz7DjGmNMXwnhr6E49MEuNTuDn9HmC81YcckTs9GaaP4alDk0If7/FrrUe4aGTtrAfERApCgE+k6FAYhSGBR7AGlJC9Qcrlay4RQutfEn0dAKXJL5zgpiSm7wLgvIgsGwN+JgUCziXg2uzGkKIYJrTcSzuTZ9QakoJwgPaVKQDWEnQyxnkMSKtH3lG6K/xSRmRRZJmLBusgcQ9sx3EXqG2AtFw+2PFbQkvzrXDq++PvJdydS5yCdrcnL2vUEeD6MDGJMIo3yU7eVurve8NxHNrWXgJkYuJPnI+ABeG/FvsLVmrLX0zOe/3ivxFRdOY9JsijyZX9FpBGnS4KdWAUiixvxeahKVo4Yk7xdSbq8XCWGOIZBRSZSbU/oQfY5TUzbjomlsVMzAvGqHGtdJB0ZMQUYGGWOxoydnVGSnWfQdxySvVrXIq3uk58vdXVGz170rq43BP6LhXTeSgBY3FZZJDZZac000dWaixlNy+NW4oNrJSTpdJnus6RyAAguMmHZnUPsWYznNv5MVWVayFFTXmslab4qy5Jc29d1WrCJHurgAyfYwaeu2bQ4JP7cyGLFtFcVU7dv00hJuXmeJK3xsyZJbuuGCbxyDpL0fEseG5nO6N1Ui3nq30Rmk7xWFYVIV/vU5wljCKRiCJfz5+WikgocwdwoV5ZKpJYLXyrPEJYr6NmUn4/5yKDdiNGLQBPAOTATU4OTbzvWinhPia+TgK0oQbaWn3Xxs1DqUu7NX4373Xvh++HGXZ3WcuEjs3iskTyd8XO3bs4l94b4+SV/Y+LCl5pOKFc/W6bwpbQd+9jbe7wRfZvbAUlxXNtHqrSCzuw5SfNNsytxARfXgI0bpDnfiEW+GM+8ccHMXYzfz+Ps7Ozco91O6ZfRdR1+5Vd+Bffee++579977734pV/6pRu+7kte8hJ8/OMfxwc+8AGEEPDII4/gJ3/yJ/Hyl7/8pvbr+c9//g2JwatXr+L5z3/+Tb3Gkx039VfoLW95yy296Fd8xVdgf3//Se3Qkxkqy6B0JgX3+Sgr3Pb5AVIm3qROxdB2ZECn1cjeWDuG9ihFuWiMmI8+xWFgSqtMsDG4USa5qaEuHTLZVaRjgEjSJB0z1XGYrQkuQBZG0mOV1gxIEfZSzaZA3VA+Juxr6tXTEkqSZewcBbhPTcsE1XUtIHNICaxqoCxNSSE7nIPa3x3BhCRJ6t0FpZiS8qumE4RHryR5I4CxUgRbk9nZlGzTwIl7ZLhioBPsVi2LNVA2lqH7kXFyDuHoWFJGZcLa91DDkJJC/fHJyJxEH6YwLP7klJPW2ZRgpG5Gj2YE8LHKQSkuWsTUy525pP0KIyl9l/GY/aOXyXrE36lF6hyDoGJIkvfAejOmmVoB1Nal66LyHGG1Fp8gPbB6bzeV0mtho4P3UJ7SVRUrPfIMyuiUZBvvf4a2iOQ3dnYCZHhlUSHJyOW+1uVsDGVJLPqa13pnDrUm85N6Etcb3g/L1Shl7/oRkNQN1O4OPapKjT5KkSymahOlEnsOICVv4uSULNje7jhh7gepgdHwRydMPu0HSlvlvUHVgEn75U/P+H6SABp/esa0UfG9OWHJlYTXmPg+KHK+z+O9UgoTG2XQImEP6w2vTZYlhi6l28aOz/gZoBTloXLsYeC9p+SzR4VirFO5nd2Ecj3GRQd6ZJVUBzHcKBePr4WaENwn6bMPrIfpen62COgJK94vSa0R63Lkc1NPpEsziwtqVHOoImeSrtGjfFdYy3FxKBe7hCUoF2ZYlRJc1PWp6zSyaDqzYwfkDUYM+VFaAcEndjnK1kPMBFBqq/Kkpqc9BsM1DE6L92iomxsG6NybvQph6Pk6SqfPuWv7Q8+BKXmPR8VD6JkNEIOvkqx34HsgfRaK8iLElG2p60kJyjd7mxiDl5avOXeuto9tuxomMrdM3jUIwy30zEp9lZ5M4Fbra1J8zQ3Z2WsZ1guAcjEuxsV4yseT8WjK85/73Oee+/Zb3vIW/IN/8A/Ofe/KlStwzuHOO+889/0777wTDz/88A1f/iUveQne9a534b777kPTNBiGAa94xSvwT//pP7253QuBc95rxmq1QhkDTG/TuC2g82kfESCmyZ6hv0yYw8QQ5TL5qMo0YdGLGf8ox1Xj5YoT/dgnF+sPEpAluIuVI2G54spyTXkgvAfOlpKuKbLFScXV5hgWdEZJIlYb6U30abIaAQaUogRRPIxRzhW9b4zGZzBKKinPMvjLZMsiQ4C64SSkbhKTp5QwhGXB8yQeUP/wZU7iLx2myhMASbYF50ZPXFlQWhmlnaCvR+0u4B+9MnpnFwuEK1d5PlbrsXolyRsZTjJWoowMJj1fvFaccLY8rljjAJHsiRfXC9sRgVBkJ2BNCiLSVUmgKAxOnPRGwJT6TONk10iFiUi4U/KjBHsEkSPqw/1xcSMC6K7jxPDuSwRqZQHl1blwDxydJK9kkitDpUqV6NcCQK+gp3eRgVNSyTKtoCIDq9X5JGNJ7w2G1wKbjQBESigTWydJtJBAGL/ejNu0lhUlcWHGGKjFDP7yVVbVTCoCNLmW0ApopVri7EzAmHh0tQJ6Ce8ZBgL6GGITq2NEThj6fgSSUS4+8HpG1iuyiSowyTh0PReR+iGFEClh1IIwaDGVNPllt+ShOtsCqVIdo3Z3oEyTgqP0Yk6JuzFAwZ5I5cP4vpTn0Jdbib+cPamhbSkDjv68KFNu2nM+cx2Dum7T8HUD5TIudJwtk2yUKdEiF51W6bMNXc+qoroRySnZdBidPqfCcpUChZIUPC5MdT2PqesQNkNKzE1ewHiPz2dJup/CvuScRIVDaNvEyMbPjQSWJYwsnrsPNu8CcOMU1QRUgkfwIu8E4GN/aSfe1G0bgywYKqV5HYX5Dusa7FhBYkkjIEsBUj7wM0nkrlDZuWTdGCCUnisLlWG1ToxwcA7ohqTeSt57a7lAEhn6PE+fyxgGfLD+/1hzI/Lemx1kGKkAuBGwi/v/0unr0nnSkup+Tj77BIMKF94PN0r0vVGI0bUy3ItxMS4WHy7GUz6eTBqtPP93f/d3sViM/eKPV0dyLQh8LGAIAP/7f/9vvOlNb8K3f/u346UvfSkeeughfMu3fAu+7uu+Dj/yIz/ymNv4xm/8xrStb/u2bztXm+Kcw3/+z/8ZL3rRi57o6D6p8eT0Np9qI7NANU0MJgBOOoWpiqEPMaUxgosItpTUfETvnX723anuAXkG5f3IhMZh+LXa34P/xEPQB/sEMsL4nGMdu17koXpcebYWwJCCd0LX8/lOehZjRyiAsK6hBZikKhfHipiw3qQgoOTfi19XVWK2UqS+AyWjbSdpqp49lP3AyboPCJevctIiLF6IwRVxxb8SwDydjJ4iIAFtLaEy6AeCjsV8DGmSlNjUMelcAn8JUMeVejlvYaBkVwGJvVZerrMPBJtVxe1GQJbTs6miFDTKBo2Bf+SKfM0k3sjOwhjp6qxTRYyKrGvdpIk1gPRaiJI/kfPGCSLvy4yT2N/5OJ8jHa1aGLC43wm8T6sEYlVVpUWB5Ald1yMTntlUX5JYTGuS9zEMLd8PZQGsmWSLzQYxoIcpoB5eAEe8xnGinybasbNVwpxSz6ZMbNkF6KD1QmpJckmqtbw/Svosw5WrAlbyMaxJ6jrCpubzo/9SfLOqLEdWP8+TpDys5TiCT/sS00QBelyDZxgN6gYhSjVn0wSaYt+irxsYYYTjIkUMIWNaci0LTIb9inXDaxKZQiD5DnWsZYrqB0kU9f0AtVwiBtaErk+dshHoqczCnZ4laWJAf04m+lQPc7DHfZOwJErQJQVWaofgA3y3VT8S31ddhw+u3ol781eLz9ukmqAoH0ZgaJm2ZHS99ILCGMQ/oyrPzvWZqhggJvLruNCUkmJl8SdV/8jv8+LFLt82VbUAwL35q9M1vRa4bE9OR2BJz7XSGt4HmPmMct9h4MKBKECCisc4IMgiUmKmQ59A2nagjtJKmMpxoQ44z3Ru71MEiUFpfhbOCyiA94Ys6kW/uq/Xyacb/x7EBcjgHF46+2ogePh+oG829gTfYFy7HzGNN7GYckw38ncm+e0Nju9xxzAAvSScX7Mvyhjcm73quu1dgIuLce24uCcuxlM+PgnQuVgszoHOG43Dw0MYY65jNR999NHr2M84vvu7vxt/5s/8GXzLt3wLAOBzP/dzMZ1O8cVf/MXJm3mj8au/+qvcvRDwP//n/0S+tbCb5zk+7/M+D9/8zd98U4f4ZMczAnQyzMEmmZ5SlKZicMC0GmWcIYhPUCZzkW3YmjwCAKQWRYmXTJWlyL06sku9pGwqDazJisZqDvQxiKdHWAlbFScZVTnKoSR1MaU+AmMtB0CgNogkKh8ZuSTJDQHhuB6ZMGvFgyqvdbrk/jTN2FG63gjTJPUV63Y8F3GyqBVULiBnGMhg9aMvK4jMEEbYyMmEwTVyHdR0IszzAHTgOQISy4Se8lE1qYRNmSSPVgwsij5bMgibNMnkKr8b/boAkFnK/LxP0lA1lwTT41MEqXhJ3knvk/8T1ozsdVnAny3H157PoNZr+JMzAn4zwJ+cjky01DOo+Yz3XMYQoShDhrC2qT5GRwks0rXWe7sjaBfwmO5dSaQNwwBIWI2qylF6GKXG4m8MXQuFHACZwLQgIamuPno1owxSgnKij0pJ8Eh6v8RJeFwEcJ4O8Fi5A7KTMXUUQJLbMvhHvKMn7ZgyG++z7eCTVuTrAm4BSpuV1MVEiTQ7Yn3qcoRIaFOab8h43LqAX60ZxNS2/F6R89oLy6OiL7huYKZjB2Bi7W2UnDcplTg0LbykEEMYafZ9ZtyeKCMoUy1Tp6VeTEdQHN+bPoxskySjwmhKO32Ays/7PG/HiAsesHY8hgjK5V7zkrCd/KxmTJd92d7X8vNR2EBklh2deSZfN9A781FlEnyqRVF5nqTU22DdS8Kq3lkgtC1BeOynbNvUi+rremRd476u1vxeXIC7xjtzI9b43uxVySv4oe7dwoYawEuKrQS4cQFKFt08JPxoVGIoY+jBTpJiVt5Eli4CpsgaKmEBo4d0m627VmqLpecC1KRKacKhH6hs0GrcptHsCl2ueB7i/WYtlLzfvEjf/XL5uGFC16bXbqfR3ig0aPu5H+rfg3vzV6dj3H7u4wICUWaErruOwbw3e9UTSs0vGK6LcTEuxm0ZnwTovJmR5zm+4Au+APfffz++6qu+Kn3//vvvx1d8xVfc8Hc2mw3sNR59Ewmn8Ngb/4Vf+AUAwOtf/3p8//d//xMC4tsxnhk9nbNJAoxqbxfYmScmJDFFsf4iskaGtQoEKHYM1tldSECP4gQw4yQqBvagj31xVZrIhL7nxFlCSVLyp3TFxQlKOD2TICFhgjKb+ijVpOKkOzIgUh8Rmpb1H4DIabM02Y2r+TwHLHWPPZ0MF5qkyV2c5KuyPMfsYatHL6Ywhu0qldjXKR2cMQgnhn2g69JxRrCNYRiZpViZEGsMuj6FDilhrfzJaZLVqv1dqeywo/cws1LDkPH/lw65bcNkSRbUSyiNyJjD0Qlip2gqtd+ZJ/lZlKHGCT+yDHpvl0FSVTn29C1mqbdQC8BM6b0C6lRRjAsXEcwK0IW1nNyHkKSsoRWWQRYYQt8LIA4EgJJUG6TiRM2mPJexTkQYtCihizLrEPyYHipetBjEk5JKY1CThKEEqXsJct2wrnk9YnjRMPC6BT8y79JVGX2ZEbzGMJrUSSs+yshWJ7kvkCSQseIhyktT7Ya1QF0naWoMpQonpwjBw5+eJUl5Cn/qe07IhaWOQTcROECAiz89SwAxJYFK1Q6AdMyse9HJT6xiR6rUTiSFQpR4GiNdv21iKVmFtFV1AfEges9tS0BW6niN4VI+PG7QzCc7QtfzXstzCd/JxwWSPBvl1TvzdM1jqFjoB7iz8/L7JJ0OYVRNhJCOU+X5KLWNC0bGJJuAP1slL3uoG3qc4+eH/E5c5Ij7H1lZv1rznAqoSp91W8DqRoBkmzmLDF58nAuukYWo5O80RhbnpEtY2M9z5wNjB+j2dnRmx/vhGuC3zSKqVPu1lZbrZdElz9I9mraZ58njGa9dvG7xXiWDq1IlzM2MeCwf6t+TWMyXlq9Jybw3qkZRmeUCjYwbVazIQV7zf57TbZlxSv19ggqWC8B5MS7Gxbgt42no6fzGb/xG/It/8S/woz/6o/j1X/91fMM3fAMeeOABfN3XfR0A4O/9vb+H173uden5f+kv/SX81E/9FH7gB34Av/3bv43/9J/+E970pjfhxS9+MZ71rGc94faUUjeU7q7Xa7zhDW+4pX2/1fGMAJ0A2EtYFgiTQpJhM2FE8lSZEtNtw2I6ykWlAw3OsxKlZW2GmrC6JDSt+CEd1IH49jqyWNEfE8NFUgVKBLgymVV7u/SuZWN9QjhbpnCNCPDUbEqv4nxG/19ZkmnRRqSLWQo2gt9i+/IcYbM5Vw6u7ryDQKmqBDBKbUvbJdZJSd8cJ/6scNHPvnusaokhQwAltcKaAUiMWwQ322AqSWdj5Udgz6c+2BtTgYGUDKyE4YEwcUGSWtN5nU4J/KoSYX/B6/GcO/koxr5MhuNIEMikSuBI7+8RnJ2tUmekj0mdkWXbbAgmBbyGk1PuiySRpuRSmcwq6ZZEBGUxrMRo7p9I7wCk/kkA4zmKkzIBpTFFFABrKyQgSimdUl0jMx/E45jqfiQoBF2fknljxUrYbDiZF2AQUzND3ydWKC6uqKIQD6wd99kHWdAI9DrGZNO2TccQ9zes1qn6J2zI/ITYQeoZxuNrOZacacYhVqIAvP4xmEm+r/KcAV3Bn2OvUrhUTIGN5nfx/SmlUw1IEMYzxDAVWdiJQV8pvVakj3o2JftX12S/pdIonnemvVbpPg5u7D7cDr4JQ5+AAiD+QvHk6umE721hnFX0uMo511sVLrdjKEmCjp7XFB4VAo9Xa8qNNzXv81jxBKmCEVCkpxN2kcqiQ6xW8WcrhI71Q7qqRiAYOz+rMi1ywTnogz2e3/jZENOJyyJ1UAJIDHB8byMuvm0FkCW1gqK/8/EASfxZPB7IPuqySMAzhkTFfVCzKSXp0YtsNGIlCY/PQz/Gdn0/QNkMH+rfA9+05352v3tvkuVCaYLf6MWPdVLR66rU+PmoyHjq6UTupSwl27L+aZJSYX0/nPMx32jcY+7DPfqV3J4kKce6lHv0K+H7IYUzRUAd1Tr3Zq/i9ZS/dfF1bjiCP/d1XMDhf8O5c3I7/c0X42JcjIvxWOPp6Om877778Pa3vx3f8R3fgRe96EX48Ic/jA984AN43vOeBwB46KGHzlWZ/PW//tfxvd/7vfhn/+yf4YUvfCFe+cpX4jM/8zPxUz/1Uze1vXe+8503bBmp6xo//uM/fms7f4vjlkHnd3zHd2Cz2Vz3/bqu8R3f8R1PyU7d8nAeKMhIqnWT/FqYTQkg7ziQigkJi7h6yufMJsB2ubx4y2LHpJLUTVQlZaT1KG+CBIekESntCT165/5ISsqhKstUZp4AqLX0csbgnUuH48q2BKZEvyCirHAYgL0dYG8nsYsqy0bvqKTkJrnkagOs12SFtIQubYd8aJUmyyH2I0p9Sgy5QQg8n5G9GiiDDSIz5SQoT+cy9H3qYkw+0BAoW53PpEaEgF8JcxB9r2o+I9CpSp7PxQTqOXdzQtg7YDoBXODDWoLRvie4UIor/ZF1ku7NICFMiZUTuWCSvkYfZVXy+sp5TB7Zrme3aFHwIXLKeP7UbJpqVUKsg4jHlQJafAp4igAl+XED5cSqLKCfdVdK3Q1bEzM1qZK/TUl1hLJkOMl+DuK5LAig2xZKafjlisxdlOI2EsiU5wRbgRP26IHdZunhHCtJovdWjgP5WBMSF09iyiu6nkE1SkvgUgQrVZJ0puTSeOzWsvpI6o/Cph6Dd6TGIgV+OZ+SQyMbGM6W47a8P/feVHme+hQjAAw1Xz91S3YdpYRNOybVlgWln1HSrvTINnsG7iTZMLYknVKbFL2SXj4vX1q9NjH67vgU7vgUXpQFvq450VZaUlOHx01dfSpGrChKgT0CniPT7lfrEXj3A0GyBB8pm5HFjAFIie2kNFgvZjy/NX3IwTm441NJp5X+ydjPC1AWKiFCGIbEvoemPZe+GpyDOdgT1QYXqyid1iOjLiAstC18XbPW4/HADwiefD+keyEuqFCpIe+PuL9yzVQuibv9gNjB6usaH2zelQKMgC3gJe9l316fbrsN8mL1SmS7/Wol9V52BGWSvBzvU5U+A8eFoJQCXjfs/DQGSiu45fJxQdz97r2437+P/1H6HFN7v39f2r979CvH/ZYFsXQe5fXT6zzBCD5ATavxPSTn6h79yvGz8mJcjItxMZ7uEZ7k4xbH3/pbfwsf+9jH0LYtfuVXfgV/9s/+2fSzH/uxH8O///f//tzzv/7rvx4f+chHsNls8OCDD+InfuIn8OxnP/txt3F2dobT01OEELBcLs/VuRwfH+MDH/gALl26dOs7fwvjlkHnW9/6VqxWq+u+v9ls8Na3vvUp2albHpLqGXJOfvwdO/DzEiEGCxktLGQg0JxPOZFxrLNAlEeGMAYZGA3MplAHewRrhcgFJxPg2ZcS0Ig9lQwzKhPYSHUVwAhI42ReWExVlgRygxuli594mOm3ioAohu3AU+6IqiSzWeWciDVtYjjV/u648j2pCEL7gXLj6CPb3+XEJASes9jvJnJIVZZJwgXNMB+1tztKjKNP6O47E/MXu/jIkjiouy5xtX0rUCcCFQBks6qS530yQbhTFgViBc0W2xHKjIsEkeWTephQWoTSws8n3O/dHaazTiepokMt5pJg2on/qeOkNLOsM4nMrdbQd11KgCfKa3nJvCQEi0ew7/mI8kJJ0ETXkYXZmuwlybUdJ4sReOvpBGp/j4mfEexFWerpGYGrgPXUMxsl2ZOtyZlU5QBg2ujOPElsVVXSvyWT0tjbR7ltSXa4yM9JEkcA2IyeOWNSMnRaNJH0Xn9ySuAnTFmsBtKLWQIbBFoCBCKIdm6UBzqfpORRmpqku5OKLKewx369EVAkICNKXfNMkmsno6859sYWefIRxvMWU0kj+0b2aGRUIsPkl6vziwvGnPMNbvfgRs+tX2/OszgAWU8zMsS6LMhmxmuPEXD4pr2l5M8nNYxhWM5mMwI178mKbTajwqEoxPfbJP9lkGRpOC6ipPRZkawD4PsA2GL/AszeDhckzrgIomdTSpbjQlXbpRTxtEgiktsY7MR7msFO0QMc7104xxTeKM+W+yQ4dw783AiAppoPH0bZdww4igtnpdTZBC68JDlyCOeu9zZTeI7tVKKOERZTb4U43O/fNwJR/76RQSwKHsOmTu8TX9dcxJP3UKgbsuPCnEcVTkrzzTNhEru0/Sca20A5VqZs79/2Y/v44u/cKkikX78fGU553fv9+8aKmRvt38W4GE/BuLifLsYfhLG7u4v9/X0opfCCF7wAe3t76XF4eIg3vOEN+Nt/+2/f1n24ZdPQY8X4/o//8T+e1m7O7eEWJcJ0BlcaZMct+r0CunVwhxXsZgJTSxy/J9Dy8xJ6cJTilhnZMyP9kRKeg7YDStFmKwU/yaEdQ3LU6ZrAqe+BKD8DV+vV3i5/3+jkX0uVLlLVAYDhMwf7wNEJ2cj1hizVpEoMLZp2ZCfbjr+vFLsdH7xMpvOQqbn0Mo6skjo64X6sa4LyLT9j3F9/xw70TIKAqhIYHPzuFPrjjxIIdiI77rpxX5JMtxew74GpyAy9h3r0iF/v70Jtau5zZrmPwgBCmGJohVDlUAMTezGpEHamUCcrbqPMRxA9q1i1YVjHoXqRh2lNMJhlQGHhyxw6yusSgHRkmwFKqOPPJxWCNVDew08L6HiOpeeTHqmSxy4VMYktNwzriaFSgEwQI2Pb99zuznysvgkiIT5b8nxHuWrs84xMtQC/VKUzFfDetiLTFqlflMLmGdR6Q8b3kStkiq1J9Rej5Fj23W5VWsjYluwFH9gbCaRJvspzSgqDFxaLUjg1JcMae2fR9WlxIUlRI0NYlZJc24yhPkWRmCpdMr02Jar2Y8orfYEZZY8SbhS6LjGncJRvB9nXoBhKoopi7GeNHmt5D/q65oQ+LtpEEJrFuowsLdZQViyJyo4LFcGL5FdpmIM9+rW1l2obPwI3y0k/JffiDY/R6UpeZ5vN7TooI6FQt2mkeovdHZ7/zPLcStelO5OwmaGH3llARwY2JmuDzLU/W8kClYRJSY2K3lnw61iFMpmQWc1z/v3QOqWDUwZtxrC07dTeGHolC0SJiRX2SxcFQ7IALjBFhjLuo/gLEwgUoBRZugRutvolo0JByfkPbcuFLADQvYAjAdw9e5ajbBbAdYArbi8yjeyvvEGlSOD9kSbAiosUejE7L48XBYeWpO14PRMI9uLlXq3HxUDw88lvNjfVobkNJmMoUtyv7XOZniPHcm/2Kug8J3jfDhLaOtc3Gjqjikff4Lkqz/Gh9XnJ180yqBfjYtzMiO/Rp2s80fvhYlyM2zF+4Rd+ASEE/IW/8Bfwr//1vz6H2fI8x/Oe97yb8oR+MuOmQefe3l4yn77gBS84Bzydc1itVsn0+nSPflHAZgau0FDzHLrzcJWETBQG/YyTgaobgMETZCoFtRG2aHBjoqtIyzCbEFgJS6pXLdzBDKoZoASohMlsTP08W3L1OXYP+i2vmnjvot+M3rsKkO7GWNeiphOESUHQ5T0n6PMplAQIYVNzsur8KA2NgDN6SLc9UcMAHO7xOIIfvXLindNHS37tg4DTCqp3BIzdkI4/TCdQXuTHWiPMKqi6QagquFkBeyyAbrkmQOp7YCKBNdYAjex/nKxXrL9QnZyfreNSQ6DkWV5PzSjpUytu24sPz09EjjyIZ64f4Gf8mdudQnkP1fZQ7cD9mc8IqM9qhMwiVBn0WQ1kBt5Y6GVDvyhA9iWUUGdLLg4U0ut5x0FK6mU68VrYwmLskAR4PcTbhq4nW55nPD8rOUfLFRcbMvFATgrACfAbPJ87nwLLHmFB1lxdlQmkNQnAhsxADR7u0h7MyRpqdwdhXkEdL8Wr7FIwU7pPhLGMnkYoRSZfgKgq8lEdItUVSarMEwRA/KmR2fKeE3+RTSaZ7hZ7CsjCTFXCr5dkQ0/PxB/sASN+4aIQmSzZr+Tfc35MPxVZdxiG1OkJCQc6B2hFDqtnU0p2lyvua5SIOocPrt4JAHjp9HUE0Fve6CRBR5Qp2pGJjR4/reHPlokVhHhmY+F9xI4xsRTGjIyr1HPISaS0tCivC6B5qkeSxbf8TPDrzejzM5q+zWz0scYwLkCuT0zMjn8HZFFA7czhrh4hxEWDCEYjEItp0fMZ/NExAAJff5WyfiWfjyk4KKZmx2TgfAzoimyeKguy2FEN4dwIAAfKoh/L13mjiaYXqTWUTmA7pRFXZN61vAfUdAJ/9Rg6BsYBsmiUkRkM/hzQBcDrO/Rj56+M7efc799HhtHQYsEgsa3jk+CgpJCJ93tkXGMAnHiEr6v8upWh9GMmyMbUWGUzhKFP3aQ6z8+BziecYKeguS6dh/Q7zt1wkn4xcb8Yv1/H03HfXrw/npqhcOsezVuLEXr6xpd8yZcAAD760Y/iuc99LrS+/jP9do+bBp1vf/vbEULAG97wBrz1rW/Fzs5O+lme5/j0T/90/Ok//advy04+0fBGwecaLlfQncIwsTCth/IKqnfQMjFq75rBrnrodgC8hRo8QmYAZ6GsQSgs1LpFmEZAQy9eKDOozkHXPdA7uJ0KUBjBlnNkFasSOFsh7C6g6jZ1U2J7cjEvhYHckOGKICbLROKbjwmWG/YpxnCjcMceJ2NHZwwbAkb200qYTyFe0TMBlG2PMC2g1u0YMBOBdQgElgD3/eSM6Ye7c07aJYhFxaj+gx0CIgEx6nQFe7qCP+S9oKxMxFYSpDMVSedAUKUGh5Cxo1K5QJbx8jHcp90Fc+UM7s4dmIeOeL60AuZTgqoZazgIzAJU3UFlW0EYXYfh2Qcwxxv4eQFzWiNkBm5nAvvoGfylXehVS0Bd0UuqugHD4Zzg1AW4/SlcaZE9uoKfFQSzwwSq78n0TirgbDlKLZUiy3xyRkA5m7Kvz4HnzQ3AUAMHe7xHJzk0gGAU4AFV5gSWueIxbtjX6nencAsLWzfwRQaVLaDiNcoywDsy9Wc15eRKIZgAXbcImYUKAWrTksU+3bDXr+3IFAWLsDeH6hzvD6WAxZyLERU7MaE0QlVALdfp3lZ7u8Bmk3oufV2PAVUAYtdr2NRkEoeBVTJtJynFBUFWx6oc1uFUqW4ldJ0AHDlOTd+fX28owcwsWTKRmgfvCSKbFvAe7spVggGMctDEaPqAoMgi8/c9wnLJhSGtoLQduwUBxOoGJQsGsapHKTWydRkDWryk64a2Zf2N94wrF9mpP+uhFBmfCIIgQErHYBxZHNIxCbUfgEAP8m2tgfCB96D4GM3OYkx51joBdwDJyxhBdkrE3gq3gVK8j7SGns8RQ4CSdFgAO/2gFv7oeOx5Xa54P6zWtC/E15UFB0r4O74uogyZIWD+bMUgoaocFx8knCiNLX/gY03CtlNSVS4AMnjxoOYSviVsfjsAuTD8TZN+lkC7eB4jcN1+ffqCe/GANmNFioDNCN7ScYpMfjsd95xCIXqam5bXIVZnRd+6PN/XNSABRvfmr37C2+PcEH9plAOfO4dybuM9fo+5j6zlNfLym9mGMjkQ3ycy7tGv5KLBDa7bxYT6YlyMxx4X74+naDyJNNpbfv7TPGJA0WazwQMPPIDumt7mz/3cz71t275p0PnVX/3VAIDnP//5eMlLXoIsy57gN56+4UoNowDbBoRMI1s7uELDZ0C3KKAc/0hna4/V8ypkaw+7cklCiADo1sEsawx37cIsa/hZieGuObIjTu5dyVNl6h5mWSMUGdxOBXO0pnd0WkKdreHv2od+5JggJfo6h4H/dx64ekzAWFUEuScdsL+b+ilVS6Y0TIyEA3XCvE6hVjXCpIK/c4+Mays/syJZzezI3i7mqfJEna4Rdim5DJkliBSfI7wbvVi7C+DkDLoRIOs93N4EWiko56FWDUIVk3oNQpUjWJtAkXKSBlxkUE2PkBuomj4t1Q3w+zPoJWtAglI8/4s5zNES/mAOc4WyU7c7gV63CEUGfcLSeb87hdp00JuaTGvPSaWblcD+HPZoDV9k0MtW+i4rqM7BLyouLuSSPrtpCTy1htl08KUlEASQ/79HgCLHcDCB7hxMzSqSlKi6NYlG15OtLHKyoRGcCeMWfatuVkL1DqrtEayGm5XQ3QC1buEv7bL3TwOqHdA8fx/Fwyv4WY7u+XdADR7Zw2cE2wAXD+S4/Q6l3sFq6BoISiEUBvAFfGVhT2qes1kF1fbwkxwhM9Bx8aEshJH3GJ57CN300KcOYVryWt3BhQR9vOb9MZsSeOQZlAT5KBdZl0wSaTNgUkFLvUSsdoCkf8KyKzYmlLL4ntLcsB3yIxJKZQz86ZLAU0BkYhM9/dFhUzMkZT4bPaiTitdEgnxSHYj4UdWEHY9xwh4ZX1/XnPwWxciuSggUPWddYjkRgXJdJzYqheL0PZTTY78pCGYig6Yno997u7dSBfHfDcP5yo7bMKI/kfJ1PQbYKD1KXfMMWFOyH+WbCUT6kD6blI0+426UIEdLgbxd1JT1TUzCzaDadgwRA0GT3hO/eQwliyy6VEyllNzZFIBKVVR+uRxlwsAoUwYDdbb9gdsj/v8e/cpU05EqnvKciyvx/e65qBclwv70TPqDtXg+2xQYlYAlTxS3tQ0s8xw+qQF82pcYmnNOjivvFb9aQ+/vpgAmZWUhxlB6H7ZAZtjUiPUjvq7puRcQfo+5jzLqm/VcKi1M8btHee1WCFI8h3o+hzs9SwB1e9wM4+L7AWYyuY4hiIzvRQ/nOC78hxfjYjyN48kEA93eP9+f9Lh8+TJe//rX49/+2397w5+72xjcdsvc6pd8yZfAGIP/+3//L37xF38RH/7wh889fi9GjCiu9w2GUqOf8d+gFbxFmgB1c4N86aE8oAePdjdDu5fB5xrdXo7urjl0NxBkVhZqCOgOJwhaoTnMoZxHvxDpnwvQ6xZ+ViDkloDirj3osxr+TrJbYcIKF3/HLhkFaxAO9+lPFIAY7jpkGmKRi7zXIOQGoWLZPaoS7q59hCJHmFUYDiZkyuKkMARKTa1BsJSuhirnfpUZ2TGRqGK9gYq+SqNZxdIPZLmsIbBaSMdpZoBNTXY3MwjzKfz+jExaJx7GukvF4xEAhYkANPm5350S8NWNgJ8CwSj4KeW0fofBLz4zCOLvMg8dAUrBZzwfwx1zqKYDrGbdDQjSVDvA/r9PwJcZglFQw0B/5sECfpLDLGvoDSWgwVqEzPA85jaxi+Z4gyBy1eE5hwKkNfSm4/UrM/jdKfzhLs9lZsfQKKWA6YRgcz6Tfs4C4Y497sN8AmggZAbD/hT94RTmtAZcQCgyDIsSbk4APBzOoIYANy1gmgHZ1Q3sSY3NZxxg2Ckx7JQ8vxlDf3xu4XOD+q4KbpLDTzIM05zMbe/h5iX06QboHYa96bn3y3BpB2E+SfeIffQMqhn9wG5e8RzlFmHOezVMS6nNEPC+WNBbbKTOZzEnE3q6TOAwtExdZiIpga7a3UmJoLFSJlXSxH7b6PeTWpgk69UqVT9gWnE7UrkTRCbKlFGyaglwbjYpxGe7gsM3LVNiJXUUSiS3Aq5iMmhKH5ZAJX24z+MtC+j5PHVQ+iXZYyVJwqMMXifPXzxOXzfwMUhpK4gmyW61GtnQ2zSUFWmvYeKsqqS+SDyXSglwFuksg61KhLbFh7p3JyAWnGOoT6wP8j6FOfn1Bn69SYFnoetZFxXrUTTTfPWUaoYUfON9kgDHSiFV8HyHph3BfJ5D7+2yQ1TrsfJHPJ9Kq/NM9uOMFN4jvk4dZdyRKVTs6kWsLZHuXXaxDun3PtS9G8qYBLTS9qMsvR+gywJKq+uSYSNwTCBN/MvQKklmWbPV8b4PnvJ1CQhjvyzfM1F6zGunk6cUcYHhZoY8797sVdczuTIoIw7jMQbP+wPXA87E+N5gO+l9szXu0a9MPvfrvv8HdFwwWBfjYjyN42lKr306x5vf/GYcHx/jl3/5l1FVFX7u534O73znO/EZn/EZeP/7339bt33LQUK//Mu/jFe/+tX4nd/5HUrJtoZS6rYi5McaLldwUMg2AXbjEKyCtwqm8XDlKMO0rYe3Crrl903joQKwuTPD7IEGuvfod0pkJzWCUugXBtnSYZhlUD6gPShgaof+YEKmzgdkxzWCJQgNRpNZ6x28MEYARjCYGQSp/eies4vioTNg8FLbohEC5cAhz879jjndYDicwWcaygeE0gKna8pRrzpKXtuecuDI3iwb+HmZfJNq8Ah37EE9cpXs3HzC3wPGkKCqTMfiqwxmMYPqBu53aVlRIoxqsDoF98SlC/O7l+Ev7ZMByg2G/QXsaUPWc2dGj+Wa+603HeW1TQe3O4FZNVDrDdzd+1BdAV33sCebMRjDBbhZTulsbtHfRSCVaQXVE1TqpoObVzBLApP+0hzZw2fwixLmyhJhViFUGXxhoTvxBOYGynmY0xpuXqF+3g6q3znFsDeB2ZCt0Mer5I3yByIl7ghwQ27JPjqfrm/IDIZ5juy0gasyZFdWBMNlBmjAzXNkV9bQneN5lHvE1AN8YWDqHv0+2Vbdh7QfPjNAphCirNUH5McEZP08S37QMNUw6w7DHQuY0xpa7kNfCGOvFKBKMvxKwe1MYM5quOkUwWoo7xOzr3oHNyv43N4hLErouoRqevhZQcldTBHt6a9MtRp3HCQ2GBEoRNYzA59nKAVk1YsEcsnkWi1mlFUvV6mWQ0VZb9OOMsy+h54syDh2vfSu1ufY0SS3jWykJMj6pk3S18TMylDWMv03z8nUdf0YHrVcMR05BMoCjRkZTGMAE3133TjZB0FS8AxjiucMgAQJIbGOymbXTcCfyuHXG5idRQrJCesNWVvZf2UMvCR6K2MQNpRU+9UaymZ46fR1BAJeM9wrLkYUBeWmsXs4JitvAehY1xOcE0Y8J5N3sE/Pc5SSSo/udiBRkDoV1qJ0yScfE6dVWSCst/rHhHW8UfjN9ojgKHpu3WpF4Bnl2poBUGG94WKf3MsxpRdb7F70csZtnvPmRnDVdQg+nANlEZCdA2rip41BQrF7Ova7xtClsKkRGgLR9HwgdXzqKBFuxPN8zXE/1vngOad0NgLJ6xhj9168bPF66DyHbxtAZQlo3+i5NwKMOqYmXzt/UPq8tDi+zgXwuhgX47aMP8gLOjcaT6Z381af/3SPf/fv/h1+5md+Bl/4hV8IrTWe97zn4Z577sFiscB3f/d34+Uvf/lt2/Ytg86v+7qvw5/4E38C/+bf/BvcfffdN0yyfbqHaT1CBQRNqa23Cqb16HYNbB1gav4hG6YGQ6VRrR26hUF+5tDPDKrLA/pFhqAB03j4KkewCtoB/dzA5RquUChPHIaJQbewKI97tLsZfKahew+z7qGbHqob0B9MeaM6TqLN6Qb9wRTBaOjeITvdwJZkkuA96mctkB+1UN6jedYcdjNANw7Ns+YwjQMWBYbKQPeBjJ4PcOUBJ90HTEX1O/vQ9UD5JICwM4GXkBk/yQmAlYJ/7p18jjEEzoNDKAuCtuUGUJSXZh+7DH/HDvrdCvmjZBGU1IWEMkMoJQwoBKilMFGLGfTxmXgPAXvCbkQ4Twmr1fA7i+S5hFFQTQ9VOKbOupD2MxiFUOZQvYNZtzwG8dPqVQN7xMleyC3MqoGfFnA7lNK6nQrBaJizFt3dC+jOI1Q53ESScgPlqG5nAhgFXfcY9qdQvYfZcF/s0RrdpTmy0wZ+PoEWz6UWKXF/SICWXV7BTwuyuGVO6Wzdolg1CLlFdrTBsD9NwNHNK/jM0BccAF0P8LlhSDIAhAA3ydAtLPIzwK56DFORsisF0wzoZxbZsocvDcyqh5tYmMbBbAa4SY6ggH6vhF12cLsVgtX0OdcDTCMANDcIWsFNS2QnLbo7ZjCt4/3cASoySYOHnxvozqHbL5Edtwi5RXP3DMXlDdz+FOGOOcyqpZd0XkLXPaXOl48pyw0BsBLW5TxBaC8STKkrUnJ8/EePXmVhk5BllPTOpqPUWZKIdfS87cyhul78yLL/B/vAZpNYUWgJ4BI21Gz3bEaGczEfU1d3d0YQvbuQlOUJlAQGYTFjUJawqNtAMSYc+5PTsQtYKQkxko/eLBN2V0HJ68Zwo5tl6J7M0FVJlqwfAGF89e4OfZ2x5xiQyhNKOv1yCTVhLYffbKBn7DBVir5cZJaVKK1IR/Nsq1eyljRopgmrwwOo5YqBQssV3/MbsqVe6of07g78kp7NEBOngdQzS2uBFYm15XadGVNlgTGYRoAgcH5SFT2D8WtdsKfXzGY8NwI2UzJ17Gf2Pklfo1Q7svBKKwRPxu9a/2PwGqqwvMedO+f9SZ2d0V8ar4GA6uAcUHejF9o5QP62qZj46/3I8iuVQs3C0ElWwFjRAzw2eNv+/v3+fbg3f/W5c3jtc4JzBJzydWKJb3KEruf7+Lof+POe6ItxMS7GbR1Pd5Lvp/x4Bspr1+t16uPc39/H5cuX8YIXvACf8zmfg//+3//7bd32Lctrf/M3fxPf9V3fhc/6rM/C7u4udnZ2zj1+L0Y/1ZTM9gFBK1SPEHjZOsAbwOeaQUOFQrb2WD8rQ7ZycJVGN9fYXLLwVmGYaHQLi3Y/x1ARvHpLNFBdGaCGABWA4nTAUBnkZwOlvJWBm2YY5gXcvOLE0gXodQe97uB2JghGk1Hb9OjvnKO5VIoPL0N21mPYyeFzC+WBYDTcxEK5ANMMMPUA3Qf4TCFoHk+/sBgmFu1egUGCj3xp0F+aob80Q3Opgi8NfJUhKIVhpyQ4rSzChNKwKNWEMfRXZhZqcLBXN/CXduGrHPnDS6imh141ZEAPFgScg0cw4hG0GrCagOqOXQyXmAIL78kAVwWGnZLnxpPVc1OG9fhFlaTK0IDedGTrijHxN+QWPufkVW+6dI6TpNdqglRDltac1jDrDjAK2dGGDF2RUSoMoN8hgI13/7Ao4HODYSeHqQe4WYb27gVB2ySDFlmw35nAVxl8xQmQbhyBqyew192QjnPYFdZLkYltD6t0L9lNj24nxzDP0B2UcBMLPQS0hwVDiAAURx28sI7xWO2qwzDLYDoPXxjolr7kIAsfIaNMXA8BZjOgldfupxampXQxaI2g+X7xhUF2pcb6uVMoH6A6B9MQBPtCHrkFFK9ZfrVGv0uAXT7IhQhX8bq4CSWmqht47BpM341y5EmFcGmfacrTCcFmWdBTHBeuqoqP2YR+QecpVz7c5WS6LAneFnNKMetmZFm1YihXnol8PePzrAF2FlB7O/z/jPukqmrcrqQ5p15ZpRJDhJgSWjB0BxKmEu7YA5qW94IW/+LuDvtiJxWDjbpuZGNjSFL06UkaL4TB1bPZmHzb9UwuvYk+xSc7fATiAtAio6g05cDIM8paY4JsnvOcaSXBQdI/GwEnuFigJhX0ZELgHutlnHTdyrZCPwDLFdk+YTp1VRHQeVbO6J3FWLMjDF8QH2hMNlaZTf2cKcwIkJ7UcE62vO0/POfl9O9Lz9FVlTzKvq4Rhj75XkPbUulQ16muJfXLxqRYGVHWG7eRRuqxDPBtQ3AqyaxxRMCZ0mJlW6FpCVS1hMx5L12pbkwfjtcwdqMCrBKqKr5WlOdeExpx7bh2wnlv/up0TI/Zd6oUgaHSqabmsV7vRkNplbzD1/zguvqVi3ExLsbFeNrGM1Be+5mf+Zn4jd/4DQDAi170IvzQD/0QPvGJT+AHf/AHcffdd9/Wbd/yrOZP/sk/id/6rd+6HfvypIfPFLKVg2k8hkqRxTEKPmZARLltG+CtQn7m4QsNl2tURw7Zht9XDrCNR7NvRpYlkElt9wy6ueHrG76eKw30ENAcWKyfXcAVmhP/3QzeMtglFAZBkzmyp22S4eYnPfw0xzDL0R4WZJ0qi2zZUR6cG/hMod0nIOoWBsEqBiRZhaHQAmAG9HODfpGh280RtELQCnoIGEoDV1q4qUV2ZY1+v4JZd5S5nq7gC9Z1+NKmtMfu7gUBqAswywZhkiNMS/pcdyoMc4IOegcdhr0RiMXAHN05hEnOMKaDCfy8QHZlTY9l0yHkFrruU4CP8p4MldbwZU4Gthug2h5uUaXnBK0B72FWDUKRIRQZWenMQC9bAifn4GclfMmAo1Bk8LmBvnoGtyjQ7xbIlj2vi+X5sVc3GCoyv25qoTufgBYlr5lIZyl/Vr1D0ApmWTNgqXd8rRn9qsEqhEyjvXOKYVGg28th1z2GicUwJTtuGgfdi49PK3SLDMoFqCFwMcJo2FXP7zkP5TzcJIOpHXTnYc9aDLMMwzRD/jC9hL4wKK420A1ZxPy4S8cyVCad72GewVUG+VEDP80x+81jZFdW8KWR7XbjfdR0sKcNWfSzGuXvHMOXOfy0wDDPYTY9F1dEht1dmkMNHu0lJg/7aYHhYEZ/sYAFtzdhPY0PY/drliEsJqyHiRLCaYUwKxmuFZOgxUeYEqPjiH2eSkF1DmGP0lcufDiE6SSlQmNgKigKyjrp1c1YpyIhQcgpN0dVEowOjvLzHQGtNSfV+mg59vGenrF6Jnruomcx+uliT2jc9+39B8a+1+jBy25ZiHLTI/lojezbfEY/ZVUyLKooJPGU5z2BrXpks6KfkN7bPAHZMAxJ3hz9sqFmgJgqKXX2m03qFlaZFV9iRrCkdapEQT8gtG0KXAKQ/LnR3xilvXp3hyBW9odVP9l1oOc6qW30WnadeEsHAjVgTGOWfwEkQI4ombY2BV9FL2h8zeuSXgPvx+jzjXUqab/kOdGTCgB6wY7lEHy6n1WeJzm7mlRMT84ynsuYOiyMbBh4jmFMAnG3NIK/IYg+d2zDcC51d9uPfDNSWC/JwE4WkdLvbG17e1wwMRfjqRyP6TW+GE84Lt6Lv//Gm9/8Zjz00EMAgLe85S34uZ/7OXzap30a3vGOd+C7vuu7buu2b2pW82u/9mvp66//+q/HN33TN+Hhhx/G53zO51yXYns7o3Yfa5RHDmfPqzB9dEC+JJNZnDjoAAylhpe5SrbycIVC9WiLYcpDH0oNPdAzOIg0t3p0gC80dBfQzzSyhpLMrPYYCoVuh+Bz9vEezaFFvvIwDSWk7X5OsKsUNs8i22U3DsEobJ5VoDga0C0sbCvyvz6g3TWwtYZdO/TzMgFeKIX8tIfPNUwfoPqAfOXQLyzyswGuMmhLkfAuB/QLi3aPx1UcDxgWFrZ2aHczuHwB03kMiwKumCCbl+h2ckpXC4bS9Ds57HrA5tN3AQXkpz3rNQBWkFQWdikr/1rT97lqE/NH32JO1jIz6BcFspMa3eEE+eCF9eOEqD+sYNYDzFqqQnYmUHUPvyMMaEWGUXlKivWmI4CclYAL6Hc5qdGDR9A5bO/gc4N2b4HiiF7KYDR8YWCXHfrnHsAVBsoFtPs5sjNOkEzdo332giB9aqBbD7vq4QoNVxbIlgPsWYt+t4DPFLBPCVhx1KC7NCczOMlhHz2DO5gB0JQ1+4B2xyJXAALZVd152M2AbpEhP2GNiXKB/uI+1sgo2E3PxYncwE0YdAUA+YNL9JdmBJHTip2mSqF91gJ22cNnGm63gO4DXKmRnxA8Vg+con7uDuxpx2UmSWt2kwz2tEF/OIPyAfZ4w+u3alBspQNH76/bn2KY0qvqMwt71sKXZJBVM8BPCxhJgS6EXQ6GIBBKEZQvuAARcta3QF5bGT2mlZYZ5baDEw9zxoWQfhjra+b0e2JSIeQZ1OkSmLOfFUYTqAp7HzJJUc4sf2c2Afoe/vJVMmjHJ/yAkFoUFAVgLYa7dmEfPuHzxUOaKoMyC+ztkO3aoXLAFxl03ZLlO9gj8+rISOnJhADOOaiDfYZrAVCNpB0bzedMhfGTupfbNRgAZSkbVZQcqywjMOlHaXFKQtVkMYPIb3WeI6xrghkJ7tFSkxP7OUMIo6czBTtlCL34SAdeo1A3BHUdmUXWo8y4ICDBTEoSVxka1SUGNKiQ0rfDcpUYZne25HY103m3PYrXycdiiJMj8PT9wDRqm40pyeKFVCUD0IIfyCpGMOrDCOa2q1K2Aa6E7MRFiHNhPLJ/2xJWFSt0JK02BD8uYmRM81UiDw9nVB5EWXToewJhkXczEfrmJKrX9WTKfgcfHhNAqqKAkm7Zl5aveUI29brf14oJxteGZ0l6bqzj2t7Hi3ExnqrxWF7jZ+J4qo/zmf5efCZ6Ol/zmtekr//4H//j+NjHPob/83/+Dz7t0z4Nh4eHt3XbNwU6X/SiF0EpdS446A1veEP6Ov7s9ypIqJ9qTK44DIVGs69RHnu4QlavNVBe4SSqvsMmVmmYcKLVzRRcrnHwkQ36TysRPNDuWxRHA4apQTBAu2ugAtBXBH9BA8oDKgS4XCE/cQiKftJ2h9vvdjOYhpMHX2g0ewbVVUcw6wJcRkmeVh7lsUM/0VCBEuFuRyNbB/QThW5WoDh1ZMCsgjNkVwFAtx7aBbS7Ft2uhd14hEoSSEsN03m0+1mS+DV7GXwG6B7QnSUwumsmbJhCfrUFFOB3MuSnPVxl4OcZ7LqHbhzspoeuW3R3zWFXPYJVwOCTbDVozclaP8BNx8mDqclaRn9piIXxGaWxqhvI4iklvZkOuhvgZoV4DB3coiJTFwL6/QzZktu0xxuyrdMCrjCwNYOVsuMabprTG3vXBPnVFsNEw248TEt5qhdGOX2/djCNQ7dDUNru51AuoLl7moKpTCP+4DnBfgyZgrCTLtfwhYYaAmYPrOFKAnotv7e5q0B1uUO3m8P0Hv3UQHl6ie2qQ7fP82YaAVxbi/yhymDqHsOcAFa3DlBI93p+ZYN+t4Ld9LAbyrSVD/DTAtXHz+g9HTyykxa6bjHsTzHslLBnDROAMwNoYNifki0FGMok/l9fkVUNUjMTfaP9/gS6ddBtT3ZcUnZ13QK5xbAoeV8MDvasESm0ArSBwkDJb771UWQ15dUAQYkxwLSAGiS5uHdA76CsSR5ff/cBq4qqkix2lUEfLeEO5zCPnIx9u0YLWJ2kbkzE4BnpEEXfA7MJ7ENHnPT2Q6o0CrllJdDJEv6QgViqpYxUD56hVcOGPact06D1fEZ5bdsSJHQdkAtYKDOgygmOrB3BbVnAr1a3+El480Mt5gwUOzphgJM1CGcSmhQ7hyU4KSxXvAfqhiFNIUjPZ0HZp/gJ/emSbKWk/MZOVd5IlOMGSc+OdSeIvkNEICzJtVKtE9Y1U2mlAzS4NrF3sHZk9oQ1jbUqsVPSt805kHldcE8M9ok9lD6MAVNaFv9cBJMBShkEeALOaZX2O/jRy0vwyfd7SrDNXw0En8J2lM3OsbBpH7c6Ke8x9zEAqMjhlyuym9KXSsBbJe9qrA8KPYG9kvMI5+Cblt2ZWqXz8nhy1WsnpSrPKC++xhN7ztPZkWG+x9yXgH48BzczPtS/By/beUOSoZ+XPpsUYnQxfv+Pm6nQuRi3b1x4Nm9xPAN7Oq8dk8kEn//5n/+0bOum5LUf/ehH8du//dv46Ec/esNH/Nlv//Zv3+79veEIRqGfKPRThcXHutTLCQCmDagvZagvZcg2AaYLOHt+hn6m4S1QHTlUVx1Wzy0RFKCHAG8JPPsJJbntjoYaANN59FOdQN/mzhzZymP53AwhU9B9wOSyg275vH5m0M8oyzUtgWZQlFO6UmNzSbyIihJeVyj0M4Py6sDfaQJcTnmw3Th4q9AuNPpKozmw6GcGzYGFrT1crnD6/AzaBWg5/m7G2pjidIDP+G+2CQgWOHt+DlcZ2DUnS0Nl0B0UGGYZdOcxTC28URgqTXbwjhJBK7JivRdAA3R3zTHsVhh2K4bsaI3uWTspyCUYA1MPqD9th97MKifY1ExaTZ5SR0YTSmGYF+jumHAfco1hmqHdz+GK0e8zTOhprT9tB74gKAJ4bvsdymujb7KfaLiphWnpkdWdR7PPMB677jH5xAbFo2syhJVlCNVeDtN49IsMSlhIuxmY9iPXsN2hFNeVBt1Ojn5mhc0NfK0J/X7RK2w2PRb/+wgAkK04ucpPB2RnPZOTdwtoR1+yaQb08wwqMEzIrnq4SQZXZfBGIX/4DP1OThbYBfjCoN+tyJ42A/QZmTI1cN/7QwI2nxmETMMtKspmO5e8vcEYAkCrU02LW1T0AxteL7dbcTuebKnedMiurhmgNHjW6NRkcb2wpGbdM/DpypJVOVpDn27gC9YGpYWGVc3H8Qr6ZC2sIoGvrnuoTQcMHurqaaqiUb2D6hzZ9bIAVmuoTQt99QxQGubqikzc3hxq3UoIltQRGU1f6P4uH2Uxsqh1A5QFwkzCTaYTgtZezlcMBgIw7E0RqhwqBC7ADCLv9SNYScm5EqyjBi/9sWRi4QOTWyGsYF1D7yyeqo/I60fX8f7Y3eE+KQ21vyu1TZbsq0iAVZ5B7bAiRlkrsmGV6mrgPKWe2+m/fpTdqjxPjC8gIK1t+TtzSfGNvsmYzJyzukbPpiK/lkUJkeiy1kUJSBUWXJJ2E6MoIO9aljMNCfoZ5ag9Gb2uoy+xIcCFF5CZZ8JEe+6DJ5MbGVillQDVMawnyvYiaAo+EHCKn3O7WiWeG0BCjSTECc6zqzZKjr0n2PQeYV3zIXLm2CMbK1VgLcxsCt91rAiSY3y8sS35Bej/TbLhxxoiCeZB3LoX+aWlrLyba35X0pyvlT9eyCFvfXyqAI3bDTg/VY7zYjxDxjPQ0/lX/+pfxT/+x//4uu+/7W1vwytfeXvfPzfFdD7vec+7rTvxyQ678SgHh7NPzzBMDNnIIcAVZCZR84/sUClkK4/pw4ESQxfgDf2g3cLC5QQIegDKK/QcDhOD4tTD9CGxXbYmQAxGYXOnQX4Wko/SGxDMbTzZVJDFIjNKsKJ8gN0EFMdkuoIWMGoZftTPDLIVfYPZJgAK2Nydw7QBxZlHs8uJSSb+PldoDKXC3m+0SV6LXCNbeybwziy8AbqFTYC8OPFY32mgXA67Zs2MyzWMAnTn0c0MTCdgV2Sp/TxLnsXsjACsX1jYtSQo7k6g2x56IOCy6w7Ke3T7FfQQ0B9MkB3V6O6YIDtt0e2VCLaAXQ8YSoOQKeQnPaUJvQciOA0BQ6XR7BvMf4fn0pVarqmG8gaFVpSbAghWodst4CqN/OEGQ0UGEuJT9LnG/GMbDJMMtud17vamBKQDZdK68+jnZJW1BO+40sCLLzJkCuVRj/oSg3uyjotbygf+/4QMDWtzWL8SrEF311zuWtnGIkN22pE9DwF21cNohWGeo7jcwJcmhfVEAGeXHu1zdqA7ngtTOwxTC7seoNsBw06B7IRJu156RwGg3S9ga0cf5oYhN6obJc/BGAlzcknqSnaJiwNumiE7rqE3TGl2exMEY+CnZIjM4OF2p9Ad/Z8IAeh76J0ZPda7TLJVdQd3SFDmDmbQy5bhVsKAo3eAURgWBbLLKzLoUvuCboC/c4/7rBT0lVNgUlHa6jz8pX1WeIhEPWgNc7rhzyFAVYAhT6pCKOT8np4RoE4qYFKkECtVS72PSHZVF32EICDoBlYLFcIuDQPgM4JXgOxTxWoYDOJPHUYWSB+vya4KkI0sHrrHZqM+6bGzIIhuB/pmmx4AO0TDzgxq3cAvKl7H+QxhUjANdbUhi5Y6VnOC9XVNsDaZ8H0mQTvRlxqcIzgUD2ZkQ8NGvKJdTxDWyfkKAXoykddxCHXP4CABo6oqGZ4jbCemEzK1Vcne4wgkY5Lu1rgRU3e/ey9eWr2WgNFysUhJRycA6N1FSsmNgDMET6a3KuFPz86BrcQkRnbQ3Jd+zqCiRljP/hwoVlqNk+bAMKvgHNCOnbasBbKjxxWgjLwf6D/e1AxsqhveQwIYVVGQCbY2Xb+bHdthPjcCDb7roIsSH6r/P8prtxRPj8lsxcUB0NNp53Muhmxt4373XtybveoxfagXrNnNjz8oDNfF/XAxnsrxTJTX/of/8B/wlre85brvv+xlL8M/+Sf/5LZu+5aTKh6rOFQphbIs8Uf+yB/B85///E96x25lKB9QH1oUJ/wD1s1ZQTF9WAJR5I99tnLo5ga25R2hhgAdPNo9Ak6fAaYHbO3RzwxcqaH7ANsEDCWZVNOCPs+Vh2oDdB8ZNsBlCu2uRrYKcIVKSaTlEf12Q6WhPX2kAEGMyxWUJ3BWHdJE2eVMqrWNAOO1Rzc36OaAHpDAY7trgQDYNqDds9Dyt95umKzbTRTUwI7SZk8jW1Nea+S4ujmP0RvWzNjaYXNnjnw51sx0C0tZ7kD5LwD0c/pFXaYAAdcqWIbUFBr5aY92v0R+1iNogm3dBrSXplAhYPOsCbK1Q4BKLKXqRU6qmPKqO49+amFbh/JKD+U8paqDh3L8neK4x+bOAps7M4LsnmE82ZpplsM0Q7Z0cBOD7LRjWNMx2bX84QbIDNwkh+491BDgpgZmPSDv6ONVPsB0ZKWGaQYjXtzNPid85ZUeWgCQWXdk75yntLdziWEMWsNVfG1fGJhmQHOpRHbaIxiGTzE0iAsipnYM6mkGKAEoupdeTw9kV2q4RQE1OJizBuaMklZfitSxtIBngBVCgFn1yB5ZAZrg1c0rhjpFlllxkh2UheoZCgQAwyyHbh18rmDqntsPQapqWvjSQkuoTlAKek0AGUwBvWyYdmw1WT2t2Yta99DLFsPBBHpNOTZaAYoAhoMJzLojC5tb+CrDMM1RfPwEYVYQNGbsQ8V8QsmrUnD7U6hugD5Zw++xYxaZAdoWei1etyqDygxQg5PeLEue1dD1TAMuLfSmIysbqzqMQjBWZMM9PZjLDdz+HOZoiTChLDpkRnpqLdTxkkm9WlGqWhQEq21H+SoAvWrh92fQD1E+imlFP2utUiXH7RghswSaRnOxoQTgAon8KycI+wvoTYfhzh2YZSNVRtw/FMWYGjybEkzuLqA2NUG3tVB9j4DhnC81JsECkFAbJYmrIuFcrsjWFZIS23VQizm7MbX0dPZD6qbUsyllp1UJ1ffJ7xnadgSASuND3bufEJzcY+6DLguEPkBX2fm6D6Xgj08oZ60b3v99TInN4E/PYA724Y5O+HQBubGf8x5zX/KMBQeoQaXn3O/fd64aJziXvJ3B83rAOZ7TPKPP1UhdSvAjA9z1lEwrlfpu2W2r+TsYJbBMKb4F+Zewjdvnii/oE5CJPZsAASj9o+clxteNaxjX0HXwElS17XE1synccnnuubFeZtt/egFAL8bFuBhP+XgGVqasVivkN1CuZFmGs7Oz27rtWwadX/mVX3mdvxM47+v8oi/6Ivz0T/809vb2nrIdfbwRtILpBAzNyPBF+aMKQC+gqDjl811OZtECaBf01GnHR1BAN9coThwcyI7qQQDOaYDuyWD2U6bHKkcJb1DAMKEXM6s96oNxdb0+zGH6gPUlg+kjA4qTIVWy5KcuJeBma4/1nRa6D8hX9I52c43Jwz2awwwuA1yhMHtwQD/V8IZpu+tLFtVV/oGPib2nn55jcsXBtAQxQ8Fj0l2Al+OfPML9CFYJswo0+xlMG6C7kPZRuQA9KPiMD92TzXM5PYNxVWeYGKg+IFux97Sv6F9lsA3TedmlaoBAL6LPFHRLwBssE1i9EdZ4qqFbSn2DUchWgYBz8Im981bDth6mF/ZVA2ZDYAeA4HXw8ANlo1FO3O+UwF4JU/O4de/RHRQoP75KgCs/CdC9Q3NHBbuR0J8Jf1YcDbCbAXbds0bEhVTrMkxzSb21lLsOPDblArqDAiEqCI87JttuHMpHGyCAzOdqgCsoSw52nBzqqzV7MTMD6ADVe+hlCzej/BWOYUD94RRBa2jnoE8bNM/ZQXYi1TYhoL9jxtfODNnQYpSNtvsFygfpwQWArGcIFmpAtz27ULuBICwyN4OHLzKY1QahyqGvLuH3piktVrkgFTUBuukwHE6h64EAWym4gxl8pmGvctJqljFkaUB3OKWXWBKCgzHwuxl0y2TO/mAK3XmYdUswCsDdsYA+Y3erbnsMzz7g4sHJZgQ9mSVw6nvgSHxxOf3P+mjJSX7bI5Q5/KKCqnv6RDed1KRooMiY+HvXLnQ7JMbYLyphDOc8DknHjR47AMmzqk6W0BGMOQcs1wQ2w0D27DYNJUxX2o/lhonB1iDsjrJec7yBnxdQuYUqMqgrJ4DqUtULfECYFlCrmmxjJZ3AyzX/Jkiyr5pUCGgSQ8jgIstQIJGQqoLA3dc1ZbXOM0QnsqVdRxDmufQc3MBk18FxO0qR4XMOes4QtyCAC7g5VkzlvLYxpVdNqrGGxFqy1FJjlKpiJhMytjKChBBFsBkBEiCAVFjsyBxGcBq/vke/kuFJkb2sSp6DLGO6LlgLo5Q+l6ib7q14TgGGNxnDeZMw57os4J7AL3xOkrwVsrLda3puDMO5oCKl1Q3nXddVyGwBzyg3PjeCJ1v9OCFIF0DzYlyMi3HbxpNgOj/VQecLX/hCvPe978W3f/u3n/v+e97zHnz2Z3/2bd32LZsv7r//fnzhF34h7r//fpyenuL09BT3338/XvziF+Nnf/Zn8eEPfxhXr17FN3/zN9+O/b3hIBtFNrK8OgABKK+Q5VQuYPpgh+mDHeoDIyylg22DgDb2XyofRBoJ5GcezZ6BN0B5zECX2e82sBtHoOXAxNqW29QyeVYB8BYYCsp4lWfgULBk7fKVh914rJ6dkfV0rHvZXMqgO3o+85WHCgS73hLUDhMD3QdUVx3mv9sTFB+TyTO1R3ni0S00holOwHD+8V4YLKbn+lyh3WXtigpAtiTrG4zCUFBWHATs9ROR0K4dguExFMcDph9vUBwN7IFsyAbb2kvAjofLNb2wMwuzcZg+2CA7ZmdqcbkRwKmRnw2oHmkABRRHPbxVAiYD7LJHftbDVZoBRyC7yjoRT+/pbi7BNY51IpI+7Ar+zjCjzytYxUAeFyhFdeyidLMMphmQP7yEzzV0Q1+glXCiYZ5j2MlZ/XFIabDPTdon3XnkJx39mvOc4K/zZDwFTLuphW4H2GXHXtLKwqx62NWA/LhjSJBWKB+qEZRCc4n+x+IygZddc0JrrwpQUgrD4YydppuOjOHgCawadpuqEOBmJexJTbA1zTDsTqR6xcEXGfxEwkgaep/dtIAX+a5eNsiPOwG17AQcdkro0w2Uc0yybQZW3qxb+itbxx7XboDbn8LnFsPdu1DNQK+jMfCZSbLU5tkLmE1PL6bz8PJaZs0qnZBb+oGdS95QXffIHjyBnxeAUbBHawIbzwWGWN/jJFAKLiBMch5zlcEerelxFSY2Ts5hDYHW9mN/F2E+gZ9V8FXB7Qj7GbSGLzK0lygT7i5NoBsuWvjMsKand1x80JrA3Gr4syXZOmD0MK9qqFUNvycAL/ZMaj3WW9zGESXFbpJDDQP9q7mwx0bB7UzgJRmb17jjuZhOhLWVECYBrWFWAdakKpnUfZpnZDGbBmo2hSoo81QZz5GaTgjwygJqMQOqEvpwnwDJGgkJYhVISsKVUJwYPORryl4xDOz4nM1Y3XINa3ZDX6eM+917BUj2CH0PPZ0Q6ISQKmTCMMAvV4mhVbJfMRApejrv9+9DGPpzYPP8yReljQTvbO/jvfmr+fuetUB6MSNzCyTpaWj4/+jdDF3HwKemTT+DD2SFHUEbe2f9CPKewHe5fY7uzV6VvLL3+/clIH0uSEgqXqKM+BxT/Bivuw04lTHnami2k3OjtPYe/crHlYdegM+LcTF+78cTvU9/341noKfz277t2/Cd3/md+Oqv/mq8853vxDvf+U687nWvwz/6R/8I3/Zt33Zbt33LTOff/bt/Fz/8wz+Ml7zkJel7X/qlX4qyLPHGN74RH/nIR/D2t7/9XLrt7R79zMAXGtmGjKEeAoaZxfpujenDgM/EW9mTCWwOLEzD4Jp2h9UnrFnxKI579AtLme3GJ2nr2fNLVJcHKB/QTzVMB/F1Au2ORnXFIShJC83Z49lP+Ae0PA7s2dQK/dygPPb8WQjsbGzD6P9sA6tdSm5j8lCNk8+oMHl0QLtnUV4doHug25FJY2bRzxQKAZ4Qz2G3Y5AvPfqZRr50yM8cmn0mtgYNdHcxpKZ6sMXm7oIAOFfIlvSSwgcME4PyCkOIglFoDnNKWAdWcpiWwHqQxFy7diivkhlzJRnU4Y4Cdu3Q3FXR2+ocvNVQhYEaAoaKycBq8NBgtYjdDDC1Z2ep0fA502U3dxUoj3qmtsY3tWYfJWI1ySJLq1L5UQtXWihNhtRs+tGj1Q7oLs2RndRMybUapnVonjND+fEV+v2KrGkr0tbWwbQ6yZrNuoWbTmFqB59pBAP0uwVM7ZCtB17rRcGk2JaJurHmJVszgTY7a8n+GoXykYbJsbMyge1gFfyihK6FzdAADFNkddNLP6gmGPQSunO6QUzcVFOpxKmyxEYGqxiM1PTo7yZoMsLeeTshyGt7glMAZtPDzyfsSbXCFh+v2VsqTOlwMGPYz0YWX1aDgN9NqlxxEshTPLyGGlivAg/Ys40E6YAeOoBMk7XoD6awxw3cJIfdtDCPnpFBlUmo8h76tIXbmQCbGia3Y5dq09OTuGwTc+0Npbq+NNCtgz3WwOUjVp8ArDipGyCbIkQf88EEZtXzvGcaGkB+tcEwL5A/ugGMQvbRqwiLGc9RZmCvLHkNmhaYT5moWzf0RIr8NPlXAT7XGLKuPhBsNS3Zvts4hsM5r/2s5D54QDeUFZuzGn5aIJQW5uoqeXXhPVDsQF09RVhMuehx1sjnDq+/L3NoCfaJMmJVFkDbU45rDc/NQFm6unTIhYB+IANtLdTBHn+GFYGu91BtR+CVZQw5krAjPZ8RmFrxpDYttweMYF/GtWznuUAa52H2ds5LWLWWgKoNJdW24uJAJrU+oM/SN6Ok96XVawEQrEXmkrUfApqNISC0NlkwojxUGTNO2JRG6Afo/T1WogC8T7SmDDoCf4AJzEpBybmC1lB9L/Jj1sgoQxDvmxZPFCYUWVoA7GCNYPba58n5fGn1Wnpvh/5JBQkBoEe2H85fo+ChtIXvuseXR19Iay/GxfiUGBfvw0/98YpXvAI//dM/je/6ru/CT/7kT6KqKnzu534ufv7nfx5f8iVfclu3fcug8//9v/+HxeL6VMXFYpHSaz/jMz4DV65c+eT37iZHP1EwDmh2NbINGch638BuYhqt/GEOAcVZQLvQqB7p0e5Z5EsG/5THHutLJk0CTONhNw7djmV351UHnxFM2ZrhPqajdDVfetSHBpPLTJg1K4bRTB8iWGj2MlqSVEisXe7ISLqc1H11eUB9aEW2quipDMDm7hLZJpCRi7+7ZPAPfMD6bovqqk8BR91cJtgZK1Umjzi40kB3HrNPOPRTk3yrccJSHkkvX8uuy/x0QLbs4EpL9nAgC1weOTiRv5rGUxFYjoxkt2tgH3Lwhj5VgkXWxPhMITtzcJVFftKiX+TIVgNU71HfVUIPFt5qaGEzYyVKt8f6ElcY5EthN4UlBIDsqIYXFmqYSZVKAOq7S0x+d4MwU9ANfY1ukqXX9oWF3ZANtid1kl0WzhPM9A6+sCkASPnA0BVJV/RVRhlvppFdXQNao9+vYFYt2cHOifQ4EGQPniBzkhGcyrnOHj6D3p2yXsSQvXTTHAgML0ppqeDXCGH0W+5WMOsOZtNBna7g9+eUoE5yBKXYh2k1dEMAYDadsHBggmzj6GFUlOYGq9O/KVzIarhpgfzRFezVFUKZwe1OEIyGbinRVV4Y102HYadE9nAL5RyG3Qn3rSPj4icFWcnOwbRrhConIG4GKGyxHsMA5Tzylh5S3dKDiMxSqlxmDCPanZJpMwrDsw9gr6zYlVpkUCXTZN1uhX6Rw7QO9pTsuln1MKuGIHNapYUI3gCFsLBkt3Xn4Uu5TpOCVS3rDXD3PvSqZhruwQ7PXTxnmYXqZIGjboB+YChO17MyZc6wFwDwd8yhj0Gw5T0AJpOqaTUyXLdhqOUGRmv6bCUJ1s0twm7B+3RWpoUcdzDjcxoBM8Jm9vsT3uNVRjl3NzBoLLfA8QB3aY/9qgC9vlVBj23vyEyGQAC5XCEc7EGdrQjw5L4LRQatFPxMGOc8g/IEfW6ngrmylHqbKZliTSCq8ozgC4Cqa+BG1STYkooqjXvzVxOUNW1K21XGEORtaoQg8l/xVxJgSXBPrNxJfZ8usX33Zq8atykx+sE56KpEcI5+0wjwrgFrKs/o67WGKb/DQBmxIUNMdY2Ep7UdQajRrJuJNTTWjtJtUL770tlXP25lyvaxAPTR6lhN81hP3/qZzuy52pibGUoWOlVmz+3b/f59eGn5mutltzgfCHUx0b0YF+Ni3JbxDPR0AsDLX/5yvPzlL3/at3vLS5Jf8AVfgG/5lm/B5cuX0/cuX76Mb/3Wb8UXfuEXAgB+8zd/E895znOeur18gjG57OANZbbNnkY30yiPHYpTVpfYxsM2lK124vkcpgb9lFLboVJwmcL0kYFBPEoRlAEoTiIgo5ezOHWYPbBGtvHIlx7FmSfQbCkNNbVj+NDaJSlmedyjelQmkAqUtFZa6leQmE6ymAS0TryT1aMt939N72cQJrOvNPqpwfRBAormwKI+tMhqj6z2mH6C28uu1KgeOIOWkJrqwTr1jHoD1Hdykqd8gGldYlx9Rs+q7gl44/f0QKYzZGQYTeORrQZkqwG2DjDrHtlZzyCmXCE7YzWD3Yg305Ix4hs5IGQaxUlPX25LFjRb9gT/LsBsHFTvYZpBfJImJZdCKQw7JUFCCMhFmmpWDcorHVTvkJ22BJZnLdTgkR+1lNEZJtm6SZ7CaoLV8LmhP7EmIO53ihRgFM+BzzSTdw27OfvDKYYFfZ3DoiCjqpQAVvaMBqvhCwJru+6ZMjvLMNxB319790LApiQhN3wOwICeoBRDgmQuOCwK6HaAXjb8+WLK48otw3S6AcPuhIyldF360sJXFsFoBPGbwnv0+6xa0WcNQsb+Wl9YelJbh+zyGn5awE8KsqzrFuasTsBUNwLGNZlgt8tAH+U9wXCVwe1NoNeUuIbCwu1O4aaFhPIoqOUaw+EUw+EUbqfi5Nmyv1NvOobUAOgOp+gOp/A7EwYhWQ1XcFHAzwqETMOXhl5LuZ56YKdpyC0XUqYZ3E7FcybBMOkhbCflxx5m1cKe1PQqnq5SFRBCgJ9VCJZgUa9bhNyi36sAqwl+J5WEFVn6GOMYHMJiSqYwN9wPLZLmSQVVVUwdjf7T2zDcXXsMnppkBOInNbKTFtmyS9d1mGbwOc+hWdbjgod4Gk0j96fVBPiTnMFU3QB/xy79rJsOakPJduxiVSFQ7im9k+HSPitw9uYM3NqdUN5bWPh5yQWdg6mEQvVcqOgdz//uXECVT+dQLeYEo31P8BN8koamEf8vktMIamIXZqpksZTwqrnIXGfTlOysIkjUil5LAbBQOnVVBh/Os6nCXoaO3a735q8eGcVY97I9dub83nQCLOYEuT4IK+7H+1b2OZwuk/8TAPs7u076VwtKZfUT/9k/J50d+gSkt8e2v1MZk4KFHitp9vFGcC55WK8DkNbe8DW3weY95r5nlqTvYlyM30fjmfzei+m1t/r4VB6/+7u/i49//OPp///lv/wXvPnNb8YP//AP3/Zt3zLT+SM/8iP4iq/4CjznOc/Bc5/7XCil8MADD+AP/aE/hJ/5mZ8BwGSk260L3h4M+6E/U3kgW3us7rZMhW0phwUI7kCMhdWzLMpjj3bXkEm0SvyJTrozyUQGrVjBollnYlcD6rsq2JphRdoHuMxg8miPfieD3Tj0M1ZtWOlidIsMdtVj9js1fGEIvjSQrTzy5cDUz85hmPD7i99coT0o6QfVCsWVFsp56J5Mna0dqtbBHjcYdkuGvHTs54zb9DkTUqOc0JUG2VmPbr9AcaWFm1joAcjWDi7TyNcEr9XDsmrvgZCzD7N4pMawk9PzqBXlrUpB9w7ZWYd+zgmOqR2GnRxmM6A46ZFdqdHeOUG27Clz9SCLItJFAALcJNhp2WEoK5EcO7ip1K60Dn6aQw0Bpu4oHe04MTZNl6SgoWRyLLSGPd6QKWkGBOPgSws3schOWgZeySJ8sAphUGniptuBYTd7E+h2QH61Zh2GKqFaBysTcrvqWOlx2ozMoFJQbQ+3M4EaHLwknSoBksoH6I5yXOUVyt895b67gOykZbXHvIRddQz38Z6BOxKapDqPYbeE7j2BpmEa7LBTpP0JRlESvuzhc4OhypGfcULtKrLUQVtkhqFNuiOgr++ewK4L2HUHN8nG6yOBTD438LMc9qRJUl0lScuwAAIILIyiD3l/in5qoXbvRH7S0vs6eLhJxo5X8eK6nQp61cDfuUewDpCV3qngSgtT9xj2pvDlAmbFQCHdM0lYTTL6hzuPMAS5RzxUD2HpuAgSrCYbW1gBoAOB7HL0y/IEy79SEaM3Xfq+WjcEiVUGLb2mqhvQ3TWHXXbQroW+fIKspY9VDZ41I90AHPtR8inAVsm2sisS6NL3ZKPyKcLRowyGuY2VKap1cIsCPtfITlq0z1kkxYLtHXxpkB83vBekTzcY9uH6nQn0ckWvp2Gasi8sdCPpxk4W1qY59ESSZmUBZSgtsqMN/KVFYvGDNdDrDrDs5LXrXu4xLnwEo3jPZwaYlXCzArpz0F0LP8mhQ0CYltDrBn5nH/rhK2M6cN3Q86kVwd9W4iqAURoaPFSeS1eoIWs4CHjedFAnZ1BlSUmvUgTMk0oY6sDkXWFpUdcIbqvuY6u/UmlFf6h0e4az1SgNjXUtApJVzrojJd5a9APCwQ7PuXhYY3qtylnVo6YT7mOUBg8OejqBP1uNNTI3UZdyrsYl7lPwSQIcz118ri7KERg+gXT3scaNUnXvMfeN/aePMx7TP3sxLsbTMP6gy7v/IB/778fx6le/Gm984xvx2te+Fg8//DC+7Mu+DC984QvxEz/xE3j44YevCxh6KsctM52f+ZmfiV//9V/Hz/zMz+BNb3oT/s7f+Tt4//vfj4985CN4wQteAIAJt6997Wuf8p19rJEvGdITFJBtArK1h20CqiMyoEH6GYMC1EDJbXnkETQDgehF7EbAJqCH3ZsMoGn3JHm1oAczehbtxiE/o4fP1PSA5ic9wWumETKN8uEN4OjftMtOZK+deCMNVAjodzJkqx752YBur4TpPOyGXZj1nSV8QTla+cgazWEOl2sMe5TAuVLqILb+LtsV60F8acgsuiAJsMLcdh7ZcQOXMWE2GLJbet0K6KCU0677FDTT7+Ts6hQQNMxzdDu5sCEa+WkHNYSUQNsfVnIMCv1M2KjcwK7IIgWr6aXcDEyvVQr5aZf6NO2qQ7tfssKiHZhQuumgnIOuW+i6Ff9ZA7hAYOsCmrvI+mHwZPfKDP2iQP7oWgBIoCfSeValhLB13AFuUVE6agku3KLitSwp74yPmADLZNYoTZV1HE/fZygta0UaVqP0i1y6Qg3cgtfVzXPoukV/1wLDvOBE28pz5sLk9g5wwmp4hib1ixzNswkWhkUBNXjYZYf8lAsBPtNQQcBmTmbc1g6mJ4iInaMRfCqRICsX0B4UaA8KPm+ai59TYdgtsf4jewzWkdCZ9pDnB4HSXDfJYDY9bO14v3nAGwU3zTFMLAFhrMnpBoQyR3NHhX63QL9bYJgX6TyxTibndZlx34ZZxtcByIoLQPaFETmwRXtQwFUZ3IxsnauyxG6r3rHOJE6+h2F8KEW2senTQkQMVlINq16gNVnhSY7spKbXEUCYTgjQDAOL1MkSaAS4Gs1EUaWAKZk8tzNJCb7IMsAYBkTlOQNh8sdO7PxkR/T5siu2gBV1gW5dugeGaSahTx7rP7SD/rCCm8oxzKbwEzmnOb2UPrcYpjn6/YqfV8D4fhG5sgoga56TEeW2fAqDglYYZjncJIOuO6YCe8CVFj4z9Pkerfl+nZXQzYB+f4JQWriDGUORAGE9KY9VNoMyJgGTbYbu3uxVCeQF56DmsyTNhTG8jt4jHO7yPR7ouQVAcCcVLlHaygd/HhnOJA2V7SDjZ7lSOrGb9+hXko0si5EptWSH3U5F1cKiohpCou7DDr2svHc0UFXcx6qUZGEP5GP9i8ozAkhza0wnd330nsbQoGtH8qM+GU+n0oD3DCPaDiLZYqG3xzOZWflUH7dy7p9p1+mxjucCdD2DxzMwSOh//a//hRe/+MUAgH/1r/4VPudzPge/9Eu/hHe/+934sR/7sdu67VtmOgFAKYWXvexleNnLXvZU78+TGi5TKFYDdKbR7Rp0C4PpQ70wjmQBAVBqWwc0+wb5mYfpwewLF5K/TUloSz+zMK0XL2IP05ok79TtAD8YtLsZVE9gmR218AU7LSO1Hlf33UQqHjRZPdM66I5JuMBYIaJELaUC96c9JCtZnBA4686h360weaiBqwxcQelgUEC26qEGSnMBMg3ZccsUVh8Sk2nWDI3ppgRopvewpx1ZxWULN6/o45oVDLAxCqojADcDWSblPNNYa6azevEYhq3V6Pyoha8sOylrh8lDNYJhV2U/ncA2Lski9aaHm1i0h+z1NJse8EC/KFActwSqNas9dOhSQilAn2OUxmZX10yAjT4n6Vvs9ycoP3YEv8PJpF63ErTRSWgNg2siE+dzjeyIIUaqdwiVRVBk+4Iq0jFGP1XcDzcj02jWHXyZwYPSWDejLHYQ0OcLg+yUINesetgHT+D35zDrsccwKJW8n27GbbrCsGImZw+prge0hyWvRevgKmElC0MQ2Ht5f2hoFZCtHXyupcLEI68HLkpYwy7SLfBWXKU8Ww0e/Zw9pnbZodstoAISgNY9Jcu6G9h32lIpQCDTYZjnDHKVftP8lF5hn2tk0f/aOIZSuZisqdihO3i4iYUV9QF8QLebU9K9HujPXQ0IILCG5/u2OGq5oDLJkmw7+mFj6i2qkpUpAvYAALMpwskpwWEEfN5Db1yawCvvERzI0PaOFSqTHAoEvMEFqLZnmI5SQD8QSAg7FZoGqioZ+ARQPhroZUOQaxDlmrdxxM5Xr5igbdceGOjDNg3rfrggxu/pjvdMyDTfP6Ak3xcGXim4QsPW9JrrzkP1Htm6SzVCPucCjjlrE8Pa30GvqCsM2v05E6F9wDDhZ+gw4fsuP+mQnTboDirkVzYE55mwyUr6Y02UsVuCxnj9lEqdlvdmr0qALoIbIHosq5H17nsCu4GLSv1eheykYXiRUvBzpjir3iS5uKqbBI7Cak2Zbkyble7NyHrqmMi6DQiNYVXM9kWSYCrVU+nhVQZ7mYFC7nAOveng9ma8nrVck5YLAAkg1w2lwbECJvZ63iIw3GYa43FupwK/tHwNwtCPMmUJLbqV11ezKbDZnJvAbwcsbY8bTfL/oLNNT9fYVgrczHOfSeOZdjwX44nHk5HLfqrLa/u+RyEVZT//8z+PV7ziFQCAP/pH/ygeeuih27rtmwKd73jHO/DGN74RZVniHe94x+M+901vetNTsmO3MiYPbmAqJokWVxr4ysIeb1BojX6vSuFA/czCrgdUlynX0nUHNytY1yAVB7rtEXKD4pENhp0C+XpgSMYQYI436J41Rz/L4HMFuyaTM0wtmkvsfCyOOvSzDPlZj/aAEq/8lHIx1itoqN4zgEZkp+asTYBU95zUQgHl5UZkvprVIxowDUNWhgkn306SebvdHNlySBN3X2RAoOQVnhNZMkElAccZmRw1kOULmcYwy5n8K+ClXHfo9iqeK0mMVZKMGpRicM9JmwKJholFtuzQ7eTANGPia+8l+ZSdfqZ2lLiKhNnUA4IA03heNMAO08vszLSrBt2lOUw9YNibwKxaHh8AVTv4SQaz7uBmJcwqFtkrqGaAmxYwayaZAuKPNIYcv7CSbpoLs6lhT2r0BxM0d04JbHZKLkoI29jv8ZpGUOMLg6ApPQ5GsSfzYAp70sAtCmHAKanNzvp0rihHdHATC/e8Q07Uvccwz2Fqh26RSXWMST5OKCBbDfC5RlBAc3cF1QdA896mtDUw6Gnjkpy3283ZRSq+ZuUCzKpBfziFXXYw9ZDCdLqDism7M56b6sE17Jr+wvaOihLrzkOvO1ippdEdz5M5azHsliJxBuXMpw2GRUH5rQL6OVlLsx7EHxgwiDw7Ll4ABKlm3SGYgvLz3qGXoKhuN4ca6Dn2uUb+0FnyDNo1/bbFo2tkl1eUPhZkZVUz0BtaFvRoxp5O8b+h6yihbMT3O51CNaxygVGUfJY5dNPBPngVKApKQ89qVlPsTJnmuz8lOJAgoNhVicwmWW0o4/3bI5SWXtIQgEB5O+subn7SfqtDdQP63SmvRUspqxMp9TBn5VBxxAWfYWKQLQfYU2Huiwz6bCULYR79zKK8XKPfLaDlPTzMLIIuYFci6TQKdtVj2CtZdyT7YdctmkuU4EMpuNLANi7dF3ZN77NyFmoIaJ41Q361TQnEIdNoDwtUD24QjEJ3MEG1qsfPB5FmBq8TKPpQ/54EAJUxCD6wF3M+4zlfzHk9fIBqemRXPYb9KbR4SeECnHwOhYxsKCYVwpUjOSjpFZXXTTMQAby+rsl6RjlpUGmflLWUAwtrqZoBbp7DWw3kGiZjpZBet1DrlnVd6ZpSqROqHKruECYF3KUF7G9+HMpaqKqEu3oEvbMAVk+QXrstr9UKwAgit3tF4/PoQ+4IqvObrPu5pqfTn5xC5fm5bd+jXwmd59cB2GufcxEmdDEuxsW4beNTHETe6vhjf+yP4Qd/8Afx8pe/HPfffz++8zu/EwDw4IMP4uDg4LZu+6ZA5/d93/fhNa95DcqyxPd93/c95vOUUr8noFM3PVTmmNLpPcxKwNrEInt0lRgps5KkTEmWDNOSiZYAVKC/yFdZSuW0py2rADKme0ID2UkLKxPHfq9At5AJawhoDjPMPzYwWXY7EVO8b7obCOx6MhrBKGRHNZxII8m6es7OlWKBe2ZQPLzCsFuJz8nA1AHlow1caTHsWGRLB9VQGhcn8HZFWRwAhEwTcG8GuNIiX9P/53KN4kqN5rkL5KcdfaCbFrYm2zHMcgbqiKTRrnq4wgpg7jBMMvjcoNvhNrOVeOXmOUzdC6NlCKQ1j7V51hR2o2GXLUzDwBufU24W2TBXWdizlsfsgeFgAqaONpS65pbBJgB8Vci1qCgpnpfQm56VF4MHFAh6tYabZlyA6HXyihF4k+HUjUN3OIVyHrYmGw0Xxsn0PE8VKnD0D5vNQMA7zaFrdlj6TMMtCrjCEIQLYxk9c66UpF4gTdK7A7K3+WmH9qCAaRhQ5Y1KVsPIIPmJhfZI90BcwAhGob6zRH7GxQcjAT+T3z5GKDLxz0lYUpmzjmSey3uIPj41BGTLMTW13ymhewez7FCtWrSXptznKoObWjK4OwTi7Z0TyqKbcV/bw4qSaTlnZGQtdM9/ASC/ssGwKMf3jPhfoRTscUNf8FHHzlIAZt1JB61iBU+Zp6ogvelQ1n2qd4HH6LGsyFT6SU6gKF2RqXYinujZJElA7ToAQw83rzjRd7JwVJVwuxPoVUMZZJVTcuwcdN1JCI28vnNMIV3XY13IlOcMmaF3NLJrBX1+GIbb2tXp5lwkQ2AyNRdFWrgq44LPWQ+fafSLDPlJz0WO58xQXGn4eaq1KB743neTjAm3BunzzdQDuj3eX5n4wYfSQGca7a7F7IENuv0KpvfodnO+5xzl4/a0Zeq2vG+GqWXn7pIy8GC46KWGgOxsQHNHhfLRDZnvPIOX/dKrNYIP5wBT9HZGSa3Oc/i6hikKBve0Hf2amxqhsPCTfHyPA/BTA9V7NHfPaD+QmhxViOy161Liqxe2M4InVVVQeQafels1AHdOlqrzPHkyVQjj3wur4ResIIL3o5QfkOOt0ueiO5hBL1vYjz3CRGaAFS2xtsU+vnT7XJCQhDEFH84xXOeAnnNQNkuhQ0okwI/LPm4Dzn6AkWqXa5nOly1eP/aPxrHF1Ebm7QJ0XoyLcTGe8vEMTK/9nu/5HnzVV30V3va2t+Grv/qr8Xmf93kAgPe///1Jdnu7xk2Bzo9+9KM3/PpTZajBM1TFaugNY/thDMzVFUKVA4PE9tcCNsuMISIDGRk/Kzl5hIE5a0TGGSjbFP8dJ9qFlNUzbMI0Flpkrz43KK909F1ebhC0Qn4qQSCKvifVcoJHBodppH6SwVuN/KRm8mjT0evVkVkLWqM7nMJuRELWulRxoHyAaegthFZJ2gZgnHQHeh3zo5YS1BP+a5cd7MDk0uKRDSf2Z2R5TUNwapoBcAG6G2CWDb1TWiE7axP7pzuXfKJm3aE7lG5BTwlm0EiSXxgltS8+pb16WMr2cgNXUcJsz1oErWGWHZpnTZGtCOz8JOd5yy1UL5PdGb2LxcMrTsQmU0AzKCVOPFXdIyxKuEKjeGSDUBjopidjMadf0eVamOGB/k0ENJcY5mSXPXTn5V6QJN/9Cm5ioFuPbr+Abtkr6jNLmZ8C7GZAP8+FwSLY1AMnzUr8me1hAbtxsCv6d5tLJeya1Tt6UAiZhpHzO1QGOtfIT1oClRBgjtasIhkGBGsxcbzH1OCBwUOBqa3BavjMjB7fEOB2KQfWg6cfVroaQzZO6FyhoXuHYa9EP7fIT/oExnVL9jHedz7XqB7aiHeSsnPdBwyTLHmjGdREaSffBwRWunMwZ+NCQsgNfGagYppxCIlxDFpDDf3IQPaOx9oOBAnTAqqhjNbtVNIvGT8stgCmY/gKBCzEVFa0PXTdQM0nY6BTN3ARqHbwO1OgkwWWwRN81j0/QzygGwJROEdPYVmwLzHPJdV0y3ztHD9/WmHBnYPKM4QQbivTaY+YSIzBEywbjW7XQvdc/In+1/xqi2AVXGVRXK6h5PNH5fRJR6a0n1oUR2T/AVAOu1+hOBK2Vynx8ko42vGA5o4S+UlHNYBWaHcsbBugjAJUSSlrYTCUlKWzz1Sj3S9QHLVo9wogZ2WTaT18SY+pGlwCX2p3B365ImhyY//kuQ5NCAgrckqui5yAz1p+1rRUU+iWsuP42ZcfNximOYx37BzdltvnI3uu8zyBTzgHTBZQHcFZrAeJjGsYBvh+EIaUnbzBxM9xk7qGQ24ZwCX9t7rjQqmfcgGTFgIg7MyhHr3Kyhcf6OssCgR3CuDGoHA7ZIkXj75KZTAexzUjdF0Kabo3exX/jyeQI24xnTqzUNam3zv32v1wfTjRNf+/AJ4X42JcjItxc+PP/bk/hytXruDs7Ax7e3vp+2984xsxmdxma8+T/cWu6/Abv/EbGGJww+/hIOPBwApfZQhFxiqIu3cArclaTAupndgKYTAK0BrmZJMCQ3y1tUpe97BXVsgeXTHQwjmoTceJZMtKC1P36Bc5XKHpZZszoCNYsov9zEINkohr2Y0IgP6mZcOHACSfGYQyh1nJhCEz9DleXiUPYz/POEH1XO3XLgjjRL9brPRIE/zek/V0jgxwphn2sSjIsPoA5T2qB04JLs9qguzWQTUDzLLmcR1MYdc9shMBBpkhuDIKqierRbbSITujXJcBIYDLNfLLK7R3TJhM6cms6c4xmCg3lH+2jhNJpSgZPZRJawgYZrxusfMyJlvaZQe77DHslATtvYebFfCSutnPMwyHU6je0U8oKao+t2RDBfzZOnaAeiZj9h669bAr+nh9QTBu1nwEo2CXPYOSUhIv0zehFb1wmYaW0B5T9xhmZEjMpke7l8GVFuUjNYYJg37avQzVQxuUH72K6uNnZIVan4Kw9EDwqjcd5XO5ZYdoN0A1vMa6c8KaB8BSMj7sVpREy32hBi5EDBMN0zgyXoB8X8NNLPpFjn6RozhqYOqBrO3ZIL/vJY1W2OnAa5etGII1zHNuq2ffqQqB9TGaiyHQlFYPizKBSeUD3Lyip9gomFVLcD6wrqbfq5KPT0lVhHIOPrdwU94zbn/KYKnTOr2ubnsC4NzSa1mIRLLvCS6yLcYnpsUGz1CgltLvYPneDmWGMK+gG1a4qOMl3O5EAK7n9cgtQjGu5YXCJuZH5RmZz76HavlIbGbXEQBLVUoKHbpNw08LtAclQmEwSH9trEFxpU5S2yAdx9mSFgFIPQo065PsuksqhRjkpTuGEWXLLt1HUCK3XvbITrnoRg+oloAxjeJ0gF3z74nyAUMlHcAKMDXPiz1tUjKz/f/Z+/No27K6PhT/zGZ1uznn3HurkU6kERDFDngxKooDKcX8GPpUhIeGX2xeFMQGf8HEp8aYxOCL+RH0DavsoibBBnDYEVSoGARFYwgiREHwByptVd3uNHvv1c45f398vnOudc49t6uqWxSXO8c4o27ts/faa6+19jrzMz9d7WDXA6uqWsdUcAVeX/FctBMQM2HHkqdT2E5ohXCw4vkoCv5/8OhOVehPVhjmBNk+11L9pNPxid796AmNLF9oW7J/IuNF7Mzc1AyWqsrkfQw+EHgVBRlROf+uYCK03vTIz2wI6qUmxVdMR1bdkB6Dh3TxaoQy5z2gyNO1HboeYUN578VA2gVSVTlG/pg6kwTaCwYgPdM8lzLiosBlxxQ4GoPQtgl0ThnV4+pa7vSvOVxFg49/v931FrpzY9wY18O4HitTXvnKV8IYcwhwAsCnfMqn4Md+7Meu6XtfNejcbDb45m/+ZsxmM3z6p386PvCBDwCgl/NHf/RHr2pbL3vZy/DUpz4Vy+USt9xyC77qq74K73nPe652l6DqOgXKRHYQioEuAMhgyKQQwMgiGsPJe0wglS5LKHCCmrMnTvWsNVHNwD/i3sMvSpi9Gma/QfnhfVTvPwddDyg/uE/wqBiaku91nASLjDJ6DvWmg2oH+DKH2a+hm4Er1HsbOKl+CFqj+tB+mnya/RrlRw7IkOYa2dkNsjMEgWbdIjsYCIyaQTx8TNXVdQ9d9yx8Byd09kAmi2uunPtZnmo/7G4NIymxBHIOdrchK5Jbfu51y4RKD+i2JxCWYxuMTNIzDbvq6cO7dYFsv2PP5kBgpBr2e5r9muAs8Jj1WwWGZc40YRdSGAwA2E1PL2TJzkVIOialxmViX6EUwUvn4UqT0n+7nZwAVpJRddND9x7Z6RXMuoU5u4LZ3cAcNCjvWsM0A4q7VvRGniwSAFXCKHYncmSrHv2SAR669+iX9MeZdQ9X8lpwM8oU3dwyXKdjUE6/XSDf7Vmp8759Xq4iIa4+tI9sv4Vdd7DrDrpzKD68z0kmGIgUqoyS3u0Zr9+eVS1BC7NZWPTzmJA7sCf0oINpBlR31SlJmIC6R3G6gT3oUJypUZypMUhybbbP58HLhDcj256JPF13DJXSnYPZ0HdqVg3ZTKXgM4PszJrXiCwQ6d6LzJkLCSqEJIX3FUF5UAp2t0F2zwowwvwIo9OfrJI0tv2kJZUMHkz6jMyt1tBNB71quBhl9Qjm6oYBP1p+BBSG7QVCbjHcvIQ5cyDBRpL4WxAMhOWMvZJKpf5RKEVA7CHgUVNR0UsqbpRDzmdkxgaCLgwy+fZBqjCOTwe9P4deNRJqZRkktBkwVDoBw1jtE4yG2VAqHzLDRZXYUykLAsHopLYYZjalCHc7BeyK13a/pGpkmFlKadc92U9APLoBLuN2irMNdD2guruBGhzsRnpDK1l0GAJ98OCCFtlTyCII7/961UKvxJvbD2Nfp4CtOO50r+LvlBqBW2TbPCXC2bk6qTtcrpnEXDGkzOe81/ntOQFd7FcdyFbST0p5b+h6hGHgQocPBFo+JKkrn0NfJJwH+h72oOWiU2GSjJ3vKzVIi4JBYx78myaLAs1DFpL2O4N76E28royGKguoksD2ikDaJGFXGZNA5QVBRJmdvOReLJY4JynAxQV+zXhspuMoQLseANvHO2i+Ma5+XA/X7XU/HqD02ttvvx2PetSjUJYlnvzkJ+MP//APL/n8tm3x/d///XjkIx+JoijwmMc8Bj//8z9/Re/14he/GP/lv/yXCx5/yUtegle+8pVXv/NXMa4adH7f930f3vGOd+AP/uAPUJZlevxLv/RL8apXXV1X1pve9CZ8+7d/O/77f//vuPPOOzEMA2677Tas1+ur2k5YzMZaCRmqJ1MHgJNAD044NeBnBf84Owe/LIUZDUyU9B666QnCHMFRKCwrRKzmynLLleWQGaBu4Gc5u+vaPvkNs90Get0y2n9JiZ/uxqTVoMicxNoPX2WcVG9V9KwNHrpuybZoLfI7jZAZmHUHu8eOOobo8HPmd+2TQWwc7PlNSlNVIaC/eUEmc90hO7Oi9LFzYxKl+ITUpuUkOP47MlEd+yshxe6qoWdUN9147BWrIuz5DexuTYDkvbB7A5QkoAIMJHLLHPABm0dty/HUrGDJFPLTG7jKErhKb58aPPR+A+XcCMT21tDdgOKeNex+y/1ct5RqRi9vPcDNyDJk+z1XogZHJni/pkS1yvi55TVq8IBzMHt1WsCo/nYX+V378nMAs+lRfYCLAvMPrCnrVkB+vqNkdZ7BNA7l6Zry2sLAbAbo1sE2ZFazfTnXwhrHBRM1sG9U1z37EFuH/KM8njExMxQZZbbyLY5s/jRVV3cO+T7ZZWiCdjdj8rA+kAoaAYAhZ8BS9I8Fq2GlO5O9iA5QwLCgzFk3PUJmkJ+rExOdnV7zumgd2ocsefzFu9vdsiCD5gKG7XysrRFgmib9Ap7NmnLvkBkgMynAKn6/848cQK876JbAJmgNXzGF2OzVQC/KBIDfZ0k7hVIIswphZ4sAL7MImU2LU3CO330f0H3ySW63yKQfkgtQfs57iNl0GG5epuvGLQogM1wE8A5hViGl0xqTAEeocoSK3tK4YIO+FxAsk/bsitwP92r4eTGeUzBBuzjP73QwZB/LD+4iO7dJvk84ei2hNbCpYffbpNxQnotqdt3zD64PyHdb9MsM/TJDcXojtTyAdoFhbJZMXr7bwbRUIgyLDMMyp4+6JbNuVx3gybb2J+htd8ucoWqBnn64d1X4AAEAAElEQVQAyE+v6LvtB4Y4te0oeZ2yahE0CTOnjCHIMyZ1W0a5rNn0VJ1YA7vpYRpP9n/FRTyzIasLDaj5jMm5AjR9PyRwFhnPuDgRZN+UMQkMK5sxkEqYV2wtCSadR7dTJO+8nxewuzWvv/0WZr9NtT4ho/zYtB7QDGoz51dQs4oBWh3v4V7qgqaT3kP/jixi8IcBX/CHejMvSJeNtTBXq4AyhiA9yy7oUY0S5GmVyp3+NYe6Oe8vwHYDBNwYD+S4sdDw4B8PBNP5qle9Ct/93d+N7//+78fb3/52PO1pT8OznvWsROodN77u674Ov//7v4//8B/+A97znvfgV37lV/CEJzzhit7vV3/1V/EN3/ANePOb35we+47v+A68+tWvxhvf+Mar2/mrHFc9q/nN3/xNvOpVr8Lnfd7npSRGAHjiE5+I973vfVe1rd/7vd879P+/8Au/gFtuuQVve9vb8EVf9EVXvJ2QW8ArytUU0zT9vBgDfCRO3lUVdO3J4EmCafSUoR/gJX0yDtWKbG7OBFO1v4Y/ucSwVSK/m9H1YVERFHQDexzFP6balhNZCAAWSaCue2Bw8NszWZm2aTKsW/HrGEomYTX8ljCQAjZUDEbpBoLcjCvgwWqEvEz7H3JLBksYAbPpoXfX8Dv8jOagIaCVSb3q6T/DQGliZH2gWZHAgJwM5u5dfubBw37kLMLWPDFHqhsQBLzFlFjdCED3fB8nEljV81wFC1QfYkqtkiAd5WNITIA5aPhZ6p7AIzPQ5w5G1qigj0sfNHA3LWHXLRNGVzVUZmBqMr1q03KSv/JcBGgkHbRpgSyD3ud5xkEPZFauKQ9fWTImbYswnyVZHTySrDVr6MfTK3DSOCsSGwlHMJd1TMeNjLNu6R2GJN66LaZ6mm5Ikl96F12yxPqtCvrMHgNnpDYCAFQzyPmW2p8NFwLcdkXGfpHDrihphGP3KsCAJnNAwKfrnkqBGNAj7Nswz1ihkRmp2SDLU961RndqBj14ZJuOMkjv4ZeFgBYg22vhJaXV55YLDyEAA1OX1bJgKnJm5HGRKWcG3YlqZFc1uACwoqw5FBn0QcMFI7l2zbpFf3ImwTMDQkGZvN5IuFQz9s3COajBken0nom2AK+pkqyRn+d8bWUTe8SQr5B8c9DiWQYo4y/4nfO5hVk1I/M5q8iASQ8kBlm4AnitrJsku43doOgm0ttrMeQcdydKFGe4QBPtCb6wsHvCDAtoy+5ZUfFR5dzfYWD/bN0j6x1cVCaA1UDFaQblRL+3LzOYuodZQ3y5Cghc3OlOMW041hfpekDWUIECpbgQ6AJclhMEawFyzqG4W67bIQgIlFCmeH9QagRCEXgeAaBh6AmmnBXgb9ICAPMClMj/xT8vL9dtD+UlTGlqMXT0QIahT4ApDD37QqWKRS/mvPcAqQMTAFSeQcduysGhu3nscjUNfdMIGZRIuEMRa5JsCi7LdyWcbCvjgpeEWcES2CHLGFaEw5PeQ+xiZDJjxYywtVNZa0quNQah60epsFTUTJ9zdKT3iOfCByDPGaJ15Hm6KBGGHm/ox+1Eb+79PY4m4t4YFx83jtGN8QkxHoAgoZe//OX45m/+ZnzLt3wLAOAVr3gFXv/61+OOO+7Ay172sgue/3u/93t405vehPe///04efIkAEpjr3R8+Zd/OX7qp34KX/VVX4U3vOEN+Pmf/3n81m/9Ft74xjficY973NXt/FWOq2Y6T58+jVtuueWCx9fr9SEQem/G3h7DDeJBvNKh2h7DTsU0P2EgVTdA1T3M7prJnRKhHzJDMLRppbScqZ3DQ3ZS7YJqKXV1J2YIZQZfWvjSIiwqTp7WPVfGAbitCrrtyRys2tFrl9kEktQgnrvYKTkr+O/SQu+tycZoEKwZw0lZbjmZajoJx5iycEOSCsN7Mg4HDfTeBr7I6FtzgaxgO0oxk6xwENDd0RsYEz6DlNSHRcXnlNkh+bFqe/hTW1Cdw3ByDn9ii/sjTKfakGlUm5bgV2pRvExe9X4Du9cQBPYOZr8lwyZyULNqyX4J4LbnN9zW3eeh1huoVQ11dk/Y1wwhJzOgT/O6MQc1cLDmew8D9EEDc26dFiPU3gqqc7Cn9wFhZlGVDH2JwS0VA0zi56BMV4vPC1B1Jz9kF2L1hS9znhPLUvZg2NHIVFP2++lNR3Zi1cgCRMvwmcGN3aFynslcYFI4r6HvPs+uRwkz8QV9impw4ze5d3Bz6Qvdq3mt9176JimZ1iteK6YeoAYy2D63siAi6c9GSc1FBy1MZezKLE5vEIyB3Qyp21PFa1cx4EqvO5H8kcVl7YWwuSVDZ3RP9lr1DKfpt0v02yUnz3stA1TAvla3PUN/05yAIAT0t5JdDEpCi6yG3W3oQbaa8tpGvncDJZdBmHiCuo7HFZj44ShpZKwwJb6687yuxMuqejd+V0VqH9lCLaoE5T2l7HmewrwwDFA72/TzlQxKCjnDhmJ/LHIJsonfc3vtmE69bqGajpVH0QsooTW65gIJnIOf5QlQhVKkxbOCIEO86MNOCXt+AwT2ceZnaSHwuYXZ2/DnoIHPDRdGmoG+84ELMvluO95jek/FhzDTPpPFqlmevJP0gTI4yJeWipJugFk19DNGxjKC05jYKiA+TZQnLOTI1FGSTbAb0kKMPb+Rc6/Sd5j3MF7H+mCDsFrzRypR7vSvGeWpEImopBL71RowY40LlIbvOi5OAHz/tkV2wL8NMUXclQbKI0n9h3lOj/7CImj6yCFBc9mqFxZWpN2zivtQ5BcNBIojAjqllXznPLTUmShhJQGCv8jgqjzDne5VZEIF1F50+/41uCAcaBjGAKapfFeSc4/u31FP5/05boCpy4+rOUY3GOQb4+N2XGN5bdd1eNvb3obbbrvt0OO33XYb/viP//jY1/z2b/82nvKUp+Df/tt/i4c97GF43OMeh3/yT/4J6rq+4vd93vOehx/5kR/BF37hF+K1r30t3vSmN11zwAncC6bzqU99Kl73utfhO77jOwAgAc2f/dmfxd//+3//Xu9ICAHf8z3fgy/8wi/EZ3zGZxz7nLZt0bZjdPr+vnjgqhw6TtQt+yazc8K6TT1cSnxwpWXip6SI2j36Ff2SkjlU+bjKrhTM2RV7Hg2lrWT0cqhugD2/4cTMSGl4z+oVmEltipEACReg+y75JFVm4E8tRbLr4LdKpsbWPcy6RvCWoLbp4RflCCRyy0lxkVFmtumhjMZwYk7gAJCtshpuXoz7VEnI0byEGgZOcjUBFjTZUXeSoTvK6CRXDhm9rwoWwVEaaPbrJMNM53BRERRaSSQ9t0KYk/WjRLfnd7HKgIEBRl4rgniAcsiguY1ZxUCWPONELRa3zyqyU3tkmqF1YjXUqgaWc2B/RfBoNP10fSAjupxBnT8Yr1uvua9dz2MijKWfkeWKiwNRXhwBFgCyocZAr1p2NR5s+JgcDz8vYITxjRJPtGRrKcNV9PtV5ShhjsfbaoYTWcrU/ExqTWqNMC8JEKOEWCmEKiMbKymXdndDNu78CigymPMb1gTJNRgyA6UIKiHBOjoys1YDLaClNsVtV0CpYPY2UD17b71MKGNvIyXpHRlmYUzT59AKqumRn+N1q9dShTMEynOlH9fNsxRaYw8ohzabgYnH4i/kYo+D3gzJX2nPrwn+M8N96eQ7nhkuNlUETb40/J4LSMXWnNdCXGgAyHzK0Os2pd8qSblW8tl0M0iCsoOb55Q6Ru/zfg2/LAlUY0pxlvF96iZ5Nv0OU54jeNJGcyGryBHO7yF4j7DZHHsfvD9GyAwDmLSCOdfALUoqNkC2nEmx9FD6WQYTAlTbI+hA+WqUDdc9j0dVIFgGZkUJuM81dBEXZbJUdaPaHiYzfF6HpALxuU1BUVRu2NSP60uLYVnA7oqnuMgo2z+oea0nT75j5YmZ3JdEHhp8uGCirLSSoB8BSdbyfPWj4iUYBWR5WogxTY9hZyaJsYHfQz0BSRNAllhWAZ/BOSilWKEj8lrfdYdAVvABYbOBWUgA2ukezUMWcKVGvssQufKja9QPm7OnF4AeAuyKAUv9kpLlbjuH3fD+oc/vAU1HhhWX8V2K7PhO9yruL5CY2/hfAIdksAhkQm/Ln38oofloquxFwYdWaaHg6HPUbAa1OsZycxS03hgP2nEDxN8YH6/j3shl4/MjRomjKAoUR0LWzpw5A+ccbr311kOP33rrrbjrrruO3f773/9+/NEf/RHKssRv/MZv4MyZM3jRi16Ec+fOXdTX+T3f8z3HPn7LLbfgcz7nc3D77benx17+8pdf8vPdl3HVoPNlL3sZvvzLvxzvete7MAwDfvzHfxx/+Zd/iT/5kz/Bm970pnu9Iy9+8Yvxzne+E3/0R390yff+4R/+4Qse96VFvtfALQqozjFIJzOcJC6LUf7Ze7jtCvY86wKGKJMDfUH08cmES1bhg3RIcpWbk3o/IyhVVqfwBrNXw21R7qc3HVkymYj4rQrBAMpR9kdfmUy0Cgt71zn4h5yCagbYOMkSoGHPbzihXdVkXi19nax+sdANnz+cmMMc1FDRo7U9A7xKEly9x4ReWMP9rzKGq1gGrqi6g+pcAs7wgXJOkQmG2CuoAbXipC5kRjxwEixSdwgnlkm2q7wnEFSUJ4YqJ/PkJZDGBHZqGrKA8MI2lQXZvKaXiYihvy0GsmQWAPcnVDmPddcRPPgwAjkdQzA80LSsQLj5BFmJzEK1HbcVPUtGJwbZz3L6cberQ0DK74xx0rrt4Re8htzWFhcV1i1gQPY4stEaCEUGd2IGXXf8rPAIW3Mes0pArtZk5Hv6hyMDHL3J/uRSJMsdVNsJcBn4fK0SkDAHNetGqhx63dD/OeONzlcZdN2nQCi/KMkqNVx4iAsEETybg5qTaGPgY42OB/dh8Ajew23P6KvTSBJeJR7d5DcDkrTY1AP6nYJ+3b2G212L9BLg4wetsPZ8rV118LIdv1WRRYuAI0raJewn2Mii8busuwG+sqIAkO/xhsE3yLLRf6Zk4iv+7/5Uxf0Qto++5yHdQ4LVsJJAq9oeviqg5V4TigxqbyULH/3IWmaWQF/CeHw5ppHCaIRZBdWTgb6gm/D+HNEj3XRwixK+ssj2NvCzIjHtUQ6uQ4DatHA7c/G6+iR77W5ewB6Qwbf7LZn2nQraBWTna6ocQBYYSsEtcoStAsoDqnZjWFk3+q4jux6ke9XnDCyL93VongtzUMMtK57TEBiqlZlRuQCwO/PgIElE41DGHKpMGWWeHqhrYDEH6lrUMIGLAHkGXUvgGxgG54sMbquCHUYApKxFGAYyfibH65tfSqycspaMntK89o4G8hiRwir+V/VOansCe5A7B7OhMsGuJl5LDWweWrE6xhIc57sd+q0MpjH8PG0Hv1pDS9IscBGZamRpgXH/jKEXeegPvTZKZFWRIwhQP5pce1TCexzwVHkObC14fUnAEgDKgLWiJ3oyDkmAb4wb48a4Ma7VuA/y2kc84hGHHv6hH/oh/It/8S+OfclRpWgI4aLqUe89lFL4pV/6JWxvbwMgUPzar/1a/ORP/iSqqrrgNW9/+9uP3dZjHvMY7O/vp9/fV8Xq5cZVg87P//zPx1ve8hb8u3/37/CYxzwGb3jDG/C5n/u5+JM/+RM86UlPulc78R3f8R347d/+bbz5zW/Gwx/+8Is+7/u+7/sOofX9/X084hGPkJX2Ej5j+qI96Ci92ipgNiw5BwAdOGFy2xU7EzsPs275h33OP5ShNMmTiIA08dfiyUMIXLXf28AvypQyGww9iHp3RbncvIQveXiTrzEXgOcc/LKgLAxA2FoAGhh2Sgar9Jw0sR9OqkJOzlnB0ZhU0RETOX1FCVUoc/hKALZzBD5as6dSQGOw/P84mYLiJNydnHN/9jbwiwp+O4c5vyKgXJScjIUAZDl7RKMcUVuEMU+K3XGzAqEwMJ1IByUkJuQWWjxrIbfQZ/ehNzVZTa0BS+AKpaDWLVBkEujhgbpB2FlAiTQ1sqkxwAhVCRysga0Fd8Q5gsoom5PJC3eS8jw0LcGYJXsapBfPnFuLd68cZa/dIHU2ZMMI8HJ2TmaGUk5PSTYCwbSbF3xsmfNYezJfw6kFfKaR332A4aYla1oisFUKGkB7y5yJmfAIs0mPpKFXNuzMmXIsslhfmCSDTdeytawgAVKPX0oq7ga4E3Mo7zEsMmCRETwYdnnas2TZgrDjcA5mf0iAzm1XiV3yVkGLBzf5GAH6iTXBuZsXcHmRQLfJDXsPSwIN3XboT3BfzWZAf7KSsCbDCpzMpO5FX1h6fa0lS+iCBGp1EoBkEXKkBSWf26RIUK1ndUuVQbUWGNrR/2flWhJwa1qXPK7IRW65LHi8I7smUupQFokJ15uO3xulE6sMpRDqBmqQxROpboqe5TCfQR2sKZUeBoR+gCqvoHbiXg7VdlBVlhZKlPfoHrKF7Oya9xE5P8N2SXn0LVuSXiygvucxyIQ99lal84cQ4HdK2IMWw1ICpvYaMpUHbWI0oalKsbsN5fR7LYZtsR6IR3nYKpmw3Dv0O6Wk3moEy/or5eQcGd4XzUSeDACh7RjiMwmjiSMCQR090t5DaS0LXGQUY7hYqMisQlNFAhfgtkr4nN3CGBwBLgC/d0BWsKAMOTGeEO/jYo7QbgiEI4iLjGhcwMvEKiJSYt17JvXOLMxujbAouAAkw+cG2QHrbqp7WnRb/F5kB2SiMQzAcg61proiSY0v4ouM+xz7QlVGCbJb9YklDT4ImBcJuWMgUhgGHrvLXoQTT6dStHR0fdouAPh+gBE/9AVj4hsFbrBpN8aNcWM8uMYHP/hBbG1tpf8/ynICwE033QRjzAWs5j333HMB+xnHQx7yEDzsYQ9LgBMAPu3TPg0hBHzoQx/Cp37qp17wmmsdEHSl414tFT7pSU/Cf/yP/xF/8Rd/gXe961145Stfea8AZwgBL37xi/Hrv/7r+G//7b/hUY961CWfXxQFtra2Dv0AgKss+i2ezPz0BkrqNUw9jNJaiOzOsKeSch5K7cj0MIZe9bGEnJPnxAC10R/DcJfIsHiZlPuqINuxM+c2JWRD1z18adGfqKDXDX1TueVEwBMEhipD0Bp21cGsO+j9hmAxhooo1nuYdYfu1Iz7FsYJrj5oOXlVKvkrfZFBhQC9aRlIVGVwEbwYBfQO3a1L6XB0aYIe5iVgNUH19gz+FL1zcZII8bWpRlJoezd2xYEMT8gMVOsITsX7phxDlNyyGqWyZQFU7NeEsBro+tSHGHIrwTwVUJXsWC2ZFBx9usFquJuW9E/etEPp4qygrLLtEKz4LvuBAR8xQbLtubI+eHqBhZn0mcZw8xL1o08yYXd7RmZYZKhuUcItSjmH9AMHK76zZQl7dkXAuczhcw1XZSlpEoi+Ofoe24dsQXeDyDQL8UVqDFsF8vMN3CwjyxOHUqk/EQLytLCi/VaBfkektw0De3wljJpC8k3GeplgDFwlktC9bvzeaIZOhVmOMMtTEFCwlgsgkqAMF+DmTE/WnXjv4vbnOX3Q8zxVOgTLtFs3p/fV7NZjDYkoCrLzNbLzNZT3yE9vhBkrYFYtA4y2KsqW92u4rQrKewZihZAWiNyM171qBibJhoCQGwKUwYtXcKBUVEBC8v8NA2BFSl139B53PJa67slIC0DTBw2v81WdalQAMr1uZ0ZpeB8lzR6haQkk8hzILGtcGllg2XRQB2tgLosfzhGYTOS+9/vI+d3xWyUl1ErRXy1KDCeLAcrzWtMNK076EyWZ4CxLScBuxm3pnuBID4FAUinowUNH/yuAYauAL03yxdpVB7dVsALEKC44nKjknGVMrg0B6B0XAdrxnISC3k2fW8rCrU6BNJhV/NHqkCfwAmAS2TIfyLZJgE+yHoTA76YAPN0M8PM8LRAFq9O9MAwDGc6qhMpzSaj16X2U/M2Jx5/3wQngBBCTbAEg9PSlq26AWfXId1tJAA5cvImJ14Y91dlqQL7bw1v6Oa30CU9DzTAMXMAbRvnwBUP25U7/Gl6L2VjpAqUp/5UKmlRFM6sSG6mshd5mcvWlfJcRWD5TP4e2hu0ZVJEnyXN6Tp4d7xGdMJ0XY1Cv5bjhU7wxboxPgHEfPJ1H8cpxoDPPczz5yU/GnXfeeejxO++8E5//+Z9/7C59wRd8AT7ykY9gtRqD19773vdCa31J4i6Ovb09nDt37oLHz507d4Ek+P4eVww69/f3r+jnasa3f/u345WvfCV++Zd/GcvlEnfddRfuuuuuqzLDAiBj2VAu15+s4Oc5VOc4qW6GVA3gtiogUKZlDzq4ih5QAOhPzZmaKiFEPhcwUVpJsywIYiV0w+zV9HwVBiHTlOIWWWKjzH6D/kTFiP/eITu3gZ+XiZkLmSGDpRRX9UVi56sc/c1zTqqMogxRKZgDTuLysxuyS6VN8f4ht8lXGkpL2a1MNIZTC4xVJusROO1UyO8+YOWBhAi5RTmWnlfiIzy/okx5XjCsSYKRfFXAC+gLWqcwl36H7GCU4QWtKR01TBeOrJcXIOCiXDUGeMwrAkBhmMPWjEEwlrLSIMeu3ynR70joTy+MqnMEBm1Pie/WQl5r0T/8BNMileJnkwlSiJNVDThhxnXTo7h7I7I+MkHBaoIYGXHiG1lct6BMsrt1KQE0jlUvjqEyPudxdZWFrgeYTY9st4bej5UsrQB46SRc5gRzJYFUAqBaARroTlJC7CoLX1hkcUIaJ/gRkCoFsz/KHENmkN2zopxyv8WwVaI/QWDo52S4hi0yiz4z0HVHoJgRBISMzGPIDZmfAHoxBdgOWwV8XGRoB9Ze+AA1UKKuG4bPRKk4gATKU+dg/H5kJtVhmE3PbfWO6bXtQDDQkRVliqiF3a0xbPP61u0AaM3vDrh/uu55H/CA2l8RXDQtf4TxDEohWN4HIjus9jdJ9hyHzy3C1pzdsJuOXuPo+/UYQ6VaSrtTSExVsiZpXjDxd0ukAgNVB8pahnwd8wfq/hoRMKpmgFm3vFdKOm+wGnYti3DOp+MwLDJk52qysd7Tb9kNCAryhzbArFou3GwX6E5K6ncMYZJ+4OzMmtfZiTJJphHYZaucQ3Z2DfjALt7K8j4gqpHYNZzCjwSsDvOM24z1NW6U/U/HM81zD8s93atG4OccrwGlgL6H32PgmJEcgJBT3QCQvY/9pMOJGZNuswwqyxDaFkHqUiLYTfJekSVDKktiiNCd7lXQmR1BXc+eT9b50KrgZhmGmU1qGzfL0udgf2pI/iMGD3GBiU+Ii6nmEIi7LHBSmtLZruNrppUpk4RbWCveWEPALZL1SwX+HKpi6fpU33W0k1MpzeN5ZL/i/sfz+UAznTeY1RvjE3l8oiy6PBCVKd/zPd+Dn/u5n8PP//zP493vfjde8pKX4AMf+AC+7du+DQBVni94wQvS85///Ofj1KlT+MZv/Ea8613vwpvf/Ga89KUvxTd90zcdK609Op73vOfhV3/1Vy94/NWvfjWe97znXd3OX+W4YtC5s7ODEydOXPQn/v5qxh133IG9vT08/elPx0Me8pD0c7V9n24uPrwoo23JTiKAoUG54U9GqZLynFhlu23ySOo+hlkYDDsVw0ushP+IL1KJ704FSiSH7QK685wMd1JrojXMLiswst0G2W5DIJAZymcHj2GeiUSNrKQ5qBPDqps+9VIyfIPVKG5ZIoZtwBLIcXI9J8iURNg4UuKjeE4jwHOLQoI7kCYzBHKak89ZRi/jLCeoObmEXxZSBxFGnyvAyZjWcMscbpmnDkVIUI7esEMxFJnUS3C/de9gVi1DkM6t6ck0hh6+OBl3IaXF+kVBCWJuU7CLXXVkQcDJqq57TvS9Z4diZgWAk/ky6w79o24h01V36B6yRQbKGMqUcwuz7thBGVlmI/U7ZS7VO11iyH2ZwWcGdpcLJLGvMDIRwVBybfd4/s1mYDdqK+ejoMdwuGkJX1gMOxWG7ZLpsp1DdmaNQfo0Y0cpFOCtxrBdIj/L7s/4PkHSQKMvzlcW/U6Bfpknia0KAT7TDMzSEiQUAux6IJNlFNQQDr1nyAyBRBkl3+xyVJ3j5+mdAEphkcEJb7wWGLozFtlDI8kxu5MV/Z8TphxAYmfU4NFvl+hunlF+DSTpZVA8N1CKXkIP9rRKKrLqfUqYjZUnUR5v1pIWLamy8ZxGsKF6uX/03C9fZJTDykIIQkAoLL3hRcb7Qgybkv1WHROu1fkDLup0HeWywwCsxoAg9vFKVUrbceFkUyPUTZq4X5NhyDCzgzKCByUBakj3El33rGgayD6rJi68eeh1C19lotBgP6evMmTna9hVh+x8m5i47mQFu0f2PhRZ6hOO4VHmoKZ/XQKgzKaDaQZkp1dwM35X4v07KSXkPuNKS4a16dm13DDcK1j6EGMnpRLWc7zQPG7LnjeCHGPo55SFJL1c8pwLix8MmU2faQxbtELASZdnLbL/qbTXuVGK6twhX6IqC95/pK4lPacqCeoyCywXKZMgaA2z6VlvoxTMqmXatyyo5mcbJuq6AF0PcBUBe36WoWIoC17bs4rHJ4K4i/giU5ovyOCyy9QdG8REdjZAVSX7Sev6UPLyne5Vl5+gGpP+TlwQqnSJwKAUUGSee11Ogq/Hz3RjPDjH1V5rnzCLLtc4vRYAnvvc5+IVr3gF/uW//Jf47M/+bLz5zW/G7/zO7+CRj3wkAOCjH/3ooc7OxWKBO++8E7u7u3jKU56Cr//6r8ezn/1s/MRP/MQVvd+f/umf4ku+5EsuePzpT386/vRP//Tqdv4qxxV7Oqd64BACvuIrvgI/93M/h4c97GH3+s1DuMozc5GhWwelOPEYSoNhPoPZSKBOYaBj/59GqjRoT5UozrXp93rdUY7YS3qi0TAiN3RLTjBCaeHmGfsv1x2UJFZ2JyuYZoA5YDBG8mHKKnOcMJu6h5ux1HzabekX4g2KoToNvXORoUnytsxAr1v0p+aw3rNjsTCpRzOmaQIgyDpZobiroezz/IYJlRAgUI7BKm6ZT2R0DsOJmfjtMgHUHr5it15kg1MKqAbMgaSzasDsDZwoAfRkAjyemWGSrYR+qKaHBdi1qHVKUI1BN6ruGGoiATm6GYB6gInskxw/X+ZksberFOyhpC9SteK3jCEfIZAlXhQwm4H+SwDd9hJ23dM32DDQZFgSsCvpE/QF5ah23aVz6gsD3dv0e04AB3o92wHBaLS3LpLsMA4ywCYBRZ8b9hZqACoCQg276sVLKmEsnUMWvVJKccIepv+vCW6l69Du9vCzHD7TZO48KKG0RoJ6xoTYNNFuuXgx7FTjdjWlk3AB/ckZgtHI7zlIKaO67pO82KwpSx12ZlyE8ZwgD9slsnO1TOCZfBt7codT9OFGVpMhQJw024MuASA/z9k5utcmZYDbriRZmFJDL9cBjJKEZwvdOZHTMpjI7G24b/PZmCgLgKFMA8O25gWDp6qMCzcxDCn2+IbAvtda9iXWJq1bApQyY79qZoH9g1T1gqoE+oEMN0RREEFP7OKdVdcWcMr+R1+3m+Wp9iUtXGkN1fsU5jMsct73DjCmzU7qVHTTiVKjSIFuqnfQ8nqz36Ze12AIFM1mYKCQC5J+qxMIVYXlPXleQPkAKF67kUmOUu6Qq3Q/DplBf9MC+d56XE7VCros4Jv2cHCQABsykQ7BAWYYGLgDINaOROWLDgF9VcGuKdkHMN6L9xtWKsXgp+hJDPJZou9xcuxDP9aDTAFeTLQFAHQdFwCXlAn7XMOs2POsQIDvqniPDfClgRpC+j4PpcFQzVF+hAldYVYBu/tQD7kFuOcMgIt7Oo+yjWOAGyag0KVEXkh9lcrsKFOWcdH0WmGY7/SvwbNufSG6nRJ5u4C65zQAw47OPIdaLhAm6bUxRCju++iZvfrxYAd1HwvZ8I3xiTk+YUDkVY77kl57NeNFL3oRXvSiFx37u1/8xV+84LEnPOEJF0hyr3S0bYvhmDlG3/dXrTS92nHFTOcXf/EXp5+nP/3pMMbg8z7v8w49/sVf/MXXcl8vOpQHZUQBsJsB2V6XvES684dWoPstlp8Xp2VlWibb/U0VfK5TMJDunKQ7DmQ8AbIpqw52v8WwpOzVlxnyczUDiyQ9MWQGTryeCfBIXYtuKSNicizTSqME2Bc29QlCKYQiS5P/2GPntioCAFnp181AFlTqS8y6S4Eq5Qd34RYlPYbbVfK6BavpuZxRumXWHcz5jQQSyeRfKQLP3KZ0WbJhTPd0pYWTkJBhu8CwLWzkLB/9YD3ZsDhhD9kkiXVRpMeVo2wxAmb2AxbQbY/+5gX0gTDS4kUMxghTaFOSZZQtxwlxsIYy67YXjyEL5JVz8FUuIIUy0Ox8LVLSnuctgGDHBbIZmlUQ2W49sizgtdDvFDCbDvagpZxtLtUKWsOVFnbdwzQDeyuFyYZSUAMZCt0O0L2HXXep01J5kab2jmx2aZPUcFhkCFqJ9DEAWrFDsItsjXgkM/Eq905qeOjv0jUlvz63fJ9ukD5PspyxqzYyKHavIVuteb3ZgxbZXgNfkY1WrSxa9A5mv07JuOxZoCfTVwxc8pIEDY3D18TgUn1K0JRoD/OM3Y8QtUKVQQ0O+T2rJHGNrFOQdFMA9KVK2nT0WiYWVdJaQ5Wz47VtCfjiT9slz6pet9BNB7NuyZ5XlE9HxsvtzHj3jP22SkEftCLTl+/9phYQa8lsGdbzIM+ZKFzY9J1GFvsJ6QGFD4fByP09FMFaf7LiOegdA7S0HmWOVsNXlO1nZ9dc5JJU21i/E79vEODIYB8PX9kkiQXEy2zpQ25PlZJEy/d1s4yVOaJU8RllvfG9stMrWfwaUv2TbntKcT98PnX9qt6N4TpefvqBLLMxF07eJ0BFGUOmbrNhgmzd8PhLkq9bFNCDp+JjcFzALDJk5zZkqvshpTzDjGxdAmUAvfOxWiVWVkmdym3Z89JzDnV7Fhn6rSirBYbtHH5Zon3YFlljzcXA9mSOfjtHdyJHe7JAeyKDq3h/i9+1WB+WFCk4HnRNgWhkalVZIDQtbsuff+h3PsqAlaKEvB8YqDSpjZlex5ea2PrC8HyJZzR6SqE01EQyNmU34/7e6V9zrybN18tE+94A0xtg9uLjxrG5/PiEOUYPANP5QI+nPvWp+Jmf+ZkLHv+pn/opPPnJT76m733t2scfwOGtghUGyDRDSqYNlqvDcdVBDwFqw9+r3lMeKJP8bJe+Jl9Z2L1GZGEELmYzwM0zWJkgIVhWOBT0/PnC0lOjgPwc/Ydq4ieKPX/9zQyusS4wFbId6FXEIMm0LkkBvUwMTC3eNfE8mjU9dhH4OPGFhtLCFYYMEgAFj/ah20yNPCHeKknm1OKttAcQv2oOVMLOKSWSOkXGs/eAB9pPWiDbawEfCLRaByt1GJFJjsA3222YrGp1SnyEB6AVhpvmAhKlqqZjqIufFyK5LaXcHZQon1nDVxn67RL52c0oGVbjQkCUgcZQJx5zLhpQykfpbWRcU/JwlFSLnM/NcyZltj26WxcwNc/lMLOw63D4vArwswednAOwkqc0sAfs+4yM9jCzUJUVeSDZd7sRVq/uEWYZPWGeoEY5yqiDVUxinrBsyo+BGvRK8jOS2aNHL1QWZsNr1QwepnXwmUG3k421IhpQjWfiaAjotzLkux1UQxbX7JNld1u8drzUmPB64TH1MzLhunFMj1156ducVFb09KV6q5Gf3SRpdsg19AGPgd50KRQKAPK7D8jQVjEhVvG9NgR/UfIaMkoO3TyHXbcIxsLPLFRvCKY3lKv6RZkCwYImOFGDY9DMakPpITAmh25a9vJqn7pFPSALKWSeI7vLDyBqhlyOUQQW20uClzxD2NTsZqxKwB9mkcy6Aw6kW7ZuKLOcz1gDdI2G6pn6mp1hfZQvMwKrzgFljmEhigwX6FvXJRcp1i3U3hqoquTZTr3D3ifwb/ZbdgFH8GQ18jM8V8W5Vu4HYP1OLX2sdY9hWwCpUlCQxaPFmLwNTYAbGXh30zItOrkF05FRlSloLWQWymbwXXcYrEzCe3RmCXSco482z8l+7+4nNUe8jinrVvT3x30CoELGcxjHRDKbQoTETwlj6IGM7F1geJSuGOgUnEMYepiTO1xcyymh98bAbjwyrZGdq7F61BK25vvYjYOXgLJhPvpkAd5jEf2kAMGn7N9xoOuQB1NppvAKCH999x/xZXN6i3zTsnqm+2U86/H/bGTnxfMLXKG0FnE/PdD1F/pFJ93ch47vRIb78ZJgO2V9789xbwH3JwxwuMrxYL+OHgzjE+YY3RsQ+SAHnT/yIz+CL/3SL8U73vEOPOMZzwAA/P7v/z7e+ta34g1veMM1fe/rpujK7jfQrUsR/dCKUrroQZJJSJRQsrokJK9nd6Lk8zc9+pMVGZqSv3PzDLr3aG+qCFa1otzMB3bFzSz04GHqAe1NnMTrzqE/UaI/UcJJyIVyAcpDiuV98nH6nJ5SX5rUwRZyI92Ck3WBEChRawcGyxRRvku5me45KU/VGzFIZuLRUyFg2CrZKZpbJnG2A3yupSahR3frEtDsUFTNAFiNbL9LwNG0DkEBw3aJ5pZKAJ5DyE1K6wUYbjFs5+i3oqwWUm8gjKT46mLi7bDN2pt+p6S8r5WU4GZAtlszmbXuRY7Kzx2Zj5g87KqxY5ISWDuZ4HKS7LYqLjCUBv12SYYlBjdt0UOVnW/IcPsAuxk7A6NXymc6PRaseL0qC90y0XYQb2VMlqQcMGCYk3X0mcYwyxh407jEZEIRMEMYjGE5Ji0PWyWClvCUgBQS4gsrDHRAf6IkEJtlMBvKc73UnJT3NGShrLCaVjMoJgDVR9aUL3oCx+HkHMPJOY+rMIVunmPYLjiBDUFqSzSZUKvh5gWBeWIuxSu86ilVj5exVgz6itJJrck0y2KIl3RgKgEmbJ8LE3A0nhO711DqKUyyblmx4HNLyWvdJ79pYlhjP6ZzyfcJo4GuQ5DAr6A1ZbkClNW6YedubseuXKPI+gJJLoxBgmKiz0++u6HrEQ5Wwr4yNCruN3a2+Pwo69w/gF+P3s/7e4zeVHolzUFDL3wITJU9aKFl4cGuezLfqybVxGC9ZnBSx75KtyQwjEqOUFqmQveOPwIUp1L8KKdPUulFkfo8oSD3aLlPDj6lfSeJvuP3hqFkNrGjcA5oOv70wwV1KYnJk6TY1N/pA0HhWnyhwYvyY+D+uAA96eMMSkmXLEN8QvAIQWpXIrsnEtLgA58jYWaIn2HSSRk6qiGCc5SoSg2YcgF242DbgH5J4NudmqG6p4UrNVwp92f5PhTne+T7A7K1k/u6BayB2t/QM9pcIrn26HUy9AibOu3vM/VzEPph9IQGz8dihVVMnhXJ+hWDrCzj32qjx0Tc6X4cXYD5OAScwMfHPj4Q4wbYvTE+Xoa6lz8P5vEFX/AF+JM/+RM84hGPwKtf/Wq89rWvxWMf+1i8853vxNOe9rRr+t73iem81iWiVzpMPWDYXsJnGtleK5UlBmY9JO8QAHhJaNSdS4ATQPLPQTGUJiiFkDO+PwgDBQDFmRrDsiC4XRDYDYslsnM1uptmCIWBXYs0te6h87Hewhf027jCAHOL7Dw9TkoCc+xeh1DqNKnymgEuMXxFbwb021zBZ7IrZcGRxfWFgVn38JUAUel285lJMf26G6TzUEO3EjwzL0Q+Oogni5Kt+pO3kJ8bV5hj7QmlowquoFSrmDwnBh0FQ4YzEpxKKcr0QKAIpXgce4+QhwS+zaZP545prZrhJSEkXyzPl2J4BwC3lK5MwxTgGGITpGcQnh4nN8uARUHpsTJkeEq+VvVOWNIAZEj9mnbVAS5gOFHyeSFARfVeZqA8vWkqAK40yPZadMIqm4bA0uecEAYlQUCZToBSOc9zoRSGhUF+vuPvM53kjzEVFADMupcFDG5Xtw5BpL8+E49o71O4UHeyhGkc3MxA95QqmpWwi3XPYCvPa1L1kowbGUqR64bKJkBBz2hPr2wh/X2tG73QIWCoLHQLGFnwoDfYkM0C0uKHr/g57LqXtN8h+fVcSX+sWbcYdmZQwTHhVqTgcC6F0aQOWKPTvqAkexiZODfLKXl1fJ5bsNoGHbeVpMldP8pctU5MXeyMjJJSBlz1QCZgp3fQTQe/KFMNil/OoM/usZqkW5FB00rSUZl2zQ87Xv9YsONVWTJv1/LuSn96DntmDZWZ5E2MizgAGFgmUlu3KGF3pQJmOQfO742suRnTtRnURRm+0kidp26rgK4HqOAk9EtC19o69YLa3ZqLBKK4cMKkD5IqrbxHgMifZRt2zXvmMM9h5drEwQo4xVA7VRY8j/0ABJUmu1OwFyWkCEHkzxnPU2IiGZTlMw3deVHSuLR4MGznMLvrJAH1DRUhymYEnNE/6hx0DJPSWmpxanl/I1Jblbahz+1C3bJEdtATXNvxPq6HDN2JHKYhOGtuKeFyDZUrmMah37LIDhzcVpTzksHGZsNr7yIBQnFESawyRpKXuwQ0x3AkiBQYUF2PEOt+Jtu5JMiaAsteJPzpV4epAmXtpcGl0teMRXwgxoNp3x+IfXmwfNYb48a47LgOmU4A+OzP/mz80i/90gP+vlcMOr/6q7/60P83TYNv+7Zvw3w+P/T4r//6r98/e3YVIxiN/FydvEbsBHTSs4Y0wYvpiL7KETQnEiHna3xJJjJoxSoJYTJ159Dt5MjPM7kWSmovxH/ng8awLJLcit1pOlU/cP8UXGlgGibi5vt96rWDAuDA1NEQMEgIT9AKqowhEcCwpMTWrHqCH6kUsOs+BfzAKAwVP2suQR6hsMIK+CTpzfZd6lPU9cBEWpE0DTMLK8yU6pmGqjuPfmGR73Ywqw7tzTNkBz26nTwBnLjPWlHSbDc9g4V6hs0My4y9cZpg3lf0RvncAAIQfW5T2q7da6CNkVCSsd5E9Ty+sb/SbHoWzK97MoUdfYouJ+OkvOc25N9MS3XoT80Z7FRlMGsypn5O+SwDgtQoEw1ATI5V0TsobF+/nSM7GOSzMXzKFawbMa1LEkU/t3CFhu4DvAoEijmBmysMdMvjlJ2rpRoFSVqnYhiLo2TcNI5Ju1tFYvd15+Et02TdzCK/Z826nsEj2+sEADp67YxGf1NF6W5uYFc93JwsLQwZMF9Gv60wNCJ59oWFX+awsk0/z8jYVQZ2t0EmwUg+MomKCwT9dskQFq2hhXFXXryXmaHHccbPmZ1Zo795QfZekqODMZS47zcIJSt3lAdCCAgVQUDy+vWOvZzep1CueO3oqHqIALssDsv3QoDaWyHMKoRJFyNTmC10lN5OekkBBlqpTYdQUYFgz28IXJqWgTGxEkgCWVIgTg4Ju3IMoskpl1fGXNMwodhBGmZSMeQ99KrlYs1Bw4Cv3EKfXxFA9w5hViSWTsWkW/GghzLnceoGIDOHamoAym1DaaG66DE0Y10KhK0u6Q0OwsybTZ+82r6w0I0w1l6hP8kQMFOzPzTba5KPN9xycjy+DX270Vd51A/InRRQ2PdQ2ZL/n1n2Ta5bVqKIb9dVlgtKE3WKPegI6nqRzGcZwlAnaS1AdvXLqn9If2mRp8US/t5xkUH7xH6y01MTiM9ymPUGupO/DYbXXbY/JKDmSo3yoGO69czCtAGu1Mj3Bgl58txH56HaIYHK4wDGNOV3mh6rSipWDgFCkQbz37Lvsyodi0sBmEN+14w1U3B+rJeJI2eq7QXbmYDWiwUifbyMBxMIezDty41xY9wY13bUdY2+P6x+2draumbvd8Wgc3t7+9D/f8M3fMP9vjP3dpi6RyjJEjBNs0uTReVUCq8JGYGlbnr6D0t6M4PV7IjrPbRI5sy6l4mqh61dqlzg5J5+vWDo4QrGEDRCmFKjYGo3MjeFQbZPn2S+7tDeXKC8u4ZZi5SxMrC1g24d+u0cVpjZYS6gMwS40iA/22LYzpnW6wKygw7dToHsoE9dlsVpSvJ8lWNYZNzfXHN/Bp+AlJtREkZpcEiJktl+m2S72jkob6BCgK0d+mUGPbNkz0rLVfYpCymVHAhIIUPxeCsBmbaNx5HgMYYf0a+lJKiDLIgXMJG8X92AYbtAfvcBhpOy2OECstNr+K1SklllX3yAn2WpexCgHC7MJYWzJqtqYtWMLFhoYTS8pWeTjNvIIEevEllLg/J0I72OvN7sfgudW0p2vci3c43sTA21HL24g1TMaOkvjJPT9tY5ggJM5+FyjWw1kfaWNvVfunkOe8DeUh1Av5lR8OBksrtlDtV7DItswuJHoM7uTJ+TfR8WWZIQD3NZyDDjgol2HtkepaL9DiXmEI+ZXnfobp7Brnp0N88YmiShWPygPJfZfotpiFDQo8Q3ZBpBiacLQH/TPDGnvqL81NTtofNk1l1i6s1GKo0cGSRfSncuIpNmyZo1Q0o4DlpTqTE4oBBJvjA5YVFJB6RNoUAhM1CtH/s7o9evpV+4v3kOuwdKkUUarKwBTIlw9jwvmapEaBoCthNLfsdqudl3HT2mIPiB85NU3WswBodQlanmJeQWfkFvZwSMPjPwt2zDnjmA355Rqj0voFc1QsPz4XYKmL0a3mqYMwesKsotFJACeADA3r2HYb4j7OqKXvbYTwzI9x9AT9lsPLYhtwlY6ZppzMFqdnnGBO0MiT3X6xZq3SIsx+CZIGmywblLyvqUZoAQ8pzJ29YkSfCwXQCBUnsnUt7Ui9m7pCYBBOgaI8ygSxJUnedQVgKlhprVOEdBHHcYUARaPgazFYYSevD97H4rFUsCCJ0XST/9nQAwLCyCoeRbGcNrXT7TURB+6O2lbzPti/hd0baHamDU5DNDKaiigFtvoDY1gSdG3+DF3if9e1PTp58A7MQTG0SWfOQYaekavdO/Jvl0rxYw3ZB5PnDjwcTm3hg3xpWOByq99oEcm80G3/u934tXv/rVOHv27AW/d0fTy+/HccWzml/4hV+4Zjtxn4f3IxgQ75Der9E/ZJudZcuxRkS1PRBiATgnx8qzmxAukE1Zxb5G8YwddAkAqWYACstJU9NL2iXDioJWKQgmaFCuCUpKBwFrvqCMMoI+5QOyVZ8AZpTnKudhfIAXEJXtdSxnP+B/4QEVKN3slxlMqyXVlFJIFrJLf6JhL6IK7I108xxDyQAguxEJqlZkvKpMACGTeV1pEYxPAUiuMDBDgMswVirEuUdA8jT6wsDlGvkuA3pMPSR2SjnPMJ2DFr7K4WYZJ1FbRhg8lsbbc2u47RnUQPZO9x6mkTL2FBRiklTTNEPq56PnUwG5QX7XPoZTCzKw+w3TVNcd/NIA3kgpfUjBTnFBQfkg6Y8ERT43iNxM7Bh04jlUAYkFgZbwIB+YTLvXwW0V6THlA8x6SOy6kw5M+ED5XkH2ONsXQCIAMKYWu4pguj1VwtZOpNYM0QpaAQ703RqNSM0GDZjayYKDTdUqyjnYDXs63TyX/bHJg9ltWeS9h1sW9Pn1fD8nINwVlVyzQH6uRntThWLTI5QEkUGkwv2Mx0Z5pO+Gbnvpoo3hWHJsRf4O71MXop/lUN2A9lSJ8iMHcEumMkdA6EoLlWmR1RJg2xXTaPUBJffwPgUjqVUvk383+u+iF01zlUNL52awmvcVYwBPrydBrkMoc8B75PesUhq1Wbejl7PvyehEKW+WjTJdgKDKKOi9AwLNIufzAX4vr9WwrBIatsskMQ+5hTloUjhVSrsu2IvKQKaOLGCeEQAqxV5cpeDnJdUEVsMbxURsqcMJi4qLgcCovkgLOLxnuoWFcSEx5H7O2iOmZrPHN1a9QHPByucWdncz1rcoBQwDu38BeAFQYRgByaHalMCuTCXJs8E5LhYIyPHC4EalgOodwiKDWUePsIYvMmjpZQVAhY210HmA7zoom+EN3S/jtux5BLZFQXCrlYDhCRMqxyQM/NuimgGomFzL+xHPjZtlh06njoujPqDbzmBan+T6vsqg9wJgDdnc44J5jowpEA5tS4ltCEDXQVn+jfFtM8p0vSeLGxUEcv1fEmjI8X+mfg7sqVNUE7RSbSNMtNKKfbl5dkheq2yW+lfjezxTP+eqgc2NMJ37Pq70mF+vgPMTEUxf6jN/PHmsr2hch/Lal770pXjjG9+I22+/HS94wQvwkz/5k/jwhz+Mn/7pn8aP/uiPXtP3vi6ChPwsT6wiALKYFaWjrD3gT0zJhMT8q24YZWAdI/x161KgSzAq9TZG0JEA4UoYQQkNihJXV+ox2TAjIDUdq1vYM+dT6EsMF/IFuwtdwUCV6FHrtscOtkHCjILRcJkEwAxewAZrCli1IYzpFtN0fa7hci2eUg83ywl8dztkuy18rjE14Zh1L/JSTrbzczUfk/3SHSXCuvMCzMbXKgGv8AS7dj2QHe1ZOxKsFukpZai+zOjxBFN42dcngTDSX6rXnYDVkEBGLIR3pchQc/FaVdIr6jyUZxek3adsT7dk4IZlwTTOQgBfIcXvitLRYWbhqky6LXXyrAGAXfWpooDsM8GZCkjezX6bzA5BrGMQSE+psnJc3LB7nHxrkeLa9YBsn8c9aIVsNVDqGqXc9UAZtFEjmwwk1jSe+ziCoeTRS4+nbnqoIZC9LQzBZ6z+KAl6hiWTS3XvktfUZxrF+RbRi+pmWVpIiWy7z5TIj7mNbDWgO1Wla0H1Hrrpke22ydNs1h18QQmu3hB4ps+jRNbsCYgIKsfPW5zepEoXCDvmKgtT9+LtLgGrYffaJPEN4hlN52XdsnPQOfrr4iR5eyvdCyKAcbOcizQ7JZ8j/Z9m1UAfNPQel4cBAEAwmYKBkkw7kFXNM+67yH6DMWQ5BwnRiQxnuHZ/uYKwhPbsBrrpUlhUMKx8gvfQq4b7WOZkEDvZv35ICb2xt1TXTKuN/lTVu9Qvy+2yukbX0pc6L4DBM4gnZyVO9GnrTUdp/CAAVpKNlSRNh8ygvXWOYZHD55qLUACl9MtyXDjQGqos4CWEJoKS6VA2I8BSmoBsuRjrTwYqXHTH76MvjCgxgqQ0j35/VCXPszW8t282BI4SVvRM81y+R9PSc+oclPh7lTG4LXuesIh6DBLqe+YEHHS8H8u9B97DnltjqKhUYBUUk7ODVsj3eh6XOROwzUbktZaVJGi7C8KVpuMCuW0hQVnxOIlc+U7/GiithEUdEIbhgpqfxEJe7np0DKhSy8WFXZ4AwjAcqkWJ+5/eQybBD8aJ7vUOaq/0mF+vx+ETceHiUuf8wfo9vE/juEqUS/08yMdrX/ta3H777fjar/1aWGvxtKc9DT/wAz+Af/Nv/s0193leF6BTH7RQ6yYFeqhh4KRJCsujhMwvSgZUbFWpjzAmU8Y6EoAslm4dst0WetND9x7ZuQ0nRQJ6WEjPiZGrLOx+A7tbozjdJIaUPY8B9jyBbQqEkSoNLUyhqR3yvQ6m9XClHmVUA0Gs7j3ysw2Gigxethr/4OpJN12si2F4EtNiXcWgGwbKUC7I2H9WSMSJHsBJW3eKTFDsI+xOVYBh0JAXiZfufOqRDJqMlu5kUmQU7F4LFVhRk+33qXYhppgGowWMEbjqxh2qOtHD6G2F9EeqgXJoX2YEvX0ESBIGEgjyAAjjq+G2uA12BJI91DJBC5Y+UnvQIdutU/F8fnoDKDKFKayoIjPoKpsmfkEpSu0qyoFjyJKNQT2Dhx4IMttPmst+kXgMIrlVnhLpYc4QnzR5LAwGmdSa1qE/UaA/UQjwR/ITB6lpIXvMxQvlBQhqlc5Vf7Ki1Fpk1D7XEiYlixUSfDTMMibz5gau5E+/zNEvKb8ley4LCEYlGTAkFEn3lHLnZzZpkUJ5n3oY1cDrvd+pRhnznMAhglMCZIf+ZMVrKoaVCZCJclSfCQBoHbKzG/G/8hobluyWNAcNAU7JY+nLHMMyZx1LIYDImFEpEaWVAGCZXKvrHmbTI//wbkqFVi5QjjqPtTsg2yfVIW5eQJ/eI+DdXiA0bWKw0LQMq5moM5Rzo5zWkcWDD6N64xoM5SlfDrmVRbqM6bQSVKZi76ak0VK+r5OnE0VOgJpLeuyiRH/zgsdBOnfdohDZOY+Z6ly63+p1S0l8N9BTOnhJqBVmWbYRJM0XCuhPVLznGC7wmGaA3Wu5AAGM94y4sOiZIqzz/Fg56Z3uVWQ5xcOYOjQt+1RDS6Ct6j4lXMeEX3vQwmy6cf82NULfE7jmGUEjkAKLlLCaKmOSLEAgFQFc3L/gWQUS01rV4CX9m+nouvfQUvmk+5AWNqFUOiZm1aL6uz3k92woWReJKhpJgL2MbPsC0CfJy1MJcRyxUxPWjh5NpZjSPDnOx247/t6/BtAKbllRecAN8z/OIbRtWmA7vF8+bfNKJrkPJDCYvtcnIig5bkzP0fV2PD4eQdb1dg6u1Yjy2qv9eTCPc+fO4VGPehQA+jfPnTsHAPjCL/xCvPnNb76m731dgE4YBbQd1KZlSELTQ63pOVJNn+oJ9Old6IMN7OkDBppsKLc1e3Wa4DHGn5UmkR3R0TMmQE3X7JWEh4CsZizhNkzsNPUwTqJDYLJi71kZ4ELy4rjSwFUEEz7TMI1PITXaBRTnWqkC0Mj3usQ0pZTDwiaGyJUWdtPzvWQb2V6XvHJmMyRZlpuJdNaaxHgBlPGadUdGQilk50WKpcluQgP9dk4PVWGEncPIWJYmyZnhgyTRWth1Rx+jsMcA0O9QspnOYaDXESGkrj6fW7j56K2Lv9ONYz9kRpmuKwxcZdN7RBDsyywxdQB4XIdARnOes95kuyQo1fSiRgDsZhmGOZmNVMUTh8yBzKZP9SlB89w7qd7xVnyyB5y0agHf8PL+AvCy/Y5sbUwDlbAgVxIYR9CtBwYJAUB7skS+36PfIlvspdMzAv84SY0BQXoYq1JcYRhkVDDsyFsBqIOHGgLysxvYzQC7GZCfq5Ht98LQK6m5ob80SJ1DvJZ1Sza2vXlGT56n37dfZNxvTbbYrjvuXzNA17w+h0VGoDLn8bcCxn1pREZp4JYlk3LbPjHqAFlFs2pFfuiQnaspgbXjOVetS+m7vswo7cxzAgBhxVAWrAIRgOVneXo9gASMQma4yCVdoLFrFrLwAAD+1DaQWdYHeQ9VFmlCDmvHCqPBk9nrY6KrSqm6OMIa3Z8jspkwit29a/oQ0TuouoeScCG4gGFnBidsolvQ2wgJ2YmLb1EeTl+94UKS86xNmmfwpcVw04L/XeQIlgs4/U7Jc2UU3DKHLzMMJ2aHQpp03UH5wA7dpmPdjqQnh4Kfw5es3DD7NdD2PJ49JdRh6AlSJOH00Jgky6qikAWBwHApWZAIcm+MtguzGZKsX9c9P/OJJUN0RKabfJCTlFglIVFJxjv53Z3+NYd8lCrPgbKA6gbYTU/2XysMiwx+Z54C7+I1FFUo8RyFjGoO3Q5MZ64boK653Vl1yA95dBx6bBIixPCjcSEk9Z0CZDqTxUV6ZnHhxPa4bT9TP4fHzo/++0Ppus6P0uXpqTPmqibODyQwOPpenwjA876ci2t5bK7Ftj8W5/L+eM+jiyHX4j2uu3G1LOfHAdv56Ec/Gn/7t38LAHjiE5+IV7/61QDIgO7s7FzT974uQKdqJpPQXuRyRicpEbpuLFrXGtjUMHsbmZwHuEWZJjhQAiJ7J/5MN8rt4h9E58bkRZncsh9OJl1ac9VeJHVMp5QJXpTn1QOy8zWy8y1BQTeIDJVMXawxiRMKJZJV5X2S+KaQnm5IFRrxPX2mk0xYS69mrIhJ3jrDwJwgTIJygZJO6WM0rUt+QlfFUJ0B5UdXnOTVLtXRAEgy0yjZ7E4wVdXutQiGsmOm1FK+q0JAd7IisNAK3Q5TUSNgDlIv4q2Gq4SFK6STMwRKXOdW+k8DTEsgqiNr1nth7SwnwbJIoAayw6r3iW2O/atQClomtbGSRDmfgHz88QIeAR7HKL8LmYEePNqTJRnm1qFfWAGiA1lIRbY6MrM+l0mqUWQgey+TbOnbbAZJzNQigfXI1gNDfzYD+u1cAosMj/d6SAyMWZNVV8K82v2GoUHWwO51GGYZZdLC2uueEuShMkxCnjCNkflVziPbbWE2A4aZHTtHZ/QaZwc9t73IAK2SlJse07GWiOEvOa+t/vCkUjcDirvXiLUrcVEAWqdaDS31HmRSuViROjQ1RjYTUq0joVFmNXo80cdeSLJV8AEhz6CagaDKEYT57Rn7J3ML1XSUjmYm1Yv4eTEGlcXAI2th9hum0MZeQ2PYBRpVFiHAL0rer5yj9BYAMnthN+H9OCj5dtAHDQGzLPAoYZqGk/NUC2P2aql2IiONWUUmsOSCV+yJtSsmKsfO3tRN7HhP9Bl7c03dw8+yFMzmtopxUSeA50ekq8rzGonKC79gbVS2y2CryDrrujt8LKuSPyL5PCr7BMYJljIGKs95vAd6spFlTB027P9NYWdAChWL9TJ6krILpRDq5jDglITX0A9jIrHz3E/Qt/hM81xhXSm3hXNAP8BtVRhmGc9LoOdb1exNhR8X/lzF68bnXPBy8xzDPJd7i+exWMzJGnbdheFFlxjRj0z5t06SYQTPfQ0EhfE4X2nqcjwnd/rXINQN+1Dj31gBpDqzF4DddEz9CFCPk04/2Ma1AL3HfearPQ7313G7L2zztVwQuL9Z8PvDw3lvjvn9cYwut43L+TQ/Ecf1yHR+4zd+I97xjncAAL7v+74Pt99+O4qiwEte8hK89KUvvabvfV2ATgzs1wvzgtKqYeAqt8jA0PXSv5bzD71MyFXTAwMBHxQlk7HwPY6QGQRhPOKkEgDMWiZGLUMuglLQq5ov0qAsVCZcEFlhkvpKwXqsccjOrOmv2m84qZAAGzWEJCuEC/RNuQC7W0MNAWa3FsaRCaVm06X3NK1Dfr4RWSEBT5yk+Sx6K5UEHA3IzzWUjwoA072H2a9HBvRMzYmq7LfZ2yTQFVlHs+6kWoaHwQowcluF1FYoDHMykcOC/kDTOrJvMwIoaElp7R281cn/qUVGCh/gM4P2VIn2VAlTO/Q7JcNnBqmCKS0GkdaauqfseJ4lNoBBPIEAWSaQuiOwGypO4ILRDNypB+ghoNvO4Qot3sEeCGCQUJUh22uSrzcGSWXrQYC+hmk9tOxX7NxTLpDdtiOQVImN9AgKSRIcAaXqfTof7LjkdZuf78jmFoZyRqNl3wzam0RiXAijvSi4TavGz9e7xASrQcKPhOn0RWQbR4BlNj1Czh5Zu+E+GPGd6t4lxlq5IP5fMuxm3YmCgOC2vakimK1dkjcrWbjpT1ashKkZ6mRWlLkmH3QuXtxMQ9cDpfFKpUm2XrdULeQiSe5ckmenpFQn0kNZ5EFZiDdP5J1bFUJuMdw0p5x3lhMs5Ra+Krh/+/UoQQ0BvpTk2mYEjPrUSTJbpfhCq3IEKJFxBAhIpecTzkNXYwLr/T2C1XDbFRk0pRiGs255nnObFBpulsNtVwQ4uYGued8jsyngVdhuX1guTHgkOW5+vkEe7QVAWnTyUa7ac0Es+qWV92mxwBWG1VWdo3fUR+m8IUNqRjY7AluzV/P+78m8qqKgBHQir50C0EO1HSay3vTYBpFBR59r0BIYp8h6x9TuUNoUPDTd7nTbKjKu8fd5htD1FyTBBu/H/csszPkNF/885LvjkvQbSsHut7D7LcyaYXRmQ4WK8kiJ69Ca+1c3/IwTFvGioGUC8lSeJ9nvBdfRpC4lJvHq5QKhHa//i01YI2h8pn4OWea4LTXWowT5fk6TbtN+pX7VCft6CfD5sZo4Xy2guZrnPhBA5P4cD1YZ6tWGTz2Q73clI1431+oavyCE7RNpXIdM50te8hJ853d+JwDgS77kS/BXf/VX+JVf+RX82Z/9Gb7ru77rmr739QE6m47sxMEGWG3SpA0h8L/bS/70/chsrOsU8KE2LdSmhb7rTGI1fZXBz0UOJSxoMBIIIkxmCifpnQTfVGQ6NhLMIb4i5RzMfsPiclAOGCfXMNID2LACQrcD7PkN7PkNzGrsulPR8xMDOVpOCoNhqI3eyB95kQDaXbITZtUyYGWZQ4u0EoqhR/leh/J0TTncuoXdrWFqSgYjO2Z3G06ovRfP7EB2xAWYg5qMU9PxZ5+gO1sNSdJrV7343kICmQgENxFcA0hMYXeCE3NfZsmv6oXl8lYln2R+rkV+rqVMtTRJXhwiiyoJwikISBIx3SxLbKwrDetszjcSSmKQn6NHTMmk2pWcIAet6KHSmpNPPwKkYauALyzys0ykdALwdOcpqRapcL806XzFyXK237L4XZJX9RDYMRt7/CSxMgaFKB+g1x29o8I+6qZH+cG9xDpn+20KLMoOxIO2R+ktg6D43q6ystDAACtXshe0286TpLfbzkTmrMW/zLoRvY7VI/R2hkxLp6v0MtaDgD1Ozs1+S2+tAI5gNUvsl6wY6pdZ+pzDVonoXeVFD8AoSdcdH1PRY6eRvIJm1fL7O2f9Byt3JHhKkoaD5mIPa0pmCaCgaSl9lQoRveHE3uy3qdfSrLjwY87ss0NR+nj9LCfQ9Fw0gYSroOkQNht2NzYy6W87yrFlcYvhR/Iaowl2jL6mPZ0w0ks8o8cVlgyy3xLQrQlKdc1kYl8ypCtM2GNfZXALss7x3hZ7OeMiXnNzhebmKvkBh2WevImmHgQgBbk2pdIJgPL83hhZiIJU1MSgMCOLHaqXhaZFIX5PBSg9plhLem0EKM/UzxkBjNKHgGC6twMEaCL3DKVNyoFY9QQwPEnXLaXJiucrnrMENic+yMio8r5vWKEjwOsN/a8CSqcgISipbzFKvLCGHbuWDP30PKRT2gwMnOsG6HXHhcANryWCTYYQocwTULuovFYApoqe5wkze2iIbBlZDL/yCJv6EIs5HYcmrROwGJxDiN+B4HFb9jw+V9HfrOyRzyuBRtPQkvjvjwULdalxNTLG+wP8PJiA3SckSPkYjOl34IF4nxvj+hqf/MmfjK/+6q/GZ33WZ13z97o+QKfWUOsNV6jFx5MAZ9dRNrep+QdMJpawlsBTKf5xByipOrfPovS6h71nn0yGc9AbMifqoIaqO4TSCstB0BUUwYSSmoXkBwMYZhIrCOrRL6nEPxUZJCgFfdAmmaAvcwK8waftsgvPQJ/dp6TJBZjdzSiD1BCJrgShrOht1YOHrjvk52pAkQHUK0rUzKpNoExtOsAznRcAQ5kmEfgAEHLD9x489O46gXZYA7O3oQdrkB5Ow9Af5QlMorQ1hs4Mcyu1A0xizPY7sgmGQUtJQicVJq6KFSVBZHYtuyy1gl11KbEVQEp89BlrUFxhJATJwbROZKaUBzIMJ2DYzsmaBYY0ZQcEd8U5XiPDIsOwyBJgiGE3QYH+1YLSX1Nz21p6MPXgUd3dJDYv+UYrm3ytvsgSK5Sdr2HWHbLzNexeA7vXoLxrLSz3AHNuzeRlYfWCUbBnVmQTN/wh2ydg/mRBuXCcMEv4EyDXQs+6BT0EFOfb9DnzPXqETT3Qz9YMXGwxZBntQUsmQkGCisiyKu9h1r3UclCu7QqT/H/0Lw/I9zrozqH86EqOIz2/SXpcmlGyLSxXUEqYXYt+u0zSTYivOmRG2FFRIwyUgeuOoT/QZKqwtQDqmgyLc5w4b/hdUk03LvRkZuyTjNtfVqOkduCCDEOLDLJzGy46WZsm1qHvmVbadWRUPYQR9MjOrMe6llpko9fQzwmAwFqOt5tliB2qsevYS5eqW+ZcNAJ4fCPLOfA6iH7m/iT7W/uTVXputtcg3+2Q73ZpsSAoqdsJ7CF28yxV/kCBNVTg/cvnBiE3UK0TC4GBzy0Zz5yVVSEzY+K3UgRj1ojqYezPZN+j+BMjIJKAm+ADOzWnYTXFKNdW3RiaFoyGXXdkvqsCblnBbVVAkUFl/AHAwKDJ+8WE3CSjlvttfP84OU++SKOp3ikySsWtSot3ERgz/Vunzlh4IDu9ptQc/O7odctwnjzjdQeMgUIXGdO02QiK1QRUXjCCT4CZ+24QJn7NqZf20KRVgG18THn+vY7HK6bjHhdgdKd71QWy2ssBnCudMH+sJtYPVoB2qf260mM+fd4D7d98sB7XB2J8PMjOH+zjepPXHhwc4G1vextWK4a9/dmf/Rle8IIX4DnPec41T64FrhfQWQgj2XXAckEfp1LjH/hePFVeGKSWE2UYPYKpWBJflVAHNfTeOoV7qBUn92rdCHvRktWz9GehaSmt7R2Zxpqspqo7gszTu2QK256vcZKy2w7iEWNHqKo7hJiQOXhKypys3huJu89lgjijNFGtagGLBH4pnGRVy/bpNbV7DSe6APKP7jNYpcwZGrJuCHwjcB5kXyOr5MFApgNKCUORwS9LKWaPDI2AQcduP33Qwh50yUep1x3sLgNeYo2HXfUJWEYAFKwmo6KVBC4xQEd5SmOz8w1ilYiTlX9ddwws2SqYmiuezOjRVI4MXXauHsFiS0bCHjAIKPpCdevQ3sKgDnPQjd4yF5Dtsz4Eit2tMUzHrrsEpAFIHQUlbsNsZLUjoxl9oiGjtNbnFnavhVk1CSyqth+rI9bN+LNqoNYEqcEY6PMMvklhIucOoLqeBe2bGma3Rna+QXZAAOhznVJ3tbBSrrRJVuszMprZfsugks4JsKBnD46Sy8iWR8Y2hkMBSOnN0JQtZ+ebVBkERMBLAOwkEZnb5LFlNRGSly5Kw+1By+vJcpFCy3H3OROQfclr0EkfapzcR/9i7IWkfFkB5/eA2JsZF5+seC2NSQtHKsi2PHhNysIMg8jc2P3ZU2YOkAWEFm8hQDBiLd9vcEkdEIOTeDEahsfMInC7dgXNGESyqjEy6wAZSi33H2F6Y3gUrBZWj2FI9C8HpgjLvcIedDz+zhHMyjmNKcTxe6I82Uq730BLOJryBGIx8ZaedpdqU+xeIxJmA28VhkWOIfZVChvOsKfJPR9A9B7GCfAb+l8FMPF0SrIsj4u8xgcJBOJCQ5SLB6NYi6QgFS7i7Y7pw9YSoCkyrmoqZw3iIc5zhL4f/aZajUAnCDiVxRnV0X8d/fIJqBpmGPjKSvAdr3+3XaFfZhhOMKkdxiBkIv+dMOeX9HQGnwC50mrCYoZDEttD4Ey2Hf2o05AkhGM6UuO242MRiOPwAkGIyc5HxlEJ8NWO+wpO7utzLxvg9DEclwx/OjLuzT5fq895Mbnxg+W4Xm7cF2B+Keb83qYG3xuwel0C3OtIXvvmN78ZD3vYw/DUpz4Vj3zkI/GGN7wBT3/60/HWt74V7373u/GCF7wAP/uzP3tN9+H6AJ1TNk5rgkdrUtn6FBQhBAK3+Bql+Jxh4PPbbuxbi54vgNuVBEGGFPWU8pYFJ5LOQ7UdVE3WUJ/dG1P3rCVglUmVWtVpQqQ3bQrvgPdkT3vH9MiDGqHIoepJCFLdQDVkStH3nBD0AzA4+GWZjkPIM05WXGB/3qblRDf2kjq+B4xCqHJJ/G2AXkJoou9V5IsA0uRDtT0Z2H4gIzEr4GcFP2s/QPWcLOp1C3v6YEy5XDewZw74+boBetXAnm8oEa0HZJICHENvYpqhbgeCZhWlxZPwIimKz8/VaWIbJAxoWFBKG/tXu5tnlPENAcMip/RSKUpo89HTWL7/DIqP7EFvWmSnV8ju2udn6B3ycw3yc42AoA72zJoTvpxsC/2nTtJsAbthkrHyZBTzD+8iv/uAwO6ufZi7d2HPrqDP7vEceF5HlPcFqG4i/1NqvAbbFvrMLqVyw0DWPi6iTBgbtamh95jYbM4dILt7BXv6AMVHKQ8tPnge+d0HsOfXMOc3sPtMZdUb9trqdcv9jGw42FGJEFgXI/JGMi5I1Slmr05gyufsC0QAzF6dai/0pkd2viZgEemi7hj0Q/8ngZ7uBti1JEjLdyWIH1J3DnbN61gJe2g2ojZYlNAHbQoXUi2BlpLaFZTiI4tJp1mW+jtV3VFKvrehJ1sYJzUMCZRGQKYkwEs1PZRzlNoOHlhvqLDoh3EBTO5XQTo67RmpllhteM6FdQ1NO4YKXYMRYo1MzsTtfqdI1gDd8HhGlpO+TzKTflEmmwGve42gyLCRlZaOX8ME2/gHWEkYWvQ90yOsGRJk2B87zJh06yqbfLtJEmpU6m01645KhXWfGGg1MGnXVxnBSGTIpM5jyiYm2eb0BxgXIPs+ef+Vp5rE7rcpvM2umOasojog9pc2LX+UokQ6ht0EP4JaY3gPL+V+Kc+5QHaqFUPqsslinnjAQ27Td1z1lPD7IoPygJNwMUBAcW6B6BvOpMql66Dz/OITRKXxTPPcJEUO4gVNwVbTJOAI/LQC8gw+dpzGzzvxZx59vwh87/SvGf9+K3UYqMfnHl2AiYBextXKaq8WnFwNEJu+5r6whFcyrsUk/2PRu3kl27qvTPa1Phf3dXuH6pyuMln2WrD40+/tlX6ejxeAf1XjOgKdP/ADP4DnPOc5+MAHPoDv/u7vxnOf+1y8+MUvxrvf/W78xV/8BX74h38YP/mTP3lN9+H6AJ1Gwh8yka+lVDsBT/EP2uA40SwLTgSrkhPDtuMEz4/gD3HSl8njbXs4LCKuvjbtOEkB+Me2KhFObo3PjSvFXQccrFOkPkCZGDsaHYKwmMFK8q5WUG1HUNj240qzUpx4ZNnYM1pkSe6bGNSul7RWP/bfGa6iR7CgGklelP0PVQ4lCcBBgHZiYuIx9UCQqpaQW+i2h24ZLhPKDGHGWgU1cLuqc5T8Fjl/FwNIUjcnmQ9V9zDnN/A5AzQOVdoA/H+R32Vn1sjOrGEO+LugNfKzGzJoEj6Un63pq3QEsPnZDSWfjkybcgHZuQ2ygx7Fh/ZgTu/DrFv45YygIDcsr1+WrMiJ8ulu9JNBugtNLYsITS8TVHozVcduSYK+dboe9d5Gro1sXAhRauypAwhAlBo9h97zWlOK1+xiNoaDzCteW9aO12IIDM+KDIcw2fH7oBrxDka53lbJRYkQ0nUUrwU4B70rx1s+eyaA22x6AsN9+intuTXZ8JxeOF1Tnqu7IckzzbpLdx9enyH5keP1Gns+p8dd12Rs8zPr5DOO15I5aKDrVqSGHmZ3Q48iCFzVQDAaqoxgvh/43Zwe37qB2lszNTm3CBlZO8p0/bgIE/3acu0SiOqkWvBFxvtL7P3sB4ar9P24OKbl+7ipeS6jtDIEptceCae5P4dblKyOScFoPL5uUWBYFlC94/mMwE1R7q76yJAKaN5nJ2/7sC3oRtKee7L4wzKHWTUwq0bYfalCynTqqQ0a4snmftlVB7sekirAlaw2isoDnxvWtjgyrNEL7hb0CwclgVDx+GrFxNajQ9i82NEZ76vpJ3ozBfSFzJDhFHbf5wbdqRmvJ6vHxUhZ+FFaJcA5TVkNHRc1p0xgChEapK5F6UNy3+hJ5z3LS28qMwViT2ew9H5GK0JQKgV+qU2bQvR8XbPKp+suPUGM7KTSlIUbDVUU6ZhNvar8EGqS0Dwysgj+imoa1PYW72OxWmg8OBfdvyjBjXUk11JKeCUg4Grf+1oF0jxQksr7M3znSrZ1X4/X/c3YxnGlTPZ9HfcFVN+b503f9/5674/HcT3Ja9/5znfipS99KR7+8Ifjn/7Tf4r9/X0897mjneJ5z3se3ve+913TfbhOQKfEtsdy9a4bC9iBwyyoD+NEve1TeAcAAgAjvs/4h1NWrtnpZwkKrKW8VQAcqhKhyicdbz3U3opJukWWpE1hVslkVa7I9YasZ9PKBJa+slDmo7xLKU6CI8tV5giZhV43CGVGAJTFmggJn6gbejEBAr44uY4S3SkbJumVETyrzfjZVddze/LeflYkNlG1ZG/UMMAtSrhFSTDpxFtV5WRIoperkNj/SdAIlCLTuybQgVEIpSUbOlBWpSSYJspLoRT0PbtjInE/UJIqUmTVDPQ27tWABCllZzdp38yq4YRst6b3semTjNAvZ9I7yI7EkNtUvRHZ3+jpGxZ5kv3RU8hqhWFLUk2F9dINJdaJhdxIwnFk0QeXJNfsZ/Q8TzHNMTKbIhUOs4rHrqrEkyzXbivpzE2brknWfxiE5QhoYkIzQLYLnosDoch4zCRYK1Q5r2lhzeEhCgEHJcCTUtOWANoFqH0JvxIApyUAS7fS+9hywYC9lGNYkl63sOfXafFCdcME9I7+tyhhjcFBqhuAnvLkBKQHD33QJvbV7NUw59b0UHcuAVjEdM34XU+VJfI9b1qY3XVilcyZA/lOauiz+1zYKcZFItU7+rDnRQqYQUvpvcrzMaBGvnPqzC7UmV0CFmt5LccANB+glE7+wGsx7Ol9MmS5IVAGA43MuoNd9wzWkhoOKCTgGfsp0XW87gsee7uRfmMBZcqxEslLUFFMEWa6tIE939CzaSWF2UuatMhltVwfrE0RL/V2keTxynsBWR521dED7IMEgIV0TlWWMYQmAcvx55nmuWNoT/Cj2iXWbelx0dLnDPFSkkSuG4f89EoSpvV4n1RqTHqNXseYYqsnC6ByHQQfDoHSVKOS8biquk9sZtBK/O9khPsT5ZhWviHrG20KDB5jGnmYdDmrLOM+aXXp5Nop2IsLIM7jtvz54z5PWWKA32VR3gRRaExDiY7K/A4B154LpAm4xk06B2XtBSA3ntOjXtEHsvLhuC7Oo/9/fwQbXe2YAvEHw7haFvOBZCTvzfhY78N9CaG6ksTje8M6X5cMZxzXEdO5v7+PkydPAgDyPMdsNsNyuUy/Xy6X2Gw213Qfrg/QOQxjWqQRn+Gs4oQ9yziRzPNRRhdHXXOiH5kjgCAgSm/rhtuTSSj6fmQuAmVbyDORxQVZYVdkRuNrIgiIIC6z4yQ3k0AjgPvqPdD10LtrPuY9gUGsfQEEiJGhUgcbgg3nR/+m9NOppqfEthw9a4ktBUbPk+N7hu0FvUYzpnEyrVHLcePx0E3HyZ4EdARhPSObAZEMqm4gi5YZhMIyHXfTcdV9cFDDwBCWWE3jGNwErfm8zCbmIJTC5lqDMCu4vRNLsqZFDuTZRJ5MuXIEx3rTQu2toJoe5vQugc6mhj2/PpR4qtuejOYsg1tWGHYqHiutRc7XwhzUY4hKCDDrPvW4+nJkg7Mza2Qf3YM+u09WbtUkiWXIxee32vDzRe9S0/E4xwlv6sVzZC+HYfRM7a94HU4nYa2A2mHg687v8XUA1O4K6vwBnzMMZA7lulS1hGw5B7W/Sa/h+2z4WAhQ6w0Zz7ohw1UVXASIQElreqAjo9+2UPtr6L0NFydikrQH37MfoNqOIVQtE16jRByDGzs0g6Qz97JY0ZNxNOfWZEUdFyrM7sggB5Efqk2bgnHi4gg6LjKo/TWf20w6NPuBMsJWvucdvyv67J4oAXgtA0j3AH2WMmm9t6F/uxtYa7SquZiUZ5JGy/OjIpu9qRMTpw4YXBTkPHDDCr6urynTSeZsEObREOx7wJc2dWZ6+b76LErUKb3XB02SnlJGrkRazcRjXxr6PAef6n5MMyT22K56uC2R87bcvgpAfrZharLzrGFq6ekFgGy/o2phCEkWHzLeL1ij45MfPSkElCK7HCYgMH3+8d9Kq1EuXRY8v8u53GsHqasaoIaQvJ1uJkFv7cD7mDbJxhG7K2PiagS2ib1UitedMKJpP2IAHpB6MUM+eteD1TC1pENvBiZYC0sfrOZCmHjfY5cw+6FFhhoDhKKi5xLjEMjzcdGW1SWJ6Yyy4RjQB1BeC4y+ZIyBTVMG7k7/msOMpvNc/Ij3uWkdilbj30lMEogFzF4Js/exkIxebPsPFGi5v9i7e7u/x4ZHXcG4Vozk/TUut9hw3LG7msCrq33/48Z9Aab3N+v78T5iaOXV/jwYh1IKamq/OvL/D8Swl3/Kx8kYGNAQgRv6RtIo65FlqCqZwLtJZ6ce5bF9z0lHBJTa8A/0ph5B7OD4B7YQVsma0XsXgZqOabTyviUZIjjP30UGsCpH1nF/xf2NoDZ6y1a1MKzyBzmyIr0apZnCZKqDTQouCWXBCX4rk6sIeCNIjiyKGSf48B5qf42ws2S4UE4ZYpgVfHxRcfJtCDb1ilKtyH6OjLLIhgPTRJEBasXUYD8rCF7XLRnDmyqWq3svQUosglcCBNlrOiSWJfpBk+xNm8Re+TIHMsPXhECf6VKCXIacrzFqlCQrBbc9k7qaAWZ/QChz2PMboB8YFtV1nDBWBRcDIrCK8upBOvS6gX7eYaDUVcA8AF47XQfVtDzuWkJrimJkWOJiSLymhmEi7Z4siMSSdivsRXxd2wlQN4cltoM/vM/rWq4beV8l4FdpSjxbBpygiLLQfvzv4AA1cP/i9jJLoGZloaCX/ZbvRWIp647fjfhZ8mz8LBGURs9wlKAKCwstN8bMAgc1wqIar4teJOGbjr7OuoPSwpI1vXiqDRQmnu5pMEnXj0yTMFIqyp3j8+qGk2Kfi4e6nywQyMQ/eYI97yFtPyoqNJ8X+oGsJzAeQ0V5rYp1EW2L0LSszriWf7gyCyfeU+U9vZUa0K2D7V3qylS9A0oLXQ+ixlDw2zNoYfbMQQe3zLkgM3goKGTnavQnKnZvyr3Bl4bBXBnZTbOWkK7WsQYlfn/lGPZbBbK9Zuzh7Aae587R3znPYFqyy+agIcscvY5tl+75qsjhV2sBMeZwiFCYdGKKumEMmpP+0lkO1bFyyjSsAQpWwbRcbIr9vCgyLmICE/a0l1NMqa2KTKf3o5Im/o2IoFg6RVVeCnM/JOl+lC4rx4U7t6xGi0LvYCRkS/UeyivoroVuBi4exRCtPAP0mA57dESG7FDQ0DBAWZtkyoe6R+PzygJhdx86s9x/+Z5fEnhMFgJC8LQqiJ8z+lyVzbgYhMMT2iRfPrKYMGVy7quv7rL7fy/GxeSwVwMkLhYMc3Q7l9ru5d7zqL/w/jiW99dzH8hxuc993HE87thdzq95te9xuXE1jH/c/sXe52rkww/W83ifxr1hLh+cmBMhBDzjGc+AlUW8zWaDZz/72chlXjJcy5o2GdcH06kFxG1E9ldM5GxlwQlkLhPtkkzeONn1AgRk8tF2SZqIYRi9os6TCdrU/H3T8nGlRpnt4PjHPQYRecefKKGMj7U92TwnASXeA/MZQUiUYXoG5HDfdAKTcOJLLQqRXgpj6glio5yNfkMDv5wlKW1MjkVJxjCmPIYqp2RSmE21qjnJ3tSU3HYDJVnrFuqc1MhEwDINuhFmMFjWGqjzB8mD54VB1U03spxKsfZD2KyQ8xzoTStySAGPyxKhYGhNCtYQ75+qOXkNWtisflw9j/7FCFQBwJecRNK32IsHUCc5pz6/Ejns4coKtd6M18wUtAQPfX4f6kAAqVYEHHEiFPwomwVGNlLrEbwkz7F0uw4CNOt6lGLH12o9Ca8S6WkEaTG1Oco0tch2N/VYIxToM4uhT+naDZ4MbGRR224EiBEQlQXBc9ePqgEJPBrZFCmhX8lEt+v4E8GataNnGsAYjqRHybQoA5BnE9aXxz7MKsoq474DTFWWRYvYvcvvih+/f52A5niP6Hv66oyByjNOxkXKl1QOAFJHYZHztavN+BkiYIi+zIZyWrS9vK94oyNwD4Fdjm07hpsFPy4UxX0DLqzwuL9H3UCLP1Pv8zqz5zdUK0QFQDfQ9xmQGGQAXHzpJMl4q4A9u6F/2XGRwJf0WiKM6aox1TfW0KTwqYysqFvI/U0BwRDUxaHrDr7KpSKFclbT0svrC4tQZGRrewLSlGYuTOcUHB1imgTcBAkbUkUx2h/MuIjgSwJc3Q6AAtRA1t7UQ0q9DhP59KG6G3mPQ/UpYgsI8v1SWuFO9yo+z1D6OvVhm03HcyVeWdWz0zKmD0MDoaQVQNci65eOZy60jf7Wo/v4TP2cVJGSjs1RVjgGWk0kw+lzxSGLacGHiwb8HAJE5rmU4sq2lBJvedcnljNWpqiCoUsqsshxX4BxP48mBePqJsAPVKXHcWMK6i4XFnMlAOdKgPL9CQ4uts/HsXwXe919Oeb3Jun1aoJxLsVkXikgfyAZ7nt7DV3J9XIl5+yB/Kw3xtWNH/qhH8LXfM3X4Cu/8ivxlV/5lfjBH/xBPOc5z0n//zVf8zX45//8n1/Tfbg+mM6uB/TYi5kmzrGTM07e4mNlMXo1owy2yOlbi5P3XBgpa4B+EuASwcKs4iRWC/Cy8nhVUlJnJYABGENKlBqZToAyxMhIRfAKjAEjXZ98mGFe0I8z9QQ1HSfh3sMvKoZ87E88gxCFYZUTVAX2zo1hLTIBXDXyOXv+0Y4eQp3Ra6NlItQRNIRZISBVjbJhAKjpj9NndhF2FgjLOftMVy1XpmXiGhN0Q6EQjEl1H4CCrzJo5yR8SI0BLnFFv+lHdhiAn5f8zJGd6ocR3BgFFTT3tcyh92tKfF3g+26EmYyMXAykilU4schdAakDNkp5I0BMiwGRrct4/Xg/eixnFaDcyKZHb23cTmRYBgdUct0pOdexvxEYpeBT31M8FtaIP9GM8vAIdmJgVpGPizPzORmduN9TIC3sIL9LArTjIghwyOcLa4HVmvssnmR0IkNfrcfPFkF5142sZvxcQUCrKQ9/r2PYVwS/mxqqyPn/ZUHW1stCUtynph0/b/quzuR7KJ8hnqfQkcGJoTEDvZ4qgnprgGBHwG5EAr8hw88FBWGtfAD8ME7Qe1nMkgWB4EdWTe1sj58xHoM8oyohnodjugnv19GyyxUa8FsVQ2kKJtqqZgAKg6CZMOyrjP7ctmdybfTGx5ESVgkk7e6GYHKWj0nTRpGVzi3MuuW2256gtvcYljnsuku+SSjF3w0Ow1bJDt7CJimu6sLYbxyYiuvmGet1JFEbwBgUBFxQExLZsuAc0IFKmOjrFTk6+5Q92dVlkfqFjfcIXgHSJ6wm34/g3CgdnYCi+Lvo8VRa87mR1YvXWQROjtuMPmzlPFQnypZZDrco0vFVzZDApxoYbKVcoNJhI98TSUVWUekQh/z70AR60q0ZmpaAL7NAN6bGHpLHHqwOf3b53TPNc0egOjkO8TGztYRfrca/fUbA7N//TDzTPJfHqm2hMsugJQC6KEXaa6HyHKoo0H36J8P+978EcO9qhqaT6nvLSh0Fjh8Lpu/ebOdS7NflHr8SFu5KtnNv9/tKQ26m1/b9HUh0lCW8FPt5OVbxYvt+b8f0M99b5vu48/SJBCrvTTDQgzVI6Id+6Ic+1rtwnTCdsdg9MpgRaC7m9AJuzxG253yuMDthZzECzhgeNLgRsEQw0nWcPBa5TERldTZ6P7WWlFuRNO4fjH65OKwZq1gA/r7tyFZuaqalFnb03CS/pFy5RUbpbAzJiBPnqkz1GvrsPsFjZLqAxNCqFcOF0A9Q95wbfX1RhhkEeMTPBCBVFShNFg8QUCdhQ1GeOU3ijLLQxYy+tsFBnzvgMYxDfKpBGIoYHBOBlDm7YjquSJlVO9CDVzcjSwUkBk1v2jF8px/G57Qd1N5aZJ1KWLyBx7FuKA1uREYXK3aUYgWJAMO4DwQVjvLTOLSi76tuuJ2uG49fnhFoToFPZAWjNzJet8akfkYCj3YEtnEiK0mpOC5YZhjIktcNsLPFx2JCcwzF6UUu23ajdHx6TuJ5NHo8DuKvTMxqVY7HKUpEVTzvEwA5OGBrwe0v5mkR5hBrZ8wI4lsymaHKx8UOmSAnWXBZHD5Hi9no347AvJ9ctxLMBaPZ2+tlEjyfjd9lgOdIK6jFnD9VyYWiLEty+hC9aXFRKLP8XFHaV+TjNRIXiiIDPZEhKvnOqvls/GyZpe96xnOvrKXMEEfYsmsxtpaJ1Yx1I0yzpu9XNwzUiiBGixxeec9r1GiY8ysoF9iPO8QKEdZ3uGVJJrS00gPrAU0ZqFuWCIWBLzPopk+9qq60KVyI3bC8l+neS+enSh2vvjTcriwYmYOG/cMS1pUSteW7ljyCEyYs9kEqraDyLB17AGmxRzes7vGZZk+sJF8D4F9PF/gTZenyc4H8M4FPJcw62di4D3f61/D5EyAXOoaQhdLK4p5c45lN1VG67qHrns8BmU7EBboNPfgo5PuTjYFbUxCojLlgEjntNY3XbgSZYegTAEzXaVmMTGQIBKhAApc6s7A3nYTZ3oJZzLF63ufhd//mT/HXP/BEPOud5/C6P/+v8PMcWMxxz4s/D/+vn3sj7KMfCTz50+gPVQrm0Z8C8+hPAT79Mfjdv30rfvdv34q/+tFPx+v+13/D//Ezv4u/+76nQG9vXTbJ9lIM3MWkq5faztHXXo4du9S4L369qxlH2bor3Y/L+Rmv5HdHAdrRz3MpVvRyjPDFpKDHgar7EzhdDkDHz3ncPlzMA3qpbU3/e9x7TF8//fyX8npGxcNx4zgZ96WukQdTkNV9HtdRkNCDYVwfoLOMjFQEJJToxQqSKJ2c+iVTDcg0xTXPx4l9nGTHyXHXjwEMxcQPGSeX8f1nE2ASh1LcXpycRj+osDM6gqO4vcFR2hr9lj6M8t9iwuZ1PSf9EaDG/QJE2uvH8JPI8h5l2eKIctn47+hJjKFIURKaZZNjFj1ULX+6bmSRgTEp11pgXTPEpSFoVftrSsCi1xVgGFJJH6mKVTZdNwKaohilqjoCF/EYiu+Ox0OOoxbPoxPAGEOhjLCCuUzEIhiL+5wzuEmtpYcVGFnLOIGMhfDxmERmUQnwqJsR9MVrIIKNOFGLrGQEyxG8A/ycEXBOFxKm5ygy9VPGPu5nUYw1LDEQKl7nRgKn4n4s5iMLH6XdUQ4ew4+k9D1JcasSsS807Cy4nVklioEwJjrH78VyzuM0n43H1AcBk5bfzyiJnMVqCoMwl+1EcOv8eB1n2cg0AyNLX+RkrWJq7Kzi8+KiwjDwseixlSAhNU2yFiAbK4sSAI3nU8C036rgb9oZ9yUmnwo7FY9z8B7IM4SD1cj8HqwZrNUNCLv7/AjGjN7ra+mvcCJVV0pqlURyLrU2wVCFoDdd6h5NwCfLAGPgd+bQdScyU8eqldYBVifvpe7Z2xoUmcKQmUMsqS+y1HELgKyiYueqWXcS4tPxcQBm00ldyKQ+pHcIxsBvSQBY6lj2o4cWOAz+gAS4QrRSxAU977gIYAz7WlvWIMWU4hjQE4wWwDzZJiCAkuqQaeBNYlq1PrQgEZN0+QFNeq6SYDr6l+Vvj1HwizLd99mPy/A2eDA5XEC+25ak6+i1jiBX2Pjoaz37jX8PuigT22pPnhh/r8Z7ZmLq5d/m0z91/AxxETN4ymHjfVg+e/v0z8RdX/M43POcJ0LdchPe8vKfBgD89df/FL7rxN8CAFafXGL1pFvx9u+7A9+x8wHs367we7/xn7H6+4/G973jj/CUX3sPnvJr78H7vncUaL3/q38GAPBNW3fjXd96Bzaf/6lp0qun534yLgUOr3TcG0A6ndRfDlhdDIzEbV1uG8c9fjmgeannX+w9rgb8HffeR8/BlTJ709ddTu56KaB1Jft73LaOHoPLSUqP7u8UCB4HWC8n6T3uOZcDgpcdwV/2KVcD+q8Xf+f1VJnyYBjXB+iMq7ZajQEfkrp5yDO3f8DnxaRMmVwwgEh8N6V4JfshSYaSpyuGpdTNKGeMfrnIHLXtyEhGj1oQxjROIttuAoAFKMaJ0uAkQVbAZN+TcYu+nOgPjV7PyIAJ6AgZuwUj2FCbNkkZw8mtkT0Cxgl0PEbAyAJ5N6YGRt9rH1fznYTchNF/pxSBzkrilqNHMQKFGK4TwSEwgtN47AY3ymd9SBUdaURmEBjZw647zE5HRioyff0wVt1MFyYWM2GgZbIUJbZSOZMkmF7k1tF/GSdS1kqaq7xf7HSVpFJ+noHHsevH6yden5F1iIA+VvxExrgUf1l+RFIdFybidRqZPWvGGp/FfJR8xoqVuCAQw4diMnNkDSNgcl62I+C07VKXbArfisBWKWBWUcYXpYGaicPJ37uoktc4lBmBZGHht+f0FucZQZ1I0mP6cZSXxiCZICDYb8+TxDvVupQFMJ8hVPQ6hzJD2Jql7aaFjbhgkef8/IOjj1LGKImU765cT2rdEoDG7QgwZ+UMmNxrpSPSGDK9i1m6RkLTHpYw9/24wBCC1KpkY7hY/F5dS7Yzz0aPt1L0WWeGfbSy8KEb1qLE7tGgNX8f7y8SQAQQPJrNALdVMEVVAz7T0J2D7hzBmVzCKnBbqvcM5WkG6N5TGgswDdcw6TZ6QO3uJqW0AmT02PVKX6hyLiVOU5ItC2EiXz0krZ1IPIOXmg+QWUzBUMbAbzZp4dKXFsHwmrYScKTbAXbFcCQEj9AP/PE+Xb9ThvWQJ9GYCzyW08RWNb3+FW0H8Tu1+pQF9h+/Q4BZGISC3aUhM6xNqXvaGLpBnpOnvx16NsM0NVfZDG/9l3fw+gNgHvFw+IODNAFNz5tV+J33vgX2oZ+UWMb/3w+U+ND3PpWfKS4kKQJq98hbyWo+4bFYfd3/hif9m3fi1f/Xj+F//vAdeO8LP+nYS/L0Z2u86Y6fSf//h0/6DQDAm+74GTytBH745r/ED9/8l3jvF/2ni17W3/byX0sAYLrgcDGwcNy/45h6JC/Gal4OGB56jaT4Xuz1R4HI5diziz12sc9ytXLfywGYo4zlxZ4Tx5UA9cu956XY0+OYvkt9lqsFSNP9Ow44Xy1LfSVs4KVY0Is9f3oMLgeGp2P6mqPbjPeti4H945jZG0znjXHcuD5A56Ym4IneyejRjL6zCCQiu+PdJPhHgMZihpRkaw0nH9siV4yTfudG1rFuOLmPabBVyW0u5iNQiwFGdSOTfz0mg3oJBIqSxghkHRNkk69QG7KeEbhGiW6sWFHjROWQ/LIsZOLVJTCg7jmHVPMSPWoTL2nYno8ADuD2pv5A5xGWIlOOYM/oCUBpR3AVAXocB2uRT+oxoElpAWmyH1HmG1fNozy1afnceK78RM4YgSUwSiob+dzTxQFgDIeKzHBkI4MfGeUIrKzhokP0RUbwGyWgMV1WFifCwWpMoVV6PE9Rolo343tHABJZTWsJylPlSDeGAA2O1840PTmzIzjJRA5a8HynWpaYLBuZdWsRthdk65aLxP6F3CIsKvZ55hmwnMMvy1H+ubUgqFRKwNyc7HlVjnU2wirHih51UI/M1d4KCIGM2aoRZs8xLKrMoOqW9T5aM3m27tKxVrsSWlV3aQFJdQNB2rl9/nQuSYGVpPuqph97XfthlD9HBjx+p1PFkU+p1yGC/yg3n3pjJVgrnktVd9C7BzxWTcdwowgw4zUoQUVxYUZF4B/vKU07Xr+x7zEmSU9qIu7vEZQi+MuMsIlyDrxPdUl+lhPU5NEfHSQULE/3naAxdvd6n/zZqifYHObslNS9h9lvKUUNAbp38JWFWXfiiYyvoWzU7jWU3g8+pVWbvZr/lgTqYNiZGz2gqpbQqf3V4cCvia/yTv+a9P/x39FXmc6BpfpB5XkCyPBI/slgDAN7pEOWoNyP94bpcZbtJtYzBC5CSCJs3L8w9IcY0eBcYpTjMXOVRXuqxB/+Pz+NP/qJn8bffcUcwzJnsq4wsV7CnsheU9KMzCT/eehYT/TeH/tcmJMn8N7/53MBAOf+98+ArircfdvDcO7/eDL+7p+T/VTG4Hfe9Sa87m2vBwC87q2/mz7be7/oP+EvXnwHfvdv/pSfscjRP/2z8OH/80n4vd98Jb7sLX+H3/n91+At//6n8eMPfSseY6mI+Ovn/9Sx1+R7X3DHVVzBx4/nLc6nf6ubTqXamqlH9Znmubgte96h1x2Ve04n78dNtC8G7KbbiONiMsjjxtGJ/9VO3C/3vhf73b3Z/pUyktPXHcfmHgcWL8dWHgdyL8YOHgVfzzTPxTPNc48FSJc69pdiH+8tu3oUeF5sceRSoPbo57saz+h03JY//6KfQVn2HR8Flxd87iMS3esBeN5gOu/fcX0ECQEj6wQ9SiZjAmYcUc6oFIFLDANZzCS0R167v0or+SlgZxoMkWWUskYws5hzgloIAOo6yglbmaxm2cjwxT7FOMmJ/r4o4zUG8AJaQnZYbgmMk1prEYqcE904gR4cazsAHossA+xkclsWI1saZVctk2mRGTI6sW8upohGOapMmNT+itvNMkALcJIo/cSGxs+ZwKsdWdKqHNnAeByk/zGtlscajpi46CE+MnOhzDT+O/hxsSBKXqfnKzJ7TQuc2B6Zuggo4ntJn2P6jDEZNrLK0xGDfYyBivLpqhxDhiKTFF8bWY4YjBP9wMAYGgUIy+kIDuNxi9eI8hIspQlgrKEXWK6JQ/1QrXzGiQRVrTcIy5n0ZYbx+okS8SxjHYa8Ru0dJEZV1RhZIkvpITJ2CKo1wz7glZxvAeBZNvrd5tW4MATw9V0HdICKTH/6/GFccJhX6fuh0rUu120vrGiqhYksfJSlQ77X8tg0MfjIUEUhoTI9z8tizv2rynExKp4rmPG9YiCTJvObFrTybLwO4uKNDwhuWp1i03UU5PsQ+p4hM9PAqPt5qI5smAqKIWgt/ZjBavo0lzkZPWMYUCOPp55O8bObmiBQeaa8wgfYg5YVLCKvBQDlPPw8h881dOcB5REUyJQGJsDGGhy/LLm4YMkGJ+l1CAiwMHvszFUePL9KAJb0FR9ajItDgOYz9XMQfZbp34D4OkUOXteUZ0sPKQDouiUbLMFmQesE2vW65bmOndpybaUkV2FUlQFCP0BXJfxarmWpbUngVNjQmGCrugHuBBPIlQe0G69b24yAP9bW6AZwOQOGgtXwhYU9fZD8+Enef7JD85TH4P1fSWbxB//5L+L2P/kK/M9/cQe+6C++Cl9+6oN4z8vnKXn5rF/jlJ5f9Hr6m//3IzD7KPDD//QX8AOv+EYASLLZj8XQeY6wrOifleCm4JB6ReNjcVxOLngpBu4oULi3TNBxzzkOlFwMLB23nxdj1i7Hsh2VgB63zem2LgdwLuYFvRrAdSmm8WLvd+ziwKQ39mJjui/HMbuX24crkS9Pt3mpfTlu20dB5tFtX+zcXQz03umZFB2OJlfL0Dtb8PsHCM5dlJ2/LXselFYX/P5L1Fdd8lg86Me9YS5vgM6LjusDdMaJe+wx9GYM75lOLqOMEjLJXsz536YdJ7xGj5PJCNaKfJyIxveJHtAYDhMEVET2LYJJQFhYD2RqrFxRE3YtCMM2gJOloSfgTBUTQWRLAzAvEwOjokzYmrGmI4EtYWiiby4+HgFiCosZDif4Rt+iFmDuwYlwVUApBfju8Gr+tNoheuSkguCQT7Rk7D32Dzihi2EWuQH8hOGLnqOYFDoII9xhTH/N7Mg8dvU40Y/gKbLZEdj7YZSznthm6mmUqU5ljx6YFssnIBYno5NaBHQd/bt1PQmkMuM1GJnkuAASASyQpKihbnhMY+0NAMwXsnAxyjuTFDv6VwcwQKppR+AapZ1R0j0XH2PclnNQB7yuVSPPqcrJYorIiGNQ1p5I0SNgleTilOIcg3wcvXUAxDsm112RAzAjaI8S55hgK2wSrEksbQpqWssxjaxgrF+ZVYcXORRGtj3WXDgPQFgj77gPZTGyidYCJowJwz6ka8nXNUNTdrb5HnGRIQJVLb7XeH1OFwPiZ0m+1zAGjUliaTxGyuaH70shJMCZAliiD+9ajcwmnUtKpYXImL2D2fQime0oLZVKIuVYbZQ+TwjwuVSiCBMat8Xt6fRf3TmY2jO0KAQY+W9KnwaYjN07vi76i4FUpwStOSeaAM6QW8D1CNtzsrGTbmZWz+hx6Vl8nQl0BI/gNQD2s4amhVougA0RpFq38NtM4Y5BPqobuNDU9lACStNCEpCsGjE5d8p2qriwJgsQyhj4fkhMaPBekm3HYC7lPHxuYOoB3XaGf3nm0/DPb3o3hhngC/mcJkPQgPUBegjwUiHjSguTWaoAAKiyxOv+x+8AAB6fvyDt8j+YNXjJN90KAHjzZ/wmAODLn/APYU7Ta3wpwAkA7/o2spS37z4cb/+/7jtjeZ+G0vD9gLuecRIPP39AifswiMydC6kqy4C2oXTQZonxPgQAJqFTFwNrlwOrcUyBwsW8fce+drIPx73/cUDyYlLS+8NjdzGweKUga7qNo/8++tzjgN/R51wMkE3B6dFjfnRfbsufn4KxjtvWcef+6HG41O+O/juN6eIXLrwerpSZvhgYvhyrevRxs70NbXv4ukn3SCiNMPToH/cwZPccILzv7xAcxkU8YYyfqZ8DnedQeQ4fyRhcyHx+vI7rgbn8iZ/4iSt+7nd+53des/1Q4Zo2kF/bsb+/j+3tbXzpzd8MqyehJc6PrBkw8di5EQTFiW+c9Lft6JOMI0wnsjLidp0fJ97A4T8OkXmJX74InKK3rpCKB+k9JMjRk5oO2f/VhhPtuHI/uJF5mYKguA8H6xFEVBVS36I1owQ51mZET2SUk04nuLE/LQK3CNCUGhm5yGJGaXA8NnFy7im5PARWgCTzgtRTJAYqMl0xQCcySDEkJ/4+ShOn6avRFwkgDMOky02N4DWyTbFjcSovjuc67V/FcxCPd7wu+v7we3o/AsN4HXlPpqosx21GmXc/8BzErs74u8iExqocIPkpE6CK7xn3Kx77+Fnj8Y7e5KmUO4b/xG1GBUB87lSiLJOykY0V8BPBkJr8f5Sjx3MyZZfjdyIm104DoaYBSQD3M9aepONl5ZiLL1YbqgvW9fiZ4nez6/g9iQstVclFmEyuixjmIhU/CSx2TDdV4mMOTUvJY/yuRn9zDNaKLH28Hs/tMgU2XlcxICZe1+LjnvZvAmAaZ/zuzipZuKK0V5UF/P5Buo+9vv7PuBbjWY99KT2xVsBUZshMHjT0hVdcyFBNB78ooTfRd+2ppmhbDI+8FbrtWd3ROfjcQEuoEJSixzJKb4907PpZDlWT7QtG/OfdMAGRLgFLNUifcVxQmlZ1pOt/ZK/V7moEq5safr0Zwd/02pwkBeuqSttTIkf3Z89DP+KhlJEb+iV9TrCua6Zux5oZfc8uwmot77lJQJMAcvR16sV8rEzJLPz+Cmpewe/tE/zId1jlGdTWEv6mHbhlDrvf0rdpFOqbc2xu5eee3cPPUOz28EYCmMQj63OD5qYc879bQR80eN2bfv3+u4AuMx73n78N7/2Hx8tojxv/4L3Pwrv/4hEpGOi+jK/4tC8CAPz1HY9G+Y4KN72jhx4Cig/vi/LBYNgqYP7Hu/gC6eb1EZQC6fqI8ty4QHFoKI073auOZ9Muxl4dAZFHg62O+92UlY2MUtqny40pc3URFutKx6X8m5d77Djwd7H3SEAo1u1c4faOAraLAbhDQ2nYh34S0PdwZ88dvkcc89xL/e5i10J6LXDs6w991iPna+oDnh6P48blmOxDx36yv/HxZz3+nwF1A/fRu+h1z3OoqkSoG7zvPz4e8z+a46G//QGEc7vwdZ1C0uL1aba3gIffShsXwO/U3j66do0/wG9hb28PW1tbF93/B9uI+OLJX/cjsFl5+RdMxtA3eNurv/+qPvPtt9+OH/uxH8NHP/pRfPqnfzpe8YpX4GlPe9plX/eWt7wFX/zFX4zP+IzPwJ//+Z9f9HmPetSjDv3/6dOnsdlssLOzAwDY3d3FbDbDLbfcgve///1XtM/3ZujLP+XjYEQQFBMuc5G+RklbHM6NQCHPx/CTOAGOvYNdxwlnZMGiDNP70ZtX16N/NEoJI+sSWajoK4phL4NMSGO4zRQkR4Z0IsNLwAsYJ+QxVXSQoKNYWN9IYFCRj57Rrh/fuypH8BeBQgSvU1AdQUWUvsZuysjI6snNc9pVOU0RHYT5jWFAyfupR4YosqHxGETWNQIO79klOU147fsxJTfPxwCoKF3NMq5gR8/mdFEhslaaHscQWShgDHaJk9rNZiJB1uPvouQ2y8Zgp0xkmLHmA4BazMdjGUHPdMEjLkrEAI4QRo9uPCbAKNeOx05PwF1cCIjXtRaAHUFjlIJGIBYZ6BgY1Ml1EBdR+p6gLqX1ZmP1S7x2gLE+JTLZ8bxHf2QEp9PUXqPpJ4vJtvH6tgYp5dL58djG8xaPSy51KnHxJoLzELjPVTmC9sSMuvF6DQEoc4Q599XvLOX6yVhhIt+ZxEIVOcKJJfyiYliQtWRho/JBKX4ft7dG8H30ezv5noWu4x/o6H02wu4WDFJK/lz5zCoeU3MNb88+0PvaDQSR3qeOXzU46HULvUdPtV7JvScCzmFAaFvYj5xNgT56v4HZb2H2hCFse6Te4yD9wFLLEqyGXrfwSx7vUJB992UOX2RQdQc/L8jOebKkan8jEloeRxUrXjYtw9IAPtbIwlD83klv5Bv6X73wGKixc9I3rZwjPb7eaITMpm0Go2BWDVTvUpps7NBM30GR6UaWM3Vaeob3hLYdvzNFAX3TSZz+3z8N/vM/EwAY9BNDhE5uwS1zqCGg3ymh6w4+1yjP9Zjd45GtAj21GjC1Q5DX9csMwyLD+qEFzn+qwbNf+eYHFHACuCLA+V0feWr69/vf9ClYvm9kyG/ffXj696N/4x8DAN43rPC+YZUe//NuoiaajOEzHw0Yjb9++i/ioV/2AXzg6x3u+fYae595Eh/50pvwN19zAn/77Ar64Q+F3t6CXi6gH/pJnGQLu6O0oo8tz5JEF0ACD1rO8VSinZiliX90+hooPb7H9HeTkKmjibvTfTq03cm1e+g9JtsEcAi4HqoNOgpwpyP+fvqDwyE10/c8Dmgdld5Owd9R8HisXDgyZJd4j+lrLiY5VsYc69+Nz1daoXvsJwEntqGXS5gT2xecI2WzdG6m5yL9GzjEWE7P93hI1YXHX34/3T+l1fg+wdN/Go9ZBKXHsN/pcx85Xkf9yGkfZX+n52Q4tYD7pJMwJ09wH8oC6uZT0DedxHu/6D/hN7733+Lgcx9Gr3SeQxcltPzN1JkFHnYLzn3OSQyPfiiGRz8U4dZTVBVEX/XH64iKuKv9uYrxqle9Ct/93d+N7//+78fb3/52PO1pT8OznvUsfOADH7jk6/b29vCCF7wAz3jGMy77Hn/zN3+Tfn7kR34En/3Zn413v/vdOHfuHM6dO4d3v/vd+NzP/Vz8q3/1r65q3692XB/y2iKnrG/iPUQhoKTvR8YxshyRhYhMWPSJRS9WllEGGieP0dcYweowSVGNE+mYDhr9iSGMcty4TzE0xwv41JqTTz0BBkFPUlcN0AyHy+idJ9sDkJGLgO6olzHWu0QWMLIvEbgAI9iMssFeGKfIsG3qCQgvRiltBMqRSZ36pwoBCM6NjFIEgdP02eivjMcvejxjeBFA8Bd7E6chPGryGaJE9CiTOQWb8fmRrVT5CE77np8tgsp4TcTQnsh2RQ9fGka8ovUYJBVBegwJioA8ymAlcAdZwetLa6ANo//Vh0l1ShhBXWQ6U60DxmtbTcBYXABxXqpO5LwsZiOrGQntecUQll4m5lsL8fllUHAjeOyEMYznLwYjRcbducNJuz33mWE0QChzKOmfVY0AW6UQcpW6BVVPQIIoxcx5DpVjWqlqewYGAQjWJKlgKBj0Mk1kDsvZCAikhxIouS2j4G7ZAgIQSgtzVv44FAI2B0ev9JwBS8M8g2kMUBVMR+01lJ+N18ymFpBseD1E+bCbfPeMgV4ukZKg84wTA6k3UiEgVDlUU0DF8znx912zEdlhAGEunz+qCdLkSKUQn9Q7LNJ7pTSg2ampDxqEKoduKFnWG0m97Qf4bUqm1aYDjOLXtx34mQefrsFgDdNyC9oazLk1g6Y2kkC7nJFtjIxm8o3zvsUQIT9K8OMCQttBl8VhmVesM5FU2zghVNbyc8Xj7jyULNzpTcvk5dym4KBQZpTz1iK3jQBTjZJeeggzhLYZJ6reQ+1s43V//NuHTskTfu6FeMx/+CDWX/RYLN74HoJ8F+BLJiN3p2ZQLkA3Dnbt4DMD0/FaaW4u4DJFGVgIWD0sQ/V1H8W3POzP8aKdD92/185Vjq/86y/Db30qg4j+P3d9Lv6/n/RnAIDXvenJ+O9PeCQ+6+aPoDgPqAF49nu/HK993O/hJ/7yS/DjXuE9T/tPOPG/DB536gUIH+C19NffQED71a//Drz/2T+b3udx/+mFeO8L7sAHn1HhkTVB6+uf8DrgCfz9a580w7Nnm/T8p7z/hVh+oIddD+h2Miz2D+BXa1oeACAE6J1t/ltruLvuGQOhjJGFIrl+TJ7AA8HF6NOVB6HzHPrkDkLbktnOcwY7QUDhYg6VZQhnz8tLLB/b2QY+fFfaVHAOWvp+E+Mk20jvOWHLUtWNVnyfqkKoa/hoC5LnpV2Ni5eT9zt2TPzI6bgcGcfJcC/FBk6B2agSmLyH/B07GgR00e0AyVsd92/6/qoo8MFnlFh8uMTN/yPHsCyQ/9WH4PcOUrK1KgqEtoVezCnTjn/jAVqh1MiGJ8+wtrKv4hWfViQZA9+06d4AADo7XK8EiC/ZOZH/I207WQImEtejiw46s/Bdd9FjfdyCxcGjKjQnNW51DvpDAHa2sXnsSZiG7/Mpdom9f3QAe/vNqDbj9yjIfHH/iSfhnncWH/qTmwAA+e4cDz23D3WmGecdH4fj3gQDXe3zX/7yl+Obv/mb8S3f8i0AgFe84hV4/etfjzvuuAMve9nLLvq6b/3Wb8Xzn/98GGPwm7/5m1f8fj/4gz+IX/u1X8PjH//49NjjH/94/Pt//+/xtV/7tfj6r//6q/sAVzGuD9AZPKBGz0/qc4xl3eVkUplYTQGSEVROuyutpHOu1lCz2QhqIihJXkC5ycYKgX4YGa8peOqH8bFpME0EwdFH2HYEkm0rDJ6s1md2lPPGVN5Y+QKMoC8Wt8fH4nvFUKQIbGOiq3gSw7ob2bmmlSoSjN2G0ZcYgXcMzXHhcDqs1iNgjQm0kVGMbGTbjh5FrUem0oUR8MZjE9mKyP5Fb+U0XAcYFxEiQxZrZOpGklvNeKzjIsOUiYvnI066o/+zyMcFg7T4MLm5x8ci0B+6EbAaM4KzLBul1PFcbi35mvj7yHJGcAKMzHD8+9AKKNqaMZk1z+mvi/tTlQhLqckR32EopJPWqPT8eOzVpmX1yLzg8deAnxcw+yEFlAQBDXwNQYrqxIOWGQAWbllCdy69xpcGOoJEraFDBl9YYFkBCgw5yQxcyeOse5+8fwAZJd3xD12/sMj2LXxhYCR8pZ8TUJt6QL/MkR10cJWFbvmerrSwm551EZlmBYdVUC7A5by2sgMBeEUBJ5/R1A2PrwBOaIVhnvO1Q4Dd9HCn5jDrHrrtgXhsQwAGh7CIbL8FqnwEzfE61jqBp2Gb9ySfG9h1DwNwW4ODwuqwb/pajRAIqjJ+T0Nm6RNPLLkH2kG83F6Cy+gTD20LtbNFAGkN0FMNEeblKG21OcGmDCXpr3AOalXzGDU9wduaVgK1bpKvE1rzPeVeo5QfJbiF5XU+lcjHnuJp33J87wgIHdKENLE/UQYuKoh4r1ZRhSBhWSFN3sHvUTwuRlGSK/J7dXIHWFT43Ttfg9t3H47XPfspwEfvhpJtvu5//h4A4O/9X9+GP/03IyP4V99yB8A5B/7BZz0DvrS456lz9Atg5689dE+/pisZ6uQyhdlHeXw3nxSrrgDTBnQLoPvdh+C7Xvqb9/76uILxqX/wj/CLn/fz+ILy4szZmXqOR9/5Tbj5pn3Ub7wZT/y8J6LZL1DuKmz+4Gb84fxmzFpAOeDvXvsofNrWC5FtAN0Cn/7OF+LW93ew9QzDjOftMb/2rVj8rcaJGnj82RfiPf+IHtL+JK+vd//jO/DYT/1HF+zHFHACwP/8Ib7u5ecfhe858Td44k+9EI/4rxu0J3NUH14hZAYf+Xu8T2frgJvfAKBu4Osa+uZTQJ7Df/ij9IFLPVJiv4IfFx3kutG33oIzT38oij2P5Vvej3DrKagP3sXXnNxB+8k7cLlG9afC1nc91l/4qWh3DE79Nyqtwv4B9NYC7pEPgS8N7P/6G4SmTenHykDAyISxlb/t4VMeCn3PLhchP3wXNJD8xFNZp9neIgDSipaDYSBAlRFBji5lsTZ+ThkXk6heADin/bTxIVmk85sNGWatEbyHcj4pQELbwtf16MWcMMB3+tfgy+Yv4LasTWnhYVMfkoTG9xqe8nj81bfcgf/73GPxX+9+Av7Fo34J3/7j346HvvE8zN98COrUSfSPOIns/XdjeMTNXOR0gf3AZ88nO0+I6jAAanuLdolh4CLWvBoXNldrLuZtat5DBdCqT3kE9D1nOU/IMoTzu1B5htC08HWT2HaVi0oLQKhrHB3BBy5IKAUz6Qn3Ufo/9GTTxW4SvZjBB9z9rA7v+9JfwKN/6x/jsb9c4AO3VfjHX/17+M0Pf1ba/jv+t1/Byx7zONz5T74Im1ss8pWH8kB5d4Mv+f634F/d8hfA54z787SPfivKv54B77hgVz9+RpCfq33NFY6u6/C2t70N/+yf/bNDj99222344z/+44u+7hd+4Rfwvve9D6985Svxr//1v76q3fvoRz+KfmoXk+Gcw913331V27racX2ATgDJg7XejEAnPyJDjOAzAofo6zMTkBMlrnkOtcDIXkQwBAioseNrgBFQxgCiqYdtGkwUgVgEXjGQJlao1I1Umnhg8COLE4FuBLYRyDl32Ac59TpGkJwSOEUCqO3oq4yS1MgQGDM+VybhKXQp+lCj7DGG1MQRQWVk9aaMQwSAcb8jYIufK4KuyBTH7UVpazyWUbIYJ3jxuM/no8QxspNRdqnUCOzi+Yrn3U/Of0z4jbLk6JOMVTjTcKjEzoqsbhhGebZ0tIbtOZmZuKoYz7lcf77MoXJ+xqA19KYFnBorbWJPaaq0WYwT3bmwidEPV3dM/XSTib8xgAuUKjYdFDQljxlBQqhydi/mVnx2GXxh4KoMpuEfNp8bKEkg9bmGHgKMBKqE3DJEph3gCwslq+zmoAOkc1P3jr2NQAKKEXDq3sNbAQAupH8jUCJoGgfTerjKMkylshgqA+XJELqC4NZVFkEpuNIiWAVvFYLNgQB4q7jPrUM/Z1dk0EC3nUHXBatcovTacOKlegfdGwyLDMoHuEIj6wb+vyS7YpnDNAOPjSEj6BYF/1+Atdqww1FNgFFk9/TAYzXMFdpTBcrecz9cgJYwnBAXkK7FyHPpC84OB5NpPV7/VhZo5P9VJ9/nGPQk32XlpcJEg4sh8Z41CRkLBR8LloskobBQqwZhXvCYLGdQHTuKI7AEAFgNX1rW9sTgJ++AvBTGfFxEifUvymr4ivcdMwzA3gEDLtYbTriivzmyD6JIUbFuSJLHVZ6xZ9bRjxprW0ImNTIAoITt1ZqAHIA/ucS5zyJD9qKdD+FFf/ghPPZXvw2P/8m78KGvfAie/K8ei7f94B2HAOfR8bp3/D4A4En//oU48VcO9SmD6qxLjL1pPPKVR7/F/Sh2HYaZhjcK2gHZBuhnlKne30znp/3sCwEA7/4/78Ajbj6HLyg1nvr25+Ctn3M88/SWz6S094k//ULoHOg+sMDsjIIaAN0D1WmgOuPRnNDI9wHT8bZc7AfkKy4cmS6g2+I1kf3/2XvveM2vqlz82eVb3nLOmXOmJZNJm4QUkggkdAyhSiIlICII0i9C7hX9Cd6r3CtXsSteuaAXkKLYQUAUpQuhGEInvWcmmZlMnzntbd+y9/79sdba+z0kSAIMJNHv5zOfOe1932/ZZT3redazljRcARRLAc1Miu52PC2xnrc8/j13+3peM78DAJkhPfi85+Hz570dj/6T16DaEPDkC4iVPbO3F+8pnoqFa8ewgxp1P0c9n6PPip4w14deXEVYXoHauJ7W/6Vlku93OsBwiPqEBfzY//dv+NSeM1AtnYC9jylw3Oc6MKMGi2fNYPkUhWqDw2kHiaU1yyPseV6D07bsxnj/cchWati9OVDmWDqzjyMPVDhl9TioXXuB8ZRCouzSHsEGZu60E+BzjZVtJYrlPsqDFTK3CThwCNpMKZXEzOuY9VCth5vpkHph1z6Y3FEPWq5tJXDYB0CARnHNMzG3bg0TB+DO9ZVTMmUA8HVNALnbRXPm8cj2r9J6vDIgZ/Ompf7HrYNaWomyeWUMlM3Yndjhyea5MHOzxA63LdwxCwTOVyqoW25HGI1iX17V6eDGn6G5/8sLt+CXF24BoHHFL78N5xSX4IT3rmJ0xiYceGiGzZ0tOPiQnKTsE6A80kd/zyzskFo86WFFnhqdEu2mWUqcjluY5RFGpyxgvJE+s3OwRb5Uwx5YJjPALIOyBgceswEbvkru8/X6LvJeAV/m0IdXoZeWaW2apzVFtY4USbv3EhCX5+YcMfOiTFpeIQd8kDhqej/RczOUlK5rqKKAAnDrk/4cALD94nfgzP2X4JMv/gOcYGfi/JDjdetvwjufdz4uPPubuPrIFuzevhHlvh4+semaO82tO57eYN2n5+7ToFN5+ndPXwNQXej0URQFCklA8HHo0CE457B58+Y1P9+8eTP27duHuzpuvvlm/Mqv/Aq+8IUvwH4XrdWe+MQn4hWveAXe/e5347zzzoNSCl/72tfwyle+Ek960pPu8fvdk+N+ATr9wiwwAS2euk8Z++EogQ3O3AejyB2ydUDOIMN7CsBir09HkgfukQnNTKEAIGGjpKWDLIhNwzb7wgDaFHxN1wsCU1JekYM6BpO8gYoLrACqaSMfgFtzMIgSUDepUl0okOTDQJLrCoM4/btOmdq5TDX4Duu65AIJAKpItWgiFxXmUXqeAogOo2J0I+6zAEtYpz5D2moA6b19oEVSJLzxPVSqbZzpUysBYWaFvVQKKLtJMir1d1LDOKlTtlHqJyW4lnY1Bdf5apcCbjmP1UG6t/IMvafXSjAudZuFRZjpAK1H6Heovg2g+r2p2tKQGeiqgc+JZQllhqALZsYCVOuoZ+a0fNkF+H4ZeyYGpYCcm8crBXC9me8xoGoaoKLx60sDVbcEFBuHYAy/DnF+gN0voymLI+DtCgM7atB2M+gJBeBQ1CojaE1zi8FpO1fAVA5tN4MZtwTcSgs7aGLdmfIB8GLMwixkh+ZLvlTFc1LOQzcEHj2zobEtjFJwhYapPXxO7I/P6W9cSYF5yBTQBrQdAxgFr+i9zIQSNyG3a+ZJyA3qjV2oNiAowHVTbaxq6f+QkxOrzzTcDAUtdmwYUGo0sxnyI/SsgtSbszmYMGZtz/J5anz+/5F5yoXP+BmEQpP0d1+bZH5H4zAa0FznOxknIyYZ89OGajJXpQ+t98llV6T2geopqU8soIYVQr8k9hY0jgR4Imi6N52C5Ko1MZa+a6kvaD+1JplsLKAbBv4DBztsoHygcdSjxILyHm0vBzTgSgO72mBwArEis1bDzHTx0U/+Pc585yVQLdA5BHz99W/Dy3c9Ble//Rxs/PBNdD9mZ+Dnuhgd14WuA7rX7cXoRGK68kVaM1VJnxMyg2pDCZcrtB0NXXehXkEJv+6vZXcClL/21A/ghc87hIsu+mnsefy6+PPvZLjTORygHdDb36LtaGAM6NrDFRp25OGYYQwWGC8YFCvMVlUBLlf4w89fhOOf9Dd3YvnkuLkZ4AFZ/24MmHSUh+j/x117MfYvz+C0v3oVVKvwCPwkzt6wF+8+/rK7fJ0ZE6Ds7VIwFaBbwFsCnq5QKI94Apa1Qj4J0C2gWw9de+QDoOnStXYOAA2fcr7ECTx4vPHIaQwcvvvjioe9F0AH1/z8nR14X/36t+GlO8/H5752Jo57wEGsjEsMP7YFug5YfCBw4kd7yFbmse+RsygXA+a/sh+TkxZw6Jwc66+tsfsJOT616Rr85qZrcMrLXopbn/g2nHrmS1BePYun/NSXcW7vNrxg5gjOOEKgfnZHHzc/jsbGuT/3XCxvX4cTPpajWrA48AiPf3ram/Giva/B7O0z6F93iGIYpYBeB76bQx9cBgDseFYXrhPw3574Cfy/bzwO3Wu6mL29xLqvEoMoDuzCnB185AbYSUA9o1AuesyFALU8hKpJwu4HQ6jMwp1wDD3XOw6mZM3eA+xf0SB4NqvhvpjAlBQ0eOoDy+ylFoB63Cbc9vQONn21hM8VTLUe2WoL7QKWTi1QHvbo3TEL/Y0boWe6xBLmyXU/+AB35olYPLOLfDVg/8MVslWFbLWLrYeWSCY7v44+q99bk6iYPq7+/96GJ339Zbj9qQbbn/02nLr+VXjbM96JRxSr+OfhcfjXxbPw2WtOh13sIFtVmNseUC7Oo+5r7Hs0oGuFfEmhf0cfq08b4HEn3gwA+PhV5yDb18OGqzvo3z7G4MQOqhmN//raf8Cb3/ETCAoYPKDFpi+ux+A4hYXre+jfWMDNFDjw0BmaNy5AOWBBA2BTtnaG9tzJxhLL2yyyQcD6K0pMjqU4qdzTgd59AKFpoeZmEGa6cP0SWpIk0+s9gEtf/kYcY2a+7TzZ/pR38/MCcM63/TNs/7E/w0O6FwN/+e3/5l5/fA9M5/HHH7/mx7/2a7+GX//1X7/Ll3zrfh9CuMsYwDmH5z//+XjDG96A00477R6eGB1/9md/hhe/+MV4+MMfjoxVQW3b4ilPeQre9a53fVfveXeP+4V77WMf83oULQWPZnlMTc0zAz2YwM124t+70lIQzJfsMw27NKZaMwEDAiBE9igAEFjLmkoPQXHQnC4gFrnsdAZC5LZlkcx5hCmzhsDLaJTMfQQwSYN6yzWE4ipaMFDudblmUK1xcY1uuJlNbqRGR1lFZF/HE0SzlzxP1y1mKUUOP9shSeWA2r2sAXbTYFkYhLZNdZLcmiMUNsrr/GwHemVMrMdwlOpkrSH2LTPwBTFokSGqqe+cfLaaYttkwWx7Gep1GczEo9g/gpp2luXG9b7MqfVD6+m1/L8eThIYl7pecUKdliFI4qBu1rqoAmlsiBwvt3SuXMNGMkwf+/vJ4TsZVOWoHQVnjOEcMYkiGQQi0HTdDJ5ZRCgVmcagFcyYGeDWR1CqGJiT3JTApxk19HVuEBRghw3aXkZSV0MgkN5IQTmPtp8BnsCiMJoAiGksTAICSsFMSPZqJi18ZggYVh7QgBm1aHsZ7LCBz2gsem6nIe/rC2JXdRvgOvR127MwtYdiV07pn+gzxcxi6qloxw5taWAqH9tNtF0NO/ao1lnsewyw4esKczePYFbHkRXTwwrIyNkSAEbHlNAtmbW4QiEbBZixgwrAZMEiX3YIGQFYV2gCuZqCAm81igNDNAsd2NUa5vCATIkAtAs9NH1aG5q+wb+9+U8BAA/53UtQHgm4/I1vx1PPfyYwGuNjd/wxjsZx0cmvSfNdakwrrrmVRFEIxDxOvkWmX9UI4zHU+gWI8zSBx5ay86OKEijMPAJAKAxUxYxhblIfyYxBk9FoZi3syKHtGPiMGOumpwhYBsoeZwPP5jkKrlTIVz2qWY3JgqKWIRPCtN/4XwQapusJv9Mx8BNc3xj83BtejbqvsPGKEfY/tIt8FfAZUCx72EmAmXiMN1hU8wrLD2rwD0/8Ezw4//fdDZ9965PxwVM+hac8+0X4xAfvWQT20Ddcgs4hj+JIg2ohgysUJvMKs7e3aZ5qhaZLiZd81WF4bAblgXpGYfUEYOtD78DBQR+POu42fG3/8fidB34If7HvR3Hl3i34X+d8FBf39qCv0zXc2g5wRXUsnt1b/bbndfaXno/RYhfl7RmUA8YPqICRxfZnvgOnvvdVuOV5a8H0ub91CVwBBAN09we4QsFngB3THEMAuoccqjkD3QTYSYCuPEzjUa3L0LC81uUKzQxgx8B4A4BzV7B1fgnb92/AzRf8xT26t9/P4+k3XYhrb9uC7T/2ZwCoxvTkh+/EJ874CC668cfxsdM/eo/e74PDmTvd/3O//lz0iyq2tXn/YA7/55YnI/z9BugG6O+uMDomR9tR6O9u4AuNS9+5Flg1ocX5Vz4P9s/Ww5UK2YDUGTPXHwEAzL77CK7YuwU/9YBv4t8ObsPSB7ZibnuN4tCY6usPLQH9Hg487li4Ethw5QjKA6NjCsxesR9YHcCvDOCnVVdArCmNDGW3Cxy3iX65ez9Ut4Olx56Ey970p3j6TRdi4ix2L65DdaALPVvj1Q/5LD5+4IG45asn4LS37oF4XLgtCzD7l+APHAKcw23/66H4uxe/Cb+9+2l4/7Z/jZ//2P/6s+hfegPcWScDABbP6OArv/XtEz53NYa/3fFbh87A39z0UJy64RD++bSPx5//8dIJePW6nXf6+2ff+mRce+mpOOWxt+HcdbtIlgrghmaIM7Ie3juYxxM6u/DjV74M/qPrETRw9guuw4ULV2PkC+xv5vChtz4e403EvCLQvAoPW8H/ffD78Ce7n4jtH92G4en0DGauzrH5a2PYxRHGJ85h6RQLlwPHv/d2VA84BtDAv/7Vn92ta72nx8qqw/xp2++z7rUPv/i3viv32q/8069i165da675rpjOuq7R7Xbx/ve/H8961rPiz3/hF34BV1xxBT73uc+t+fulpSXMz8/DTNXleu8RQoAxBp/85CfxhCc84W6d50033YQbbrgBIQSceeaZ3zWIvSfH/QJ0PuQFv411ezVlf8cuBkjNjIWufaS6devRdkwMqpWnAEWOYKh2q5kxsGMKYr0BBZ5GQTeBjByaQIFu42EGDfS4QigyaisgctPMRglWyC18ZhAyDq65DkyvslyzaYGSQSSQpJt5nuo7Rfol0lox0xCgZjX0oEoASMAqS4j9TBndH1XVwq3roV4osHRqjnoOqOYDwpYJ/HKO7i4TZYjVQoAZK+TLJNma3dkiW2lI2th4mHGTAh8wONQa7XyJZsbCG4XOvgnafgY7EsmmRj1rYcceumFwpBVcaeA6BvWMxniDgssp2MuGVO/TdoHh8Q4hDzAjus7eSSt46LE7YVSAVQ7DtsDj52/ApYtn4KqDx6K5fAGj4xyxL/0G+Y4SZgy4DtB2A9yGBghA78YcxRGgv7dFcbiCnjQEjPNUb4ZJvcZkJYJSfsZoWSKpVARAEfS2ntjHuiVpa25JGhvrwggMQqnIDsl49GVi6s2whS8MoJg9UoiAzw4b+MKSTFUrZIdH8J2MmEiuS5T6Tp8R621WqshM+kyj7WXIVmqqtwQATcyKj/0WFXTlEIymHou1hysYJBQ6gj9Te5ixi+BKuwBXikFLoCb3ga/RB6iW2Cyap9S/ER5oZi2yQYumR3PS5xqq8Wj6ZKLS9DVUC7QdhWKZAGHT1TBVYJktYCb09epWjWwMPOlVX8Tvbb4S2/7pZ/HGJ/0d3vS65wMATv/v12DPJSei7edwpcHomAwrJyoEA9gHLePqR/wtLrjkZ6FcwGTBoC0VsmHAZEEhXwlpnXGAmXjYKqwJ+C581gsRlMJkU4HRJpFFeiydqu+SWQEAfcxNd/nz7/X4sUf9JvTKhCReACUlWKoqtaWR4bTMbGuWTB9ajkmxMNOF9N10cx0C+B0DMyHZeQSVWsGOqM+kbgJ8ppitc3CFRttdq+YIGggKqNYptB0CnKYC6ln6f3xMQHe3gqmBtgMMzqlhcoeHnrgTf3fyZ75v9+kBf/sqdPcoYtYCMXXFEQLBg61APe9x63P+9G6/30VPez5u/ak5vP5Z78cLZw7drdc86I2XwI6BcsnHce0KBTtOCaCgFbLVFpP1FtmI1v22o9GWCvWMQj0HtD0gmABXBhRHNOyAxuro2IB2xgOlgxpYqPUVwuECqlUIGytcesFb8C+DM7ElW8Qze8M15/Yjf3QJBbuamEzXBcZnTBCGGRaOX8LXzv37+LeP/JVXwRua85QgIMZGt7Kf0tdiiGQqDzukZFTbtxgcIxJtoJqlfWG8KUnZfAbc8PIfco/Qe8Gxs13FCXYGO9tVfGD1R+4kj5Tj2maEs7IufmHPw/DmLV/FKe9/JQB82/H85Oufhtu/ejzWXx1w5CwVa2kffeWzsf/gLG590p/jQX9wCeZvbdG77iCwtAK3tAQA5ADbKQl0Bg+lNCYPORk7foLWnAf81QSLZ3Txd2/4Q5xivzPzfvafXAJvgO4BYPTkVfQ+NoMNV6xADyb4yKUf/Lavu+ipP43uW2jeffCUT33Hz7k/Ha/a/Sh84sqz8Prz/xkvm6W6vYe9/hI89r99GR/+14dHg67v93GfB53P+M3vDnR++PV3+5of8YhH4LzzzsNb3/rW+LMHPvCBuPjii+9kJOS9x3XXXbfmZ29961vxmc98Bh/4wAdw8skno9fr3a3zrOsaO3bswCmnnPJdyXS/m+N+Ia81VcDo2Bx27OE6VP8RFG1aunJRtoeWmBE78vCFhjcUPHgD1DMGwRC4AQDdUvCjHWLgVM9SMBsU0HYVBQBbC2TDLuu+6Xf5SgufUX0NQD9v+hSYy7kVyy0wV8Ku1hS8c6uBWBMoBkNFscZUxM914boZbdxKoZq3GG80cDllfmdvoyAyP0zyYtfLUc9liW0CgYiVk3MsnUk1Um62hVm10Ls66B4GmhlA17S5Kw+0vQDXAbBPYe8jLWa3Gyw+ELAjBYQSvT10btU6oFhM8o9ihdiqej6HqYmtqtZZND2FpqtQr6MALhiSWQVFgYPPgfFpFQV5B3P4XKHtBvRPX4Re7eCETUdwZETmL01rcNWhLci0x6jOkFmHU3oHUXuLwjqsnjXG1k1LqFuL2hkseUqpm9zDLedQxkOZgNHZE1R3lJistyiWLHwOFEtz6BxsUe7PiOkFUlKh12EDFGpV086VyPYux2SDqlsClsxuwmpy58wMkOVrA+ycahL1xJHDp9EwgwrNuhJh2qDDA65HjKauHPS4hS8NfGaIjexlNJ45IdDOldATB6UCXGFhJzXauZwSMS4g6BANbVxhqOaycrE2EqDEynQdpfIBIdNwpeEEjWX5E6BHDq408AawI3ru8AFNj5hQ7egZKwMEr2INp3YBbaFheXw2pSaQysFnPWsRFLGLdkIBqG5IPtgWCqFDgezq8RbKAeWip6SJBdpSkTw3VyiWA6p1Cu///CPxe8+5EtsvJlnrs98yFWT9y2V4wkteDldquJyZlQKod87g6fMXYs+Pari+x/xVCivnj9H/Ugf1BSsY3Ugby+ytwGATcM2r34E/XjphzTr18Q/9FQDgR3/hlWhZgDHarFGv+8Hn/Xym4Tb2kB2k71UICNYilDwWpcdmIIbbdzJoaRVSkFNy6HbgWEKrQpJHB6PQzGbE+jZ0bboJqNdlZOoUACjA5UA2oK+VDzATAvBNn+r97DggGKCaJ7AEDbj1DaCAk447iNsWNmLj5mWsXr8BatnisY+84dtKO7/b4+bnvx3bPv5yzH0zR7VAsk6XA+c85Sa8b9uncep7X4XfPXwaXrf+7iUHPvYvf3uPzyFoYLIesGOpgyQJ/HSyTzcBbY8Na2qPpkdJGUqQcoKpAnSjosTdTmjv6+xTCAcNqnlOCg06aHse+aJCW5V44mU/h3WzIwzHBZ756L+OrU62dQ7CW0rgVRsd5q8y0A6o95Ro1jdYPNLD+wdzeE5/OZ6nz0gxQBJ+cPKM9mHdePhMxySaCsBkfYZiqcVkPtVB1x2FziFiSvMVAqttB7B3rSDGHx45Bb+0cOs9vu/31eMEOxP//3aAEwDOymgPffOWrwL49mBTjk+d+S/AmcB/veMReOtxX44//+KDEsj7w1e/E6/85MtwYrsB3R2WPPC4Zjqsm8XopDl0bz6MUGbYeWGG7c8koHP2rkvwgVfdPcAJAFf93P+DhsY7l4/FK+b24oMPmsHr//pnMH/jv//6vReswxWn/N3d+oz72/H2rZcDWy9f87Mf//nP4w0br8Ubnv9FAPcMWP1HOX4Q7rWvec1r8MIXvhAPfehD8ahHPQrveMc7sHPnTrzqVa8CALzuda/DHXfcgb/8y7+E1hpnn332mtdv2rQJZVne6eff7hiNRnj1q1+Nv/gLUobcdNNN2LZtG37+538eW7ZsuZOp0ffzuF+Azrqv0WkooAWAtlDIhp5YosLEzbktTZTBwQf4UqPpamQjD+0CmkLBtpxhn9WAoq/tJETmLygCSbKhu5xlTT5ABUV1YEWOtlSo5tLmTp8PYtwcMN5gkA8CcGyO4kiLrGOJBa1EOloAmqR41UKOeoYAbzWn0ZZAPUfgLChw0AbYIaA815htnIXPFGe6gfHmAOUtfBaga4tgAuwKXWN5KIOuEgDPF+k8V0/20I2CrhWyVfocOwLV3SgCltkK4HitsmPANAQWsjqgLRU6Bx0m6y2UNwwuFVaPV5hscUDuMTrNA6sZ7MYxFmZHmCvHaJzBxs4Q79tGZhpfmADnT62HHxzO4N9WSAbwzzecg/GggDYeF55xHZ678BX62w3X49yvPxe+MTiwPEPAq9XIOg2aIyVccOhuHmAyyvGIk2/DFXuPw2iDQbCWAMoiMF6v0HYyjDbPIl/x6O0ekQQTBBSVC1S7qcgMBpiDWa2hq4akg6WlVhBDMtZxHQsz4pq4xlFNRdXQzQbg+ll0bXU9aj1jRg2q9eni86UazWxOjEA3sZFgxsAzo+mthnEBnoGAagM53TpmQXoWPtcwEzInkWBPV46MW1qRTFPdoagCzMTDVCRxhFVoCw3lbQx0lad50cxYqsvUigBkrgAEGscNsbSmCmh6GnZCTEfLAFu5AK8U7JjeMzA7ghBQzWrYccB4PdWvtV0GlRnViLkCGBynKYlhCNQ0XQWfA/lyoL+r1b9rsHLonByzO8kttDysMNH0+dfuOA6/+Yy/x//+ysX4+usJsJ564JW45VF/AzwKOP/qZ2HyIIsOgJ/e8QT83cmfwTlffj6ufsRaoFG95Ajsh8hWvukB7boWp3/hRbjx/B9c4UuwGk3fIpgZZCsVUDVwswXfZ0SWUlyIEcD1qIqMpEquxwzExDs2+3GlRj1nYEfEeDeFyGcBO/IkkVQ6gtFq3kC1tC7U6zV8xsmnglj15Qe20J0WJx5zGLft2YDHn3EjPnvjaWiDxvVPeTsKleGfT+9i4jNc0LkDwD2rT7w7x/YL340H3nYJOvsJdE42IK5Ntzzv7dj2Tz+L1118dBhpgBKfgcvffa7iWG/7PHeQwH2x4qNiQLmAtkdzNxsG2LHi+lhiGk0dEDoERl0J9Hfzvc8pwao81W+2ky4Guotqc4sHfvFnUO2hLPrsSUuYbOYE40GDbBjQ9EkVo+sMbc/i9296CnDaJ/DAYi9crmAnAU1XwU4oMSXJKIQQk7s+V3FM5KsOKgTYkcdgK4HibBDgSsVya5pDxTJiIudbj19auBWnXvoS3PL49+D8q5+FL5zzoaP0pP5jHNOA81uPJ3dabL/4HdhWvhzzX96Ihev6qNdlGG8wOPRwhzc+8b343T/8GZSHPd77k28BQD4J17z6bQDuHjsDAJr3zFfM7QUAPLu3ilNe+ib8l2te9O++buGpd9ztz/iPcLxh47UAsEZa/5/HD/547nOfi8OHD+M3fuM3sHfvXpx99tn46Ec/ihNPPBEAuc1+p56d9+R43etehyuvvBKf/exnceGFF8afP+lJT8Kv/dqvHVXQeb+Q15722t9Bb1gCnjbdoCmjairaYOUwVUA+8BgvGLQdkm8KeGt7AapVsKxwtUMCWKYGsiHLP8V8tAGqOZI3ac48N12NfOjRFlSHpNu0CQYNCsBZnkU/xBpXrGwUYiDuM3p901MEantAtUCyMlcwW8QaftHymwosBab3k0Dcjgjk2jEw3EoMpm7pdXZM30MlQ4emn87JFfQ32YB+D8XmD4buWbbK7Jf4BmUklcxGAXWfQPLhHx9DqYCws4tsoDA5uQa8wskn7sft+9djft0QLzr5y/jGyon4wq2nYHZ2jIdv3omt5SJOL/fi+OwwNpoJdrUzuHpyPC7sXYd/HpwDw5XahW5wqJnBycUBrLcDfGLpHLzp2K/j02ODJ3YcnnDtM/CYjdux0nawfbAeozbDkVEXG3pDnL/xViy3HZzbuw1nFPvwydWz0TcT3Dg6Bp/deSqqKiM2tNKY2aFRLIYY5ClPQdtws8H8TRMsnVqic9jBjjzyw9RAPmQG9foS2WoDPWngywyqZSYx0zCjFp4ljcEq2NUa8EC1oUS+TBLXWCsJMrXRtedEA0nEo2TcKpixp9YlhsHBhKS4mo12wM9VBQKl0UBoikmP/Sx53tiRRz1HTrOASOPIEbaa1SgXHcYbqC606VF9ZfeAJ/lkToFjNqRx3XYV2o6CckA9A2IpHM3RpkNyOYCY9nqWDEO0A5oOjT1T07gdnBhQHKb3MRWNRTum8dp26H9X0GvCiSO4yuANj/owXjhzCA98+yW47lXfWYL3kVGJ3/nVF8PlCtU6hS0/cRs0Av75tI9j2ydfhj89/y/xs59+KTq7LOqFgIc9/CY8eG7nd21kcurfvQqmUlCnDvArP/IJNMHgFXN7j5q89glP/j3Us8QY2yEF9k3fYrCF2OJi2cM0d2bTlAuwwwb2wAqaY+fQdi3qWa639UDb0XAFAYK2RGyjozwlFrxRyMbEao43Esj0FoDnZz3DSbljAjr7FMabA3zpoWuFzomrmEwylGWDB27cjx9duBlXD7biYTM7cE65Cy7of7d1x/d6nPKBVyL0W7zkoV/E/95wPc6/+llYnRQYXzOPn3vWR+6ydmvRjzCvu3fxbnS8dWkrjs+P3Mno51m3/Bj2D2cwbixWb5lHZ79CeQjoHqQFvukb5CsusvGS7AMQZei6DVANmVsNNxlk48DutmySlSvkg4CmR4oB7Xjt7mnUM7TPKU/Px9TEtvqMa8gAjDcF9HeS9LheF9DZTwxk06W577pAdfYY3in4iUHv1gymojnbOeJR9zWCBroHHEzDtdMZrS2Oz7/t0D4LRWMLoDWq7msUyx7jDRpNj9YOn9PY6Tx9P379AR/Gkzt3bg647Z9+NiocAKrF+9UNN9yDUfCfx/fj+NZE8n8e99/jvi6vfcTTvjt57Zf/5e7La3/Qx4knnoj3ve99eOQjH4mZmRlceeWV2LZtG2655Race+65d3Ld/X4e9w/Q+Uu/gzyU0Q3PjgEEqgGUWhyAQFU2or8Rdk4AlM+4ZmidR7aqYcYE7LIVyhJ7q+BK3nDZ9EBqk+yYwEjTU/BGQQVi+QRgBk3nYib8WZbAngpAsRxIGjuhnwsQreaIcagW6HXVAjOZLckGXUnnCzAI9XxujB+yVWZ6RgGmDmgLCp5FDqUboLcnYLI+1UzB0+f0d9F9CxoolghompoCAAR6z6ajqC6SgSlAgUnTBTK+/y4Hxo8doNupMRwVaPd1EGZaqIFFsWWITXOr2L88g42zA+w7MosscxgPCmzcsIKT5haxa3UdcttiXBN7O2kybJpZxYHVGWSWkG7VWHTzGkYHzBQTjNsMmXY4sDqDMm/gvMbSEcqgKh2QlS28U+iUDXpFjVGdoXUaIShsWbeMvcuzqCY5XGWgjIevDfTQoDyoka0C+Qqb3Uh7D0X3ZrKOWhUoR/d23S0VMYmVw3hjDm+B2e0jtN0Mg+NzdA6QI1/ToxrWbBRQHqyINZ3PMDzGYP7mioJGrtN1XcNMO7F+3tJnepPqCAHATByq+Qz5ckuy25kMZuQis+kKjZBpqnlkxkM1JDkXUxwBG8EqqIYYLDHUCZoSOsHS9TY9hfEGRUDxwUfgP7uA4Qke8AozOxTKRXqv0SYKRptZoN7UoHdLhrYPdPYDq9sCOntYbmyAan1AsagAD4yP9ciXNKpNDltPOYAfWdiDy/acjNw4HDw4h87MBOM9xHCVhzQlkzY10KXD0868Bk3Q2JgP8CsbvoGPjjb8u+Yo00cTWpz+D/8NP/eET+Jd770Q111yZ7D6gL95FXStcONL37bmdZm6+yKSZ93yY3jxsZfhv3/ohSgPKVTrA9oNLbZf9K6jBjovePobqf5WA56ZsWrOYLiZwHx/39r6wXzVU3ehOiBbaZAdHKLZ1Mfg+CICzKZHEubRJhpX+UoqV5i2nKc2NIrlt1yjt46Ag8tprZQkl/JAMxNghwr1BgfVaxC8wuz8CK3T6BQNtvRXYLWHVgEXbbga5xS78c6DFwAA7hjN4YzZ/fg/x3zje7pff7x0At7yLz8ON+vQu9UiWGCyiQAxWgV0HX7/fGoZ8rTeAXRU8R3ekY6HfuOnMKktnrHtGvzOpqsAAGf82wvRKWus3jqPoAN8x2P2eotiKaBcJFWON4odmnmecm20N4oAnCIg6g337ZyRunB6htWsRjYMMenjMkpwynpG0nZ6v2DoOdVziElOgPYIO+JnG2hez+7gcysQ53ozE+DzgHxR0553BCiPBIw2K8zs8qQaakNcx1xOjKxuSAkBINZqA0jJrHHAZJ58F0RN5Eo6r3U/uh+DSYErH05Sym2fehk6/QrjlRLPe8hX8aqFy/CEL7wa+Y0dvPinPvU9u97+53HfPx7ye5fgE//9jdhkvv9qif/Ix30ddD7yqd8d6PzSR+69oLPb7eKaa67Btm3b1oDOK6+8Eo997GOxvLz8nd/kuzzuF/JaMwbaBdoASd6agFhxhIIYgECUZ3YwX+KaIk8Bj2aA2NmroRwFQvkybWJhQrWYtmL2sQC6B4SVpMyud8ROirEDSYb4BAMBRBXo62yI6NbXlorPizZdpYCGAaeAXOUoMNctgbzggfIIAdCaXa1zBpmsrkXbIzkhAsmPRWoYDOBq+vzxegLSoy0emGlhywZ+Xwem0tFcyHUUyoPEYhbLBK4m8+IiSCVf4A1/sBVw3YDikEKxDNTrgLYxcLlGM8yALCC0Gnp9hcmhDnYOc5jMY8+BdfCNQfAa2no03uCrt5wEOIXgAdtrETwQDhdYnekCQwvYlCsZZB5KAftGBjABIfNQrcaIpcEqIxbbzbWoh1Q8utp2sGoCsiWDtu+hGoVb8z5gA4IJ0GMKcIxTCIb6wQWlErPAjHHTpfpfYQSUJuCu2xydIx5m4uAzhck6BXNchySHPYXJGRnsEFSnVgCTBQVTZ8hWWmaYgKVtBcolj85BGpyq8XA9A5ermHBoWF7aFAACnUdQZJalWx8l58Eo+FxRrbJS8edNT8PbADsB2oKBqAIU/4GZUJ00AtD0NfIVD58hSfQQOEikRMfyzjmEM1ooE6CLFqNJB8opjLaQcZOuFNpNDeY3raKzlepj9+7YgO7mAYbrpNCRnmPtDeAVVKtQHV9DL2U4sDSDL1Yn49SFw9i5vA4XnnUNMuXxqXA6zty8H9+48UTYRYsNW5YxqTN8+IoHQVUaZr7GJ9adiXM37sKO6tDdqvHKlMX2Z1Od00+84g8A3NlC/n89/R/wjh3n4wnXPgO/f+oH8c6DF+DVmz6NUzMXgcfHxzku7NAzFBltw3rJO9wYrdd47Zd+CqETMNkA9HcrjOsMb13aip875jue5nd1eAM085bZci4/KBSyEY/HeZJX5qsB1awCArn/kotsAIzC6JgcTY+kkMIyt10Vk2RAApu64QRbRiA2GE6wBaDu0thpOzSv7BDEUhv6Wb5Ef28GGnqxhCsDlmtqZzUEcLhdBzNX49En7cCf3/5oAMChVUo0eaex49B6XLW4BV3b4NSZg5izY3xk11k4b9MunNg5jG35QZS6wc3VZiyYIXq6wqovcdP4GOwaz+P4ziIu3fMAuM017N4iljL4JYWmr+E21cDA4i07yDHwr8sRurbB3tEMju2uYufqOjx0wy40QaPyFi/b+AU8ptT4UtXiyP5Z6BWLj9kz8TubrsKnxhb1/i78RoWwvoKfUG/ZZpZMUwDa23QbgAlinX7TJ3dq5bm8Ycwuv0bRM6mpJ648j3xAwJTk+IDvELvtSqq395aAnK3o+QfNUtZ+2mOUo7/RNf2sARl6mQkBWd3QvlceUphsoO+rBWJEs6FHvmKwdIrGwo0uAk1hOsmhmFQYhqXD+YBOfryeVBpmEpCv0lrkLcl1ATrXvfvmoYzHa/edi8v3n4xye4HJAu1BH/77x+Dv1z8ac7fQWleqxIi+dWkrPnfkdDxp/XV4xdxenHrpSwCkvp9NaPGU634CT9tyNW4fb8C2zkF8dP9ZOH/Drbho9iqcN91W6z+P+8zx1arBpq8O8exfeA32/qi+2661/5GO52x/0ho34Ls6/mxlczQput8cYsh5T19zLz4e9rCH4SMf+Qhe/epXA0BszfLOd74Tj3rUo47qZ98vmM6HvOC3oTqdKM0Khj0K+Mq8lL61iUlUPslRmx6xdT5PdZuaGStwLaepQPWYLYFYO0KSzLI0SHl6v6D5M/nzRW4mmzUABgf0tZ2E+LdNX0XGUv7eW/58Nl0Q2/m2pA3fcD2rqRLAlho83QCjY1gqXIGZSgLabY9e18566IlGO+dgVzTyJYXRVkfnZANUpZEvacxuD6jnEkPRlncumHYlSxv5qBZIdqecgpmwtHJjCxQe+kiGYAN0o6AaBd/x8DZAeQXlFOxAoe0H6IkCdEC2qlDPUm2SK+mDzZgY5Wo+oDjCQKniALbie8imJfky1WPZEaL8WYC4rpECHZOYbzE50k26/3SDub9cToFyPcvjS4yoakou2AmBstHmdP9l3KhAf+8tfW0mQOewRz1DgLfp0s+FKaxnFPp3OEwWyOTGWwrkmz4FdPUMJR/KRXJHtlWS13kDkqy5JNet+9ySAkBvn2PzHGHBCSTVcxam8hhttjSOHcnNyV2SWZKc2AafEWve9BETN+NjHNB3yHfmqOc9dKvguw5oCUwGE+hneYBqpmS+AXB9B1VroJiiyRoFdByUDgiDDHZ+gnaUwZQO2jDTazwmgwLwQPAKOncIQSE0mt6vdKD+u6DERstsSqsAwwM6owmqyxYzM2OsrnbgWw21nAHzNTauX0WZNRhUBS7aeh1O6+zDm256InLjcGixT+0rVzOgcDCHchSHaF5rdmGVNUk5njMZMNkcYAdcE9fQ/Lz2d38RR+N49HP/T2xBUS66mIhouxr1jIoma9mQkmqmpkRIby8lCorDEwyP78GVVDdOtZqICpOgAFshliQgpLVR5lJMojFT1XBJl+e56nKg7bMypQi0JvA6HRN6PCeTCRmdpy9YBh/ouQYNBEtjLGQBIQuAA8xE8xwPyFZ1XJOhANfxKA5r1HMBulbwRYAdqVh64S1Qradz8VlAyGXDUQg2IMw20EtkqBRmG2BioGqN0HMwnRZlp8Zo5wx0o9B7wBJ+5pSv4pcWbsVfrW7Aozu34cLL/huM8agPdGCHGutuJHmyrQKyAbkjSx10PaNQLJNBmLcE1GSPk/vg2TVWBbpf9YxGPkgso89V3O8mC5rGa0tqGAGQwBTT2aW/pVp+VvQUKQEq65cd856Yp7W0t88DAZis17Aj2j87R1z8fF0HuA4pLnQdqJaV1SVtl2qClU/JL5eryLw2PfqsZgZoO/TsOvvZIM+RbD8qObTCZANwwkW34frbtqDcnkPXBPLbrRMUtxDD4TOgPqECvII9kMGVgU3b0rqlFiqcsuUgPnHGR+5yzr3+wNmxNcZ/Hj+44+y3XIL+HeS4e9OL1qpVHvTGS/Ccl16Kf3vZQ6lndpFh51Nn8Ds/85d3cmr+9w5xDH7tvnO/Z1XFvfXY9qGfBfot7J4CN734rktUTvuLS+70u/s60/moi37ju2I6L//Y/77XXvMXv/hFXHjhhXjBC16A97znPXjlK1+Ja6+9Fpdffjk+97nP4bzzzjtqn32/YDrbjoLqUhxiWsCuUjBsJgSSpFZMwAVAwEi3FDgHQ60VdIPopGcqCbbAMjMGcoZYTleoCFpj1t4htmLxJmWVsxEzCYE+JygAHrB1iHLYeB0+GQRpR8CuWE4yJwmuXCHGQQz8FAX52dQ62XZpoy0WqUbKDuheiEOsbhjwNBRsmFUNMyajiuKQQb3OE+BcJoDT9Al0SCBjJ3Qe04FNZHUb2rizVQ2fB5gRXVuxBChvoTgBIMYY+RLQNJrdggmABQ3ki4oy6Qx4CLyycy7oPpoGMPy9BPXFUkA1p1AM6D5oDvQ7B1JSoFii8eBKuteqovtlJ4Dn5LcdcxDNwbJm81pJMpgJ1S5N1wL7nGXbloLBpk+f1XaAfIDYpkfXKYATo6qmq6Pk1U4ooBptVPFcVo83XKNL47bpk2RV1wlA+wyYrDPo7XfQWsPlGnbokklLRskTMchCoOfQlhS8+VnAsKTZFVSHSYGd1HiRQ3Td15ElE2bK1ECxO6CaJwBa7jdQ+wxcQdLXoAF92FKAyHWYImWXhu8yBxHYrXPRwJUUPAKAOmwp2HMAlnrQHQ8cyeAUOXy2FtAZ/d5MFILNCLR4wFQKQRvkSwnoyPOQZzw5xkMfsfBlQL4zQ4UOOqMka8eREou7yghk3nvDj8L1aCKWey06E0RlgaksdB3QsqQdWmojhYKm6808YCe83kxIyl4c+bZL3vd8NF1FPU4BjDZZlEccmp5mt99AwMmAW1xQcI5ALtydAxXUqIZpuiR/DAnIqECyWlkvpY4YoPtnJzSONKs07JjWCoCTPCrNC93SfAoGKA+TfNuOyNSb+juy1LJABEXxvnLm1hWp7tcbWv+qdUkpYPhZ+VzFhKW4oLaV5np5Nq05wrWKJdUwZiu0FikPhCq1SWo75Lpb3F5QcrDvgZWMk2dAcBbqQIZKl/DrW6gVi+WDfXwgewg+sPMhyG2Lv80ejk3zq9i7Zx7oePhGo+2oqDIYLxj09qeb23QVvDGpFrcJ8IWOihpniQUlya2CAsvqPY21KbKPzL4mIFd1VuyYydoSC4ANjvwUqMzoGes2QNWU3BxtoPFkOEFXzSpkXEdqJwHd/S6ai5mxJ0BZhyj1D0ZFp3nNCT07ppIUMb3SnPTNBirOu7ZL5+2tonnYABiK4RiplHRL51QsKtxw5QmYu4Ukx+P1imTDt5fEukMSigXabkB5QKHlZJ1upkp1Bh3ccngrfmX+Qfi9zVfeac699/rz/hN0/gCPx117MXbu2oBZNji86UV3Zi+PuXyIj+++AH1Dk94MJjjusxl+tX4RnvlqAk8P/9VX4Su/9XY8d/sTo4GYHFfXY1z8mZ+DWrX4yrP/CB/70CPwfy4h0Ck9N+/OMf23n50oPK6893FB+cYxup+jTfquatUf+PZL0FkB/nFI13FPQPu9+uAY6R6/5l58PPrRj8Zll12GP/zDP8Qpp5yCT37ykzj33HNx+eWX45xzzjmqn32/AJ3gjHzQZK7iDdWCBUM1eGKKEoBYe2kieFBsnBFQz5DpCTE3JCdVgVgIw4Y7wZNc1VYBxkumNzWQV4ECE1OlUSfZWAGTUiMqmVrd0GYu5ie6ZumqExZNoTzCNaPMoEogpXjTNwxghWlr+gTW3Dy1HDATDtiZKbUDoJmjAEs265adDJVnyZTVyJc4WBxS5tqOE7gUoG05YJHAWjcchI7YcIiDs2CZNZ4kwGu5ZYJck6651ainaxcm0Tl+L65rFUBvar6mHCiX6Nkprq3MBhRk5MuJfZNg0+fpfwqwGIgxgy1mGcFSEE0BzBSzKwy4I0ZHXB/l3lkGht5yMMs1bsKguhwUgPI/qRPOhuTM6I2K9W4ZB8GmSsYa3lJvyGpOxbYTYDDdllRz2ZZkFELsJTFZtuaESUnjNF+h2kyXcT2yYza/pPHiLeDY8EPxMwgc8LkcyIaKjbQ4IC7TOCr4OgPfz8geK2aKTLrPLgeKw3xv+TrsMLHqbkKAoenR700t8l8AizomhFTLQaBSsa7ZFSTRlI3AjoFihUFppaaSKAH1rEJ/h7BfieGRADVfpnEuoIieFZAtG3QO8hpBalRaPzzY2TqZiBGbJrV4iONDxpHLpbXSVEbq+3xkw4DxBgr8qR8mgcdi1ZOEPATkSwF1X9YEZrULhWA1gefKQ3kN5QlItCWb0QzJpVj5BK5VmFKcMCtmahoHbTepBBSrSqQUwgSaT20JlDw+VItYvw9QUq1az2vHlGJBnrXLeW5XADwlnnyW2GQ7AtQgmahJzb2AT12ltUuSlvkiAUhJAMr6BtCYcgWPNw+YoeYEW4Aeq6ic8ZlC5/aMXhcsDkwWoGYbKBPgxhYq84AOMIcyqJYM5bIBAzsPDI/VMVFTHiaQ6DNeg7gXtcs1s440MHWLKFcl13UyybONBxpgso5YRJLZ02fqRmpup5KmSGyvKxGVNqZeqzAqVgKyMa0PbSmfD1pPjUI7p7imk1yyfaahG8cJHXLiNpVH29ERdDo22lOOVDTRyXbo0fQ077H0XLJVxNZfuqbvaYxQUsyOqa513fWsdvBAuZjWejuWe0XrrB0r7i2KuDeaMSU+zITUJP+y4yxcOHcVHleG2AMTANzY4j0rm/CS2QPf8/z9945/HnXvZEx1bzj+cdjDW257Ij5z1oeP+med9heXYO4WYHYGuOKXv71xnB43yFdycqV3AcFq2NUKm75h4jnPba9w+p9fgmwIvPziCc7q78Fr5nfgq1WDF3/9Fdj02QzVOoWn/s/XopgD/mjxZLz7hkdjvK+H7c96x7f97Onjl2/7CfzMsV9Cplr8jw+8KLKFv3v4NPy3+Ssxq7+NNfMP8ripj3wloL+nxn4XMP8tnm3lYerj/ouXUt/rZz7tnXfxJve94wfRMuWHcZxzzjmxZcoP8rhfgE47CdAGbLAABL0WgOmapZgst+wc9qhmdWwxka/S3wt41G2IG5wwjlAsJ8wVtVhp6e+kJlO+95lKtTM88KQ2RVi1tqNi0EQyJ7UmqNEO0GNusQLazOq+SuwBEEEfBcUEmKflnVFeW1P5Y7GUmJzgmXVbpnPIlwWYIoJOnwG9PSzhZQOkbJAcbQEBuioFf0ssuTL0GZIhlyCxzQBwbZCco3KIsuS6z6DVA16CRg5ATZOYVWAto2vqEEHItKRZzosMkehZt90km9UNB5YhZegxUSl4nJDjqtxrSRbIZ3pLfys9UnWDWI/pLWL9pwoUuAqYDZK1p5iSGAJ+/3omsTASGAeRWRvFyQZKjjQ9GrsS0OsG8D0Vx5srgFFp0D3kKFDjfpg0RunzaF7QtQhoFuaczkFFFkMxAFCefp8xC9WUzDg4oNhPc0RAagRtLGcTQEXANER2nxIwac5lLMWUe2snNLZMTfdN8TPxhkA5zZvAzrgECKhFCte55SqOXcXsjXYhfc/3O1+h+lZy0kRMTlHtI83DbMQgnc/NTCjhRdJlOlep7XY5zUuqhVVRVipjzBnFPReZbQOAOnBC4+jtXNJ2Q6SuANB0iO1GoDXVZ2RWUyx5NF1yCw1GoS0N8tqRAZHiGjx2pQU4KTehZIeUCOSDwOBaRadVaZ+Rr9JYlnnSdGm+2pCSH9kIEVBlYwY5DRB4vSoOT7GtvJ4ASOUUQ6T2Uo7X2Cknb4DWL5lzUnIhSYxpszZvaV2q+5SMK5aSsytAYETmSdshYKsbRMUIQGZu8f1BQNVUBmFg4LoedkL11z4LkQ0PXP+dryag3pMOEIqltxNq9dMW9FxMHWINuGJW0GeUVBCjJuVJtWDHHvkqyfttRe8hSUojfZtbgLtV0JrTJrULJUD52VYBISAmcLNxiNLf6K0AlmAXKjLSlg3PPLtkW+9J9ov0Gu0CzNgDhg2QBIQGROWFckC+qtacrwbXHjuKAWQvb0uN7kEfWeGmN5X4mCrNEQbf1OSLUC56VHOaun7w/TU1sLp7Bn+x5Uex7phP4Qt7t+Gnqxn83cmfgV7M8KY/fTZe8t/vDIKeu/2JeNrGK/HCmUN3ew7LcXU9xll5AQ2NgZ/gY4uPwJ/s3IBPnPERPPaaZ+Jtp/9t7Mk5fbx58ST838ufhB0//q57/JnfzfF3+x+J23ZvBM46+p81uwPo72lQz5nv+LfFoTGgyfFdhYCgNbLVBo975SuwfKLFln0HEXSJLV8Y4yvNj+DSUx+I1zz9nXjB+36e946AmZ0O+XILIMcff/GJ6N+UYX4V+KsnbcDEZ1jHTOp0z1o5VvwYV+/Yil++6nnobV2Nfc8B4N2fegKe9Kxr8bC750v2fTke+o2fwtfO/XsAwM3NAA/IKLOVrdC+YIYtTrR3hg6kWvLobb+f1TX7QP/u6WvuxYcxBnv37sWmTZvW/Pzw4cPYtGkTnHPf5pXf+3G/AJ26pQBNOUDxxijOmroJaNn1Tuo4KAPrKfhf9dSHkAM/ynRzNppBqfScCwxsVcs1Hn2NbBwoEzyrUC75aIgApCCE2oyw22idZEo6UHAstaLTvIZuAjKnklyXW8D4TNgrDqJHdN6dwwEuSxmWtlAIYiG/xPdkitExVWLglGenSZ+y9spzFpdBQ8sAMRuC3QI5GGqBfJwCbAHCyrPJRJuCE90CUgtJtvgqykF9xoyeSLcskK3K81LxHgkolqA+KGZfmEGmcwgwYm7RpeDWM6jMBhw81hzQchCqOPutPGAHIdaCSqJCOTJJyVelFojOwWtiecQ4QxwgNYNPkV6L4ySQmOJsvDZZ4HN6f6mvsmOWv/LIINlxgKoYEE2EQSNGw3PrHN1QUKpYpljNGthJSpAElRjiah3XsUktM9csC/CVGtR8JUSg13YURcpKGKEQW8jI/RYZMo0LfoaBpObeUq89SqCECBBic3c15WDZEmuPhsaDuHTG2m2Vnq/0+5QaZwKAIV6zAHSZn9KgXhIJ+YqP7WC0YwdPTvKoQNddLnlOQtA9B+gedg5R24e2UIBS/Nme54swtnT+2gU4k5IXUICtPLWWMAp55dEWGqae0qZ+n49ihdYql5OBTNPTLOmmdarpUVIuG4YILFUAzJhlm2WGyTpxRCUgSRJPVnAwqC2W01rorYqSbtMEWr+qkBhvnjdmIoZEBJqkBjkbJrDadglQGE4GoJPWCAH/AFgWzCxok8xzLDOz2ZDWiqanKDkpqoup5JNd4v6TK+zmy8mZfJXGRdNJrakARNY9aGJh81WaT9Xc2kSMJIrsBNCHuBbRAn6oyaisSWtq2+NERc371yI7pDeS2STZcN3XyFc9J0p5rngCV96kJJnytL6MNtKNIsaOZPAyzyXJE4H5twB6qX9XnrexhvYpqbFtmCWXe2FqSuaKn4H8nJjDgPEGg2LJ34lZcFxrKj9Tjscgb5imDshGnlrGFCq6rXvLyV6RFCtJZKlY/kCsM41/Oybn7nyVvo9tmsD3ogpx3RRTOTsOlNDg9zVNQLtX43PXn4Yv7z4R4doZXBEWgJ/9TEx2SA/f6eMblz8AX8tPxS2P+7fYO3H6eMFtj8PD527Dm7/wZAQb8OgH3oK/Oemz+LWDZ+Efd/wIHrf1Ftywsgk7j8yj2d2D2lQBZwB7Dq7DxQf+K84/9Vb8+QlfiO/37FufjKsuPwXztymcufsSXP+z37mN1Hd77HMDXDY5Ft+4/AHoLiqcsvJK3PqcP/2u3us3Dp2JY7Ol2J9Tjpfvegxec8yncMmNz8fnz/5HfO3X34YnP+8lKI5UeMwvvhJHztR3usZt//izOKNeRDvThZ3UQJkDVQPlPdTYwUws5m5rgabFSR8dw6xU2HC1RX+PxcO2PgezO2jtzJcd8uUGelij7Bls/rxFf3cFUzn8xtefBreYQ7wC/veGMa5/9F+vOY+/XT0Vc1/PsXBDjTvOn0PmgV/cex6OKZax7kaFX7jhefjigz4IADjl0y/FrU/88zvdl/OvfhY2dQf44CmfwlmXvwCnbTwIrQI+eMqn7vI+irmdh489T+V4/slfxf/Y/xD8weZvRsAJAL19ATM3raBZ6OCcS1+JVz/ks/iF+dsAAONQUWnSqMX4rKO3b/3n8f05vp2VT1VVyI+yGdr9A3TWAVoHtB2yZPelBHggI5E63WCp3Ww75KDnMwUzoVoSqaXKRgQ6ZMOSAEw2cVfQa0X+6HIKpkk+FKACgSCZynbMTe25NjSysBzg2CkljMg0SXYYIjhSLkRQ0paJvYtGNy21tegcoglPoCixSAJ8xNCD6qqmLPGj865CseITsAhqDaj1ViGbTNX61BSQ0YfSdRmpVfS0UYvcVoISuU7FtVnSo096hgJAaBlo1yGyjNWsiuBJ+SlmMwDlko/1aPK6oBJAjxlwBg+6EeaSkwwmfa8CW/CHVMvrrcha07OyE2G3+bUZAKeinFKBsv0SZGVjajVjGByR1DgFurqhZIGpyTxDBfpegE+xEtgQSEXJmqnoPZULCBm3SzDMaJiUSAhGRSbVZQoOLCFzAb6kZEjGwVnJAS3A4NkLkAbLhH00FTINP4+AWOMp7CJA4yM4heDJ2KjpkBLAunTd8jkC/qbBonYhsf8t4LLAXwuD46OLJoFpluIxCAZEGkeJIzsmF09KLLG0fkRowVtFw0kDLfcxtZVH06HrKo60aPoGllluO6H1o3ugJbleyb0RvYfLdWRiqW8iBc5BpzraOOeFmWagLQG5gNqjcajGIxvqxOrzvWo7NMcoAcQ9g2taF6jmPSBfagDvkQ19NHWhtZIBLCce2k5Ko8lrgyEVA80vRJUIGYghjjMzIbdwgJ95C2Qjz27LtK5IksQbIB/SujjdpgogyWXlVGz9omtOKOQK5RGPelYl8NEkNi1oSqa0bLZkJXkgbGlLbKUw/roNiZGT2nqP1NoqJGmsMPWiHPEZoirEKJ6vzLqJusACzGYSe+ozAsFSxqFbutbxegOXk7qh6dL6Z2ofE166DtAsjVda1mQ6x3qOrsvnKSkloDAmXzymAH0CneUR3gcCYqlGNqBSgXw1oC1AztmcsLFVSvZol5JSwVCLJnGwbXmvreYNikWap47Nk6K7LbeE0XUgV14+L2HbVaC6+2jg5hKw9pLE8mDH79SKivY/UYMI86lgQoh/Z2pO5pmpdX4AlNtzTE5S6K9OKXv4s7989SnAyZ/B1+sa5+U57nCrMJVCeVDhL7/yKJz2WAJUL5g5gide93Rsv20z9NDg1c/4V5jZxyG/toMv5SfjzD1b0dzeh19f48v5iTiwbw5oNPJVjTqjwDG/sQME4Ms3nY0nXzCHT535L/ifB34E1372VLiNLUxl0azzeMQ3fxL/ZdtlAHAnQPe9HE1o8ZbDj8YHb3wwxPV59maNsy5/Aa591N/8u6+9op7gwTnJvcahQkcVmLdD/Omt5+MVzMS9/sDZeNH8l/CZq87El3afhOr2GTzg0Etgb+rghGYIPaqRjUpsuCrgwb9/CUbHAsc/dDcAYP5qAxgF3XiEIkO9UKLcOUEwBrAaZtwiW6GBbwYV9HCCcj8A08ft2xewoQ4oFh3K3cuxv3VxpIZqM+jGwaxO4A6vQ2+njgn/cd3DL+49D+uzIX51ww148e2Pxe7hHM39icPsbbQH/9OVD4btNpjRwOJXNgEPoteH5Qy3tas4yc6svc/O4JvXnQScAigVcMNnTkHzgDEOnESD71vbwPzL4oPx/kMZDk36+KcHfGLN7x7W2YEXf+m/4A8u+uaaez/YqrD+yzWADrpXdHDj6ZsBBp2rvuHSrwC9/wdIy/4gjm/Zr+/2a+6Fx1ve8hYAgFIK73rXu9Dvp3HhnMPnP/95nHHGGUf1HO4XoNNPbeR2xH3MrGI5okc9SzM+Gzj4QM3LixXP4IWaTpsJgYZ84GPtiebNT5hGhBCzm9KCgjKlEujT+Sgv4IU21ck6He3eKXCf2ugUYkbWNPQ6M+RgCikbLDJTcReVzK4AqqDZOGlGpEgqsr2ABDEEqCXQE9AYGd5MgKCKElGpaREwRg3GKciSnpHCHAWlkgSVs/2OQZRnM5LINrM1vh3Rz8U5WNxV5T2VA4wXYEWyRsmM03lR0FHNkROjnDOdb1gTIAnwzRkoiZmFABy5/mqOAjphWKgVgRhYcKDUUhANo6KZFPUvTY61gARv/Hxqvg4vAUwK6CRTHo9vYeoAYo+gwMAbkX2yFeL1mjpgMq+JSQxTsltLbTB0E7j9j7DlKppqeIuYcJH7n69yCxnu1ak8g94hAwhD5jfeAN1DjiTMHZacyurCzBb9D+SrPtZMCrAgwy4fx4bI05UPyJm9oiBVpUC7JvALBegqwHI/Wjv2MYiM7LsjoOUKDTthNoXBR2RMJh6ho6OqQPocakd1ZQTcfQTV3qYgVXqkUkCsYIeO5J65Rj2rOQnCz8yFxFApWn90G2KCJGi6BjGmORqHqT0lBlqVnIhLhXxF7l0C6z7n+1HRffOZJjmaJhATnbQ5cUUGUSomt4C1KgcyzOLen9nU3OLkGpSK6x3dq3S/lQswIofmMgbFqgPdqDinRDpezTJonISYqNACJhSNd5exTF3k7hYsu1QpiaYo2RE04Btah7IhrR2TeU3yS1ZB1D1O5ElCUSVmT9rBiMs4dJI4x3VkmNaEwMymSIGlrYxywHijQr5K1+cyxDIRV9KeI2MusFSVAl8V1/GUjKO9wlSkQAhGcTKOQLJihULQ4MQenXow9JmiuqG1JV0rscGUrIzjriLQ33Y0l1ckc6KYUMwV8oGP+249awiQMkhVXuTsgIckcULca3QT0GaawS2vFY2Ka6rsXaYOUeotKgRvgXzF0WcFRCdxzXXu5aIn8M/JYzuiumZXcPlHBnQOeXirUa+aJGEHoGtm1mfpB0uuA8Ahg4pJ2O5tGf7s5McAADad/DHcvm89Zq7PEAzwgi+8AtltJWZvDxgfk8NcVwIdoLO9wIGHzaG8PUc9x/NvqPHQb/wUdA10DtH83HloAQDw99eei3II9HdZNDNAuU8jXLUBf3TCM2ksPffDeNWc6La/t+OyKsPVS1vgb+9SixpD9y3/7Czw73Rm+JX9D0ITDLZtugyrocUnhqfiYDuDxlss37AAnEt/97XFE3BcsQg9Nqh29VEcURiXOfqHgbaXQdcO+WINXxhkA4diKcPKzVsBAHkVgMD7vGZJd25Z9aOgawefG+g8gx5QD23lAuygxdxNJezEo9w7gKpa+l1QUM5TXLNSIWiNhas0xLsBAILR2D2ex1WLW7CnmsPnrz4dqlXYxE7zncMexeEK481dTE4jVU1/F712HCqUBwye9rVX4lfP/hie11+M9+vAvjmg1vDwOHZuBQcX52Cu7eDt5zwMAPC/N1y/5v5ev7QZBz+2FfUjBsAD0s//54EfQVfX6GxPk/bHrnkevnDOhzDe7BEyAzNpsf4ajcsfexKeU83gfxz3MUxCH9WxDeqFAnM30jz9x2EP53cOIMM9c369tx0Ka5UXd/c198bjTW96EwBiOt/+9rfDmCQ/z/McJ510Et7+9qPbLuiHCjo///nP441vfCO+/vWvY+/evfjQhz6EZz7zmff8jTjL3vIG21qSsFHjax0DaCjOamkdmRw7oU1Danbk/yST5PcsyV0yG4YEFDnzrCSwzyjbLCyHAKwcAr6YWco4o86yUzIQYdOiPEnxcpZt5m1I1xAQa0nzQYgZNKozIlBD14ooF3OFgh0FzihzNnZEAbVsxJ7Nkkg2SBtgsSJ1WQys+N7AJomSBIwAt1DxUg/GBguWWWelAEUbu2G2QTP4s1WAb0V+R8xGlOG6BIqns+jTs9qz5A5In9/0VDIVUSGCUzEZ0S4gG4NbfSAyjboN0EtTrpdTsk1pISH3lySlzPpxUG2rFEQihFRb6wgI5wNP5lAzKrkHMyiyqz4yz4p/B5MSB1Gma6UnLJ2/BsvJGKR0DvrIMtkx1WsRGEhuleL6qDI1ZXCjoJ1P0lq+TnF41jUblQQOqusA41nyauVZ8HgIAboSgOrjfSDW2sEVmqXYIdZiCfNB9zbEZIXcS+OIgSOArmFHHqZWXHdKNIapA1QbOGgM0ShHWoIIeBFJMaZ6r2pHzp8kAWSQWAe0PQ07dmhLEx1agyIGs+1qdgTmJMPYI2SKGXVy4DRaAZ5Yw2BUOj+AJZgt2r5QbIi9UaMz8FE6hE2Sr8GMnG4DnE7mbKIUCUbBK8AOPFTjyF2bEzX5aojrnXYBipnn6aRXNPUJCsVBx3LwwOsrA3qrWIqLbwF8DBQcIjgwTeC6YE01tjY5hYsiwdSIklFyK/ex7t5bZtbkmnNaJ03FTt2e1lTlQ2ThAs8HSTrVfR0VCFKDb3mdifM2UL2sgFLd0FwzzTSbe2d2WNaUjNd5SlzR+7Y9RIdvAGxOlmpGKTmg4AoCn04cfmsP5xXa7pQjrApoysRMyjlKIkJqvnUT0DnikwqiZVnyt7h6A4iJQeUB64mxbAsgc4AHP2sjCRdEVhcMBqNSokmMaAzcFYP+gYOy1CuVnnuIyUVTcQ1pplDNanSOeIwXNN8DdujleldKNNN8I1M/DdOwGRZPQTvy7GSuIfXgAVMJBzYslHW5XAwoFylZ0vQUHnftxVGanF/bwYN7z8PJ80fwtYXtePHcFXAnjmHv6CAbAfs+S6DoVde+HMHImgvMX1ZSMk8Bvd3EnvXuoP0puyNH2w0wY8VARWG0uh6dZbkXgL2qh7f/yHHwqxnKw6QO8qvc9zQAM7fTxb7xk0/Dq1j+etnE4zHlPVdcfHycY6MZ4L2HLsA1t2zFut2U2KhnySG+vws4+08uwTU/d9ey3iYYfGb3A7BnvA7HdxfxtUPH4/brjsVDz7sFxRGFp9zwVPzj6f+AQ6MePnfkdBSHNTr7KSmDjkPQhtZbpWDGLYLRMI3H7O0pmeeNgu8V0FWLkBnYsYMvCGwFq6HHDdxsAZNbqJUB0O1A1S2UD9j0tSHqhQJqXANNA3RKhMxATVpYoymplVFZix2nBOzC9TW+uf5UzN2s8JnjtmLzTQGrJ3BJR9WgOKJhF0fY9PUMd8wUyIYBM7sq/Nyeh+OnF75EdZWfmsP/XHw2nnfRu3DYD3FzU8AezNDZq/CrBx4M5zXaLrDuJo+//PQFAIAD58/gT7Z8BQBwbTPCjp2b0LNAWxsM/ASv3HkhfmXLx/DpO06Dh4oGfwBw5PPH4K9O2oAzHrQTrrseZmmMzrDG8icW8LUHzeL3wo/jb7d9FOuPWcFw8wLmbyTTjzdc/zRs6A3xwa3/dI/Hz73quB/16dyxYwcA4PGPfzz+4R/+AfPz8z/wc/ihgs7hcIgHPehBeOlLX4pnP/vZ3/0bcaZTMphAYvqI7UzSsaA11Rs5YTo0MTvRQU9Fpz2pERTZkHIhGjKoZqp+bcpuXnohOqOmmJ6AcpH+PvD5KKvWBJVOQMIkxP6H0YV0ih2jbDi574oZC5SKsksJYryVOsYknROwKrIpALG+T1hcX3LdFTOfdhJQc5AujeGVV1GO1nRUBL4AOe12Dos8mT9HJQAkWX9hTosVuS+c6S6YEQ0UKKFWAMvpZFMX8Agw0O6qWMMn9bICUk0ttXkhGlgoL++hYiBA50nvqQLVopEb55QrolaxNsNnwsZOZd8NAR3tAvSYkxM5M0mTqfNCMpYRYBzZdCAyFAAzOYJzfWJf5ZzFtVEYPzFFkvpC3VAfxqavo6SsWJYa1BBNsdqSJLtBKyiVkhlkpkSg1Uzoei03nqdxpqL5CjEGPJ4LYtVNTQE7QOOWzBYQQbC3CroiUxAzpihdFRqq4cDTmhiAKg8orrfUlY/MOYvtqC4biFJWXZPkmHr+UQCiAmIrJDtyBDp47LbszpoPfGw5oQJghw5N3yIbtPCZRvABOgBN3zB7raECfT5YHtj0dLxm8CiJsj0GOgDgNQODiUc1Z2EZcCpHbS+O1hHZd2Z6JNHlrWK2jJJ1IkcGGDiO6L77IoMdOSivozmVkSRIS5u0aYGWrz8bkuw5MvchzR1KDhAAyQYOwWgy5mpDZKqDVrHdj9SHx/XMk5N4PqDEC/VFZtBS0XsDiMkBSUrR3E9SSTsmiaidhCi1l/FMLVUCFCcpxGiue8jFZy0g045TqYWwsfkgBbtB0/fSdstOQnTgVpyckNrUaEbG+5UkPHRDAFnWjGxETsOa6yllziXXWHG2JZDmDWLdO5BqrPOV1I5E2sioKZmpy1R872KV98xCrXXlVlyGMUwMODBVHwp22K6TPFhcce1IkrKkUgqc1BDZOcDqltrDF6wQWHJwHR2fsQriTE00eOcwPaNsHHiOJ28A3YL9HALMmEyMNCtqZI8AaGySC3dSkNA+wdJ5hVi+QuM9sESeZNzDOofLgcmCwtyOgPHKPK48rwerPfZVc/CVjWNqZie9SX+3wmReo1oP5IucbCtpbPTvIPfpbMgMfhcoDlGrl2xESSDdMrPaUMnP/E0ef7/7PKw7bgXqqwtRsdHf66PyBgDWXa9x5hd/Btc/+q9xXXUcHlPuRRNaZOruh4s3V8fgU5MN+Oxtp6K8I4NhI7jxpoDeHaTK6u8G/usdjwAAvPW4L695/Tnd3fiHfefhy4s9XDN7DHpFjd5Oja90tiGbAW7eeQxwOrDQGeGbe46D4f0wGwGh5YSSVfAdCzNq6LnWHqr10VFbhwBXWphRC9U4oDBw/Qxm3ELXBER16+FmC+hhCRgNOA8zqAAP2MKQrb6in8NqqLqlXOaoIifcSUC+0sLy3mYXR5jZUaJYYgOr/S1clsGOPfS4IWkvgOJIhc1fMTCVhxk0+NgXH4Jbz9kAOwFmdreo1mW4oRnit/f8OHqmhq4UyqWAmwebsHP/AuaWgO7+Cpu+Spmwj4dz8aofNXj71stxVtaFPZiRkdm+En+9egq+cvuJ+NP8AjTeYHmxi64F7nCrOM7MQHng1z/+bGw54wDKTgY9aaGGE8zd2qDpZ7hqZguu2gocP7uEW9avx/wNrPj48gJ2PLCDv5rbBGD73R4797bj/uhee+mll/7QPvuHCjovuugiXHTRRd/z+0jd5XgD1zQqCv4D28aPNtGGFG3yu1xnoWhjEfOCpq9jfUYzw1nclr5WHrAjBW/ZEbBQ0RSh7YgMi2tCmQ2zI0IYTd9Ek582B2wMMDjT7hEDcKlHmXa0zEYhWvCLRA8CSgJJepQjSVg1y9cqgaIKkcWUuisxoBDTH2+Bao6bgTMDKfFgNUe9y4RllPYvQRPgNE0yjZG6SOUDScTY7EWCbamBMRyoiOFEPJSA6hAlqmJKEsokj4qMK4RxYpZi4iEtSiSLLWA0mV8Qs6o4PiBWJwV29DeI2WUJVIsVh3rGJEdkFe5U7wsgseWBx9eEW02Uitg2nZg3Gi/cAJ3lXXZEYDTYJH2WwFCcXw07PloBtlyfKC67kZ2V7/l10dk3T8BdGHqqqWWmgwEbPVNiFIJWicXgtxIjIGLEk8yb6hgJ1IkkFy6Q4dA0S9jR0a1SADFAAWc+IDbUjB1CppOkWwOuNAQ6RYbLQTbJwgmsBVY7NB1NhmAmJTZI4sfJm4lHNU/LYDb0ZFTHpiO69sQ6lhSwuFzHIJTcbFNtJxTNc90EGJ77XsBYTf0tSU5O1yXz05UKplHwINUFQGypJCuO1tF2DK0BLTHIgaWrih1BxTxN5qCY/AQNmHEL1bb8rFmuOXBcl8csQq4QJJkDCtrzATmjqpbrVZl5BjhgHHqW66dnLe6iMcnECTExlwEICEg5hXYBZtXH5yT9mr0BKWLaVGcbNIEltGvnTcusH7GkXAfKMmIZQ9EsTsoLgorJPYAVHk6SX7w+hCThl+uk/rYquvEqFxBYogpFUnRXMAtaJFfobMjnORIwmICObkJcXwE659amfYSSc1jTroeAFO8LnCRzcd9AlEVKIpOeqZxPiO8ndbHGpz1EEjlNl/cBTrw2XR3rnbMhmWdRPX1K7HmroqR7OvkE0Od6A/jSwNSe7pnhz1BAPcvzsQlrjNzIzEzFxKpnAyBf6JjANg3WjrtRWgtkzzaVh7RVIlWLirV9uvE85+l69t+wHsWQzNC8AWZ2OSyfafD1a0/G6HTabNoOMLPLR0m6uF1LglcURbKGZUPaz+oZhf7tis3SaMyI6Z8d81hg5dXqe49D6FKyRLwNJAEl+3ix7LGyo4/zZ56Fw6s9PPi8P8eHls/D72y66i7Xkmub0Rp33MsmHj5o/OvO09Hu6WJ+F4BANdDdPdx3m9fFSz/KOtlXrAWdF/dvw2+ONcyigdte4NADxugYID9oyR26yXFjE7A07iDc1Ee+QkmnbBgwe22GZhao5gzM2MMujTFthCUJVSlzcv0MdnmCYDSU8wjCVALQE0fjWClgdQhkGVRtoGoHqwH4AARP4FNTnzvV8gTxQP/2IVwnI6AKQFUtFq6vETS1GLGjFjO7+Xysjn+jCoferiGdC4C5GzVu6GzBMasBxZEa5WGLDyyfh8uuPxX5TI18mZ7bUlUiv7mD3j4Hs1JhZjvHoJ0ePrXxDGDr5QAQW+iVBxT+6vZHQOuAz+06FU1rEFYIkP7d8oPxSwu3YnyMR3+HRnl2g8mGHGbcwNQW5f4hiqV1qLd38YruC/HSU7+Eq4/dhrZH++n6ax12b83x5zseDeBLdzl27hNHQNxr7tFr7uXH7t278eEPfxg7d+5EXddrfvdHf/RHR+1z71M1nVVVoaqS9m9lZQUAbX5uvY6ymVjwXwNAWky1I5t7kVyR3EtheKzhQJ7+iT26GPZIn8fJAmWKxZkUIOdKaa0hpj7iBFmxXbepUzPsbER1l5K9FzMfwV75KmUd266GRoiBnm4Ct1yhjZjqS9wUY0WMXwxCcvo3DbionjDETT0YRGBiR+l7CcaDUbFpvPSxVE7YDAoY9VRgKcEMyTDFUIflouz6K61hsoFD29XRPIVMjwK1S2G5pLy3mAkJwyCfQx8qbWxSYKcCYj2ky0Xuq6KUk+q/6FlFSRSboUhtrGcXXGpNotFoHeV/oNtErMk4RAdey20BAgfuYjol4Bvgc2EAFDxJKVXgWqk6cP0VokxYWm7Qa9kQhGW7ZFjCrTgC/Z7k5HS/SaqsYuN3YU7kfcUZNB9Q7YjUDqs2wIuJTSBprDC5bQaoSaqrMxW9Fk7FWi0yVVIM1thgRREgcQbIVgl4aQeELEm3fEFfm4lH2zOpH2xL40LmAQKBPALJOiaYdBvQ9AzyVWoRAxCgj7VgDrGPn3YBoWdpLIiJDj8DAt4KoQeYOoFF5QOCp7VFHK9NQzXj0WBFSW2uio7GIjF3hUKYMRBDMoCuoenoqTq1wDWKifk+GockDghQqOjybSYeio2W7IQdQfmZEJNPDAK0jsybrrhWduQ4UKYxl6+08fl6gyhNjQfPVVoD6PctKy1I0qTQcg2uBIhSoyjJvsj48/iVAFp6MxtuPxON1ACaP4HOKY+ANdXtFcs0z0Rp4nMJ6InV0E1IxlBG3LIT66ibgHyV63ZDoBpYhMgGyhiRtU0cjQVgaU5USdJB7pmdJDUF1diqyJ5aSVpxK6WoIuCEmAAZO/JwmYlKA9qr6P6Y4LmMgtduQ6DHNCH21iRjspR4MT65SSMgyoMFrJK7e+qBLX2GVeBSAb5nbclmKy4x7t6Sg7PcYznIyI+VDYpk8d4glm5IjbU3ydhMJLGZtEgyiEnKoGi/zVYdmbFVNBfEzTZ+rtShckIktkZTyYkbSPOhPEQMG5lxKdghra92RDL+9d+wqNYBNy2fCM3ybGrJlBIxxWJAeRix37IoW6KCwnnkKwouM2ySKCUsHnaEWI/eMhNcLnmEFUTVlqlTe53Ubkxj/npgX9gM1wl4ybt+HvVZY9wxXofLvvRA6GNH+IPzPogjro+Xze6PjrF/vHQCrlg9Hi/c+EW8+WtPQLm9wMwiG1xt1OzkHGK9dNAK66/xd7k+eQSYkcamr3sMtyhk3+gAPrlB2wnwjoMX4OCBOcwcojrabEQKmJk7gH0nKAyPUSiWNOoNPWRLFVwv4+dB9yJbpjpNNB7tTAE7rBE09Yn1ueH4he6L7xbQzgHOQdWOWE8AsAahLKFaT2uW1lCTBiHPoNoWelRBrxooLxlVj2LfKpqFLrLFMZr5DkzFipuqpTXKGqjWQw8rNBv70JXHhitHKI+UyEYeZnGEheuAP7/0AphKAXtyFEu03m6/YyO6I9pjVdtCN3SeG762hGDW4UEzP42nnHgDXZMGugcCDly1iUiXRqE9eQx0HcYbDd76lcfhly68Ff/lCZfizz/yRNy6exPyh2h09xoqxRk3mNteI1/NsLoyj7d/8yL4E2oMj6Es+eyOMcp9XSzv3nCXz/i+cqjA4+AevubefHz605/GM57xDJx88sm48cYbcfbZZ+O2225DCAHnnnvuUf3sey7W/yEev/u7v4u5ubn47/jjjwfAIKQi63rNzJtpOPNaB0hbEqplBJkyMMPTlsJmIbabEDt1w26qZJjBRgGFYrkhSzUdSUpdQa9pO1yTlFHQIv9cnv4XQ5ymr9bIUwUcUe1piFnoGHwq2fSZoStUBErUZy1l4wEKokT6I8GDAFYJFGJtai6uiYp6gnJQ1XY0Z3KpjcG0jbxcU6yTZIai6ROTE8Ql0bPDH6c4mq5C0zcxc++ZlZJrzIYeYtBkRz4ywMIiAmCZL2XYi2Ufvye302TSNN27NGgyiRDTKDvy7K4rgDPJ7TQzgwKQI+id+r3mDLqd0PcSNIqcTaTUYuiUr7oYPGuuHRSG1HCfSTG30iIvBRte1FL/yiDQpWSCGO6IOZOwFi5L9WqS6VeOAIS0xrAjByUyRgZ5PudMb0iATGSw+SCZ8ESDFA2Yhlm6ioGgsI8csEugppuAtm8QpP8nS9AIHLNEFQx8pV6ZAWwwNG58ruBzjaZvYt2lGP0YBgQup7YfNAY1fK64ljUgG3quM6VEiIwdSQS1LLGXNUJqFdsu1QlRPZxC0zNwpY7MeXRUbWjcJhYhzd0ojxRZZ5TmpTlFNYsqBopH82g7NFdlvjc9k1yIOXhXAjSZwVceQMuqAp+YHZ/rCOBME9D2TFobXAKEkkQCEsOpGx/vYSwHcIHqf7kXIyVrEssU2Xwe43I94lJsx54/Q0WQKaoAAaZNV6PhdY1kpVwbyc+FFCYildRxLQEQ52liXIkNE6fxtlTE0Lnkeh1MOvfomFoLgxdiaytheqV1isxdccYWBYGMXQDRL0AYMUkqKR+mWj1xokzznBJZtUkJAEkmSushb6ZUFBkZQOWrPgF9Nt0Tdj4bJdMsGlOJ1RUZr8vBLbBSzf50ck6emeOend5IHS7tjS5X8IWm2nAG1nYobDspC+imJAWKnkpA0HpKSRZvuaVMTtJ4kVJLLbmYTgX+Z0cuqnamFRpUguCjOoR6WNPXscd0oL9RjcfMrgblkYDOfkVyaU6CK0fJSzv2KJYZTDV0z/NldtqWy+OxVy76CObt2Kd9qkljQbe0TsrcDlp6nIphYtpDs2HA/A1AcZik7mFviS/cdCr6tyvom3p4y21PxG/+29Ox4seoGosz3n0J3v43T8XNyxvxss++DNnuAr07yNdhvF6jWAool3xinNWU+iYA71lZ2y9wve5B3MOLRWp11vaSK3Q2DLh5ZSMwNpz0lQQLvd/8dVSX6AqNtmfgOxY+03A5udKacYtgTXy+ZkJmQLpq4AsLnxu4wsDnhlQTGkBm6e+9B+qGJLmsYghWI2iNkBl6X9B5hk5Osafm35cZQpGR1Hdc030YNbCrNQHa1tF71y2C5lZ+dQszatDfXcEOHTnrDmt+NgrlQfo4XXvY3SXL2D1U7aBGNf0LAbO3N6iuWYdP3H4G2l6AL4BiyaM8rJCtKKy7MUDv6gCBY5AVWrRet/4m6AboX12gOXGC8aYCvlcgWI380AjFssPcrR69O4BsX2q34TOD2e0Bve+fGfIP5/Df5b978fG6170Or33ta3HNNdegLEt88IMfxK5du3DBBRfgOc95zlH97PsU6Hzd616H5eXl+G/XLrb1YkAlRfwqCCigDU/qUiQbK3JFKfiPbqL8czFr8BnW9HUUiQb1HaS/rebof+knKD0upUYuG3MPQ3YNFQmprfgcXQo0pRZHpLXyeWBXUrGk9kahmlcYbaKAiTL+xNCIFEwCt2SMJDWfAT4H6lkdr0GMcCLbCUQreTHakCyytD0QgKYct7DggCgxaBxQch9RcbV1GWVyhXEV1rWdyuw3Xc0y08RqyO9I/ou4Wbmc2O3oEsj3bA1LJAG/R+y/pmvKZkttmEhcRTodLfA5+NFNCpRl7Ey3viDrfs/uxj4GjVTH6KgGidkjCVQkwAO4RsgF5AMfGVkJ9iRxIaY4kVmxwv6yVJrBozCh2dhHNiq2BggAdBprrtTwuY4BgIAiOYIlMCjBStCAXW1gJp5aZwAcXOnIwpI7aZKQKo/YV6/pmfgc2oKYUJH0SVAHgKWpBCzJEILldpmKSQhhvdquRts1kWVpSx0laGRmE7idgo/MmMt1BJwSdIk7a77iY09PgM+1a2KgLqyZYRl6WyhI43sJ3OsZQ0mmQlqzhCjJleRD0IrVAyngk+dwNFlOOao5Yl3BwKLtapZ1c5AeJcxrEzkA4HsFxGAn1vtFZ+cQmc14nY0wpQxwXGodIwkqzfefpNjM6rNLqSSl4lrNcmBb0RinVhWOn/vatjjU+khYSGLJqYbTQ0oMCCAqft6ULGs6CpN5heExmhNlPL6473PTTcymAGqpk5USBTErsmNp7ZOMqiRZZWoCyMRk0v1VXEsuhknKc/KqTSBP8X2yI7/GbE1JIqxlKTkbgEn9pWOHZ1HIyP6kZC3k8adrStAIeyg/i/dW9it5Nk7Mygj4kNFXQDbwCRgH2Ts4IWWS+RFA1ybzKCYB2RRQEgmiatC1j+x6TH60iemLCb5WWrCQG7NuA+zEE5PP8zmCb2Y2Zb2Xf/K9YRYzG6Xkm3xG0JTocrxOBWFcc43Z7QHlEd4PAxAyAvz9O1rM7XDY9HU/FYuomJwxTYjSdW/pPUmREiC9gsWxX/oVi+w9lqMYqenV8Tqk7joC0KnlRp5hvhLQ3U/J+O5eBX04hx3SXr/r0Dxsr8H/PXIuDt6+gPr4CvVcwP6vHov8jhy9PTQ2pDer5tpxO/IxaRuBvQLe9I7k6XFFTUXYPg+x76y3ZJyVrxB4zUYBt958LDp7KPEYXcg1s7xj+lwxHWw7Jq4fvjDwBX0ftIZiZjMohWBIOmsmLeywga4dxxQKvl8CWUbrXZ4R6LSyhiqA5bEw9HUoCaSq1kfigxYQqi2VMjDVOKjKAZqTvXUDOE/sqAKxp3ULs1LBDhqgpfrPdTd7mDGt18WKhxk1KA+QZ0ME1Rn/8x7Zco25mwMGO2dRHFGx7lnurWkCencAgefD7M06PgtXBCzc2GL2K+yWpkH1sZMW5d4h7MRjbkeN2e0UBzd9mgOdg20yX/zP415zXH/99Xjxi18MALDWYjweo9/v4zd+4zfw+7//+0f1s+9T8tqiKFAUd+4BFNiZMtbgtEk26CoVwaRnGVesleLMrZgBtYUiJ1BhHIEYlCgGq14DCClAKZYZrKq19vg+A1peyeMmwZKGWIPYJsmrE/OEOpD7bh0imBLHzbhIBbLeFxAUa2CMIqtcIIJrO5EAkM7FspkCXRzihjPdw5MWaQXtVASrYqoEcMBgEf8+Oijy10EB5ZIjgxGbAJO0lpFATBhGYXpclsw9pk03aHMH1FRNoeiR5Xx9pmI7GapJ4oCCNzb5X1rWAFPmRJyFjz0z+X0FjCYpU2IVgkirwDVi7IoJgDd3CiI1twEheXIAHEuSVaotCoqMRbwBBQIc6ND7UkZenqkESNGNVsbyVMJCc9BLdcfs5qo5mKo9dO3hLclX7Yj7Wgpr0VCNn7yvOO+qNsB1DbLVFtDENLY9Q5lYBh7NLC0nWlxFuykgApLJVt3X0REzZCShFGaM5lxqVUSSWWaYeL4qg+jsSQCG3aqbVHfquGepgBrlQ6q/U6C+gexM7VUCJCLhBOF9Ov+aglEywGHQqokN03UK5MnVVJySfayjjkwfm1k1fRPlcZqKpgABAABJREFUjgRg09eSOFIuxH6ER+PwuYa0doISMEFMgqkpeWImyUil6ZPUEVrh4IO7+Mavvg2nf+FF2PKeHCJrl2QJVDJJkzniSs2SSv58Zk7l0E2I8kWfE7iXFjryh/Kehs2IXKGhJx4hT9JumbPyGhlLktSK19NVMLXmJAbNR8cma8WKx2ReM6Bay/g1Pa6trmgsNj1SwEwzoJ69BATkEQOpYx2qGFmRk7pK9YohGS7JvZuWBwvzqtuUVBQwbZok+QxaUSsRTo4oD6BOLDMwJZH1aa0Th29qGeRj6Yc4WMc6d1EWTkLcm8QVOF2TIjM9LnPwXpISUyY/EIk2J2UYcMm50fmlJFrck8UA0AYUyy2CKDnElMzRupINHM9RluGCWcoiradyjd6oaEwk42zaXE7qXcH7lZM2SZbYURVAkt2KgnHPyg0AQBvQOdhSvWnNY7pJc4uSuR661TBjn0zGGORT7TslgKLU2BPDJuUDimvmEWjvkT0I4FKWDsnUxVE7SC9rQ4loUazIddsx1V8Xy9zyJij0diuUSx6T9RrY3YEvAt5/60Nw5gN34ZYvnkT72BLJXzuHfQTy2WitlDy10KJyBYDKmeQ40XrsbFeBTRMMN3dITjum9V32N6cVNn/BYPUEoFhBktZzMolq9qmmty01lABeF+DlvgPQNTOctaM90ihul9LCdanliq5bQGtiM3slVN0CrdSXaGI+lQIiuOSf8RGyKadFAKptCVB6DztsGHS29F4tS3ebFigy6FEDNMRu6lEF5T1UTcne/q4JVk7solimhIweVthwbU21xOOGgC/vbTAGIdPo39HAlTmkxSAAasGznuqq54YeTT8jQuaIxyuv/RlUrcVxn3coDlXYuFijmaM1P/CkUuMG5cEJglIojxgsnULXW81bdPdW6A8q3JeP+6O8ttfrxVLFLVu24NZbb8VZZ50FADh06NBR/ez7FNP57Q4dgYUE3fTQpVYhZix5kzFV6r8n5kLkKkMy2siqNfyejiW5NdhgIkQK3Y6p1tJw5lFACgEC+ieySZGiEqBLzIY0q471J0B0zLIc+E3mdTSNieBCjFXy5AYoR2SEVNo8dUu1NSIL0g1iRlzMfqJkKrIYiQkAiAmdNudxBV0PyYrZyKOSzDjitaw1QqKf1zOaQK0AxnZtCxgw2JSgQ7E0x/EzI/mjis6G0dBIIQbS0qNOxodcX1BU0zPdD3TaqELYGgF8IjtSTYjNy4lB4yBLnovUcXEA5EpNLqgesVWGjEVvGARKwMSS3aAATDlVlkcalEeaKI8W5pLqfDkQHxOYFMkeSef4mXFQohsaXwI4TOWj6ZE3xGwpMQARea3IrpyHHbYkObJTAJZ/7zrktDfdAkU+jwx4aAxKTRKdGAPRNrArJgciKoEDl+sItuU5BAVyphbJIo8VyfLHv5Mgiuu9JFkRGTAeZ02PerYKSyk1YIFbekgwSnW0U+BFMuDcUsjnKrHThU71ufy+sQ8rJ45ECqdciHXT1OZIRXbsaB1tme6ryxXL6FkGzwDL5zrJ2TlZMNxs8Y1fpTYHN57/l2tq3iIg4Q1X2qFMS0jN2EU2UN5XWlsI+wIgBvhtR8cevyKXbvomyn1dSbXBTU/TXMs06jkT769cq+fnSo6xiRGTundvU+2zfC3KGarVpj1FaqJtFaI0VGoZpbSh6es0Xt2Uu2s9JcPWaSzZiWcjoRBbCYlMXxh0OyHGMB943mtSrbvIbOme+1jGIPLWyGLx/3ZM8vJ81cd9Sw5Zx0R6Key0zIUo22wTexyUqE1YzcJAWz5L3r9YdtANm06x7Fj2hennIu1aBJh/6zyQliq6DWg7JiYpZH4Gnt9RUcJrIdVumuQsPRX9mKm1T3lENYjcX9Mw0AN/P/HRdC+wPN1MPCBS/ZDGttx71fi4PvhCAA4nRAOQL7UxaS3PTLdU/wlR/LgQmVQxSaJrUbFFWkpwqVR/zmu8qDOiLL32DGCxRiWlArNnbLrWOeIxe5uDqQPyVaCdc9C1Qjevcd0tx6E5tobyQG9fIDNHpPlDLsKIyVbt0viXf8Wyxx1uFQBwe6vxO/t+DD9x1pXRwEpc+MW8SqT+pqL5aeoQe6vKszYV1Y+Ck6swKSkS1//cIljNbCS3WBnV/DN5djwHtMwFRaCS1yrVEGBVDD6D1Qw8+bUAQm6pB6hSkdEMZUZAVcaI559lGTGoAJRz8fVwLOe1BnAednEEUd7pJgCtgxm3JO2Of6vJVdc5kvEOGvT2OWQjVplphfJwi2I5IFttYSYexSKgK3o+B3cuYHT9POzYQTcOZljTfF9XIJSWxnjdQI9qmGGFYrlFf09Afw8ri2oPu3TfBp3RSOie/rsXH4985CNx2WWXAQCe+tSn4rWvfS1++7d/Gy972cvwyEc+8qh+9g+V6RwMBrjlllvi9zt27MAVV1yBhYUFnHDCCXf7fbxhu/4CUNJr0aloTOHzxGwFtoqf7usoGzx4c6EG2LyYG7KmF0MXqbM0XHNJDoKpv5x2gObMoYAuBHLzU54Alm2Te57IS7wmxkY5zsrz57Ts2iqGMd5IY2+seQ9qHJ4cC9si1UyIoQ7AbDC7l4rtu6kR5ZqxLtBLdpc2OjkPbwEdmO3MUjaengOgoQhsR/lpYGZUxUx53U8shyumWGo2Cmp6KhpuyAYursLRdIgX6sAsi9ROCjCXtgkisWw7CjlL8YQ1pqAqAWGpbdPsfKtY1jUtF5TEoZjtkJkMrzBc+xMTCAJWJBjXKrK5Utcph8grY/CUp9rOtpukQUYANjMDwrD5XCEEFc9VgiEz9qjWWTLXKUlGqxraYAFmc72LBkvT9VsAIrsbjI7McdtlEyBFmXtfkLurZ0dSXXlMFmysRZX6LjGtMCwDtAx6fcYsGh8u1/BTgdc04FSOTLdofHLiI0+KBZFdxt6yHFjKWHeZ5kAoxGRIbKlU6Bj0Sc1W29HRuASQMU3S5ck6MpvIV3wEmAIkBJgKe0w3W8Xxka96vlYVHZgJjFI/Qxm3R+uYzFMiIB9IWQIFs8QyUX85McMShhlKYfFMupY3L56EN3/5iTimSwZbUT5cJ9MuSXYAiGZSMQAWSZ9JbA8Fk5x84HEmrJdng5fJvI7mXtITeTivMTheobsXyHRiROm5q+hqTIZuGr6DOLeCFkYzkIFOkN7EiLWPqZ5cJZBZyPxB7DU8/bzaro41hIAoLERGz4FzUAiemBhar3wE3i7X3B9YxX1DSa2+S/LieK0qzROp6ZS9hZxJfWSVZP2TxGJ0IF51UQYfzZkgyYG09sQLDYmxljp/SSqJMZCoG6T9CCWMSAIprXpEsTGdDGw7OsrdPSetYnkDX6v05qWWSDrWe4oyBRCVDr2wnqEWGOSwzlsQtz/yeZLNxzp0j7hvIoAUKI1HYGbacU2oCsR2ihcDwAAzo+tsCx0TMHFtqlJ9I7H0Bk7puEbTmGHTOQXYsaME3FQySpQQylGdKNXMikrB0/xrJBFE980bQPEm5nhO25HHdA0a9UamtZ0ctVOCwJXUAiY/ZFEeADoPaqAyj5m5MfQ3ckgP7tFGg3LRJ5f0OnB7Mw1w0pbKI3hMtwEXfOC/45bnvh3bbMCX9p6I4+aWUa8TF16gc7DF6okZzTVmirNVg2xA40V5kJKo9vAZOQc3XR19BkSaLUy3Kwx046DHAe1MQWxnpuFLS062PsAXFqateQ3S8PMd2NWKmEnQfQqZgR5VCIUlFpTltr6TQTcOqkrAMlhWX4WQ7nlGtaUhM/R6fl+0Dsp5MitiSS9cAJwHyhyqdqRMqfj9lIJZrWktbunNpQULtCZZLhBN8dpCA55M8uzYoC0NisUK+YCke+WSQ3eXpTpzraZqWMkZunN7Q4wsmydBKwK1e/gSQoBuHMIU63ufPO5HfTrl+KM/+iMMBgMAwK//+q9jMBjgfe97H0499VS86U1vOqqf/UMFnV/72tfw+Mc/Pn7/mte8BgDw4he/GO95z3vu9vvYKsBYdqSdADVnmosV6q8oJgqO61ookObFfMTW8BWzkVPAzVaAy0KshRQQKP0mp2v8hEkDyJzIVIgW7UK1e5ayiKEFQADSW44PQvonLnbStiW6SDYcLE05YHrOsFNNIp2DdmmTF3tyYkXZ1Vcyd0ggOhgVwbhsUgEcJLDELKtTaxfpERoDewM4AwKFLV2v64lTL0semUmOgU0L6DDlghsQ5a6SNIj1qRwIQQHj9bSY5gNPvfWYjfC5SrUtDPyNyKc7OhnjZIAKKvVxVYC4bkqLgpgdl+byTXJ1JYYzORpKwKM4Kw0gmsgEpGu2Y8moUtBADCW3TonGQwQWg051QXT9HJDHLH6I8k7NGzkCyHWS/86Vmlw3Q0A2aGP2m+q/CBirxrNpTCDNEY9nOVyh4ToGuiZXWeqPSEFh2zX0t1I3ZHXsx0j30ceaYMfBTKwlFqdTHyJIA0C1a3WqfYMSlpAAhJn4CCpFSi9jmxQLKja2l/eUfpNqKsiRVgLTR+z92co4SX+jXICemjfFMtWTCRsk9ZHC3NBzo1rJfNXBK6njpbVozTzTNG/ieZg7n9v38yDWRxIt5NDtyjRXRQIvPRJNHfALv/1ePKe/jF/Z/yD83uYr8dZ9OUYbgZ5TsBNE6aDUDYPVAQBIOh0Q1wtKQhBDJoGpJCeCprkxmTcYHkf1RuMtHqpW6O9Kc1OuY3C8QtsLqNYpHHlMje5NBWZ38Ll4ToYxQIp9RnOZByGyIyQjpesvVjjRJusRQlyXhCWS9xYTnJZrPG2VHHTF8VrzHEtsDz0HAW1inoap2kRh9SWBZ6aSVKIOkPsrwFFKOKZfK3JpAdetKBV4fMle0XZ1bF8Cbmclf5OMkNK4tDIPGXDRedDvsjEBz2lGlJ5HKinQDtDDVHupPGJJhkiSqe8xjRvZR12W9lEohVYcqSXBB9DaxsZG0q5IapN9rkgWyKoQeU3QQPAEkIyjZJ+t0h5rao+2k1h0qdcEgwjxk2hZ9UFssY7teeRZtB3Da0BiIEUhk6+0UUkSFOA6BnbkUuIxKrcCoAlgSSInII3PePhAzJu0AHH0M5FQK88JkjbAleK276cUW2Q013Z1ZJgBYOY2Mi/afc2x2PRNYLSxwMxuF5O65RGPbOh4PWCzRE60ShIlrpug5MTGrwPjn6qQKY3MOty8byOUofnXPdAgaBX7WxIYpvpOSSIKEy3S/KDZQ8CJGSD9PlshYEdruyaAWlFrFNV4+MJC1456eI4b+DKDch6mcnAF13B6T0DOebrlnTwCR9kLdNWkcS8xYGah6grBWijHMluAmEzvgdYh5BnPFUUAk+s+4/t4D8AAWqF/ByWqzIRkucSMZgRgRe4LIEBFpZiZkNokr9o49/IBl8EEkoKbCY3RuVu5DV9gVjjTyBdrYJ3Uh6goJQ5c52oqzWuGolrZdq28+L52TKtm7slr7s3Htm3b4tfdbhdvfetbf2Cf/UMFnY973OMog/O9HpwlzYYh1sEBVDsjbSwAwPBHSR1frLUKmDJsYBBWS/ClomlAy4wH1Q+AgoWxSGlAE1ylWr0oIfMSoNDOQM6AJHX17GzqcjrffJWzlaVmU5qUQRVDFWEHBahZZiqmpVSxoXes80m3S1wSfT4VAAXJgjIwDwks0T2m82i6KXASBig6DnItl8vXBvfS/oRYgdQ7LBslwyEJoFxOwFrum88oiWBqqmGsZnWUeskzBhAdN02TAHvd1zEo0w2S66+AEQ7C0piQeqMQZZYkKZtK7gvAZllo0IprvHxkuUS6Ny1bkwAwGK714cCr7RnkK23MVAdNTGGUGzZr30OzxErGvJ04eCfshARvXGulAkzro+unMwyEctosCWSLQRLVVOqaZEgyLX2hIoh2peaWKB7SqmbaLVeC62DYwZTrUSnoQqrnaSnjTWCG21NMyTSBFGBT7RZioCUBrxg+Rcazmc6ac0DRArDSHoeTPBzcSyubabdUl4skG5EBj+ZNXGMpwVhrpLdiiHM41kaDklnSOkmMi4TNnQaTogCIPX17htth+GhYczQO/4wjWNk9B7tsYE5dRdapUH9+A9WlQ2G0WaHtAWZEAbodAbuaBXxkVOH3Nl+Jl+48H29+7rvx2j97eUyMtaWJddqGpd8SLAvDJeuYYQdSaRfSFjqCI+WB8QaDwfFA2wuYnNgCrUI2tFjdFjDcqjC7ndpEuFLBlfS8XAfI7ijgCmC0me5d/w4GXIoBQckGaopLIxytwR6pjjn18qTyC1mjJOklMj9JXsozl3GkXJqjJMsHMUpVcomWsaO4FhVAvDeyBwFpHTZNIHATWT6RUfI5y4RViPWV03V9UrstzNr0Z5BShF4joFzGsqhPBJjqNs1BOFrHREoKH4AsmWYZlRK8AoRkrTARNPMJ8CG9aikJFWISyLgp1tipWL7gc3peIlkXcC0qH8Nrm8vp2mgNET8BQHFyJGiFYrmNhjMiLZdESihILkmMOTHsynPyUWFqf5aewwQE3FS9bjCKgTRSwkMlKXDQithofpaWZanKi2OujqAucPJT6lGVpiSjrjwgZkgMsM2YGGxvFZxOCQ/lKOloJuyILnWKASTnZeCtGjH9UVHR0tvXQgVg66cpKbn+YBtbU9Hc97Htz7QRE6lpeDyFpEpQrYdyGmd/8Ofxexe+FyuDDvy+DvIJzVvHaprySEsu4gzU82Vqy5QNHbPj5PQblELGbsYk/SaG1Q7b5NHgyWAoi3shsdhSkmPGDYLh0hBOCOrGE7DUGtI2JVhNclaAg0ZAuRa+JOfaNU6mHvCdgupEpxnOqqHP7xYE4uQ1lgyOVE3vp+oWKs8iGO3toj6gAOCLjEBsoLnpu8lJFi4QaORWV6ZyzF5z8v5whbafQbVUZ1rPGNhhi3zRI5zUodgk0xAWVzcBvldADytSm7EjsR6lfo8qM2TShPv4cT9kOn+Yx9GLan7Ah7S+kKBguo4zOr9xVpPc2ULMVEfWLqSvpQYuG3GdnEoSMM3BqryPOIiaZqrOBxJ8JMYr1kxyQJsNE8NHzGyq9VIuuXqKRFXYrFhjEkJkTGKtpMIalipoFVuxTMv6pA+lOChS8JdAZrxelQCXdiG60Sqf7O+jQ6VPQZAYSMQ6S86+2wlAdukEFoRNMMzSGu5rKM60ic0i2W025nshtbYtMTTRAZXvs8hrxdluuu+d4obd4LrfepYXUctyW62iFGpafiYMYWSDTGJt5B6IiYYAaYBkOdP1hFJfJm7ArtTkEsqf5zopMAlicmFSrZIkEXyh0ZZs6a6SlFE2XXE6ltojqlWl2kyqhyEGdFqOJkxu2yGA6XORAvK4mxBoi/0YA9XMJYZMxXpTy8HMdGsVcVyUWj+Zm2vGrKJ7R6zH1LzjemyatyoZQbWI1xgl2Z6ZD0m+yLOfqssSdYLUW0+/j7TTAZDqhYCYTJAaMHFilTpumicyDkNMZshclDmVLjZMScJTX0Fpy3K0jtVBie7mAdr5Fr9w1qU4fGAWxRKtTZP11J5Atfy/A5oe8P++8Ti8ccdTAAD/tmMbfvGvXo5qI4PlbmLATM1JCWmdoxAlrlLHLnWWUT3CpkHxWTcBZgL4LEANDPSI5HJmpJAvK4yOAUabFIZbgGACMJUsNBVQz9C/4bEaPpckHKIrNQK4hRXWrNcArT/U95eSGdqRgmZ6X0nPOkxJ5tP6J21T5DBjx0ynzEkaD+LsKTXIsaYb6f2iGzjXYxNTRwkjXfnk+suvEcdfkfAHReu9qXxk4ETurqU8gAGijF0VANWEmKRpxWSnDrGFTeDEqhikQfNYF/DOxlPTe4L83JvETJJngoCPEBlfqfGkX6SxK6A2mrkx4BQDp6gMEYZVHGx5nopSQuSYdG7ESsa+vj715k21niGxRcIGToF82atV48l4LSM5rMt0XPvEj8FOXDp3ft/4DDkGaPo2gUytuIaeb6YL0cRNgF3srxxSzAEglYGIbJcZzsBjKMjewmty4H1LxqrUmmu+5uhmLcZe7OgrSW+powUEFAsrrFKbsJAA/fTYWP9NhX84eB6aQyXsqoYdch/2PI0tGROuYLMxTojYsZ+q5w1xX+wccbGOtC3Jk8DbqWfCz7/tSTBF/3xuyDzIaPiCjHgQAFdaqqtsHWI5gEeq2bSaZK2GJKm+kyGUltxsTUri0n0npjyUOUKRQUyJQm7hy5z25szQ7wCK2EOA7xYk52097GrFfg70WjOqEbo5YgsXNjLSdQtdNdCTBnpCi50dNDRevSenXlD8IIkVeJpPbZfax8DT+DaVQ1AKvsgQOtQGBkrB9alnqWp9vP9BXH3vo8f02n5P/t2bD601jDHf9t/RPO5T7rXf7tBNgO8IK8kbgUsM3LTcRFxTSYoJmLEUpytugC0LmorsWmxnAiRzBkvJLzI7CHHjkz6LyqeNlNg1ROmmyJEouOa/5c0egUwxpJek1JZNM5W6Sa6TtkpBb9BT1wz6vJCnwIUWTHYOzUKUhLYdllQVid0T46FgBFyJwQgBQ9WG2KJCAjD5XoyCJus0twhYaw4TN98mNaAXS/+gqGVFzZIOYgoU2iLVTYkcjz6MbNSJqRZQI2CQ2S9+/k1HUabP030XR2EyhCIptiQABNCQXEoBVXr28gxUS7VEiQFIMsE1420qqx9ZUAkKGJCJVJlMh6ZkYmAQCqRgyVFw4XINraQVBaLbZ9CGWTraGOghgTLvpYntU1qWU8EoyqzKM/IJiK4xDNGpBlKelRjSSP1fBOsq1b1N91eUcSL3Us6FTEHSIBdWUUypqDcf/X7akdhMjX+S+iEyk/KZFNzz+C+mWiS0EgCG+KwAcH0sIhseGFimuadiNlxATKwlC2LzTw2/rbg0cj2vMPoyRkQmLDVHqqaxKc7DR+vIMofRcgeoNW4eb0axM2cHbUBXiAZTUMDgRI9NpxzG6MAs/v6Mv8Fzt1+MsL9EMEBxSMfAjdpTJaCgG0RTGzMhhQFAktqmp1Auem5PoiIAEpdZYaKDCchWDJm6eZLatiX3TC7pe9WqBJY8rXvZhO+vJraLGEtm3BStFWJIJjXxkqSM0muXkhrRiAtg1l5q5afUNDYxKFCK5NeSlOA1oi11dJqNMnGXgnVde6hAwZ7McyhEcBDXDQ0oJKm6PC8qWZDgHLHnrrcK1iNKV0VR4boGOrBBGp+DcQEhI8OmbExtT4wjx1OopEJQbarXDkYhMHDymQJaxOCamC1E9sbnOgJSMi4KbPJC/0tSQNZGuaeyvkUzNZHac22a4eSwyCtN5WMiWc5XcUJRgKlStAZlAxcTa17AM/8OAMzYR9Y+uq4z2PNct6q4Lhg6JRqDC3HtUVyrq9uAtmcj6CIjObknWBOvSGIPANo+SW2FdSYJsY6JwMi8mlQ7Le8lTrhKTNXaJJWNZkdTa37TM1ANjVGEAAWux800jZuOgZjFAYhKMFFsKN4HzcSTckZknpw8FIlv9DVgtrdc9PjyTSejv92gmQEmG4HOPloz8hUVExIh07Ajx4kHvsce0CDljOb1O19pKSaQxC9SMkLHmMgS0Jq09HXt4DqW6imnypjktcp5+E4GxZJvYkiJ/YwJXMP1mVouXDIKvJdmBnpYEWjLDMQwSIyG1nwvUl6jAM8JajExqhoEa6l20lL7F2mxgtwCDCSVcwmAehBAdcTuwtD/00Zu+XKLZpbe145SUouetcTAZJ4k0t9g6Rwl5pCaWEzu60ZC6b7co9fci48PfehDa75vmgbf/OY38Rd/8Rd4wxvecFQ/+34BOuUQMw5hfShoURGISU88yZqSfISCimis4yigyFdTXYNyARB5IJDknbLJT2VysxFLr3z6HOnV57r0/q5QyFe4FkPR+8kY1VPnmKzs5WusLb5n+YywqdoFBJ8m/LRFvq6TPNDbVN8EAPkqn3PgrDqzv7KxUvsLCq60Bjs4poy4ZPSllpIyt0DO9TsSVDqWMHtD7WzaDgMTlSRtypP7Y1BAPgzRNTf1L1ybSRJmiFquALpVMVMp91OCRNNQUCstciQLaycB9YwhM6VMoS0RF+C2k0wlBFwBoLYdNvVDE6YsgNlfBiCJTVfkVCesn0nAixjSEGWpsSZU3G5FAlwmV0yo5HoZRK5kp6R5ngIb+SyEAFXqKAULmUbTEYkwIPWE3hK4lwHZMltlarKat2MGD4HnCo//COKcMCHCEk3NN74v2ZCkV4HnGwWQOiYriKEOkdWN84gTKDrQNbhCRxAY+01mdHMEfE7fFwEAEEMYyHPjec2BuyQgTJ3OQfpV0vxXVDrqAkKgn3sxRykYPHISQJJGTV8xazTV4xAMYnPp40jjMOe+huKyeTSO8VKJbSftx+1XHYcPfeU84JgW5UHLAIcTRx4IJTWIP+jXw440fnv/4/HVW09EsayRrVIfY6lhdTmrDHKFcjHAI7lxVzMa+TDA58BkXrEMXrPaAUAwycW5CWh6QDMLdPcYDgwR5eXZKo+lEjBjQLFBWWQGKxkHQLEU2AWW5sk06JLaUMcGcMFQElLWK28pASb7SdPTkf3W9VTZQOOj+y+A2P5JVBcCzChJmZQzMYmlAgEwk/aYuPcEkDlKE+BMcuqO0kyRNDLbJK2ipO5RFkJiKT1Ug2ikIiUCyQU4QNUM/JqArHUx8UdGImkuA6J2AIIP0b3V5+k+QBy+wXMmV0AbYNiMB0BkaadZSfCaaqbZz6l1UBlJdrK5jux9KkApFcEmwAGyZ+WJODYzoI3M3PT/mhk4/r2OvgkpsSiS2WAUXYtidYenpJYki6JRVptKfLIBOYBHwC7X5DnG0GltMBUxpvJzApdrx4fIXaOSiuMHw9LZJElGlNIGq2JLKaphTOwoADblSUZ5UicMrSCu6sJ6QiUVkShCdO0BKdHhBEHIdHyOUttM103/y31vuwabPpNB+YCRVwgDUlu4IQFpO3RUcsLrq3KeWERFEuPUj5rku9Lqa03JCn92jN8CoJznaw0JaGpyXqfxlEB6ZPBKS/Wg4ybWZIYs4/VBPgTfMmc0zLilhEE358/ytNZKktRqqLGj95xSuwjApPtG61J00K1bAn0ixe1QP1HPDKkGEHJiRoMiBla1VL8arCYAXZPkFsym25GDahzylQb1bEYsslPQqw20UiRF9jqeQ8gMjw+OV3IDs1LB39utXP8DHhdffPGdfvaTP/mTOOuss/C+970PL3/5y4/aZ98vQKfyAnh4A+ZMsp/Omsa/5c1asrqV9I9UsU5TwJ0vqH5Qs4V4CPTejmsrI3CIWWrJYJPkcw3ryLUkFFDTouWn7MCBFBBLBj3WqjCTJbVtSd7IckdmeYJL7xXrmLinpnJcr2ZTPaRk8QHZSBLjFGUCvLlRn05EUyWfUa2r1FEASHWFAmRaAFIbZTGV0Q9Ui6m41YSAfc6C6rG02mDDIpHcquQ6nFpqMKCVQJEdfk0jQWgyrojgKUo2VawDksbswhxEKa2jQKbtKGSDqYwnG48IQKd+qCoabyjP7QBqYVNJutZ0dJRPC0OppzZjaZURNKheygPRDICDI83/i0uhZJXlmZo6BW6UuWdQz5bywvpI8EVjmFk4ZjalLrruM8vI4EGYXDuhIFxAqzSQJ7kcvXGwiIYkgeV0VLMn9v4J7MnYph8gOkimTLNitivEex/PJ5rAhAj+JVEEMNhg0xECHCFJsF26d8IgCBsqCY9Y19YguhvXMxrKJ+k61TiTm60ExVKn5TOaW9J+51uVC3YcotlNNvLxumNrmaNwZIctbis2ADbADJjBaVndxc8AANp+QL6sYAcazfoWH7nxLOQ7izUJIJcrtF3QWBrR/R4eo2AqFY3N6j5QzynohoPzLI0LnwPNDKBrTuB06X87pHWBzMDo/c2I3i/KZBks6Jb+RtyM7TBd67QRWXTTlKCY13HVSFJLca/MdO7SHilnZ1A5pASCkjeY6rcauC9xYl18piAGYsLSt10T128KuteCKwAsmVSExaZkoxLEi8TRMwABQC2dZEzX0q+XWX5m3QAkE5uaWMKmo5FXLQEXqQu3Oq4VAnDEGdPnmvr61kltESWTAdEpVDXpZ1FC2hDAMAD0yMHzZBWDG9pHfGJ4fUpgyD2O+zt/H51BQWCTnLhV+r1Or5+ubYVaW2IwDfSk5g0uQEx5QqbImMUDwXGSUyu4LrGAIq3XYAFJNJFRCNZwskJo3wS4he2UftR+Woqp0/ON5mVGAS49l8DLYsiI+V2jCgqA5aRYNKqK9ZRTdbqgRJgoN2gf02vGYvBJlgsBw/y1SHyjx4EkDEJKqJDclvbVuA5ymQkZ/rA/Q6UxmdcYbiU/BvoMYNpEUVhKSuYq2FELXyS3d2hS+IjL/ZrSBrmv3EZFaXBLJ163czZ8KtnrQAUCuUbD9fIoSw25pc1Za7iOpWdbBYhzrHLp5ppxS0C1ISWMqoiBVPAsUef5ZTVU2yLYPEpuYUwEm0HrOK6IEOFxyudD7GlKElHLlQCfGV5/HHxh6T6yNB5Gwefca7t2QAWqVWUmOWgFM5oyR3Lskst9SYPR0G0b5bxm1CAUht7nvnyk3N09e8198HjEIx6BV7ziFUf1M+4foJMDzOmaLslMSu0ckBYccmZkgDa1GAJT0qp2WuZD2XDD9Swi7REAKAsWWCokr9Mxo8Y1X9M1DAyApIG4y8gQCD7ErLkYpURGk99Ps4PttGQsMrtK7glfizgwOslWIwLuVJ+oIhMamlSTStJUleRk4xBdSZULsc5iuh3LtNkLIJs9/15cKpk1FtddxfI3AZ7CnFL9l2wMnIEMWOuGyM9nTa0IA1nNVt/TtSnRxa+hYDc65fL5xCBXEhGe36sJ1H9PZJbiQsktEwQ0CVC2YzJrajvcMiAmEmSzZBDl0uZO9zDE2i8zIbdYud7YY0wlpiUGGz7VR4kEV5xC5RkLawHweOBsfTK9UjGwi9l2rmNzOiVcJNiXmlQ5qBdhSizApfpTNXW/XKGga8QWOhItxXsTn38aPyKlk2ciQFQAvLcqJkBckRI+UvsZkwQx8OTECDNcABB8AkI0/rlGrJaaXQ5GOknyJvXcMflRCFih9h6mBhqpi2p4vk+Ng7ZMjtFtkUxIIpg+SoduFNpBBlOryPJ4ltfaloyDqgUAOkBXHLAtWagjFnZMQaByVDdp6jRvXMF7tEnrLEC/b3uIQI5+SD83FY9RS7WjqiXncFl3dAvqkyzrgyTrOP4RsDkte5W1V1pMtfJcGPhQ4on+Jl/1a9dam2rOlaO2JuCE2DRolHpmqa+Pl9WmxBhJzJIEUkcVBI/bjKWOoKBPWhBFcBgCFF+nKFXEKETadgBgsKMYGKno1gwguaeG5L6tOFmqeS4DiG6jcu4CzATBeu6l+601yZRsIXAVwEBCQC+DD3peIV2zyGUD4LomXg8Z/KlYMxaZsSZgaubS/QMgjqUiN1WBkrriACvnqzwQMhX7KkvCQRy+laP7rPj+AaC+xOy8KbWCUU2iEIGdAqBbD6d0VJXwiRITqhUUpK4+MDMpGzWv3cz+SpsVACQFnpLNyrMDeK2UkoGOTix0CHFvSCaA9DmiDHEsr5W4JFgFNVVrLnGNK1g6Lutyh02HHCll4Pi8RbbOY0RavcjXqiW5tq5obBuObaL7LzghxAy5HXl2UXbQbUA1b0i9wCAWwkYDBDIzzYDdEfCN0nRWgfFrTFQPJMBv+Fk1sxmy1TbGAsJEK/D2FNigSIOBpwKgaY8rFMxKhVASmAQA38nJmCn2+BSNd0iA0wXqdSmtSIDoNKwc4DsF/c6SXDZk7PTIcl5pr+LLjNq9GAU4S/dn0hKAlPKa4GNNKtXt8rg2aZ6olhhduMCsKa1RumrgNhTIlxtKuuQaunEEYMW1XdP994WNSaZgdbzW+/KhQmJv78lr7mvHeDzGH//xH2Pr1q1H9XPuF6DTTDyUYZMH3kxsNWXwwIGxz6ccZSW4Z3lfMp3R5ODHNVvRqY73YyuSOQ6Kkvw1LfTKB6jx2uypyFJoQ+Ug1ih43hnI8RZrMofRXrxQMSgEEliWn0nN2jSoiq0xxJlWgFrg+1AlNlHMWWIPMWnkzhuR51Yn0QxEWALeECV48xnSIivSSZHBNgLQAZGYRikyAyQ6Jw7W5FzYAEGArnKA0gk8SR+zmJnl5y/GNVGixQx3BIbsficGPiqAazQSEzwNniVQdFOAmH6egHbdV8hXfTSCkTrfoEgybKQdjUJMIEgNlgRGIrHzVhGTOGUs4grN90LDZymwiTVVSsZjAsuycURWkZMxUjcs2WAB5wLwogGUUSRbC4h9Taez57F1Bd+vtqNgJoiMeuyzGPuO0s/rvorjGOAgLpfrTEGmZ1mrSMvkmYiMKtUJc1sUI6ZQiDXeBHJD/Cy5rpjs8Olzo2SJW7sonUCZCikIFODoLeBZvlfP0POazCuYimShbSHzgpg8AUUSlRNwY9kyA9igNSVAjuK+ZUeI5h/5MCWWXAlMNgcUh+hn+RGN8bEe+ZKG5nZH3lCLFXmN8px86VA/PQBAldZigMCNmcjNl2dItyEbMUDVQLHEANYCoaSvLbOb0RCIEzyGM+i6WcuU05hIc3jasEcUIJJUiuZxrJbwltowtTzXkikOrxEmJQGjxG9K7QLw2mMl8cHsj5WkD98AhVh/F9ubcA2nYvl7UIhAiIx9fASDAI1vMxaqDxBjG+X+f/b+Pc6vsroXx9/P3p+5JJnMZBLIcDEMJIAETAjKkYZWz/HUmKSlFaGR0xeCB+W8jopthV68I8Wi1Vqgp0XbHgSr5ai/VOrta4KhtVUKYlVCEIJiCCGEMJFkMrnOzOez9/P741nv9ay9P5+ZzAQGSNyL15CZz2dfnvuz3mu913rkrEKbEELGdG1/POaHa0Xca2StkXWLnlkmOKL3UOO9CY6yPHrsfAQtSYN7p9ezKMPxWHyPGNtoqHTQ8ITceC0tJVWqaryDgeKaJ5JkzcX5mozmSOoZ6l1tYZwao11az5Umm0hCJgAKWp0P4Qlsa43ZFJouY5DVCyiGAI6/LJFypTSUCsthOFMQSKCcjPiQpAXFPYfvDdlQczALMI9l4fjh+znXgoeWRnNZ+8XoBe+DN68tKZ7nKcYRAHCMNWXogngwaSjzbXIuc+oKtFme/6xxmxlj36OXnl5VpbVySzQnB7gsJHLzQj3t3JmqEYsZmfP2cOZq1hE8fulIBmYH1twC1P1Eh4kGvWhEYUK9QEtPQ3I9GV/paChD4oNe6Brh/M/CMUsIfetndQbgJ9RbKkjJaNaUSMen4ZrA8kiRILBkavvroMfU55IYCFCQGcaXDx7QDmYFd0iG69IvbUAHQjklX4NmJM4BL2ePM+Y87HFO+zrEsUpiK8ZttteAPMxBgslktBFANOsqZcyntYVrOCZzSWho+cVHohyFMZ29vb16Xi8AeO+xd+9eTJ8+Hf/4j/84pe8+KkBn3u6QemOhkpgrTR1foN0KQGt4jb1hRs10NFdQCUTPEtPeh+xnSWExj4qiKG6AHh5t4w6DByxaQQl+VYl3EGDnjWcP8rlTqxsATbqTGNopQV2ZTsSyFpOeQAGYgrda3ByspdPz2aLM0JOjmfGMshfKEIBjPGoiUoEtfbImnlp6IdsOBG+DAqa2oIjn7Q6NaRDPrSijYo3U9mN2R6F+amZbcyZnSB7h1btGD2NQPqHeubhpyGcEHQ0PZoUM7xVwnTJBiHi6OhO1tCotjACHYF+y86YHc+RtiSTnkU1Z+oTU5/JZkowdpDeUNFtSlxudYVPW9hEF2GalpOEDDsiYlMNkk+Qh2gD0SJOgcEZvEyCKXT1X2h5jNp0HstQBHdEbldfkfe3hvrYDlgrtNXsix4cqIKII5+0xTpNjySY7CnHTiSoZ8R00SIQx5KXtFfC6WB8C93B+KwQ0On2WZopOYgw455VNqpHUeQYmx1UAVvUuoHNXTHoFxKRKpGnnCMcS2SQgUyGuDrjMIe8I7VvbL+3ugba9YczV9gcQ2j4YrN61YQGW4s0ONObIUuAa5DJoWvRcDAn16UDHYPSmJmY+Zx3Bk5mOAEz85U3oga8hKN5iqKkdgMZw8lo9Modz21AUVVkWyjkzgefUDdPghXEKTBNoVnJmEHfRi8OkN+rJEYMWWQKM51UQIACSiYNisq1o8OIaofd5aGKiECedhBO5co/EGk+kniwPIOC1LcZrMj4NQpXk2OJ7QvIi6HwgwGp0pjELbiNHXktiplJEpTsZyQJoYls2giswo2dFwCA9m0y6RENVmMcx07aTbKDKukgdktEMjemirrgYVhLWQKfl4hwMdGagMaNN+z8woiSrrEiSecB4YwhEXR3BO0ZqsIBqnqGMNILbkHxL1rDMq3czJ7iA173V1+Leose8dKZgRl4mIQp9GvYHOMDXUgWcXo6sUMCZx7FpQw2UOs6y59LpXNtNfHKh7cRQoCEgEKOhgMt0ONdjv5iQyOfQeF4v867QV4nT2HWOnwK+JviU50AAta85dO7OMdIT5k29K0XtYPBYu0YewFcj1DPrCO3IcRSSWznZ/ySsSYysADQelwnqEsm6mnW0IWHso3jvNQN5zYWYUAHHGuvpfaD1muRMuZzp6RoZeLxKnkZ6OhyBrGQt76gFKutIXcvossxkrhVAmrYFWi7BbOYB8Uz6JAlavQEUvDf0VY5sWluIVfY+7HW1BGhEfVHHVSMTIJlp4q9kf10TH7kR48HtSNUjqDmTOH4aL20AdkjxwKTPfXmJV/nmm28u/J0kCY499licd9556O3tndJ3HxWgM6l75J0xIx2teqTxaJY2DwFgCFY9KgLtiR7ATM8aY8BsMggmoUiycEQAY8y4WVMR4plvVgkJSlaORgeVEyoE8uy2khInHs4QKyjgThQDG6dKGi0BkHrQSN9IhWIiE4cgzyeRgqptI//yyI9EsvnSWum8ixRaTSITqaFURIEIHpntNHieArBmtlyb5YzeSZ7hGb6QfwjEBODnxnBIb4QT5ZN9rF5gxksJyCWYDcpaVNQyZs8VYO4yrxnwNAFJbgAXwnchuY0r3Kd0WcYVIYJaJiZKMog3lMaCRI8uYP+wH9nZSV3GiYCvuFl6Bdy1A5lS7iDjLnhZcgU6NHBonKaLgFPHufUoimKlyqqJDQUSNaqko16t6fQw0kOttDMXlF8mnFLlTSiqgBxXATF8tBlrizfUb7GmNzqFMZCSagvxQvmCwUQ9SowFTqJ3mGOfscD0wMFDMybzOyaX4nxnHGmmBgUqdNKvCZBNg3rmGp0hxnO0O45t+HBkC40CPCoon8LVmcan9t0Oo7MkOY8PwC4cVRLaIO8A2ncLWGSm7VFDkxUA7/JAieU8hzNeT0SwCYjXczSAzXSE3n3IGYUynhsOtf0BcFqWhNLiD0LHhW13VbwNXR2I8zJpMJ400f7LkmhM0yMyENcdjVfLod4/m+DDJv/RNqkHLx89QZqILikmAwoxv8LQEJqjHsNSZybSNCR8kaRUBB4wY1eVVKPsKNVUwE/4ELpeJlnIxhspgHGf0EzRPA5C4uUaM2q6Nrgs1/cmdfGYCQgEoOEtTpLaJCNmX9TEX06NBATJoe3luK0kvNslMGcuR0DABCyk73oxkCV1r8CAZaMQSKZKA3TRWNGZoLY3EwBlqS5Ag/GdEEOfBF9qYigx0GVtkmE0B8KREwHoBo9yjlwMk1xrE0lgo+VgnG4ticBTxldjRqoZZRk+ErK5Rl1Dsx4L/ZbjK4TbJEpzRS0wQPLEjF1KIgCNoJpHDnH9JHBsc/BpEo/t4RqPUHcIo4s6GdtJwTDHrfF20ygOQD356Wiqc9HXXAA8tSSMA+fVk8zkStn0NMYDE/SkMk/kz6wzRTqchT0jdTKWEvXgZx0J0rocUyN15zPT4YY4GpyCvUSPCQEyOe+1Mb2G2oHSGmEAYYhVDoUK7zbHsABgvCRywHekcCOBbus7BLw6F+ol4DeUKXyn6wQg8ZphQ3K5eDUJPKUMLhMAPBqMSLWRBly9gbyzhmSkIfHM4nEl40FAvKtL4iMreQ5XDqI9AuVopNe+9a1vfdHefVSAzqwzQcdotMqRXqUbMMe9WFj5GZUI0mTU2oi4uJJKF5Q0A67qcZOk94mUVh6RUUgIkYW4ImY8ZOIeelrb9ofyMwU/j1LgZphmsUyAKAz0pLUHuq96OwFNfpE7AgivC6ce+i0JV+jNDQqglNtLOn4XFXqmf2fMaWgPqygQiEI3n9qB6DnmppQOE4RJP2QobEIuk7LUIhDN28IGRECq3gvBJIxnpSeSdDZ7xIXLPNr25QqqSekNbRapZErTZvwkNy2hKekYyDzccAS1cA71GXLofD0cIRHGkoBxSR5FGq2ThAipxDc2xECQybE7jc6iMsCYSbalgrU2p15UBZyQNq3nmkTIejPqQjVXT3HqovLhHLJOjluzUXpJmsM4PQJT8ZJnHU7OsEN4ViOAFmYG1TLnUM+wWlY9dJzkkmSGSrXGgraTIhVBCCnyzACdzXCRtu198OQKeCCF02WisBkvP+tjvZP0dnKuh4RBZg7WoWCRxhJmcB7tCnHavgY5KknK2wYd20DszxDLzPUGBerZVEjSCKAwqQeqbN4eykUA2bYveGvTYfFWSl+37RVDhtQj9BskWQ4ibc7EVQPyriw8RumXdQGewyh4TKnEp3Ugk/nvHeLcH43xxaHtJImYgEqeyUchPZ90Ul7DNYPxm5YJwD7xicwTJ2DAGLFcBvUghhviO7P2JMyZzEcwxpg0H/cmn0Dj+Vw9JpECIgBkIo9MYtcopImG32WcG2UneCYz5G2J0laDhizKKWPklQEjayo9jsyImwWlm0l14j4r+1A7F4Tg9fSkinqJh3bRIEwgp96dWhKPACEgzgj6PfQoJxfBQ248lQS9CjoSwENA6mgO5+L+xHNw2UbKYjLgMqn7kDhGPLv2Whqgs2nRi8w9x9ccchcBbChTGGuNrlR0gxTJiKzVEtrALPfqNTYAOREvGNkyeUcSE/jQoJmGMabUag89vzITwzrHpobYSFu6LILQcvx4ImwaGi45fgmsGdIBeM1wCy1bfKejoZsJspzE1Yp3VuNIZVxx3Y9xxOEZ6bCPcz4PXvgApHM1kKTDGeCAxoyajoWwiURDBfc/IMy7YOyMgJVG6HQkHCHi6gHYhfGaK82ZXsZ43ifQaEtVz+A6EijAqT43qQcqsJ6nmhiPtvdodLUhaU+R7q+HGMwOBPps6gLgTKBnh4ZzOJMACNNEy5pNC0edJKOZrts+TXRdCXkOwrqgYVKNwHDLOxxyF6jbjZntqO2JWYAKINcZ/bYR9YMClViOkPHWS3AkCo0pk71nkvLpT38af/EXf4Ht27fjrLPOws0334zXvOY1La+988478ZnPfAbr16/HyMgIzjrrLFx33XVYvnz5hN+3e/du/OAHP8COHTuQ53nhu8svv3zyFZigHBWgk0ps0jALuYl9IZUVCUGfD8qAsTQqgJSEJYwFouWQNCPGwiUCwJiExXlZz3LGivrCJk6gxkx/wbvlFYDSu0TaYwRuEZClo3Fx0o3NJBhSWpaInoEG6Eal6f0bVIJNhljjVQoxRF4OdA5xnD4V2qSXzIxtJSqnALmgPDpNvERqY1T6aAEPSjlpirTG0mvHcuRtweNFkB3eRaAg3gJSPx3g4BRMA4HqyyNhLOCkh1m9ZD4q3RqLZkC5tQgrCDIJbZB71A5GEEbAq/GRAj6ZVIZnujIRU84DsPVaDirpUFpoJbtqXgNgKHUhFjJ6lOE9GtMTPQOU3mQ+rz49vL8+XTZtiVWzxkmlKaWhT+mN5FzLOqISq+n1pUz0ZmZaT6d0awLOENsW2qLtoDf3RYND1mnihFw85oblZ6In0qWZlZYGFFK79Mgh9o8AdjUqSD+y/jVhSxDcUlwWPKCc514t9L4wbl2OcO5mZxjnvoZwZEUt9kHWYeZuYryIHgrup0JqwxFwyYkxEfR5A74Zly1eD3o6SQfmfAkeelNvyQTe6GDLhvvJ5sgkXjMRqj6PWwnrHVShT0di/CzncUKgnsZkbDQOhdi9GNqQp07DBPJU4nQzADSmACCjJR2J48GnEWxqkhopU0jYgQg2E1GkHcvl1KsJZoeEKLlsq5oDmScu98hhFH8aW+S9vi1BliS6Z7F8teHMeCRZ37A/BGVfYirFuOp8HkLGslCmXJKWcJ1xWYhrIxXRm/WoELtIcREkhMRhQN6RBhCXB8+NV4OOJJ3hZwpMpG0EDGbtwXuFFNGbm4cY1cx4GjVZoICGdDh4dhzrlXDQyWucoVk7oQDnCIlOEqegPxwFkiPvSGW9FEBimC5OwnPgoZmq2ec0XNOQnUu8ZGAYyXCRzN15W+gXZEb/aE9Ub3FcP6UPGSOaZAEYh44RyqtQl3OhTyuNukxt5Pom+11qQinUqJNBjZeOupP3IdbYUldlLQ3x8j569RHbyV4XPIS5nh2r8YUyrvKEIDxQkpXBlIRcGnoWNPtCxin1p8a0VAxecR32AmDztiTu4yn1gkypyvSoc+wHwB7msBo0nEPayFHvSkN7OzFypA5JlsOjBLjlWYnPVQdgMi+fOqQHGsjbU81bQL0ub0vgptXESytAz4VOyzva4rriAuh1jSQYftrD3AueyzQk8hmmFV4SjwkFuTG9FoAnk/3UEng7R1MnHnTxbEpG3bwjnF/q6qUkSSRXSZvp76HWOKLlBYjp/PKXv4z3vOc9+PSnP41f/dVfxd/93d9h5cqVeOSRR3DSSSc1Xf/d734Xy5Ytw8c+9jHMmjULt99+O37rt34L999/P84555xDvu8b3/gGLr30Uuzfvx8zZ84sxHc65yrQeSjJay4cAG42hmQ0l3MUUVSUM2N14/1iuSElT899LAHDQF/M9Z30hBEYWrBI8AlAP7ceP43pYeylLLB6tAOpEg5K97WZ8ZwXEJq7SKFEVAqs108X69QFHEvAImVL6tBzu+rTHWp1aAxfWDwR709iXZg8SRXtPBSAHtOYUABg4iRVuPh6OVZFA9elrXwN8cytDKiJd5bKoI0BoWeIChmP5dDMpwQlxpORZEKtzaLXGIBuhI3OeGwIE9Mwyyggm6qJm1XPce6j57Bm+rUWM9OmkgE4uGgNxVOuRR4VfhvTyVhPelk1Q7NV7DKPnJZ4CH0qdZoRmH1C7yETIQFQEBzmCevptF80qzDHmNJ6rDc4gv3g3aSBJQIajYWS+CbWUYF+g8YDAWpZTGSk8cbtTumuPKczeCcj0KQCo950Veah49DGsORC5WQdNUO1i3PWHtGjYzWNc0wVEXlH0kBIqnMwPCebBhQMQ+0xIU59RqCyhjjvcIzIVEntgAALMhMQQGfbfqlPB4SNAU1gVjsYjSZ2jKaZXK9eKYQsrUm813nA0cvbBh3rpN22HUQ01JDeL4A2MSDSGU8mDXR5TTLgWrBMPGgom0k9ZBIOWZfjmHWMdXPGQGkMv+q5SI1RyAFOwGbWmeh6CIQ54+p5AASk4YqyyeyplqnhUxeTs2gyHmOw0vnqFdwQUOp5lwey6JUjpdAhZL70AWC5eo4EQflORnON/XJZ8AL5NFEFPDwICsD4XAWtgHraAlXUwcFHjxKNYeIhI3UxUPhSnU+pgEQHHzy6BgCQJqoJi6jIZQASqOKfjgbAmbclSPIMmuhF6hYozXnYO8XDG5Rj7ktyfrUkp1EDnezL4Z0+xDDWvWSpR6C0+rhnOLIh0gQ+9QqAmFEYgB4fkidxr3POrDWG/glAz8r0NYfMCTWyMy0YeDimrCcdNHq4+DxSfjUJk4x9JgFKMtGTZBxyHfUJYj+ZtdVLe/ha2Ech8zIY/mVsNWLbcNw22tLgLbRrD+nAooe5nCyjPBynkwA1YZ8EAyc95mKQl3hZfY6wfPL2JABv5txwTtkB6jGXue1dOPva5bl47818kvpmiWSQBdDoTJHWc7hResWhScO4pwKIhh9ADPnBWF+f2VbY28ORI2I8aE/CnAVCrKcH0JYi70h1TAemViZHu4T+yTpSIEGkFBvWRqDNJuGnLdLTQywq4BANRT4L89W7HC4FfBrO+CTg9G1pBJ7iKQ0DOSZA8jUBwyOTBGwvNZF9bdL3TEJuvPFGvP3tb8eVV14JIMRc3nXXXfjMZz6Dj3/8403Xl2MyP/axj+FrX/savvGNb0wIdP7hH/4h3va2t+FjH/sYpk+fPrnCPkc5wv3eQZjdL617OYzeIZuWFCmFAjbymjlXCigsogQISvOAKN2OGwITssTELEqz4QLKeEJZeGjl9LI5JmLV5IKnXk8Un8HYJt5vk06oN0YtYFEx4ju5gak3xlKPvaGHBWwC5yWpST3ck9diBjxfQ7Tk5tG61+iU9pVyJKT4KD0qPJsZZm38BpVOazlnwheCnki/hFoDaweExtwmVBhRgoIXWQDqQa8xigQZzBhH6iwQE0uFbMRRYWRinqw9nF2ZtUcPImPbbBbbUC6JSxplu8Z+J8AjILIUTia+odA7TcBcFo27QqwX41kBqEdPzxttd5q9NZEjTtjHjWmuMC/oaQ4Viz9ePNHh7DQfwZbJXkfPlxpKRuK1AHRsss1U8eZYBgr9BSDS1H18f5gPxfkSjUnRM6vjUhTV2rDcW7fxyFCQFOORCSLtugF9DgFQ0gjsgNgW4d7Rrtge8ZxHrhVAbZ+cP8nx36AiFUBd3o7Jb3CHIwLWEjmyJKmL11GU1LQOXR+SevCMhqOawmdOzqUkwM46xGNvjQvy/ITeUgGZga4cngEPtO8Lv6cS8xfmPtcoH8cixMNMGq2AUo7dEPbg1ZuqRzDJ+qPnsoqXm/OI454GBk2CImOIzAvnxfDihH6n51b6eKRGQgU+jnGOxXRYaLQyngtCDwxxlYR7qIFOjFgE5BoPX88la2hQohnDSSMf51wykgmYsh6qXMe1pbRaj2veLlljhZqYCgWYhq84dyUxWE2UVtmvCIAdcwO0JRG01hIFMsH7k4ixLtJCFVDJvsLsmshDTB7EOwMgAEvG2BFwEmAawOmyPAJhxrIJlZaG0WxaGs7jlORBpLbadZEGEe6LzPwevKXBG5rxGa44ltRjLsJYRU/KbErvPJSZwThcjvPyvUBcg0ldZeIiTejjAiCxCbC4j2jIQnsS2krGqqt77U9d7+16LImo0pFcY/p1zuQePAaGXugItAB6kVw9N/ua03mZmXhWjdHMvO77ZNU0ZqQx07Aw2cgoAuK6lnckOk9pzKAxn17lvD1FOSaPTAayA1j/8HcAxyHDbx5ALXVC0U2S0UB3zUiR1n1T5rU5DgfUoRKoxzJvD2eA+sQh66yFz+Rzgm2OH++cepOpy+VtKbKOVHVdnVdmrQhsCDKD0pj0Kk2QtwUvpx6n4iXxkfdK9aVk09uQTW+Dr6W6BlQytoyOjuJHP/oR3vCGNxQ+f8Mb3oB77713Qs/I8xx79+7F7NmzJ3T9tm3b8Pu///svOOAEjhJPZ9bukDoq267glQnJCGSCex/PMTSeSG6C9kgGbxcwIFBgEqGQyWLN73IH9YqRCpYYzTHE3gi4zWjBznUjd+Y6VUgcIgU4hQJPGwuZJ1Exp+IdM9pF5cHlwYJXToYDDz2Xjn8H7wzjQwhQoc9M6vG4ABvvCkCoP6H86jGgwS0LFEqew8b62gQQpNzSm0flnhRO3puMevWgMGNlOFQ8xrV4054hAQ+NAVHBssYCX4tJR7hhM0FJsELSux1uTetek8h4h+CtlRgZq5DxqIbgPQ+ARzMeUxmQREtsbx3TEksYY8zi+EnNcTSadMN462vD9BiFe3NJlx7PMC1uqhpfq8aK4nekPWu8K4+KacTrvLQBUldImJQejEm9mLmVSZ443gj0wziSeD5SW+k1z5wqNKQ+avuRupvScyx9552hMns1VNCbBLiQHRVmLhjwS1DTvs8rSGOyHKXGS4IKtnV9hlNWNM+XrE9jY8pzecZkHQqYGNPp8nCm5VQKQVuiCiXnGhR8MUac8z8AT/GQ1uL8yDpCe/K8zmCwiQmRAAGSIwSiHj4P94VYT6FEj1IZj+Vkpmwan7ystfSi8sxixgza2EDW06f03PvY71yeUgfUc1mvI2VajXGZKGhCv8w7E2WWKLslC4q5pVRlFlwyhrLNSXIXKvuG0ULjUSKvpqfIGGXUk4UItGP2cfGUakIZj1w8Y3ktgXNhzyH1M68lcEkxBCR455NoTJX3oC3OiUJMKXxkiTDOLwnlVuDlAiDPk+glowGWVD8kEZyo5072A55ZWRABMyGGL1An05FM+95lPpxdWUuRjGRa35B5NdO4Q+d9oFl76WeJv8umJQpy1GNuvGch50COxvRQ/gRQ7771Auft0ehAYyq4Brninq1eURpIQzU182siHjJ6iU0nBAMCmTz0DvNoODaf9xrjGbylCZJM6pPHNTGM3ZhNXZ9pc1Rw/Mr+GhPzJOqNVxaUrInMZBzGpdPQJDJq7DinA4ChMnqElTn3k4Z9a9hUL7KApdyAcWbZtQw3xu5qOyo4ivoB4/myzpA0gPfn7YkCTQAKIvOaSSgk1wX9wSxqNKoyqzqCwSRvT+ARDRRhLwWyNIJgZfBIfdGAsBSK3tXgiU8i/T7z8J01Xf80CZB8F3QbczIDOE5qcN7HDNVkdHDdaTfncrpgqCgYop0D7NnMR6A8l0RCe/bsKXze0dGBjo6OwmfPPvsssixDX19f4fO+vj4888wzE3rfX/7lX2L//v1485vfPKHrly9fjh/+8IeYP3/+hK5/PuWoAJ3hIF0UFjEmlrCZwrzET3ARVdprGuMu1BJJTwwtZO1OlXouiEq7E9pHIStZFj1Val1SSzoKAFkTVYAKVvRuIo9lYSIE6wnSzcuHzYKbHmmAXPwaJqmAKvlybh2VRz1gPXFKNaQSxGMJ4GIbW8UMiEDXJ0CeRKVIqVSMSTftlEu8Jz0kANSrqBuneC0Yc+vj7XosDg9Dp1LlMjnnUrwn6WhQgjUDpHheCBbhDBgkBTWRcWSyDVtaXKaJoeIGrwlrzMYRKb5AY7pTcMs2JMCrHchRn5EgZAmOygt/z9odkjwCIVVSHa2u0M0nt55YU14ndOLY13HDt3Gaakl3UOuyJuiS9vIO8OacS7XGM3GLbGA8VF4BIg0GqmREoAMIYJZYaY2xM55Jvhsu0Lsa08R7It2USGKqRDzObP+0Hsd8ZCNAdQyluOk8hMYNque4FstLKRz9IaC07SB0U29MD4ANCMCy0RaBlWvEdmTisHiED6ZMsraY+ZljIesI5UnrACR7rRMqY8I4UwGceQrkM0J8ZSbgT9dFUgR9BKyAJPSR9SPvDFmKfSLGBRc9p3xvbRgFWnrwtBGwSXu58Bz1kiD2HeT3ZNRQAtNoEKMyT9osx2nWHtZZZvnMO5IYzzYawAcIElxU+m1oA8O7rALN8w6tlzN4/4Sm2OZCUhdSI70pN4Akj141fsc1PRgthGorCWi8THwCKi+eDF6jyirXbw8wrjM3OrLOfxcNkvp5I3rMGC4QXhATDykFVbJpKvPGmzkr2X6DBi7gRBKRhDWwyE7SGELu5+30DCd6jitcULy9Mc6RtqghMUCkjQqbh+UFED2ITkCCGB8a09OYLR8IY8ImlHKIWWLFCE1Kdmh/Hz2RHlAKrvHAA4j1ak/08zyV9zkHL6ElemQKadoSN6jUcDEE8HPODdJOaeDQsgkQZuIgNTJ3CivIG6ME9QDRVThWQvmdHl1GAyLnHp+pz9F2lHmsdGEx2HrOH1m7jeE0GC9C++UdScGLSUMofDDUc87ocWCGcaRev8zMKeO1B8I4prcy6JIJ7BErTHIU5q3X9uEcY1hDIjqFY/xvRxKy9brYJmSt0aikBhGuAW2x/Erxd6Hf4EI2eyAm0dIQFnPkk+qcpEo7B594IA9x3aTweqGp0xtPsJlNr4VsvuIR1fHrPXx7CsfzmY9UeQ4xnfPmzSt8/JGPfATXXXddy1tsXGV4hG/6rJV88YtfxHXXXYevfe1rmDt37oSK95u/+Zv44z/+YzzyyCNYtGgR2traCt//9m//9oSeczhydIBOLpbGumdpiFQYVbnqTDQeLizoQUtoSIxLbTiHz70CHCaoUSWMSoQLGT8Zw2ctzZaiq/F3MtFTsdbB0cJkAKwP9QgHt3sFuo3OoFirB9NLrIAubihY8GNCIm8UOa+WSWZT1SMFfAR64ZnULAAmAtIgfB+9DFZI1eDz6E2m0sgNnZ4EnkUJUWQtyC17nDXmMfMaLxbfGZPURItoLoAxgBh7HEcAZ+F5bCcASDzbDKD3NChFxtJr6MxgmwNKUSItOqmHpCc5M65K2XgER6RHi2LpI2jhGE4a4XzDmC3WxJ7KOAmWaOkPjSdBobzR4xv/9uJFdJnX+CCfhOy7HM+hYl4SX0QrfsjwSM9FnFdMPsCzUHWhppXWISQISWM8D59h30nwx3bgvxHsRdaALReNP6TAZpLVuWBQMRRrjZsreI+8eEfD9Y32aMjJLQCm5148eSGZWJzjPoF6UENyqTAHG3ImJfuU2W2dOfvWZUF/msojU+gJYPuGdcCpF9enEQSTkqpzpRGU3XQ0PCeVMzK9tCuz9tYOxDq17+VZqgJqSUdAfH8ubILagXitHqHkod7kpOE1g2ha5zwIxxcldWNwRDRE8kgjnyKu/QSESVRwc1HqQvxtooo02qAeFi+KbTqcaxUIKoE4FzTTqKx3mrkWwuoQWq7GxQF6pqdrxHjGvIOWP7lGYq+slymt58Gb5KKXFaIch3ZIoleHaxYpfVTEvbSNxKkmo17L4ktKPsD5RkW3uB6qMi9AkywWXT8bITFToHknQFqM54STRC8pzyU25Ueg/erxEtaJZI63INXQJTYeEXq9GnUYPgIBEdLvyL22lUskTEGN2rkCRq4HNIa50Ekgc4pDXY/KcQJQa8HIkHUm0dBqsmp7MaKGozu8Zid19ILBxzKkLpxeQbpvI/Y9jRrhPQGccU13HnCjEfACCMmRGMPYadrC1IHjlHtQIuOVcZlqcJa4+9DWTo/pYV6FQgiJABlvYi0hdHU6DGzMa95uACNk7RfPKMEmz5eEOBqS0Vz7nWey0gBBo7WyXDzHSTSSBAaNZIXW82ohCR4Bpe6bvVqp2SiB2xzx8xxhHUikTWuh7r4tGC3IokrlHPnQbgYMe9L449gr7KPUn1gfdVYE405Cw0Ye1iiuPZxznE+5nWw1MWx4j3p79N7pPWmg+4/OascRLc8BdG7duhXd3d36cdnLCQDHHHMM0jRt8mru2LGjyftZli9/+ct4+9vfjtWrV+P1r3/9hIv3v/7X/wIAXH/99U3fOeeQZVnT58+XHBWgM6bsd6qAqwcncdFq6zzyNIneFuei5Y4gwBeThOhGIpNWKYFZpKDqWY/iTWQ2N/U6dsRzuajoOkcroS8kuQnltBZlWQTJz09NCn8CTheoPR4RpCWNcERL9GzFmCUAulhpRlgJ3md2UErIdCh0IbHoM8EFwWjWSUUCamnLBMgnTHNuQK+mhxcgEGJjEMsAUhhpVXUKckPfRU8cj5ZRgJUQULoCNdDGwLiMYBIKOAlowlEQHlm7oXAiKij2vWrhh49ezjancRRAeIbGiAGaKTdQhL3SeG3iJL7PJkgC4kYd6iDtL6CH4EdjGjOvz0gkUyfrx0RVTGikZTQUZqURD5s2Ei+eEy9MyOALpURyTCWjHr5mgBy9BmJ4SZUSGNvdJkwqWGQbcW5wnhUyMiMCWpeHo4WCgSPOX449pU5KW1JpaaLSC0BvTBOqPMepudflXhXQJAtH0FgWghoy6tJGMi7Teugz2nRcneBbvP4IoM3VgdoU0pKoDPP4JM2ebc7fVEAsxvfgCZKvxXscxm/IRsskQbURwEv8pzc7TO2g0MhzSTbW5pCMAEkajRfWsGGzW7JMXCOZ6ZheFJd71IaFim8o35xb9PRrUjjxltCgmOZRgWc8pgd0feZaQeo8RgyVteFDDJ6ht2kCHe4jQuON+QOEfpmF7wr0Qq5LLsQW2thwJ4o4gKiEAxjtrqFtb6ZeK+4JpDzCh6MkXMOrcZTGUA+nawLbOS2FYyjYcLEPAktHYuxTU1fpDxrE4pwQQJC4qHkQHItRgUbgmKkdAo5i2+oZnYDObWvAiF4lAbyMw9M1TgxDNZMwiPGNsqQwxlBZNZlHbTT2K2NjkUTaJwFRLiBPx0vuQ2IWF5g5mfArlTo7KoBCzt/UJG7i8a4dyBTI6fcemshI9YUkfI8kKYCzwOhxCji9Q0iKJHqFb4vjBYgMEBrcWWfOXZuoSj1x7fHIDvVy0tDckEJI4poEXpMYaSyjRwC6pKXK+k1mgMs9UAugVhMimS7XkAgBwkhi29CjG/NycJAGUOcFCFojLQC4LAfEgGtBR9jn6RSAGkBjckXEJEUOSA9kyKabzMuZN7kHvI5nnmlpwR9o7K+FORGAMhTUhj3OtpuTcIUYOw0EJgEQHBXROBadBKqb0ouZecma7ZEiL8ayZqEfdb62RSND0BMTM47CETFHtDwH0Nnd3V0Ana2kvb0dr3rVq7Bu3Tq86U1v0s/XrVuHN77xjWPe98UvfhFve9vb8MUvfhG/+Zu/OanilY9IeSHlqACdNhaN3kilYGReqW16Vlt7tPQBASApnS5xGkdEwAegeECy01vFKh6VVqt02nTxpCwopQ9QqkI8IDsq1SGuzcckPC4qxfSM2ky2VtkAIl1Fz+Rk/IA3bSXl5LlhQDwk3VJY9ZxOoACM1WPEI0FcBA8EQfRi8gJu6oCAQ1XOY9t56cO8wyFvt6DLACLfDFx94vUdpM6p4UDiTBt6/IYZPiWAZ+l5BBY+iQqZ9qkoAdarrTERJomNev1EsQtxUgZEOiigV3osrd4+jgtSffm9KqeIscSRhkhKqSiLqdM6hLEEtXyTVqznryW+0D6BlhsTM4W+E7Dt6WWN3tXcMjXEyED6L2m/2oaIccE2JoUU4qQRzzFlFmGb+MvGsrCemmzIxTWAmXo5R7L2AI4KNEL1EkMVAM6VVLyTmcQzQ/mTcp6qKgGxLDSQqLfDQ2KDozHE1+K8oge/NhzKVYtHpD3vEow50chSzLQdKpZIoiR6EJ3Uq0AxFo8us8cyltg1Yp0BwzaQucgxCMjUMJR9PlsTMYkBQBMNiccyqXvU1PJPBSd6XgABTMPGEEGaKLPXOipRXs9wziQJh5076u3LxWBARbPNAW2MM5cxIwq0DcMIHrYwZpQGWzfjIwseUHqYHKj4h7KRlhkK6IE2A1oBpAfl/OE0xONpJk/IMxyQDocjEhQkpXaMR7CfdQawp7H9GfQ4DPad3ieJiyLbIJwnqu1DJdjEr3LtVHYEpL0QvJ7paDx7UUMXhIIIRMOEnjsoHivuxRagEgw4eoXEq63GVR/7RdlMMsZIUy5IgpjQRtcKGbpCzw3HgrgImrS9ooIOxHEACafJ2x1cvWh4sZ7AcJyKGCzbI6PFGlTz9kSNdByHIaYWIZMxwxCk6o5e0cTpMSnKGGO9amFcImXiGcCNWtaVVy+pnkmrBghDO6ehT3SdzBhjuZZzrkQae2A2aVulERBz/+M7OUZSmc/2c/aPk3JwbGiWUQ/QaK1tmTrwTEo6EbK2GOLkfDQUuzzE9tJ5oew7OS+VdF9lUMn8Um9okghNH9FBQRUjCUAwnFQgcyoPTAG7/6nX1iwVyh5ySdS7SN034xIwOgjngjGyFoxNidNEZDYembHK+qI0GO6yWmkOHWlC4/Vk75mEXHPNNbjssstw7rnnYunSpfj7v/97PPnkk3jHO94BAHj/+9+Pbdu24fOf/zyAADgvv/xy/NVf/RV+5Vd+Rb2k06ZNQ09Pz6TePTw8jM7OzskV+DnIET4aotBan7VLEhNjfco6XFSceT6bKE+MiyF4AYTWZhZBBbXyOwDx3kWPFgBdcJQmJwuepYAkPKKjLVEPlBU9GgUGYMl9WgbP58QFNNzgdQFUOhxEaWAMiCjiPLOO59ZlHeEsUR6UHjdxFEBELkDRxjbSEpRk9PiVlC0tQwRzurGL8p11hg3JUgppXU9H7cZeBIw2uL9A7ZVNhGMiUilLwFlAUsje6SWeMJRXz+xMze88b5Fg15fanZstk4dQSWPbC72adchTp571EPMCvV7jV6V9WQ+enRgoL8YDiLgZ55Lohc9Sai0ZACnLCG073bRYblESeC/HTVBsQ7sxtjSvBcu9y6Cxv9YzG8BU3BypkNpNs9Hh0OiI4IeU26zDaVIh65GhdTZSlY3SIOOPYFkBgVCfrJdS206MVkrzNkqoJmXypv3knrB2xHgaJnayIEQt95KZ1c6TRMYVIMl2khiDPFWiRijjDSYgDjF0UC96ZCLE8vP7pBGOX4lhBw7IzRgc9UqvJ209xHd7yRAc5hzLolmnZQ1Tj/soIsioxbbPTIIT650pxCsnUbEK1xUZF5yX7NvacB7jviFznIBRvDcKPERBa0xLCgougBCf1/B6L0RZ0zhEY5DI22SMp1Fpz+ToLl8r7jNexpUmVTEJb0L7yDvqIdEJM8YGo1Lw4GWdiZn70LGtnlEj6tmStmD5ea+2oTBnFChmZNe4glFGvaCieDPjfJhDuTIiAGhGzkJ2T3lm1iFnerK4HtAMtDrQ5RazfmiYhrwjUC69jDPJBuwMmKVST69bKZ6RcYZ8Fo3NFqgRoLHdubZzv7fGr6wzCT9kf3Bd47rF6jmo8UA9tIZNxPeEa6lTyJ9GkdYjiurSJlwPuAZK2+Y8GsrG8TJGOIttqYCJelMtjnM1Aggo5I8djxzzbItoPGH/xzAhGph5vcZrcwyk8l1ixhT3N+lfX4tsL3pEkcQxpyBZzkZ1Ps43skUsm4n3cPxZ41Nu2oJ7LZ9TO5jF/QyIY88X15W0nqs3MR3JCkwolifJiveosRvBmOKFim8Nyewb7dNGNCzYNVPfJzTfPBUatjxH4+AbwZiSmLX3SBQmEprsz2Tkkksuwc0334zrr78eS5YswXe/+11861vfQn9/PwBg+/btePLJJ/X6v/u7v0Oj0cBVV12F448/Xn/+4A/+YELvy7IMH/3oR3HiiSeiq6sLjz/+OADgwx/+MD772c9OquyTlaPC08nJFi1EopwI3VYXM2N9oKfIQ4CVWOGFU6XKp7UK+xqQDpsYBqbzzmNsZuHcxJJ1xFIhmHU2bLC51gHOwcOr4qQLFJ+hG2ykUTImLnhsubCYBVaSodBqbwP/Nf41jwlT4tERcUFUi2MtWir1Xy429ATwAHbEcoUHCYD1COd6af9J/1DZF2Dr5OiDvBYAYWN6zMqncY6emysUDHkUDQSsi2YKFNCm4JKJfSTWhhuyM31p2yWW2yEZ8fBCWW0C3mmxzZOGR5YSvIji3en0cz2ah7QZ9h/bh0lILFh0MJtO9I47D4zOjIYNUoQICnlOqHpMjRVTY1ggnydUNKHjXLO3OqH+DHsxWvjoUTKKsnrj5Zka69tmx2yxDMw0TO+fxqASzOWI3gqEeVgbFs8pAaCXceRCuSKICfRza2zSpFEyPzSWW8Y/veWZxqeFceyFlcCYUespd/o9CtR/q/wlo2Y9oqdTYiKnShjDZGO2gRhDGTJpGhp9TroxNBEZxLDB82mThtdjlAjEawft4uUKhpyarKV5Lby3YMQxns+kEZIvKUiQTLQ2hMLlzI4tBiDNoFg0OGn9kxbgk0nF6EkkSMnkwPVpaWHuMd6urODBjH1r4FIqpxonpCwyTpN6eDfjwBOjoGuyJQGkyajQIqnbCh2ZCedIj4OAT3oOrQFe6y5GM7vXUdkkhdTDN4HRPAnfcc8L+QDi3oCk6InTrMDm3Ukjh0f4N6+FbJtMaEJl3pk6AjEpir5PFnpfi15XHmGjCvRoLteHDLXJaK6eUyB40pKRmCk2m56GWMKaAR7SF5qkyYwlejn1jEYX5ofScWsxlCXrSCIV1PZxewKXFg2BcAbgCzgNrAsHnpULIBg2dFwD9GSRJm7Hep4ACbzOcX2XVoihCFznPTxPA8jCPdEoFw0vvJckEI0TzaOBhYY9NeDRYCTP01Aj7gn0eHLMuPgd9yRA9hGleUK9cOqRluRLNHwB0YigtF5pM8aLM0cFWQqaiCkzhuQEaEyTjMKkF9Pl6UPWWwJxbV7xEurf9EYaCmsEe9AJk9fCfFfWg3MxAzR8oU+4b+v4poEsdchd6BeGiGnseHuibUEDtIropWxfuJKnVBgPzgfvrp0XKOlNR5w8B3rtZORd73oX3vWud7X87nOf+1zh73/7t3+b9POt3HDDDfiHf/gHfPKTn9T4TgBYtGgRbrrpJrz97W9/Ts8fT44KTyetQQx2ViuTseKEC6PCR+pYuB+aVIhWXz3PrWHAl3gMfSJW1gS6QHs5tFgPAhYASSDB7H4a7yeLM4FEwiQTujnwGdH7xX9JHaaX1gaNW8tsjFWDHmdC5ZYgs5BaXcEjdPEkSMzbnNIgefZjbmiZwWtFYACl8lrF1p7XSSW1Pl026gYUZKgyJudikkZEwJ/xLEMX4+U0fbqxzsbzPaH9A4jyws3RgD3AWMQRFVZmzy0DhhCXEdoqGTWWQHlvtCwGZSsXwBuPiCH4FYWYccAeGi8X3gPNyEowbWOV1euWBk8hLfpKBxcwTtoz+yZ4GaT9UmN9N8eKaBZlM9ds7COfk7dRgSq2He/hWNWYoNzETnvj4RLjUaPDqdKgY7EWDUNBsQhtxYQU9EDav10GOWcWhcyJITsuvfrFfuAY5rpgRY8FUo90KE9jmlNAbr0I6mHLY3ZYe46ketLlbz2mpIEpXZ3jua6ISpiUpWCMYluL559Je8LagejBkH/TenxWOup1DeS5m2m9aITSZGQu9oE9R1U9UCNxHsJDz+SNWSWDqDFP1/mYoVI9MNZzp8aBuGbSk6geiprTpCFsCxr97JFG9FCRQl4IFyDN0qw79sc1JJFSu1NjJud4VEIRvauGOkhPG/clemGTkTzOX1Hw6eHhuLaJVXTc+jhvQ/hEDtKCGb9pzzINbJS8AJbYJ8zwqvRbiWHj87l3hfMbvbZjMKbFcZKn8WxmhiQEkJcUvAqhz8zz0wgCwx4m4Lu96MUsgwCePWnXe+tRYrlsGE1kSkVDR9YR20zBKJ/lzLPAvS8aLQkKyOLyToyKpEFyPTV7HnMuUOdgH2r/+rh+E2Tw3VoOrntCMc+Fjsz5oMkPa3EscsxkHYkC2XQkD78ncf5Zo3cB1OQxeyqTb3kHBZyxcNAyaxIlqW+SQbNMMx7WUn11fOdewFUERnwv9yI447W2OheBvKGJO9J52xJdt2hMJQNA4yfl/Xq/afNiuJTT/U5pxzTAeK9jJY4jaB8V5kMedUqX+zBPXXxH6DSva5R1SOhYF9Cl6yH1PVlLNIY889pOBa/qUYEyji75/Oc/j7//+7/HpZdeitQc57N48WI8+uijU/ruo2Y4hEkKjePj4s1Flvx59Vy1F5NK8PiLtO5VeVUPmljnlDYkCjkXFwDxvELZJGiVLNBlXbTWqiKQE4BE+l78F/F9iIodf1fFgZtIzaniTgWHwDiAQ1L7wiKScRM3MSE8v88uFFSSA3CJC63SRXxsE4IA9VQYSQygJOXZtqkeJeIDoOAh8mwLgrQiCIoKrp6fyAQm8hzSpZkRM685jeuNFJJinfVvF0Ezy5M0EM92bLAPqRCH9gkeoKiQso6kHeoGI5s9j9yhtZAJoAplkfFFIwdAcAftl+gt5PdQirQXKq+ejSZl59mK9BLFxFpsjKjkJQ2jfKXsG2Z7NZZZQ0FiAg8aHVj3dDRuZCFWMoxLBX8ugg61BlN5s0Yb2SSVpuxCWwYjUKQFK41Pys04XtaVfRM33DgeygYdxsKq4YptyTntAE1CIfM2GQWQoKAEaXuyHAR5dV84A/V5F85XAKTWqsEt92JUkrKSLSKg0hod2E6Wbq9j0OqLnlRLGvjC743p0ZPFNYZUPwULNRqPfKGsXE/IFCnEKXIdTB1gxxHBrQCowjqJ2Hf8sRZ/fUYSKGSaDdXQ0Wg80X2G2ZprEWAy9tJa0HnsCgA9m1I9v5A9wcWET5YyzncHr36kHmoiF5adSqmsSwRyQAQ4pLz7tqRAGYz3xbZ1pI8mTCQUwRXptKQTU/JSfBfDT7huEiyU4+tsUhTuO5ECGuaYfTbnPJkj4WWRVaGUYlMOjnF+x/1aM7RmESz4tiQmI0oiKLQMiQKId0VlvOAdNG1c3ts03EHGMe9X44MYwVThl+N+IgXXKbU/rtsRNPF+xuMXQlVsUh0TxqCGHgeN92QsqfYp50AePO0EOgAKZ2daw4u9V5NrJdCEWd5BQaUemWLWTnok1WDPueugIVW6P8GUhfM9lfKK4Vc9khwniSmTjDGtC9kOUkceWZLUvRxvg1jWJIJXNdhl0bOqWdmNXuYTKQv3VRaJbADqumJko6HGrmVhnCZaB58gUoyNd7MwfmQMWdDszbvDOa9R16FeqjqpYW0dsSJGikn/vIRl27ZtOPXUU5s+z/Mc9Xq9xR3PnxwVoFMVmby4IDBmUy1TPnpuXEa6nZO/w6JI5Tc3i3xQEoD6DInHkLg+UhTDAgUFAiwTrfzhg2j5LFioRLHJ6NkxVkvG7UUPpNOJrYoQQUtNALMos6pwcSMzG5q1mhU2ZXmexrTK4s1kCVkHKV6i9IlXhgfBx7gwJ20JBYvqmYUopS72SaDgxs2VXlT1LNbi9Sxj7Fdo/6gHsN0skKSBFnhlxXdlHQ6N6YmCVu1DBZ7FDcBKRk+RI1iAjkPXiMDKPlM9osYjoEqIKgvGs2f6JlL7ZExobGH0NCeSCbUAKmSjC/SwMB7Zn0FBjEqYJssRxSHG80VvaUjxHxVaL3HATUqVj2PUHs/DucaNLi21LzMukxXAjH/qCfARfGisKpUnLxlgeU/mCwCecUtq/bZDw1rEfVxHYhtHb3Bt2ABrUX6VUiT1BOdjHgE2wS0BbmKNAHJPTuVpqoQKMvvWlJPHIuk6JGtXbZgUznAPPfj2fGMCSkA8D5Z54aIBi/HvAWQGpoP2VSHJF98XlDLGWnL+qNcUKCjJlnau8cOkkiP2gwV2lpJoPbg0rBBcRCaGKOYt+orj1Mbnh1/i2A8ANFGQY0Ekr/VOzjGVeZ61m9hnF5VZq/RHDzVppsX6KQNB6wU1yhCwFjx9BM30LFFhTZwqnc11NwalDgPwZTzRSAZjFFWwQC8XYruXpZB4D1CGU3hv0RNk40/pKQJQiCWNdOiioq+eaB9BLQGm4zyV/dTuSbpOpNHbyXeH/Amm7Im0kZOySZ9mbc7oGGZs+tjO8OE6DXtx0ctG41xqgFqguHudu/TScazasaxGniSuU+o99qy/jGmh6rKOPkEAVwbghkaXa+gB9XEeKsuG/c8xQsMq40brsje0mTnDMmQxXt/SawsZnwly7brHbqeOIe2jOgfnPNuX8ddCN847kqbEkZop2LxTmQNpiBnVJJLyrMSEj1iDkRokxLCjhkoYZk8O9eTHieLiDz2W6pBBIR5cDTIuHiuonlUfx0HB+EqAznnciLRm1TfL3uojTWgcnOzPS1jOOussfO9732v6fPXq1TjnnHOm9N1HRUwnSN2TTbMxTWKJGBsjCm2j08Ezq5xYxjVWi7GHAtZI9QLChGp0OvWm0JPnnSRnkLjQEOtAmm9M5ANws450VIBKc1zU+AyCFl3UrALFxVsXai4oKASBFyySBA98NzdG0viMMmy9q65uFkBZcCxl13FPkCyTluoR+sLpoq4U0/bwIlU65NnBiu+1jvRc5HKGGj2oFqgDECpvMfZH68K1TqiV0fMRvZvh/U6BiFo8zXuo5FL5BQDXECVWPXtFwKq0TtlwC/GCoqhHRcGZ2J6wkas3ySjSmfHOaz2tV85HQKPJVEyfBC+w0w2bY52bODcJ66FNR8Q4Q08jvN6TjsY2ZJ1BOiD7yoxj0rY55wiGYwKD8BzrsdJ4asbwsp2oMDmoJZ/XwwM1mzk2N+ODioT8rlmpzRhnxmftx6S4FigQIcUWVFy8ZnJ2Mq6cjOdgvBKQ56AZtfkOPW6mEdehKRWZH4yr5HxJGkCjJ9SHCbzYhkxcZim2NLgwAZka4bRe5pVpfBaTW1lDnVVyrIQ5GNdFrvPhO87NwNxI6Tku6Tku93B1GWttCRI5EiMnWBEFzNcCLZUAS40TjahII/eqnNITEhT6UCilpBGY0NvnYz2BOHa4Rigtz3xnPV+NzhgHzb2FcYmxn8KYrh2UPAESNpJ3hJhFGhC0DY1HS3cr8VzxfFF7NIc3NFgAgGRbjTGRUOOLKvKpC3pYEvsrjkED6IxyyyMvOFZI34vtC/jEg55wbT/dW52WiffrHKvz5WGs2X0tayt6s+ix4NhsTE8jxVTGLvcphmCkIyiAYl2bEXULIOyFuecYAQBSy2Of56kL50f7sF7ynMZE4+sj7VXjM12Yawmc0iYLrBEaiEzCnoInMjW0cdsnZmzbOaZHAxnaLg2kThgL6sHO2BDxfgAh3wbL4KBUWwVk1Msyb8Zp7NPwENmjpO7BgBcBncuhx4E0iXjteSQLn4807r9cI3jcTC7sJu8gx4QF40XG+cV2F12K4jzCMS1sAuoaEK90yasfHSa50tBJG1daay2B8yE+2qdOj+xRJoXMcz0OiuOBez69+PJvOL4nMDog7awJKX2kFiNHzPCrY93UNQ/lPrLFN+1JE7rnJSwf+chHcNlll2Hbtm3I8xx33nknfvrTn+Lzn/88vvnNb07pu48KT6czrv2QXVHojrIY0yKmllRZMEg/oqKXmAVRvXxpVO7pDVWAwQ1TrZwRsBQ8KN7rtVl7BEdU1ug94ud6LAQtsRZcyGJhKUM2eyolay9ucPqdeZbGTsnmzraiJ5N0SsdzMGEWRB/bJcZGyFcJldOSIlqL9xe8HmwjuTcCE8R3OrMZG7oHYx+Vcil11XKKF5teFGeUCAXz3lBbPNQYobRh2WBzOeZEaTH0oNoNWDyjpHAqXU+OeLDUFlqSSUUqHN9BC78n6DNjRNrG0hhpibbPcDnpkKa/Oe6d6TdDJ6P3gxLO9TT1oPLBmEDjwVZacSnVP6l/BU+7gjWjFEh7k3oYvOhxfOv4NcqiPp+eFVKvmEBJDAEc4+xPxjYXMp2KccTGq8V4mdim1qNEj3Sq8aWxXbgxa3zbSOwHO1+VlpsV260MnJ5PIUBR0GeUX3rryXigeDPGawd8VBzlGgWhIPU1hjaE+rH9YsW4toS+8LrmpSVqWSizj8ojQRY9WgbkcI0uUIgR1xYatex4t8YDtkfh71zCEdIYcwgE0Kcgjm1amkd2bBc8BrqGGg+Vi8CJgEoz+2aRxk9DiH1m1p4oaLLUWY2R03U5AjSlEfvYZ57AOi2OYdZN10AB6ZqAhPug2XcKCi4Q13muqfSeOsCCwxBHajrPBWpgXkvUQ1TwbKshVdYjenAl/wA9SzQQsJx5e6JeKp3XBA8dSYjTkxjbvJ0xtlCWRaBqc8/3agywHkNdJ9I4HsD9Q+YEwzE4fhV8GUoz8wfQsKBeJ449rnEch1y3FMzFNlNDmyMFMtLKdc54SZqUOE1kxaz9rI8dz/Rscv4p/ZVjENBsvhr3TK8YpQSMm35PQ1820RdZ9jzmqWCixnCjlNECTj6HbdYwgJMx2LlXw03h3jx6VJ1HITs6IG1hKOBWqHtwzBTO56axwrDkqNvwLG0gUvDV0EJDLriux/sBaEbbaEg3RhHqVt6X1gHA1fMCy4vJytjvYR42P7NQ3yP9yJSj0NP5W7/1W/jyl7+Mb33rW3DO4dprr8XGjRvxjW98A8uWLZvSdx8Vnk61noObgUxC3fzjdeEXs4h783cSqavcFBWAJQjZa8Ujas+BUy8cIi3OZrG1MZIui0chMJkR47r03EqjPGVt0HNDmXCkTFeIHpxYx3K9fRqy1gVreqyfHv2RsQ0NdYPKORcxo0Db408KHqrEnHNqFMO4KEsdDeCxCn14ly/WxyFkYTRKZDlGq3At+zgPDaH9jFimhJ4Is8HyeT6NZ/dZEGyfr/3l46KdZF6zfdp+DLEiiDEzdsN24d0uA9IsvKuYebhoOAhW7tAX5fhDD1KXY/k0A6tISCpFBQVaP2t4sDRZIFqGyzREpFHJIZU5xjbH+6zH0bYVFUQCPfVkcDxkHk49IdEKy7JE+rwZi9L3pO4WM5YWrdZsBx1TxtvsfWQ12OOKrEfZQxRGMR6w7W1cDOMOY9In42mW8aieMSkj58iUbltcs2See4n3zgwIDZ5Or0qqpVrBcU3wehQOFW2Xh3hUL0aa8GWzoqMAzRrUgHidGW/0dFKskYF9mpQOpweKCp8miivN+8LYARU26DpYVuISE3PJfvapg7eKhkc0lpn12nrM6eUrlNMFBoX3zBQcwT/3inB9KGOjMzyPMe1O6L6Ji55ONfqlDjY+UYGvzAsmZPE1oS07B3HAgeEhdl2DUTB9LXgzqaArS0f+zToT8b7E8zEJxvM2F5R0GlOz+A41shlwUqDLmrXfrhNMesc2Z2ZiJx60mEPBoyZxxFlnqkp11uYK89a5GANbOFM6gZz57eIeS0Oq2dfsGqUAXva/PAHS0XCvzSTNDOFJg0yr6NnKUQK1HHM594wwt5X6nsS9LRjPLU29qENxvCvDi/pF6tCQo81qB/MY0lRz8C6Ofe8Az6O2ZKyqQajhkXcYii2HEA0d8nukLCMK56R4IGGu0WzOhvGk8dgeemYkgAJ41LN3ZY7ZMz2Tuo/nSvMWu687wNH4Ygw83OdDvGRpDSJLIo+OEhoTKDFO04A97rHGe660ZYZ1yJpeMOpKA+fUBWUdiUkzQ0V0HZAyktEQ+i6Hd4mWXw18Lo4zpaUbvTPUqegMOSIlN0rCpO55acvy5cuxfPnyF/y9RwXozGvBw18GbB5FpQaiMKo3ykEOZHcFmh2pMHy2Wh5lolk6Ht8Vkp/4omWSz2iTjcQjHibtocBNk99AFk9SD51Qd1ITQ2EC19VbZbLU2Q1IgZeAmmguDN9pBl+pF8/VU5qbgFDWNWlAF0teV/TyCeVIPRrSNzk3OtNm4nXT3yVZCJVAXWRTs7AqRynGjUEpTtC4QuuhUmNCRsAWQailrlhABDaVgBdSEIPSwwEWx0DBUGGUdoJ6Wa8jgDfA1dJdfMKMn4aSZ635aaT1JBmQG+VZtywXy8QxZ3Tw0FbGyk1vfniO6Wtacel9Mx4iTXogfaEv8NAES9EIJOBBLLHJqEfeZqhbebGfeG/5dyp6CorT+D2PSALM5grEeUQAI/OB7IUkC0Yd1jXrcIXzMRWUcXzoO+VZPFKAwF7LF4cJHL0D8k4BATZbqlrhnTSn9/BwzR33PIs1ynnnkPpcs2+HIymA+gyn3msgGtA0JswX53ViWBHpqGtSOBIeOWO8zkBc22IinDD2FdxnXj9jnypzgXTLNNDCGtMSFL0EcYyxzJx3yo7w0P7VYxsQLfk6vwF9f+FMRpTGb2rmC99PA4TxjOY1p2NVY/BG4rNiNujiXqFGEwIrC2zbYI7zclE5BAL1LwcyGvbMmPRJABRZe6KsjrwtQe1ArvPdAnTSJ31bTNRFr59PXbDVOeg5pUpZlr5XSj3XP2b87EhQG86B3GO0K0VjmlNjKcdK7WA8U9J6U51HOO4mdXDiDaZeUBuW86DTeCY02zKTcudy3mDwICJmV4ZXwFaIU5Q5lEv/KntKQg/IeGBegcY0ycJu1mn2WVKHJsrRt5qkZ+moQ9YBYDTOXQCaTTmMlzgvfBKNBzROJKaMalhuc8Bwrs9rTHPa3zHbvUNDjNVZGrxXIcmSlMMYMtkPeXsS93Ppd4K8MLl8gRIcniO6mOyDOua4Tph13PNvAaGWlaCxo5C1wzAbmo6Xyb2CM5sVWs/utUYUQxFWg1Et6kv6GfdHGXsRBHItMOsEqa1Gz7HU1/gcH+vjos5CZ4Fei9gHTWtSI+oyClAt68PsOd680xpFCVrpfQ1ZiqMhnULdIZ/qjWyqxefhZ7L3vIRl/vz5+M///E/MmTOn8Pnu3bvxyle+Us/tnAo5KkBnOhrmZ5nGABQnkrWMJlmYpumwD8lmOFl8nLy0OmoslkjWBiQNY1FXMFtaOIr6lia1UU9JEsusHk9aQYGoOHlobGMhoY3xipmiy3e27KIAyef0UNh6FT0PUVmy3szgiTCeJy78BmDYDSXGz8TPXO6KC6W0c9KIgJzKFZXPcrp5xtAAbDNottqCskcPHa1zskizOKHMCF4ZKrJUoC2tmeOHChSKz9JnC+hWT4/QYmMZwspOKiUQqTZJw8N7Fzc6bfci2LHjhYl51GJqvMtq1GAmRSrc1gsg9eM19l3aTtoGcfMJnjhBA0A0IDi+g+Ms1FuP6sl9BKWksnOzQ+xfvod1o5fF5TaOVOZZiQrMuhe8mAbYZHKNg/xu1oekATQ64rXBU8nnOfUwgK9H9G4qOLLjyhgYgMiGKKxTHtDDxj0AUYSzNky5cM5RMQ/n/IZy6NmbPBZFvUamXgaI2XVDDRQlkELRuYLwDD0b0sfvy0Bf782klb2ZyzYsggwCRCW8kFm3HNPkEY+fMDTUgtEOkYLo8pBQLmSyjop7SIYj17bFmDh9Z4EuBzCLsqXX0YtmY/7tnNIYcieGp9JeAGMMocTEIkw0F5KGMTsojZyq2Go7owBkotEJ0RskbeZyADUU6gEgeLuy0lwkDZexawIydN+TZ9DTGI4iQhGIJeGZ7fsiCM86XKCv+3CPGmvFcESqPveVzOw19RmJ9rOCMie5GvIIUJRO6OPentqYaGMU9DXuKYDzDi7LleVg1yO7btPbTEkawthw4Z6wPwbg6bJgzKodzJXyStq0Gg094J1TsE4DmMaNAxr+4rJIx7ZeWL3H5Dwgrds7oD5dclzk4pkf9YX1ouDJ5/g33neNdaQBO5fzwDuS2M66qMbnsT4FO7r5Tj3U0i/W6KTHxMApuwL0ypdOKCgcKcPx2+Y0aRE99UqrJWXXrivUKYDoLRdvp2UxWUaVGnlKdfYusLRyhpPx2aTX1pJATaeR3hjVQMaGvI/rM72U9n0FA7yHli2My2hUI8XeNbwa3C2jkIbmI1oOhy77EqfXPvHEE8iy5iMmRkZGsG3btil991EBOgGoAmzBhU5ks8gz4J9CoKOAEVAlMhGaKGPp8hTweTyLDrmxEtuFkd42GCVTLJZKiVRPktxG8GM9lfxVyuAVPJnvjKJgz5iUakQwIYBIY82ACLboaXWInhcuNDDKH+vJBdmLhVAz9EZqCAEnFYRQD2c22tgvYXELE9vJcSRKixSrZ2gfQwkxbcNnMAEPLXHa3yVLKQ9ZBqBxlmp1FyofGzAoTREYWlGqm42jzOxGgubdUPqEtLny+FHvplhDLb0WkHPa1MMc68zkEgXDitCdYzrz0H6p2QyVRqsKPwoKaPDgRU9xOipGmtGoXFqvftjg5b20pNaicSCOLfN8AAXAqX3r4qar3hEzdsxm7kQxpxKl7WviE+1Y0A3XjA16kC2g4mflYxPYZImlTyWxu0lxSjIgR0mRceYeB3gIPc1FTyAQQepUSDqao96Vajvp2boZ0L4/jg8gjnuXRTCq9a8bpTuJoQXq3bbdyrUD8bxBtrU1ENH7QAXfUqR5jYJAH8d8iNcL5WDMUy5zJ2WsriTTKFgIKASvpPoZBSyOL2dCEaB1L1v4dR9wMHMvUjrjxXEuM6u37ltWJzDjKcZ1unh002hYt9iXegQKzylOQls4F9eyPHVwNajyG9eCSP3L2xzcqFflmxlfAYJRr31Gz55lAnlSY2VvUM9WEipFmiDj8+mZ4wDL26LBh+dv0vDU6BQAJUdh+Vrw5Np6F5gUEnrjpT2DpzjumS4Lc089h2Z9sQyYRIxumi2bII35H8rz1se1UhMDAdEgKO0M0KDH+4KHM8mcSSjkdZ6R+UEDX709QduBvGDssrkd1LCcOA2xScXAlbVHb2haj8amRgf3DxSopgo+aXzlPt/ukIwinP8LIB0OoEapxQ4FT6PuO4aiqnPI7ov2fllHcgHx3MdyWXscjCcacd3VDLt8lq7b8b3WcKCeTbsWSFmBWA4FXrbb2T5evOBcQ63hnTprW4gRT4R+bg3Vdv2Rpgdp+TFe1ezvYB+zjPEa1Yt9HM80Bid1D3Du0FtujHs2nIlGtqBb5GGd4jpoWEhcaxslw2ElL558/etf19/vuusu9PT06N9ZluFf/uVfcPLJJ09pGY4K0Jm3xTOEdaOpcTGPm4BPY4p+X7YUo7g4W0oEPURp7goTPKFHh141o0w6uS8+XD4X5QUw5ZI4LkvNs1lu7aZWfKZHtEAXvaBZh9NkR9bjRmEsgY3zIRWX5WTZLIDP26DHG1CxLiinpU0ifM5nFUGNboSML5KGY3IFxlmod0Usr8GKGPs+vM/HjHWIC36Mg/SRvmdoq6ScWLDIRlbQSHpYae2kwmc9O4xV1ezBBgTa9lBlRrzO9M7ov6Uxou80m2mgyMi7FPUVFSUL2vR9ZgMvg0xK03tTpwm6qNSF62K/uAbg4CW204wjFxXl8CwUlO/CJmnLIP2n3k56wzg/UyqOCFZcQwsOFFoHn/pQXzNmlBpFwMLxa7zCXDsSo/hbq7QXoFBmKyiwchIjaTwqltYP019q7JL7qOTZdz/fovNEvGkEDkqJRAAySVtkKzgfy6YedTtulBpqPuZ6IHMuxubK5xYY5GIEkmuVYtcilk4t8nYc0VtRz2HXSecj9YsGPE3EUopX1m4x3hF7zmsElMW1D0ChTr6kWJO+Z2P9w7UBTHGNDV6TOM6jpzWuG4ybdrkHGlHBU4pmTsMfFMQ4WSdo6KKRjZk+GY/KcU0vuCYw4TptFsF0NJ4JGIF/EbxmQplNsui9pFJv24rgKK9FkEkgrEYMPpbzn94jobg3Op1e5z1iJm3pZwIKsh1ix0HLRaOeT50YRsIljNXX8Jmc90UjFEFQqJiMZzEcBu8QogEXYc3TZHsCzkNdwjOYdLAhMZB5mwNGYNZNOR5MdBCfyDFuPnxHjzrHD9c0n0RDKpxk1jaaID3C3gXwWPYyh3Ec1/EkC563WiN40fNpEn5Tc8hnhHtrB3OQeusRjRPK+OlISm0Z53cYr8W5E9YrMWiAc8TE7Jt1RZNKMmkVwa2ZX/R0hgd5uMQFgCo0WwJLzYYre6pPoNepwUpE1xQDtAvXmvZUjyVjRMUow/jTaJwNP9p/XvqYdfeRaVGOCw33OzEy+eJeJ1To0LeG7mvfbevDj5yT7LVRV7RAl0U+oiUXZWHS97z05MILL9Tf3/rWtxa+a2trw8knn4y//Mu/nNIyHBWgE0BRyTC0g6RuFypEz5FswgDi4gEEi40sRBrvZSeZLoIu0BI8FHAGsBgV7MICbcGHM5tgk+IWFyulXI0GxYQxegF8RoqL9RjGe+VzHz1+SQNIMx4lIZtnZjdR3uz0PiAq/cETGS2fNlYIIPWICn20lttNUml4ZiOzXhalJdLTV2tuP37OsqoSK9TPggcxiWDJns8I+CbnGoG4VWi0DUsKrh0LauHmBuDp4WPCnPA5qTw2o2qZhs3FvhAfQYWQdLFC/3CMFWNxC/GfMkAU4Mu4UeOJAs8IgK23Nq8Z7y0TynDMqfKOaKEVb7VSFOVvrXdu2tRFpcZuclRQ9BwzYTBof8vcSXxUYq1nO8kAnxfb1vYxPZi2rtqWMnd5FBDrTSONjakpZJPkpixjmesHxy7p7jquAJBaa2m+sY0w5UJFNx1GgQZN0XNmTWyPy5kMpqi4UCHSsYJi2wZKuAAVAlgBLPAIR3oYD6ICW3ik3swxD8BFRgMQ/o3rWkT16i1WZTQocpwT9GQULPS5D1Z/+Y7zHJCxxrguQMd4MRkUoiGwYcCTid1SWmBi1/GiF5WMiyaash0nZoyoIU7mPsGpN9c5TypqXOu9a14LafDTjNkMG3HFxCDW2MX60Bga5m4ob9buYjiAWWvymiROIsMmB1BD9OLJswPzyK69xTVK6y3eZRo6CTDT0p4UjaqlsvPZ5bknY9vSJa0+oWDRxefokUAEjN5rm4T7HfK25jo1OqK+wn+DrhHAedKIa6IeT6M6SwRzAcDLGlRef0vHfRV1HEQjt6m/5hKoAajHdrChLGqoMVTdrB1oIJwjm9a9smSyjjjGQ7KkRHULxtYn9UDhrQ2beNl6ruNHaaVSRmXYpA71DtkjGiGBlDV2qLAf7TFcMt6tl1xBo4fGnROEgowG8RQm4sFn0qoyE0IpuGbvI9tBvag+fmb1MnqqLXMLANCIa4o1iFhRJ0ELFBjXXQNKk8gmAaCU34TjJo/7oHfB65m1JXHNZR1e4lTTQ8pRRK/Nhfpzyimn4D//8z9xzDHHvOBlSA59yZEh1oNYUOpFAVDLmCsqd2HRgi4MlsbBrJX2qApOKD2rU0AQzL3OKpp5cXHXczENILPp9rVY1InqZlGVcZwYS3dLBQCAKvtU6vTBUuc0fs7NkUDFbiAFD615vjOLGsueNLym27blseCJG1L0PotCIZZTnrOoiga9LFnzv05oj2qlHpWNkspbFutaaFdX/Jt0V9ZXKdVGOdFjLkw76KHMmdf2UI+RBVDS5k3tUkrowzay7Rufh3j0hIn/CO0fKTAFS2PTcyLFr+BF5LVKLy968MqeWgJ+O/Ztn1hvH40kQQFvHhPxOBc7uBjfVexfVXJorPFF7ySfp/OIyrodu1KmtI7CnLXtxblHYAVAMhN7jVmKsY6+CRQwuYaWLS8928W20jXLeH60vqWyPd9iPYwAQruMBjorQUk4BsVrfaxRi2VkUjNLnwWKfep8OGPQSlhjw3yvHczD2qHzIPY3AAV5OidZHnofrbdpJI+fl+ssHg3vEJSreq5lKXicWQwxMoT6G4o111Wps10b7HpH2r8zzJViPYpU5vBl0AwjRdusiSZJG72xNLipUtnKyu7ppfVqiC3OaUkek7q4/tn1ycR0Umx9AJSAYqSKqqEwESBq9w4f90ubEZ2ALil5hdjmSuE1bZBkXrO9lqn3ZbDJenH9UZ3BGDG0fIU9AGI8iXM1PCx62rwpr+0Lm9DMlssbQGOVVa4X9PrSsFiIcXVQgwjnJ6nGGhJDXUTGkDf3FOYZgXUSy1jQa8x6kbcL9bkW1/eYhyG2L/dUhj1Y9pbdp7J2+V7iUwNTK3pg69PD0UR5W6y7GglSp3uCrxU9nsHA6Ex57Dh3Ok4Kid14vRyd4x3C+pObsVhz4TPzPN6vwDaNx/PEMYCCqNeUehfHTwtg2AR+klhPSnlu8agU+z3bQduwpBOrIUFZRsXn699yn5fMztagRppuuK5VZY4g4byc1M+LXejx5U//9E8xc+bMps9HR0fx+c9/fkrffXSATi5y4mFIRwNtxYI0bqJ6pl9uKJWSkKcQV8TJRaWgRLNj/KUqmCXlmRuU9b7oJLWUNlXiwmBlsgcARc+lbFZl8KvlkcnO2DVuJtww2B7WM6WxUvSi6vOikqN/2/cYQM2yFZQmfXdsU/UqZXKWmYAmG6/FmAZQ0QJ0AlvQoMouFV4DFgEIMPC6YTqeNZpHhSQxn1GBUEqUtFUhEYDE3EawW+wX9rkFGSGbMWI5pCxsV44LbvoKdBuxPzn2FNx5fiYKlpQx0fp5VbhJVVRlSGMhvXosE6NcWYCsXZkU281umtYrA3Bs+cIcsLRJvcfWRT2YZhwYj4YqNTTCkGonY0ETuJjxzhjMQiwg+8AYeMreRAv4kjrnYix7rGNzGxRAGTdx+UwzuhK81eOc4bhWJdYAuykVndvh36wjdgDnoW2PcA/nVDSG6NEbNI6UvreGMfVOZ77weSogMVLIorKMJJylGDx3QfFTjz3PAM0RknrIs33NBRCaODmc3qwZcp/+7WP/qXKUGkXR9IVmlPacs5JtNYn9bddNtlkrI5WKpaJZbzLbyMxL22Y8hzYd9eqNtgY/fYZVOjm/XfS+eKHDEiDQa2UNVdybWF5V8rk/GmWTRsUCvdgFgJJ1BMpv1h6VVAJGC+jivm3mOueSuc55WR8Sc6+MaU0qJvXKJW7RhhTw/pCp2YMGaK7b1jBkDW+quLPN9L1eE4vpeZ4luqVmfbZ7Z276JA8GsbRu9AZ2acL1vTjeNLbSlMX2qfaDt+VA0Vhq1mTbD2xLpd/6+Cz1iJqf3Biz1TMsazLZIHmb00RGCticK8wdDZsQ8ELqMOdcJmevZ4YWyvIRPGvIh7SNJrgyfUdQmLcnESxmHllHouMla3fIpiXhs44EBZDGOZpIlnLPceU0Np8086yN4x56bA+/y3nebeIC/ZeAmECZPwTUQsNVg4FZO7QpzL3sd7IQ7Lxtos0aBls5U701pkfdqAjqaTTTNRJHuEwacPpCP7wU5YorrsDQ0FDT53v37sUVV1wxpe8+Kui1SQNAzYMZZYveG8SFjMeVICrE4RqjeMMHYJB6jW9S7ybPYOQ9VI74LqU+Iix6ubnPTPxkNCou4UMqoOEwZpdJBjd5snqwvAlAp9IKxIUkjUAyLhKxgEGJYZIae1ZguE7paqJYaNZHloE0M0PDTFBs6wLFQ8shF1DRcUBSz5G3JWK9jZ9TWS30n9m8Cu1h+l89SMabpmODon0f+zsRC7zLA3WYn1PSkUjlJEVZpTCGEPvaAJPy995QnDg2eZ6mrS/Yt7Y8OVCms9rnWMVIE/fAAHhDBw3tAKV/uQzwbfG9BSMEx7XUWcvEmGJv+ofJp+TZqaT3t8mqAoCIvxdidoEYi2bmiKWpJjCKmwd84pFyjtGbbjxURStupAwxbXwBxPJXKY+e00vQKyAGqQOyuKZ4s3PnbUWPqzIJXATvVorjXdrUev2mQKLhxsNlkSYOhPGSkf2RcTHjGgWzBpm5mocLdP7rmmNeyjAEa0yjwg+2U4xN8kxoYb3ZHnBZrtfDUFm9hx7Z4ayhIPfxDOF6OL8S7EcX1/+sI9HjEnxbBFXaZvQkIY4HMmLCBXxhqbGpiBYooYzDz9W4GOiL0WvspE05xnUs+5hPwHrI2Xc2GZx6sgnMZO2PYCSu6TTIKLWZQzoBGAtWNkgRvBRowqYNlEGShLo4k+2Zl7kMge6Z+bD00ZtpkoHp3gTO29AOqVC9CdaiwS88nMnsaAzSMxClHzUmXvvDN63Z4aWyRyZxHBT3Iaf97zIP50IsdN7m4VCkVNtzGO2aVfZw6V6SmTbkdzZ2PWEcH4qhHmx/jlW2g+wj6uUthflwr9M2MGUC4rnh4LjSRIsm7t/oKFw/2WeWLVDwPMt6XD56I679Mk/bm/dAAAWQze+VokpdLpdyw7zbjOm85vSIs+iZDplgFVx2huNi6BHVY3EymD6FzmlltRmhnlqgryaBWq2J/lKj75UcAfas0aRAyXXqkbWOCW8SatJpkavxKQy+sE+HmHibeNA7wCGMUxo5GCqQW5aglFPr4xCSJB3pUZ25GTSTuuelK96HNaosTz31VCG50FTIUQE609EcTihMBW+NLy6mFmDYhYHiZV6GGADD/zeJYgqJRTSrowViJgYS8X4YZUIzrHLx0+sCGFNgZr2AVOoYR1hesJ23MexmmpvYhFwUdAECMa4HQO6RKAgT67kBTvQmhA3MqwUsGc2L8Znyv1aLbGIWIyDUNan7cFRBSVnR8znpKfBRsYEvLmM+Le6L6kngRqN0YWPZcxGkUNmtNXLj4YhAy8siXhs2m1pGqqrUxYAi1KPiZssBQIwAxoORQw9Dp1JgY8iKvwPejj/vFKRzbFlrJRMCFOtTVBydUboUNBrvTFQcBOiVgBl8Do1XNUqncygCVBnLiaXwSpnTkVIb6cYa/rHn1Gr/lmJdAusger3ThmljLbDUQ+ptr+F7rVdJlYIcBeDtOYeT8FCrwPlakSpfPN/SIa+VEUlReeIc1fadKiEQY9+LF8m7sK4l9HiRAZI4paIysZallnLxKY8P6wUCBND6RO8NXqU8Pi9DUPpcoMmyrYse9AisSHFDjnDcDI1ENBQ24vrkJKMtctFFuQ/Qw9eIFHSlArO70miUYxyxznfWPyv2W4Ge7oC0EdZpAm+up+rxlTUkUTBsgEgW11C7dxRE5pDWfdTrERxqmMrK10dPcAASkRbLNUmzuZpyADJPGLem65AYf7n+mbKGo5rCPKJRpXyuJJVgrs01gkbTrs5HdodPPVJjdGNinpTU3pJeldTD+NbjaIAImmqG2cA9yqzDnrQ528Z52Ku89+E4v1Sytss8D3HhUl6pexgLovjbzOcwa62MPQ0NcbHfc2OMceZ7ADo2NQbdISiX1hiXez0KhvVjMiYgeOxSwzZxo0JZrQEgm8DFdU/bQtsmrmVKswbBGdQAlOS+QC1O4LVMPokGS9S8PtMadJLMw3tmsGXuDo73ovFaWVWuFJ/NkB7STe06xfYhNZ57AxkLlr3jYz9bCj/nTmE/bQSHRt7uVN9SgwfXKmbzleuth5JlL9DocyDxObxZgPM2Z5hMXtdWsgyS0Rx6XApp3rlH1pkE3ZDrXBqaPcnDc9IRM+dNv4Z2L657MAzCI1aOopjOc845B845OOfw67/+66jVIgTMsgybN2/GihUrprQMRwXoTEZzpMjF+xA7W+MDzcbDM4V4SHFhU3LQw2596iIIc0WvAxUzeyYTN0L1gBbE631AXCxouderDE2BG5AucLJhJCHvfVTebfF9Uqg/ypYyCRJXdFr+XRayqKQbsF06tBoOADfkUsyAVUj12vLv3GtkkY7WPlmsqSAaoERAVm5LzaDHeAzSzLhIt/rX9AsBbSFjsBcFkWU2l8d6GAWSYJZjwl7Pa4BgAfPxsybjQYKg3KetXhrbjIV0dV/sZ0DHNw0E9jNrhYx1i8DVntnFdtC6AfoMnwDJiO2juNkAYSxzjjRZa9l2bCtpP91ws+JYK1pyubFZQJ0Xxp0FwGVvof2sDHI1IYYq/BFwqZfHriX0EIiBxqcO3satljytCk7H2JCit4MKy9RtXOlojtpwApcF5cflHrUDefD2ZR7JSB7pmvRoSHnytiQqPPxuNBdF1IUxJJ9bSQzo4/NcI4/sCoILn8Rh76MiWlivNEYRoc9TB5/JOGxLFCAn9Ry+lsA1xDiSOjhzcDc9YT5N4BiDxHqmRpmih9QDMGcWAqJgZ3mhvt7FfcSpki5KYBKvKRuKopFN6lUwkjZ77sv7UtPnGqYg/ySl8W7WqjJ9tLCWm+ub3qMGGIDJhuLZffHidDiXLMS5rg9JI64BMYN1AvjceMaKXlTLsijQBaWtgyfO7KWsmzw+yePaYZ/F47NcIwIWfSd8AAmjvnCeIwCgHsE9M9FiNCrgFnD4xOw/3qPGRFPlBDdsbzlH1e6hNBbruGAZx/Byq6HDA6kUphAmkfsYjwlEwwQBlHNIfTTIkiauBtfSKQAEnvDQrPHwQG04VyAKWjBpEHaAY8gKxxj7tx73l3i/6TtEBopLzfxBLF+cq0VrTWoSIrksngcNAGnmdG2h99Enwn5y0VtL426Se822XDiKrKT/6H1MnJY6OLJ3fFhrXC7DknpZ5gOqV8Nr3rS3MYQjZPtO4dsS5HJRAMLQLOBhXXRAQ57lXDhuPolJxpysVT5NooOFTpHctM1o1DH4GRDXi/JRc0ecHEWgk9lr169fj+XLl6Orq0u/a29vx8knn4yLL754SstwVIDO2sEMaaNR2LTLFk6KT43CZK9VSw/i93axKFBgXNN3VloCT+/13a0yxsa4hvKzoPSm1DyrYFHKPHwtQTKSFRVqS6kwHqYC8LP/el9KCW/KBsANx2cEpSFXZbFQZnqFxENolYPiZofme82eUP5Oy1/2IlNpUEXJt3z2eO1LxSZl2wHNio6RsuJVBjplRSFcCKW4Rq+rKNJGuQsvYJmKlsIC8LfPNl6/slXUp6EhtP8A9XBFj4YTxbk0bpWSS88uQUX8HICOaZ8mca4QVLYoU8t+5KvNBh3OJAtKuwIf+73MWX5HS6+OWbnXGWUfYrHNa0kESAjP4HdaDilD0sjjnCqVjc/Rsmq8jY9tyPUFVG4S/Z3v1TIA+oypknR/I9RLkmW4zKN2MENjWioxlnkEUxTOs2EX6yaf+zTRtuE9HAvxOsA1cqR5Dt8WOHiunsm1qZ5fqOtwud8B7d8kz1tv7M4FkDkimjOpoc6F+noP5Dl8msLlAkidC4azEfNOJ2edl9fJVmOZ1bNJUoC4/hUuYjlLz9J2LD0DBjxYz2mZAkrQVf7OfM7ryutxk9e0vO5ben3puvI5wnYNKIgapmTstNo75ZpUQkwAmadtCWjYVclbzHFj7LAeH7uuFOapi/fr2pFAPUt2H/epgx9h3xDo5IW9IxqNi8/nO0gZZ9mDUSq2ddM6xWKbNcfSG8ttYuvDti4Y3rzZE9n3jNkzlNZinKoZT7ar+LmXz0mdR6wbjXXqjM3oVS/1F2K8pXrwchgvvWWmmXXAITIguK/W4tpR2Hv4HDM22b9c110uRz6Zdd7lPvYNy00GltWpAMB7pFyfjDFA2SDSJ7xX4x/Zt3Itn6XPluuzabUwNjxQO5DBpwnqXUGFT0fzYGhLom6RHoi0hpggTNaYBpAezMBswCHcKom6CGKZa2ZPVFDMPuXaz/ckQDJqxlHiUDtQRyUvDfnIRz4CADj55JNxySWXoLOzs+ma9evXY8mSJVNWhqMCdCajGdI8a7mBFT1bgE/yOOEBwCMqO+WNyigIhYQpJeAWLHOuaEXLEa28BA3hpHjZgDx8khQXUaDJW8uiWAVMlWzWM/NF5VDe72thEdHv5J36XLv4a+VszJosNHV5H0Gmc0Vdqk0U6NwbilkeN2y2jdyr78/zQhsUykhrslHGLaiA2Tx5n7a59LkTq3S5PfUzggw+Ly/9a5XBJCn2rx1T0h4t25Pfe7PJyHPKislYnxW+Z2Iioxzr+DPKRmhfXpMXlV2rSPOzsd6nHj4Xx/0Y3yWZB+p5Yc4VyjVWXU0dCmXk7+W5bBTmhPFFvJyKG1BQqK3irtTXkay0NrBeiAq1jufWa4RPHdJG3vSegtLAe6lEiYeP7yqACil/S4PL8yjJaAPpsIPrSOEz8TqNZKjlXpSQXAxwce3g/HMFw4fXMuvf/KyRF9sBAKTeSaPevGZyLcsR18nyfLKKtf2hMtuWBiAr70HNrHdm7LiGKEKZB2pJXK+8iXWxe0fpMwuGVdnX5GLF9axJ7BhW5VbWQuqJRtFTEA7ouqc9oLQ248ktfZeMmv3GXKc04xIAA8I4T0Z9NOpYscYb2Ru0HKkD6nnLea8KqtlLy+/2To7EMG3HuWLbj+EhEzFiakzqGAavMh1a32/KHtpCAIsq5Xncg0FAgYI3vgDsvQdG4/XaDub6QqiKrY/MsbzmkIxiTG9vK72nbIC3jCEgMBcSASC2zkqfN+t0bJjiWCkYz0rGO2W9GKDm0ySsMTr+Y7m1bg1TF2nfVgwmGse8c/B5EscLy24NvMhUB9I1xPShnZcuz6MepY1n9j3eZ4wdVj+KNPHC7c36gTIIWux/AJA46fc8JFXLPLKOFJA914mnNe8Ihjx7JqwKTzJIE7gkOj6SETprhInSwuiceBOilfkw7jMfjHdtqbZ9APpmTRcPqS/V94iT3KPYIRO956Ur5TM6h4aGcMcdd+DWW2/Fgw8+iCzLxrjzuctRATpdPYNrxADIMkArLI6pUSxKVNTyYlBYUID4vARNi02rGBs32mLgmcXKIVelyTlXUIDt9QrMqOgYqhiVu1ZgzI3mEVxxgYZRtDOvQfMq1hJpgKJdoJ1VlryHz+meIeWtuCi78u8WePpGk2LnfCxj4d0FYGU2DttcZaW0tBl7glG7udjxMQboczCeE6BAAS2Af9ueZlMsjC9bf2m3wvXm98L1pbpa5diVPzd9pfdyjLUoh31+wRDADSQHXM14MEoeOmfmmqUU6htoJJFnFZRntg9KwrJ6DyQl74kFi6bfCt6xvDg+bHlaAWEan1oqpxKnU6COJkmTkaEJSJln+cbYY5agQ+nh1vs5BeJGG0iSpAB6k9EsWLFlw6FiofUg8KFhxypabWkEmaU5zvqHORINbw4A6F13bYDPw/ssDgUK4K4wznVselWW3GgjPFOs74Hu3GJ+J+YZJot3YczxbwJSO6/MeNQ5TINeq/637WB+d+a6wh5glWpzX9P6Zt+TJFFRt21k1/48B5KksEbA+0J9wutarEXldcj8TUDrkyT0l9kfdU3k79aox7Fu32fnFb3idkxYoGo+Y/s0gV3j0dNy+GagrcIxaNcsltX7Qv+qgasWUuo1GVFte9k2K39e/k7Kn5T2o9TkeSh4/0vGVa7DrXSKspE0GN/M98ZjThaVlteOG6BwndbBevj5tylXeE6ua6rWgd+bzwttVPKSlvvPAfBqzIvGT+s1ByKIVL0pdU19oeM/E/2mvEdb9lG5PGOMbdZBhWsQn2nnF+ejD4a0NKMnPC+GFWSuuQ88ggfYGP5j3xpjKllKww01JBYcIcr8i+uv7ZfAgMi17UOd4v0uC46g3K6HR6B4n8P7ydVhste/WPKv//qvuO2223DnnXeiv78fF198MT772c9O6TuPCtCZHBhFQoVYFJCgzMokJL0QaN4AxtpMLdhrdR+lacPyMQbMSitFTO/J4yKj4NEF5QmAs+W3io95hFVMmqRcx1YgprAwcvMPZXCljQZpEts3cXCj5nnWCjiWlJ9X/qxcx1aKmKF0sJ3gHJxt+7FAnnlWXCydGCLy8Iyxylx+13jlL4+TsfoHRfDTpLSMeVPRA9vkObcbUYv3lN9VVuT0GiqToyjOhxb9omMnNYaSsrIyTju0rOZYxgDrqWa5Ddi01Numelklt8XnAIqKbskA01SmEt2xFagdC0g6+/14a8vzKO7gaADZo2k0Xo02xAubRYt3Ng6QtMCMCnkjK15r1zOuDZxHXlCIl9imxAF1q1WPPVYBFNdYrgecnw0BziZmnb+7VvPS1oWfF17umv8ea0/g/S3Ws5ZrQ8n7N15b2/UKZY8LENftVmvgWOOpXGcLoMvlafUMAj3uC+V3lT16ebi2FYjRV1qv7HCj+CWNvC3mE9cAAEVDsAWQQJPnuBCDTGOVqXfBYGWAvL53tNhf1vCm7y9RxPl52aitX/kA+ApreImBUwD1hYYw3sfyXOS9ZryPx67R91JaAKeWQH6s9b9chvJn+uwkGnz4PFNfx7IUHZGFcIum5zoHN2L6rdV8RNxzrD6g+yvXGJln1Dl07tnnJMXOaRo7Y7R7wag/2ijMR2vg0b1Z20weW2Z3mfcVjC8EhKN5kT2H5v2y8Lf0R8HAmklSyDJTLzvC6bXUayd7z0tUnnrqKXzuc5/Dbbfdhv379+PNb34z6vU6vvKVr+DMM8+c8vcfFaATjQbgG0ZZLE3qMTwP+nt5cdNJl7W+r7yQGst6+LtFGctJe6wkLngAskyVsMJOUlYGWliwJqXQj7UBlEWpXnYjKLVnXnqfjQs81ES1iY2a3tHiXp8DqUSIWC8H72lV5nJZ2Lb2HYkL92l/lt7bqk3zvHjdeO3eqm9KY8iVv7f3tRKO0UZmPjLW3fJz7EYyhhLpWo0tPq+RFayvTfUob9zjWTft3Cl7MFtVVV/VWkFoqcgDxZjIJsA6xtjjdxRDTdeysH3L7VwGAuZ65DlQS4vP5HflspCGPZUbV6MB10ijV8EJHZagr57HcmQ5kNu0yWbsOAekKdxoXTxtWZhjei05i7ZNM6O4yTP4Xp8Xn01J07DOszxWElcsn5W8xe9jgWcrXIfL37Van7UMVsltZV1IWrcNQT7X/1brITBxxadVojjKWM8ol6sQy1vejwy9wb6La5HdC61oghqZD+V1sbwW2bVsIqCllSGgDHzsfa0MC2WjGue93fftfa0MD86FMAg0K+5Nhq5W66c1VDWy5nJTWhkXygYMe13ZSMoqlNcxXuKNt7S8lhaMCuYre68FoGU5FOB0Lj5Y9boxxq4x0AMIbdDwY+9B4+lK5f04z1XHcciK93rzPetTNoK0osCV91juu6ZMBbZSA7p3BO+3PV8NgTEHhHXc9FMT6HakDZv+y03/kF7bat0w7WMBuV7H8uZo6ic3hVTNF0S8ByZLr32Jgs7f+I3fwD333IMLLrgAf/3Xf40VK1YgTVP87d/+7QtWhqMHdOb1ZqDRSlpNqFbWuEMuiAibLpWdPJuYUtBqsS18zkVKNuXUvKNc5vK9Y1ihm9453mZrpawIt1KEbFnMQjZuW9jyJLJQjgU07edpUlRYD1uKlsrC72nSWqkdT8ZTYCZiBNB7qKwdgp7Xqj/HUozHo3m0UsTGu6eVAtfqmlblHeuzFgpn4fOytGqjlqZ+xOvG+76VUt7qMwuGknR84DEW4Cgr8kAAU5l5r3PNMchTIY0sAD3n4DIDdBqNqEx4KhIlBY4KeKhIcew0GqW+Lc01/mto2+G6TJTFrDg+aMW3HldbDio644H08cAIMMbYG2PN1TOLTDlpPKG02iu8j8/U9xulTZXFEtDl+mhlvLVSHlEAI4cyiDXN6ZKyz35setYYBoKyIXKsfaN8TVnh5ZjSaxjikLae/xYMW2llEBhvHaGxwb6n1VrJ3/X+sl4xxlpq32OvsSAeOLQ+UV6DrEGY61CahmzLZcMq4u9jhvYACp7H1VksaLZgaSywad6t80ZB2zgGddarldixQ+NHeR+1ehsA5I1D9yPXwrKecCidoPycVlJui/H2PGkXZ9qoyQhbL73TtiXzSCRJ8bq8NNftWCnXgUYg20/e9D8whvPgCAedeQ60Mh6MJy9Reu23v/1t/P7v/z7e+c534rTTTntRynB0gM6RuihJLTaGQynBYykg4y0YtExZC9V4wK8sh1Le7WeN5q8L7y0/czLSatFrBWhaWZTLzyh/bstkrcf2usMpsy1vq/o0gagxNrCxDA11jN2HY9VvInVpNWb4ORC/4+/l7w4lra5tNU7G+s4qzeWyjFeO8nWTGZfld44lJYpSQcaaz/a5vH8sxal8b6vrssyUo9WkNFKuS5K0LicAjNaLnx3unJis1OuxbcrzmV58fp5l0RtnwXG5nOz/MRJuFaSVYWcic8kqN1bxo5e1LON5DSejQJblUGUdD9Ta+1vdN9bzWwCGlu8b63kWIIxXl/H2wLHW+4kYpMpyqP3SuTHWodHma8dbI8Z6Nud0LuCsvG60ekd5nZvI2mjF3tfqWa3atLD2lJ41Vr21bVtQG8vjaSL91WpcjDVOxurTVjpSK13iUONivPW+vK+X25u/A2PvWePJWECxVTtOZJy0Gt+tylYeG00GjkPoXuXPW61N47Vrq3fws0OxmlrN1yNJvAeOEk/n9773Pdx2220499xzccYZZ+Cyyy7DJZdc8oKW4agAnb5eD0OibCUs05Im4tUQcS5BUzDwWNe3UmAmQhdtpQCVytmyHIchTc9pRT8dr272nnL5JyIT8QIfqg0P53ljlWW8sdCq7zQeLGwM4/WJM9Zn73Np++bNP1xn0pr7uvbTWPeU38Fy2PuitLYw2uvs/a3qVS7HRK8bq8yxbsX3tnpm+d5DyljzbKKW1lZjor0tbKxtbRGo8fM8bwl0WtXDpenY4GdSdXl+xDcaRU+EoTn7RiOUV5QMT+8nIEpwLGN5vHmfj70eNlHdffMzx5JDxSmOpcxO9tiZsYBiKzAw0fJOhTR57w5l9BpnvL1Qhg6WY5z9riyt1rOJrlGHksIaVJrHPssK47u8bo1X3lZrW9P7tC4tyl2aM56JvVrWq/X6c8g2ONS+HwvFh44/RsYy+I9lmGj1/PFkrFCIsphrWvVZse3H71PnEh0HEypb+V7p2/I7m69rvd+Xv9M9pBUjoLnwrY1/rfpxPANZK1r2ZABV7oH8yI7p9HkOP0lP50s1kdDSpUuxdOlS/NVf/RW+9KUv4bbbbsM111yDPM+xbt06zJs3DzNnzpzSMhwVoBP1erMV6HAsDWbi+YkoNBR73WSUnLHKaRZsfyjrYKsylD8DmhMFTKR9Jqu4HW67l58x1vvG2sgOVZ7JlGssyysZOxN4RPmalvf4FolxzLWHes9Y75ho60+ojPZzsboWrjN9MZl28S0+m+i9h5SJegTG8xJIXJRrqwW6aFsb0NEOjI4C0zqB4RH4kZFwbVby7I1V/rpJBtHK2t3KAzKVUm/Al2NLtbA+fKfzvwFvM3Yaz7FnmYFivTI0r1nUjwluLeXrMMXneeH+Vn+PJ7x2rOtckjSVs/yO51um+vmHesfhvv85lXsMz8rztt4e6v5WHq+stFZNwCs2kbVtzM/HWqOYKG7cN4u0WqdL3x2WV/hQXq9DefafL5mIR9B87vMiK2WiezPHns+yAPbGMnSZ68v3TvQdY11T/s4z7n2sfpyI4aiVQWCifTdpwCnj1leezpeaTJ8+HW9729vwtre9DT/96U/x2c9+Fn/+53+O973vfVi2bBm+/vWvT9m7jwrQ6bMM3nNCliyok/WwjeVdG4+ONZ63YqzvW8VutEoo0uqaVu9uVdexPKnla8Zro0PFmDyfFv3JlMXKeAlhJuJxHu8943mAW903lpd0IjS+w6X6VTI5GYNW5krKjAfgpsnhyXv2AjNmACMj8COj8KNH9kbqG2NniW1qmxIV1tvsjby/1SZ7qFg+eiTGitGe4Hwt3z/m8yZw73jfj/X7VMi4z3+e1tzx3nG49XvO7fJSWf8mkh+i1T3PtfwTNahOhUzUq/lcZSL07ckCIsrzYfguzS1vE+aUv5+ox3iqZKr7aTxD/wTbWnXzSl6S8vKXvxyf/OQn8fGPfxzf+MY3cNttt03p+44O0DlabzoAuZJKKvkll1aJPCaTVAhAvm8/XOLgumYAO3cFL2CeB6vzS5RCMxFpCZrHSshyBNezkkqOKDlU8reXskw0gV35HsqRWu+Xihwqod5EnzHetYcxPg8VdvOSl9wD7ujydLaSNE1x4YUX4sILL5zS9xwdoDPL4CcYx1FJJZW8iDIWEBwLHLbKNDnhud4qUHDs4EE/xlc+A9yefXCJg5eskL6cDfO5yIsA9rT89p2+XmrbrMU1Y/RdWXkcrw4VmK2kkmZh7F95vh1qfTzcd411FM547261Luv99ViHCYtZY8plavmOQ5R5vLoczXKodh8vi3t5Dyg/t9An9Um3sx9rYz1SxHug1QHbh7ynklbyoiO1T3/60zjllFPQ2dmJV73qVfje9773YhepkkoqmSpptfmP9ZkeV5AXr+HfL+CPb9SRj47CN+ohuYT9/vlok1b1m0pp9U77eav6jdd35evHq8MLUb9KKjnSZLw5eahrD+dd9jnlZ4317lZzfLzPJvrTqkwTeUerso1Vl6NZJtq+z3V9Lz+n1bVHmfjcH9bPZGWyWOjf//3f8apXvQqdnZ2YP3/+C3rW5nORFxV0fvnLX8Z73vMefPCDH8QDDzyA17zmNVi5ciWefPLJF7NYlVRSSSWVVFJJJZVUUskvszwXQ8oEZbJYaPPmzfiN3/gNvOY1r8EDDzyAD3zgA/j93/99fOUrX3k+ajyl4rx/8fzA5513Hl75ylfiM5/5jH62cOFCXHjhhfj4xz9+yPv37NmDnp4e/De8ETXXNpVFraSSSip5wWVdvnpKnrssWTUlz62kkkoqqaQSSsPX8W/4GoaGhtDd3f1iF2fCovjCvWnS+KLh6/g3/88TrvNksdB73/tefP3rX8fGjRv1s3e84x148MEHcd99902qrC+0vGgxnaOjo/jRj36E973vfYXP3/CGN+Dee+9tec/IyAhGeFQBgKGhIQBAA3VMOk96JZVUUslLXPbs2TMlz20c6ckdKqmkkkoqeclLA2GveRH9W89JGn5k0p5L1rm8f3d0dKCjo6Pw2eFgofvuuw9veMMbCp8tX74cn/3sZ1Gv19HW9tJ1wr1ooPPZZ59FlmXo6+srfN7X14dnnnmm5T0f//jH8ad/+qdNn9+Db01JGSuppJJKXkzp6el5sYtQSSWVVFJJJc9J9u7de0TtZ+3t7TjuuONwzzOHhy+6urowb968wmcf+chHcN111xU+Oxws9Mwzz7S8vtFo4Nlnn8Xxxx9/WGV+IeRFz17rSucMee+bPqO8//3vxzXXXKN/53mOLVu2YMmSJdi6desR5bqv5PBkz549mDdvXtXfvyRS9fcvl1T9/csnVZ//cknV379c4r3H3r17ccIJJ7zYRZmUdHZ2YvPmzRg9zDO5W2GZspfTymSw0FjXt/r8pSYvGug85phjkKZpE5LfsWNHE4KntHJNJ3Kge3d3d7WA/RJJ1d+/XFL19y+XVP39yydVn/9ySdXfvzxyJHk4rXR2dqKzs3NK33E4WOi4445reX2tVsOcOXOmrKzPh7xo2Wvb29vxqle9CuvWrSt8vm7dOpx//vkvUqkqqaSSSiqppJJKKqmkkkqmVg4HCy1durTp+m9/+9s499xzX9LxnMCLfGTKNddcg1tvvRW33XYbNm7ciKuvvhpPPvkk3vGOd7yYxaqkkkoqqaSSSiqppJJKKplSORQWev/734/LL79cr3/HO96BLVu24JprrsHGjRtx22234bOf/Sz+6I/+6MWqwoTlRY3pvOSSS7Bz505cf/312L59O17xilfgW9/6Fvr7+yf8jI6ODnzkIx8ZlytdydEjVX//cknV379cUvX3L59Uff7LJVV/V1JJUQ6FhbZv3144s/OUU07Bt771LVx99dW45ZZbcMIJJ+D//J//g4svvvjFqsKE5UU9p7OSSiqppJJKKqmkkkoqqaSSo1teVHptJZVUUkkllVRSSSWVVFJJJUe3VKCzkkoqqaSSSiqppJJKKqmkkimTCnRWUkkllVRSSSWVVFJJJZVUMmVSgc5KKqmkkkoqqaSSSiqppJJKpkwq0FlJJZVUUkkllVRSSSWVVFLJlEkFOiuppJJKKqmkkkoqqaSSSiqZMqlAZyWVVFJJJZVUUkkllVRSSSVTJhXorKSSSiqppJJKKqmkkkoqqWTKpAKdlVRSSSWVVFJJJZVUUkkllUyZVKCzkkoqqaSSSiqppJJKKqmkkimTCnRWUkkllVRSSSWVVFJJJZVUMmVSgc5KKqmkkkoqqaSSSiqppJJKpkwq0FlJJZVUUkkllVRSSSWVVFLJlEkFOiuppJJKKqmkkkoqqaSSSiqZMqlAZyWVVFJJJZVUUkkllVRSSSVTJhXorKSSSiqppJJKKqmkkkoqqWRM+fGPf4wLLrjgsO+vQGcllVRSSSWVVFJJJZVUUskvuaxbtw5//Md/jA984AN4/PHHAQCPPvooLrzwQvyX//Jf0Gg0DvvZznvvn6+CVlJJJZVUUkkllVRSSSWVVHJkyT/8wz/giiuuwOzZs7Fr1y4cc8wxuPHGG/Gud70LF198Mf7wD/8Qr3jFKw77+S+qp/O6666Dc67wc9xxx72YRaqkkkoqqaSSSiqppJJKKnlB5NOf/jROOeUUdHZ24lWvehW+973vjXntPffcg1/91V/FnDlzMG3aNJxxxhm46aabmq77yle+gjPPPBMdHR0488wz8c///M+HLMdNN92Ej33sY3j22WfxpS99Cc8++yxuuukmPPDAA7j99tufE+AEXgL02rPOOgvbt2/Xn4ceeujFLlIllVRSSSWVVFJJJZVUUsmUype//GW85z3vwQc/+EE88MADeM1rXoOVK1fiySefbHn9jBkz8O53vxvf/e53sXHjRnzoQx/Chz70Ifz93/+9XnPffffhkksuwWWXXYYHH3wQl112Gd785jfj/vvvH7csmzZtwiWXXAIA+J3f+R2kaYobb7wRCxYseF7q+qLSa6+77jp89atfxfr161+sIlRSSSWVVFJJJZVUUkkllbzgct555+GVr3wlPvOZz+hnCxcuxIUXXoiPf/zjE3rGRRddhBkzZuALX/gCAOCSSy7Bnj17sGbNGr1mxYoV6O3txRe/+MUxn5MkCZ555hnMnTsXADBz5kw8+OCDmD9//uFUrUlqz8tTnoM89thjOOGEE9DR0YHzzjsPH/vYx8as3MjICEZGRvTvPM+xa9cuzJkzB865F6rIlVRSSSWVVFJJJZVUUsk44r3H3r17ccIJJyBJXnRy5aRkeHgYo6Ojh3Wv974Jl3R0dKCjo6Pw2ejoKH70ox/hfe97X+HzN7zhDbj33nsn9K4HHngA9957L/7sz/5MP7vvvvtw9dVXF65bvnw5br755kM+76677kJPTw+AgLP+5V/+BT/5yU8K1/z2b//2hMrWJP5FlG9961v+n/7pn/yGDRv8unXr/H/9r//V9/X1+Weffbbl9R/5yEc8gOqn+ql+qp/qp/qpfqqf6qf6qX6OgJ+tW7e+wAjjucnBgwf9cXPTw65vV1dX02cf+chHmt6zbds2D8D/x3/8R+HzG264wZ9++unjlvHEE0/07e3tPkkSf/311xe+a2tr83fccUfhszvuuMO3t7eP+0zn3CF/kiQZ9xnjyYvq6Vy5cqX+vmjRIixduhQLFizAP/zDP+Caa65puv79739/4fOhoSGcdNJJ+DX8Bmpom9S7vzb0ebyx5/Km362kr3g5sp/8dNxryt/Ze8Z7znjPA4Da3GPR2PGLls9rJWlPNzDveL22NvdYfOWxv9R3JNM6kR8cblmXserJZ1z0qzcAW7cDAFxHBxo7fqHf+7mzxy0fn8f6lOtv/wXQslzlNphoX0zk+7RrBrJ9+/W7chkmIofqU9ad/+q7pc/u/I8P4o09lyPt6cadT/5Nof39yAiyoT36HvYFP6vNPRb+wAG46dP12Xvf/GrM/P/9oFC+i371hjHHsh1rLEur8rN9Lj7tDwv1sPVKFp2B/KFHm97Bz8vvb9Vmk+lf++60awbu3PaZwngb6z5bV9YLQKEf7TW12bPxlc03F8p90a/egDv/44PaHuWxbt89lvB6AIX5OpF7n4vYeiaLzgAA7Z/y2lGWseZJYR00fdFKWq6TPd3Ihva07H9eb78rj7XyWmLrOVb9AeDCOW/HV3d+tuX1rdYKW5bx3tHqnZRWZW0ldt6W7+Xv5We3uqbV9WO9237HMT7RepbX8vHafiJlKl8zWZnI/baO4wmvG2uMTLRPJyutnnnOX9yCB/74qjG/H+t+jif+/V+++XH85wXvbxqbhyrDWO+0a+FLUVqV+7//r7/Bv/7fd094TkxWJjK+Ws2bsebDeOvdRGWiY2Yi8/lQ8ps9v4t78C3MnDlz0ve+mDI6OopndmTY8qOT0T1zch7aPXtz9L/qCWzduhXd3d36ednLaaXsFfUtPKVl+d73vod9+/bh+9//Pt73vvfh1FNPxe/+7u8+p2fmeT7u989VXnR6rZUZM2Zg0aJFeOyxx1p+38o1DQA1tKHmAuis9c9DY8tWrMtXAwCWJav0dwBY0XslsqEhXPLy96Pm2rAuX40VS65FzbUhmTYN+cGD4bPeK4G0A85c03nciViz/RYsS1YV3tXd3a3vx8OPo+Psxcg2bEStL3Ci1zz0CS3HsmQVaq4NF896e7xHhN8DAH6xGzXXhjTtQLrkbNz1wJ+Ge/lMUw4AwJ6DwMOPx2f+YnfxHcMZEvn9za/9FL7jv1p4N5/FuqSLF2Lt+uuxLFmFtr65WLP784Vr1+WrsfL4q+Cljcpi7/+O/yqWn/MR1KRMHWcvxsWz3q5lYHusy1cX25j1efhxfMd/FSuWXIu166/Hit4rtV62b5clq5raVdu8by4aAzv0ezs+vuO/imXJKrz5tZ8K/daiPoW+KX3OOrz5tZ/Se5Ozz0T+4CPhu6QdcG3AL3br2ODz0rRDy1SbNrNQvmXJKjh5NgBcPOvtuNuMIfazA1DrmhXeAaB39QOoHXciGgM7AACXnH0d3Jathefw93TxQq3zuoc+AQDaHvZ7frYuX430+OOxbuD/xmuOPx74xW6kPT3IfrIJifSnfQ5+sgn/Iv3N8qeLF+p4yDZs1DZl+fhu2868l3+vPP4qrTf2j8Y+kPmz59KluP8L12g/8xl4+HEd6xfPeru+o+ba4nx++PH4vMG9TWPn7oc+gZXHX6Vtoe0q7241PjkOKby+3C/2vqmQwhr0k01Izj4Td7G/zNrBed4Y2KFjkpuoLSvrr32+fxTd3d2FdWZZsgppTw/WDt6KZckqdMzsRbZvX+EZhfY3sk7W0Itnvb2wRiSmHryvu7sbK5ZcCwBN61y5/rbP7VrAMtlypAsW4G5Z0+y4sXPD1rXch+W+5XvHk7ul3raN7DrDMpTbi22STJsW51NPj9ZzvHfbNqvJ+rQuXz1mW9r62rYY6x3lPrZl4rtsWeyYm6zYd4x5TdoxoefzunK97Dsm0qeTlVbPTDs7C/Nw3PqZ72uyX/PvdHpH03wbr8/GKxMQ97rnuw2eL2nZlu2dh+y759KvExlfhT4qrWWtrhvvmgmVaYJjplW5Jv0ucQgdqSFwXTMdumZOruw5wvXd3d2HbLNjjjkGaZrimWeeKXy+Y8cO9PX1jXvvKaecAiA47QYGBnDdddcp6DzuuOMO65lTLS8pgvXIyAg2btyI448//rDuX5evhu/pUmC2LFmFPZcuxcrjrwrKKQD0nwAAaAzsQK1vLpYlq6LCe/op2HPpUpx+w01wnR1Yu/56bPno+ViWrMLgolloDOyIgLNvLtZsvhHp4oVY0XtlYaPk89ZsvwVrtt+iClAr0MJy2/JayTZsRP7gIwEES7nLgHNdvrpZwZE2sNekwtHONmzEiiXXYlmyqqlM/NuCACrJy5JVhbqs2X5L4V4+f12+GtmGjQVwmjzzrJaRz155/FURyCxeiGXJqoJCni5eqP+yXMuSVVg7eGuhvFYpKwu/88MjSHt6tF1W9F5ZMASwzLbdan1zx1T+bZ+xTdauv16/yx98pOlevoP3WVAFAL5vzpjvYXnGEoICXm/bsbFla9O44nVr119fuK88Jmx5+S/7lsJ6Z0NDhXfwmj1vWdr0WdrTo+DbtoEV+3m5n8eaSxakrstXo/uO+wr9bMFAK6Bg/+Xz+KyC8Ur6vDGwQ3/n+Of7k7PPbHoe+yVdvFDHt73nhZRW77OftZqTtf55Tfe06hc7Z3nNunw1sqEhBd/Zvn3aBpwPALB8RvS62L5KFy9E2tNTWIfLZaWsXX+9jsvJ1L9c5rJwHW4lHF/lMTaWcHyM9X7+bduo1jdXxyQQ1gSuC/ws7enRe2hEZdvb6yYqE7m+PKfGq/dk3/VCz4tKKqmkEkrm88P6mai0t7fjVa96FdatW1f4fN26dTj//PMn/BzvfSHnzdKlS5ue+e1vf/uQz3zta1+L3bt3699f//rXcfDgwQmX41DyooLOP/qjP8K///u/Y/Pmzbj//vvxO7/zO9izZw/e+ta3Tuo5adcMVTqyDRuBzg7drAfPcPDDI/DDI/irR1+vSrYFNAQX+YOPoPebj2DB3/wcvm8OzrvsRvzsg1djy0fPR+9Du8MGf+wx2HPp0qBsdl+BteuvRzY0hJXHX1VQAKxCuXb99UgXLyy8t6kO8v39X7gmKhQvO1GvXzt4KxrLzgUQwFra1aX3UtEpPLuz6BFelqxCNjRUABvp4oUFMGLvL5dz5fFXKUiyShD/ThcvbAIdVD4aAzvQGNiB8y67saBANAZ2FPutJBaUlAHbeGKVLyAod9nQELKhIVWg1w7eWlCO+K8FBWXlNpk2rdCebAfrueR3e96ytAB4W5Wb91ljgH1++R4L8st15LVUisuKWvcd9xX+LivGY4F31lHf12L8LktWtVSg9d3/eF9TXThWxjOe8F3lOcV7rAeufG2rMvIddv6U+8S+f12+Wg1TZYOLG9gZ6yL9lg0NqWFjRe+VyB98pNCP9tllUGSNH7ZPp1JsmTavmlVoCzIV1uWrFWguS1ZhzeYbW86/dPFCrFhybZNhaUXvlWqUawW+KXZ+u9NObn5+T4+uteU+su+zdZso8PGNetPzWhnysg0bm9a4tKfnkGNoLCETYixpVf5yu3FtLVzffwKyDRt1baeBkddwHE+FHC5AnAhIr6SSSip5oSWHP6yfycg111yDW2+9Fbfddhs2btyIq6++Gk8++STe8Y53AAihhZdfHo2xt9xyC77xjW/gsccew2OPPYbbb78dn/rUp/CWt7xFr/mDP/gDfPvb38YnPvEJPProo/jEJz6Bu+++G+95z3vGLcs999xTSJ70lre8Bdu3b59UfcaTFxV0PvXUU/jd3/1dvPzlL8dFF12E9vZ2fP/730d/f/+knrP7N87S39OeHgyfHtzH2YaN+NkHr0b97Pmonz0fa1/3cgBFLwU9mCuWXBsU1v4TsGb7LRhcNAvdd9yHFUuuRf+HQwapXa9fAD9ax+AZwXW+ds/tan0myFuz/RYFZ62U1TJd0AIXet9WHn9V+L6jXeu1Ysm1+M5d70X+2nOARlbwEhCMum3G2yVAp9Y3V0FhrW8ufv2/fQxAUNjXrr8eey/e0+TdTHt6MLhoVkE5WbP9ljE9UrbtrfJsPcDp4oXofWh3S+BFJZv/Wm8YwR3L0sp7yOtWdF9R9FyKgpg/+Eg0MpTeYZ9JUJmcfWYBVLC98pK1x3pG7fNqfXMVaK3LV2PwgjObvGUFz/g4HogyACHA0z4z96ZdXQUwdyjvt/XKWODWyovJz6w315avlQKtYNF4wK2HxkoZtFoQQg+wHZ/r8tVoDOzQtrDsAgtwWhlV1my/pUDrLItVgP3+A/p5K+9/+Rk0bFhwwnq3UqhbghvxBk612Pf2fyisceV+XpasQmPLVr1urHHlBnYqnd4a8mjsKQBaM6b5HmWilMQyVFoxRlYefxX2XLpUwT6NWFzzWhlvyuJqRdqrrZ8dl2VPNtDs3aeM9z4LiA8HsJY9ytYQtC5frQZFGpp03ohh81Dr+KFkMoB+Ms8c7++J3FNJJZVU8nxLfpj/TUYuueQS3Hzzzbj++uuxZMkSfPe738W3vvUtxULbt28vnNmZ5zne//73Y8mSJTj33HPx13/91/jzP/9zXH99NGSff/75+NKXvoTbb78dixcvxuc+9zl8+ctfxnnnnTepsvnn+VTNFxV0fulLX8LTTz+N0dFRbNu2DV/5yldw5plje0zGklmPDOHJ65JAPRoaQm3dDwFExbZt9zDadg8XqK5uYKd+v+Wj52Nw0Szkxx2Dx1f1YuXxV2H2PU9hXb4abmCnbuS9D+1GNjSE/g/fG7wZS64NikhnBxpbtiI/7hicd9mN+m4qBGvXXx89gqK40CO1dv31WHmKJEfqPwE4f4l62NY89slQzv55GFwUPBHJdx9AY+dO7Ll0KQ70h8Ds79z1XiTTpsGfOBen33ATTr/hJlWK/fAIvHOBpnvcMWj78WOozZmDXa8PB72edF2u7ZCcfSb2XLoUgxecid6HdhfamKDcep3KVDvX2YHlMy7Hunw1ls+4HM54nLMNG4EtTwNAE9XTCWBfO3hrgQKrCp3cB6BJ2aIHFwhGAHpTy9Z8gkirJJavIajMH3xE269MV03OPhPJ2WcWPi8rhxaQLEtWKcWTRgV+3sqr0qRAGioxyzuWh2TtntsL95aVslbeJStlg4h9Z1nKoLfVc/3wSAH8W5BbLku5Trbs1gCwLl+t42qs+2wbU/FOFy+EG9jZmhVgns3vOQbZpsn06YW68fmtPF2tpOxFXrHk2iYPlDWYlNtgqmSirAEggpbyffS2WeNVmXpPr7EdDwB0DfXDI2HNFNq1fTaFRh7bd42BHei+474I9ul13rCxQEOdiAeuFZiy48t6Wcv92coYVm4n+91EylU2BrZ6Dj3mreZsmaVQNhgd6r1j/d3KUNKqbIcjY7XLWO1YSSWVVDKVknl/WD+TlXe961144oknMDIygh/96Ed47Wtfq9997nOfw7/927/p37/3e7+Hn/zkJ9i/fz+Ghobw4x//GO985zubjqT5nd/5HTz66KMYHR3Fxo0bcdFFFx12Ozxf8pKK6TxcufM/PoiTLt+C4cUnqVKyLl+NNdtvwXmX3YjBRbPUc0cF1tKS5q8eVO9l/4fvxaZ3n4o1m2/E6TfchE3vPhUA8Lrln4Ab2qeAiUrV4AVnYtevvQwA4DY9GRSgDRsxvPgk+MeegH/siUC3JNDqPyEoAxJbumLJtViz+UZs+ej5cAM7ke4bQbp4IbZ8VHjXbW0hWdEd92msTtrVhe477sP070SFKD94EGvXX4+fffBq/OyD4WyedflqPP7HwQusnqX58+BPnIv7vxCzALMu+YOPYNad6zH77k0AIgV3y0fPR7ZhI/zwiCp2a9dfD98TKb6Uu/Z/HsuSVbhr/+fh9x8oUgoFQBEIUHlhP6xYci1cZ0cxrlO8d62s9VYZq/XNxcrjr1KAV1bUGgM7kHZ1Fb1SjC095ZqWtMwC5ZExUg8+UqBNAq0VolYUSYJr6x1dPuPyAr21FcVW269FjJpVbs+77Mam+K+C16ajc0x6rPV4tvJ0WqnNmTMmjVW90z09TR4u65Wmt9I+xyr91iO6Zvstet3K46/S8WqBjH1OK2U027CxCejynfZ3tgGNMgpuD0SPp1WMOaZZt7KiD6BAO1UvvBm/E/WAPp9Sf8O5E/LGla9Z0X1FnBM9PQVPYBmQ8zOOKb//QKEtgEhZzYaGggdagF3Z48dxyPWnbJyx675970SlFdgv151Ua0DiJru61Mhlx46V59KHrca1ZTnYPlqx5Fo0lsU+tWDUGjR433h0+MMpc3msPN8GkwpgPr9SeYkrqWRi8kLQa19qctddd+HrX/86vv71r+s5nfybP4crRwXoPOcvbsHgGxehc8OTgCQAWpaswuk33ITuO+7D7Ls3BSC15Wk0BnYomCIt1T/2BPo/fC/cpieRLl6ooO2Ujz2AU/7sx8HLJ97T+79wjXrY1q6/Xp+9Ll8NNyN4Q9LFC1Fb90PkBw8iP3gQtf55mL4jxA09vqoX2YaNGFw0q0B1mr96UGm92PI0+j98b6C2PbUtPLOnB66zAyN9MyK1dv489TayLstnXK6JOE6/4Sb0bvRwT2wLVMRnnkW2YaMC7/MuuxFr11+P8y67UZWQZO4x8PsPKD3YDezEgr/5OfZcuhSuswNbPno+Bt5zfgCIQ/sCuNryNLDlafUkUyF0M6ZjWbIKj6/qVWXJJr0oKyr+sSe0rrW+uQF4zy8lLtnyNFYsuRZ7Ll1a8Jiu2X4Ldr1+QeH5NmaViUss4FGlbnhEadF8f9rTo97oViB2PKE3xypKzFrKf/kdvatjKVVjKXJlmvC6PCTNoVeYYN7ek48MF7yZ1uNHCrPSzFu8M1YmbaKXqvdXjCmtPKArj79KP+e/TPBklWAq94yrPe+yGxVoMolWGRwTJLLMY8VOtxKOBdsH1jAylofPeq5ZN+uVZZ3KyY1atakFNHzPVCqGbd/+YaFeZSo0pRD3veVpXXvIMLCeQA0LQBxfjOkEAMyfh2zDRr0uOftMbPno+U3GkbK32RpF+Gy2KdutMbBDPdnr8tXF5HETkImAGsagAzJ+01QzI9tyHq60Am5lJoUNFQBiIiE3sFP3KO5/Za+/reuh4kmtTBTwPZ8A28pUz4VfRqlAfCWVTExyeGST/DnSQedb3/pWXHjhhbjwwgtx8OBB/O///b/17wsvvBBvetObDvvZRwXofOCPg4dr1+sXaCxjunihgiXfNwe+bw5cZ4cutnaT3v6/zwmfzZiumV0BYPdFS5CPDBcUSQIQKlVbPnMsdr1+AX79v30sKNDG00EKaGPLVnQM7Icb2of5qweRLl6I2XdvwuuWf0Kfk23YqCAZ/SeEOCWTHMXN6obvm4NkJNPyZxs2AsMjQUFb98OQJbZ7JpLumUh7ejB/9aBSz6jA1/rnYXjxSUo9PO+yGzH77k3Y/YrgpVmz+UY8/r7F2oa+bw4aAzuw9+I92PX6Bej/8L048Ys/hxvah12/9jJ4E3B8+g03wf98SyG+btudZ6F21hCS3XsLfUZanVX4k7nHYHjxSVg7eCvy444JhoCBnWgsOxfJtGkBYAuo6X1otyZ3osdh9j1PhT7o6gp9aGi5FlTSA0ev2bbfPVVp0QSM/NEEUKKMt0oWNdYGbgEcQVo56Yf1/lmlywICSsFDZGI5LUjj55YWy/sIpu37lXbMJC4DO1t6BG28bqt4RvfEtgI4t94nilWgNRZzaCj094OPaNvy+XwWY6tZ7nX56kI25xVLri0kSkqmT2+iCpfb0n5vqa5sF8YJMva5lbeSyaiAyBZo1Xa8pwyebLnK4GKqlcJyZl1tc2MsYGhA79ceKnzH9a8Q77h4IXa9fkEB9AHBu6/0efG85YO7Q4z0g49g/qce1mfQIAREowkQs0izrHZsF7I0H8KT3UrG8mqW+4liY9DdrG4F0Ha9fy7S6p127DN7LYV7G40uZeNFefxPFsAdKnmben6fh0zM5bAE+6wKJFVSSSWVTL3keX7InyzLDvv5RwXovOikdwMAZt+9CV5A2Nr11yPfsxe9D+2G2z8Mt38Y6JoBIGzivi9QBGff8xRO/OLPsS5fjU2/F6i0/rEncN5lN6L3m4+g1j9P6X08/9NusP3v/AW677gPeUcaPB1D+zT+krReABhcNAt+9x64gZ14fFVv8Mqt+6Eqs2lPD+Z/6mHUFpwCN7QPs+5cD7drDwbecz6e/NtjgeERjPTNQNuDjwdwKAlTACCdORONZecWNu21g7ci27BRFcO1669HcvaZWLP5RnTc96iCWirrs34ypN7NBX/9cwBB4XBDwbNx4kUPo/eh3djy0fOxZvstyHc8i96HdgdPXf8JQP8J+NkHr8buN52NPZcuVUX+pCuewiMXXqfGgDXbbwle0207MHjBmU2xbR0/eAwrT3pPiKtcdm5oe/Ea+52DsVwDO+EGdoYkM/Pn4f4vXAPf04Unb38ZMH8e3NA+ZENDCkxXLLk2jAEjvm8OVp5yDfpuvhe9D+3G46t6C99TwVt52p/AdXZo1mJ6PMrHnzR5w8SzTil7ltRjLhTNxsAO9SpZjxCllTK5Ysm1TSBtXb5axx2vAdAEpNflgYKu3q3Ht44Jmni2YlmoxNKgk5x9JtLFCzWzbNlDTDBCT0va1YWVp1zT5L0pK/Dq2eqfp2ftAgEclrPAulP7mxI8lcvOeUygbI9GWbHkWsy+5ykMXnBm0/FELBO9nOdddmMhk/SeS5cWAJI1rJTfPx4VdKo9O7sX9WCkb8a4yrwehWOSLrU6MoVtMniG075HV/HZ9KQvS1bBtbcXxl1+3DFqhLJGEzIeyh77bMNGDT8gICXdlffyuKpDSSujRDmhFt/Bvs2GhnQ/4HyhceJQx7Qcqiyt+t0aPcmW4Jjh2O2+4z5dL1vVgc8/XPA21n38nN7VSo4MqfqqkkomJr+M9NqplKMCdGLe8ai97ETsev0CpTWuPP6qkHr/8a0Ynj8Hw/PnwD+7K1z/eMjG6PvmwO/eg03vPhUrj78KC27fji0fPR/J3GNw/xeuCckpmLnRHEFCryQlXbwQHT94LGQ/HR7ReMnerz2E3q89hG13nhU9MY0Mj779M+j9ZlC66UWpnz0f6D8BfuAXAEJspJ/djRP/aQtOeNPD8PsPoP2eh4H+E+AGdmLlSe/Bit4r8eTfHou1Q7fhwNxwEDhBUTnpzMrjr9JjEQbfuAiDZzg8vqpXY+Z43ZaPnh88qtOnq+I+0jdD6zp/9SBWnnIN3GknK4ij4rMsWYXBMxwGz3D6Gb2KjYEdSDo6dbNb84u/DYmZNmwEHt8akon0dGHk1aeh8dQ2pIsX4sDcNixLYjysm9OLkb4ZGHjP+VpPN7ATbmgfzrvsRrihfTjpiqdC/Kmc15pt2Ign//bYAFBndGqdmJypsWUr0sULMbhoFhb8zc8LHkd6n3b9ynEAEONqe3o0wRTFKl302HEsWvCk/bJ3n8amWW+B9dhR2bYALF28sJi91tAMWS8tq2QRpSJMGisp1UCIZ6XHztcbTWDPxlKWxXpmy4lkCmc7muvUIyPlzPbtC7HBAtD5s3b99YVxp32yZWvBG2dpsJbO6g3jwf7LdljRe2VTAhariG36X/Oasi1r/86Zo9677jvuU9o2EAxf9MLmDz6CbGiokHmZ/c2flcdfVfDc8fvx6IbPh/R+9SF856736rvtuzjfliWrCt+vPP4q+N171JuWP/hIwUjCsATfNweNTZuxLFml4M/GsA6+cRGWJavgTz4R2dAQkmeeBR7fCtfZoeBx7frrNZN4uR22fPR89H/43kI5s6EhZPv2BQ/0+uuVXjtZzyPvtwCtMbADK4+/Sqm0aVcXGgM7cMEjg2pgKxtrJuphPdT31uNrZeWx72gClB0D+5VdYY09ZTA7GeBp16fxjlqxnuHDocOWaf1jzctKnh+pPMeVVDIxeaESCf2yyFEBOrOf/BRrnrxZM66+bvknFJAAIbvrd+56L9B/goIu0lP96Cj6P3xv2Nj37Q+/b9mKlaf9SQAPpwXqLMEJN975qwfDwi3eU9fZgd0XL9G4xi0fPR9r99yOtXtuR//Ve4J375jZwKxu/OY5y+BHRwuZL9t2S7zd/HkYPr1PP/d7g6fRzZiO5ITjQp26ZwId7Vg7eCtOeNPDeO0b/wKz794Ed9rJhVhH/l7rn1c4+qH7jvswf/Ug5q8eRGNgB163/BMAgmJJSmsysyuA8p4u1Nb9UD17g4tmwfd0BU9aowEgZpJNFy/Eglu34mcfvFoBj6UI5yPDSGfODIp59xUAZPObPw8jrz5NKc9UPnof2o1a/zzMvntToAWf3oeOgf048Z+3YsufnY8tfxbAJxMardl8I7618btoLDsXj6/qVY/kCW96GGu234K166/Xz3xPSMbEzff+L1yDTe8+FY1l5yI/7phwCL0k5ph99yaMvvzEcJSK0AXtuaOsI9vdJqmiB6TWPw9r118PN7QvtMWBA00KcdlLohlUxYPChE4t47E6O4IBYfON2rZlLyNj8EitXpaswq5fexlWHn+Veis5L6wnFEDBs14WSzPevGoWav3zogdKaN1NMhwPMR5cNAvoPwHJ9OlIpk8P3rJTrkHnhif12b5vDtKenng8hoDW/LXn4LzLwtmRv/XF7+lY9vUQQ43Htxa80rW+ucGzLDTtMpjm/f0fulf7o9Y3Vz3+bmAnGjt3YstnjkXa1VUAtenihfD7D2Dt4K0672zWXPaJ7XcaH9Qbbb6fiKfucIVA2f98i4IoIADy3m8+0gT0OKazoaFQdykjE6ftuXRpMPTJmsv6U9KuLs1OO/uep0LdJT6xMbADSFP44REFjwDghsK/+Z69BYYAs4drSAWK8bIWHJWPPpqIWOYF3+n75qgBLNsXEsr9vxt+A9133IeRvhmFmOiJyKGUfmugUEZLVxfSnh6s2X4LGjt36nU0snKMlw0+Ns7+UNIK4NkQkLGuLxh1WoQGTETGYllUAKmSSip5sSQ/zJ9KWstRAToB4LVv/As9XqRjYL9aiN2M6XqMCJXp2d9/JijpLztRDyJPe3rQGNiBbXeGbK/YvSdY7B/bhH/5tw8gXbwQI30zMLhoFpJp01Cf1YnlMy5HY9NmrF1/PdZsvwWzv/cUTr/hJriBnZj/qYdjMovhEcy+exMamzYHj+vwCFx7OINz251nBbrorj1Ipk+H27YDnT8biOXoPwFuaF/I/PiNHwcla89erHnsk1h52p8AAJ55dQ1AUNJ6v/kIer/5SKD5nuEC9XfWTLgZ09H/oXtVcceWpxUMdQzsV+po2tWF/LhjQgzswM7gbfqVs0MbLV6I2f/+JNzATsz+/jPY9WsvU0/a2vXXB29iT1dry3RbW1DY9u4NAHj+PLj9w1jRfQWyDRvR8YPHIiAcHtGkQo0tW7Hr9Quw6cpQTgDA8Aj6P3Qv+j90L7Z89HyM9M1QIPub5yxD5483FzwhaVeXAisCCB6Z87rlnwAe34oVS65F76MeB+a2IX/wEawdvBXJSIa0pweb3n0qti6bhuSZZ+H3H4Dff0C9klQyGVeYdHRqlelFZj1WLLkWkAzApMbRq2I9ChQb/7ksWaXnxVpJF4eja/zuPWrwcDOmF2INNZEL7zHJYrrvuE/fM3jBmQHcGW8cEJXOlce+o6XXcqRvBpKzA1W6d6MPsb59cwoK+J5Ll2rW5VrfXOR79qpySuCSHziA/MABjPTNCEcQ7dkLbHkatb65gap+wZnovuM+rB28Vc/ibXvwcaV9f+3MOXGMSLnX7rldaaQEDytPuaYA9Oz137nrvTGrsdCbWc+VX7pX++7Eix4G5s/T/qMnb+2e27Gi522B6r97T6Ef+Q4m4Ep7euDaAkPBGhxYthfCw+NO7YdfcFKk4Q/eCn/yiRoDzXLx+7SnBydd8VT0nsv4m333Jvi+Odj2P06F27UHvQ/tht+9R8dR/ZWnhQRlQ0OAxL6z3lv+LFBl6cFuLDs3hEDIWpLMmR1jajs7AsBpa0O6eGEhC/fKY98RjnySs5cbAzua6NGHkhVLrm0y/tiMuRy/vQ/tVjZB5+M7MbhoVoFeOx5QIsuglbS6j4aRbN8+ZY7QsLOi+wqs6L4C2343GMwsrZ5Srg8NK+O9fyzweajPliWrmsIUJiI0DrR67ws1FyqppJLnJkfjPJ1sEiH+VNJajhrQOX3L3uD12jmIp/97b/R49M0JWWg/9oDGa6157JMYPr0P+ZxIk3S1Gva8ZSn6rw6Koj9xrm7gtOC33xPiGt1pJ6P9p9vgTjsZaU8Pfv2/fQwreq+E370Hp3zsgUAPHLxVAYqNA+wY2B+AZGcHBi84EzO/0h0A31PbsPn9S7DtspcHUPXOX8D/dDPyR36OxpatcDOm4w/OuBtdW4ejErtvP5Kzz9TMt9Z71NiyFQv+5udIu7rgvA+xj4jHdgAS2ypxpoBkwJVssfQU1tb9ELXN2wEIzTZNgqV90+agWMpzwwMyzQwMFJW1Xf/1JMy6cz1qc+aoJ6OxaTMG37hIb1+Xr4Z3DiOvPg1J98xA05SsvD/74NVw24L32pt69j7qUVv3Q/jhEazdc7t6AWonvQzdd9wHN7ATg29chOHFJ2mZGIvVGNiBjh88hpGlZyDbsBG9D+3W7MQrllyLtt3DcLO68bMPXo35qweRH3dMUPz27cOB/pmoLThFvZf09jjxOALNgG9w0SwFoSuPvyrGoHVfoX1Qzh7aKtYsOftM1PrmKhDY9WsvgztmNpbPuFwBk828y5jRdPFCzYJMD8mWj55foNS6oX3AlqcLniMgAOhdK05XwwQ9TQDQMbAfye69yDZsxKyfDGH2vz9ZSEwEALPveSr2+8AOzdprqaQsx3fuei9q/fMChXtWN9ZsvwX3f+GaApPhO3e9FwDgTz4Rg4tmRUBeOtP1vMtuRG3dD5Xq6Yb2AXJ+KNCc9dZ6lgicZ9+9CY0tW/EHZ9ytgDxdvFA9cfy71j8Pp99wE9YO3YZ89x5kQ0Ooz+os9CkND2sHbw1rRL0Od9rJSrPleLdnTE6FPHHD+XpMk/Me+YOPxLAE79VIt7zrrQEgPvNsKNfQEEZefZrWOduwUcH14KJZOOFzD8PP7lbvNanm7VuexcjSMzQ5GQAk3TOx8th34JTVuwvrUufPBgpnG/vZ3XADO4MHf18wSvjZ3ZrJmvNt14rT0fu1h3D/F67Blo+er3GfE21H5gIoH+tiadvr8tW6/tT652GkbwbWPPbJAvg9lPDa8Y4tAWLiJb6PFPJ1+Wr4nuCdXvmDrVj5g63YcOPV6PzZAGbduT7Mo64u9TQvS1apt33l8Vfp3BlPJjv2bLv97INXTypzMADNsdDqvWUafCWVVPLSlKNxnmb+8H4qaS1HBeiszZ6tCsraPbej7+Z70fGDx/SgcWZ0ZXIfACEG8plnsendpwbAODKCWQ8NKXAb6ZuhSvWa7bdg07tPVa/oSN8M7Hr9Ajz933uRDQ2h7cHHVRlz7e0hZqz7CgUoFFqcR/pmAJ0d+vzebz6C2oJTsOBvfo4Tbt2AxsAODC8+CXcd/ALqrzsbA+85H37/ASyfcTmS7z6A2oJTtFz5g48E5XDaZQU6nib3eOMiPP7mXgwumqVUtt6HdgP9J2jMZb5nL7o3H0THwH4MLpoVskquDkl79ly6VGOyOn7wmNIxKdmGjTjvshtx3mU3wjcaWPny9wEI4IXHIbAtXXs7fKOBLX8WvJNpT0/wHLzsRLhZ3UHp7ulAx8B+zQRcW/dD9H7zEaxYci12rTgdvm8O1g7eqgCFbZgNDeF1yz+B02+4KST5ePIpNJadi+HFJ6H7jvsCVVNomU//81naRiOvPk3pw27/sHq/BhfN0tjQ02+4CXjqGU1u1Fh2Ljq+8QPk2wewovdKbLmpG0CI53Ny5Azj3tYO3oqVx19VAGlAAF5uTm9Q8vuO1QQhTjw5VNZbWQ7zBx8BOjuUZtt9x30Ynj8H7rSTYwxYlinYIb3ODeyE6+zQ7MUrj78K81cPoj6rs+BZZhwi7zv9hps0KVc6c6YeDWTHAGOfd7+iB6jX8ew7AxV41+sXBBbBlq1I2tuRv/YcLRdjNgmQ6S3k2bXZho3IZ83EeZcFyjBZBp0/G8DKU0Rx33dQE9iQ/pwuXohkdi9qc+ag95uPFNrB7xwEOjtw5levAxCPQLKUwBVLrg3PEsBMr+zK0/5EFWB69ZfPuDzGzWa5HnWUzOpGrW8u/uXfPqDjlfPG983Ro0Tu2vcPoZ5792Ht+uvVUztWUpnnS07+4L2Y/6mHA6vAOSTTpuG8y24MhjvndP6P/moARvTwJtOmobbuh9hz6VI8/uZeNbQAgQ6f79uHx1f1hkRoAzuBPAfyHL6nC50bnoTbFgxRT//3YNTyJ85F/uAjahQiUCocvSTgfvCNi4BZ3ej82UA4empoCG7bDr2396HdajTr//C9BWbAeG3pG4GKnS5eqIYbu8bZY0pWLLk2JCsD4Hu6NO7ces3LMY1jvZtAvpXwmUyalfb04HXLP4H8wUfwuuWfwOCiWRh4z/lY+7qXY+3rXo7Tb7gJazbfiNFfOwtr11+PwTcugt8dMo5zTSDzYrKAEIihA2MJwwgoY1Flx5K166/Hpt87dVwvsJWj0aNSSSWVvPSkotc+v3JUgE5/wrFKWwOCx84Pj8DN6sa2O8/C6MtPxOjLT9SEPstnXK6Zbk/52AOB1jV/HnYv6sHw4pNQ65uLzscDVXddHo5nmLcueGayDRvRueHJoOjPkeMZJCNobd0PVSHJ9u1T4EDL84ol14bkPet+iE1XzsOWj54fPKedHfAzOvGGf30MmD8Pey5diu/c9d5Ao1r3Q/TdHJQnggo/o1Pj2DRrY+Kw8rQ/weAFZ2LwgjMDYOqbg+477kP/h+7F4BkObmCnUubomVuXr8bWO+Yjr4XkP3sv3qNenHX5aj3aZctHzwf6T8Dse57C8OKTsO3Os+AfewK1OXNw/xeuwf1fuAauswOjxwcvx5rNN2L+p0IsZcfA/uBxrdXgOjvQ/6F70TGwPyQNWnYuGk9tw4o1GzDSNwP75nUGOvHJJ6oh4cnbXxbiSSVBEQEHEOOHmMxpwd+EzLu1vrlIRjJVkDe9+1T1+J3wpodViTwwN9D0mHDKbdsBt/0XmP39ZzDyW69GtmEjFvzNz+Ek1rC27od61I47tR/Z0BD63/mL6N19bBPW7rldy7YsWRU8O42QYnpF75WozRHv8PAIfN8crHnskwCCEYFejTXbb1FFn6Ao27AxxAl2dcH3dClQ3vLR89H5481qUEkXL4TPMgxecKaCTVLeGgM70DGwv0D9Tb77AHb8z3OCJ7p/XiifnL268ktBeXft7aEPfvsVSlvk+EkXx7McBxc6+L456PvCBqU/+pNDPOzof12Mf/m3DwD9JyDt6UHnjzeHeg0NBUaAAHogHL+z59KlcE9sCwmn9u3TuGXf04VNV4Z4yU1XHI/eRz3cwE4cPCl4jt3ATuS7BuFPDMmc3MwuJO3t4TifffuwZvONOPGih5Ft2Igzv3qdZpm2cZf5g49g07tPRbZhI3ZftCS03abNOu5oiLlr/+eVsssEWOnicHyIpUevXX+9niEMQD2d9KyO/uqZel05lm8qhJliXWcHkt174eTMyXTxQrgntmk527/7kxALvexcrDzlGty1//NKL+3/0L2Yded65IOB8eAfewLfrn8J/R++F2s2fR+NgR3Y/yvzsf9X5gMANr37VOxacXqIs/7XwUBFF/p0x8D+sMYMj6jBwQ3tw5PXJSFmu5Fh8AwH/+wuDJ/ehxO/G45q8ifOxXfuei8WX3NTAFvSj/R0nnfZjVjRe+Uh25Jtnx88qCDNApsnr0vU2HfSdbkaJKbvqOOupx8MnlgT02nfN9a7y+9gOXiPG9iJwQvEG9p/gsa7H5jbhvu/cA3mPDwaEtx1dmD+6kG8bvkn0PGDx7Ci+wp033Ef/PAIuu+4DytPuaaQBdh69ycijGsfT56P+OMF/3drS4/xRKm+lVRSSSXPt+RwyCb5k8O92MU+LOnt7cXs2bMn9HO4clSAzpG504OnbecgVvReiZ1ntSM57ljkO55F/9V70L7lWbRveRYnXfEU1uWr8Rs/ehq+bw4GLzgzUGS7uuD2D6P7H+8LVM2+kOlW4xP7T0Dthz9VRcP3zcG6fHWgXB44EDwpQus78aKQYZaKS2NgB568LkG2L8QRLrj5p1iXr8aCzw+g/8P3BhpiV4gV/dZZge47+56nsLzrrQFgzJyJtKsreEy3PK1Zc62HD/Pn4a79n8fw/AgACS7p5Zm/ehCjLz8R/rEnsKL3SjSWnatxkI2He5B89wEkZ5+JRy68LoA+qTuPdpm/ehB4fCv87j3o3PAk+t/5C+QHD2LXitNx5levC56j7plo2z2M3q89FLLJzuoO1K6hfXBzerXtan1zgS1PBxqlgMK1r3s52v99Aw4e67Dr116m53oOLpqFEy96GLX+eej/8L0BdAudj0IwjzwP3pO+Odj/6pPR9uDjSr1jZtrGlq2afZYxsG6bOetv504gSdDYtBkj3anGAeLgsJ7RZxPZNJadixXf+ameb7nnLUvVYwQIXfbxrVjxPTkzdngEvtFAbcEp2PX6BRjpm4EVS67FBY8MYsWSa8OYHNqnVMvyeZuNLVvhF5wEN7QPj7+5F5v+5/GYt+4ghl95iraFOziKpHsmZv3TAwCCsj//Uw9j1+sXBA/Vql7ke8M7sg0bA/1xj8fui5Zg05Xz4E+cq8fgrFl6cvBEizdr8AyHdflqvG75JzQhDLY8jVmrf4Q9ly5F/4fu1dhGKuIjfdPhNj2J79z13mAYWTQrPK+WKpDG/HkK6N3ATvR/+F7MvucpuGNmww3t05hjHknETKn9H7oXvd98BGu234LpW/YG5TfPkczsiuM/TZD0zsLuN4XY5OXTLlOg3Hg4eNuZVGpdvjqcLds/LyTE6uoK1GAxgNjzPNOeHo0BZDIrAh8K44jpveU1K0+5JoCB/nkhVnbdD1WR5v2/8paJeX0OS7IM6eKFGF58UqBnz+nVuYos0/olXWF8tu84oGcC+745Wsb84EEgTeEGdmLzB85R2u1vnrMMaU8PZty7CTPu3aQU6O477gvryGNPIJnVHQxBXTOwdv31mtCN3q7h0/tw0lsDVdufOBcL/nEHHv+js9C54Ul0/mwAT//zWXBDISFS3833aoKsdPFCnPjdUZz43VHc/4Vrxjzux4pNhmWBH40I/X98QI0Og4tmYXjxSRhcNAsdA/ux7H/8T824bKmgh3ynHKczlvi+4KlvLDtX45LJVFmWrELnhifhe4IBauWX7kXWmQBZBjerB7U5c7D5Q69E2tOjMdas02SowLynFUC2QuYEENagQ3lGW93f2LJ1UgBzokmbKqmkkkoqObTcfPPNuOmmmyb0c7hSex7L+6JJ7V8fQKdrw+ClS9H70G6c+MWfh0Q4PV3wW54GeJDp/HlBKR9ajGzLRnRvANDTE7ySc3qRnH0mkn0H0diwEflrz0Hy3aC0u117sHbfP2BF9xUhCcpjT4RsgQM7UOufp5lta0PdgWYoiWnotTnpuv1wc+ZoXOeKJdcieyxYjvdcuhR7L96D428eRv7ac+B3hzNF8wMHsPnKJaid1Y3+d/4Ce964CL0P7caCv/m5JnthzGM6sB8rT7kG39l8Y6RO9c3BeZfdiO6hoUAVfGoANdeHu/Z/HkA4KsMPjwBpqjS03a/owcpTrsGuX3uZKuE26YTrCECBlvLZdwdK6ezvx6MteIbe/E89jDWi7O36teD97X1odwDunR3A8IgmpqEStCxZhRNu3QDXdyx2/drLMBsS//QF6PEHK0/7Ezz+R2dhwa1y7I1k0p2/ehBrh24L9d8/jGl3P4Gt/28+5n0kV6/2nkuXomvrMGo/3YZ8cDfctGkYvOBM9H7zEazdcztOv+Em9K+LVMIVS67FrtcvwN6L96D/6unAzp2a9XTbf+/FCbc/jA6cgDWvnoeRpSdh22tPxYK/+Xmklm15GiOvPg2dPxvAmv8xD7P3PwMcd2wAKAM7A+Dt7MCa7beEc2ERkpMMLzs3ADAAGWIWW6XJ7t4bYnb/egR+/wEMvnERZt/zFDoGujDSNwOdG3Zi07tPRf+H70W65WnM/9TTcLO6g5f69D7M/v5O4Pg+7Fo0C71beoJy+9DucGzMrVuDIit0290XLcH0HXV0Pr4Tad/CkCDr5negtnMnvGSZffqKs3DiF3+O+/7ib/Gb91yANZtvDF6X/QeQbdiBtg2hHstnXI784EH0DvwCjX0hGywk+7DbP6xAPoy/HcDwCDb93jws+L9bMX1HoED63SFJzYreK+FqtYLH1jsXFNHROrKFJ4ffsyzE8C47F70P7cbafHWYF3fch9kDc9F9xw41zFDhfSRfjWUXrcLyGZfDnXZyaKfdezC4aJYemwOE+Ohd4lljvOyKJdfifjmyo4EAZhobNgIDOwKNf3QU7rSTkQkdOV28EGs336h0ymzDRtyfr8ayO1Zh5v+7D/jHia6Ck5T54cigwTNc8BqaONcVS64FNmwMY10Mbbtf0YP7H/jTsG4gxOhCvGdJ90z4vjlYcOtWrJE1o+PxrXBzetVANPuep+AXh6OJZt+9CRgewf/3wDqsPOk9gekg8YArT/sTDJ7h0Lt4IfbMbcPutyzCcd/eHsohHr3GwA4k06ej659ehsaWh5WZ4Xu6gMe3Fo4EohBMtgIwyTlnYu2P/rSQiZUxyTQ2NDZtVmA6+56nQpboJdcCj29F+4zp2MLEb4AC9vEA5Yol12pSoLJXlOXYc+lSzB7ah87HYwbe2oJTMNw3Ax0SD07wv/Z1L8f0AxvhZkzH6Py5SL77ABb84w54YaeskTEGACtPes84A6NZWEbeb0E5JV28EGvoxZf4zskcH07jXqtESGMJvfFTzQqopJJKfnkl9+FnsvccifLWt751yt9xVHg601e8HOnihSGmR877w5anwzmS/SfAzZgON2O60krp7ar1z4Ob1Q0eoQEECl1twSlo/+m24PHom6tKUbZvH3zfHAVuNo2/G9iJxpat4bM0eHAG3nkQA+88GOhpO3cWzk+rzQlHQMy+exNOui4wwPfNC5lP/YzOUJ9HPfo+M00t3szKmG3YCDz1TKABb3gSg4tmYctN3eEcvb45mnmW3lD3xLaQGVSOy2BmyMELzgT6TwixlwtOwex7nkJjy1Z033FfOH+u90p03PdooHrt2gN/4lxsevepmL6jjt6HdmPX6xcET8mvHBfOsty3Pxy58KgHsgznXXZj+FsSwADBep/PmhnaeufOgmcjXbwQ2b592H/msRhc6PD/3fdNrFhyLX79v30sgEQ5a7P3UR+AxJagYNpYST88gl2/chzcaSfjpHf8QmnGblY3Zt25Hu0/3Yb8uGOQHN8X6MJ3b8LTV5yFlcdfFcoNaCZWbHka3Xfch5OuCEobz/YcXDQLJ9y6IXiZNz8FN2M6vnPXexVwajKYoaHgOd+9J4y7TZvjua8A6mfP1/hKtlG6eCE6H3kayfQQN1k79hil0LmhfajP6gzZP3t6MPryE+Hm9IYMxdsHQtztwH5sencAv6Suov8EbHvTPI1Ba2zaDAh441E1a9dfHxJDyTzY/IFzsPkD52D23ZtCjO2MQHsevOBM+BPnBgr7oyHBzolf/Dny447B0j9+R4j5PfYdYVy+cZEq6rX+eZqxmUdO+J9vQSpABPv2I9/xLPIdzwZPvRzj0f+he7Fm842a/EQTzvSfgMbOkFTqdcs/gRXdVyB/8BG4XXvgZnYB338wzMHLFqM2Zw5q636Ikb4ZeN3yT+D+L1xTOP+QVFdStc+7LPT17ouWBObBvz+JkVefFo7w6ZsL//9n7+/jo6zvfH/8ec1MZibJZDIzgQxJSIZkuKdEsBaFpShKJFG6Wlt6tCy1ntJT/eq2wq/qaa2Wg9Vdaw/arVT7LfvVaqluaa2uaELjamVdUJYKhHLPJExCEiaQucvd3F+/P95zfRJQ682pu6fdvB+PeSiQzFxzzTUzn9fndZdOiyR4DHukxQZJLpgGQGPpf1cJx2M/JyIrZrN96Cla9m3AVFiIqbBQsdqtua3Q3qU2GD7uRbSuSbJ13Q8OCotptylvNqDUACDAy6jYIZFUAE9PJEeVAMgGUFPFrcJWFxeRqSpTjDmJJAR7cL9wgPAyPy2RzSyvvIDuz/sIfm+RAirhSybhf/QELfs24HklwIT9wzA4BJkswYed0t/6mQWYShzi162fhX78pHh1QQWTGSneY+e9zmlu76F3/XdVW6VpBO9bJAx9bBCyORUu135XPZlQH97HCs8JojKk/OePSgnet0EF55zP7hmM+1tPr0MvdajP1otXb0Q/G2a4vEB81ZksnpZjeFqOSd3MwACpGVUURBPSe2wroH2lW6lWVECWx/l+l8c5c34g09jgsXc7X/DOuqb3m+beTUTmut6VhX0/RnMccI7P+IzPxzUfVlpr3P6SZmRkhHg8fs7to85fBOjU4oOqssPwwOipFOFLJtG+0k14mZ/wMj9NFbeqL1B8lSJVLHXI4irYQ8dKCdtJ1JXR3LuJmi8FZcHPaAqnSnoN9YnUNZGEfG9j8HuLWLr8QeXrLPm1k5JfO5XEzliIZtsOk7iwVhapzhK0UD+D1Xblm4nMdalaEnt7v2KdQHa/g/ctQisoUItm94EovlvOkKivUWEsurcMi68a0wWzxbs1bQqfPXwWc/0s2ldKsJB7myzS67ZGyDkK0fsjox5RUJ15F6/eCGaTVMFsjdC9xKqkqYa/EfILhxWzlQfP/cIB8FXKAvKNU6oDUgt0KnmfxVuO541TApiPBMgtmc/pBRbqfhlh4R0307JvA4PVdhr3niET6lPHbSx8FKPR3qVeV+eWXaqT05B56aUOtg89RXiZn9z+Q4qBCS/z07ZxrZKtmetnwTGR0RmhNAbQMbo9nVt2gVmktz1fmQt2m1oYmUtLJaQqnzJpHJPFV62YPEOKa9qxF3tbp9qM0IYS8vOZDNpUH6bCQvSKiQpgg2xM6Imk9Fru2AsWCxZ/LdpMPyabXTyom7vAUaw8zlp3H1W/6RJP5ixNJdGCdNhmQn001a4j7bKra8/XPIyveVgBimzbYbBYcB+IMuwr4bXtdxFd+Ukl0cztP4TnlYBIei1muS5fOCBBXhfMluPPv+ZmhwPPm6cxlTjQYoO4frOf7hskqEubNoWmaXfS+ZQPELBqLOYtvmoyoT5VXWTxlqOVOASQWuWck07T3PmIApDWuE7g9hkqOdoWGqJx3r0KWI5dPGfbDqsNgJRvAkV9adnEuLQG2+7j8kN2m5LUp1123AeiTL//YcKLJ2M/FpLPh9rJKtCmZd8GteA3KnbGJhpngl0iQ3avIXLNXHkt8mqFj3MxrZ3sVsFVxnEYnz8rnn1DgSajOsXwt+veMvRoXCTFU6rOYfWzbYcVC5kJ9WH6/RH1eQQI+KyTepUG00os/lq8j+yk7u/bVMKqe9shwsv8LHfcKOE33cJwBm6fgW9tnKaa2ylu60X3lpFcME3qeoqKyA6K7LqoLy0VLRfUkb6g7oOdC0vBOSxnZK5Lpb+Gbl9Ebv8hjt29FveBqFzH1gK6b5iKPjSM/9ET8t7Oy6ONBGB4d3ltbv8h5f00zt3Y13lsCFGje416Trn4gHyGupzY4llRmziKoLQESktoiWwWFY7NLBLvFbPRkml8zcP0XO5WKpH36ts8f8b6m3337DwH+I2thjEmMtelAPT56b8fdMZWUI2d8x9rfMZnfMbnP2r+q4LOoaEhbrvtNsrLy3E4HLjd7nNuH3X+IkDnrw/8HRev3qgSNltzW8mNjEiIzj07cR+IqnoP/+YuBTwtZcIIJuprSC6Yhv9HJ4jMdWFv65SFg9kMiSTB+xap9D99aBiQxYl/c5cswvoj4iN6PYUtNETJr52YS0txbf298roBJBfOVN5P644/YGndQyYvs3Vu2aUW+J5XAmRCfQS+XMHQ7IkSnpLv+nT9Zj+1D+wl+NhEefJ5PxQIgDA8hwAkkphOn1WeoN/MmkDSW0zdg224tx0iG4uROdVNz+VuYYnK3GjlE1Q6qeFpcm7ZRWCNLPi1UL+SYBossef1Tjyvd3Lx6o0SKDSUkDCaumpo76J+7cMKNJkKC4UJHpSakebeTWSCXZLEm0phPdotxx7swfN6p6qs2H7DJUqKq1ksAjLbu/C8cUpAcl21WiiFbl8kctw8y6qF+lXAiMGKDi+ZQftKN0V9aald8NdCsIe0y05uRNhpfWhYzm0mqxbFpgtmq5TU5t5NVL4aQS91qDROA3SbTp+lqXbdKLtQ6lC9pi/tbZXXqX6WPO98OiqDQ5LK2R8R8DdtCrlDJ9RiVu+PSJiOy6mCgV761+fRz4ZpX+lm+8jTQD6BNnhKwoZKS0lcWCsM5qJ5gHg8QVJB69c9rHyy1qPdqm8xU2whU2yRhNn8NZuoKyPbdpji3SdpMK2U1yI2yGvb78J0wWx5LsmE2oShTkBdrjAPCOuqBfheM5dEXRmJ+hr0UgemEgeFZ3Tx/sYGCV8yCe9jheq8mUtLCd63SLHN9rbO0eM81c30+x8m0jST6CdK0RNJljtuJO2y0+heg+eNUyIfbe86p8bFuWUXieleBQrPlwyaduzFFhpCs9tkEyUWQwvHyQSFjczGYuRsZvTjJ8V/+non4cWTCT42kfaVbmHltXO/eAyAm43FSC2eQ2rxHOVVbYlsFibVVy3BRPle2Y9rNJdThVZpgU4513mZ9Ys3fFqpJpYXf0mY6ERSFBLHTyqJuWlwRCVBdz05GcvkKtmkmeojvmohHd+5UIWpGddc2iVqDsvkKro/UyEHU1eNZeIEcgODaHYbnpZjmEocJD+zAKxWlQ6dmO4ld6af5o6NJL3FWFr3YAsNMbTIz/ae/SKdz4O/gmiCgmjiA58P4zM623YYzysBYTCDXRSe0cldduE56azdf11JZkkMzSXSdL3UQfC+Reo6MsBkfNXCd38N8x3J7zZjwVpLZHNeNj8kyemJJJmqMk4vsEgtzMNOGBqGoWEF1myhIa647AHZ8MvpxGsLSZTJY+pnw+oz/f3mfFbz/YDfF+9+eZTlzbPWH2YaTCux1Ez+UL8zPuNz/oynGo/Pn3pyuvaRbn/uc+edd/Lqq6/y4x//GJvNxubNm/lf/+t/UVlZyVNPPfWR7/cvAnR+btr/D0B1/zW616hkTcVsjhnLHCkhzw0LkzNcXoC9rRPsNt56ep3sIttt4KtE95YJE7hkvvT8FReJLzQvpwXQXKW07NsgMjXEh5iNxdie2ML2xBbcLxxA95YJI+OrlFTOAguWaeKNNBZkgFrgG6C2aMdR2r/gxjRJQGb0sxewfegpfF+PSkjEJZOYfv/DKkHTkAoaTF/gb6dKWmnrHoLfWySMTV21KmK3lJVR9fRRQBb4xKXv1L3tEE216xSjVLUjRfC+RbLQNySiiST6iSDhS2sIXyq1BRev3kgm0IGeTBKZ6yJ94TQqX4uoupbU4jnoU6qwTK4S36TzJnnN5t2LqbCQRH0Nk3ZnFCDuXG8iFx9AC/UTXuYXhqC/X9XRNHdsVODWVFhItu0wla9GyEXjysubCfUpFsfiFWno6QUW6n5wEEvrHgEjmQyRFbMx7dhL6HZhrJMLZ8riqdAu4MBfSW7/IWFKE0kaS/+7kmwXntHFg/XGKSWDVkFUY7ojAa6eezmay6kAj5GOqieS1G2NcGbNp3Bu2SVVJpk00+9/WFh1s5lcfIBMVZkK0rn609eiuZzUPdgm7J23HFNhIeYyt3pcS+se0i47lkAPvu/spG5rBM+bpyGRJFEGTdPuFJn1Y1LdonX3KVbQkLWa62dhPxZSLPhY/+X0+x8mt/8Q0+9/WJjP6V65xvNhTZbeMKZTIfSjwiA7t+zClMyqehAD7DR3bKS5YyNFfWlVeaSFxEdrVJE0d4yGNLXs26ACf956eh2Dn5fX3FTioCCaECVBVqTrhuwyMteFnvcvWlr3qGTTsYt9g51tX+lWmzqtua1kTnWrFFCQTR7NahXm+NIakcp/KUjd1gjhZX5Mg/J5ZHg9jWMO3rdIhSZ5fivP00hPbe7YqK6fj5PpDH96MpEVslEQuWaunOtusSUYVTwApspJIrvPB+VoVqtiZlNVLmpuPgNIIjTpNJG5LlWR5H/0BIn6GhL1NfgfPUG27TDWo93oiSTNnY9Q8ZooC7RkmsDXp6MVFMiDWswk6ms4vcBCpsxBzfoc/id7+Zef/SOmfLWSce1osUEK/+UPXL3gKqmTmjiB5YWrFRv3QRehhqzTAGTH7l5La24rq+/eRq7AhJ5IyvurSgB3xSNWSKcViPY/ekIBbGPGyv6NsXjLaf/mnHeAMsVunscSZoJdahPjpb2tmIYktbdp4s34bjkDBQVQUEDTszvBJeB8sNpOS/wJAl/yMjJRo+7BNiIrZtP5xOTRJOUPMR/kPBodthev3pgPDyv+oz9//rTmtqLHBt6VhR2rDBif8fljMy61Hp8/9fxXZTpffPFFfvzjH/P5z38ei8XCpz/9ab7zne/wwAMPsGXLlo98v38RoDNymSR3Gt6qnpvmKJBx6+deUj48LdQPiSQlvxamSDOblUw0UV9DJigBQJGZGnqpg7RL5Iq0d2E92i3hIHlPpbHw1EL96B6nMK37D6ldXlNhIUuXP8jS5Q9Kcm0+ZVEFaSD1GqbCQrVgAWFBxnacaS4nvu9I0I9RgdI4717Cl9aQjcXwvHFKakKCPSr9r8G0UiocGi7CfViHYA9mh0PuJ9/PaXY45Is+kwFHEaHbpTtTPS+7Tfk7A7dNZbi8APcRnchclwCK2KDUQlw0QzHJux56XLE1LfEn8Lx5moKopL5qoX4s/lrxe+k6mVPdIvUalGoWo5rCtvs4RcEBMg0Xsb1nPyW/dpIbGaG5d5NiLcfu1De618giqk48g4Y3DpOmGFvTBbOVtNlgk+u2RqRSJA+imjs24nlD0o2rftOFpXUPr22/SzxRHidFfWksoZhiOrOxmAQX1a5T1TeGJ9b13D7SV16kgDC+SmHXOkbk8R1FhBdPZsk1D4nsM54dTXQN9jBUJc/trx9/lfiqhfifCuF/KkRLZDMd356PJdAj/ZC+ahJ1wrS0xJ9QC+ftQ09Jr6zdhma3YZlchWnHXunMzD9fvdiuUm31YjvaUILqVe0kP7MAvaqcpmd30vSsAL2eL8+hfaVbNhvqhPEOL54soTlPr8N9RMfir+XY3WuVUsB9IAqOYvRonJyrhMSFtUQ/Px+zQ5hL0469ChQbrGBTxa00VdwqfY4lDrXpY9T7GCFcuSXzwW6TQJR8omrjvHsp/3Gh8l0D2I+F0D1ObKEhJZ2OzNQYXjJDEmwdDsXawSjb1ZrbqiSVDA4x+/n1o/6+fHK1xVsu0m5fJZlQH84tuyTQqK4aLTYogGNwSPUsju0gHdsfqafSqnKpcd69NNWuU57Tj3PCszTcLxygadqd8nnkqyT400my8WITINWybwN6sV0FpS1d/qDyshr9xIZ/3HhPug9Ese0+rlQhr22/i9e236XCz5p7N6lOXo6007JvA+GLJoqfOv96J+prsLd14n8qhGn/MQFUmQxXz2+AggJVAWTxlpOY7kWb6lMbQ3rFRK7a26skrPFVC9+XdTN6Oi9eLYBf95ap33nxhk8rxnvgc3Gau34om1o2M0MLpmA92q1+p7l3kwq1ei+Qlps0QXWgBr83uiFqvN5jQdfFqzdKf/OTvQwtmMJVjddjOn0W30tDhBuny2ONJGAkQfP1i9BtBWhDCWEdF9+P756dVD1zQkmaD127XgWkfdBRGQT5BOb3GiNB/K2n18lm0BjbxQedricnv+t13xLZPJ5UOz7jMz7/KZPF9JFuf+4TDoeprRVs5XQ6CYfDACxevJgdO3Z85Pv98z8zY2Z58ZcAqHr6KOFlfmFGFk5RNSL6kARmuLfJgjI7OEh48WRaIpvVzvlYCZ61W6S6yYUzae7dJIv2MYDH8Jhl2w6rIJjGefdKiX35BLXgMtfPElmev1ZkvnlZmxFWMrbTT+2WB3sk9TWRFFYpkUQL9Stg7XnzNBZ/rQABpwTztOa2El+1UFi5oAAnY0EZuWYup5+XOg5z/Sy04iKRjNpt6KUOqn4VxNK6h9Dti8g0XERuQHpG46sW4t/chefN07hfOCAgI+9tch+I0tVQqGSRV89voH2lW1VD6KEz0N6lzlnz8e9LP2DbYQlSytdWTL//YZHCbjukfFr2YyFm/uMtii0wzqshb1bjq8wnEg+SjcVkcWI2kxsZUYDFdPqsgJEjebYzz/x5XgkoCWhTxa0SglNzuwqaapx3L8fuXkv7Sje20BCZQAen1mucWq8p71au76y6XhLTvXz28Fm0aVMo+O0e2WyYNkVAsaMYdu6jq9ENwyN43jhF8e6T9Fzupig4gKflmNxPPqHTXD+L5utlUaoXWtELrTTVrlMLVm3aFBLTvdhCQ0o63DjvXsV4+zd3QSYr8uVT3fT8RjpqNVepMHjBHgE/vkrSLjuZQAemoiKKj/Sjhfp5+ZOVvPxJ6dOs+Ok+jt29FlNhIWmXHbPDwVtPCwveOO9euabPygeSsYFDsEe8qXYbWqBTSWKzg+IzNdfPkusyn1ap6boKwdITSRKzKpRP0OItl99tO4zWewbr0W65xmxWJXvXYoPqPUywR4Fk/fhJInNd0vd4Ioj/0RMUt/VicjnpfMp3Duszlpnq+c0cBRy8jxWq94KRTG38vPF+MphfYFQOWl8D2axSXIyVLOaWzBfwjDChxnE0d2z8D2F2pty9U16LwSH1PjECzbQZ8kWzdPmDqkcz23ZY2Nl8V6URUqWF+gV4huNgtzHsk88i3VtGdnCQRudNNDpvQj/awbCvhKaJNwMCaE0Ty2iquR3PKwHZUBpJkW07TPcSK4HbpsqmnLOEl+dXiK851Ac2K7lIFPtZ6Hx8orDNoX7I5VQC8/PfaFDyewMEfZAx/NqGFxogVS4bV6kZVbIBdlq80PaTEX73k59y+rqpOLpExjs2SMjwFJ8/xsZktu2w2kwcO2N/562n18lnaDJF1C+WgkR9DebhPNtZu47A7TMI3D4Dgj1E5ro4fOdENv36aixH5ftmaIjn7skAAQAASURBVMEUlWtgAPGxz+/9xvjs1r1l73seDfXF+c/jg47jV+8ecPQfCTjH5ZnjMz7jM3b0jyCt1f8C5LV1dXWcPHkSgNmzZ/PLX/4SEAbU5XJ95Pv9iwCd7t914D4QJTcyIgvNTIa3nl4nASsup0plHF4qIUOay0mjew3XHOrnrafXqaqC3JL5mEtKJB011IdebJc+tGMheZwxKazB+xaRCQqTZS4tVV/kSW+xAmXzb32Y+bc+TPtKt7CKZ8PyxZ0P1Ej5JgCy2Mk0XKQ8juFlfiIrZvPW0+ukW7PUAQUF6ImkeK1KHeihM+jFdvHK5XK0f3OOqoIwuuqMhE4DFE/+2llVK2CkrBoLTkYSmAoLqdzchm33caKfvQDdK72fieleYcPy0mIQWa7RradH43LzluE+oo+WpJvNaK5SFZwztsYg09+vmK66rRGysRjJBdOUrNP4ewOEZ9sOi3T1mrmqKsZIiY3MdRFePFn6VmODEtzjLRdJqb+W1Iy816ywEK27D9MFsynY366CiVr2bVAbAbrHKamidptirSftzqCfCBJftZBJD1uZ9LBVFtLeMrRpU+QcDA1j232cFy6sVt5Pi68agj3iD85kCN63iHQJ5AaHVI1MxWNvi5yyv1+xJcYYwSoEexSTLVLgLFp3H92XWtV1aSShgsikmzs2gqOIKy57AMs0P5NX5SsfzCb8T/aiTfDIORlKYO0WdjrcOJ1cZ7ckDI+MkBsZGU2LBVKL54hPrq6apopbBbQb0uFslqbadQwvmYG9TdJe9VKHgEh/jVy/3jIyDRfREtmswKbnjVPoiSRJbzHtK920r3TTEtmM/ViIbCwmTDEikbb4qsmcOfuOzsDm3k2qOsfscKCn09T9QKo0ciMj4pE+WCqMWCYr4V+OYqquO8jFqzeeAxaMvsHKzx48Ryp55avHBZAUFwmgDUnVSibYpc77cseNAt5uOoXFW45t93HFthsLcsNfnC4tIF1agGa3EVkxe7Tj0HmTYl8/zp5Oc2mp8pO2f8Et6cL5jmBDydG9xEpLZDP60DCmC2YTX7WQ5uPfl5c7zz7riaQAzkI74cWTKdy+TyUijwUeXc/4Kd59ksDtM5QiQx8YpLnzEcLL/PL5e6qH4PcWcezutdjPoupwop+fT/C+Rcr33HHPRVS9HMLxK6ew3XYbgS+LP9Tz5mlsu48TuWYukWvmfiAQoVlE1qu80/kEZ0BY91A/BfvbiczU+MSbXwTgpdef46pZS5j0216VOm52OJSSwABo7wa+li5/ULHl54+xeWQcd3LhTDI1E3EFMmiuUvXZuOuhx9GjcRX4ha8S94EoRROH5M8WM5hMJJ1mbv3cS+Lhbzssx5VPHTYe7/3G6KIde4xjf6/BtJKkt3i0rusjjmfPGdVDPXaS3uIPnYb7UWdcnjk+4zM+Y+e/qrz2pptuYv/+/QB861vfUt7OtWvXcscdd3zk+/2LAJ16uUcYNYdDFia5nOyoZzIqcdN9IEpRcEAAVKkDslm2Xb/4nE60nM0syZOgGDkQX43BPhgJo7XfexuLt1z8eL5K9Gic4PcWKUlma24ryYYYyYaYyF8R6Wz9uodVAq71aDeelmOY62cxXF6AZXIVS5c/qI63cd69FOxvRxtJoXucpC+oo/v6fCDHhdNGQUaxHf/mLtwvHFDBD5G5rnOrSAwGdyhBS/wJLP5aupdY0Y+fxOKvJbB2hlRElLnRp1ThaTmGNpLih0eWYX3joAAzb5kCGZn+fgngyYeqZGOxd0be+yoJfM1Hc+8m4qsWqvAikL45rUTSYPXjJ1W6qNYtYUU5V4liUZp7NxG8b5F0TL4SwFxaKgXxbZ30XC6VIe4DUbQyYVm1oQThZX4KogmpwIklR1/L/n7xrOUX9p5XAqq3zhbPN8tNr0XPyzYjc12cXmBBKyjA83onph17Me3Yi1YmCcDG9aAVF5G+oA5T+QRwFJPbf0hY6Lz8MjHdi/uILkzl9FoVbBX9/HwiM0c/oC5evREtJmnM4WV+NLuNlw/v4OXDO2DRPAGHTdPJ9PdT99BBaO+i53I30evmKeAFslAMrKmWtNveENpUH0lvMYnpXgJfrpBamdigMCn5upKivjSmKZNHU3QRsKcVSLqnLTSk5OaNrx2luXeTSjk1VAP9/12YM9ubR+lcbxIfYKCTzKluUQ/s+ANLlz+I53VZzBspwvZjIfyPnsD/6AkuXr2RwJpq4qsWYnKWKFmwESpk8ZZL4E6+Wmjs+zK5cCamiWWS3hmOKymuIUFvPvO4sL+BDiy+aiVlNWSH8VULae7dhGXiBCX1HC4vYO2s3ypfsvF+Vv2/FovIiIeH5bgSSXmv5OWiY0GE4V8t3n2S4t0nwW47J0QsOzgonsl590pP58c02gQPzi27sHjL8X1np/JpG5s3yc8sUBtI2cFBcvsPqQ06A4C4t4m3OXOqG+ID4tV1u7C07iFw21SuuOwBVVdVdd1B8Yof0RXDm43FaKq4VTbJHA5yIyPU3r+Xxnn34mrPCFOcH/+jJ+TzzlGM/6kQue5eAGHEE0nqfnBQqqMeKiKyYraqEnm/GQuejNepZd8GOtebaKpdpzypeiqF/8leatbnSHqLuarxeshmpcLpQFTes/EnaIk/8Y77Pf/PhvTUULecz+QZHuIG00q6l1g5e0ER5kQObFb++p/+lZZ9G1h4x8303DQH82AS82ASLTpA53qThNgNyqaKXmjF2TFCy6enko3FzvWKvkty7nvN+Qzn+R7o1txWbLuPn2ML+bBz8eqN6LaCc6Tnxlha97wj/OiPnd/xGZ/xGZ/x+T+btWvX8vWvfx2ApUuXcuTIEZ555hnefvttvvGNb3zk+/2LAJ1aX1iSYfM1BdmBAekSDHZJx1ueKdKGEhKMEuxRSZrGl6fnlQD2tk7aVwqIMapXDBCXXDBNFq3Hv49lchWa3UYuPkBkpiYLa4sF33d2cvHqjZKWCVRdd5Cq6w6ie8tUyIr3kZ3CVMUG0YeG0avKicx1MfC5OLrHif1khJ7L8+mXJ4J0PjEZvdBK2mUnXVpA5WuSbGo92IkejWM6fVYAYTSOVuZWbBGgqkUMD2du0gQSdWUCoAeHRDJZPoFcz2nqfimyzcCaajh8Qljj4wG2zXZjcpaohVHnE5PVooj2Ljofn6gScyMzNRXzbwRJ3Pq5l1RqbNOuk6MyxEwG0uKlyo2MqETHzs2T0BNJTFHpnMu2HVYdmvZjIQK3TaX9m6NF7FXPSGWBlpT7anSvIRPokPJ6hC0xDY7I447x7xkBIHoiKb11vkrO3jREy74NCpRmGi7C9au9VO1IodltBG72qeqT8OLJ4v0ttst9221YD3aS6w1JD2Z+InOlhsfSugdnx4gwOvtHWYe3nhbJbPA+8dS6XzigAIABTK+e38DV8xswHwjA4DCe1oD4YGMxtAovrkAGR1dC1SsYGwO19+1R7NzYUCEAz++CwvjVVUMmy/ahp7C07iHlFXmkIdM+n42x+GvJ1k9l22w3TbXrJHDJW4y5tJS3nl7H5PW6BAgVFeK75QyN7jUkFs2UlzzUx/aRp7G3dRK42SeBXb5K9HSaxHSvyMTzUnHfPTsF1OTZdq24SOS1EzwEbpsq6cn5TZemaXeiF9tVpyiA67l95MIRrEe7CS+eLEnEeYC8dPmDSoJuMEuG2sHwTCfmTVEJqV+8+2VmP79enYMrX83Xp+Sl2YEvV9D5xGRZGCeSmFxOOdbYoKoJAkbrKoI9BG6bKhLS/Gtt+M5BmFst1P+uTNifavTQGcV0Wvy1opjIZGXTqK6a4t0ncXaM0LNU6jYyDRfRVCsbSmPDbgwpbibUJ6x0Jkum4SJ89+ykIJogFx9Q6cxG0FZBNCEbCoWFZEJ9hG5fJL2eDReRGxkhMteFrT+FPXCWBtNK5YEnkZSQsr6z5OZNx73tENY3DqoKoV0PPa4kwuHG6YQbpyuf7nvNOWmx+zZgrp9Fg2klJb92opc66L7USvelVkzOEvRiu/hLgWFfiXznAD2Xy2aXUnjAO0DS2McxUm0NT+dYUDf254wwuUnPnSBrN0EiyW9miTrGve0QlU8cHH2AZArf16O4XzhAV6Ob5uPfZ/kzb2IJxWg+8/g550Eln3/AaZx37/unKfsqzzn2saqWDzLuA1Febnn2Xf8tvmrhuwLfP/bn8Rmf8RmfP8VkddNHuv25z1NPPUUymVR/rqmp4brrrmPWrFnj6bW/Pv6/RQY1lFBpq8BocXm+oDwT6JBE1hWzpdbDV03TtDtVKE5u0gQJr7DZ8bxxSu3I68dPYmndo9gv8uXw0evm4btHgKZeJY85duFr7AhH5rpwH9EVoxdunE5gTTXZwUGS3mLeenodvlvOkHbZSVU4qfpVUFiuqT58a+NKjrfjhTvQuiW0JHGhSIfDyyQBNxuLoZc68N2zU+0WGwEmyc8soP2uenL7D8mx5dm3pok3y+J92hRVI+F/spdT/zQNU4lD+dhy8QHVg5o5WKrkVVpxEek/lKrAGvcRHT2dxvPGKZUK23y9yOKSn1nAttniszOXlBBYU60krcH7FpGNxbjS+kVqbj6Dnk7L4n3HXhX+49yyi0ywC/+jJ6jakRJ5cKiP4GMTCaypJuUtQY/GSS6YhmVylXhgz0Yl7TXQQXjxZPGLPTcHc2kp1qPdamHYsm8DaZcd39q4CqoxPGwKJN02Ff9Pu8i5Ssi5ShRYNBJHsVjQk0miX/jkaIJs/SzFggCYByU11VxaSm7JfHUeW/ZtoPZ7b9O9xCr1Eb5qmmrXkS2yonX3qZ7Zzp8J86MPDbN0+YNYpvnRC60UBQdIlxYw8Lk4ltY9Im/sPYOpfKK8F5wl4KvE3tZJ8HuL8D9ylEx3D+FlftpXunnpwKuqB9MaCPHSvz5PZKZGZKYmGysrRjtHw5dMwjyYxDK5ikywC1toSHpbJ3jEL5b3ZubiAwBo5RMo3N8pmzdGimtBAXUPHSQyUyPpLWb74M+wt3WSOR4gczyA+4UDWLzl0ndryHftNjmGwSHxq4KAZkA/GybpLSY1o4qXW54lvHgyJmcJXT8XQOQ+EEUrLmLp8gdpdK/Btvs4ntc7zwlvybYdHq00aTuMtW8YW2iI3JL5bJvt5tC161Vf7mPPNalkXaPHsOo6UQM0925SjE/ONeq1hlEgklwwjdoH9kr1UV426nklgHPLLoL3LVJ1Jf8nzNH7jtnMyERNJMineqVaqb9fpS03vnaU1jfupuL/3UvLvg0MlxcIQG7vUuC4/Y454l+32THZpGoo09+v7AgAJmcJJmcJWrckYhtprM4tu+i4W4KlKl+VDa/h8gJMhYUU9aUxH+xgoN6LxVdN8LGJSnYeX7WQyIrZhD5VhGaxYCrzSFBXbJCrpv+VUlsYt7GWiPNHsc/5ICF1asYAxrpfRqjdGpWU37kufnH/VYRuGaH4SD+hWwQgV70UYrnjxnP6K9/L/xj83iLFro/1dI5Nr20wrZQaoLkuJVFPOs1gMatjG14yQ3UgKxWK2YSezVLzsxNccdkDvPyVJXKfzpt46+l1NNXcTuO8exUw/yBjbI41925S1/FYcG1M+0q3+rux1VQfZq5qvP5d0+adW3aNM5njMz7j858yOTRymD7k7S9DXhvLKxPHzsDAADfddNNHvt+/CND52fKvypfu4JCwI0PDykdoeKgic12YS0vRSx24tx0iVeUi13eW8CWT1OI1t/8Q4QXlmGrER2nasVdJYQHpIAS1AHJu2YXFX3vOF2wmJLUDYxkUQy6b+dRMdX9VO1Li+8r3qjX3bsJ6tJuCaILErApcv9mv5IdFfWl2vCAaar1KpIX2dknidf0hRoNppVqMBO9bRPC+RUo6F1+1kKLgAHUPtgmbkZ/W3FawmEej7Q3v3uAQNTefEU9rHuxpVitLrnkI3VumqgQs/lqaezeJnzGRVD2i0c9eABaL8pRm2w6T9BZTtOOopNf6qqF2MnUPttH+zTlY/LUSnFNSgrlqErlJE+j41jxe2tuqPJvZtsMKxAVumyq1L/kp+bWT6tYRTDv2ko3FsIWG0D1OkUbOqUIzm9X9APhuOYPmcqrXCWRRZjBiBHsUE2Oun8UVlz0AQO3f7aPzESem02cxnT6r7q9x3r3C5tWVkVw4U0llTc4StCFJ7g0vnqxen6aJN6NN8Ei9Qz6UpaniVrQZtfgfPaEW9CSSbH/uKXAUqeun6rqD6JkMur8Ge+AsxAfUz9te3E3N+pxIT1v3MHRJHaTTUoOS74LNhPqove9tUnNqFCCu2xph4R03E7xvEd2frSZ8mY+rGq9XoCg3MoL7QJSlyx9EC4kHunO9CX1gUPmQ/Y+eIHeqV73W7Svd5EZGCC8TUDxyQY3IDyOb0UL9pOrKRa64uQv74V4FsgxVQXZwkObeTVRdd1AUBojs0+hAHKoX/55RNZSNxbC07qEgmuDqT18r7+8ZVXx1xk61sdHcu0mlkEZWzEb3ONUi2dgcMpgu47MApK/TSHPNjYwoFk9zOem+YSqh2xfBonl0PzeHpprbFXg3EnCHFkyhqeJWCQAL9RO8TyT4Hd+eT8e3549KdfPH4T6iK6ntxznZWIyKx96W55VMoOd9fpYyeexts91yXU6bci6IqKtW17jvO9KBjEnDNGmi6unMnekn03CRUlwANJ95XFJ6S0vl/VpYiO87OxWDrXvLJJBnei32tzvQXE4iNw5CIknFI1bx+uUrVdzbDlH1fBfdq2eATSpcmjvEs+9uPgKI4qHRvYZs2+H3BCyKrc17Oo1JeotVcJV+/KTq3HUfiOJ5JUDVDwogPkDVDwrY9dDjBB+00/Xz2ncFY+dP7dYoTRW3Kin3+cdiqEiMGiAY7ZUlkSTrsDH7+fUU7z5J1mFjefGXWF78JaV2SS2ew0t7W7H8+xGCVxfTfPz7vHzs32iadifhS2s+cKiScc4M5nfsOTQ2acZO1Y6U+m47X377QaZzvUltzp7/emUaLvpA9zcOTMfn/5b5U16L49f1f+78V/V06rqOpr3zeZw6dYrS0tKPfL9/EaBTs9voXG9SjE5L/AnCy/yyqM97ppxbdoGvUgWVdDUUolmtFPWl0b1lRGbKrr972yEYSajUzPAyPx3fnn8OcGkwrcTiLVfSOqNCBEZZO5DFwljQ2tVQSGpGFe4DUekFRRY41t44TbXSD6qF47LjX+JQpfH29n5mP79eFZQ3d2wk+FCRAr/m+lnq8Y/dvVYBw/AyP55XAiLzKy4it/8QkbkutSudi0Sxt/eTdtlJTPcKC5vvKLV4yyV0x2qV87sCVfuydPmDIt8q/hLJzywgsmK2kiO7tx2i+zMV6jEM/2VywTT0YjtD9RVo4TjJhTOp2xoRsOYtJnnJDPHF7T/EpN0Zrmq8HkvrHpqe3SnM4eRKOteb8N2zUz0WoBJ0LT6ROhLsUf2QANRVq/5Uw5OngkKyWXW96N4yee19lYp9NgB5c+8mTBPLqPzsQZWwCijmy2ARrW8cpPq3I+jDIwT+dqpIsf21FPWlebnlWfFpNk4XtrB+Fs3Hvy+SRLtN9ZAaC81cNM4VN35FJaGCMPf4KjFFB2BomIy/Es8bp9CGEqoXU08ksZSVUfjKAYKPTaRmfW7U07toHrmkBAeN3YxxPbcP/5O9THxbeiW12CCpxXNILZ6jQJj9cK9a9PtuOUNywTSGywuEiU4k1YaMpXWPVAD5qlVnacFv90AiSaN7DXoiSUE0gTZtCno0TnPnI5DLKQk8wR4yDRdJJYy3HHt7v6pmMcBc8e6TaKF+qnakRutUSkvl900m9HQay56jbL/hEiIrZtOyb8M5cj8DQI31v6pkz3z9h1JLlJWhzajlisseGAWkS+aDxYL3EamkaH3jbg5dux7SaVYcipA7fUaBj6IdR9G9ZQowH7t7LY3z7sW/uQv/5i6V9Gu8l4201bHS/49j0ldeRC6ZGE0PtttI1NfQfOZxNJdTfX68W4DLsbvXqoVQtu0wpvIJNDa3oXtEVrx98GfYQsJIG4qEptp1ArJ8lQLkyycoea+5fhZ0nOJffvdtTKfPimrEYqH6uzle2tuKtTuK+4hOYlZFPoHZCVYrhWd0FWzUOO9eioIDRJqkW9f4TDpf5vpuM7YyBYRxNTZBTGUe+Uy0WGQzJT5AvLZQ/v1gB1fc+BV8t5yRzswxcz5QMya3/5B4ofdtUBuG5/+eMY3z7lWJtdt79oPdhrntBIeuXU/3DVOxBHowTa7ENLlS5N++SkzJrFSrVE7C/w/HaKpdx/LKC2g+/n3cB6JSgXS5mw8zRujRHwPUr22/SwV8fRDwff74bjmjvPnnX/cftH5lXGI7Pv+3zJ/yWhy/rv9z57+avHb+/PlceOGFaJrGFVdcwYUXXqhuF1xwAZ/+9KdZtmzZR77/P98zM2b0kYRIhuIDEsLhvEnCOfIdc0bSqcHKNNWuo/YBYcZe234X2bbD1D3YRvcGDXyVdH/epwCKc8su6n5wkNe234VlcpXyJBr9dP5HTyj/IEjAUHzVQklBzAMUQ5patSOF9Wi3kivpiSS23cfRC60qzVT3OInM1AQ0T65ieeFqcl3d1KyXBdgVlz0gXX93DHPFZQ/QsVLCbJo7Nqrn3ui8SbxQLcdk0Zf3jYF43Xz37BRZnNuFXmznX373benPDPXjeSWAHo0TXuan+czjdD0rTNOM2w+ovj5L6x6WLn8Q0+RKivd243klIL/nLaMlspm2jWup2xoR32Q2i+4tU3Jb24u7yZzqxn4sRNJbTOiWEUkqtZukG7ThIop2HKXrf5kw18/iGzNfAbuN8IJyDl27Hou3nMhMTaXadq434d/cJTLhUD+RFbPl2Ds2CrM1BvRn2w5j8VWLBNtXTdPuLrShhEpdNJg425tHAbC392M92i1VJKUOLBMnKKDWOO9eMp+aSdplV+xBbmSErisLQddHu1XPhrHtOsJVjdfTOO9enFt24XnztKTa1txOavGc0eTfA8KCZIJdaDNq6Z9jpWZ9Di2ZRkumVbBKJtiF7i3DtPcoeqmDRF2Z8j+SzaJnMqQWz8F3V0JJQDWzmUyxBRCJrAGc3dsOEb1uHhlvKQXRhBybxYJt9/FRRvnUaVJ15TR3PsLS5Q9KUFC++zIy14VmsShfL+TZfosFvdRBUV+a+KqFBP52qpK5G/23RjKunkqjp1LqdbKFhjCVeeT9U2xHC/XL45Y6VLqlnkjy2va7yC2Zj+eNU3Q+MRmtfALhBeVoU31oU33Ko2n4ArNthwVALJqn/i3TcNE5Pb7vVg3RvtItQHkogS00hOXfj8iGQn5TqtG9hqXLH+TKV4/T8pkLMdVUKWVFNhajZd8GxZw3VdwqDFqpBCPZj4Vo/+YcYb+cN52zWP8402sLj4Yk3OtAVF43h/hXL169Eb3UwXL7KkA20SxlZXL8+XRolWDtqxYLQ6mD5usXyftqcpVI74cS6NG4Yq8Da6oF7Bty6URSqTG0UD+Rv/6EAK6CAnoud6vX/RNvflHCerYdwn4sJOd88WQSU9xEZmlSk5XfDNHCcTy7RZJsbDQqJccfGYPpfOvpdRDsoagvTdJbLNeB2YSjK4F+NkzVjhS9X5vPwOck/Orlw9JVZrwvDT/9WAb9/ebdvIkKeLZ3oZW5ydpNLLzjZk5fVY3ur5EqG+D0dVNJTHGTmCLy72zbYQqispGQqCsj8PXpkM2p96XhvS48o3/g4zK6ZQ2gbPzb+cfdOO/ec+TpH3aaezeRWzL/PZnY80HsOPszPuMzPv8RI/LaD3/7c51rr72Wa665Bl3XWb58Oddcc426XX/99fzkJz/h5z//+Ue+/78I0JlLpXi55Vly8QF0bxntd9UrL2V81UKVdGoAwUywC23aFMwOB43uNfIFWldN1d90osUGadu4VoEVi7d8dHHscUr/Zb7CIxPqU3UNRv2JaXIF7m2HZOE1kkIbSbG8cDUwuoPe6F6jduM1iwWtu0/SFl/vVDUk7m0i79s+8jQmtwuAH0VrVDy/fjZMwdvH8f/oBHroDE216ySAJJ8W2Zrbil5VrhYC3TdMlcRYqxWzw4Hnt8fl2E8EZYd/0Twyfknb1PLs8MWrN+JbG6e5d5MEzfiq1QLa3taJXmglc6pbhaLAqKwt6S2mICpJuQZ7ZISJWLwS8vTa9rsk1TLYxY4X7iAT6sPe1snLh3cweb0uCzj3GvRSh2KnjFoWfWgYfWiYmvU5mjs2knbZFXAr6kvLIiW/wM3tP4TnjVPEVy0kvHiyBPT0R3j+Gw3kek4r/6ERlKJ5XDTVrkM/G5bzkU+TzVWWj3b5hfoZrLZj2rFXpGvL/JhLS6n7+za0UmEnly5/EHyVqpuyZd8G2bTIZCQgyuPEtuvIaMBRsEcA+jQ/aZddahJC/YQvmkj4oon0z7HSvtKNqbAQ/UQQk7NEAffIitmjC3qk6mFoZpny/J5cf7GEaCF1C0qyl69aCDYVKdYr8OUK2r85ZzSwafIkcjYzjfPuxfrGQbUgN+S2mRnV6KmU2jTINFxE44tv07JvA7bdx3FvO4T/RydkEyLYQ7q0AEtZGcHHJtLovAltggdt2hSVgqyF+gn+g0t1D+YGBtWx122NSP1KKkWDaSXWo90kpnvlOjoekNChUL9avBodmkZdi+eVAK1v3K26My2te5T/z6gXaqpdB45ikW2eeZy6H+QDWwbzPaCaBGYZ15tWPoHuJVYee65J/MMLynFvO0T6gjpVoQEoGanx2RSZ6yKwphr/oydkEyAfhNZUcSvm0tKPNb025ZsgGxX57tpcz2nVI5xtO0wulRIGfHOX1Bvlp7ljI/qJIACdj8h1HpnrUn+ne5wCRIvtdD4xWQLQXHb8T/aKIqPEgclqVUxiakaVpNq+cEB8of1hyg6m0E8EyYT68H31NO0r3WglDgmESiTxvHka678dwn1YR5s2heA/SO0R6TTkNy+MTSmjJ/n9xmDo8FVKknjrHhpMK+l8xEnB28dJX1CHva2TqpdCeB8rVAm2WXv+K9RuUyoTFbT2PmNIuMfO2JCmlvgT6KUOkk4zBUM5sjbps7W+3kbVlmNMeiVE9xIr3UusNCy+X87vSEoqlJJZfM3DdH/ep57fcHnBOWFqH3TOB4Lv5ekE1Gf8h50G00pMO/a+67+Z62e9b5DQ+IzP+IzPxzE5TGQ/5C33Zwytvvvd7/Ld736XJ554gvvuu0/9+bvf/S7f+ta3uOGGG7BarR/5/v98z8yY6fzOxZgmHWP70FNosUGqdqRonHcvnldkEWowjsYXZXzVQln0XTOXbCwmoCvYw/DS2SSme2madidDC6aMFmMnklxx2QMkvcXSzxnqU+CpadqdwqDMKWR7z36ponDJwotcTm4mYS53PfS4LOyzWfUlHW6cDi4nej7NtaniVuX1O3yPVKiQySpJlOeNU4rRSy6cie4to2dNvWIzDTmbkcbZ/gVJnyw7mJJjs1igTvoOL169EdNEWTibB5MEr5JOwcBtUzHXz5IkVFAgOXf6DE0Tb8a5ZZf49Y4EsHjLqdsaETAQ7EGz28jGYlz7w1a0UL8wd6E+Vfxe1JcmN2mCYn3M9bOw+GtpdN5EpuEiAn87lStu/Aq5/Yeo2xpRgD943yIlf3O/cICeNfX0rKmXLk33GunRm+tCC8cl2CbUT/sdc9CHhuW1GB4ZDRbJpxfbj4XQzGZM5RNU5UaivobAV/MJu75KCfJZPJlG9xq0k90q1RWEGTG6A90HoiQXTCN94TTCl/mUrDjtspNbMh/9+EkuXr2R3Jl+9FKH9EaG+olcM1cW1eE4mstJ+0o3zUf/ntZnn6TwlQMK2LgPRKn4iSTpJj89B22qpL9ad/yBmvU5PK93qv5XfJVkaiuUDzi+aiF1WyNK4nj1/AZJku3ZL9dhxymO3b0Wz+udUr/z6Am1gI7MdbH8mTex7viD+KQ/NRPbriNcvHojqRlVUpsT6BG/Y37ToH+OlebrFylAmY3FSNTXUBBNkI3FKApKuvSha9eTHRzkyn/eh9bdp85tc+8mfGvjtH9zDs4tuxi54hNcvHqjsFbBHumUzafcJuprhH30lgvYzb/HmypuHe0uzfeAWlr3EF7m5+LVG2mquBX/5i5ac1tpqrhVXYutua3Sh3qyk+beTaMbBwZLCphKHGguJ1p3H7R3MTSzTDpl58QkFOj1Ttq/OQdr8CzUVSum1Vw/S21mGZsX/qdC58hYO9ebVPjZx7mwtsTE65q6tF6SuEdG0M+Gla3A7HAIAx7sktTZN09DXTWN7jWYJgpgrF4dBLtNelAvmsFbT6+T52EkEK+Nq4ohg7nM9YkMtX2lG9q7KIgm2N6zn8g1c9Fm1JIbGVE9web6WehV5aTKMwT/wcXIRE16Q8+GyVw0A9cfYrzc8izVq9rzFVlZCRPzFtPcu4nm3k0fWOap/J1DCdzbDtGa20p81UKqv5uj62mfVA/lQe1weYGoKKID7HjhDqmIWVOtwJghrX0vNs4IChrrsTfmfHClH+3A89vjpItNTHr030l6i4mu/CS5wSGID1D7vbdFXVNbSMu+DSSmyHltffZJur+Zpm3jWloim8UvnFcmfJiQH3P9rHMqf+DdPZ0G2DYSuT+od9QY43y/2/xHdXSOz/iMz/icP//V5LXG3Hjjjdjtdn7/+9/z85//nC1btrB377tvDH6Y+fM/M4D7mK7CGcKLJ6u+x/Ayv/LF0D4qMXUfiIrMMc9+uQ9ExctXapaU2tAZincGoOOUkk4VRBMMl0uPmBHC0ty7iUSdMBfWuM7V8xvEI1rqkMTRSyYRvmSSCmO5en6DgOB0hroH21QwhQFUjUAeMhk0u42ZmwZ4bftdhBunU/ViLy1LZ0C+A7CoL40tNKTSEy2+asXaGguo+KqF1G6VhYYtNITrDzEoFOYheN8iARn56om0y47vOztJeoup/VUUQIUVaXabMFt2m6ohKOpLE135SWGE8oxuNhYjNzCIuX4WPzmyWE62y4m5tJTwAvHI2ds6ZYc/z+qB9IxqZW5soSHqfhnB3taJ2eEg7bKTCfURmeuibmsE97ZDwrLFn6Dy1QiVr0ZomnjzOZ5S0mmO3b0WfWiYqtdTtMSfELmnyUSDaSXLn3mTnpvmKImjVlwk0uZoXOSOb3fgPqwL8Gg7jJ5MKh+ZnkpR1JemqE8Cehrn3TsalhLsEVloRse5ZReff+soOIopePs4//K7b5MbGZHEzm/NQz9+krofHEQfEq+pZU4MfWAQvdRB3dYIDaaVXL1whXR+IpUMPZe70aZNwRYaovBoSMlDO75zIZG5LsKX1owuzoI9ZEoKuHj1RrnG8n2FieleWXDabcRXLeSqWUtwH9FJXjJDZJXRGElvMYHbpjL9/oeZfv/DjEzU2H7DJaNhM/9+BK1Ywo0K9rfL4+VyWCZOEFbPbqPiJ3slVGjhTPTQGUwXzJb31dEOFaijxQaVTPDlOaXgGvWuNs67l5RvglTJfG8RRUHpgBwuL5DXIZOR81ZWpmThZLJYd/wB2rvUdRW9bp5UsRQUSDpuw0W4D0QZ+Fz8HG/vWJ8uyALYPEFqjmy7JJjG6PTMth0mNaOK8OLJwgCazeqcQ94najbJNVjqQBtK0JrbSnPvJpLe4nPqk8z1s8h19yofW2SuC98tZ1SAzcc5mVLZzLG07kH3inzWqJTJhPqIXDNXAWVPyzH0YrtUIS2Yhl7qINt2mMg1c1XP6mC1XYDfonnofRK0lQl2nfN5JAmrZky+ao7dvVYxu1cvXCE1VXNdUiPiq8QWzypGfcbX26hZn2PiPtkYatp1kt7bU3SsdHH1whUcfWQuwZ9Oonv1DDS7DVMyy8WrN3Lx6o3vCpCMOT+9tmXfBqlF2XWSpcsfVK935UMFOAPDtK+dRaZK/P9abJDGlgNcvHoj5vpZ54Au4zmfv2lggEwjKOj844BRGanhnc99ciZ6xUTZGPSJBcO5ZRem8gl03zCV7SNPs33kaaVM6Z9jRbPZuGrWEvEZA9Pvf1j1q7719Doltz3/sd9z2rve0ct5/nO7ePVGdV8fti7F+B2lUDlvPghTPT7jMz7jMz5/uunr6+Pyyy/nU5/6FF//+te57bbb+OQnP8kVV1zBmTNn3v8O3mP+IkDnwDUDEsYSG8TzxilZZFhkMah19ynJqTaSonO9CS0sXiM9GldBO9m2w7gOCFPRflc9gdtnkB0YkCqWCR5A6lCC9y2iZd8G5euzHwupZEN9aFgFBLkPRPHs7sOzu0/kZkcCwkA6b5KKkeIitNggL+1tFT9kqYP0BXWS1plIkgn1sfyZN4Wx3XOG7s9UoA8NC8N4tEOkUuRDUVIp6el0ORVr07JvA+4XDqDpukgAQ/1E55aiDwxi2rEX9xEd0mn0ox3o0TjWdgn0GC4vUD2SiboyBYwuXr0RzW7jrafX4Wk5hv3tDunPGxxSvqLckvmYPALCatbnCC/z89Jrv0bPS/VAWGPTBbNpiWxWgSlZhw2sVnVeDaBv2rGX3JL5Ki1Ts9uwhYZU32G27TDhxumKCbTFxT96xWUPELlmLrZdR2SBX1oq8tf6WWy75TIqnziogndw5D1fjiJ1f5GZmkgv82EymYaL5PxOm6J6+0BYqUm7M+jeMvEUjqRg5z7M9bPY+t8uh8Eh0hdOo9F5E2aHg9Dti6j9u32SbFviILlwJu5th6QWZ8VsARrBHizT/DA4TGK6Fy3UT9vGtSL5HkqQddjQw1FMRUWYS0uFoXzjFJ5XAiKf7u6TYKNjIYr60uo5abFB7G2dWHzVJKZ7hcX2VfLW0+sYLi/A/cIBxf7WbY2ooJuyg5JKmWm4iMiK2USvmweOYvFkupzCJBYV5gN8MnR/tprer0nVkKV1D+kLxRNsrp+FyVEssuWhYUgksf7bIcV0ZwIdeN48jefN0+jHT2I92i0e7J92KRbUqM3RXPl04gtrJZCpdxN6JoM2oxY9mxUQvsyP6zf7BVR5nDT3bpL3ZrBHvMF5b7YRHqSF+mlf6abBtFItmn337CT7Cb9KR22qXYepsBBr8CxvPb1OMTO2F3ejlTgo+bVT3sv9EUnk1TTIZNRC2qhSqm4dURthqb+arWTN7gNRxaYaqbEf12TtZkmPzlsFjJAZc/0sTIWFeHb3oQ8Ni2w939V1fh2UnKRKyOXks8BixnywA31khLTLjsVbztLlD8rmQrAHLTbIyLK56L0hdd6NMRji2vv30r7STVFwgOHyApoqbuXUNy6kc72JXIGJ3OnpNF+/iJJfO6l78AAp3wT8W9L41sZxBeS9aD3arT57Dfb63eb89FqjvugbM1/BfiykfIqWQA/mYUlb3v6rn0nisMVCy1Jhd7Vw/F2B1vmv31g5bYNpJbn9YsM4vyvUOLbXtt9FvLaQYZ8kYXd/pgJbaIjQ7YtITPdS+WqEKT99iCk/fQguuQB7OCOKlsbppC+oY+nyB1X6dsu+Deq7ITsmBv+DsOmGPeCPjVEDY7D673ftnv/vzi27iE61SiAg53o4W/Zt+D9+L4x7QMdnfMbno8yHr0v585bXGvO3f/u3xONxDh48SDgcJhKJ8Ic//IF4PM7Xv/71j3y/f/5nBqj5Vly6EoGXdm1T/Zyu5/YRuH0GZLJyS6Wo+kEBuscpgCcWA0cxeqnjnBAE9xGduq0R6aiMREnUlamQEaOX0/D16aUOYR+PBND9NejeMgG97V3ofWfR+85KcXqFpMNqNhv4xDsZWFPNVY3Xo3vLaHp2J+lSYWQMv9P2Gy6RJzg0QtVvukhfOA3nll10PePHuWUXnetNhG5fJH5SXyWJ6V4S9TUk6muk53FwEP1oB+1fcKN7y/C0BlT4hWvr71XqqDbBg+4Rpsnz2+OYS0tpzW3Ftvu4KkB3H4iSqK8RL1tVOak58lw7H5+IpXUPltY9WI92o3sEiOjHT+JpOYZp0jFyIyNEr5snzz8vl22qXackjebBJMQHaJp2J+4DUaz/dkixIIaHNdt2WPlGAcUUeXb3kTt0Aq27j6hfgnJyNrNsAvgFvGZjMdWXaQ0KC+N58zQ4Swh8uUKe23QvwfsW4T4Qxf/IUeq2RsiE+nj52L9hCw0pr2HV6ymqXk/huytBxSNWineflFTdtXHIe0MBtEAn2G0URBNQJ52siTLYPvgzArdNJVMzUSSrdptiiwxfo94bQq8ql75DZwmLr/sBi6/7AWQyWEIxsgMDEpQTizH7+fWqyy9RVwaOImGIg13Ydh/H4i3H80pAZL35+hD7MfGCRea6aKq4VTYu6qqFrRuUTQasVrBasYWGCC/zY2/vV+mS4UsmYfFVi8eu1EGu7yx6Isn2kadJTIDMkpjI0BsuwnpQNmG6/pcJHEXyPjWbyU2aIMm9CNOdWzIfonHIg/tMqE/AaFA8hUV9adX3akjJ7cdC0N4l/ukJHtq/IGywc8suAUGggmoa3WvIxQfouWmOAITBYSJz815ARzE4ilXqs3PLLnkPlpZi6egd7edNJJWst6lWfKDZWEw+bzxObPEsQwum0BJ/Ql0HY0O8DB9swf52Od911dh2H6eoL83ywtW07Nug2MWPe8K3DokkHtQGTHzVwtHQqeMBMJux7DlKdnCQyFyXpGnv2yAhWLXr5DMwNoheMVExxZrdxvbEFnmOBQXqs0Gz2yRYasdR2u+ql99LJAV45q9LbFaYXkvVjhTLn3lTdWzW/OwEvlvOcO0PW2m4/ssiS89vRKVLC8jZzFItFRxQnct6bwi9N/SBZJ4G0xlftZCm2nVcvHojgTXiX9fCcdVnq5c6uKrxerpvmMrQbHnOTdPuJHOqm7eeXvcOr6MB6MYCnviqhef4NseCvgbTShVqBiiLSNRvofszFVT9RtQXhWd07G2d9FzuZuamAWZuGsByJk7vogI6Pm8SC4PNjC00REE0oVhVQ3K84lDkg18oY47l/cbYtBjb6flec/6/x1ctZMJjO/E+Jp8JY9np6fc//H8sNR/3gI7P+IzPR5msrn2k25/7tLS08NhjjzFr1uh6ZPbs2WzatInm5uaPfL9/EaBTjw1w8eqNDNVX8KOogC5tKEFuZATfPTvJ9PeT6e9nqL4C88EOlcBoLi0lE+gApCZC03VMRUUikT1+Es8bp4iu/KQKANJC/UpmZ7LZwW6TEI1EUlJej3UoT5SezaKVONBKHGQaLhJgGOyh+czjdK430XO5W/r+hhK07NvAy5+spHj3SciIrEzr7uPllmeFSUqnwWLBelQ8hb6vngag6rqDVL3YS0E0ISEtCJtiCw0RfFhkrblkgtr79yo25arG6zGXlpK6tJ7kgmlY/LUEvlwh/YpfcAtrhfg42785B3tXjFwkSvtKN7ZdRxRo/JfffRstNkjNN4cJ3b6I0O2L0BNJqQVZPJncyAg4ilS9jHvbIQHiiaT0XObZXMhLEu02yMsmtak+CR8qLcXzeie+ZvFluo9I6qLFW66CWDLHA7R07kGvKqfqmRNk2w4rL6Wmy+ZBz2/mQLCHlG8C4cWTyc6aAskUwQft6vHtx0LCWh7toHPzJLTYIN3PzeGqxutp2bdBKgsyWYbLCxguLyBzPIBlz1HIZEfL1jNZckvmo8UGCXyrXqTJoX4lT6v7wUEa3Wuo2pHCtO8Y0c/PF9bPVylAJy+p1LNZJdcMLyjH0R7H0R6XzY1iO6YLZpNtO4y5tFQkdPmaGNvu46N1MAgA0L1lNL52VM7pBA+5vrMkpnvxP3qCXQ89DnYbwfsWoYXjZGMx8RiDWrQb8uFMoEOxYp5XAoQXT8bRJem40evmoU3wSKpyPrW39r49soGSTGKKDlD93ZzUpuTDWkzRATL9+WTdfCiTXlUudRnt+cTpQIeqQ7G396Ml0xQFB0h+ZoF4sGdPov1/1mOy2ckEOqh78IAKxsFuw+RxyzWWZ7NzI7J589bT68BRhHPLLtlEORtGPxum0b1GXa/Gojdw29RRf6Xd9o406OB9i0RlMZLi9AILRcEBSTYO9ZHbf0j1fxoePz2RBJ/U/3SuN6GVT8AWGiL6+fmjKcp5YPdxLpSr/ke/2khzv3BA6pX2nBGlx/+sx+KrRiufgDbVp96/BjuslU+QQLX8n7NFVpLeYjKhPhL1NSx33IhWPgHSaRUsBfI+UzL4RBI9lSL42ESVGKwX28ntl5Tabbdchn78pFgOvGVgt7Htv19GQTSB7i3D/+gJtKJCTi+w8Nr2u2iJbOboV0pJTPfy1tPraNrdRdNueZ3eDzAZTKfRhTnwubgC5OFLa3BvO0R16whJbzGRuS4qN7dRfOiMStm1lElegBYbVN8tY4Hm2NfRuWWXuh4MJcXYnzOqcwCyDhupGVV4dw9T+WqE5o6No73QuRyVr0ZUUBODUlEz886jomho3YM2JInLxrW3dPmDNNWu4xf3X/WhrpV3C/I5f6647AF1rTfOu1elTH/QMZKMz2fSG+fdi++eneNM5fiMz/j8p8yHDREybn/uk8vlKCgoeMffFxQUkMvlPvL9/vmfGSC6fCaRmRq2F3fT8umpAhKL7Vh81ZgKCwnet4jgfYtIOs1oEzySwHj8JFpF+ajMMpGUMJepPkzpHJrZjF7qYOBzccVqZkJ96NG4hEC4nOj9EZwd0m146Nr1yoMXXubH5CyhufMRmjsfwd7eT84mjz37+fXUrJcFA4vmQTJFU+06UovnkKivIdPfL51n/f0svONmmjs2kqivUTUXnlcCZPolDbb7uTlc/eIeaO9isNouzE9+qq47CL5KLJOr0KZNEbYrGicy10U2FpMwjF1HyPWcpvaBvdKv+KMTYLWyYlc7+Co58pXH0K0WTCUl1D3YJuxMfgHfVP0NQMBI2cEUZQclNfKKyx7A/cIBFchirp8lmwAWixTBz5GQJH14RDE6zi27hO3LMx6GPFKb4CF8aY0woQUiAdVPBEnNqCIyUyMyU6M1txXTpGNkHTYV/pQJ9dHVUCi+s7ku0gdL0ew2em9PiUR6RTHNnY9Iv949O/G8cWqUKZlRS83NZ+j+bLWcQ6DJe4swHKtniJQ17wXWCgrI9PcrZlGvKscSS5IJduE5rDO0YAoAWpmb+bc+TEtkszBj+d4516/2ohUXQbBH/JCaRuj2RWwfeorOJ8S/5ehKjC4scznaV7qJzi2Vx5/gEalxLCayzDyYsbwl1TDGpsg3Zr4iFRbFdkzOErqXWMFu4xNvfpHAmmqqdqQIfM2nmOO0y077XfW031VPdevIaO3JbVOJr1qowo26GgrBWSIbCtG4SCp91dR8KYipwguZLJrNJpLY2CCpGVUCMmODhBdPxjK5Si5Wuw1Py7HR6pKqSfTPsSrvtJ5OoxfbhQUN9Qtb9s05FO48xqS3MqSWfELqSQYG6P1/LhKv5Jl+dI8Tc/0sgg8ViR/VW07wYScXr96ors1sLCYyYZeTbEwYWrXw91VimRMj67Cx3HEj4cWTVfVJJtjF7OfXU7VDrnu9N0Tt995GPxKQwKNF8xSTC2OknHYb2bbD1KzPUbM+h15ohfYuARKOYsWqtkQ2f6wLbSN527g+PW+cQu8+Lf7pv2+TH4oPjG4I5efi1RvJdfcCkl6bCXZhORPHFhoivmqhhHhN9ZE71UMuGse264j4Yp0l7HrocUYmavif7FXMaNV1B+m53C1y5BNBTIWFDNVXYMropBbPQXM5RXI5OIylNzzavwtgt6nQuB8eWUZJhwn7Pgns2rZmKdvWLGX28+s/cKhNfNVCEvU1+NbGadm3QQEnzWLhrx9/FXtbJ+5th9BsNsKXTKLy1QiRuS6GFvlVf3Jzx0a1wfdur5/pgtmK6TR6LcfO2CAidu4DwNI/qM59JtCBxV9Lc+gxCPZQEE1QEE2QmlGl/PeB26YSvG8RqSoXeugMZ29ZRHzVQmyhIZo7NqqAuA862bbDfzQYCaSD2gD32bbD6n3xYUb3lqmKG+OxxsqNx84f6zcdn/EZn/H5U01ON32k25/7XH755XzjG9+gp2e0FaG7u5u1a9dyxRVXfOT7/fM/M0Dpi20Sye9wkLiwVvk0yebYPvQU1a0jVLeOCGALdEg8e+UkuhsnqvvQE0lhFIHBKhvUVZP0FlOzPqd2oEO3L6LziclKetn5lI/ub6ZJLpimuhyD9y2iqC9Nc+8mFWYxNHuisGOBDsWKta90E68tFF9lqYPh8gLsh3uV/Cq+aiFvPS1SL3t7v+oCbe7dRPIzC0h6iyn5tZNtC3y031UvXrg8qIzMdWHxljPsKyF8aY3Ubizzk7l4Fp43T6vFvBGCkhsZweKrli7F+ADbrl/MsK+EK278CsO+EhIX1qKVCeOlh85AsIfE7EolS7a392Nv7ycbi0mK7DVzhUl2OGjZt0H8pxYzwccmEmwqIheN0xL7/xRD15rbKvUcBQUQ7BmVRw4O4frVXlr2bSDwNZ/Ur0z1URBNqNf0isseoMG0knitsB60S9qmf3MX1xzql8CbOSKj9q2Ng8uJ/6dd1K+TBaXFWy5+TbtNNiJig+jeMipfjUjC7rM7wSRvk6oXe0XumWe6hpfMwFw/i4JoQiWTaie7sfiqcf58l4RRAXqpA++/RRS7YSosRJvqI7XkE8rDlx0cRJvqo23jWpYuf1DY7GyOrobC0QTQion4n+zFdSAmfYSJJJmGixR4b3p2J75bzmByOWnuEGDleeMUjfPuVRLxTKgP/1MhSCSZ8EQx/id6eG37XRQMQOiv3DS+sAfTjr3UPiA369Fu7Id7cb9wgGN3r1WBL0a4E+GoBEhN9vIvv/s2en9EmFqLBT2TQa8qx3TBbDLBLgrePi7M4OBw/voz0f3cHMjm0KvKcXQlpBOx+7TqEsy2HSY3PEy27TDNvZtkw2HFbOq2RmiJbKYoKJJmg4GvfEIqXbSpPvTjJ+WcrI2zfeRp9ESSmptk08BgbwyZsNGT67tnpyQ1L5lP+0o3vq+exhLowVTiwPNKQD3/1txWSn7txN7WSXjxZCLXzMXkcjKyfB7dS6yYD3ZgcpYo9tQID2ru3aSY1GzbYbRuCe1pzW0lUVdGNhZTr+fHKbNNX3kRV1z2gFQ/BbsEsJS5hQWuq0aPxlVPsQFm9FKHgK6CAjINF1H+40IsM6YS+HKF+Olf76RqRwqtu08SoQssZAcHxROYSnFV4/VU/aaL8CWTSHqLRQ0BsgFnt2EqLCTzqZl87sHtmA92YNt9nEywiz9c8gsOPyDdu7nTZ8gWWcFZQmK6V/X9/uTIYto2rkWvmCgM/s59sHOfCtN5vzE6dF/bfheBNcLgHrt7Lbseepxw43SaF05haMEUqTrKZBTLD1AwkKGp5nYVvuXcskuuc94ZqpPbf0jk3u/R5ZltO3xOEFHB/nYCfyOMaFFfWm3ULLnmITSLhWyRlWyRFVNG3i+6twz/k7IpcGZ+IdRVs3fTWvGedvfJ90k+d+CDjmFl+GNTtSN1Drg3NtY+6MRXLWSwzqmYzvMf//w5X5Y8Lp8dn/EZn49j/qsynY8++igDAwNMmTIFv9/P1KlTqa2tZWBggB/96Ecf+X7//M8MYCopgcEhtDK3+kLNnT6DPjAodRr53WDDK5lbMh8Ghyg8o0uCayJJNhYj45eKjJGJGu0r5b6ybYe5ePVGWbiXweQvHEMbSmDxVVPxiJVD166XtNx8Aqz/yV6u/WErje410r3ZcoyiHUfxvBIY9bAdP4llToy3nl4njF5sUAJIPE6K+tJE5rrwvHGKpcsfFL9hXRlkc0q6ZeuXXWT3gSiRa+ZS92Cb/Ey+c9EIJCkKDuD6Q0wxU5ZYkkygQwX24Kuka0udfKknkvi+s5PAbVNZ8ewbFAUHxBMUS2M/FlJAK33hNEmXzCcE6yeCpKpcpKoE6GZjMTyvBMi2HaZnTT1NFbfi+s1+wsv8+L4Wwr+5C9PEMppm/E80l5PAbVP54ZFlWObEaO58hMiK2VJ/Eerjpb2taDNqaaq4lWN3r6XReRMgTKj1aDfWo910NRSqLsS6HxwkfeE0OZfROC8tlcVK+mApqSqXAKHTkrrlfWSnMEveMgV4tXz3kMFUdK438Y2Zr0i66Ykg+tmwWoC5D0QpbuuVoKqhBJ3rTXTfMJXkgmlku09jmeYncWGtyDs7TinPXKK+Bs1qRT8RlDCmN06h5dNkja7D4fICqURwFKlaj9bcVtlIyXdF6oVWErOlU5BgD46uBC2fHvW8NlXcKoAukZRwonxdiLl+lnhP7TYBxUPDNFXcSnVLhAmP7aTlMxdi8VUTvW4e0evmoQ8NE/wHF+131YtsNJ8Aa/jtcBRJ+ExPn/Rb1lWT+dRMyGSERYwNqgAVPZul7sE2Cf3p7oPBYWq+FASzifYvuEmXFpAuLUDPZvG8eVqk6v5aup+bQ6ZBQJJpx15RHozkmZRgD1m71Iz0/GYOR74/A9vu4wCkFs+hpakePRpn9vPr0VxOIitmkzvTr0rvE9O9eF4JqJAwyzQ/BHvEF3xEV57w8DI/+tCw8nw2LL5fmPf8e8vTckxSpYMD+B89oWSkmt2GxVvOsbvXqseE0YW8XlWO+0CUpcsfVJ2SxvX3YWsnPszYQsNYg2dVGFJ4mR8SSZYXf0lkohaLpPjWz5JOXG+ZyF1TKcXWW/uGCawul57RaFxCypJZms+IbNvYqAJUbY0ejQt4at2DubSU0O2L0GKDoj6pKsd6tJvmBdXynvRVErp9EQvvuJlZDwiQ6nrGj6V/kOajfy/VPB0bsYWG8K2NkzstgVlXNV5P93Nz6H5uzmj/Zn7ejRHTM2mVOttgWon/0RPMfn49TRW3svCOm0k5NXJDwxS39VL1gwIiK2ZLInU0jqMrQeAGC+FLa9T9Gem0rbmtAuLPG8Mba6TZjk2sHQueLl69keElMyTQqsiKKZklfGkNjS++TdJphglu4v4i4v4igk1FnP7sVJFMF9vxb+6ialuvbJrlp/nM46pC7MOM0Xv7XsCuwbRSBbyNPQcfZt56eh3pYpNiOsf+fmSu648ym+OAc3zGZ3w+rsnx4X2dH118+n/PVFdX8/bbb/PSSy9x++238/Wvf52XX36Z3//+90yePPkj3+9fBOjM9J2BggICa6rJhKQqxTS5QqScvkrltdHCo/H+kPfwmE2k5wvbZB5MoleVU/mq9E7qiSShbyxSKaD2fjCXOtHPhiGRpGB/O0uXP6jYgoK3j5OqctFy3QKGl8xQxze8RBau2rQpwgIUFVHzpaAERbx5ms7/Vi2F9yeC2A/3EpmpSX1HZ5S0yy5AeiQhjMNQAkt3P9f+sJWey0UWpxXasZ+MKC8YwR48rwQY9pWQKi+SgJVQv3gpkUWOARp8Xz0NwR7Cy/zKY7Tt+sV0rpcFgPWg1KpkGi4i23YYU0ZHC/XTc7mbTH8/WoEEeeRsZpp7NxFftZCMv5L4qoVUvhoRL2xeGjt0SR2502cIX1ojIUvROHXfP8A3Zr7ChCeKaTCtFKlmJgtIlULaZUdPJGmquJXOp3wCyF/vVIFJljkxWuJP4D4QVd2IeiIJ2azyjPq+s5OCaILgQ0XSYRnsEqnlQ0Vki6zU/eCgHFM+BRgk9di3Ns7ywtWkXXZME8vOuZYic13opQ5eOvAqmUAH3scKqXr6KPZjIcxlbjLHJenT7HBA7WSw2/ji3S8zXF5A15OT6fjWPKncAHAUq+CbpprbcW87JBUlvSLnNhjzTLALCgowDUr6adYuoSHJBdOwdkcJ/nSSSLTz74HqVVJp8vLhHaNSVU1D95aR6ztL99/MALvIko0+0UygAxJJBcQ070QOXbse3z07xfva1kn7Src8/+Mn0aNxOtebyJw5qypCem9PMTx7ErnLLkQvdRD/G+m01aZNITs4yPCSGaTm1IDHBZpc63W/jJB0mkk6pTdVPxvG4i0nfMkkvjpjJ/a3OyiIJoQptlrR+87KwjObZccLd9C+0k31l08x886jACS9xZiSWZH2lk+g5ktB9FKHANapPrqfk9qcscyK/9ETNB/9e1oim7G3deJpOSbVNskkrq2/Rysu4odHlqnwK81VKnLg2KC8F7pFmozdJtcxokwwrkOjLiYT6lPvU/34SdpXunlt+100mFaS9BYroPJxSgajc0vJBLtUZ6N72yECt03FVDlJ/JZV5eJ3PREUtrLtMNuHnsJUOQlLmdREmfpj+B8P0ty7iWwsRmTFbAqi4iEkGofBYdW9qnvLsL/dQTYWw34shGWihJ+1bZSQG8PP2vn4RKirFjYemLRrEMtIDrI5rrjsAXxr44QXSCpu1W+6ZKMj2AOJJFfPbwDEn19z0ylqbhpltI15P4Bi8ZbT+NpRSn4tCcmeN05hjeuYfZNp7thIssyqNgm0GbWYUjmchy14XglQtSNF1Y7U+/Z0GmqWVHmRSsx9t2PzvBKgYCBD8L5FmIdTtD77JIOfj/PPN18uvbyapjaAUuUZXIEUFBSQdtkJL57MwNxyWj49lSsue4BG9xqm3/8wTRW3KmD3QWesz/T8MYCywf4bz2NsINIHGSOc77Xtd73j/o1k3LEzDjTHZ3zG5z9i/qum1z711FMkk0kaGhr427/9W77+9a+zbNkyUqkUTz311Ee+3z//MwMMfGEB4UtrqNsaYeTai4XpKrZjCfRIQEWpA73UQXPnI+f03yU/s4Dc6TMU7DkmyZjeYvQjAZqe3Smda5HNVD3fJYskbzmVTxwEixmtfILUevgqJSHUXyuL6qpJWI92w0hCQoEcReAoYscLd5C4sJZskRW9qlwWqMWSMqqfDVPzmxC6x0njv/eqnkkAcjqWt6RsPXFhLTiKyfWGSEz30tJUT9UzJ8i5StArRCbcVHGrVJFENiums/tSK+7DuqTl/u1UJe3TwnGpKnE5aYlsJjJLQx8aVmE9E54oxhYaInFhrYQIhYawlJXBzn1kQn1UPnFQWON8pQrIAsHTcozQAulxTHrz1RolDrQyN8WHziiJb2TFbMhmwWSiadqdFL12iKsOxtCnVEmYDCJLtfz7EVWR4bsjL8scSajAJO9j4iXShhICHhdPlscrLpIwlHz4E0DmYKkKXDLuz9IbVtUhmU/NpPOJyeJ5ylfu5JIJrEe7CaypHk3aTCRVDc3Mf7yF4H2LpNM03zVpMAmRuS5VjZOY7mXbbDeeVwKU/7hQBZUYPtbgYxNFEhqNga9SGK98/+jA5+IMfC6O2eEg+A8uco5COp/ycfpiC/Z9J8WjOTiE764EpmRW9fAZKbBXL7hKJKfhOFqgEy3UT27+DCpfi0AiyekFFsJzCrGGBlTyszGJOglJsXhF/mr46NwHopLkms2SOVgq1waAycSha9dT2D2I63tdJL3FuJ8/oO4veN8iCl85gOXfj0B8gOzAAO0rZVPEWEDr0Tj4KskNDFLUl2b7ohoF0PWhYbTCQpGhlpYSuWYuS655SEDxitnoqZRU6+w+jvVotwDXBeVorlKO3FpCfNVCkc3fdIrgfYtUKmsm1EduQLxzje41Atwbp5NcMA2tzI0204/uLeMX918lTGR7F6TT4kfOy8xzIyNYe+Oj/Z/54CRz/SzVI6x7y+h+bo56bbVpU6SCAyQwqa1TsXMDX1z4YT4GP/RYvOUqqEmfUkXtA3sJXyIbF1psEHyV9H51nnr/gHi4m888Ln9Ip4V5y4eyObfsQhtKKBkxjiIlmSbYA4V2papIzJuiQm70Uodc/7FByn8sapDAl7zoRzswn45QciJO+NIaCqIJuj9bjXvbIQmpyp9nzW5DTyQJL/OT6zsrCcm+SrnBO9jO88c0f7b6/ytfPc43Zr6iqrc6HxH1yfD0CSxd/iBnbxpi2FeCZShD+0o35uEUla9GJOH5WAj7sdB79nQaY2QEDJcXYK6fpcDp+SC1uXcT1sOnmLJNrsuG679M5WcPYu2OQnEhutVC07M7aXp2J/4tadmc1HWsR7sZ+FycdLEJvaqcf/ndt0UFc0RHTyTl8+IDjqH0ea8xnuPYjs0G08pzPkM+yOaJ55UAodsXvev9G32+4zM+4zM+4/MfMzfddBOxMfVaxgwMDHDTTTd95Pv9iwCdpc/vE6/RUALHKwfBWUL7SkmuHPuF2VRxK7mREdwvHCAXiargGKNL0LbrCHpO5/lvNKid4fDiyWSqpDw+uWAaGX8lqQrxculHAhJ2c8kkkWraCsBZQqbCA3ab8orN+Z8PS3VAoJukt5gVh4RF7WoolIVRfIBs22F+enQRZLI0TbsTrbuPXHcvqSWfQC+0cu0PWzl9ZQUjV3yC17bfRWK6l/AyP9FPlEqZfKGVTKiPpml30jTtTkDYOv/PQnJuYoMCKB3FZBouInemH9Peo+jFdn54ZBn+x4NQVy1gKhzHnMiRdtnpXmKl9ntvy6LR41IL1fZvzkE/fpLsok+o82ssvL27h2nZtwHrGwJMMZu4unkfROMC4G6qUJ7S9m/OQS+2oxUXsXbWbzFFBwQo5lnX6HXzyPWGGPaV0Hz8+9JTmcmQ9BaP7tgHe9DPhoVtmamRmSybAoGvVkv6aDaHFuqn7sE2/uV338biLad9pVvCmUodaIMjNE28mXRpARWPWLG07qG5d5Mw5w0XkZpRhe+eneJ5tdtEOphnrAyQrhfLBkLgq9Uq7MR9IIruLRMJ4K4jSjZsad0j8rrCQlIzqtCL7crrG7lmrvL6uV84gOf1TqpXtVO9qp2W+BOU/NqJqT+Gb20c/9N9YDJR94ODdN8wlZdef04WmPnHzrYdFvbyVLcweoV2CYMqKCDYVISWytB541SqdqTwHBwRAJxnmYOPTST42ESRUeefa0E0QfBhufaN5FPqqvHds1N1gYYXlAuj8uxOhj+nYT8WIjs4SCbUR7btML57dpIbGSE3MoKeSGKun4X/0ROKDQSEUQ7HMU0sw34sRPs35yjGSisuQk+lMDscqm/QnMgpJndk2VxyA4O0f3MO3deL3Pitp9dBOs20r+3GuWUXttAQLZHNHLt7Le4DUSV13T74M1kg58GKoytB9xKrbFrlpc9j61iaezeRabiIYV+JqABKHCrsqSWyma4nJ6uNF+P5A9TcdArnll2qciQT6qNx3r0CmvOL9WzbYd78+TulmX+q8ew6LZtMB8STa4pIVU1kpib+87yc3tiYMMCRZeIEZTcwjrVzvUk2SkqkgkgL9dPovIlc31mlgtDsNpFR59OQ7cdC6MV2paDwffU03ddWSwhcfmMwN3+GMNWhfjz/KsfjfWSnbEIlktJz6ptAc+8mWiKbpZd42hQa590rm4ZjGM4/JlXO7R3tz1w767c0mFbS3LERc/0sam4WOX7RodMi4/16lJxZwxKKiezVYaN9pVssDWO8wcacD3aNz7Xm3k24tx06R7Z6fsVK47x70YeGMY1I1kBXQyGW8ok0H/8+4YsmkvQW07xwCs0Lp1Cwv12+m/xSm+W7Y5iRCRpJb7Hq6gTAV4l/cxcfdMz1sxQb/sfmfIbSkIiPfV5/bDKhPv7H/3iR2c+vf8e/WVr3jAcFjc/4jM9/ymR100e6/bmPruto2jurX06dOkVpntj4KPPnf2YAbXotLZHN6GfDkgYK+J/sxeKvlT5GQ8rmLZMv/bpqtJl+tHCcpLdYyW11fw2x/3YRBbEkyQXTqNsakaqAgATe2Ns6sXSeoauhkER9DdsTW0hVufDs7uOlf31e7qPQStxfRLq6TJXdV74xjGVyFUOX1GE/GeGfb74ccjmpTBlTkO67KwHFheLPLJRj6l5ihXSW57/RwKTnTlC8t5vp9z/McHkBkZma6vWLzHWR/MwCAT/FdvGbZTKQStH1pPQpul88JCmggMnlJDcyQud6Ey1LZ6jAIQCs0hdqPdqN/9ETbB95Gs1iQS+0El7mJzJTo+7BNqLXzcO0Y6/a4ddC/XR8ax7Hb7TSsPh+kRPHBtFLHbwwuwwsZgm6+GmXePFiMUk5zMt7G0wrZeG2zC9Mcj5UyOR2YXtxN43OmzAPJklfUKfCizzfDaJNmkhkxWziqxYKg/jmfvrnWKn7ZYSqHSl0jzClWoWXKy57QMJ0NneJfDTUDwODvHTgVYqCAyLhLCujqXYdVTtSWFr3yN9NrhLP65pqAmuqZTFYJIxu3daIAsDuwzrBh52YLpit/q5x3r1oxUVkQn0qFKNx3r2YKoUZN8572mUXVgjxX2k2m/SeTpuCNm0KU376kArgGaqvkE0ORzHt35xD1W+6uHru5VL+PmuKuqYKogks0/zoRwLkOmXTI3Oqm7qtEcKfnEDNr3uxHwsxWG0XkDAgGyA1XwpS86WgWqybS0tFcnzLGVIzqsj4BZgZALuoLy0McPMR/D8J0nz9ItWBa7BpLJKu1uB9i+Q85OXKOUPSnK94INiD7pFk1MBXq3Ef0aXcvnadsP0jI7TfVU9rbiueVwLYfh9AG0oQmeuiePdJTBPLqNsaoepXJ6VLsVZ8bJmGi5Q3tql2HUuXP6i6M43XIPmZBRLENWMq1t64hLJ0nCL62QsAlAcyOyj3YWndg+3F3ZhLSiSsK9BB3YNtNE28merv5nht+13nLLpb9m1As9tUlYgejir1gXPLLlWV83HLBzOBDtWFC6CHowDUbY3g3LJLgrHyUnJ9aBhtKCHP3WTC9Zv9tOzboMBWya+dAsprJ1P3g4OQy6HZbGjTpmAZymAZytDcuwm91EHVjhSZUJ8oB2KDKnFbz2Soer4L9wsHVBeqeTiFuX6WpEBnsqRddvmM64/IteOdiDV4dlTKWletulebam6nqeb2D+SL1SwFwobmPeNj2Uc9kZRQnOERtOgAmE042uNkvKVYpvmxhGLY8x+bbz0t3a1jpbVGPYo676E+WnNbuXj1RrKx2LuCKcNbqoX6oa5a1Bn560KfNIGmiTfjeSWAZShDdk4t2Tm1pC+oI1NsAZOGva2TVLUbV3sGW2gIa3ufOj4jZfePzbsdk5Gq+16yWePcGcf/YSXirbmtbLt+sdp8O7/vdFxOOz7jMz7/GZND+0i3Dzs//vGPqa2txW6388lPfpJ//dd/fc+ffe6552hoaGDixIk4nU4WLlzI9u3bz/mZJ598Ek3T3nFLJBJ/9Djmz5/PhRdeiKZpXHHFFVx44YXqdsEFF/DpT3+aZcuWfejnZ8xfBOjsXeISRtNXKWCF/KIqk0E/G1ZStqS3GPKVBVoyje5x8tr2u/DsljAY0+mzeH4XJF1qwxYaUmE7ob+eqvoOsVmp2xrBFhpieeFqBqvthBeU03D9l8kVFtC+0k3KqfHKjrsJXzKJ8CWTaH3jbnSPU8IfkkmswbNgMpFpuIhEfQ3ZKZMwe9wEvuQFTZgGRhKYqirw3bOTkTqX2kkOfM1H3dYInlcC2PvzUs72LmFsEjkFsLXiIgHipQ6qv3zqnPNlb+uUzsGGi6S2IV/FglMYG4aGJVjEUUz3DVNZ7rgRLGbaV7rxvHka9xGdyDVzCS3JSmdiOgNpWVjWbY0w85uHsfQPCgNisYgMzeFQzEgm2AXZrMT47zpCZMVsCTryVePskJRhW2gIfJVqkW+unwV1UthuDZ4lE+ggE+jg1GNT0W0FuA9EcXQlyB06gbl+liRi5hdq7V/IB5rEBxTTicUiclh3KfpIgqum/5UCiZn+fsKLJ2Nv6xyt1BmR8Cj/5i78m7tILpxJ189rBXS1iwQbXyWRWZrU55w+K72m+RAO3VvG9p799CwdE66SlDAcA2Bbg8IMWbzlNE28mdScGo5+pVQt/qd9bTed603kXCUU7z4p12cmo+SZelW53Neb+wGw+EUarfedxeR2YXKIZDq3ZD5pl11qMs6G0cNRBj4nTJdROaKVudHK3IrRiqyYLZsYmawA2UAPSW+x9MHWz6L7UtmQaF83S0JVgj3yHsz7i7OxmCyKgz34N0vJfcu+DVKP4SjGUlam3i+5mT607j4s3nLqfhlRzKJe6oDBISxeCa9pnHevsM+TJ8HgkLDc+VqjtMsOmQxaqJ+UbwJadx/2w700TbxZ6m3ydR5jF8WN7jXseOEO6ek9fUbqTDIZOn9WI+cn1I/ruX2KlW1sbiPTcJGE4XhcuA9ECd63SNJaHUXnpJQabGqDaSXNvZuwt3VKUNnAANl+SUq2eMtJLpimNiY+TnYn+ZkF6v/N9bNIXziNnsvdwtheMFuu67yUPHLNXPTQGUnQttvYPvgzpt//sPjeNQ3nll3khgWY4qskMW+KBDAB5rYTmNvktepcb8L+tnSv+u7ZiV7qYKi+AgBtggeyObKDkh597O61Ss1Q3NZLeOkUCqIJqb1ylarAHD0ax1RYKFL6oQSeVwJcvHqjbDR5RO7+foBFz6RpzW2V1w0JzjE28jSXk/iqhQR/4kV3lRBYU82wr4Sc1USqwkn8gnKyVkh7CpX3+nyQ9G51HwZ7eP6/jU217b5BmHpr8KwwlI8cpavRLV5oZwmWUIwVm19jxebXJGW6rZOC8AiZ2goSngKV7Kx7nAqIf5Br6t38kwYje76/U4HD/Gez8fzGPo8P8phLlz/IsK9EvjP4eEO0xmd8xmd8Puj8RzCd//RP/8Ttt9/O3Xffzd69e/n0pz9NU1MTnZ3vnjS+Y8cOGhoaVLDP0qVL+cxnPsPevXvP+Tmn00lvb+85N7vd/keP5dprr+Waa65B13WWL1/ONddco27XX389P/nJT/j5z3/+oZ7f2PmLAJ2V207hfkF8Y/ZjIcjlJFo+k2V4yQzlFbOFhsBuE4/S2QhaqJ+lyx9EL7TSfPz7smC3C+Bs2bdB9Zl5//kEWmyQTb++muBDRbIY6e5Dm1GLs2ME16/2krOZSZTbscyJ8ft7HlNhPe4DUZom3qwCQnRXiQQCTZrItT9sxfbmUUyHOtAKCqhuFYljUV+a7r+ZgV4oXYXWiHTR4SzB1zyMFuqn+4apJMokMGV46WwlW9NHRuTmLaPBtFIB58hcF8kFIiPOhPpU6qMWGxxlOEcSUuReICAuE+ggsyQmXsM8YAx8uYKivjSelmPM+naAoh1HScypIjGnikb3GgmZ+etPMDTdg++WM+hnw/gfOYpW4UUL9SuJoVYmLEm2fqryR+qlDunkzGRVx6bnzdPn+IMa3Wvo/my1sAzT/EqiCCI/0zNpVeKOrxJL6x6qXhc21egbNBipRI2LVLn4TbP1U4WtHZKwmqK+NHo+UEf3lslCL78IJ5HEuuMPTHiimOaOjWg2m4DYYA/+H8kCOxeNK9bdCJu6en4DFf/vXkyFhegnghJe5C3DuuMPJBdMQy91YNt1RDr3LGas7X3M+McYieleEtO9cMkFIq8dFGmq583ThBdPlmTRPNAaLi84x4Pn39yl5KKZGeJLLYgm+JfffVukiy4nmsdFxSPi86p8NUK27bCSChpA3LlllwDPqnIic128tLcVW2iIquukoqTuoYMSvPP3bQpkWrzlkMuJjNbhkOstX+dinJfivd2EG6dDoR3P7j48u/vQ32oDi5ncwCBat4QiNc67l8hcl9pUykWlS7HuoYPoRzugoADfPTux+KrJxmJKjZAJ9WHtjYNJIzGrguYzj4/6T+uqac1tJTcwSG5gUHVj2ts6efnwjnMCV6q+b0H3lhG9bh7xVQuxzJjKttkSAFTxk70iw3fa8D9+UkCjRX4+23ZYvNb7NtC53oS5fpZcH/EBcvEBSYfNpAWk56XXRi/ixzm2F2UDo2epG13T6LpSvJTB+xbJ9ZX3SwIqdMzwxi933Ij7iE7WYcM0nMRSJqngDA5J6vfbHfJZ1t6l+l5p76LquoO8dOBVAan5jY+iHUfVxlLujIA9LRyncd69dC+RxFYGh6WipFuuD2xWUjOqxKdsF0aVfPK0sYF2vrz2j41mkQJsY1PA+Lx56+l1YLXi+e1xqq47KF7OOTGSTjN//eN/YbDaTknrIcr3phissqnPe0N+/G6vYXzVQgXqDKnt2Bl7zEbIkl7qEFtBVTmmRVHCC8plQwRouXQaLZdOY/hCH+RyROa6MA+ncL5+AoI9DNY5ob1LNo88TgGBi+//QOfFmD/mhzWOVwv1n/N8je8jeG/Qb6T3gshzf/eTn5I5+E7Z1rudy/GezvEZn/H5j5j/iMqUjRs38pWvfIU1a9Ywa9YsHnnkEaqrq3nsscfe9ecfeeQR7rzzTj71qU8xbdo0HnjgAaZNm8aLL754zs9pmsakSZPOub3ffPe73+W73/0uTzzxBPfdd5/683e/+12+9a1vccMNN2C1fvBcgPPnLwJ0MjhCtl52hRPTvRy+c6KUw8fiFO/tlpTN48IM6TZJuU1cWIs+NCwL4XyghXXHH2g+/n2R31XcynB5gfKzZYJd1P3gIIeuXS89cIUieTIPJtFm1HLtD1spbuvFd8sZrmq8nqzDhnY2inY2SvfqGdjb+7l49UYJK/KWQTTON2a+glY+AZDFUsH+drpvmIq9rZN9dz1G53oTR24twRLokYX18Ajmgx0k6muEyQMsQxmKD4nvKGs3oc+bgT5vBtm2w8qPky2y4nm9UwB5exdmh0Pi+H0lwtaE+qQWwZPvSTObyDps4mn6UpCkt5iWfRtwH9FxH9GxHz2NXlXOSwdeRU8k6Z9jpX+OlcGlM/E/egLP74KYE8KgZufUgsVM4EYvuJxYvOWqG1E/fhJLoIfIXJcALcSHmhseJnjfIjyvBAhfMkmB/JZ9GxheMoNEGWSOB8gcD6gFjRbqx/9kL93PzcEWGlKhJqHbF2HbfZyWyGZSvgk0utcAshAyJbPkbBIYFK8t5KVd29Tmg6V1D5EVs1WPZnhBOYG/nSrnaWiY1JJPUBQckB7WRX7Cl0wStiYPbE0u5zmbGCyaR+C2qaqKJPVXs3G/cEDJLe3HQujHTzK8dLYkBnvLaO58RCSIB7uxH+wmU1LAyESNnKMQbYKHXM9pqUrZt0GSe492q2AbEJ8pgxL09NLeVti5T55Pexezn1+PqaiIxHQvuTMCRLVQv0hLy8qUVNB+LIQWG8RcWiqbMHnm8qrG6xUb+dr2u9AcxcJ2eScKeMoHEh35wSw0u43s4KBK1TSCQUK3jJA51Y2n5RiZU92SaNx3FkuZeKi1qT4BpPlxbzskibShPkyTJtJUu05As7WAxKwKckvmk5juxVxaqtQIlrIy9D5RFlha97C8+EtcuaMdzW4jMtdFU8WtaFN9aFN9o58ndhsL77iZrN1EYrqXmvU5rvrHHbSvdIun883TNB/+Oyz+Wppq12FyluD61V46lxehl+U91sV2ea/lQ3WaKm6lZn1utJ/TapVbPuhK+VPzjOi7AZI/5aSvFJVDxU/3oXX1Unv/XqqeOYH7iE4m0KE+p4xwpfb/WY9+tIP2lW62D/5MgqR+f4RckY1w43SGfSUinX/0BOHG6XI9uUqp+8FBkdzWVWOun8VVn7iM3JL5Iue2mdHy4VuYTIxcWY8W6if4Dy60cBz/oyewBs/y0oFXWfHY7+RaiA8QvmQS1uBZ7Id7FUBsPvr3omqZ61KS6qbadX8UMJ0/BptnSIcb591LeEE5iXlTMNfPYmCyBd/aOG89vY7mhVNkc2WuH1toSElXW/ZtwH0g+p4VI84tu1Tia27ShHc9DkOe21QrEtVskRXL6ShabJDJ3xjAs7uPYV8Jp6+sgJwOOR1rLM3AX/kJLcmSdchrgq8SR3scrcKrGMSm2nXSD/0Bx1AkvB+oCy/zYy4tVT+ngtL+yBhVSsZcPb8B3z073/Vn3y+9dlx+Oz7jMz4fx+R07SPdPuikUil+//vfc+WVV57z91deeSU7d7775+E7jjGXY2BgAI/Hc87fDw4O4vP5mDx5MitWrHgHE/rH5sYbb3xfVvSjzF8E6NQrJ9L6xt0M+0oYLi/g5FfvIJdMQF01ga/56Pj2fDq+PV8qK3ziF7LtPg5mMy37NpCoKxPP2oxaGhbfL4v2ggJs8SwUFKhFoOZy0lRxK1c1Xk+uP4xmsRCZ6yLtsquOxJf2ttK+0o0l2KcYwK/8j5fQi+14fhfEfjJC53oThzdI4AWZDJhMaEMJ0hfUSc1IIsnVC1eQOVhK7a9yqq8yfJmP9AV1mJLCBE7ancES6IFoHMucGDteuANTIoMpkVESvUzDRfDmfvRojJyrRMkmPW+epritl5alM4RdKC5i2FdC2lNIYrqXq376ujq/w+UFLLnmIcX8hj9dTdJbzMI7bsbkcuJ9ZCfeR3ZSsucUureMlN+Lvb1fwMpgktSMKvw/7SJz9AS5aJzAV6vxtBwjet08hhZMwfXcPgr2S70HFjOmykn4N3ehe8twdCUk3GRBnTBjOwPUbY1wzaF+rjnUz3L7Kl7atQ2A5uPfp+q6gwpw5071UvWbLvQpAmgLognI5fDs7qOp5nas3VGGywt4adc2nFt28Yk3vyiHMJQhvmohIxM1Je10H4hSuzUqPri6auxtnSosoyg4oJj2Uz8sEebVAJuJpDDJOV2lvhb1SfcpddWyEbFgGiSSpBbP4fQCC7nhYfTjJ2mcc7dc3/EB9PgA/XOseHcPY4oOkKgrQzOb6WoopKl2nQQaZbJYvOW07NugGGVMGpG5Lq5qvB6A1XdvQysuwvf1qAT1nIxgmlgm9TozqnB0JUjNqaFp1rdomvUtsFq58uU/oKdS9M+xoqdSqls2NadGJbRiMWMqy4dqgQQkHT/JzE0DpGZUEbxvEQXRBCMTNSxDGUyFhXgfK5TF9/DwOexs85nH1fGrPlCQa3/HXqngyLNuDA6Tnj9VeZDtbZ1oLqcCd3oyKTJzbxkWXzWmoiJenlNKJtSnpLKRuS4ic11Mv/9hefyOjbi3HcL24m4VmvTbqz5B3daIgMF8V2rz8e9LvUgiSebiWVS3jqhexJEqB5hlQ6PRvUbCbvJApDW3lWwsRjYWIzcwyOFvV8hmTP6100L9f7Sm4k8xttAwkbkuTBVemDwJk7OERH0NnjdPYyosxN4usvDoZy9A6z1D7dYoJkexkjVn2w6LN7XUJh7QRA46TqnzqsUGydRMlITqbBZtJCXdrNkc6dICHF0JbLuOEF48Gc9vj6MXWilu6wVnCROeKFY+7MCaan4UreHZBxpxH4jS3LtJPPS7tqHH4mjhOPrxk1xx2QNo5ROwxbO0r3SLP7nv7AdmOxtMK5l+/8PMfn49F6/eqCTrzi276Pt/RtD6Y0zYP0znf6umYfH9JBdMo/uGqQSbikS6WnErje41NLrXEJnrUuzcWLBmbCQYn83ngy7jOIxrJOUTUBr3F6E7CsFspvuz1eiFVjpXQMmpDOHG6YQbp2NuO0HJrg6sfRaCTUUihUYCfbqv9lJ13UGV5G7U+XyQMY71j4G6xnn34vrVXvVzwfsWfWh57MWrNxL666nq8d5vxpnN8Rmf8fmPmNxHYDmNypR4PH7OLZlMvuP+z549Szabxev1nvP3Xq+X06dPf6Bj/N//+38zNDTEF77wBfV3M2fO5Mknn+Sf//mfeeaZZ7Db7fzVX/0Vx48f/z84G//n8xcBOqOzS2mquJWi4ACeN07RsPh+zt4i8evVrSNqt33bbDfFOwNM2p0BXyXtd8xh+v0PY3+7Q2L3NY1kmVW8SYV2io/0jwbs5HJSu9K7iSO3ltD7P+ajJ5M4uhKkSwukI3G6l0+8+UWqW0dobD1I9xIr3Uus/OL+q6QjVNNITHHjuytB2W6LSKZKHWguJ4m6Mv7ld99Wvii9SHajjaANbSTFyESNgmgCU0bHXD+L4t0n0fOdeiW/FkCsRQfQohIG077SzXB5gcjX6qrh8AlJV7RYIJ0WT9WCKbLAt1hIOs0U7A1gad3DP99+BU3P7iQ7OCjS5Bd3Yz/cyxfvflkW5LuPMzJRE6DjqxbQkE4TmeuiIJog8OUKCd/IS3f1aFz8kTNq8f/oBHpVuQCw4ACm8glSc5JP8tSL7ZBIkm07LGFGPwlS9HYQe1snnV+ZgTaU4IXZZbwwu4zoyk9y9cIVSk6cabhILVw0u031bjY6bxJJtMclx+NxwuAQRX1prrjxK4xcezG+r0eZfv/D5CwCNiufOCgAK88Aaie7VehMeJkf97ZDAopO5T8YMhn5r9UqiblDCUnZzGSJ+4sk/GmuS4FRQ4Jm23WETKhP5Ko7UmwfeorMp2ZCTwgsFrVRUPlqhJxFI+WbILJDs1mYgUQS9wsHOPyA1HoYNR1pl53EvCl88e6XSXrFN7ltYR2B26bS3PmIHGd8gEywi6LXDlEQTVAQTWANnqX58N/RfPjvaHxhDy0XTSJ63Tx5r103D9dz+wgvnozlzYPCVAV76HzECem0BJx4y7EGz2JyFNO53oQpo6vEzEmP/juZYgunflGH/ViIpml30vHt+SJbztdcNDpvInifvH8Nn17TszuxtvdhLi1VHmwt1E+mv5+/fvxVmns3Kelt8GEnpqIiqUS5Zq5iu/RoHL2qnOB9i4ivWkhuZARAySJ99+ykNbeV5fZV6KkUFm85uWhc5Mulcn0aj3Px6o00TbsTfWiYlw/vwLRjL6Yde/nF/VfJ82/+d2Evgz20RDarzyoDjJhLSzGXlmIqceDdIQFbmVAfywtXK8D5cbI3uf2HcD23j1xviGzbYbpvmIotNETwoSJO/aKOwJcrxCKw7RCZM2cFIDmKVHKzUelTsL9dNuUG0kT++hOY62cRvW4e4cWTMf3+iLp29d4QeFxSSRQckACt4iKcW3ZJfUpeXq+f7lPKDRDGrGXpDJwd8lnSNO1O9EKrMNEDAyRmVWCqnCSpzakUSaeZqh0ppSj4oGOc65JfOxW4bdm3gdyS+dSsOU3K7yVTbMHRrWPpH2S4vADvIzvx/2M3TRW30n39VNq/OYf2b87hrafXkW07/A5v5zkJze8yBuA0rteCqARrObfsQhtJMVDvFfl7kZVZP+inuK1XVdJoxUV03jiV6tYR/P9wjJRvAvqJIO4DUSpfjRC8bxFNE2+WB8rL7T/oGMfzXqxxtu0wufkz1P0eu3vthwaF7m2HSF4VOyf19t2OwZhxZnN8xmd8/iMmp5s+0g2gurqa0tJSdfu7v/u793yc85Ni3ys99vx55plnWL9+Pf/0T/9EefmoQuqSSy7hb/7mb1T4zy9/+UumT5/Oj370o494Jv408xcBOl/96W0EbpuqvGiW7n68/yZBMtaj3aQvqCN9QZ1I/qrKsb24W/1udeuI6oU0nT5LUXCA4ENFhBeUQ3wA9z//gYy/ksy0ybzc8ixNNbdTsw0mvTmIVurE2h1lxwt3UPEPNs7eOoyttZSCaIJ/vvlyUuUZUuUZASbpNJg0bKEhhmaWMXJ1DMuZAWFe891pTRNvJnzJJPoudtNz5QSmvDhAyjeBmf94C7rFTGZJjLSnkFRpAVo4TuC2qaQvqFPsre4tI/hIKcFHZEF77O61eF4J4Nr6e9IuOya3S5i7aJzAVwVMF712CIu3nM5HBQBp1gK29+znte138Yv7r1JdhvFVC6G4kJZrLiJ9QR16IknVi73yuHnWKbzMLwugkZQkmIYeE8/i0W40l1MtnsLL/KOeq2CPAMN0WnxxDRdJfyj5HtX4AKTTDC2YQuC2qVS3RBiaPVExQ8YY9SAGMwWI9NJuQ9N1VYujlzoI3Jj3l/ZH6J9j5bXtd1HcvJ/wpTVUt45g2rFXZLK+StzbDhFe5pdF7wSPktcW9aXRLBa02CAtZ/9ftOIicqfPMPlrZyUQymySKhi7jcZ/PYHnjVNS6TFBI1tkhVwO/5O9ItEbHJQglHAc2+7jNE27k5zNTDYWU2nDAMO+EunTPNqNNXiW5IJpWKb5Cdw2Fa3CS+2vJEiqZd8GaO/C2jeIbdcRmq9fhKV1j/TDWgvwb+7i4tUbaT7693TfMBWLr5r2u+rRRsT7qpc6VCgKgGax4HnjFFUv9uLcsovMp2bieeMUuZQEIWVjMRy/cirghFOuaUwmqq47yOf/8bfSnxobxFw1CevrbZT9fxJClAl04H+yV8B5fvRslqodKbRQvyQ5A9tmu8n1h9XC9vCdE9G94iVsvn4Rjc6bpPIokcR3yxk6vjVP5OD5CiCjdzIy16XeF/FVC0XBkN9ICN63iAbTSrYntkili7dMkpvHJEw3zrsXW2hIeZ6pq+bq+Q205raq2pbwMj+ZhovkNXU5RepZcSsNppVk2w5jmVwl12Veim3IMc0OB9tHnqZx3r3q9nGNxVtObmREyYq9j+xEP9qBb20cx6+c1G0Vzzu+SrWRo5c60KNxBcgGPx+n/Y45gCTNenb38XLLs3heCch5n1E76keuE4bu0LXr0YYSqhvV4qvG0rpH+V+Tl8yQ2pVkGv34SV5ueVaqaYrFI5vrDaEf7aD0xBA9v5nDcHkBud6QpKoODuPedojXtt91jp9+7LwXGGqquJVjd6/l+m+3KGntxas3UhBNcPW/HhM5b3s/zo4REjUuSo8PErp9EbqziMbXjhKfLYFeBkM69rGM/xoexpZ9G7h49UYsvupzXmNjs8z496S3GP1sGMvECRz+Zhkl/yYpzTmrCb3QSso3AWt7H9b2PoYWTGHNjc1Y2/vQU2kGq+1oU330XO4W7/MPDqruYaWC+BAztnrsfAa3NbeVuF/868Z5+7DTEtmM62mHUtMYM/3+h89VboyZcbZzfMZnfP5vnq6uLmKxmLp961vfesfPTJgwAbPZ/A5Ws6+v7x3s5/nzT//0T3zlK1/hl7/85fsmyppMJj71qU+NM51/ivnc7LtE+jZN/HcGSza8ZAapGVV0NRTS1VAorNVQQkrl57qo/u2I6jU0FsxaOI5lRymelmPgcqKVT5DUzZzOJ978IplT3RS39WIaSRO4eQrhSyax5JqHMI9kqLpXZ9JzJ4jMdZGzmWn/zE9p/8xPReaXTtN5fY0s7AHtzVLCC8rp/FyOoQVTqNsaIX7pVBxdCYrOZKl6sQdzoAfdbKLuF2cJXTYB3y1n6LrCTmH3IKTT1G2NcHptiuLdJwGRUh26dr0s7EocNM67l0R9DdGVn6RgfzuJ+hpykSh6VTnVrSPyfMvc6N4yam6P43klQMZfxRU3foUrLnuAkYka7iM6njdOqcVW89G/B8BUU0WqykVqTg3tK920r3Tj6BKWrPGFPTR3PkJT7TrZuc4vrgH0vrN43jil/FaRFbOld89bhvtAVGppuvvJxQco3tuNNm2K8m35Hz2Blkxz9qYhUjOqSM2owvPmaVK+Caw4FKHg7eOjwCfYo5Ixey53S1/g8DB0nKLulxFy0TjmPHvYVHM7Xc/48bxxSkJnQDFb2ViMgc/FMQ8mCdxUgVZchFZcJAnA+Z9rdK9BT4i3N9zgx94P4U9PFvCcf+56qQMt1E/FT/dhPthOc+gxAVSZLLkl84WZ8DgFVEUFfK44FJFrKS8VBJiwf5hwgx+91IH9WIiXXn8O30tDpCqcmJISnNVUuw7NZiP8yQm8fOzf0I92YLLZ6X5uDphM6NE4rq2/5+LVG6l65gR6NC5MZColr0dsUC3amxdOITswIOE/FjPm+lkU7G9HHxjEVFhI92dHZbFG8M5Lrz8n8tpEkviqhTx/2SeIzNKU11Sb6afotUMqvChRVwa+SpHH+0pUZQZ2m9Q/zKjCMs0vICk2SOZ4AO+O0QW0fvwkmquU9EXT0VxOrnz1OL57duJ6bp/U8VxaQ8o3gfaVbly/2quCfNwHosKse8VDashnly5/EBbNk/RZ9xoB9eG4+lwB8RDnlswf9RBW3KrAq7NjBEvrHvEtlzpo7tiormFDERBe5ie8zI8+5j4xmxUwNbx0H9eEG/xYysoUoA5+bxG5ZIJMsAvXr/bK881kIdijNnJa9m1Am+ARKem8ezG/XkrV6yn0IwF5n8cHuHrhClWBQ7CH4fIC8caPpEi77BLcVmzH4i0XkJVIYvFKOJW5fhb2TmGcM2UOotfN4+r5DSpUKO2yYyosZOTKesxnB9RnllZQIMB02mSyc2ppdN7Eyy3P8nLLswpoGfNeDFlz7yYa3WtYO+u32PtR5yXbdpht1y8mU1VGZqKTnNVE9xIrJz9TQtvGtWixQX5x/1VM+9puFQ5lPM75/ZuGnHb6/Q9LqFw+DMuYscDqrafXqQTv4E+8IlOfVQ3xAboaCulcb5LgtHxKb/HBPn5995UkZlWguZzCtjpseHcPS5BZLKY2C1Ycen+/5dgxfKpwrvz3HWNseP2R8/xec/HqjVhGckrZY8yxu9dSv/bhd72/cbbzL3/GNxbG5z97smgf6QaSHjv2ZrPZ3nH/VquVT37yk7S2tp7z962trSxatOg9j+uZZ57hy1/+Mr/4xS+4+uqr3/d56LrOvn37qKio+JBn4E87fxGgE7sdLZkmczxAZKZ2Ttqp5d+PcOzutf9/9t48Pqr63v9/npkz+5KZyTLZJwtbQCIoRUDENZKIu3Jr3a1WRepVuFV/1VsvV6u9aq/YxeJat9p6S11agQRRUYqCVAVBCFuWSTJJJstsmX3OzPn98SGjtLZWv/Xe215ej8d5IHEyy5kzw+f9eW2C3dgqdhKkImG21X3UmfcnpdwWwYS67FS83EsuGqO1/QegKKIs3abDsyyC0jRLsDb+UZx7VcYuiGDZM8yip95GiiVJN1TieruHeImOubdcz9xbrkeur4XKUqragqgmPYOzZaragti7Ekz59l7M3jFSbgvGkTQDN6cxDSYxPxsj0DwJ/WgcpdBM0UdxfN+Y8ElsPrCu7QWq/i1H+101GCJZrL1JTl54n1g0Z0SKq3GPCH4ZZ5LUGZPxH+8k5dLjvXueYBm9/aQ9RSi1ZWjjaTSpLCmXnopXB3C93SPK4XceFHLIhu/S22RCKbQKxs0neirrVgeFrPaaKp5ZuUiUfMcTGAMK3pV2sr5BwUx6ygULdijgxrW5j9zQiEiiPCRbVby9SBNrSDZ88uGw7BShIYFZxVSvyBGtMhKtMuI7qwz9Ph9rpjqRXA4h/bNakYpcaJM5ko3VlD22g3iJDk1JEZ23TgdAOa6BdEMl/ac4ae15iIrzd4uhKDIG82YA0H57GZFL5lJ9uRf1QDf1z/jzDA1GkSaZclvAU86Pdq4T6ZrrD3D1tWtxvir6XVGytM6tgc5eULJoXE7W7dtM84w7Me73k4vH0Wzajt4XYuGvtuYlo8Ezp/Lwi4twvXYAjV0MYsahJLJvFNeWQbHIjydYNPsM5K4B9L4QulAS81vtoGTpuHky1t4kLbXH0fWvx6CprsCzTEhF24JP5Fnvjm9PQM1kaO16kNzg8GFBROPQ6PWcvPA+Oi4tQYol6b9qGvHjJ4IkUbg7zU+97+Bcs4esUcP6/o84o/kiwbTYrGLwMxqof7w3HyQk+YZQ66uR39/HmXuCwl/t7ee8/9jAef+xAew2NKksgfmVrN2+gcBUUz6tUy2wkjprtkgW7RvEf/M8Ie2zmtG89SEAq15qQWmahaTX59lqgLof7hZeb4RMeNxjOT4sjnspjTt78M8259nuutVBlD6f+D7xVOXTknMGLa7NfWIAPTRwqO5CvC1mIpfMFWxfOEqTZrFgMhsbRF2LuxBX235cbfuRjAbO3BPkuMseRHKIdNHxhNM/14n4t4BpKIOaSnH6mweQCwup+7UYuOXJEwhdOBMA7+Ol4vNqt5GLjAlWTqNBTabIOIy4H3qX0Wl6lDnTcL3eITa1hkdZn3hOfJ5LinBt7hP+wnRasPdGjfgs5HJkrQbR35lMCR9oLAmBEPLEerS7OsT3tcOOlFaof2gf0SojaiolNtmiscPqVlpql7P+pWfRRlO0RZ4SG2dXXJ3/3vu8xWuTZrGQds+4k2Qh+L4xgfeeWy56QQ90A4LN1QUS1K0OUv9It2D0ksJioW1sIJdIkEsk/uJjRS6Zm5eaSw77n+3EHEfKbcGwoSDfJ62mM6LrdHfBYWE9sWklWDsjGHvDNLfupKVsKdrdXfi+kyFr1CB7qmhcvpLjLnuQm6a8/vkXyB/h89KA03YpH4Y13lX6ReDcFSLpkg/bxBqHaUT9Sln/I/jfiyMbC0fwP43/F3ntX4vly5fzxBNP8POf/5z29naWLVtGT08P118vlDrf/e53ufzyy/O3/9WvfsXll1/Of/7nfzJnzhwGBwcZHBwkfOg7GODf//3fWb9+PZ2dnezYsYOrr76aHTt25O/z86CqKqtXr+aGG27gwgsv5Pzzzz/s+LL4xxg6M2mS1Q7kKROFX7OzF6VpFqbXdiLVVH0iVTsUAALiH7nU7InQ2Ys8sZ7RaXoCTfVCqirLhC6cyckL78N3rvhH0LijG5KpPMMVOK0eqy9FfNhCuspJ28mTUUcCRCsMkMnw3nPLcW0ZwLVlANVizCfASok09Y90i/j9jn4yx0ykZ5ETY6+oebC9aMf+w36ip44RnCKxd6kNKQf9882YhlXUihJKtqeJza5h0fxz6D/FiX5IZrRBRu8dQb95N/rNu1HdhWxcfxtqYQEdN05AU1oMgPTRAUpfOogulqX2+x9CNks2HEYXSuL4oe8TSbJFI1gCgx6sFtoiT9Ha9SDKvoPsv2MZ2ngalCyBOaWMHOtg5FgH69peoOSDLO43RD/m2PH1JF0ye85dgbaiNL9YDZxWT8fNk1H3dQn/XXmpKGB32EVCqcHIurYXkDe8T/8pzny35bq2F/Lvn/35Ldif30LFqwMiObGxAaVH9AhSV3XYex0672hcr3fQ2iUCQrI729G39xKtMFDxq4P5ABkplhSs2j6xKJzyHcE8BM+ZTuj8GagmPd7vz8P7/Xki0GNvB8YPu8i4TNw8+zwCzZOIzamjdW5NXoqL1SzqN+ZOQVUUWnseYmH50cLPG4rQ+7yQfacrHDz1WEs++dU8JBaXgdMnitdkNJA1aGm/vYxc3wDmoQzK8AhoNaDTka5wkN3ZTs8zIjG3bnUQvS/Evh83UnffTnK+AVHv4h/ldP3FYLXgXVVM/SPdSBM8tJQtRVNaLEKlYsl8uI5kNCBNqSdeohOL3GiM8qd2i1TodAbjzh5uajyD3BQPKbuWMyYdjxSO4nq9g7FZlUhaLcgyireXnEGLc1cIVVGQVJXcjEm0nSy8YJLRwOP75vH4vnl0XO5Gfq8d564QZzRfRMmzOwCxYA9Od2DeuIdJ96wkfvxEyt8Mot3dhaqXAUh7ikRy7bYDZMNhpHCU4JlTxRB8aMBTD3pxrP4gLxMcZzrznjujgcLdaTquqcoHhcmeKsFMFlhJTnKj+IdEny5CJZE4bTrSxBqyVgOl2xSRGr2zXdQAfSocZUNu9Z/41lrn1ojexkOhTOOL9k9vnv2tYeoYFd2Kx7hJHlObZ9k6Li3B+buPwaCn+qo+6OrDt8gtPMbeflSTHslh58xVb6E0zaL4wwT6fT5UdyHGfYNIEzzMP/+HGPf7SdY48xVD3pV2jO0DYmCsqwKzCdknEr2lkiLS86cJuXKhUzyG0UBsajFjDYUEji0SHvA1e8TvAqe/eYDRb8byHnDvSjuLpp8CiKFtnGFNzZ6YZ+f+HMZ7OsdR//QAFb86SJNmMTmthDSxBsUi5z8XKbcFtbBA+F3ry8kZtPSs0OC9ex7eu+exIbf6z0pMnbtCtHY9yKR7VhKYX5mvixlH84w788NdzwoNBn8M1+4EoQl6nLtCrGvfhHFnD/VPD4gh/RDyvb3ROL+85wyU+nLiCyZT9pCekatiDLZUoSwQC5LxBO+/Fs0z7mTSPSs/8zyOD5fud4L563zSPZ/NTP4ljL9m90N/mtbo3BX6UpLgIziCIziC/1dk+TJs5xfD17/+dR566CHuuusuZsyYwaZNm1i3bh0ej7C/DAwMHNbZ+eijj6IoCkuXLqWsrCx/3HTTTfnbhEIhrr32WhoaGjj99NPx+Xxs2rSJ2bNn/8njfxZuuukmLrvsMrq6urBarYd5UwsK/rTa6q/F/5qh8wc/+AGSJHHzzTd/4d+NzCjDuKMbVS+LFMW6Ks790QbSC45CSh/uEVHcBagjAToXO3njmSeJnzwV5UAHFc/vJ35hGF04Q6asIC/1rFjnZ+yCCLlwhMBp9XhXFeNfkMXelSBaYcBcHKPjGzLeVcXEF0xm7AJxu9zgJFSrCdVqYt/VBRj8MRac8wDJGiftd4owjWRjNWgkyt5JkC614dyrYohkWV33Orljp5AuUZj0eOKwiPvOxU6MHSNIikrrwQeoWOvHOAqlWxPCI/e1KShfm0LWaqCl4btI/lFq7/5QDMzdQbr+9Zh8zUlr13tI7mLBDPpHGbuhWLDAFjNDs1XSbpsIx1EUZi5dSUvDd/MLi6zVALJgego6khR0JJl7y/UEGrSkK514bk9j29IlfFVlS4WvCxGO4fjNdupWB8mlklRf7oVoTKSY9vaTOboO5bgGzph0PP6b51Hx6gCmEZVsqZMzGhbger1DpAMfCi/K9Q/iX5AVTLWnSoQkdYqh0eCPsXH9bULa6S4U/i6jAaVpFsrwiJCMWS2UfJBFLizE+4BZVKhYzUj+Ufqvno4hks171LI726n7dZC6Xwep+NVBlDnTSB5Ti/xeOxhFiqfZO0Z8wWSIxgieMx1CERbNbBLD6dF1NM+4Uyw0391B5ug6wT4mUwwfY6LiF/vwvTQtz7ZpUlnxHLM5yObQ7/Mx8Zk0TKkTfZzuEsFKJpJo0jm0BQXYXrSDoohgFouRKbfuQyp0orEJnyZWC4t2DkEqjXuVibV/aCXjMglZYDIlhuFP+Uix20TqKEBnr6hcyeWIzawgO++o/M20vlHBPtZV5cObrJ0RUWWkKDBvBlmjJu8TTLktggnW6YQfNzJG2UN6yh7SiyE5JYKCpHBUVJoEIri2DuJcs4fMMROpWx3E2LpdBAod2lDJLZgp/MPhKGpGfO4Vr7gWxhnX1rk1IpFVrxcSz4IC0eHq7c9LaxVvL/qhOJ5W4d2NVhlJTnLj/O0uoR44JAEclxxmHEYMr25DPdCNNppi029vIXWW+GIf75sd/++WWlFFo4wKRlt1FyI57EQumSsGqAPdTLpnJZFL5n6l8loiImxMU1qMfvNutFYraixO7b3bkcwm0hUOUedUW0n5U7uRwyIFWBoNoxZYWTetAMO2AwwuS4PRwLq2FwicUEV2ZzvWjXtJe4rYuP42sFrAaqF6RQ4MeuGJPtAN2RyxxjLRNTu7RNTbeKrymwcd/zwJ88Y9WDsjuDb30dvsRM1kRDJtMMT6b8yh+tYkuQUzkXxDeJYMo0yszPvbg1MkkXpdovvcAUiSdRx32YP5YC+lows1FmdDbjW2LV1IsaTYbIzGRPVUTwgplaH/qmnIQxEM/hhFT1nyipomzWLxWfgMZHe2M/WVFXi+9y6u1zv+5Haffs89S4YJTneg944QO5T9s2j+OQROqyc2VVzz45J084sqri0DKJVFuLYOkpMldOEMikXGs2QY9692U70iJ1K2v2CQUHZnO869av67/9N+409vqIxf53Wrg1+YmTx54X24Nvf92fTaPx7Oj8guj+AIjuC/A/8dTCfADTfcQHd3N6lUig8++IAFCxbk/9/TTz/NW2+9lf/7W2+9haqqf3I8/fTT+dusXLkSr9dLKpViaGiI9evXM3fu4d+jfwm/+MUveOmll2htbeXpp5/mqaeeOuz4svhfMXT+4Q9/4LHHHqOxsfFL/b7t7f2g0dC24y4xkPlHaWtp5NwfbQCN6Lrcu1RUAsj7epGMBuqfHmBh+dFY9gyjbWwQi5zVBUICZpJFlUpjNaTTNJQMocyZhr0rQfWKHPoh4fF0vd5B9YocUx4ew/WsFcveUSrul7H2Jjn1iqvJmvVkzXpq1ihIo2Ese4bpulDDxKcE43fujzaQ02kITTSRcogagcd/upKmi69CG01R90IWbTBKxixR/cxBXG/3UPJBVgyzspTvcZt44QF0oSSe74rhZfgYk/ChJlNCrnpcg1ioTCmk7oe7UdNpHB0KZzRfhGoxEj95KliFvJVUmh+/9Tw1axT07b2CqdDrRTCTkmXvUhstE2+Fd3egJlP4zqtC91Enuo86CU6R4GtholVGGA4QOK2ejMuEd1Ux8ob38ymekk4sLLWNDbRs6wWrheYZd6JxOdF7R0S1SV0VFc/to/XA/RgiWbIGLcEzRRCHc6+aZ1CkiTVUr/nUxRCNsW7/O7QOPCwG70PnSPINgdWMWmAVcummWUiBCIE5pWx+6Tukp1XjWTKM8cMu1AIrSn055W8GeevRxwHBOmgMRlH9cCgMqbdJVEsMXCf6IWVPFXSKJFh0OiFrdhcSm12D77LJaJNiGHSu2SMGJF+IwPxKUrMnUvFyL4HmSVRf05/vscwZtMJbp9XkGU3fdzJkXCYSRRK50iIh9ZS1yL3DQkr6u49RKgqFf9k3hOSwC1b2kGdWtRh55aYmkg1lvPHMkyyaeyZvvPldIVEF5J5h8ZjjSKdRRkdxbR1EMhmFZLSkCF04gy6URI3FhWIgk6F5xp1i2D1UIyL5R9FGU+QcNiK1JlJ2rfARh6MM3ZAgF4rQ2vMQsqeKgetmCt+tQUu28VB1gtWS97I2v/Yx6kiAtuAT6AcixD02JJ1Mx1VlQvYbjqJRVHHehkchm0NpmkVuwUzBnpcUoVSIAS84RQKtVny+PeW0BZ/I16oY/IIh10RFJ66xXUjM9e/sIRuNYvDH6LimCu/d80Rdx2gQ/e4eIbvV65FGw7SULeWC+9bnfYvvPSf8yxtyqwnMr6RJszi/aSIFIqgh4U3ckFuNxm7D871387/zVUEZHhGbFqEI0sQa0GqFX3liDWosjn53D0TGkGJJUrMnojn0upC1SP5RIpfMJXN0HdW3JkGWWTSzSXifGxuQilzoQkkal68kMKc0X6PTcWUZwXOm56Xz5o17aL+1WEiUw1GSk9z5odG5V0XN5ZD8owydVoXVp6KxWbH0g6ZKVCD13G8Uz7PYRS4UQbHpxKahw0j9E73UP9ErGOS/gPHh5b3nlrP/jmXg7ce+uZhsNMpxlz2IGosTmFNKbPYhb3kmg2rQEZhVLLqStRrShWY2/faWPwnY+XOoXpFjQ241sdk1hz2HDbnVh/2+d1Ux9q4E6PUsvWAtQ8c5iU0Tqd9ZnYRqMWL2jmH2jhG5tQLVakKx6SAaY/gYE7pQkp5mLYHT6ul5qhIplqTnWU++1uevhffuefke0nGp+PhwPP7neMr0+M++6IaJvOF90Ovz0vVxjD/eHw/nR2SXR/C/CUc2Qf5xkVU1X+r4e0dBQQF1dXV/8/v9Hz8z0WiUSy65hMcffxyn0/ml7kMqsBE4rV4siiQJjAbStcW8clMTZLM03DtAw70DGPf7RVKt3Sa6ORsbUEcCpNwWSrcpec+ncb+fhZbLMbYPCOYMyBTokH0iTEM3BnLXQH7R0LbjLiI1WoiMkdNrCE424Vug58yfv8WZP3+LeIkO/xk1JOsKcW/Skig1knLpeXTvfPxLErh2xwhcHkXvHxNSRZ2GrNWAoT8sakiOytF76QQCJ1YzdKyWRIUV0xsfC8/b2y8R+HcPwekO/AtKqHi5l4qXe/OLZ9fmPgaXpXFt7mOsUhaSQ60Wy7Zu+k85VNlxKM3X940JYDJy6Xe/Iyo5zCbQ6SCdzg9oDfcekgufNRtJlincnc5XXejGoOJ+GddrB0hP95C2S+i7hpG2FtD/8jSandcImVSd8MVJ4SitF81DcQvPktLny3fT9Z/iJDavnlNPupdQnYy+U7Bt40Ed45LT4HQHlu0+NKmskD42VjPlySWiliKaIjC/ktL3FHLRGGqBleB0h2Cx9/uJzazAtbmP4y57UIRyxOJC+hlLIg+GPmE+/ENUr8iJtNHIGETGsPpS1K0O0nFlGeVvBnnjmSdZu2UNmVmTSM2dAlqNYAW9/aTsWhwdCoue3kTrwMNIDjuaTdtRLUacv92FwR9D8faKRVtlKYat+wicVi+uP4M+f50nG8rwLIsQK9dTvD2BJpog1lgmfJlhkdCqplLI+8R9qYpC2lNExasDSAPDee+ywR/D4I+J7k5ZFiEeHf2CaXPZUUPhPFOUri0WAV3RmJD6AiiK2BgAJINBqAwOVcAs2tiO5lBqsOIfEpLyjh4SxRLmoYwYJg99psYl32qBlfI3g/nntWHzHWID5NBAJIWjPPziIshmaSlbinKgA/PGPSjHNeD513epeiMJ0TiyT3iCpQke1ieew+CP5a8bZBnZHwZZpv6JXtqCT7Bx/W0iLOgQczMelrMht5pc/yCZo+uIzayARJJcPC4UAbEk8rQwtfduF7UzgJpKiTqWdBq1sADVXcjDLy7Ky3XHw4HGpbPjwVFpTxG+CzwipfhQ5U/rwMOfqAm+QklhbsFMFP9Q3odHNouaTBGtsyMVOgk0T8K7qhjVYhSpwC676Ni9pgrFP5QfMJQDHYIZt9vw3BIHbz+5/kHo7KX8qd35QKrgdAee770r2OHOXuQN7yMZDNQ/L6qbckMjyBvex/JuBym3BdfbPSizJtPx7QlYhhS2PPAIyDLud4Li8bz9WH9jp/n3ByGjIBkNGLYdQPdRJ3rvCIH5lQTmV37ucLIhtxpVyXzin/WUE79AInLJXOFvzih5RjvusQnZqseG633BQo41FNJ1ro6med/P36fSNOtPkmvHIbtLWPirrQCYeqL5zYjx23568Nxz7gpkfxjVpOexpxdR8l4Qy0f9ZK0G9GNZ2nbclWc65dEo0miYnCxUHRMvPEBwuoPJT4bznZ0oCtUrcjz52OeHTnwanu+9m/+MfNb5bNIsRunoQvEP5UOmvig25FajFNtEYNkf/fyIn/MI/rfjyCbIPy5UJHJf8FD5/KqT/+1YsWIF//7v/07iULXc3wr/40Pn0qVLWbRo0efG/f5FyHK+M1Lp6AK9Hn17HwZ/jMDcMtZuWcPaLWtER6W3n8DsEowdI+y/0oFkMaNJZdEmcyjuAoydIrUwl0igVBfTcWUZL877meiOiyc47rIHqf5NP4HT6jF7x+hZoaFl4q0492VE2iPg/t1B6h/aR+tF82i9aB7BKRLutV0Ye8OifD2rYu2MUPaQHs9tSSL1ZtyrTBBLMHbqFAx9ESGpVbL0XFTNzU2tVP3Oj2vLAPW/GMK8bxhNdQXBKRKLTjgXY28Ye1cC83CWdG0x6dpigtMdtN9ehlpgZddxvyQwvxJbnyI8ZeeIMJ2K33iRfENo9GKwKdsYgkQSQyTL4BwTscYyxmZVolQUimRVoOfrwueW00qkp3vEDvUhVLUFOXCVgeSMGvQH/bh2JwjMr6SqLUjlCpVsOJyX8iYnuWntepB1bS+g2b5PvI2VFYI9O+il+MMEg7Nlzn7kTSp+sY90ndjh15QU4do2RKyxjFhjGVseeISxWZXo9/kITneIQe3QffX+u7i8R78ZQ5pci/94J4aIeI/GJaDjKN2mIDkK6PzONDFYItJyT73iauTCQhGM4rDnU3OjFQakRJqlF6xl71Ibp15xNU0XXUnGJosah6ER4UHzlGPviGPZ1k3byZNpmn+PqPXxVCH5hsgeVU/WasB79zyRkhpPETz7KJy7QqTrSvA+YKbjmio6rqnC2B2EeALnrlB+6LPsGab+6QFSc6eglLnQOB0gC3ajLfiEuF0oAgYDkUvm5qWh6t4OJN8QvrPKBJuSy4nn5B9FMhhE3cheFX1vECJjgnV5fgv9pzhpbt2Juq+L7M52lNFR0GiQHHbqf7yfV2eUkWyszteGTLpnJWp9NRXP72d02qEwIIuR6uuHIZmiZeKtZBxCzjuegtxSfL0YgBVFpNV6e6lb2Y5U+MmmVOdtjWje+pDUWbPR+aN03DwZkinsXQl6m50cd9mDwlMZCgt/7UhAvK+KQsc1VfnFceC0+vx9jg9+LbXLRSiMQSs2ZGQt2oIC1IyCOhKg+qo+QufPoGXy/5d/TtlwGE1JES0vvIsUjor+1E9hQ251fojUhZL5qouKFw6iNM0CBKM06Z6VBKc7vtIQIYCehSax0G+aRcZhRHIUkJo9EduWLojGcb12ANuLdrFRMVf4OQeWHEPdD3fjv2le/nVoTCJJteNyt6gIsloInT8DyWAQHa6HcPEd6/KvE0QgU/KYWnQh4ZPsun2mOMclhYRr9KguO3r/GM69KpY9w8y95XrUwgKyVgNSIEL/ldNE1cw5s4hNLUbS68BTzrr2TXhX2vP9lZ83sDRpFiPJOhT/UH7Y+/Q1IU2uRQpEGJwts+m3t3DmExsxe8fI9fhEAq0GJvxXEv9xFrSNDWgbG9i4/rb8xsEfL0YV/xDLGl4TCdOqetjGwvjAuSG3mkn3rKTZeQ2Dp5cRrbNT2C5UEsnJpeRkCeOHXTQuX4np9V2YXt8FYzHUwgLM+8QwvLrudfwLxGBKMkXZQ3qaW3fStuMu3NviX+ha8d4978+yl+PPd7xXd3yj6Yui2XmNCO/i8EG9ecadh3lX//ixj+AIjuAIvkr8X2U6Fy9eTDAYpKSkhOnTp3PMMcccdnxZ/I+emRdeeIEPP/zwLxamfhqpVIpIJHLYAaKGAyB5TC3Mm0GyxkmysZqU28LYBRFm3L+EGfcvwbVtiPiCyehiOWLTSjh40SNgMaP3jmDwx4jUmgjMKSVb6sT30jTipQbqn+hl0j0rOf13O1j70Rti1zudIVEksa7tBYqesqC4Cxj+VhxMRt5463bWbt8AFuHl3Hd1AZ61MdDJrH3zNyjuAnJaSbBokkRgdgmurYMYd/YwNqsS30IVaSyGc80e0lVOdt28ipf+5XRa9/0HgbllZAtMRGa4iUx1Ubc6SGBOKR2XFDNytJm+c7NEKwxEKwwMzVaZ8vAYnf/k5Izmi5ATubxU1PnbXaKqY3iUXDxOa/cfSFc5RZ9lTTFjlTLVvx3C7B0jY9EQqTXlQ3xKtqeREzliZVr0vrBgPAdHkAZFAq19j0xogp50XQloJKy9YsGgGQkhT5koPH/uQuQN79NStpQZ9y9h5OKZZCxaxmZVEpzuQGOzotm0nfonellzzcngsBOtMgrPkMlIrq+f0W/GGP1mjIUXXoHt9wdR3YWi4y2TwblXRakUjKnjpR1UXz9MywvvUrQ9hmXPsFjEZLPowhnUsSiuzX1Y3u0gcGI1nu+9S89Vk1i7+bdgMRMv0dE6/IhgjqNxBpelGVyWFn2Ms0tYM9WJuVgwdPp9PsxeMbBqCl1575TcJQZhdDo0O/ajGEV9iFpRgjwQ4MwnNuLcq2IcgVxvP1seeITgdAfRKiO2F+3I08LI0wTjoaYztH/bhnqgW8j+phYzeHoZhi17RbjTocex9iZptl8l0nWt5vymAXGxayVNqad1+BEq1vrR7NiPWlYsJMtKltbhR/IhWKTS+e5LbUGBkDzPrUGaXIvSNIsz9wQFQw5gNtF156x8j6XBHxOSRRCpmy/35lNiO74tfkcdCaALJZFCY9T+YAe1P9iBqij5pOPckJCBSsWFYtPIYibd8jXqH/Uiu0uwbOtGCoiaDTWZQu7op/rFAVyb+4QE1lGAajESPHMquVgctcBK/U8PkguGDvNafrqiRA2EiFwyl9FpeuRCETCUmj0xn+qaDYfzg7o6GhQDsMkkOiwvEonQ2saGfBXI+DARuWSuGHLCUQInVhM4UYQ+jasSxtNI7c9voXXg4cN6aP/WmPDjgzTbr8K4swftux+jVBfjX5IQYUkVJXn7AIBhy14kh11sUskyFev8efm0xmym+qo+6p/1o+7tIHCSB+eaPcTmiRTqlNtCym1h3bHlGPf780oHdVQw25J/FOeHo6IX1GGHfj/u3x1k6Dgng6eU4Nom2GJDJEvOpCNSayI2s4LCPYeu9XQa86Z9YDahHujm1CuuBsRArAslD+tY/WOMD0zjQULaxgbadtzF2AURLr5jnUg2jiVBq8HSDy0Tb+WxpxeRclvQlBRx3GUP8vufPkZOp6FizUD+fscT0f/cUNSkWYwainwmkz0+pO6/Yxmd35mG+50gGYsGy95RIsfXik0jjYT38VIKd6cZunImQ1fOJD2pTPx71+jGPJRh7i3X03D/MI3LV6K6C9F91EnrRfM47rIH2bD5ji90rey/Y9mfDUb69FAte6qIXDKX4y57MB/O9teiLfgExR8m+NHe0w67z+zO9rw8+y899hEcwREcwVeBnCp9qePvHVdeeSUffPABl156KRdccAHnnHPOYceXxf/Y0Nnb28tNN93EL37xC4xG4+f/AiJs6NPpSVVVIskwM3MC5W8GOfdHG4jUmghN0GNsH2Dj+tvw3J6m7NHtlD26nY7L3aTsWmy7RzBv2kfTRVeSrHEyNrOcaJ2dK/71VeH/SmQo/YkR+7Y+vCvtOPeq/O7mU1k090ykWJJkQxnlG4O01M/B7B1D3teLZ1kEMhlOPeleFl54BUplEZPvbGfyne2kig00t+4U9SmBGJEaLZ61MTrP14sF2tRi0g2VWDsjTHl4TPj3POXo2/s4o/kisgYNzTPuxLVlgPBEK4pJg6UvTrTOjpzIUbc6iGUgy8RHMlh9Kay+FFMe6KVzsRNdRPht5ESOBTdcS3PBNwXTKWuRJtfSdftMTr3iaoZniPcgWWTA1S7YsazVQLRCwrlmD2c0X4RrYze6jR9hOxghVg6qSS+Cmw5JXTeuv42KtiG2PywGjbN/9gbaVJaMw0hyajnJqgIhtwxERICLTkfh7jQlbw1g2z1CxqLBuWZPPrVzbGa5CJuJxrB3JXBt7iNwbBGh82dQ9W85qv4tR/98c74r1OCPgU6Hc1eInF5D+Xm7hV8NaDvrGORQHKXYDqpK8Jzp6LYfpHNZA4H5leSiMVxt+1nf/xFVbUEWzT0zv5nRUn0zkn+U9HQP2rcL0L5dgDQwjHNXCN9L06hekRMLSJ1OyHfX7CE3GsjLc7Met0hIHYsy8K0ZlLw1kB9wYo1lrLnmZFyvicJeTWUZp15xNc5dIcxDGZy7QniWRfAsiyClMkilxUy5aRfK16aIgJXTNJT+Zj+ZY4T0VY0n8F0oEs/UjIImlSU5yS02BJ7fQnJGDZJvSLBpE29FHfCTPl50cwZOq6f59wdpnnFnPgRLDYWFbNRdSMuWbpTRUaInTxEMeSrLmqlOKn51UHgovb3UPzeUl05nd7bTMvFWpM5eyGbpuKZKJPoaBZOaG4uK5Fr/KOmaYkLnHU3ovKPzw3rKbRHvn8WMatAJNUM2S9IlC9mxLIPdhuouJHLJXDG05HLitYciVL2RJHBiNVIsKVhy2yGWvbEayWTiveeWH+b5G/f/puZMxrkrRMWrA+Cw4795HvESMcjHF0zmjN1h4Wm2GJEKndT/eD/p+dPyr0Vy2GnbcRdqMoXzt7uEfNldwnvPLRfXPeB6u0fUEUXjSLEkWavhsI7D5hl30jrw8F/3ZfolkJ5cQfCc6bQOPIxmqtgAqDh/tzjHnb1gMqImU/nnG5hfCYkkyugoyRqnUAQYDaCRkIpc+Ba5yaXFZoyayWDZ1o1c+En67hkf9KOGIvmOSMlgIGs1oNSWkXWaxfkqsAoJO1DQncb9TpCxaUW0Hrgfy95RMnbBlKfsWtI2bb7KJBsWNSG5RAJdJI3tRXs+fXn8u+Sz5K7jQ4sk62jSLBYs+8Rb+XjOL2k7YYIYDi1GFG8vpa8NoHR0Uf3MQYzdQdK1xTh3hfhJqJruswwE5pQexgZ+usvyj4dPbWMDwTOnsr7/o784OHm+925+MA3MLsH+vg/feVVo3voQz3dTxEt0lDz5PiVPvk/vqUaM+/2YhlIYO0fz/64UdGXpbXbSFnyCzsVO7M9voXH5FxsI/1Iw0vhrc+5VUQus+VTxP2b6Pw/NM+5E3zlE60XzDrvfDbnVeXnzERzBERzBEfz3YO3atbz88susWrWKFStW8G//9m+HHV8W/2ND5wcffMDQ0BDHHnsssiwjyzJvv/02P/7xj5FlmWz2T0OHv/vd7xIOh/NHb69IptTt7ELyDdF23tdw7grh/uVuAidW0zL5/4NgGE2hC02hi4pNaewdcQiF6XmqEv1ABE0qS8aiQZvKsXF0CpIs07nYyaqnf8zYrEqqV+QwD2UI1+hBrydbZGNoph4pkSZ0/gyC0x30XD1ZpIhaLeh9IeSOfuRADP/F0/BfPA1tMsfTPxE+mmyBSaRBDkeYctcBJIcdbTKHJplFCkfJuEyg17N3qQ3/2RNY1/YC1s4Ivc1OHtr4PM41e3C93oHW68d3soR+LIs0MIxpKIU8GkXf3oe+vY/0BDfF27NUv+yn54IyzAcD6GJZ4ic1YO9KoNSLBXLVhoRgO1KQM+mw/f4gKYdO1JBEU2jTYFmrJ2fS4funWmxvOZBiSWzdIq0wXqJDddhQHTYWnPMAabeNlsn/H7rtB/nd9aLCYMMLTyPHFDauvy0fkiPJMrkhEazkfcCMatKTKBJdb9E6O/LEenSxbL7OQh4eY/CMKpxr9hw2KGRsEK0SwSHr2l5AddmRYkn0+3z5Re/a7RtAUVD2HkDuG0G1mjAPZZDMJpEi+XYPaCTUdJozJh0PCJ9h522NuLYNkWwoIza7Bn1vEEeHgqNDAYfwoXq+NYgUiJBbMJPAidUkisUiPHTBjPxz1I5G8R/vRDIaqPhNN+pIgMZlK4l7bKTsWjHAJJIki2Dw9DL0m3fTf7IT484eciYdYzPLxdFQiK+lBGliDfruYRRvL5OfDJOcUYPuwwNIaQXJbKL8zSD6Dj8ahx29LwSAcWfPJ8yZy4Hr7R7BINRVYWwfQC2w4nq9g5umvI66t0MMHp29IlxGlpH8o7x420Iil8zF9Mp7qBUlRKuMIowmFkd1iY5JQhHaby+j5+tVaAsKCMwpRa2rEv69Z/1ko1GypU4cL+0gfbwIhlL8Q+gHIti7EoJBBJKN1Rh39ohgkayQCZa/GUQtsJIolkjWFYo6oCtEKrJ/QRZ1NEjHP08SGybhMORUERp0iFVS/EMifXpJgtTsiaLLsKAAjckkmMoCKxtyqzHu9yMl0qgWI+pIgPI3gzhe/gjNpu1s+u0trP/GHPw3zxMe2gIrgdMnYtzZQ3ZnuwgnSqaYdM9KUrMnIlnMvPfccloHHhbeznBYMMoZwcoHmieBoiDv66XZeQ2e772bD0/5KuWDmk3bcb3eQUvZUrI729FGU2hMJnY+uAy1vlp8Rg/1lOaSKeFvrCgREu1OwUymPUUkZ9SARkPFrw6iMZux/GwETXEh5hdFvZOxJ4SxJ0TbCROQrBZa59YQ99hQFQXFIqNtF4m/F7x7A1IsiVJbBkbhEwbIWDQ0zfs+sSmFZCxaXNuGsHfEsX80BDlVpGLPm8HTP1mEtrEBTSKDtTeZ9ySPM53jw92fq/wY/7lqMXLSdd9CrRAhUFJCMKqBOaWs7/8INZlibFoRA/+cwn+8k1duaqJss9joaClbSkvZUoLTHYeFCv3xYwanO3jvueWc0XzRn8h/m2fcSZNmMc0z7kTb2IDvpWlULjmIa3MfycmlVLQOCeluKIKrbT+9L0yk9wWx4ZT2FCGPRkFRBLOvlQhM1lL9X70cd9mDeL73LtrGBrFJ9FdifOj9PKbTtXUwP3DnFsz8UpUp6bpPmP1PD+zjm3d/jCNezyM4giP4qpFF86WOv3dUVVVht9s//4ZfEP9jZ+bUU09l165d7NixI3/MmjWLSy65hB07dqDVav/kdwwGA3a7/bADgKoyAs2TUApFUEx8wWT8C7JEGovBYcf7YwfeH4sBaWSmBTWdwfainWSNk2iVkbELImx88glCt1aCVfhKvvXtZZgGk1h+NoI2oeB+o59cj49FT71N0c4MPecJGZMxoKAfQ9QuAEqx8P2RzqCPqOgjKm888yRF22O43h9m0VNvkzm6jr3/7AZZi1LmwtgxQqTeDEoW/UE/qklPw90+St4LsmhmE5JviF03r+LmhVciOey031XD2u0bqHo9R9qmRZlYiX+2mcHT3KjxBGo8gW5nF7aDEQiE0Kah/TuFaFJZBmfLKBYZeV8vycZqdKEkvm9MoHRdL5pEhrW73qTsOx04Xv4IAHt3ltDtlRz8uoXC3WliNxSBovDB48vEgh6QAhGkQISUXSs8mSY9wbOPIuXSo0oSp13yTRSLCKwx+GN0XO9BTaUYuGEWE59J4/mOkNlVvNIrFo5ZlVxfP/ESXb6uI1njRB9RkUqKOHNPkIzLRMZlYv8dy8QuvNEggnEQC8RkY7Vgc8NRIbmTZfw3zxNDaVrB2D4gOjk9VUKym0gglRQhVZQihaOk3Bbqn+rHt8hNvESH5aN+VIsRy4EglgNB0W9q1ECREwx69Pt82J/fQvmz7SLoRIX6Z4V/Ep0IQGneuI/k1HKkkiIqfrEPbTKH6/UO8Rzqqth/xzJKf70PTXkp5RuDdHx7AvEyE9bOCNbOCLbfHxShQOEoqsMm6iL8o+jf3gl1VSSrBCvVtuMu1MICwQKOBPLyzVxI1Dug1UAiiWvbECm3hXRdiZAR2m1MumcluXQatFrQalGTwmMaOK2elF1Lolj6hK3bMiAqPyzmfFBO4LR6Gu4doKrtkFS0K4FmcERUhBzoIHXWbLThBLmZkzH4Y/SfIupvlAMd8O4OcXT2YmwfIBcZY2xmOWqBVUgWrQZxnfzqoPBed/ZS9VqC7M527O0yksVM/bN+0tOq8b00DU06R9WGBLloTFxXej2eZRGKnrIgb3ifXGSMtuAThM6fQej8GYBYyCYnuUVyq3+U4JmCBe767oxP5LEDwygLwkJau9hJ2i7Cy2R3Cf4lCVoHHhZy6Q+78t9dLWVL8wtpxT9E68DDtA48LNjnSW5URQFPOXJ9bf55fNXywVwwJDZ0ystEJ+vEGpEiHRpDDYVJT65AiiXRFjrz7/nFd6wjXeEAbz+6UJJ4iY61b79ELjKGxmQifoGEOhYltiiNlMrQcWkJHZeWsHbXmyBJSKXFWLZ1k51Wm78uJf8o8avsqBYjcigOer34TBplIjUSUi6HcSCB7+RPJEuBOaVoKsswbDuA3NGP+7md4O0n7rHR22TKe5I/jy0el9cCyNPCdC52YvaO0bbjLiHtnC2GT9e2IfFdXOTCNJSi4gEZx8E0xt4w2lRO+LftNrDbeO+55X/xvRtnDccl3Z/eXBiXY7ftuAvJP4p7lYm+VRPIFjvwLdATmFUsJPNGAwMXTaZ6RY7qFTnqH9qHRlEJzBIyedfrHdi2dFHVFmTtljW4Nvche0SljXdV8V99jYx7U4NTpL+4CTJes6RtbEC/z5ffXPxr0VK2lGiV8TP9oOMS/T/GV1opdAT/7Tji0T2C/434vyqv/c///E9uvfVWuru7/6b3+z82dNpsNo466qjDDovFQmFhIUcdddTn38GnEJpaIPx8h2DZM8zEp1IoRonBU0rw3BLHc0tcsKDvBJFKi3G9P4zBH2PLA4/guTlM00VXMnhrGu9KO57WOJYP+9C2dzP4YB0JtxEkCU1lGb+85wzOf/A1Srcm0KSymHxRSn/XzY92rkO1GMnpNei9I6zd/Fu2PPAIWx54hGPuWYKkipqPtjNFNcSUnw6DToc8HKHjqnLkRI7W3h+J/sNDASh4+8WiSSMx4/4lBI4VPsWG+8QCKFQvI4lNdkzDKh98b5UYnEqKyE6qRhoNo0yuIntimInPpNGFktQ/0o0uks77FKVwlIrf9eeTXc9ovoixG4qF5DKWxNoZYcMLT1O5MYsukoZMFvR6Tlx0P1UbElQvOSCkjgZ93osI5AOTDlyvw9Dh59wfbcj3HNY/3isqUX51EG00JWSc7kLI5vI767GFjbi2DqJWlLB3qS3fWZl1WYQEK6dCTmXm0pWcetK9JKsdtLzwLnuX2oSUMpUlNrsGpaIQeSwDoQjJQug/2SnOYyKJ3hcSdSJDI4IVTaVZu/FFFrXuwLizB9/Z5VSs9QtfaiotaneqCkhWFeA7u5zB2TKqQUeyTlSUyJ4qJElDzwoNgakSzb99X8hWjy0SErezjhFBRxpJsED7/SK0JJkSfrST7sV32WTUkQD+eU7kaWEiNdq8XDU2T6TIYjQI6XQoCTodak5FPdAtFvGyzMkL76PlhXfJ9fhY174pz/akFxwFnb2kS6ykp1WjFFox+GOim1SjQTXpqX/Ui7agIF8jQjbLlgcewbV1ENfWQSpe7iUbDovXOLeMjMOI4h9CYzDSudgpehfnVyKNhomePAW5o59caRG5BTPx3j1P1MkkkigWmeB0BxUvi1oerdWKXFiIXFiIms2KtNREAtuWLsFg7+sS6bM6HTjEgIJWiyadI3XWbFE5c6gWKFOgw3NjEO3HHaK+RyORcltILJyBWmAVoWFNs5D0eo677ME8Kxac7hD9s4jBMHBaPY6XdhBrLBNDzK6QCEzRaKg4fzeSxYxxFLY/vEwoHZQs1Zd7Bds1RSJ5TC3eVcUicfdQsmfkkrnIhYU0O6+h2XkN2gLR4TsuKc71D+ZDh75qJmfghlliU8dkxDyUEUN9LCm8mHOnoAslyfUPipobmxUpkabt9KPQ+0KCxfUN4drcx6lXXE1sYSNqRQlKfTn9lzWI1xOKUP+sn/pn/WLjx2RENehQ3YXk5EMbVo7xTbo0cY+NjkuL6bjcjVJZhGZfD55Xg6iyFttDg1RsVFFNeiL1ZiHjHQmIxzEaiJ88FUmWMXvHqFsdxPV6R96T+tegecadoksUkAaGhfcxt1r434Ge89xgt9FxZRmKRcZ7xid9rX2nysQXTCY2pZDYlMK/+L5pjp7K1FdW5P/7j+HcFcovvgfPn4BxZw9WX4qYx4JnbQxdLIdxRzf+06so+83BvGc2eUwt/fPNHH3TR3lJceC0evYutwgbySQ3RONoGxvyr/OvQXZnO5FL5rL/jmX5QfrTPZ3jGK+6adtxF6q7UHS0fgGo7kKCU6Q80zuOI77N/zs48l4fwf9G5NB8qePvHZdeeikbN26kvr4em82Gy+U67Piy+Ps/M8DYOWNkLFpyskTaLuF9wMyZP38L15YBij6Ki4V6NMa6thdEUmZOJVntIGvWc9J136LjW4LxK1lpxHNjELmjn9gxlWRm1qMfy2J/38fY9BKSdYWir6ylkWiVqD3pP1lIf8569hYSFVaCk03kigo4kIly7N1LOPbuJZS93EVgmgU1FCFd7RLVLSY9g2d5aD1wP1VvJLEdjNAy9XYsH/bBvBlIhU4km5WWCbeQi8aoaB3C8dIOvBdXQXgM3zcm4N4aI2PRoFhEN+iio08lXeUkXeUkp9eA2ZRf3GmjKTIuEz2X1KDZ66Vp3veRRkJ0XFNFz4XlSIk05qEMcY+NnkVOui785NI4o/kizO8cIGPX07+wiGSNk+BkHbpQkj3D7nwX31FbLyZRJIkFiKxlcLZMw92jnN66W6T4njNdLLD1elJuC+YXVXqbnfSf4hQppdXF5BbMJKeViBdriRxdgjQwzE3zN9DbZAJgpNFK3GMjWmUkWmWk9FUv+n0+DP4YrXNrcG/SilqbQ+mu8vAY/bcpKJOrqH+il/KNgoHDZERxF1D8YYLMTBGeolQXc0bzRdzg6EONJyh/ajdZlwX9Ph9Zjxt7u4xxvx/jfj/ubXFqv/8hPSs0nPujDRgDop6k54lSip6yMOHnA/zu+lOEJHhXCKtPBUUBjZB77l1qIznJzZYHHkHxD7E+9iy6j7uFdBfQj6m4V5mYeOEBETpUYBUhRUYDabdNBK+Eo5DJoK2tRlNeihRNoPj60W/ezdM/WUSs5WjOaFiAmkwBYOwOkm2cQM6gRfdRJ/JAgIzDSLxEB4GQkNUmhG9xoeVyFloup/+axnyfq9LRhe884aP23CISTvXeEbSNDeRmTqZutaizcL3eQXKqGKKU2jJSbrMIhvrpQcGejgYxbDuAISJqbsjlkMrcqBUlqBUlaApd+cUuRgOnXnE1GoddnL9MhrTbJtgeTzls/QjL7iHQ63H+ViR5ml7bKVjuQieu1zuQJtbwxjNPok3maNtxF5pUFuPOHjJH12F/fgt1q4PUrQ5if34L2miKjetvw3v3PNEfObEG02s7812S9U8PkGysFgyZu5DyN4P5BXjymFqoqwKdjopNaeIlOjxLhlH8Q2itVlE10rZfPDejQRyHfKbqQa9QDEysyXtLv8rKlO575lH+ZpBcIkFgTinG7iDaXR1C6uupwrizBwBNZTn+m+eRKyxAHfCLwXFoRDDNVjMkUxi7AhgCabJWA5FaE1dd24oUjpKLjBGYXUJgdon43B/q3aSrj94mk7AmfL2KwWVp1AIrg7Nl6p8aoHhHjkitCf/F08ia9SI86IYibNv7yTjE9ZVyW+i8ZVo+CVsXztA6/Ag9K0Rfs+IfQvEP5YeYz2NRxpnN+p8eJDmjBvOQ6J1NzZ1CxmXi2ivXMjatiLof7kaTyVH/VD9E46h6GX1QoudMsOwcwLJTBAo1O6/5s4to24t2mp3X0LXYAXyy2J50z8p8ZQ+A42Aapb4c/e4e7Js7yRm19LWo4CggMEth7fYN+Jck8C9JYNzvx+pT2Xv3UUK9MLkC11teql/UsOGFp4mX6Ag0T8rXJn3WwPtZGO+ZhU+YqHHv6qcHT8NoOv/3jMP4hTdMOhc7MR7KfPprh48jzNgRHMERfNXIqtKXOv7e8dBDD/HYY4/x85//nJ/+9KesXLnysOPL4n/V0PnWW2/x0EMPfeHfq142wtCxWoKTTVgGslw3ZTMPv7gI/6nlRGpNJBurSTZWc/LV16BRVLz3GUnbtKx/6Vl04Qy1924XJehOnUiUPa0eSVFJFIngivjUUjIWDXJMoaV2Od6Lq0gUSQQnaal4tZ/ASR50Y2De1Y95OEvv6Xauve5mgjMUgjMUAifXkLZLJOZNQhdI5INdrrxxLS21y0k5dEiBCCSSYLOQLtCBXtQGKKVC9haYVYw0sYaqBz8EWUvpliiJUmM+JTRaZSQ2p46cTkNOp2Hg5jS+RW4yBTpKfyRkifquYbRpkPQ65KEwY7OrxaCgBd8iN8bOUQyjaTy/7KX++QzJukKktEK0zk52kvB5VfzGi7F9AG1KLC6sv7FjiGQxRLJ4bgzifi8mCsufDFOzJkp8UhGvnTKRoeOEDDHlthCYXYJvgR7/D+vY/R/LKN0SBVVFG4yjUVSGjtWii6uY+5OkG6rYHJxA/bN++k9x4n5LsAvj/X/puhKwmJFGw4LBfr2D5hl3otgNIq03kaTkZybkQAzvSiHHdv7uY8GkyRJ6XwhNMkvGZSJSayK7s51FcxYh6fXgKUc7GER1F6INJzANqyK8RpbRRlMoxwnWoO3EiSRdMsPHmNC/VkDKriXX6wOg9FWvYAA396FUFKJUFhGbXUP3t27BuLOHRV9rQdvYQIt7CWoyheHVbWSn1eLa3IexfYD2oZJ84mvWrEfx9iKHU0ipjAjvqS+HbFbUomSzyIWF5BIJ3O8EGTpWi1TkyrNBpNNEak3IG94neOZU1m5Zg+7jblxveUkeU4vGUwVWM2bvGInTppM4bfph9QraxgbK3wwKyZ2iMHZ8vQh/CUSQhyOo+7rEAOUuxLizB9uuIdBI6F57H21jg5A853Kg1RJfMBnL3kOrTEcBpFL5zkHVZUdjMFLwX++jFljzNUZqgRUA3UedeZY+cslcCIvuVDWbZX3sWUIXzuT0dR8L76TRAN5+Zty/BMO2AzTPuBNdKIniH0L3oQhvGg+dAVAPemnSLKb+pweJTy1FSqQPDbyHBmRFwbdAz0LL5YBYLEupDBmHUXhQ/aOg1WDYdkB0pbpFHzB1YlhXK0pEf+eh8C0yGdDpkCZ4UPp89J/ipLXrwa80uRag/r/E5ovGZsX1/jDJGifUVgqp72gQdDo6FzsJzC7BNKyiGQkhGQyiGiWb5fTf7RC3dReSrHXR22RCHh7D3pXgtVMmkhscJnHadMxDmU9UKBqNkGO7HNT+YAeubUNUtQWp/k6cnEkn0ntVFcUocdHtbVgGsmRNMmm7RM6ko7l1Jx2X6FCTKYzdQep/IlKrs0YNulCSltrlNJQM0bh8JbK7BNldQnZn+2GhPn8O4+nC4/24xv1+2nbchcEfQ/fBAR7dOx/b+334L57Gz575Ccn6IsZOmMDw15wYQtD9rVto7XqQ1q4HxVAWfOIzh6LcR3sITpHIHF2H518PD9sZD99p0iympWypqPKKpqDQSce3JxCtMDDxmTSB2SVMeXiMk677Fp4bg3huDOI7rwp9NIdlu9iAk3Jikytl13LU1ot577nlIsiqsSEva/5rMf58/vgcjstbmzSL88oFAL135AtLX/ffsYyK1d3g7f+T8/bXJOf+veDIoHwER/D3hf+r8torrrjiLx5fFv+rhs4vi+BJtdStDpJsCWPbPcKTjy3K1w98OvnO5IuiWGTKHtKjzai01M9Bo6jCzxUVrGGyoYy0XUKOKaQKNMhjGeSYgnkoQ6pQT++FVSRKc2jTUPZunMHTy7H6UjgOZonMrmTTzx6jdGuK0Wk6pjw8xpSHx5ATOcrXj5B0yaLIO6Zg8MdoO2cWa7esYfG9baQnuOm4poq1b/6GjU8+QbpMJDD6Z5uhqw97V4JonZ30gqMYm1vLa1u+R+DyKFI0QXC6A0t/Gsu2boydoxg7R7G9aMfRoWB5twPNWx+KgA7AvS1O4PSJkM5g7RSJqB/fuIryN4NknVZk3yjxqaVseOFpjPv9eO/Vc8E9ryF3+DBs3Sf8gCYjsXLQpHMEp0iYN+3DvGkfHdfXoNh0SKkMfasmkCwxkjVoiM2uQRdXce1OYOwYwbkrRP0TvagaaFy+Em04gfehAlKVdiK1pvx7J3cNEJ5gZHBlPfEJLtxbY2QLxFC0d6mNvUsF49l+ZxFr/9AK2RyB0+ppeeFdehaaWVh+NIETqzFs2Uvg2CKqV+SEd63cjTQwzIYXnkZxF7Dh3X9F3y7OcW7BTDAYBGsFoNcTnO4gMKuYLQ88gjoSEMfeDvS+EIMnOEGjwbkrROHuNKW/68b+/BYSC2eI4aa6mLrVQZpbd6Ld60Xu9mPZH6Cldjmqu5B0vQjCUcuKiZ0+Hbmygg2b78C70k6yoQzPtwbzYUqpYgNyXQ2JaqsYMjMZ5MEQxOIikEaWwWoWQ07fIPU/3g+K6PfznVcFyRTmoYzwqLXtp6V+DpJOh/cnTowfdqEc6CA5yY0US2J5twPLux3I+3rpbXbmGaSMwygYuGwO2+4RpEQapc9Hx1Vl5FJimJNiQvZLSgy56/s/QvKL61Iym+j8zjQGj5Px3mdEiiXJeUVaqmVbN5Zt3Xl/tLb0k8FLdRcixYT/ODPzkx5FXSwnKj6sFjR2m0h5btsv/qfVgu+8qnwdg6TXEffYRHBOQQGSxZxnNF2b+9iQW8366DMwbwaquxB9OENgdkneH4y3H995gjHvfb4OvP3U/3g/qkFHtMqId1Uxycbq/HCcKy3KdwyOL8jbdtyFtrFBbEBUiDqWtdvWief0qZAXxT/01Q6eXX1IviHBggdCAEiJtBicLWYCJ4oNKeeaPYc6kBVUNUfdfTvRlLlZN60Aw7YD4nowakR90+wStNEU6ckVaIpcWPYMM1alY6xKB5ksqkkvhnK9Hk1FGYHZJUjRBMm6QrQDAST/KPtvKGNotsr6b8zB2hnBeXcPoSk52l5+jtaL5uHepEVy2EV90KHaFsueYdR9XXhX2olfYaVwd5r2u2pov6vmrxpMJFknpLRr9ghWz6DP+4ijdXZScyaL96SyCMtAlptnn4c2IRQJGRsYQipN875/GKM6Xscyjk8PG869Kr1NJvpfnvYn/39c1qv4h8gZtHQudpIpsuD53rvorhgkp9fkJb+Wbd089O5qHnp3Ne5tceR4FkxGUm4LQ7PM4LAL+8htoq90vAO2bcdd5D7a81dfKp93DjfkVudrkgBaux7MS4i/CHp+6iQbDv/J4+U/z/8A+HsclI/gCP4vQ1U15L7gof4D9HQC5HI59u/fz+bNm9m0adNhx5fFP8SZMY2InfTqq/rwn+SmonUIyT9KQXdadJcdWviMTbAzOk2PRlFFYmxJEf3zzVh7k6zdLiSSbzzzJKUvHUSxyLh2x0gVGxg+xoQmlUWOZkk7oGPxo3zwvVW89utn+ODOVZz9szdQTBqGZ2o5ccm1SKqKZVAlXWgmXWgmeGmUdLkNS38a3ZhCvNSQ98As7jyNX3+vheBEI3Wrgyw8XzAoer/oe5RjEDz7KEaONrP43jYM/hh952ZpqV1OxQ91+M4u573nlmPoCwkG5RBcr3dgHEqy6Pf7SZx7HPJAgPbby8TOOTA2q1Kwq4EQCy+8AimRRtvrZ2xmOSV3dAlZZoEVz5JhnnqshcDpE+l5ppq0p4ixaUWUblNQbDoAgmdOJXjmVNIlYpjuOacEe1cCw3CKxfe2oU3msPYmyRm0mJ8V4TG+86qwv9dLshAUlwXbi3ZC9XrMQxmGjnMiJ1UyE8sp/v0Q9p3DeM+V0MbTZK2ilmR8oHeu2cPExxR+EqomXV6A64MR2loaqVsdRPZUibTiuipcH4zQudhJcIpErseHWlbMoplNyD3D+QAceV8vuo86iU0RoUtSOErrvv/A2pvEtXWQ6e9djKTXIel1JBbOoP17hdh7FLBa8jJbrGbkinIs230EpzvQRlNI8RRtJ06EylLIZFCcQmqdqLCi3yduJ/lHsXZGUF32vL/MuN+PqihIA8NIA8NYtnaSrnQSLZNR4wmyI6MQHiPZWC06NSNjeFfaye3ej6TToZYVi5qSZIryp3ajJlPidQUiKBMr6btR9E56lgwz+E+T0TY2YOwcxXdW2WGfL6tPpegpCy3VN+d/prrsdFzuZmxaEd6756GbFkbb2IC2sUH4LTMZlD4frm1DLCw/WsiCKxzEZtdQtzpI/TN+3KtMqBYjGqcDdXA4H4KVDYfRFLkYWiiqXzquFB2EgTmlVPyuH932DvpPcYJOh2koRaLCmg+Pkvyj4LDzu5tPFbd/fj/HXfYg+n0+cBRg3rgHub6W1OyJJBur2X/HsjxD1VK2lIXWK/KsjTaawrkrlPfu4SnHNKwSmF+J7UW7YJA1GjIOI841eyh7SI9vgRisMkfXoYkmIBoTQUfPb2FDbjUttctp23GXSLjuEMP1oplNnLE7nK/4GE8uHffmfRVQ66qEzNdhF8xh56jwLR6qLHH8Zjt09tJ7nRiMlPpychMqkSrLIBrD95KoiNH7QgzOlun9d42QIPuGCE42gaLQfmsxRe+HKHo/xNhUF1mzHpQsyoEOVJOeaIVEuvJQ1VEqDVYLx8w5wOQnwwye4GRd2wuEbq+kZJvECd++lt5mIZdGr2fhr7YiWS2o7kK8D5iRJtdie1F4feMlOhru7Kbhzm7B0tV+dt1H/lwcChIKnjmVpvn30Pzqh+xdaiM43UGsTIvBH8OzZBi5a4BYmZbAafVkTTKbfvYYujEYq5bw3arkr//xwXEc4wNo5NK5+Z/VrQ5Sft7u/N/H2djxZO4NudVsXH8b6RKF0elm1vd/RCBqZujmJJ23NQrlg8fNTWddw01nXYOUzeFt0eFb5Ma/RKgy0mV2mi66EtWkp2eFBnlaGOeu0BcO+WnSLM6nL8NnezrHJbfjybvuVaYv9BgAJT8z5YOLPv3YgeZJX/i+juAIjuAI/hbIIn2p4+8dW7duZcKECTQ0NLBgwQJOOumk/HHyySd/6fv9hxg6hxv1Qm7oKScwS8H7AwOqu5DRaWIRWHAwScHBJMMztVT86iCj08z4lySETDIN/NsIJ199DQPzdJz8zWtINlYTLdeRNckYRlK4t8bQdw+TLpCpWx3k1CuupvnsS2k+7zKaLrqSH687g/eeWy66B2UJbULBGFAYvDHJ4I1J4sMW5GgGOZYmWmFAm1FFoEooTPTUMUL1Goq2jyFFE8gDAWb92xJUgw7zUAZbX4ZohYQ+orLy9wshk0UKy/jOq0K78yAl78epefwB0ZnZ2IDvrDJ8Z5WxdvsG9n/TyJqL5qOLiY7Eic+kadtxF67NfQC09jyE91E3ikWGXI72u2qwdkbY87tJSGUlokevvhxHp4K1N4nnFiF/te0ewTCcwr8kgaVfJFpefMc6an+TQ7EbcBzMEi81sP6lZ2k7bSpdF2qIVhkx+GO0D5Vg784KqarRQNWGBKoGCg5ESZ0eJidLuH8/jPnFrYxOF1UqHZe70Q/JwouUzKKL5fKDmOQQktlxny3ZHGqBlYzDSKyxjEi9WfT5BcOUblOo/+lBNCVF9KzQCMmky07OpBPnRNaSmDeJ0W+KcJ21W9bQUnw9Zz/yJkRjlK7Uo5YVo5YVY/aO0XBbj/BZRmNUr8ix5psnEZhVLFhXq9jMCE53oJTYwSBknumGKnJGLdlptSRdsvBebR0UvYmH2DCArFlPYH4lQ/80DcwmMJtQ6iuQ/7CXaDWoisLZu4bAasbYOUqyvoi1H72B61krmmlikRats1P/9ADBM6cSXzCZ4JlTRYhRKEyi1Ejp1kS+N7Pk6e1iKIjGqPiNl/S0atLTqum4eTKu1zuw7B0lNrMCzabt+SRTgMDlUep/MUTVlX3inALr2l6geeM+ZE8VyRon/t82kHPY0O/zCUntISbT+GEXdPWJYdFhh9pKqK1EdpfQcX0Njo4kUjhK7fc/xL8kQXCKhNLZzdA/TcPRoeC934TcM4w+mBabLDt7wGpBcRcQL9HhatuP75JJYlA5NExJBgO5/kGM+/3IG94HhJdu0j0rRWhXNodh2wG2PPAIcY9NeMBlmbVb1kBnrwio2dwnWB1vP6q7EF0oiVTkQr/PR9WGBGu3b0Df4Scwp5TWgYfz56t5xp20dgmpYL4KyF0COh2vnTJRDBze/sPkvl8VEtWCjVVDESS7Df8pZZRvjufl46ELZyIV2ClsV6h4uVdIz806UFV8F03AsyxC/ylOVIuR+of2UX6fTLavHywmrIMKY3NrRefwoeodxSgh942QPKYWpWkWLS+8S8YGaCQSFVayo0HSFQ6erFlLxmGkbP0gx969hIHjTRQcjOFbqFK0KwN2G4HZJbxwbzNqYQHqgW48t8TxH+9EMUr0NjsxD2XwrirGu6oY2V2SP+d/CU2axTh3hdiw+Q7aWhqxt8vYn9+CrU9BGhwhcFo93lXFlK7twd6VIF6io+7Vb+E4kOC6y9Yiv/3JZ8K5Zg8tZUvzfx8fQO2/EAPleL1I5JK5f/Icxv9s0izm1JPupeGHo6QcMOXJJZQ8asb5pJX6Z0UAWXiiFSk0hhQaI2uUqX0liWlYxfWseG+10QyOe/sYOs6JsruAPeeuAGB0mv4LXSuyu4SWsqUiRAsO6yMdf84zl67Mv1bJPyp84l8ATZrFxEt0f3Ld/7mu0yM4giM4giP46nD99dcza9YsPv74YwKBAMFgMH8EAoEvfb//EENnxaod9JwjhiT9kEw8YiJrNVDxci/V6+PoQkl0oSRVrydJNlbjbvUCkK5wUL5+hA0NazCMJql/qh9Tf5ThRj2mrw+gTSjkDFqkXA7/wmoiNVoSFVZyskSyzMTgXPGPu61LoqX6Zqy9ScwDSUaPOvSP/uYCtJsL6DzrccITLOy/wUDGLJGyawmeMx3vo2401RWUb4rRfY6N+KRiMBqw9WVEsELnKD3NomdtywOPUPubHJKSZcqP/YzV5qCuCl3XEA0PBti73EL/KU6cezM492aYe8v1mItj9P67BuOHXXjWxvC2mDlx0f00t+7E3B9nwTkP5HekVYuR+l8pSKkMU8/eD9E4cs8wciCGNpkTQT6pNPKBPpI1ThSbDs91fmx9Cm3N02lrnk68RMeYx0SiUIv9oyFO+Pa1jM2uZspDwn8ZnO7AvcqENpUTNRNRMdwNzTKjSSo0lAwxME9HxyXFyJMnUPqb/fQscuJZG0OeFmbj+tuQR6PoYlnhA3QUoBZYGTnaTGB+JXIiR65PDDT6fT4xEAKSVsvg+ROw7BkWyacFVmwv2lm76038xztRJYl0bTGx2TWYfFGqbwxhbB9g0dwzSU/30HbyZNRkio5LdETr7ETr7Kxre0HILjt7hfTTP8rBb4gF8qLZZxA4toicQUvaLhGpNRE4sRqpyIVuOIYukECxyNi7EqJixmJE7x3BEMmScltQ93aQ02uwdyVwv9ZLYH4lgfmVpIoNSBNrsPhAY7Ww9oRJkM2x9vevYNzvZ9H8c7B2Ruhc7GTt9g3YtveDqlLwX+/nz4X8XjvBc6ZjeuU9cW4sZtDr6bp9pqgTchfS/r0KcgYtOYM2H5xDZEzcx7wZYkDsGaD+WT/VK3KoBp3wqF3nR/KPsujoU3nlpibhx9zZQ8WSEAMnC5YzXWYXv59Iokyuoi38cwwfdICqIkUTSNEEOMUCXt4iJICa2io8S4apf3oAbWMD7t8PYxhO4V5lIltRiNw1QLqhUjCDyRTaaArHSztA1pLVA0YDHddUCe+rrBXJx1otsruEJs3ifJBQS9lSpMm1SLLMSdd9C7N3DM2m7QA0XXQlwXOmoyZT+U2N8f7QrNVArk+cJ93H3SwsP5qO62tw/nYXP9p7Gm3BJ9A2NpDd2S6Yt+qb8z5dAHUsKl6vR0iBXZv7eO+55X+2n/BvAcueYTAa6HmqErWwgKLtY8hDEZSOLnKBIAUHomT9w1i2+0hOcuNq24/+oB9CEYq3J1C8vVS0DQvGuciJNpwgdu7XIJXGsrUT2+8PIvlHWbf/Hdbtf4eMWWx2vPHMkxi2HeDhFxdhHIVohQHFpEEzVQRcfX3O+fQ2mTjwLTehKTnc76dZ9NTbTHl4jI1PPkF8ggvnmj041+whON1BLpFA1eso/jBKYlGYXTev4o1nnsSzZBjPkuHDGLrPQ8pt4bjLHkQNRSjcnWZDbjWDs2V8F0/E2puk4n4ZNTyG7Bvl6Js+wvOySrTKyI2OHpJFYqMoa9bnPdTjrN8fP773ATNTX1mBeSjDcZc9eFifZ+SSuXnW8+xH3sR7n6giqfvlCG8+9QShepnB09y4Xu/A3pUgW+YiW+YiWmlAo6gYIlk2/ewxtjzwCNr+EfpWTUCxiMdtXC7kteVvBr/QtdI68DCB0+rz1SXjfaTjzxnA1qd8ofv8Y2itVqIVUj606NN477nl+YH3CI7gCI7gvxOiKOGLejr/p5/1/zsOHDjAvffeS0NDAw6Hg4KCgsOOL4t/iKGz7+d1VL84wPrfPINnbYzJS3aTKtQTayyj/wRznjnQfbAfgz9Guq4E17NWAtNM9JxbRPN5l6H1+mlf7ia35yA7/2UVif8qAwk6z9PjO8lG8KicKEBXVHqbZAZny0QaxD+09h6FwbM8RKuMnPnERgCMQwnGGtOMNaY5/Z+uQBfLMfGRDO4NPpy7Qri2DVH8uJl0eQGKTYelH7znSXRcWoIqS9T/YojoUSWUbJMgnmD6Q0uIVOvxneGm+dUPqVmjkHGZCJzkwfsDA1Pui1Dxq4OY2wcxtw/ier0Dz21Jqq7sI9A8CUlVqX96AH04w8rfLyQ80ZofRMbj7VWtRGv7D/igu4qO6zyQyaDqZYw7e3DuVfG3eBg7vh6DP4Z+826Gzp6EedM+/Aur8S+spuBAlMolB3H/fpi1v38FxaTB9k4H0kiAvUttbHngEbou1GDyRdFHxKdSv3k37q0iWTh2QxGetTGq3kiy6OX3iM2po+hjhbRTT/WKHAvOeSAfQkQiCYkkKbeFoo/iuN7uwf5Bf75v8dP9fJLZRN0lB0S/YEZBGg2LtN8Tz8fWpyB3DaALJLDsHUVKZfBd6AGDnrSniJxOQ8e3J5BtqGHKw2PYdvqx7fTTdNGVGD7owH9ZoxiUrBYmPxlGG4jRcZ0Ha28SY08IfURFFztUURCNkS0wIYWj+Gfp8c8203+KU4TnFFix7BzAuKef9ImN6L0jpAr1tN9elvccWvYMQ9+guC+7DSximF00s0mkwg6PknEYsfTDonlno5S5SNYXoZk6AfVAN85dIaSJNTh3hZArygUz7LBDRNRMXHnjWgAaHgxg2LpPeHgVBeOObrBaiHtsjBxtRilzET9hEqpJn68Y0bf3EptTJypgciqaTI5EhZX05AqU/gHKn9qN5UCQ3iYTscYy1LEokVoT88//IT1PlIMsk652ka52EZsohi1NZZmoekgkxfNUFKREGnVgCMWmI6eV0I6IaxhVBFslp5YjxZJ03T6T2Owaqp85SG5oRPiEw2PkImMMXH8MSBKx2TV4756XHwBbBx4WLKysRZvMsa7tBTQmIQHWhZKCMfWUs/BXW9F91IlU5qZJsxj5QB+a0mLhh9TpGFkyj/onepEsZtZMFa9l3EeqLSig4zpPvqcz2VhNbnI1qruQ1q4HOXnhfXlP6J/rJ/xbQakoxHOb8Jxqg1GyLgvyxHo0dhuZAoPw1GYyGNsHUNNpEdplNKDf3SPSe/UyFWv9IuQsEMK+cxjVXSg841Yz6ckVNJ97Kc3nXoqtLwN1VRy19WJRxQMU7k7jbN2LfizLurYXMB4YxvpCiuLtWUq35CjfBNqEwq+/18Katl8y4/4lmHf1Qy5HdlotGbOExmQiW2Ai5TTg+W4q39WLkgUlS0v1zX/RR9c84868p9Ow7QDOXaH885t0z0rqVgdRTIg+41sVMkfXoZS56LnGw/C1cRSjxKlXXE3xjhwb3v1XNrz7r0ImXWDN921++vEjl8zFvcpE9YocvgX6wzo9mzSLee+55Uy6R7CGTz62iLIfGzAPqmSKLcy4fwmlW6KUvjkEDjtSOkuswkSswoS9I87BrxuxfNDLGacu5rRLvsnI6TUAODoU9t+xjMLdaVyb+9i71Pb/dN2MX7ufRsquzb+GLyMLb4s8BcCpJ9172M/H5dFf9WfhCI7gCI7gs/BF/Zzjx987jjvuOA4ePPg3v9+//zMDKO3Cy7Pw/MuRcjliCxvJ6iQsu4fwvDSU78scvXA6wekOOr6pRZNVcb81gufXA0hKjo5vT6DoQw2Zk4/m1CuuxnEwSbpAT3Vbli3/vJKy34vetI3rb8NYE6H+6QHcm7SkXHqyOolYOVh7k7Sd9zVMgSw5g4x1lx7rLj3ByWYShVr6TrUQnlUmZGkmPfpgmmilgYxFS+nGYSbfvIuKTWkuuG89HZeWIMezuN7ygtlE1doRCj+Ook3B2isXcO6PNhCtEAv+6hU50GlR6suJTy0lPrVUsEUaieELp+HaMkDWKAOQKtTT8EPBsIzLoE6fc5foeyuQmXTPSgztJtIlwqu4d6mNXCiCIZKl5IVdGEIZMi4T6vQJ2HvS4ClHF1fRxVUyBQYC/+5h37WiT9TSn87LNCc+k+aM5ouY8lAoP6iMza1FmliDNp5mwQ3XAqD5aD/6XV5euP0M3nr0cTb99hZUWSJrNWD2juHcFcLSD74LPfgu9JA1auDdHQROrKa160G2PPAIAC0TbwWEtxVJIn6JkcE5JgJzy8gVOdC++zEAF9y3nmRjNZ2LxXtCTqVwdxqATIGQiJW9m0EbTyOlFYjFIRZH3+FH0ukofd1Px1Xl+M4qyycQ68ag6Ac9KE4L9q4Etj8IpjrZWI13kQW1wIpihuIdScqf2InZO0b/KU4C8yvJBUPiNckylv0B7O1C2rl2yxrQyfRf3kDxH8ZEMqvBgJzIsXb7Brw/tCCZTeh9IUp/1w1aDaliA/rNwjemKXSJvk//KFI4ihoR5xJJQi1yIMWSrL69WfRvzirOS11JplDLimk9cD+bfnsL+oiKPBolZdci+UdRLLKorZhRgy6cwblmD7gc6Hf3MDBPBOxoGxuQHHYUhxnP997F7B0THmCbhC6WxbNkGNVqQjFpUUxakToMKMV2DP4Y8amlJKvFQn7t2y+BpxxjT4iNP38C1ahj8PwJJAt1qDoNQzOFRNi5VxVSXiWLxi4W2blEgtzRkzCNqMSmFmPZ1o08LZz/HmnSLAZPOcnGauIlOhadcC7SxBqGjnOKa9ZqIbuzndfOOArJaCA2pRClaRZqWbF4P5Qsvm9MoPTX+wjMryTZWI22sSHv0QyeOZW24BNUbErnJZQGfwyt10/WaqB5xp0Yd/bQtuMuWsqWMufSz5eFflmoFiORWlN+40YdCdD3/+UgnqDnkWKMPSFUl130wLrsBM8+SiRkN5bhu3TyJ+ydViNqf+rLRTBQIs1Ft7fhP71KfDcaZbJGmeFvxZH8o+w5dwVN8++h9pUImkyO1LH16IIpTlxyLQOLKvjDQQ9yXGzSaBSVjE1H/wI462uL2HHrKjqu8yC5hN/W/buDSCbRw2ruCEA0juQbYtEJ59Jx82RxXOf55L39DIzLRJtn3Elb8Ak6FzupefwBDP4YdauDwm89O4w0MAx84rUHcPzKRqwCMreM4jtLITc4idzgJN57bjk9KzSHyWVB9HKahzIYth0QwVqjhz+XDbnVHHfZg+y/YxnHXfYgO25dxfAMI4GjVfT7fFj7cvi/ZoWcyt4bighPttJ/Xob+8zJE6s1Muc+L94paUpV25Fgae2eSsWqJC+5bT+OylYQm6CEap/tbt3zh6+WzGMhPwxDJMumelfkBOjjli3mamjSLsfoEc/xpbMitJu0p+szbH8ERHMERfNXIIX2p4+8dN954I//yL//C008/zQcffMDOnTsPO74s/iGGzrqneth3dQE5oxZV1mLui6MfyxKfXEzzy39g4QWXs/CCyyk4mGBotop7g46Rq2JIGQXVYkSVNZR8kMW5J46hLwKAf5aR4Zk6gpN0nLnkRlIFGvQRldrnf0DlvRp8Z5Zh7Uth7YxgGkpR9k6Gjkt0xOucmHvjHFyiYee/rGLnv6ziwTtWkSiBtFMl4tFQvmGElNuC3DVAcIrEpp89hq+5mPR8Edrx6N751Px2jLRdpv3OSuJTS2lZ/R6aRAb3u0FGjrbRetE8XBu72fLAI+xdakOKJfG2mNn45BNsfPIJVL2MatBh7c8QmFvGwE0pVIsRy+4hmn/7vpC3GvRkzBJtr/yCoRsSKCYNnrUxLP0qla0SgTml6IdkNMWF9JwJe38yGU06x/AMI/FyswilSSvIiRxyIiekc/4YOVeGRfPPIWvUMDDXyOBsI/JBHz0rNEjxBHIghr0jLtJPUxn8xzvRxYS0tPfmmey9cyLWjXtpuuhKWhq+y+M/XUmiRAyqWbMeW5+Co0McunCGyCVzcb3eQdP8e1hww7VIiTSZsgLiHpsonrdZaW7dScXbUQyRLJpoguy8o0jWOFl71YkA1D/qFRsSl7t545kniU0tRjemYOwJYT4YEGykXsb3jQn4vjGBwEkesaOfTlO1ISFqX6xmyGSo/Ml2wmcoeBdZWP+bZ2i/s5KWsy7BuLOHutVBMi4TJR9k6fiGTOaYiUiBCBWru3FtG0JT5sYwmhbX5eAwFa8OcPLV13Dy1dfQc3Yx7q0xtOE4amEBWZcF+wf9HHv3Emwv2onNriEwpxQ1MibYuXAGTaELvP3kAkHSnqJDSawy1FbSf7KTjkuKSVQXEJtajG17P65nrXkpdHC6AzWeAKBp/j356oKxaUW4tgzQ8e0J+JckUOrLRbVEKCmkhbkcmcmV1D8/jOstL1mrgVhjGd5FFpSmWfiPF+xf6RqvCOmxCDXC+LU7/5+vw9oDcs8QUjSBRlHJGjWo+7pYNPsM4aU06Dhj0vFkiq3EysG+exT9cJzq3w2TG4vi2jZEbEoh3sdL835ZZdZkIvVmXNuG0CZzKP4hEQh0CLK7BLz9yBvex/V6B4E5pUixJCXvBYXvWVGQ62vJDQ7T8e0J6MIZ/EsSQgZrt4HVTMVz+0geU4vr7R4MW/chhaO07bgLyT+KIZKl2XkNxs5RvHfPw3v3PLJmPaq7kEitKd8v2VK2lNaBh3G+N/jVfWlmspiHMqguMdhLVgvuVSaUqmKq/k1IpoPTHSQbypBiSaIVh/p+kzl2rlzG+v6PGJsgqmQW3HAt3hYzzl0hlGIbbV8ro6A7jSGQJlmkJ1mkp/I/NCj15TQXXYt/thlNMMrgbFFdFZ5sRY5nCTUqVLyqQ7EIptnSFUGjqNS+kiF2TCXN512Gc68qvNgHhUUiO6kaVQPkcjRv2E22XoQ7WXvA2kM+CfvzUkOD0x38aO9pGEeg86zHPwn36hWVS4HTJ+JeZaJ9uSjF7lmhIenSUPF2kicn/wKzPcFJ132Lk677Fk2axew5d4UIjipbmg/HCU0vQN7wPlJpMa0DD5M9MfyZw1PzjDuxP7+FRSeej7IgzI/OfIauaycQmqih/PVh1r79EpqMxMV3rKP6l1qqf6llrEpsHn184yo0mRxJt4lolRFjAJotezCNqJS+6kWtKPmzFSR/Di1lSz83fEgXzuDcq9JSu/yv9iN/2tN65p6g2CD8IzRpFrPhhaf/5OdHUmCP4AiO4L8D/1d7Oi+44ALa29v55je/yde+9jVmzJjBzJkz839+WfxDDJ1KeSGTnwzT8Q2Zg1830nd7Dl04hffrKmuvXMCZT77FmU++Rc9CM9Wv5dCmVORNBfhPLhUM0zQL1s4IWYuMNBLgjWeepGxLnK1LV+LoVNCNKdRcdQBrbxI1q6Hv9hyFe9Js+NVTjMxyoNvXR7JQpv6XCqNTdSQqzJh3GTnhxus44cbrWH7PEmw9KvqgRLxC+Mn8s/QETqundJtC87mXApCTxYUaj5gIT7ZiOxBm4jNphmfo+PX3Wsi4TEgDw7jfECXp8aMrOfWKq/G8ogrpKKAp3Y+mdD+di510LnZi3tGLLpbD9awVxW5AKSnglZuaKNwVZ+3WtZhGs8y4fwlFj5oBiNSbSRdIGEIi0dG5VwWLiSkPj9FwSze+E80UfZTCEMygajXEax0MzNcwMF/DiUuuZWxiAQ3/EabjqnIyFi3Va4OUvRsHmzUfcLH2zd8gDwRQim2oBh2lGwWLYOwN43lsH1Me6CVzdB26UJLArGJumnkm2lQOKRxFGxdMsPmtdsxvtfPGW7djiGRR4wniZUb6WlSaf/s+kqJi9o6h7wtCOELbObPIGrRYdg4QmFNKtMqILpwiY9cTL9GhVBfj3BXC0xpn0cwmzN4xdO/vh1AE1aTHsj9Ab7MT07CKaVjFuStETi8TmF+J3hciY9eTLhWMmqaynL5rplH/qJcFN1xLZauEYtGBswDJP8rwDCOxUi1TvtOO7sMD9FzsQQ1HyPX1E5hTijw8Jgb00mLSFQ5M7x3E9N5Bqp85iO9WBQIhOhc70R70gaQRnZyzVSx7hsmYJaitJGvWo+8cAoOe4JlT0RQXot/nQ44p+M4qE/7aH72Lc6+Kec8g5ncOQDaHtTNCxmUiOEUiOEVCKjg0lL0rehWtvUk0WRV1VPggPd8axNtiFgxqLImUSEMogm44SuDYItREgkidCfPGPdR+/0OM+/3oIyquD0ZER6WSRbWacLy0gwkvXM+EF67HEFZwHkiC0YBqNZGTJXThDOkFR+W9sQDR06bRP99IxaY0re0/IOMwki6xorFZITKGLpxhz7krhDR22xCSCq5tIuHZuN+PtqAA+/NbSE+uEJsTOh3ZcBjZU8Xa7RtEv2RIbEIZd/aI2hlFQZpcS90Pd6MLJam+3Ev9Tw9CLocaioCsxbinH3UsiuRyEJhfCUDgtHpGroqJoTwUofbu96m9+/1DicwGHC9/lO+KVPxDInTowP1f2XdmpthC14WffP2n690Yd3SL4K19XSIACAjX6EGjofqlQexdYgPiuMseZNHMJvRjWfbdICp/6h/1Eq2zI3f70RQXYuiLoO8L5r8b9l9tIl5mpHNZAzsfXEb4mFKsPpXf//Qx9NEc8lgG5w4ZxSjhOz1H1qhBsRvQpLKosiT8tYmMYNAUBY1LpBfL/aMsevRtVL2OtqZp+E6y0fzb9yl5L0jJe8G/2hf73nPLaTvrGHbctoqm9jMxD2UInFZPzqDFuLOHgoOi27LzrMdZ/9KzeJZHKdwZo+NimTN+/R1ADF66cIb1/R/lz1PgtPo8mxq/ULDqabeN3OAk4j22zxyexmt1Bk9zo+wu4I6fXoVrb46cDhgOsLD8aCbdf5BXrz2ZgXk6BubpuO+apwhOd3DqFVcDoA+meeeBnzHr6h18c5lgKTuu8/xV5+PTg3B2Z7tI9j4UugWfnV6bcumxP78lH9o0Puz/JYwn9QK0nTwZcjlRz/MpbMit5qitF3/ufR3BERzBEXwV+L8qr+3q6vqTo7OzM//nl8Xf/5kBehZayDiMeF5R6Vj8qAir8Yew7tKjSX4ScODcq2LeO4zFl0CbAkdnisLdaYyBnGDswmn8505ixv1LSKwIM+upZUSqZXI6Df64FTQSk36WItlt541nnuSEb1/LyPEKiWNqSRRLRCsNVK3uIenU4NqXRZVAlcBxIMGG+x7i+kvWUrkxS88KDYYQDM1WUTUS4YlWij9MsPHJJ4QPsFdUhyQqrMjDY7jas9j/0Ie+c4iOf55EYG4ZKZeepEtGk8lheu8gkipeX8vU22mZeju1926n5IMs+26pJVWgwTiSQjcQ5qf/9TPiJToG5pk56idLGJ6ppfzZdrJGDTZvgsolB0kfmjNc7w/j3BXKsx4d/zwJW4+KPJZG3xsgMNXA8Awdpe+qlL6r0n+CBkMgQ/ioQsreyXDUrTtJuS2knXp8Z5Whi2XxnVfFovnnoJS5yMkSlp+NwGgQY6fQmo2dMAG0ouxdSonBV3I60IczjM0sB4S8ODe1ltzUWlpqljE4WzB39p3D1P9Koe28rxGpNyOlFQYXlguPWSJJb5NJyFQBOZEja9ZhbB/A9ZY3XyWjjaaEjNJqIDV3CtmaUqRwlGS1g6pVuzAGRDJx3GMjaxaSZXVoBF0kzeDsQ9IwjUTV2mG8P3Zg+9CHaVAEWal6mWyVqPSx+RRScyYjFdipeDsKdVWEzp+BvSNOYHYJRU9ZIJkiZ9ASXTCJ6IJJYNAjbS0Ak5H6Z0WdCgYdUjjKlB8NQiqNYoFE+aHhfts6AnNKSRRLYvhRssijUSp+dRDnrhDaxgacu0J0XFOFVOgS0sVwFF0gQf2jXuof9aKORUUv57wZJGucoJHIaSUxPAFqRQn1T/Si+IdQ3AWiP1FRGDylBNf7w0g2q5BL1lWhsVuJNQqFQLKqANXlAEVhXdsLDFw3E+OwhHFYQh7LoEllab+ziJTbgmLW0ttkwtg5inNXiPgkkT5s+9BHVdsYunCKa9+/Am1SBH+p7kIwGtCFkjQXfJOU2wKhMBmrLEKwim2QTNEWfIINudX5oDHVZRdy2UCIRSecS06WaD4UiIPVQvmbQQLzxUCPVkPKbUEyGEhPrqD1wP1kp9WSrSkFowHJaEDx9uLa3Jdnlmwviu7d5DG1aJwONE4HUkp0rXZ9d0Z+WNmQWw3e/q9UQqj3jtBwt49EhZWMy5SvlBmaraIc14CazeLaNoT7LT9KoRXVINM/35xPJU1PrsB3oh5TbQTrviCR46owBNKYf60IT6pOi2o1MenpEJOeDjHl4THs7/vwrI3RuHwl9vYArq2D/D4JpsEEciiOPqJiGUwz6fEEtvf7kEMJpJyKPHboe8A/imddjLXbN9BxnQelsoh0TTEv3NtM3xlFdF9ZQ/mmGDc6erD8bATLz0bYf8eyPxmQPo0mzeJ8ZUpgTqk4N9+Ergs1vPfccnwnis1B7WCQrFHDjPuXcNJ13yL8tTKGj7HQMLmPsndyfDznl0SrjESrjMy95XqaNIuxP78F565Q/vFLz92DtrGBnEHLUVsvRjXm/uT5jHdStu24i+AMhb1Xr0KbgtAEDYpFBYNIKW+/30Nokgk5Jmq1OtMl6GI5ui7UoB8cY6TRzIKbltB100RsByNEKyQqNqWpWx3Ms9Z/Dn88CDt3hdAWFOSv4+zOdvGdAPnXpotl8783Li/+IvCuKkYtcpH8Iztok2axsI98Bo5IbP9+ceS9O4K/F+T4oiFC/xjyWo/H8xePL4t/iKGz7tk+9Lt7GKvUcUbTYrJGDUOnVVG+OUrnYiev3NTEKzc1EXdLtN9ehPcMEef3+nM/J1yjZ+hYLe23OMg4DejiKlIWMs+UUv1aguIPY3SfKZN7zI02mqH/BBvlm3Ismnc2hrCCvl9HxqIh0iCGkX03V2HvSqFJ5zCOZjCOZjj7kTdZfPY3efjFRaTsWlzWOKXrelGtWeR4lnCdRKLEwFE/WQKJJGXvZDj7oTfIWLS0f6eQQIMW1WHDv7CaVy9/gLRdwtoZoXLJQTEQf70BOZxEF8ux9s3fsPbN36B8bQr9CzRUr1ew9mfI2HT0XFDGN+69hS0PPEJhu8LHN64iY88x+I0GgldE0SSzAFS8JRgN1aAjUSEYUmtvEuMoJIol4lVmwseUUvr7ILYelYKPRyn4eJSJTweQY2ks/UkGjtfxwaMz0AeSWLb7KP4wQbhGT0XrEIMLy1FliY5LdPStmkByRg1rf/8KsYlOgldE8/UWYw2FuLYN4TtT9ItmLBrWtb1A1qwjWWIkWWJEddiof3oA9UA3qkmPLpAg7bYJRiubw/1OENfbPaguO3uvXsWiE8/HvyCLNqOiCyRQXXaSU8tRD3QLdvFQt6I8PIbBHyNrkonMquCNZ54UdR4xBTmmiNChnIqrbT9SaQna4QjVvx0CZwFptw2yORpKhtj7L5Vo42lyJjEcascSjDUUYtkfQJPJETjJw0ijVTCECKYZRDCH77wqDP4YqgZUDcSOLqfqiXZIpcgWmEUFSkYhW+qk/bYSMOgp3RJFH0qz/qVnWXTyBRgDCtoUgrUrsAm/piLeZ/88J1Iizd6rV7H27ZeoWx0kW+oUnk+XXRyJBLK7BM0He4mX6HB8v5dEoRa8/YLZPLQxoG1sIFV4qKNyZj2SAoFZxahmE+b+JHuX2oT3EdB3DyPHFFRvH6q7kEWzz6B4RxL3+2nc76cJTjXz0xd+RuHv9eRkiYRLQ/2zfrKFVnpWaIjU6Ph4zi9RygvRhuMEjrLywaMz8r5lKZEm1lgm0nBLijDu6CY5o4akSxYeymgK1V2YX/iMS4mzO9s5+6E36HmmGqWjC/OeQV68bSG5sSjpCgcZhxHX6x0oNh3BlinCbzpbMPKnz7kLeShC321Z0MmgZIlcMpdYY5mQK0+RhN8VMHaOsnb7BtZu34A6OExuwE/t9z+k6aIrcW0dFL2S2SxjFx9eqfG3RN+PbPgu9KAPptF9cEBUuAyNULNGQRdKoqkoI11mh1AYbTxNf1MR1S8NYvWlcLXtRxdKUr+qm+qbwkhKFtuBMGmnnl0bJzL8NScZh1Fc84cqPVJuC2lPEaniQ9U1oSiKu4Dv3nYdWZNM+zInro3dhOoMwpu70EOkwYmUFR7iwROc4Cyg9kcHOP2frqD+pwdRbDp6m0wMNaco2ZGi6GPBik55cgkLXPtZ4NpPbnBSfkD6LIwPSs0z7sz7ENduWUPnWY+LQB8VdLEc/tOriJbKVLziw3wwhEZRcRzMkLynnE0/e4ymi65k7IIIYxdEeO+55azv/ygfJCT5Rw+71oydo1w3ZTPOHfKfPJ9x+etxlz1I51mPc/o/XYGtVwEVal+Og81C52InUx6IYO9KIydBTsKNjh5yOgnHTpnAsUWMzU8gJ3MgSaTcFqr/q1cEsIGoqvkL+PSQPi4Nbgs+kZe/jqfrwieeWDmm8KO9pwFw8sL7uG7K5r/8IH+EhpIhsk4z9b8YOuznG3Kr6W3+bHb2iMT27xdH3rsj+HuB+iX8nOo/wNCZy332Zl8ul6Onp+dL3+8/xNCplLkYPmcSJe8FSZXbiVTLlGzsZ/8SPRWb0qQKZFIFMtdcs5aid2SKd+Qo/V03U55cgnVQoebVMdxvyihmLZb+NOFj0yRdEmPVRny3KEx+MkzWINF9jo3o9DQZi4bA8eUse/SXlG/OoM2ouDdpGavWUftKku6zDKiyxOAcA4NzDDzzo0UoVj2GIGTMErarUqhmE6dP303XOVpqfhtEUqF0a4r2O8ox9Uf5zfdOx74/jLlbRtWIxYpzX4Kznr0F81COjMPI2PJSus+UKfoozsgsB6PTtCxacB6LFpxHosRA4UegTWYxdgfRB9M4OnIUvx9h0ZxFhOpljvrJEmpfyeDam6LkZ0LSO7iynkSJgUSRnsjkAoKTdWiSWRSLTPH2JPqIiiGYYfOPH2V4thNdLEf4qELCRxWy7rVfEy83C/8bECuH1lefB4tJVK4gBln3O0F0A2E6z3ocmzeBb4GeM5ovIqeVqL7WL2o1RkJYN+7Fe5+RijUDZK0GtjzwCItOOJeUQ4fZKyo8gtMdxKYWkzhtOlI4KiR+7+8jXWYnPsFF/ylOen7iIGvW55MtO896nNEGGSmaIGfSMTRTj6QV6Yuk0iQbqwWrB2jjGey7R1l4/uUAeTZDKbQibd4BRU5a9/0HyfoixhoKUVwW8frTaWI3FDHlgV6kVAZVklAqClGcFgyBND3nlBBoMOJ67QC6uEjytfpS2DviuF7vIFohUf5mkJ4VGuzvdGF/pwtdOMPeH0wkV16CJn2Iwc9m0faN0HD/MOpIAE0ig2LTMeXJJXRcWiJuYgDjnn58Z5fj3BUiGxJhTu53g6gmPSdd9y1m3L8ExW4gPMEiPJ+HIE2pZ+32DWiqK8iYJVbXvY7jQEIMvIpCYFYxyUnufNBTenIFel+YlBNc7/QjKVlSxQZqfyOuWQCMBrwtZjRV5YJFNOjJ6TSMVekYq9JhGVC4+qbluHbH0Cgq0WqIT3DR9sovUD8oQJOGM5ov4uA3TAwtcOP6OErJJj+Dy9IYPugQTCaQrC8S7JWjQEgkD0RZ1/YCQ8c5RQhS0yxOXngf9ue3YH9+C9rGBn5386k4f2FFrq9FHQ1iGE6Ra5yIfp8PbSor0nkRwSmSfxR9UGwWaP0hCIbxXOeHSBRkLa5tQ5i9Y7TtuIu61UH6r5qGa3MfgTmlLJp7Jovmnokky2hKitBUlqH78AAoCq1dD5KNRrH98hP54d8aFXeqgiXrGshLqCWHHeN+P1JoDFUnkyw2sPajN8TtW4dIlxeg393D6Zs6kWJJ1MgYpNIQCrOu7QWsH/RhHAHLkIImnUM7GER12FAdNoZm6kX9lKISmZFG8ZSQNWkZadSgG47iel+m/d4KSt4LUvujA/zh7lUU7BrlzCffIv1ImvSpYdKlNvbcO51UoQGMQnpbuk1B122k8zwZOZ5F1zuKc6/Kq9eezKvXnsxJ133rcxe4clkpbTvuwrlXZfrKJdS9+i1a6ucgTwtj7YHgJC3Bo3IEj8oxNrOUdKmNkelauv8JXP/mZeojS+i4REei10ai18Zxlz2IplQwlk2axbQOPMyG3Go0R0/F9XoHD735C355zxmkbRzmr2yecSdtO+7Kh/YsOuFcBueYueD+9VS8HWP9S8+SdttQLCrJygJSLh1SFqQsLLjhWspu6sC1N4W9K0HRWiMpuxZtOE5ogkjBrtqQQApHsfT/5WtjfJAcR3C64zN9oOODdPOMO/G2mPM/37j+Nm6a8vpffpA/QvwK6//P3nuHx1HY29+fKdubdlVWXbLk3jAYDDbYFGNwAUINDp2YphBCSYAEQgkBcoGEEgIyAUKAwHVCDeCGAYMpBoNxw129r7TSavvu1PePwQrckJDkJvd5f4nP88zzWNrV7Ozs7HjOnPM9h3yh3bop9j+w/b/+slq9XzHbj/3Yj/345yGRSPDNb34Tj8dDOBzmlltuQdf1kccHBgYYNWrUP7z+fwvSWfNfTSRGCaj3pXDu7uPTGxvpPqmcilck3nzycYbHiAyPEXnh+uPRHQL5gMDOO8qxJUFUTExJxDmo4RjMExvvoOb3AvHJGoGmNDtOvpWeY4Ksv2cpVW/mmPBfcZKnJYjMU7n1ZxcyPNpGolomeiDYUibN35YIf2SiOUVCO3VCO3XCy9vpnOfimLM34O3TGDqqBr3AxesbpjB6WZ7O+UEGponkCm2MeyTNj15ehn/rALEpBRR9plGy0VKTNLdM3XMx/Jv6EAyT/kN81L2QJzHKhadXQ5geZ+jQMEOHhpHzBudcvwJutuYlW053kysQMWWR+au24evQ4ZA4gm4gp1T6v5PF252n9Opm4nUiukNg+FtJpDwMHOLD/Wk7vVfmcQ4ZqD6LlMhZE+WSITw9OTw9Oeaev4REjWTNcw1DLqxbRE/T8fRA0aYkK1Yt48HXHqP7xDJmXnsZukumZmUGAN/2KKaiMjTeifcPKrvuHkf1rQbJKSX0HOFm0dGnwXACd292RJ0KvdOBsz/HcL1M9ylVrHv41wijrfoae1yl4g+tlDzssuy2WQWzP8rc85dYRfPpDLpDovqP/azY8751MMmSVavjl63ESkEgMruYRL0b3W3HkdBxJHTk5m60eQfTfE4J8464g0yJDXdPBrlvmN5ZNqtXM+zBGByy5gJFgegBbqS41c0Z3KNTuqaPjgvHElrfS8cpYWxDWaSMAgV+qp9souNW0SIxkmRVokgio5432HW5ZZ8Nre/FDHhx/15l/qufYqqqpQQ7ReRJceofacfdEiO4W6Xj7FoqVloqgjhpLHJbBLXAaVlENRNRBTmRJ7htmL7jyth1uc8KqIoMMvPay6x+xD05Jj/YQN9hLpxD2si8YqbERr7QUlHsOzsx+6Nsu6oRtdJKBPbsjRGvtdN7lYJnbwzTYaPsA5X2nzkww4Xk6gpxNkfJnJAgc0ICe1zFltbJljrRnSL1S9twdaeoe/ViCrfryDmLpLuqkpSs7SFd5cF02dl26LOWnbd7EMeggnN3H7HxgnUDwelAigxz/OnnE36rFzOXR17zCd1z7MjhEitECHDu7MXbnobhBCv2vE/30R4GpnvYeVstUq9ViKw7RTybujHDhUgZxbLv5vIok6rpO32spXQaBigKsSkFLCi7nFWbb6P8ie0YA4P4n1mPGfBiBrx0LBlnza5Gh6CuiqEjKkcusP+V3YSxKQWULNv2+SxqHN3rsFJCVY32s6vJ1gTw7Rhi4fzFVsWGqn0+I+xg2Z3zab6gDEZVkp5RCwUBZl57Gc2X1lDxu93YkhpSRmH5+tdQgy7UoIvqVwas78juAcYsVUmMcqHbRco+UDG8Tgq3pSEvkq3w8sZ7B/BArJbI7GJWnDubzg+qqLk+R6rCgWNIwbulDzPgxd6fwr3WUo8r34STH1iD6XVZIwGyda5b98drvzY4Rx/43Np/WoKq1THaLr4W44CxONYESFWDcxCq3jAY3zhIrkCkZ7aDUce0gWSyuaOSHZc1cuURa7hq3kqumreSj56+ZuQz/J+Et/mK0Vxy2VWsv2cp265q/FIqrL51JwtGXTMSJKRUBdl6TSN/vOJYVr/wFAuP+yaxcS6ctQn6L81Q94OdOIdMnEMmjz9wL0O31pAL2djzbRu5kMBh13xMy+Ii0uUwfVIrasDGytZ7uf/axr/rWAluG+ajp69Bi/R/5eP7SOpDLywaed9G39i/6zXIWzdv3P3qnz100KX3feUs6vxpN+9XzPZjP/bjX4q/v6PTWv5fxU033cSWLVt4+umnueOOO3jyySf5xje+gaIoI88xzX+8iPTfgnTuuXsiAPZv5Vj+4XLqVl5E8adWGusZLccyYdFeJizay+AEmeCuPMXv9DHh+g4kBQ64cxOGXaJ9sTVf6evS6DhLZ8yTCn0zvcz65i8ItOqfEyoH8cmFVNwtU75cRvVB2coeCppUfnPqUnQHVL8g0jNfZ3i0iKSaSKpJ34k12JLw9m9nAOBvzbL3AgehLRIdx7sxbFC6QcPTk2N4gp+bL78IpaKAVLmAbhNwtceRFBNXTwrPw1GaLyzHNpBi+BCFoUkuootyOPuzFD3hwdudx9udxzGo8MhTi2jtLSI1sYiiTdZsabbUxVO/WIR/Sz9Vdwj0H+xC9VsX7MP1TjIX+jElGJhuzaAF9+SRMybffu9Dcm1+XH05DElgxo2XUbArSe71YjSPjOaRMSSB4k1Z1LCX1BSFuhc0Ws4IogctkiQqGseffj7f+eZ3cA2YuPtVdKc4Mk+5/J0X0SeNwp40GT4iyoQ7e6G9B8eQQskmheZzSoicOo5VLz39pw9fVZFaeqj47yYqXupk4YQ5VqomIGzYDjaZTImNyBwd02UnP2MMzj0RRM2k/4TRVtDQcIK55y/B94J/pCPRv64J7HbSFS5KPrIsuogCnk3dFuEoK6b/QDu7llgXcN7OHLrbBukM9b/cQ3DbMM6WQYZPncbQsfUjSoCgqOhuO+6eDCRTVP++k6GZZZRssr7Quttu2RqdDqpvNUgfVoeZTGEmU9gHM9jf30Hdc4alEKYzCHmVXa+M4bVvH8XKpvXM+c4l5P0S1bcaaNXFLF/7AnJWp/zuD1DCFonMVnhZ/skq7F0xnG0x3Dv6KHu5jRWrliH0DlC0JcOEu/qZcFc/neeMRvEJ2GMK9og1X7z9v65Gd1hVGZUNTahugeF62QqgcbsQCvwcfEsDciKPEE+BqpFfGMexJoBW4CYf9uAYylN9q4GQzpEpsaGWF1B2v52y++3Ie7twRNL43m/G/f5e8mPLUArd1C3TcQwphN5oJjq9gIJnfeTqizAF6DkmyFGXXow5GMMMeDFkAdPnpu45i/D2Layy7J2FVsdrZs4469xx49UjVtfIrCDHrd5BrsTF0PyxLJy/mLL3s/g7NKpfA3QDd7+KlDOs7lAgU+MbCQCz7+6m9HXrRgAOa6YzeVqClb0PMU88g1WxxzCmjSVx9syRoK/CnRrpsVYwEi2dBF/5jJ5jgkhTJ/xNYSz/KEKfDCCUlqANRFEPGkNkhht7bwJldClyFhyxPLGDCtm9JMCEn1ihQmbIjzL68+CgpyLoW3fy9iOPYvZGAHBGofmqcaQqHPQfGmT84w3Y+xLY+6wqof4ZJthtxCa6Cb3RjGMgh31YIVPmwpQFKlYLRC/J4O0QeP3kg0hVQ3San7rnYqTHF+Ic1Gg5zcH8FZuJTSlAKfGiTh9LoMkkVyDyyFOLaD+5iJ5jghg2EcMmMvaO+7627gMsxaymYQDPw1GOOPXnaD4bpa/3smtJIwVNORwDeZLjQygBAdVvsvvTGkY9AzWPisw760Ie++0Crhz/BleOf4N54hkjCa77SNg88QyMLTuoej3LyT9/g4XzF3N8+QFfUvDWGM+xsvXekW7PgWlOHhyuxpBFDr/6UiKzCxk6PI/7VT/G5gDbfzMJe8rAnjI4/YFrOeSeT7CldGqfh8Rog3WPzSB0cD+7ljSycfsobHGV+dNu5sZrLvnaffFFtJwRZEHZ5X+V4NU/FbFqtrDU22l3N/zF9X0Vho6sxrO19yv/Jrg7+6Vqo334a7bp/diP/diPfwb+04KEXn75ZR555BFOP/10LrroIjZu3Eg0GuXEE08kn7eu1QXhHyfV/+/umS9g8JwMVWuyqOMqmfqLBorel0EQcA7rtD45hqbfW0vJJgXdKbL83ZfpPGc04Y/SfHb1FFKVDsY+nKflNAfRC9OUvWLnx797Evfx/QyflSQ6WSJTYuPAy7fg6cqSL3Yg5Q0S0xTi00sx7AINm8/CE9FxDOSx99iYd/oGXC3DuFqGSZeDLQ1F23LY4iq6Q0Ielih5L0r5eyqlG6xEVuOnVkS/e08UOaWQHq8SHyWRnBAkNlZkzwUF7Fg5huJNOsvXvkD9bw0Kt2WQml2sfPUZpJxByxJoWQK5YgdyDsbcqyDmDTSngBK0I2omxR9G2Xl1CV1z/VS+0ofil1k4fzHuqE73ojDFm1Xqn88SGy9gG86TqhS49v1vUr1aY+/FdjLFEpICncf5CW/I0Hq6SOvpIq72OPa2AZovEClaZ8Pel6Roq+ULtydMBEUjMcpF5DAPil/AEUnjiObxPBwlU+Nj7vlLEBRLxpemTgDdYNfd41ADNt588nHqnovh7dNYdMQ3sKUNa9bqpNGkD6sjPaOWjjOr0CfU4u7LE2hK03bLIaDphNa2MeG2LmJTCnBE0gwdUYmzLUbJ+wP0LihnZe9DVk3Ge10IihVsQ0EAszCAb2cM2ntQ6krQPDK5CWXkJpSxYNkHVD9jWbR7jnCTD1kdnx1Li2l/JIzutsKTYuMFInN0bElrH5BXkPvjSJFh9EErmKbgxc3ISRXPw1GiB3qwtw5gDsbQvQ4EzRwJTdLddswpo3G2xWj+7mgrcdUwCe7RGZziYf7J51ik7MM+hME4+UI7x596nlVZM37MSMiO5pYsu3BeYWhGCVp5CKU+zMKxhzN03BjkvV0kp4ZJTg1TuSZG0dYMkcM8NJ9djKibVu9kzvpck98vo3B7hsLtCqLXQ8eZVSijiklVg+GyQSYLokjVLQalf2wlUe/G2TGM7pBQ70uRqytk8Q2r6JntZmiii6GJLhKz60HXSR9WR356PfZPm+id5SByiGPkPRSvaCawdi+2hIItrZOqNvFsaEP9Y5DkmAC24RyxaYV0LAoSXt1BQZOCbThnbbfLsh7K4RLmT7uZhfMXs3D+YkrX9NL44gJyoc9nQ+MpOue5EDSTZIWMERu25rtlAbOihHzYg7M/R/9BNozKEvTaUsz+KD3HBEeO4Yqf26zah887GKXtrXz09DXU3bWVuru24tkxYPWJ1pRDXRWxkybjGjChvefPbI7/TGhBSx2Wx43G3pug7OFP0Aq92HsTVPx3E9JAgtAbzfhaRHKTKkBREeIp1jz7BKEN/Zj9UeT6URx/+vnkZ45Hzhr42y27a2h9L4E2xeqhjMYgGiM9tcwaU/A78UR09NpS0tVu2hd6kFQTMafj6cpQ+BsPctq6ARXcZSKYJrrXQeexIq6eFAW7RFadcgjBbcNkw9YYw0c/W0p0ukGuxKR0g8KSS5aj+GUUv/y1QUIApmbVLpnhQjZuH8XQeSl0p0jzBWXM+PFl2GJZXnj+15gilL2b4DenLsXXKhA52I69L4ltKEvV6thIqqs272A+evqaL5HPfYRNXLeJ10+ahr51J9q8g//sMx57x32ARbrkNDz/o/mkS2X6Zlnnj5bjfkNwV4YdlzXyyU8aUS8bRL1sEP2IOO/deRiiZhKdasOWEHFHdDKrS5hz+SVUv2aNBrScESQ2Vvra42Mf6dO37qT+V01fmoH+KrTf5aTt4mtZtfk2/K3Zka7jvxVn3biCyPwqBifZv7S/AOy9CarPa/+zv9mvcu7HPwNfTFHej/34n/hPUzqj0eiXgoIKCwtZs2YNyWSShQsXkslk/lfr/7cgnVU/M8iGHRiySKDNoOiTYZSAjKszSXBPjvCGFOENKUTFoGO+xPjHG3DGTBJ1bisVc1gnPsZLxVoT6b0AsbEiblFBfb6E4kfd5AtNvF15tjxwAFJGwXZVL5FDZMbfkyBVIZIqlwk3ulj76KMoQTv1zwzw4b2HkK0JkK0JMOqlOMNTNeJ1DmLjXLRfbFD1hkrLWUWcct/rtH5DYvkFcxh+tgp7JEn8wDDdR/kZf28cxzCYknUArzzt5+hOK3n1oNsbaDvBgaCbVL6dZ+a1l6H4ZYrecFD0hoN3Gn+NbofdF3k59d7XkXMmYt7g1HtW0zOvCLw640/ay9ChYfIBgVSdH+/mHvxtVrjR6uefxNcGms+Op9ek5bjHyRbJTLhjgOCeHM4hjZpnO9E8MhPuHmDC3QMkJwRR6koYs1SlcNkWeo8tIjpVRFQ0gtuGab/Tjr85Q6raJHR6J32zgwi6QcszY/Bs7LSCe7w2Qut7ydT4SB5s2TdtcdXqZ/wcyalhpLyBlDco3J7Bs70fz8ZOqh7eyuCUz2cqN+600lf9XnZdX4NeVkhw2zCpOj/VDXvBMFBKvIQf+5Q537gHuaaK9NQyOk7nDM2TAAEAAElEQVQqtgiZaO0TwTAQSovpnOciU2LDkAUMWeDZOxaSm1hOfWMrRdtUfNuj2Hd3U36XjLY9QPsJHvxtOnV3baXmZZPqP/Zb3aiqiul1gaYjlZYQ+rAP0e+j+zqNzClWlx6mSX7meDBM5KyO5rahuW30zPGQK3EyNKOEuudiGA6J7kVhfFsjBNoUuub6sHfFMD1OlDGleLb0kKh3U7UqBqaJsy2GEBlEUgzk4QxoGqG325G7B6204IpS3P0quWm1pCokUhUSydF+5K4ouUIo3qTj2TNEx60izoEsYukepJYe9l5iBfT0nzyO0g+z2Ld3UPKpbilYZcVkRofI1PjY/YNaVLeA6bBhG0hj3l5CpCHL8suOovzdDKWruild1Y2rP4/uc+H51FI8ey6YhGBA9ZNNOCJpkmMC9J06GmVKDdJQGsdgjnGPDJCcOQrjv8L49lqqiPfbXVS8nUYrL8S5uQ0hnbPCVDTtS52AqTo/qTo/ZnSI+t/24hzSrFAZVWPK0XsZuDhDQbOCMLoGW0JEc4kI8RTONmu/CtPjmJJA95FejImjKF2fQhkdJjalAGlrE4mzZzI/eBHBbcNQU87YO+5jwYZOFmzoJDO2aEQ1XbX5NryduZHn/Ssht1qqUnpsCAyDlW0fowTtlhXZ62H+a5sYOrYeX6c1E548uBKtLMTC+YsxXXb0eNyq9+keRE5ruPvy+HYO4n57J7n6IpwdwwR3qyAKIAo4e7NW5ZNuImV00hUu/NsHqXs2ipxUyVS6wbB6QEs+ijHpoQZi4wWKX91L+wI345YOIeRVwu/HUMI+Kz3aLiClrcope0xi3CMDODuGue/d4/HtjePbG2fsHff9VfI+f9rNSAUFfPT0Neheh3UuK7FspHtuvJrQZyn6ZwY55uariJ2TQnfJNPy6gZJP0hRvVhH6B9G9DpKj/XTcKlrJ5JH0yI2Gr+qeXP7uy0SumoWc1v6s/7JinUXW1hjPcf7Vy60EdBHqJnez/p6lzLz2MgYO9DDt7gaOPffb9PUE6esJcun497BlrK5kb7eJtwu6jzexpUAwrGCy9fcsxTkI5mF/rhp+EV8MCtoXJBSbUjDyu3niGSPvD+Cwc+5F+DAw8l7WvHcjJz+w5kvr+zpcUdBBeFXnn6XXgnUDouOpL6cl7p/l3I9/Fr7YF7sf+/E/8feGCO1b/l9FVVUVO3d+2UXi8/l4/fXXyWaznHLKKf+r9f9bkM7oQQUInwctOQcVkqP9xOtkdi8JEDnEie6Q0B0SqUoHhVsEgrtMpLwV6qPW5ogcJLPmrvuJnZ+i4vVBqt5IcdNJ5zE4W6F3lo3ydw0QIPRRhEyNj2xjBcWbdNKjg8hp+OSWRjIlVnhLLiSz/K3n8fQpJGtsJGtsqEEnqAK6Q6Bwewbf+y4S1XaKNxvU2fsZ/+sE8TFeBmdo/HDlC9iyBp4+k675RQDExorY0nDOLT+wal929iOqoPt0dKfMcJ2Ds25cQbpUpGB3hoLdGab/tIFtVzWy5oR7ee6G+biiGr2H2/j1bxfh7TYIr7Gx65UxSIpJdLrBGXeusma6gHzQei8lq9vJhWx88tg1HHXpxSRqBYZmlmFvH0R3iDRfZKWrpicWk55YzMCBEoZNZM0HP0b0egjtUqh/vJu9P3SSLffiXBnAlAVKPzAxby/B36ERH+PlkAs3o4wtQ8gqpCocKJVBbHEVx5DCuKu2oXlkkgdXonsd2OIq7q4MqkdC9UhED3DT/nMP6elVCGVhwuv6yZTYMGdMIjOlnOSEIHUvKUidEVasWsbQeSlSZ7tovqCMgYNcqLMmEb0wjRnw4vmgGW+3idwzSPJBHd+n3ZgOG1rQQ9kHqqVCNw1ZS7+KvOYTlNGlmLKAUhGwqjqAmuVp6u7ZjnftLqirwv1pO5m6IMWfpshNrabljCBKfZj0gRUoFQUoEyqtihSXE/J5jlu5HeeeCCf85m1sQ1kcW9twbG0D4O1HHiX0bif9hwbp/06WiufbMR12xLxO9fIY2G3oXgcYJs0XV5OsFiyF1GFjd0MJkZNG425Psvyt58FmI31QJVplEXsuKCAzKmilLnfG8XXp+Lp0/Bu6aDu3mvrf9pILSvQeW0LF3TK9s/3UPnE389/Zi73Tju/hAUrWRbA3R9DqK7CldHrmiAh5FfeOPmwJjXEPdFG4PUNsSgEr3nwOUxbwveDH1hGl9/sqZmwYMzZsEZN4FuUpidiUAoo3ZQl/omCGC+k/NIhvb5yiLRlSFQ7aTw+jO2VMWcS3awjN/ScVJ7KiisihHgTTpPvssZDLU/+rJmsWNRhgZe9DACM3MDAMlIoCciGZy09bTu8po4j8vI7qC7tw7omwYNkHlH2gkgtKKLXFJKYUIXUPUvyoG1MUKV+XRhrOkClzkQvZ8HbmEAoCJE9LjJBIITLI5actZ+XiWaxcPAv3nijNl9YQm1LAnG/cg73X6gXNhz1fq9D9b5CbWo2xownHQB5yeRYdeSruJquj2PQ4WTmzltD6XvzvNmP29eN7vxn585lWIZ5CmjqBVIVAbmwYeTDFCY+tRW9up3/xFBwf7oZ0ltg4G83fG0vz98YiKhqqT2blq88g6IY1N6vptJxVRHy0k3idRLbCjSOSxnDK1Lwao/7RDkxFtcLgKv0wnEBQNNYs+y19M2Qic3RERUPMaZR9oFpztQ/naTnx0ZEbCXU/3/5X9+Oqzbdhfv7ZSKk8K/feTcszY8j7Jeb7L0QNODAkcMYMfC/4Sda4cMRB89qsEKlggHyhHc0l4nvBj+8FP/rWnZyww7JGf3EOMnHOTKSpE3ggVsvm6xrhg82sXX39yOPzgxfhiFh9oEcffxe/fXAR/dMlijYO07yznHnfuhBHXKd4UxpbEobrHBSvkyleJ/NK71ScvWkWHXkq/TNMStbHKPxYRjp1AE9rgsgcnTnfuYTC7Qq+F/x/17Fi5vJfUoO+SEoPPfdePvzdNWy7qnFEpZw/7WauKPj70g3nnr+EzMRSqxf6C9hHLnecfOuXfr9f5dyP/diP/wv8pymdxx13HE888cSf/d7r9bJ69WqcTuf/av3/FqRTMEzeffARErV2Wk9yEBsn4e0y8LWKGLPiqAGbtXgFar7dxAFXbiFbLODrVLG1OaleneToW66m4Hc+lGIvXdfrmDaJ8BobCxZuwNucIFdoJ35gCW8/8qh1QX2kQMm1LYiayRHfu5ToNOsudf8Mkyn3N6DbRHQ76HaQUyqj/qgj5U0GDnRT9uYA/g6Fb/50JVe9dAGxKQVEjtGo+4POxc80oDkEAntThPZo6A7QnWBLgi1tki0W0MoKsGVMgptluo5xMHSAzi9XLETOmIiqjqjqxCZZFxn1spd1D/+abJHMpm/fT67E5Gd3PYItY2BPguIT+MZhG1l15Bh6D3fRdbJO18kaNcvTdJ9ew6QfbmP+tJuttFfDCpVITwmjekRqX02SqvPj7M/h7M9R91wMKfv53XvdoO08A7UqhH27i+hUG74ulWSNi1SliOaV6DheIFEr0HHR53exRYHQ2+30Hu4CUaBznovssVP49dL76ZkjcsJja8kV2S31tSuLpytLyYYEZffbKb6+FT3kwejoxt2v0r7AjT2m4N/QxdAEJzjsVsLj/XaQrfCiild7cfTEKfulg8jhQTIz6lh/z1ISh1Tiu0JCqyxixaplVoiQQ8TeNgD5POTzOLd2ELlyFppHxjGoIH/wGag6iALilj0IBX6oKbfsdTYbiVobUlsfzs449b9qIlXlRMoZVkfkUBbPhjaryiRUwOpvHUbzRVW8cP3x5MMehuaPZWj+WCreSbHowHmg6YRf78T7vB8MA0HTsQ3nUArddM8vtt57c4TSDRo1f+il+2gPydF+6p/PEl7eijCcZOH8xQwdWU3eLxGZ4Wb0sykQANOk+exi0iUS6RIJ0+dBmB4HUcQd1Sl7vomB6R7K10QZ/0CKZ+9YSP0zAxwR2ove2oFZGEDzWYrw6N+nGZhVjNbeSa7IDpKE/+5uQhujLJy/GOfuPkJr28hMLqPqBpXYCROJnTCRXy17mMzoAritGG9nDlMSaT0bVqxahi1jWgq0olOwK0n4E0sZEjQDMlk8rQmyFV665gUxJah4tRfDJhL+ME1mSgXpGbVWEFE2x6HnWvNz9phiEQjR6of96OlrWDV7NN5uHdUjYqoqubFhXr5yHu5P2yl6+hNUv4yvKYFZGMD17k7kgQRK0I7ZN4Bv5yDe9jQDB7nAY6U2d9xq1f0AvDbRSs8VIoMMHVY6Mrvpbk9ayc2RQTIlNmtu91+EN598HP2IKfTM8aB194BhkJxUxIL6w+g5JohQ4EcrCWCWFSOEi8FndawClmWaP1VvtP/MwarjJtN3+SEoftCn1DN0ZDUVK/upey428v50m4BYuofYOBe5YhdKZZD63/Vb5+Gk1dfZf2gQqT9hfW/cLoSiEJGGLM62GGaoAD3gYu75SyjepLN10YOI3VZQ2sDFGWuW9rZiFs08AVE3EXWTVbHHvnb2z9y+909JrMGLCL8f46wbV7Aq8QTxWjtlLzQRGyOhugVsGQPFb4VnyX3DDB0aZmCqjK89i7czh7czxxrjOV6baFms1xjPMU88wwoH+t36kc90X5I2/IlYrYo9RsetIoeeey9rV19P+P0YZR9q9BwTZMzvcmRL7DgG88hDaVyDOoIJcs5Ezpkknq6k6Vt+MvUhCjcLKMVuhseaPD3pt8SmFBBeJ+HbNUT3HDuROTp/K/StO6GmHLn+q9MK983LHn/qeSMq5b7j/OvwRYVpcJLVO73+nqV/9rzJDzb81fTc/diP/diPfxX+00jnT37yE2699davfMzn8/HGG2/w1ltv/cPr/7cgncEdKeZ8djKZExM0LV7KZ1c0YkvryFkIPeVFUE0E1cSWMml5Zgx7b5zIg99rJFcoo5Ro9Mz2kSsEU7CSKc2NAQYO9hOZafDhvYcwcEgQU4D3HniE4087j1yhTMEukbYnxiAp4O7LMebJIau3cauI6gPnQJZDz93MoeduxrCJCJpJoCWPY9hk9yVFpCpsvNIwF1+rQPRAKF8l0XuYg+Auk+HREjcu+x3JShlH3KTwM4NMKUROyRP+ME33tRqGDL4ujerXs/ibJOqXpaz3/y0fTd/yEV4vkimWmPLRWcw760LW37OUHl2l7D2D799+Gd49MULbs2y4fSkrV8yg49dhMuPyjL83zdhGlcEpHsrfitF5Vin5sIeqV6OEP1HIlXlxRrIEPx0kF3bhbUmgu2R0l4zQ3Y+gmVaBvMtJ6G0npiggZ6Fkk8Laxx8DILRTxd00TM1yg6o1SdT7LOtzrroAZUwpNc900DnXia8NOk6Aq+YspmnxUp69w7LY9h/koH2Rh/ZFHpSQk855LoZvqKTreh1BlnFsbKZmZQb9c4W0dHUPZoEPV3+eznkuTI8TQTcgb4UTKQEbRVsySDmDKfc3kCkRyVUXYIqw6JAF+JszeNftQaktZmh2FUOzqwDIFUEuJCO399N608EYHgeaR0asLAfdIDnab9k0XU7C78esLsTefrq/ZYXzODvjVnpsVmH5pjUkZ1SzfO0LaH4HSomGu93qCt03vyruagevBzxumi+qsmyYLicMxzFcNhw7OtHtWGqBzaqVab6gjODuz+dkU3mSM6ppvriaPRcU4BzUqGxoonhzDlHRkLM6fYe5qFqTpeSjGCUfxRBMk5orrCAXe0xBHVNO+LFPicwpstJvCwSGphexcvEs4mcejKBo2IdyOAZztJ3gxd+moCw4hN7DBQaOKiN6Ry1D0y1FfV81S+9hMqbbPnLRftlFV2JIAvkbY6SqnLR+22TCz4aZd9aFANjjGvkiJ5rPzuAkO6kqJ6bDhlpdRLbCSy4k4+kz2XZVIzuvK0ZUdATDwNUex5bU8O2NEzmuaiQpc80HP2bNBz+GmnKEdI4FZZdjVpSQLrNUU+/rHk66/02cbTFrjhbomyHTc0yQ2JQCywptmrja4gg+L2RzpKus7w+aju8FPztOvpWFYw/HDBcih0usYyFciL8lQ+Rwi6Cs2nwb8ofbR5SlfUrsvwLTPl6MYROpWhWzkntlCd+mHoZPnUbFS52YyRRy54ClmufyIAg4Xt2AEB1GG2MlM9c9F2N4tJ2aButmTPnaGFW/a0Ju6sbbnQfDmuMWFA1hKIGnO8v8oksI7s7iiOUR8zrdC0poPdsiiACxyQbNSyoI7E2BbtB8QRk1P8qTqw2y6wcBMmWWzd0R1zjz6LPITa3G3N1K9a0GkcOD2HviaO2dI5VKX7SB/iWYmsoa4znyYc8ISX3h+uOZe9SdpKqxkosVKN4Yp/sogdL1WZLVAqbfTXDbMDWvxrD1JbDv7sa+u/vPlNXE2TNZtfk2EufMJDe1mkd3z7J6TOccCPxJtVsw6hq07YGR9FvdbWfto49ij5tEDvMgqibDY91UPd1D14kGpgCDk0UGJ4v4OhUq39RoP0UgVSUw5e4tjP1NlDE2LwdcuQU5a40TlG7Q8DTLaPMO/ov7Y599Fix7bcsZQdC0r3x8H9Z88GP23Hg1Y++4j5nXXvYlUv2X8EX11DVgEl2UY/YVl468xr59I/wFjrxf7dyP/diPfzX+00hnMBhk0qRJf/Fxr9fLkUce+Q+v/9+CdAqGSc9npWjbAxx8SwMzbrwMKWd1UqoekcFJNgYn2Vh/j5Uw6+hKcMO1l2JPWZ7c4q0KRdtUXFEF97YePL0mompS9bpJ8rQEttP70R3WQTQ8zs36e5YS2pGjaHOCw675mNf/8CRagQvbcJ5AW57Kt/N0zPez++ZJ7L55Ev0Hu7El8qQqHBTsSlK80SK40alOpDz4mwUS1SKVb6Yp2JUkuFenYfNZAESngbtPoXpVAj1pI3KYh/J7bRg2AcGweiRL30uy52IXhU96McJ5jHAexS8wPMGk8naBzrlOZvz4Ms679vtkiiUCzXmUUh9qwMb0nzZQ/XqW6muzjL/Xsj5KLT2YElZVgqJgH8qhlnhx9CbJFlkhTWrYi5Qz6JsdxN7Sj72ln6H5Y9Fdn3c8OuyYIti2tWKPW2R+6i8a8HbnyRbJZGsCuDridBzvw36xhKDplrVPEuifV03xZgNvj8qEO3pYvv41K+ioXwVBwNNnUvaBStkHKo5ImuAuE3tXjKInPMROmoznFRlB0cmFbAzXS3T8wkNsSgFy96A1C2mX6T/Ya6kx6xTioyRWP/8kiVF2qv/YT2hHjv4D7SRrXODzkKh3s/uWcdi7YgS3DVtkT5Kof7QDR0Kn/cEg9c8MIA0M49xpzcqZIT/OqGIRwOE4mRqfpd7UlFtkBNB27cUWz2P2R5n+0wac0TyLjj4N294exj6eRRhO0nJGEFdfDleflbjafWIZp736PsWbdIR4iuYLyjDLrG47M1xIxVtJssUCuTHFKIVu6h9pR3OJDBwoIfQO4OlMU//bXsreM0hV2Gh9cgwDBzjpOSaIvWuYqlVWZ2zf7CB9s4MoYR/Nl9Ui5YyRUB1h3CiKNyZJVTlJ1Jt4ehQMl/X9GppehOazIw2lqVmZwT6YYbjeRv0fMjiHDOS0RmhjFCGTp/vEcvTKIhpOXYla4EAJ2lGCdpwtg3hbEvguzJMuFah4wYZa7EVKWSQxXW63Uo+zGqUfJAl+OogadDE81oXqkQhtjCIpJgvnnkH1ayDmNPYscaGU+3jzrR8hpHNoHjBkwZqvHPdDFoz7IQB6kW/EAu2OWFe7sZuqAWg/PYzhkBg4/2DqnosR3pAh9F4XclojOTVMz3FFRBbUgCDg29aPMJTAjA4Req/LCoipqyI2pQAzXIiQVRCyCnL3ILoDQm80M/Wa+xDG10NNOe0/nfVn837/TFRcnsC5vdtKeg4GrH7EVIbQu51WncuoCnC7kLsHQZbRg16iDbNobhiFuHkPjkia2JQCCpqsXltT1zFcNpQJlSAIrHn2CUxZont+Md3zi9l5UwVSSw+RMycwNMlFfLQHeThD0ZY8o54RMCQBMatS9YZB7atJpMgwQzNKqFmZYejgYkTFYPwDKXzbo/hbs8gp1Uq03dmLWFpMPuwhXY41k3rYASOVSvsCnP4WZEpsAGjzDsYxkOekpW9hS8L47+/AkEApdFGyQUAN2LAnQA256DkmiHpfir5jS0duJOybId1HPvepgf7frUde8wnadivoSly36Uuvr7V3jthLY1MKWP3iU9S/eSG+LhXFB6pHJB8U2HnHFISURPjtCKGD+wkd3E/7fDu5QpnQpzLFW3R2fmcipl1m4tIGWq4Zi+YS6T7ShZQzKP0wO/JevwpftM/qW3ey58arWdl670hQ0hcf30cODz33Xg4991723Hg1YCn7fw8qG5ooWu5E1MyRbdgHb7f5lVUq/0r7+X7sx37sx38qBgcHWbt2LUND1khNNBrlrrvu4rbbbvuzec+/F/8WpLPqvjbktEDNygyf/KSRzIkJWhZL7LnKTrxOwN9h4O8wmPetC3ENmPQcX4SomcRHSQQ+k8leM4yrO4V9Ty9dZ9SQGCVg2ARMScDYFMD8XTHufpWUkcM1qLPosEW0fsNBrtjF5h8fyNzzl5ArctC02Eff5TkMWcQ1AL2H2+g93Iav00BM5XAOamTL3fjas/i6VHxdOt5uFUEDxzCsfvEpBg/wobpFqm/WUN1Q9r5B81kyms8ODp3N1zVi64gS3JXBvbOf/hl+0jUeAp/JvNP4a2xtTmxtTko2JHB3C9Q0thDcZZKqECj4JEL49U46j3UiJxWknEG6HOR4DmSJnmOC2NIGQ8eNwd2vW5Ulsoywpx172wDVv+nEOaQhDVhzZ4pPonhjGqWuBKWuhGS1gGNvn3VXvcKyACePGkugJc9rjQ+CAL2HOSnYlWR4jI18uZ/qlUlMr4tFv13H6GWXEflujmwJDJ+T5LaHHmPnjeXMPX8JuseaFxqYJlqzlZ+24/7USjQM7E2hVIfIFEs4hzTmhPbQdJaX+FlJircqFPzOhyOh031KFdkKL4Jpkqg3SVYLOFsGKT7RqoTIFgnkqgvoO9SJPQmh97rIV/jxN2eofU1jaGbZyMUsNhm9NIiUM6i+qAejtRO1qhClroS+Y8OoBU7s3cOENvTTd/pY8n5LNes/NIgadBH+3VYiV81C2NaEPqGW8PsxbK39mA4beNyIuztAlnEOgrhlD+KWPXh2DFD8aZb/vmwhvqYEQ0dUUv9oB5kaH2JWJVvhRUpmKd6UQ/xhBPunTQwdWU3onQ7qH++m95tjiRzmx3TaiI2TkHMmBU15SjZmLCKcy6MWOEndkETxg+KHwclOqt7M4ehJUPlWnkS9mxWrlhEf4yX0bifjftGOozdBzxFejmy4hGyxgOqTyYwpJF9oR8jkkfIgd0XxtiQwHBKJiSGSk4vxdRnEx3h55ufz0dwS7zT+mncaf41SFSRV5yc+sxpPn4mrL4fqtaH57Lj7VUJvt+OM5tEdEmJOI1/pR04pBHdl8bWm0AMuvB0Z8pV+PDsGaDkjyIQ7enB0DLNg3A8x7TY2X9dIbNznF97DcRiOo2/dSa7EyXC9neAuE+egiuYUsPclef3EAyjepGLIAuF3B8hWeJEHkiMVO/3TJSpW9RN+cTfpqWWoZQGUuhJW7FyHGfCy58arESKDf1J3DGuGdGh2FcWfZlEmVLLkkuUAqAVOgrtMssV/mRj8b9F3Yg1mUQH25J9kpL5vjqP97GqEvIqg6piDQ6AbDB1ejuGUKF3dQ93PtzN8ygF03CpiSxvEa63vZebwMfh+0UuqwoFeW4pYugdsf5qvHft4FgqDBHflkfLW+SgzqgBBt2aISz6IQnsPng+aGZjuw/2MFagUPcBNslqg5SJrPcpSBXkgSabMsi3jcYHTgWPDXoo36RiSQKLeTWSOTmSOjtE39m+uTInM0Zl7zM8wZIHVLz7FqqPHUfFOisT8yWz9fiOaW8LfmqVvhsyM8zeTLbZT0KyRXVqBp09nxaplrFi1bITkrtp821daQEs26uhbdyIcOpUFZZeP/F6aOuFPBPWZ9cxbfAFj71cYnGTH222i+AUCrTqGLFCwS2RoRgnD662l7H0D57COc8jA05pg1UtP0z8ziOo3iR7gRvEJKEHLhWI4pK9N7Pyi0nnouffywK5jEdI5Ws4IjpDPLyIyTx3Z9o+evoZwo+tr9/kXMXDXKJJVAu80/vorH//i7Os+7K9M2Y/92I9/Nf7TlM4NGzZQX1/P3LlzGT16NBs3bmTGjBk8/vjjPP3000yfPp1PP/30H17/vwXpfPfNqexa0khilGukH6xgi0zz3Cc46dQPML4dxfh2lM5jnQxOheJPs8RGW3OfUg4OLWljzwUFDB1diyNuIk+KI6omtpTOpOP3IuVNWk8Xmbr6uzz6q/tIHFLJqD/mkTMazv4s+QIZb0sC54CA5xU/iVobrqhO2YcaZR9q+D/pJjGpkHS5jJzSeeiZh1A9Et7mBK72OLkiazZn+m0NBHdmcCR0kuOClL+TJFsk4eqQ6ZzrZOzDCodfcymxR5yIis7u75aRLgdXJIeUgyOuvBRHDBwxyJW4EHXoPDWIp0+h4VvLab6wDL00SP3j3UiJHI6uBLV/TKIUutl5tWXxS1VIuPtV/O82M3/lVsyBQfQp9XScWcWun04GQCstYGCaEzlrED3QQ6rCQarCQc1vmrj//T/gbU/Te7iL0No2/Bt76DrGwZlzz8YRA1+nyd6zfcgZGJhqJ1/kJDkmwB9uWoC/SUDfFOCzKxpxveLnupsuI7xOouNCna5jPDgiaWqWp2k5zUH7t0fT/u3RAOhuG7YhyypsygKvLTmK2lcVqm6wlFDf3jhySqfkkwy9s2x0/dik7sUsNS8NsPzdl7GdbFk3vd0mjkiasg8yhN+36kyG6xx0H23ZK0Ov7yVbLJAtFjDdLhAEIg1ZPK/IiBWl9FyjWrOVPqueQC0L0H6Xk9LXe3EOaih+gfCbPfRdlUcoKqT8rRjDp05DMAxo78EsDCCksmTGFaNPqAW7jfCHaQbOOZCBcw4kM7YIKa9j39kFYFl3sWYBdbc1E5UeGyJ5fRLzzhLi8yfga88SWVBDxzcrKP40TdlrnSBJaG5rPndoogPBwCL1Tge5Ijuen/moeCdDxTtWNHbfDCeCbmJ+3ke56MB5uPtVuk+pRqkP03x2MYXbVXpmi1T8oZW3H3mUtY8/RnSyzJ7LwhR+lmboyGo0v2PkO+vdG+P021YTPRA0t8DgBJnZV1zK7CsuJVtkx5bW0R0C6VKB7us0UhU2lIBMpMGaJxQMkHIauteBnNYYHu+zLq5DTtpO8KI7JJw7eug7roya5Wly40oxXXaGZpRAZIAHh6upeL6dqjVZ+k4bS99pY4leNsvalw6skBrVoPCzFC1nFbHrylLknI6rIw6ZLHJaY2hGCbuvddF8uoviTTr9R5TQfe443O/vJVnjwt7Sz8L5ixEigxx67r2s7H2I9p/OspJXi6wl9F4XuSI7nXOdvPKduWRqfLz59g1UNjQRfPPPk0//WQi/HQWwEnhjcYKv7aD0jQiVa5KYfdacpBAsAI+L0CcD9Mx2gyjS8oNJOBKWZdi3rZ/wijaceyJ4dkZJfr+M0DsdqH47Ry66mwdefYziT7MUf5olPsZLripA9AAHxa/upaDZwB6zeoYxTUv5LSkid9Aoqx/zlTEI6RyeXp3CHTolqxzkyqyZzaEZJXhbEkg5A9Nhw5RFei60Eo7d27oBmHBbFxNu62Lyh2d9qSvzL2GN8RzjH0qCYdJ+psnxp57H/LW7kSJx8gGRed+6EDFvIMUyqLU52i8bReSUPF1HS2RKRLwtVtfv3POXEHqjeWRecd9MJ0DfyxNpv30Wvu1RTtgRQ0zkRsLH5olnsGrzbUx8+dYR0tp8to2Vrz5D2bo49qSJ4oNcUERSDFwDBnLWQFRBVMHbkqBzrkSiWqRrXpCZ115GoEUhsFdA9YDugLL3DELvdTE82v61x8c+pXFf5cuqEw+i+YIy9tx4Nas23zai5q4xnmP+tJupekli/rSbmSeewdRr7kN3/n2XFm8/8iiXnrecg+5o+NJnNX/azZaz5H/gb7FN78d+7Md+/G9h8vcn2Jpfu9b//+LGG2/kjDPOIB6Pc8MNN3DyySczd+5c9uzZw969eznrrLP46U9/+g+v/9+CdFa/nmbhcd8kMkenfHUUeV2An17zBNN/2sBbv5xJdkUJ2RUlVtH3Lug+0kWgVSddJvLJTxp55zcz8HQJJKsENty+FPsbAVyDBo7eJMM/ruLM21ZS/oZI4Qc2zrvhB8g5g0Stg1SVk0yFG8ewxq7LfQRaDYrei+Dt0YiPkkZ6Os2AF1d/nlxQQMrrXDVnMb5NPeTKvGRrAkh5q6dMzpnkixykS2SyRSJq0EGgNY+nxyT8scbQZC+qS0D4bRGDU3wIOoQ/0dFdMsG9OZJVIlt+0MiWHzTiaouTmKLSfXoNmlti1TcOZtRLCaSmbpTqECgqK958DlHRcHQNM/bxLBX/3UTpuzFssTxDx41h1UnTyR4xnsQoF6UbcuT9EvZhhYED3VT8oRVXd4p8AZZy255FG1XGlQcsYni8j+pXonScMwp0g+Auk0xdkJL3B3D3q5SvM3BHdcIfZ+mfbsMVyWFL6TjiJrkSnUVzTiE63WD9PUvxt2TQkzZqXo0hZBUS9W5sCZFLz13Opecup//QILaBFELamgfSHCKJOje6W2JgVhFCXmXgkCC6U0RO5FH9BuaGAB3Hu9H9To775vloB9RjG8pa5fWA7pLJhz3IWYPwW73U/KGXK4NtdJ89lopXe6l4tZee44tQ/Xbcr/ppeWYMufoiKu8UERIZVB+EPuzD1jmIc2UAPejF1ZMiXQ6JaWHScSdaWQGJcQGC24bZe7aH5NzxqAVOTK8Le1xFHkig+500fctFqhpS1WBLquRDDit51Pm5jVkQWLFqGVLSmjPL+yWEZ4p44+nf4IqqPPTMQ4TX9VP9ygCa14YZ8KIGrblNNeQiUwp7z7ejeW2kJxQh6tB3mIueI9z0HOGm7Lm92NKghTw42yx7sRkuZHCSnfDHGWzDOXYtacQ+rLD3rKXs/kEtAIumHIOchbGPDqC7ZfytWZI1LmJj7bz7q1+jB9w8+fACjHAeKQeuqImnK4OnK4N/TxxDFojXWVZq1/IAxR/HsKUtsrP845XIQ2nEdB7dZSVTB7cNky+wLLOjbv8U22Ca9EGVlrKWUeieY0fo7mf9PUtpb5jAK9+ZC5LICY1v4+vW8HVrbLy5kcisIK6oyZgnFeT2fsSsSt1zMcbf3YG9Ow4GmAU+UlVO3P0q428aJLhd4N1f/RrnsEHx5hz62GrW37MUpa7EstiGC0melmCeeAY1N32AZ2svUl8Mqc/qr8wWSgR3mYiKZfef+PKt7H1+zAgh+VdAME2EeAqt2Ad+H0KBH6XMjxr8040B02PNymbqglYyci5P/WOdSDnDqgJJpclNLKf7lCpIpoiP9mCG/EQasthjCufd8AMrKGs4hz1l4NzdR/HmHIk5o3EOKkjbWyjakrFeY2wxpl3C2RxFCweofilCx8/d2NI69rhlrVQ9Ijc/8QSqWyA2pYDTHljNilXLEPIa5aujJGol+ufXEmhK03xZLc2X1VJzfW7k/XwtSWnvQQ1Y6nKi3s2qEw/C9DjIlsDQRCeu9jiZ+gKumv4me8/xM/ZHUUa9nCO4R2HFqmW8+eTjvPnk42iR/i8RpX2vW34L1Pz4AzAMVh09jl1XFYyQun3VJPuSZdt/OosxTyrM+c4ldB8dQLcLVP+xH80lIOUMogeIdB9vki+0lvQoP3JaINBm8ML37iFeJzAw1U7siNxIRYp/QxdmwMvGmxq/9vjYR9T3jRMsf/flEevsF7GPLHcf+aegLDkN0QvTX3rOV0E8YOLIv+eev4SXr5zH76+750v7DP5EfL+I/YRzP/bjn4v9wVxfjf80pXPjxo1cc801+Hw+rrzySnp6erj44otHHr/88sv5+OOP/+H1/1uQzkyZi86FhYTXSQjJNN5ug9tvuYDhWTkkBVI1Jqkak8kfnsWGO6yuMkdMwzlkckbLsTiGTcIbMtiTcODPGiw7YEZn19V+UhUOfvvgIlIVIsUfxxDOGcDVmaTo/Qj2lIHik8gHZEY9byCqJrtuKcAWVwk26dDZA509JMdYJKfqlQi5Ygfdp1QxdEQlpiyw9jePUfJpjheuP570SQnG3/QZhVuS2FImomrQd3mOgxq2EK+TyRYJRKcb9H8jz+BsBVtCoHOeNYuUqnDg6zBG1KKWs4oIvyXjGjBxtychk6Vrrh99VDmpKidaaQHzFl9gFdknkvTM9qHXhAHQ3TKBvSn0kIfeWTYCTWlUn0zo7Xb2Xmqj7PUI/QtGIfQOUPF2mtf/8CSv/+FJayZvej3ufhXD47AsYaPD+JszlqoxvQhHVwLf1gimALZNTfjbTEsVGrRCZIo/Fmn9Vik1yw0OveEyVr/wFBPu7EXIKnScEsY5pBHcZfKHmxbwh5sW4Ilo9M8qwvQ46ZspkC0WCb3XhS2uULA3i1rspeS9fgYn2tD8DsY3DlLz+x6Kthjkwi5Unw05kUfIKoz/ZQQhbSnAbz75OKYokJ5YTHR2KYtmncSSS5ajFfvRiv2Uvpck0pDFETdGZkBFRQObTP1TEfxPJWg/qwrjC6LCq+fdgzOq4NnhABP8u60AoPEP9CGpJvadnQjpHNGpbpJTSlADDsY9Hqf8PZXy91SGx7qIHGxDTmtIPUPUPzNA12lVHHfmBbQsLsLXYSIpJqFPBpj8ywY6jrPzvVMvBcNEKfHi6EqMhK2kqpzYhrI4B6FumU4+INM3Q8bVl6X6xT6KtqkUbVPZ+dNRlHyUZOBAN7u+W4zhslndnRlIjHIROTzI3POXIPfHWTh/MaOfTXH0kovQxlTy3NX3oIR9OLriZEodeDtzqB6Y851LkAeSOIZNXDucyDkTf7uCmFURsyqGy4arO0X16ym8nTl0h1U1ErqlndDaNo4/7Ty65xfTuagIUxRwtPSTqfER/sSayc7MPwAt6LZqd3Z00nN0kMq385gVJcy89jJqf9dJ3wwn5PK81nAUeb9E3i+xaOYJhD+wiHX7AjeZqRVofocVFjS2DKUiQOygQvJhD/3z89jieXbeWoycM5m3+AKSlSL2pgi5sJPjTz8fwyFhhvzEphRQ0zDA6p4tLNwep/miKjrOtBZ3e5KiTUli4wUUv428X6Lm6gTlT2yH9p5/2TnTtMuWNdgwYWgYTBP77m6rskMQ0L0OhFQW2nuwxxRSdX6QRDrOrKL1dBEzlwdZxtH1+bbabAQ/tZJZSx5xs/rFp9AdED24gOjBBTgG83SeUY29dQB3r5XYjCiy+vkn6ZwfpPdwG0gSpsNOtsQBikL1D/M4O+NoLuumgqSaNDzagK9LpWBXkl++cCJzz1+CUh5AKfVRvjqK7oToVC/ypDjypDgkrLnovwZBtln22rMmIeUMxt+VIFssoFQUsGLNc4Q/Ubjt+0+Qr/QzPFrm0d8sonydQW5smJaLBaJT7Ez/aQML5y9m4fzFIwFBfwndJ5QxdGw9npL0iM11n3KYLRb46OlrCO4ykbsHEXWTgmadyEwraK30j63Yh3KUfagRXidRs1KhZqXCO42/pnSDRt9MgSVXXoNggC0DhWsduJYHcEVNlm9YQT7ssRKwvwb7tkffupPI4ZZyuk/J/CL2KZ3eDoG6Vy9Gm3cwqWqscKkvPOerYGzZMfJv554Iw6PtLLnSsujuI72rNt9G6MM+Foz6eov0fuzHfvzj2H8j56vxn0Y6FUXB5bLGI2w2G263m6KiopHHCwsLGRz8x5P1/y1Ip+4QcPeb5EIC7b8swDmkUrAzgRhxYEsbCDoIOpTdb2fMM5eRHGXSd0WOp+78ORu31OFvzdK02MGD338IeX4UzW2t17PbxuAUgUCLivv4fitYB+g9Mki+Koi7M0VknoruEKzI96BI1R9kYhNcGBJ0XTqVrkun4t/Uh+406TgljGCYlL2bwN2vojlFFh15Ki/9rhHPziiVd4q8tfYA+g/1EVr/edVDo4tPlx7A5usasSet7RrVCEXrbNS+GGX8r60LJP8z63HENfJ+gbxfoGKdgiOhE1rfy+4lAdA0qp9sQlQ0PD0KkRlu7JEkSy5ZjhkuxNNnInUPkhztxzaYJh9yWl2JJRrdR3pxvb4VHHZGP6bRd2yYknUR+k4fi5S27sbP+c4ltJ8OLd+UkLJWd15oQz+GTUTKKChBO8FtwwiZLEpVCHdPhpbrphDYm0JzCqSr3QyPdRH6LEmuUsO9J4prUKf+uUsZOqKSoRkljD9pL+7tllX13Qcf4d0HH6HzeIFPftJI+4lBvrdwhZUS67VCZezdw1aybsBF1e+ayBU5MO1WEJIjrhGdLGPKApHDg6THF2LabeTqCjE8DiZ/eBY98wyknIE7orGy5ee8dtHRGHYRwy5i2iWKH3Xj6s9ji2Wp/r01F0o6A4kkibO9FH2mUfbmAKbd6ptclZ6IbSBN6YYccixNYlwAqa3P6ta0Cey6t5pkI9iTJr5t/chpjR+9vAzFJ6H4JHydKrUvRumc66b3pBr6Dy9GykOuyI6/1cTXnkVUTZavfQFRhU/Pvp+uuX5Mlx3NI4NNovTFJrRCL5UNTeheB74OneBPO0hWi4Q/0ZEGErSdWYqrO4WrO8Wo5w2k4TRlK3uoXqUjqDqxcRKY4EjohN+P4dzejV7ko/3EIGI6j2MojxTPctlFVyJlNEhZNt3OeS5MCXybeiCfJ7Shn5rnI8TGC9je3YYQHUaIDjM42ZqT7DrGi71tAF+3jl4TZvvqMShPy8j9CQKtOt5uk9hYq4olOlnGEUmj1hYjaib9B7vJFdlp/u5oCncoKD6ZljOCOAc1ho6oJFNhoIyr4ITGt4mNF4iNFxg6opIVq5aRHBOg/nf9JGpt2DoHkbMGXcc4sDdFCH0Uwbm5jXH3ZJHa+hh/TwI5a2D7dC/la2OYPg+ePTG6f6BatSfdVk+jGS7kqEsv5vWFk6lZmaFqVYyqVTEyNT5e33Azqt/A1WORbFIZqClnVeyxf9k5M1PjQ0hl+fGzT6HHE5gFPqt+ZziJEC5C7h7E9LpYsXMdms+GtyVBZmIpl1ywnAk/brF6R30eBmcUsvemiai1xQiGQWRWkKHxNg6+pQFH3KT4nT6K3+mjd5abqpd7QRCRUnmEeApqyjn64oupeCtJ1ZosQiaP2dbJcL1E+1lVqCU+TEkgF5Kt7dJMij7TcH/azu4rnNQ9G8URSWNvG+CNZ35D9ZNdSDnLel12v52y++0s37TmKxW6L8LUVKSpE/B1abz55OPEDipEt0PvVQpHf/si+r+T5fZbLiBTYqPiv5uQM3/627H3K7gjJvaEiZDOIaQtZXUfaRt7x30jzzW27CBx9kwqXu4k9F4X1VcMjyicAAvKLsc18CdjlhnwMlwv490bw9kvkSmxsfPOcvpm+emfJpMtFui7PEff5TmOuvRiNKdI0SbwbesnuNvAHdGR8uAa1K3jat96w4Vfqiv5a5CmTsCeMAl92DcSzPTF9NoFZZePvNcJN7exdvX17Lnxapq/O/pvWv8+DB1RyW3ffwLfx50j6uk+5OoKR2an/xL2qzRfjf37ZT/243+H/zTSWVVVRUtLy8jPy5Yto6ysbOTn3t7eL5HQvxf/FqQzXieQLxAofT9OPm+j4zg7zWf6MUry9B4u4ukW8HRb1taqNzTK39UxtgQ49dNL8O+VaF/gxlac48q7Lmd4Z6Flpxpvp3phG/ZhiI21kXinhOIPJMTfFFH2ToyXn36Y1lMChNfYiBxmkhitU7RxmLxfQvGCf2MPxVsVircq7Ly5iNL1BtVPt2Aftqxig5PsJGpEOk4Jc/ATVxM5upT4OC81y3Mofmj7Vrl1QQ0U7MlyyE0NSDmTrSf/kmS1lXrbd2QRPccEEVWQfD4MSWDDHUvZcMdSklU2bEkr/Kb8HdOy6nncCJk8ulOkaEuevmNKWHX0OPJhD84hDbPAi+YSyVYFcO/sw5RFJtwXo/qlCILHBTaZZI2L0hebQNUo/cNuzC+EhVS+JhJeJ5Epc9J+YpDeeWHEvE4+7MHVZlm8ho6oxBbLkgu7qF6dQWruIbQ9jX9bFPeATuvJfvw7rLRMz4Y2BF1g8Q2rcPerJK6rIHFwBbpLZOG8M1g47wykrMgRV16Kv83kheuPZ+CQILuu9WMKAnqRjzF37CAfcpKfWIXqEUHX6Z1fxuAkO45hax5y402NpEplOk8sYnCSnf5DfcjrAgg56+vh3tHH3KPuRPPI5Iod5IodCJpOdIptJK2348wqDJeNPVfX0nfqaLru95D3S/QdXUzX0Va1wm8fXAQ2CVs0jRL2kaoQaW8sJj65kL5ZIr6NTmJvlBFa20Z0dimrn3+S2U6wZQ1sWYPYGDu7rgngikJod55kLZR8kkK3C2BC63esE938U84lOz2LV3SSK7Z6LR1DeVasWoZeFWb1i0+RvKqUpjOdDI+W6GocTWKiSs/iPHqxn5oXo7ScEfyTQmSaKJVBXG1xBqYH8PSaqF7QHKJl0T2gkuiBPqreSAEgdQ+CTcK5J4LUN4RZGMDXlCD8iU7lWylWtt7L0OwqcrVBTLtE/dI2UiccaNW/uJyUvN1LpsaH5rasrJ7WBJrHjrsPet6qovPkMly9GWLjBRzDJkMzy/jsikYMp4wpCkg5nfK3Yvjfb8XdB45IGm9LgpKNOqJu4kjojH84SsGdXbz0w3nsWtLIriWNLL5hFfMWX4A9oaEVeiloUlCrCvE1JQjuMjFDfkimUCZYlTm4nCTHh/A1JVAPGmNVyFT6ERIpip7wEHqvi6H5Y4mNF9C37kTKWepY9AA3aoETtcCJuz3JgrLLGfd4HNp7sA3niJw6DoC5R935rzpl0nGajlYS4NZLlyDV12C4bKRm1bP8w+WQy6OVhYhNKWDR7JNRfBL9hwbJhWRevnIeHReOBUAp9WGKUPtKDimrWSFYAgR3qziHDeSMTtviMtoWlxHarY+EEsWmFIAk0fkTEdd7uxEVjVyRneXrXmL3PVNxxkzCnyh0HOei/WTrPzelthjVI+FuT5KcOYrgR3YGZhWRHuVHrSlm0kMNtF0xmsHZCq5BnVSVk1SVk4XzF/9N6bWxKQWs++O1HHXpxXg7c5SvS/PZYc8i6ibV1+VI1FrfrdzUagp35PB0pmn9hoQSchKdZoWQkUpDKo24btPIa9bc9MHIa8jhEtz9Kitb78Xoj5I+sOJLYTgrex8iW2y9zj57buF2Bd3nQsqBtzOHvdOBv0NH0EFzgWtFANeKANmQhKc7h6iZdJ5cRq5QxDmoYksb9E+X6JznYua1l1k287z6tUFCX8T6e5bSfo+bxTes+rPH9tX6uAZMho6tH/n91xH9/4mzblzBzb+4kPbzar80BztPPANHJP21nbX7VZqvxv79sh/78b/DfxrpXLx4Mf39/SM/L1q0aET5BHjllVeYMWPGP7z+fwvSWbDXYPN1jey+1IOhiQT2ChRuBddOJ1JOoOSTDCWfZMiHHHQdI2NKAqrXxL/Mh+4A1W9gs2koXihfp2NLginB8rErMRzg79A5ZfG75IMCw99KkqnxMe/6qyjebM1g1SzXKdwksfecAiIzDSpXROk9oQrnlnacW9opWmfD051Fry4hU+pAUHWCezU0FxRtVSl7XyX8Vi+x8QJyWsWWgrIP83j6DHpn2uk71E3hZ1YP57zrryK4bZj19yzF3W9QsbIfX5dO5qgJiLrJUZdezFGXXownopMvsBF6r2skobLjFx6Wr3uJtY8/hikLbLypEb0mTNsJMvaYQrbamjFsP8Xqllyx5jmqnuoGRUF9zotW6CX0djtGZQmoKvmptXQe5ycTlsiEJfoOlUhVCqQqrPReUbXULcUvEzmqiF3XeFh/z1IyNT46TjPQXTLamEoS9W4Mv4t0iUT97/qpWNWPlMzS/N3RFG+E148Zw62PPI6gWCrblXf/N7svCbH7khDfW7iCZJVIqlJAt4sIpknFqzLFH8dIVbmZ5O1GzmhoXolktQCGpYRUrOyn+NMUatDFAT9voOSDKJICFS934+vSKXs3QcXbJuIPIyg1RfQd5sLZHMWe0LAnNKS+GM5BcHYMY9hl3P0mpiBQuzxP+O0o8uoC3P0qG29qpPzdrFVFkzeJTi8gPimI4ZBITs9R/LgH/544petNcoUQaDVobhiFe8BSeeefdA5STkfK6djSJoJdx9ujky614+qH5isl3r/vEYYOMDE0gWS1xISHd+DY6uKQmxsIb7A6EFe99DQL5y8mX+QEID7Oy/g7WyjappILCVSulBh74zC6Q6LzxCKkHEg5a0bS9FhJvNmaAInRJoPTTBxxE09Pjtg4J46BHP42lehUL8LQsDXLmM6x8ydh9NIQK1YtQyl24+rNIPXFOG7GbQS3DbP0sQdAksDpwNWfJzO+mMz4YtTyAgYnyNSszICuM3BIEFsiT25BnJLNGqlROvExXgy7adlv7TD1Fw2IOQ17Sz+24TyGU6b5u6PRnJAcEyA2pQDBtIJzMsUSWqGXzPleHK9uYOa1lzHz2su4esLrgJWUqXlkWk//fA445MLfbG2LMq6CVJUTIZ5CqSnCtzuGUuhG89mQchqOSBqlPow9pqCVhfC3ZkfOU5kSG5GGLEWNH4yQohWrlpGbWs2KVctYFXuM/kODlHxkVer8z0qNfyYm3BxBjqWxD+Ugr5ItdTFwgMT8U84lNa0cKZ4l9EYzzReU4d/QRdGW5AgRKn/PkvoGpjmJzcsxNMlFTWMLsSkFqG6s/RxVcXWn8LeZ+NtM0qUSmUlhciGB5GkJ0DQKnvZCjUVCXf15Zl57Gc6IRCYs4GwdYteSRqpXJQhtjBKb4MLdlwNdx9OZpmRDguKPYwxMldCdEtsvb0R3yYTetyPq5oh6vWDZB3z09DV/VfGJnzfT6sb0X4gzkiVV5SR6oId5iy9AMEwyo0PYEyDlTTqPtTE4yWl91181yJTYqH01R+EOhebvjqb5u6O/lEL7xYt+LdKPvOYTAAS7Hc/W3hHFcd/2bb33T2RtxaplJKrtiKpO+XsZWk6zepwVr0hBs0FBk4G/XcHfrnDO9SsQ8xr+vUnmnrUBx7CBqBqoHhGlREOeFCdbLLBrSSPpsaEvzVN+HSZ/eBZFT3hY/a3DAKt3NHH2TOaJZ4zYYJOnJazPFZjzjXs44tSf/83rByuoyJ4wKd6qjdSyfHH/5aZWf+n5+xW8/diP/fi/gGkK/9Dy/ypuueUWFi/+yz3LN954I88+++w/vP5/C9KZrBI5+uKL8e2SMeM2XIM66TIBWwp2LHmIxCgXiVEuBi7OYB+TIF0qUfKJSd4vMv7EvQR3iGQSTkQdZv5kA+NP2ouchTNajiXQZPJO46/5cLCWzdc1om0P0DFf4Kk7f073MdZFb3SqjXQ5mOU5hIDK3iWFlL4bA68HvB6KP4gyNMnDqpeexjGssWLVMgamypS/l2N4jBVckRlbxJSj95IvclH+hybeePo3ZEMiZesVgk26ZcPEugMupLJMu7uByGEC7aeW4P2wFSlnYEsoeLb349nejyOaw7ehA6W2GFE1EPIqlYubWTT7ZBbOX0ymxMbMay+jb5aP0cuydM31kAvJKIVuxj6a5bgXPmHu+UtouWYsu66poOWzCrIlDjJTKjAcMlpVMZFDHOQmZfnklkY+uaWRwF5wzomy5JLlCN+IklsQRwnqCIZJcE8ezw47RzZcgrtpGHHIhi2hoBQ6OODKLfTO9lPyXhTTYaN7fglDBxdT+1oKf3MGra6Ma+5oIFXrQdDh7tvPpvmMR2g+4xF+ueVoijflkTOQKRFxxE00p0DbN4LoDoEXrj+eznkuXD0Zql+OMnhoIf42FS3kQRpKM3Cgk0CLTra2gFwhdHyzgvgoiVypm3SpROo3ldh7E1S/FEErCaB6JFSPhF4atD5zh+3zY1AgU+5Ec0roQTcFTQrJSmsf27uHGfvdHYS2pxFM0JwCjkiacVe2kiuQrE7I7izVqzO898Aj1C2L0nuYjOk06DnKx4UPv8yFD7/MRz9bim+jk0S1hO38PgJtGg6nwuzvXkLZu1Cy2kHJx2lefecQAm0G6QoYOD2LoJq8m8NKes3pHHXpxbiiGv0njGbgQBvxaSqZEpHI3HJ0l0yu2KT8vRzl7+VIl8n0HRGk+YIy5IyGnBYIjhliw+1L6Z7jRsqbmDaRjuNlfF0qek0YKa8zdFgp4pCNyEwfM6+9DN0mIkWTRI6rAklAiA4DkA97ABBVg0S1TKJa5sRH3kL+nKuZTjv33mgFnxT8tw8pp1O1BvIBgdHPDJMNOzBkgco1MXqPDNJ8aQ2DB/gw7BJl76tUvdyLPaHh7c5jH1ZRyvyEPksh5jW0cIDsyYeO1GssKL6MVJWTBWWXYx/KMeHeIVJ1fhI1DhAFWhYX0TnPZZGvlEUCdv3AR+Nvf4moWPUvutdKAJYTeSKHeWg600nFOgVjzoEofoGKn9uQKytGzl0L5y9mzB07WDh/MfOn3YyvS7XCceIpIlfO+pedMxOHVDJ0cDGax4ZaGcQeV/F0Q3y0B3tcRSn3ETlpNKUbNHITyhia7KXp7AI6jrNZVTjd/SRHGdQ+IhDamaPzVEsVL38nieKXSJfbiRweRM6ZVjXPnrx1A0OCmuuyKHUlDE6ybOf+5gxSVkNzChRu1/nsikZ2XlvEpIcaGB7vIzUuRHB3DlHREXST4fE+hsdbow6eHrj1kcc5+tsXMVzvRNQte2rdczHqnovx0AuL/mrS6TzxDAqe/WSkRzU+xrJxFm6zQrLsAxkcQ3lUL6QqRbQijeFD8vQc6aPjeJnQe1aS9HD9n4a39a07R5TOL1pRwVI7AWInTCQ3Nvwlcvo/idTMay+jcEuS1pP9rH7+ScY8ncY5pHHYNR+z6r4HmHztVhS/jOKXWXXiQfTN8rPytWfZ+NPpBHbGmfjgZzgHNcY/lKToCQ+JaQoAgvrXsxW/uB361p1o2wO425MjgT7+Z9bjf2Y9a4znRmyw2vYAl45/j/nTbubtRx7Fu3bXX32N/4nuE8t46s6fkyqV/6xqRhhKjKRW78N+BW8/9mM//i/w9ybX7lv+XeF2u3E4HF//xL+AfwvSmanW6Tpa4v3v34erVyI2VqLs/TSqF8Y//Z2Ru95ep4JtbYCNNzUSrxMZOlgjenstqUqofsGyib723Cxit1RjyND69BjOunEFda9ezOALVRz4swZ2LWlE0ARWpCZT/5xK9AAXlW8mKN6q49rqgiE7RjhPttxLrr6IXH0Ry996Hn+7woPD1Yh5nRk/voziTSrxOgdSDlSfRC4kk7oyjC2uoNeWMu3uBsJv9fLmk4+j2wSavzuaqjsE8mEPA0eVUfFKD8Ed8NkVjZjhQjIlNqJTvZiDMczBGOgmQ0fXYm8bYGiCk9T4Qpg8GmwyQipLaGOU4LZhSj9IIiWylG5QcA5pCLpBfJyX1xqOwrkngmGTKNkgMOaZNM5BBVdPisQoF5rPRtWrUYJvuzj86ks5/OpLiR2TJb+2iF8uX8h5oz6i6DceCjdJyBmDtgsNCpoNdJuAkEhR92KW7iO9pEpl1uweT+kHSTpPKGLFqmW4oiap0xNkylxIySyJOjfZRXHOvG0lhg0++tlSjv72RRz97YsoeNPN4GQH6UrrQuqHdz2J7gA5C66oRtfREqUbNBY+/S59xxSRLxBwDOSITXCjlAfQ5sTpWmAyOF5m1MsJKt5JkSuEoXEyqtuqJWm/ywnxBPJgCt/WCL6tEaTmHiYdv5dUnR/dLWPYrboZ3SmSGOXC2TGMv00hXyBgFHj46I1JtJzqIfhZCn9bnpYzgqiTa/G3ZPB2WGmZyRrLwrDrewXsWPIQo543yBWbnO0b4mzfEAff0kC+wFLhXTf7ERUTbXuAgQMlYmNEbBmDltPcmJJJ30KFQ+ZvR9jjIVMqc+ErVqBQ9xwnrp4MqQqZj3/aSNl7WZwdNpxDBraMieYW8bcI5G+Mkb8xhqCDe8Ag0GwSG++i9COV80Z9xEF3NOAchFzQ6rMN7BXoOkZGDTiIj/bga89S91KOsndipE5P4Ijm2Hl9CUVbrMHkjodCnHn3tRiyFdgS+EUPrqiBK2pw/8a5bPpRI6rPsi//7OTF1DS2kCkRUT0S9u/14IkYpGt95EIiuUJo+0aQsndiaB6T2ESwDaTIFcp0nFaGqBqkKhxoXpnYOBdSV5RsuZt8oZ11D/+a6ZNamT6pFbOihOC2YXbeVsvweB87rwmhekT8bXnk/gQ1KzO4+6BvdpCdvxiFLSEy6hm48sSLkNMaziGNdLkTX1OCzvkWCRvzbAYxr3PS0rewJ0ykVJ6dN1UQWt9LaH2vZet8bRoA6VF+olNsPDhcTft9flzRf13werxWZP09S1EDNmLj3AxMcyKYJqGPIiRqHMTGOgi/2YO7aRhbPI8nojP6mWFG/3cC21W9UBSk+YxHsMWyDE1wojwpIikmSqET3S4gKSb5APTOthZbIk82JFK4XSE3KkTv9/IYdjBs1rEv7GnHNajz7q9+zfGnnkf5GhF/m4m7X8WzJ4YSsNEz20emPkjowz5CnwywYNkHhHakufXSJbj3RvH2qOSCAukSmT0XFLDnggKUEu1L84tfpZAJU8ZYParpHKEP+/A3Z4iNc0N7D3uucyJmVTS3lQtQ/7SOf7ODuWdtYNwve4jPqMCURNIVlqV0z41Xs8Z47i92g3YsLebo4+/io6evQfVIX3psjfHcCFnVt+6ksqGJbIWb6tUZFu1ZgO610XWuxsoVMzjlnAY6vxXGtzeOb2+c9MRivD0GC048m0SthJDKsmd+AZmwTGxKAX0zZPyb7cw9fwkDl2RGQny+TjGUpk6g/re9dNwq4u5XRwj8PtK37+/lDFxR0MGqzbcx7e4GWn4w6W88Ei1oc+IseOEHDE01Rl4XrEChoSOrqb7V+LvWtx/78bfi77Ga78d+/KchFotx//33c/nll3P77bfT2dn5v1rfvwXpFHMi9pjA1DWXs/3yRhYt/oCW7wk0XtyIc0BACekoIZ3+fj/x6XnqXrgUQYfiDyQGDrSxa0kjfTNkgrtUTBEGJzkIn97O0GSDB19diLs4jahDfJzO/JPP4ZeLnuTR3y2gc64TX6eOkFHoXGji7Tbxtot4NzmJTrUhaCaCZjLpoQZaLzBZft4cy7o1R6Xox20MTjMRNRPdLqD4BXZf4cSwiwiagWvApP2bZSyafTJn3raSsvdVvA9E0J0ihVuSmF4X/laFoy69GLXAib81S8lHMZSDRqMcNJrBA3z4mzOYbpcV9hLJIeSsBNn5r20CTSc2pQBTlmhZXESmxEZsnI3OeS5yIQHbcG4kvMHbnUcaSpOsdiIo2sjcUdvpRYiqiZy1OuP0jEzhTg1fm8Avly8kVSEj5ywi03TMb9GcApHDBIaOqiFZ46L8vQzeHpVx92QxZYmKtUlGL7sMb1ce9bMAuaDI4CGFRA+AqjsEHtl1BLY0LNqzgIFpNgam2cgujFP2Tow95y7F16Vx543nI+hgipC5cpjydw1iY2SWXzCHXAjKV1v9hI64SeRgp3UXvziNpMCeq+z4ftFL8WaDbNgk/HGW2tdSsCHAnutGY7rsmF4XptdFcvZouhpHI+omg1Nc2ONQvDkLgmXDS/9Kw9GbwJYyUQusu0Jl7+sMHOKj71AnzkHome2ic56H8LsDeHoUfB05ptzfwPgH40x98HLEH0a44sQVHHR7Awfd3oCkmoR2GfjbdBY+vo7oFBvm2DTeDijerOLdO4y7R8DdLVKy2kHPj+qpWKcQrxeQyrL0HeYhN0qhZ46fgVkadS9dQv7Hw9S+MEguJDJwiEnPkQJyxiSxoozEijJEDWwpHTlrkqoCxS/xyK4jUHzwyU8a8fYY7L3AhmvAoGgzOHoSnHXjCiKHuGn6pgM16KL0Pjtt3/Ax/qEkmseO1DtE8Ekvpe/GWPv4Y1bf6V11qB4B1SNQ9KaDyb9soHOejKgadB8b5OMnplH2eoR0WCb1m0r6ZgooPgl3v07omF6cQ5aNtnKtzncXrOK4FzcSGy9Qvi6NbSCF7gDHQJbw8lYAbFf14tkbY+75S9i2dgzb1o5BiAwSm1KAvV8muG2Y0KdW1UtsvJPM6EJ6jnCTKQVPr07VS1bNyeAkO7su95Epc2KPKXi6shh2meo/9uMaMOk/2EvvVQorZ9bib7VqaipWC+y8voSd15fg7cwhKiDEU3i3RXBFTa4o6GDHybf+ReLyz0Boj8bxp55H57EiriGraurwyz8mM7qQ4E6r3qfr5HI8j8ZovlrCFK25R+2eBNKPCjAdNuqfuxTTJlHQpCDcWoScM4hOseEc1nnvgUcQVRBUAUEV6D7KT9GHgzgiaQxZoPBJL742sCcMErUC+cPG4RxUmHZ3A53He3DENQwbiHmd6GGFePbEsGXAFlfQwgGazynhioIO0pUuRMWwUqan2PD0mRR/HGPcr3oZ96te4MsXlV9UyPYRKHPbXuZPu5nl775M8wVlJOrdfPzTRmInTCS41kVsSgG7ljTiHDIZnOjENWCy+5w6tLICpLxhWfrXKcyfdrO1BC8aIY9fJGgA5adsxxFJM/eoOxF1889IX+iNP3WzdjWORvGKNF1oo/93tQCU/NHB5actp/d7eZKNUP1YO9WPteNuTyJqJmI6T3KUQWJaGHVcJcHtKYKvfEbFOoWKP7SSKbHhWBMYsdd+lWL4xd/pW3diepxU32rgiKRHiPEX3x+AO2LdIBl7x32MOX0vu5Z8fS3LF1FzbYaalSquPuuS5IvpuevvWfqlYKF92Gft3Y/9+N/gbw3V2o//TPxfzXQ+/PDDjBo1CqfTyfTp03n33Xf/4nNffPFF5s2bR3FxMX6/n5kzZ7J69eo/e94LL7zAxIkTcTgcTJw4kZdeeulrt6O8vHwknba1tZWJEydy1113sXfvXh555BGmTJnCrl1/n5Pli/i3IJ0V7+hkalXKVstMu7uBl1+bhSianPfmRaTGaIx6WWfUyzqh9Q78mx34m0UMG6TLBDy9JlMeaECeFKf3cBs3nPt7Cpo1Is/VENgrUva+jmtFAFE1CW2TSNV4uLfhbIJ7rDuvrv486bFBfLusQJnyt+Nkyk0EHd545je88cxvsCfAt9FJvsjJ3T9dSs1zIq1PjkHQBfxtCsFtw9gTJhNuH0JOqwiqjq89S/EmFbUswGtLjiJVYWPoJzWYooCYyKIWOEmX25FyBnJaZXCSm3zYQ9cxDrqOcXDvjY1oPhtqsYeWM4LIzd3QFcE5qLFy8SwwTYKbB/nVf1tBEKG1bbiP76dqTRZb2iT18xzqfSlyhTZExeD+N5/G25kjM6oAQYdIQxbNY5G76h/tofpHe6h/1qD9DINPb2zEMSQwNENFUky6vqEx9RcNDJ2UoXydjiOhWxUI8Qz2uIoadNH9A5W2b/ioXKsjpxQ8PVC0cRhvt0p4g0n/DD/mxgByFiL/XYstDbY0ONYE2HWNmyOuvJTT7lqNPWUgahD+OM/gZ0VkCyXsCeic50PUIFcdoGueD29bisoVUbxdVhjHH35wDweN6sQtKTgHFUa9lCF6gIuuuV78R/ZT90KGljOCIwmrtrQ1J+tuT1L2egTDBq///rfWhZ9m4vmujGmX8fao9MxyULTVIFEjky63jtngXo1ciUHVmjTxyYXoDpFsiQPVBwOHFlL1eoK+1VWs6p+Et8ea4xw6Psv79z7Ckbd+wKPPLMAxDI4NHgJtCgMH2ogdECI1LYfqs24U1N+9C80lEv5Yw27XUANQ+J6dLdc28vMjf0/52wL5Z0pRQ26W/fAegttE5KRI9cVNOGImjphJvE7g1F+8juITKftAZbhepOQRK965/o0Lueb2Z6l6TaTnSCsMxHTYeO6G+Xh7DUo+tk68qSonVWuyoOtED3CQmVJOvkAkW+7l+NPPp/NYJ11HSySrrK5c3SZQ/l6Gok3QcZyXwu0qggFDM0rQPODtylO0ybKaG7KA50ceNCckaqzT2Us/nMfqbx2GWpsjNtGNWuRlw+1Lef7Fx8gcWEVmaiXq/WUMTS/izScfH7FigjWb5u2wPqPij+MMHOgm0KIg6ibmYXHqnovRfQwIhkn//DzhD9O0nPiodfPIJZOudJGtcKOEfXg7c5S90U/NxX1QU077AjdD451Iqol/p4x/p5WkXLxFhVyeoZllbLh9KXPPX8KCssuZ+PKt/7JzZt4voflsVL9uoLpFUuOD7JiukS6X6ZvlY9eSRkI7VKK31zLqIeg6UyUyTyH6smVBbTkjSN3kbjSPjUyJDTmeQ3WL+NsNXG3DzDvrQjQPjPpjllF/zOKeO0CuOsDe8wpoOw2Gxkkka8GWNih/P0frYoFMqQN/u06gycTRk0B1C7Sc5sAZM+g4uYiCJgXdJaP4bSOfl6c7h5RWiV6Ypmi7hqdPITq9gJ03F7Hz5iJGPW+MEKN9JOWr1L1Vm2/jqEsvRp4Ux5HQmfzhWVYnrQgD02FiYwOh9b24Bz5X3GwSmXKXFQxnmAxOtI+EQ7X8YBLB13b82WuIB0xEPGAiHbeKVn9pUvsSwTv03HtHgnmkQMCqnrIBkonih2yxndh4kWfvWIj9jQDig8VseeAAtjxwAF3HBUmVS6THBKl/Po9/Sz+GQ0J3ywihAmwJheWfrOKjp68hPsZkeHLgLx4bX9w/0tQJeB6OIsRTI8RvX3fnF+Ht1ph2dwN7bryazIV+Jn941l8/AP8Huk8sQ06qyNm/jUzOE8/4UgjTfuzHfuzHvwL/FzOdv//977nqqqu48cYb2bRpE7Nnz2bBggV0dHR85fPXrVvHvHnzWLFiBRs3buToo4/mxBNPZNOmP+VArF+/njPPPJNzzz2XLVu2cO655/LNb36Tjz766K9uS19fH7puXd/ecMMNjB8/nubmZl5//XWampqYPXs2N91009/1/r6IfwvS2XmygafVRrJSZPN1jZQc2ot/jQdPYYbqV+E7D/+B7zz8B5LHpElVmZiCFXbx2fcaMSSB9Vfcx2eHPYtSovHra09D8YpkysDdbxCvk5GzJlIeYkfkSNSI5EIyQ+NFCrcbxMZbATipWgPNKWDKVmz9uFP2Mv+Uc5l/yrmIGpS/FSMyw8bVP2tAdYkE9+Qo/cBE1AxS9X68nTmUygLSP0vTPzNIpsyJe1M7zWc4yZS7KNqcsOoBhlU6TgkTH20Fwjh396F5bBR/miA2xk7dsih1y6L817HfQPVIDE5xUf+rJnLTajHG1+De1I6QV8mMKcLwOPhFZB71j3aAx43r3gI0j4ziF3Df5EO9v4x8QCR7a5zvnH8FbSc56TpXI7whRSbhRAsrSApsf3oi25+eyDceeoOaZQLnt88h0GLg2W1j1DW7KCxOUv+NZhwbPPQfJBMbLZMPe+hcVISgG/TOcmJbG8DXBoMTZC5a9gr2hInmd9A7y47uEFB8oDvB16Ei5U08fQaePgNvt0bhuw5Ut8Crlx7DwJI06+9ZytBEB2XT+ijalGDtrfdR+Waa4k0q2SKZqtcT5EpctJ1RRGhHlmwxHL/qaoZ/XEXzvRNoPclBqsZFvgBEBeRfF6J5bbj7wCgNYZSGEFUD8+wo6VF+hg8qpmibxsxrL0PKGqTKJQZmFaP5HQiGSekGlcDl1smj4dSVlG7I0XuYdZOi+0gP/p1DDEyzcec9j1D9ehb3gEbryX4Kd2ioN4XpmynSN1Ok4lk7SzoP5417jqBibZqSc9rIlllqes3xbeh2MFWJwu2WnXTTQwfQd3aed5Y+SnUwRmi7wcc/bWTqvQ38+JlzOOSHn6D4BfIhOw0XfI98ATgH4dON9Sh+S323J+GR3y3CsIEtpWE4QMpqBPfolP/Rzn/ddQ6dJxi0nPYIT/98IdkKD/LlfRRc1kEuJGDKAv4lneQL7aTGBql4uRtThMKPBrGlNaRU3lJFRJNAi7Wcf/Vyeme5yYQFDDskam24+62T4ObrGskHbcTGC5g2CdeAQvfRfgq3q7gjJgNTZaScQXJMgHH3ZPF1qvQd6mTOdy7h6FuuJlcgIV/bh5QzCG4bZq+aQujuR+juxwwXUvSEh/BbvfQcEyRd7SVTCnJKxRFJI68LkKnxYY9JaA6RklUOpIzC8aefj2MwT2y8w6pv6koz+b5t2HsT7Lm4mKH5Y1m1+Tbqfr6d0jW92GMKhdutBdPEva0HZUIlwW3DLJy/mO45drRIPxeP++Arznb/HGSLBXSHCIaJK6qRLpUwjjqI4g+ihD9Ks+CEs7ClNZJVNlovhwmVfYTX2JGz1kxyzfI06v1lvPTsUmxpi4hN+f5WYmNFUvep3PybJ6h9cZDWk120nuzCc1+A2FgbZVP7mPDLFGXrs1StyRKdbKPvyjxF78nkCkR0u4CnT6Ht1CJsaZOSDdZ/3jUvDeBsHaJ9gQ17QqVvdpBjLryI7iPddM3zMa9mN11HSzjahjBkgeoXRKpfEFm7+noe2HUs8Cf17KvUvfnBi7DFVapvNXC3J9G2B6C9h4KWPFWva5R9qJCaEibwaQQANeTCOagg5TQQBVxRkzXLfsuaZb9lz41Xf2XdTesZBfQeXUD1rQZCOoeY07/0+EdPX/OnipWachSfRLpM4OoZa7AlwTmkkg8Z5EICjrhJx2k6fUdr9B2tWYnSHnj7kUeRMirZ0UUoPhnBMOk4s2okSGzeEXcgGH/dUvjF+VJ9604+3VhPemoZC8ouH9mP++Yu9z2v/yAbm69rZP60m+leFCYz4PnSOr/Oxlvxai9KoYPS9akvqZprjOeYe/6Sr9zG//k57g8X2o/92I9/Nv4vlM57772XJUuWcNFFFzFhwgTuv/9+qqqqaGz8asfI/fffz3XXXcchhxzCmDFjuPPOOxkzZgyvvvrql54zb948fvSjHzF+/Hh+9KMfMXfuXO6///6/ebs++ugjbrrpJtxuS2hwOBz8+Mc/5sMPP/y73t8X8W9BOkvW2vB2mfjbDOpeuoSez0pJ1oD0XoDIwTI3LjuHG5edw+7ZT1GxzuDOy59AzkDdy5cQHwMrM2EA3K0y+QKJG+54kqOO34ycNSldnyJeJxA9IYutzUm6yiAXEnH3gS1lENqeRvOAp1PEsIPv3j4KdiXZ2FRNPuggH3SguiFyeNBKdNVMEjUig5Od2NI6mVIHw3US9uYI2SI7yVfLCLQo9MzX0avCVL9uXdBFp1kl84OTLbJZ/G4/ctZgaHYV+ZCdzuP8FH+axvA4MDwOTK8L7xvbKfk4CX4rdEMpcECBHyXso/dwG8+/+BhN109g1/crUcoD5AMyombi7TIwRQFXT4a53/0A9clSskV2Qp9B0XInHfO9hNfYqV1mzW4pAVACsPy8OXSdr/Lu1nHYMgbpap3Pnp3IYFcBLS/VYxweRwkaqH6rksbbbdJxnJeCJgNJgXQ5lL+b5vHTFqG5BMyfDFK0VeepO39uXSi1mHQeL/HRnUtJVookK0Vm/mQDwxNMPvrZUgYOcJFJODnwvxoo3Jal/6My+mb5Ofzeq2le7KLrWJnIHMsOrQQkAs0mkUNcFG3TqHnZRPHLOIY1Ktfq5AtEal8axNNr0nWCwdB4O54+g1Stl1Stl4FpTjz3BVC8IoYskKiREXWT1rOheHOGfIHAwIFu+g90EJ1iI/5QNclRJo0vLkDzyFS9kSO4Q6TswxyJiSGcg/D92y+j6ygX8TqLkP5/7L13mBX1/fb/mnJ627N7tu+yy7L0DoqACqIiTWPFEGvUqKzGgpE0YonGRMVAbFmwdzHYlSaoCEgTlL4Ly7K9l9P7lN8fIyTEFM2TfH/Pky/3dc21u3PO+Zw5szNz5v257/d9r3/qabqH23AfEXAfEWg6W2RbSwlbFi6h7nwHmZYYfdakkWNp0nfloosC1gYTFr9Kx0QNOamj1znou/JHVNXnk7yml8cDfYiPjeE5onOT7zOkFDiPhOgeaSHzoIIpAs56kczqFJnVKTJqFdDAFNUJ97GSs1NBjqRIuUQ0GdJOKHtDZ8Z5lxPLE7j4oTXEXygg+et8zEGdSKGZmpoCegdL2FtiRIflEio24ml6h9iovsPOyXdVkPe5EQnhbErw2K4zueGHK8jenSSVo+AfohEqkfDu7uWUX85F0MEchES2DcUqEc/VufOJV1CsAkWfxmiZZCbpEQxmWtExRSHhFck4nDCk47fa6DjJzOpd93HeS/NJDe1DaqjhILt+6dNEh2QfMzIqW+6ne5SDPs80oNjA2hHHFIZAf5HuUVBzZQahvjbDOXV/nO5RcPgHLt7fdBK943KQ4gLevQFG37yY5Lj+tJybj6mpB+uBVqwHWo38VFnG3OQ/xpTJQ4M03D+RNROPd+z8d8JbnaZpqsgHTz+BlFDJ/jJCy+lWesf6GPbkPhI5dnqG2bD1qGSutlGzsS9SSidcAjmfthLPs9I4TeD8a26m+QKV0AAPTXNyKPo0RnOTj3tvvI5I/wzKXw9T/nqYxmkmzGEd8+8y6fNMA3U36USKrXhrFNzLXGRv6SazKkbb6TrBUgul7/mJFgjEcgWcB/10TM6m4WEb/V8N0zHOTnCARtwn0+eDHvK3JNgXyOerOX8gWZrJjl9XYu2IY+2IM+n8hYay42/gaNEiDO3Pav8zaBajxzKdYcjf0yPLkIMpIoUmzL0JxKRGsk8mjvYUpt44ik0i0seBqStiKCzyDiHmHQL4mzEt/R4/TO4fNuP4YzeYZNa8/RKnXLnouGKp3xOHj/196W9Wk7sjxfs3nYW7MU2oxEzRJzqPzatky8IlOA5Y6Lsc+i4Hd12crCqFM268nlihnWSGROqGXoY+uo+8rXEe9ZeSzHUQ6mvDVffPb4iO9lRKIwaTUS2SdEu0zDk+e3OttpyG+ycyVZyNb59yjN3cs2ieweL/jX3996DkemiYrSLG08ftj+mj7iZYav5WsTcnzIVO4ARO4N+N/zTTmUql2LlzJ+ecc85x68855xw2b/52E8+aphEOh8nMzDy2bsuWLd8Yc9q0ad9qTEEwtj+ZTJKbm3vcY7m5uXR1dX2r7fpb+K8oOsNFAlsfXEK0QKRoHdhbBFI5CrGTY5gi8PhlT/P4ZU8DYOlNcc/Cawj108CmIqbh4ZpzmNs8AcVhmEXc+7tr2PDhKD6rfIr2CU5cjTq+D230fSeIpVsksypB4Kw4jjubqT/Xia1LxxKA6JQIh9/oT/tEN54vLchxFTmuGjJUATQZpBTkfJkkcHKSUb/5inCxSNFaP3qGk47xhrujuTdB+fNpYsV2xJSGnNDwHjRiCvLWd5O9O03nJONA8O4N0DNUIv/zuNHflFAQEwq6SaL1RyPQzBKa20bXCDOaWaB7Qvax/TZx8TyiBWbsLSKRIguO5jiWjii9g0WkUBzFaWb1ixPJqA6jWgScLWnCfQRyt6VJegRaTzORtgvsu6WSfbdUIiZSlDwjUf5KGsUiMuDlOFICCtcIpJ2wb/xr6FaNtEMn3FcnkSWACIKq0zs+RdYBjY5THBy8zoMuQeS5IjY8+RQV196KxW/su77vppi07wKeu+1RnrvtUf60+RRunbmSyRU3ICpgq7EQ7K9Re7kJRysUXFxPrECn7zspPDUCR857mmj/DIJ9RVQLqGaQEhqWngSRQpnGaxV6ro0SzYf2SVnGjopJREp0Ws7RjrGOjjYNOaogpXQ6pqZIuaD7wjj9n0rTPMVOeGTSKNQGpdl1x5MkPSKH5ywBIJEh0XiTiiWgoVokTDGNRBak3Aazl3KBKQzly+aS8MH6uxaz/q7FiNlJXG+5Of3HN+A+Aj035qPYRJJeC3XnW+mZmMbVoNN6momcbSKfL1qKtRvqZj5D4QcmAlVZhjRvvx0ppfOD387HtytCx6leRn5/PwCe2iS+fSm6RprpGmlGNRnyz54ZcVQLBMplUlk2RAXSdgFXk4aUUFFcZi68dCN5cpAtC5egmUUy90a4/OcrGbg0Ssqj03GKi5bLU5iiOrZunWB/Hd8GM852hc6L48diRDI/svLGPTNovznBpeO2Q2aK7C/jdI/LNF7bFsPRppPIklHsIoWfqUy2BrB3KTRPsVO0PknsXCO+wXKwFUtAR0pBPMeMb28CIaWi2GHIgsWULffTM9xGz3AbM8on8Ki/lPVLn0axgaMxgmo342pW2PrqKIo+DpPItWHv1IkPSSAlBGp/cgdpp0DbeDOmQILyZWEsZSFs7SKKVcAc+LNDr7W2m10/q6R9ZjGxEYXERhQiKjoNi5zoDiuaRcJc30XJ/Bjeah3/ud8+1uK7oukKhZztAlPumUftbBMd45xGBFBAZdNj40hkybiaFRSbSLBcwFutYwkoFGxKozttuPZ3U74siaUjSr8XNIJlIrHBOWiygLXehBxJY2+KIaRVhLRK/5cCWP0aiLDxvVH4PrRhCanEcmSSGSJVP/dgavZT+AnoMkx7fSv5mxO4mjSEcBRvdQL7B24Cg1zk7IjRZ7WKvTONf2Qm7eOsKI/kMePWWwmUWZg1biZSMI4UjJN0S9DQelzu419D232AqeJs5LU7EKIJNItEwSd+IsVWFI8Z1QrCoQbsO+uQwymkuEqsxEXbRBOmiEoqx5jUmzLtIaZMe4jpo+4mc6vhNv6XUlGlo5PkeeOoe7E/vWN9jL2/Au/ewHHyXz03i7XacoSOHp59ahbJDJmGGRbiPhl3Q4qOkyQWHL6AU34xl8iANJpZQDMLhPrasM9vZtBd+5BjGl2jRByLPMRVM223p1jzg/F0jjazZeEScnZE/unx8ZdsY97KJi5bsBJlcvDYdh79XEdzSC9+aA03Dtp07HXZX8b5Lugabafvq8ZE0V8Wj433iuz4deVxva5/vV9PMJwncAIn8J+C/i+wnEeLzlAodNySTCa/MX53dzeqqv7N4q69vf1bbePvf/97otEol1566bF17e3t//KYZ511FmPGjCEUCnHo0KHjHmtsbMTn832r7fpb+K8oOjOrFR4P9OGrn1fSclHacNzMjlIz+UUyzmnjprd+xE1v/YjyT39IqK8VU0Qne4eAbEvj260RT5rY8toops3YwWULViIqOolCY+Y2lq8TKRaI5QgcmmchkasSKrFQsMyEfquHM6btIpEp4KlL0udxCdUC8RyIZ4NmFtHMItm7FEIDNNJTgjibEqTdMmKvmR2/PQlXk8aHq1/j4A2ZZO6FULmKZpaQ/TE6x0jI4TSJDIlwiRHV0DrVR6RAJntrN46WONU3u3DX67TdnsKzt5vIIwkijyTQJYGCT/yGmVFaJZUBjho/vUOh9lLD3t+3L433yx6KV/sZedtuktkW4sUuEvkqaZ+TZKaJ3K3RY0Y49y59ltMu3IU5mMLToOBshKRXOJZzmMx3oYsCqlXC6k8TLHfg2xNBTuoUbEpSvmwuroMypqhAyao0UhLEJCg2kSPTnkVQdcJ9NWpnLyWZAXfc8zrDHqugYZoZxQ7KZT10jrZiu8fNjb+7jRt/dxuCIvD0s7OIFEh89qvFuOt1MGusmW7I1JrfLcXSK6A4ZRKZMOjZCvz9ZazdEOor4K3RaL4+TfNZLjQTlC4V8b7oxBIA/5g09s40WbtEcr7Qyf9EJHMfZO6D9olw5EIbabuA9YiFnK/SlOd2ocsi9g4oeUOke7SO90sT502+hKy9EU7/8Q3oA6LEswX6VErYug3jqobvGVJvi18n9wuF7N0KhZ+EKfpEpXBDglk33cKsm26h6GWZjvE6tz74BoksmLFsM82zFURFp+ayJRS/J9A7LUHarSGldMY8UIFig2GPV9B+cZKMagHntc3k7EijWASE73UTLHdg9WtUPT0E1SJyZI6EoOhkHlTIPKjQMc6IiCl60UT3pBTeg2m6RprRJYjlCihWgcgvw7RMtrLhnomUmbuYXHEDnzz/DC1nuli8cRoIArnbday9On2fBHNYwzqnnYKNhlKg4ySZ0ieEYy7Tmmxkvfqed7DjzrHkrLYQ6mtFSkLHRQlci9rx7g0QvjiEKaoR90lMeGwezgPd2NtBtUoo+z1Y/NAxqy/OliTxbIHeQYacFEBx6LwzdyGq3YyYMmTUPXNG8mHFGcycPgdHm4YYjCG39eKo6qbgUz+Kw4wpmKZ7tE7OagvF6xKccuUiYjlQ+nY3rVO8JLJtZD9tp+jjsBEVUqti2V5D5v44CAJnXX0d9g4V++Fe7Id7MQWT9LlXI5nrwFrvJ1WWQ+0P89n28h3/USOh/HcsKFYBOaGTu0XEFNEZ98NddA2X6TojhbVHQY6p+AcZBad/kMDJD+8g5ZZI5jpI5btJZllIZ1gJllno834XtqYw9XMhb3saKZpCtctES11ES10Gy9ZHQoqrhhnWkRjokLU3QtoB5iazUXyLAtYejVXXnk7d+RYC5SKNlxm97PFsgXAfASmuYO2Mo9glXI0JvDUqiUwZU9RwYI4NL0CXRXRZxLs3cJzU9a+NhAAE2YQ0YjDSiMFEh2TTMsmM6jSueZZmo9DSB5SQHlRM/fkuOsfaCfST6fdKJ6pVRIorxHzGtVoOp43nO6zf2OcNv5mIY3s9iRlB/IMEzCH9uOLuqJEVGAXqrp9WYu1Nk7dFI6M6TO8gC6ICbd0ZWIIasiONJhvnS9cYCP+hmC+eHwUCFH6WxuxP8MmnI3G872bl6mVE+xi9qvXnOv/p8XG0qFP3VDF91Z5j5lbTR919nLw2dPkE1mrLWbxxGk++NYvpo+7mlCsXYQokvsPRCP1+UEPo9tAxA6Kj71940X6mX3AFtT8+nmU9Mtt73N8nCs8TOIET+E9AB3T9Oy5fv7a4uBiPx3Ns+d3vfvd33+cou3jsfXX9G+v+Fl5//XXuvfde3njjDXJycv6Px7znnnu4+OKLOf/887nzzjuPSWuP4oMPPuD000//p9v19/BfUXTGcmWeeWYW438+F3SQzuolHrVw0q8r6N6Yj7NJwNkkkJsVMhjRPIHgrCjpiJnWc9PcOGgTlqDOis/GcktGI52nK/i2SfCFB3etQMHGBKYoZK+1YPZLBMsF2i9PcvjyDNZtHGmYS2jQMslGzldJPLU6YhraJphpm2BG0A2mM98TQrXJpOf2IEcFYtkiw+bvofyduRSv0+idluDIRU/RMNMBkkS/VzqRwnESmQKaDKGBHrw1Co5OleoKL7okUvYnlYzqMAWLzXRN/DOLmci10X66l47xDjSzTL+XOuiYnE3f9xNk7pXYcc1iTOE0iIbrZ+1PByHHVNI2kYJPBRSbhLUrSTzfatworqvldCvU/nQQTWc7sHQniOVB7vYY1oCKNaBSd75EyyQznaPNqCaR7lkJkpkWpISKf5CFw3OWIKbBd2ob7ePM+IdoBmOVaZwEclzDVSdSvmwuggp5cpCsAwrFHyt89YtKQhEbmdVpjtwqYAloWAIaOdsFRBUEBb533Y+x9ipkb5aYvXg+mgwzrt5M0qsjxVSSI2LYOsHepbPj15VUX1fJxseXcvD0l0CH4FAFc3uYzrESpjC495tonGaie3wa/yCRpFukZ7ROz2idYaPq6fu+IddM9E1RP0endmMpzWfa6B6fJuWWsHaLmKI6f1j3EqvfeRlTVENpt5GYGOHItdAx1kLLGTLOGpkxD1SQyBToGi0T90k0/1JDlwQ6x1jp+WGUnh9GQRTw1Ig8ceel5G9J8OKjs7DUWNFMAsMer6D1dImc9y0UfaJz+s+2Gjf1BzRKptVTsMyEYofY04U8suSPKHaB5Cc+tixcQqiPiKc2yUm/3IF3l0ygv4X1S59m/dKnUW0abaeDpTeB2Gum6/oYUhJc9QlcTTpJj4BpSRalb/XQNFPnR7+/DVNEYeQjFbgbNHxbJXpGurC3JbB1KzROs5Nyi6jP5hLNkYjnWknkqKS8ZlwN4GowmO+UGzRZIJlhwnM4ijWg4R8kUFopELqzwOit/NhjONh2qeRtjaNmObH1qgRLZPK3Ktg7dNyNaWovMZFZlTZibYqtxAZkYW8RmLZ6Hi0/Vch99xC57x7i8p+sRnHICIEw7sMRgqNz0T1Ouk7PRWjpJOmVDTbzjQRdY0GKp3E1xOn7bB3JAjdZB1LYGoIkMmWkUILLFqzEtaeD1f5nMDd00zs+D1MohTmskir0kCr0UPpELaksu+Hq2tyKuaqZsuV+po+6m/FX/HNJ4b8K17oqnC1pMj+tJ5kh4NsZoPbOgXhrNPovUbAf6kKTRaQEuGsNlcXeS/piDql0VMRZu+wFzMG0kVt6IIbmsBAa4MG200baKRErcYEgEC6WCRfLWDqi5G438lFbzjEUDbbWKIkcK4Xr/BSvS5Cod7PpsaWkXAKCopG9E2IDUxR+EiZaYCZvaxxLAA5d5aR1spvPKp9CTKnoAkTzBHTBiDgyB9MI7T3GEvz7rN7RAlRX0gYbfaSJxnON6BO5N4olpFJ/SQ7OVhXFaSaVYaLvuyHsnRqaBFW3ZKFaBJqmOkhkQsMsBw2zHAjBCP7hGccKtGOs4K82U/vjcnIrbRRuSLFl4ZLjiqXes/sdcwYPXT6BaRddhRRVsPSmOPwDFzlfhDGFwLzfhn+ARN6bVuKZEvFMieJ1Crok4GpSOPnhHQT7mTj8fRdZe2Dbb42IKWxGq0bRJ9+cbf97kEYMZtWcicycPofSpxce18v5l9EwfT6Ewg0pVu+6D3tn+ljR/m1R92J/HEsyGPerucCfmVZpxGAeX77kmHHUURxaMO+4/+NROfAJnMAJnMC/E/8nOZ1NTU0Eg8Fjyy9+8YtvjO/z+ZAk6RsMZGdn5zeYyr/GG2+8wXXXXcef/vQnzj777OMey8vL+5fGvOeee45bpk2bdtzjCxcu5PXXX/+HY/wj/FcUnboEKTd0j4Las59HWJmJHjEROTtC2vXnrLvoamNnD7ywBlWRsLSZyP7UzMuPzCQwQEDLSjHsiQrqZj5D3CcQK1UovqKW7hFW5IRONE9AM+v49mj43raRcRCcTQLBS8M03KDi26cSKjETLhHQhkVI9E2R6Juie6iMKT9GwxfFdA83ob2eja0DgoM0vnpyJCUrdQQNct+2MOjZCmxdBou14tO3aJ/s46tfVBrZjF0pbEcCpO0i5W+kOHILRPPMxAvsJO4KIOggPp6N+Hg2ugAZh9MksqDlTBd1c3K5+rYVdI2yIaZ0zr/mZsSEMSsvaDqm7qjhjKvoOBtj2FojtJ9iJ54p0TzFTmJEHybMn4tik8is1pACMdJuHUE13Cpt9QEGL+qg6JMkhZ+EMQdT5L5jQY4q1M8yJIr9P7saV7OK/+N8Mmo1BE1g2++WkPJA+etzEVSdgZccYvS4w2gyzL93LqESmbRDZNyv5qKpIhf9/iMy3DGSGSLJDJH2KQppBySyoeEKFd+CeoLlAtFCHVeTyta7xqE6NORoCjVmIrM6Re/MGKMermD4HyoYssSIIxEVyN0gES33krVfQ7q4C3unjhwRyN4ik7stTdoJjnoRR71I68tlHL7CRN6WKEemPcvlo7dRsCmNvRV8W01oskD+5ynMYY1+JjszZv2A1tNMOOtFfH+y4/rS6Buztwnk7Ewy9IoDWP06hesTSEkwfepB0HRUC4ifexA/99B0pkTu1jDPPbEYManSO0zD4ofu4SZSGTq+XeCqi9EyReDB3N2YojpSUqPpw1JUs4igQCxH5Naf3YL3UBJRhZN+XUHBphjDFu1h41Pj0EUInxVl8tzrmTz3enSnSv4mOHSdjYINGu+MfYpkBtReLeMfJOBuUvCXS/SMzWTAcwlcTSr155kQvjb5FFXIvaoeuTdK4b01FGxKEyk0LsimmI5iE8k4INE7QEaXjHPZEtRwNep0j5CQUhrhUjvNZwqoNh3dJOIfbEfQDHbSfShINEdi7bIXaD3NiaAZJmENFxkZj/U/0Bn0VIh1zz8FQMcklYYZAgMvrCF3g0ThIyY6LhhAxwUDqHx7BqKi0zinD6lMK+5DQR794BmkFOiFObh3d+IfnoFc20r5sghSZ4i0x0zbhX2xdERZ8syjxMsyyFxXS8v0bFZcMxnCEaZ7f0TLhcXGufZVNZaOKGmXTNolU7NgCP5BFqpvdiHm+ECWmbFsM0IwQu/g/1zAdPy0gSSyZNQCH5aATtprw9waRI6pSOE4XWfkk3JL5G1PEepnJ+dLlY4z8zEFUwDMOvMS2m9LYhoW5PDNErosYg6rFK3uRhfB1hqjZZKVvM96yPusB80sk/RaENIqtkaZzKo0CEbfeMepXkzdEQZWdjJz6mzc9YZLrbVXYdCiKIcvc+JsSdJ2qhH/1PfdNL69KaZfeCVptxmLXwFAs4gUbkghtwdouXIgLVcOJFXyTRnQX0sze1cM4NM1P0Nw2Km/fj7Thy6gZXo2G96bT5/VIRyNURSnjK01RmCQC1ddFEsQSlbptJ2jkvRp9Hm77VhOZ2JALpnbO4+9x7ECyuOhcEPKMKVau4MJ8+cet13evQEKXz987PfeoQ6ixTYaZlmpuWwJ7RNdFH3QTt72FLYuHWtXkozDCTIOJ2ibaCLtEOi+NsoXPz0J76EUvt0QKTTY9USWjBCRKKnowlLzj+VVU8XZx0WWHJnt5chsL/XXz/9GTudRqCYBS0eUU65chHVPIw0z7H9r6L8LZ7tCx1iZ7b9Zctz61bvuY9rqeaxcvey49X/tcHu0GD6BEziBE/i/BW63+7jFYvnmZJzZbGbs2LGsXbv2uPVr165l4sS/7UcABsP5wx/+kNdee41Zs2Z94/EJEyZ8Y8yPPvroH475P4H/iqLz3Os24Gg1+jFP+vJSzCEdXdaoPvVlij9WuPD69Vx4/XpCA1UGvFjBzoOlaF0WbJ3QMwJ6zzKkQM59FgQFRj5Sgb1Dp+R9OPhxP5ytGpECgVEXHkC16nSOFYn7ROI+AUe7Rs5SOzWTX6TjJInuSSl0wdCB53xqIudTE64mneEFbThaIeOcNhKZAuEJcTT5awOc4TK2hiCt56bp+06QUD+ND687gwnz52Lv0uj/ylz8QyBSaKFroo9wsUi4jxXHNjvtUxQG3bWP2Fv5dJ1kxB+k7SL2lhjdw03suGYxp16yi9wvFd6feyaqGQQNGmaYCJe5iJdm4NrdQazUgyYJcFMntbMdhPt7KFwfQlR1hLFBQn3MdI+CaJ6MahYgEMK3C7rGOGk/w0f7GT6CY/OQkipSJIEUTeHZ24MuiQga9FwbxfOJHXNIIdI/ja0rjZaR5tR5N5IYmCD7Swj2M/Ob4vdoriwn1kfFPytGqFyjZZpO7NwQpiobLy2ahfmlTFJOSDkh71MZ314Fe7uOZ4uV5spy8j9PU7I6RdwnYa8PYu2QaJjpQu6WaTrLTNYHduSoUeDYO4wiZc8dlYT7CHScJFN2WzWdnW46zk5z32Wv4alNEi4xoYtg9etY/TrhcyK4qmU0i8Spd9zImkdPI+WWiBVAxiGDna6/ytj28o9+RNvkDNJuDatfR5cg+6sE0UKjdzNYZuGLdUPI3B2ka7SVznE65pBOPEtCTEEiRyeRo2PtFqg/z8VFi+ZTd4Edd62IJahzwWUbOXTlEsIXh2ib6MRVK3LKL+ZiDmskPRL2Th3VIhAYqeBuULG3Jan9gYigQu8wjVi+lU1/HMdJ1+0iMETFud5BzxCZniEyuZ/IdI8QKV4pECiXuOqXd9JnTZiszSYOXPckgX4yRSu76ZqocugGC6aoSv5GMId07B0pFItA5/Ol9I710TG/FCmu4K7XCZaJRp+YVcASMPaJs1XB2apgimqkXAL5m41zSVTAfVjEUyPQfnMCKWXkNVoDGqlsO6EygX4fX4OjXUeTDEOqkjeNjMcBlSk6TvUyrep79H03xKDHArhrJMK355G5qZmWyXZ6T1LoPUmh+rpKgqWGwRWA0B3ghrm342hPUXNVBphkMlcfIjWwkLTHQut5hYgpDe+hFKRVrvrlnUZRJklk70oQLHeA24UaDFL4ZgPbXr4DwWZDiCaQEhpSQqNxmoncz/30+RAjFzeVYtWciayqW0TZI/v/Y9fMvF/WYu9Mc/gyJ969AcS0RmhENtbOOLGyDHxbeo5phLx7A9jaEuRs89Mz0kGfBWlqL8+m+B4N55tuXDuttPxcxRROo9ks2NsStE90U/qeH91iMhaTSMMcndBAD9sqFmP2JxDSKvECO3nrOkjluEj2yaDzNB+pDBnFYcK+sx7/8Ay8+wXEpEqfD3pQ3BYUp4SlLYxmlhCTKoKuU/xmE4F+EtaaLnon5B9zB44U/1nmepSh+2t31MxZRs9K79n9GPqzxQi9AQo+8TPk3XtpONdN92g3XddFSWTbKKo4TPtEF842BVMwRemfoN/yBKGROcd6HWM5JlYdfPAb2ZJqMIh1TyOtU7xITidbFi45zil29a770HOzjv2e/YWffndUoeSmmDB/LoksaP5eHpaOKN6DcXqH2hCTKmJSxVuto36/h4zXXQRLzXSNMJNRFWLv7ZXUzRGw9iqUvZ2i4dpyVmxf+Q+Pjb8uKL3VxoFw1hm/Za22/LiezqM/3TtbWb3rPvyDBGp/XI615zsdjqxf+jSJwjRTq849btxHq89mzfTF34hg+ct9e3T//bcbCZ0oqk/gBP7n8T8RmXLHHXfwzDPP8Nxzz1FVVcW8efNobGxk7lxjYvIXv/gFV1111bHnv/7661x11VX8/ve/Z/z48bS3t9Pe3k4wGDz2nNtuu42PPvqIhx56iOrqah566CHWrVvH7bff/m/ZL/8q/iuKzrVtg3C0K/h2wbz+6wAw9co8HuiD9NN23n/iDN5/4gyuPXUDYnmEkf0bqZ29FOXMII4mAfuXNjKrjJ6zs7+/HYtfx96tksiQSJSmUH/UjajA518OxFknUrg+jSmio9oAHRS7yICXKki7NVy7LMhxEA45mHDbF0y47QtEVWf3xv54z2sh/WIeriYN5xc25p25mh+3jiN/c5yaX9rxbTDTOtlDRrVIKsOMJkPbaWAJCLhrBUbetpue0SreGgVnS5JYvo7JmWLfwhHYulTOGbcXxSag2AQ6TnEjJeGsBbfjT9npGSyjm0REFabcvoWydxIkM0TMPUY+IIJhLhJclY8UF0DXOXiNA1uPiv1DN45OBUuvwPYHluCpiRA6rQxR0cn+MkLqrCCps4J4dnUR6muj9vJsWs/0EhyexbpXn6PoYwV9u4dIH4OZzdkos+7l58j4wkygv0h2VpjLFqzEcyTFedsqiBQK9H1bRdrvILt/D33f1Lhx0Cbyt6bQDYNJIgMUIgMUNBkSXgnNZPSnxXIErJ0xkhkmvrivEseSXgQFsie2YWsTqL7qSSJFAr3j0+y7pZJYHqgWmDB/LprZKAJ3rh6Ce5cFyarwp86TWfv681iCGrFCHTn59fKVE1MUan9oMIgI0DpdJeHTiOdaCA5WkS0qR857GvcuC9nnNSElBKbctsUw4XHJCCooDuO11ddVIi4OYOvSWfa9J7D6VdAhMiSNrV3A1i4gx8HeBmkX5G7XieVByimw/r6JlL11I+Z1HpytGmkH+AcLdI2W8ByO0jtEwBTTyP9EpGeIRMprxlljQrVA5j6R0jurCZwdY0nRFsqXpZDjhlFOfEiCzmlJMk/qJJojockgXNFFIseGYodT599EcFiayEAv1laZ80d9hZRQSTkFPEeSBOZHUBwQLBdIegTqz7VTe42E6ep2Tj5/H5XLZuGpjdM9GtwNGnJMRY6pNE6TsXdqZN7TgJjSUawCGUcUkh5wWlP4BwmYftNJ0iOS9MgUT2wiZ7UFZ1MC944WSj80JJVtt6eY9fxnuBsV5J96iBUZsvVdP6uk/nwX7TOL6bPCT8n7UPI+nHX1dZhiOhnVYT5+8VnaLuxLIlM23mOtQqLYQ+S0chLZFoKlZuI5EMsxEepjRnNbcdfF0UwSqf75yKGvJYyiyFptOb2T+1D69EKEDDfEE4T6mAn1MdP/2R40q0wkTyZW4kLPzyaZ62BG3zv+o0ZCrQ+XY+mIMvCxVmIlLpJZZqS0kYdrrwuiemx4dnfROM2Ef3gGDbMMMyR7h0rjBT76vdAGGHLWrP0ptB0eUm4T4X4O5J4Y1l6d9tO9xAvsxAvsHL5JovADY8Jq+rzbOHSVnXSGFUd1D7EBWXSOsWCp78Vdn8bfX+KT558hObwP3r0BcjZ1ksi20HifTNpjxtKdRPHaMXWGjbzL3jiJAbnsvrOSdHEmroY4LZPMtEwy4x9kfPkfZef+VmEiyCZOuXIRlpBKwecxlL75HJntpaSiCzkC2dv9+J53EOhvov75/oT7arSNl4nnWLhw8UeYuiKGoVeu45hp1N/LmlQ6Osk6kCI2ZQgzp8/hlCsXIecafTinXLnoGGM3fdTd+IdnUPv7wUjdZnJuqMNbrePbm2Ll6mXU3CChi4AogCgQzRfIusdiTAgKsOcnlXSd7DEmLmtNXPDIOhJZJkre7OCcS6/+1seJNGIwmasPUbIiStNUm3HsnOml9Uyjp/JoVmbtjwwm/9CCeRSvjWPr0v/2gH8Hp//4BkreE1g7+EPgz0Xlh0O8/LLpAlxvuf/ua//bi82j+N/yOU/gBP5vwv9EZMr3v/99/vCHP3DfffcxatQoNmzYwMqVKykpKQGgra3tuMzOpUuXoigKN998M/n5+ceW22677dhzJk6cyLJly3j++ecZMWIEL7zwAm+88QannHLKv2fH/Iv4ryg6LX/00j5eJjnHz3NNp/Lw/UtQ7DpPvTCL+i+L6B2t0jtaRUOg+tSXaV9aBoDblsAzs42EDxSLgCmms/GpcfQOh4bZKim3wJFpz7J55FtkHlRw1UqIGgz9zR6SXqPP8smHHsUcUMjaq+NoEsma1YIchVSOwpZHT2bLoycTKRDR+sRp2lVA5zidaK5IqFzlSDybdavGcvgqmaKXDGlR1swWkl5om2gi4RUofyOBOQDhUp1dT4zE2ikRy5ZY+9rziCkBi0VBNQuo1/dQ9Zth+LZ249vajZQ0ckatfpUvN/dn4AU1BPsa1P6m346n42QbzuY0oX52Mq9vwN4QRguZyd6TJPsrFXNYpd/yNPaGMBk1BhNcsDHG1B9cw+p3X8G9tRFXfQwxniZnqZ2cpXZaZuXiaojjbISMWoVNjy5l5lmz6RplonB9lH6P15DIFEh6BaZedg3nXL+Zkvf9JD7K5oXHZ9E4zYS024k+PkjaKYEA+hs+gqVmHlsxk7RDwtGuEs0X8W2V8G2VCJcKZO4J4r20CXl2F1ISQv1d2DqTnHrHjXQ8UkbethTa07mkPTDunpvx1Gr0e0ll8FMVHLixEjEF0Xzj/xkZnuTAjYaZhdBgZ1djEcOeqOCXv3kRzyGB4XfsYfgde7D2gG9XGEeVmUiREZuy/Kw/4vtKwBxSceRHGFHYyqSbb0BMQ89bxWTtMfZ956VxOsfK7L+5kpQL4jODjH6wgnDKQue0JBUP3krLGRL2ToXCFSLmMJjDEOmjY47oONqMYtPeDu4mhbZTRRZOfw05odM1RsDaA/mbFbL2aQQGOsjbqhLsK9F5ssA7cxdiDqTJ3pXG0aZj7dH4Yt0QhEYbY35TweErTDjaFHLWWMhZY2H3mZXIkoq9W8XVpCM+56P9FBkpAeaIhiBrjL1rJ77dKu/vH0GwzIq9W0WXBHoPZ2IO6uRtV3C2qRR+liZ3nYneiJ1tK4YhGkpNVJeKozWJuSuGuStG3/eTZFQ0squxCPuhLrrGGo6/xR90Y1nipXBDiugjRbgbUrgP9CLelYV/kEA8x4JS5CORY0WOqWS+7GTpy7PQRWic7kZKGMzo1Dk/pPTDCI42IycxkSGRyJCwHurAFNUQG9uZVjASQTMyKVu+pyJoOi2TzAiajhxVydnmp2xZN3JcI2ddE6pFQlA1zD0xgv0saGaZeLbAis/ePnad6vumxootH1J1TzFf3F/JF/dXIug6YkLBU58iXCSjWWWDKatb9A3Hzn8nPqt8Cs0sExqTh2oWiWVLCGkd/1CV1rOzSGVZ0C0yA5Z2opohf6uCf3gG9tYY2V+l6R2fx4xlmylan0RMquTsUugeYUJUQPXYyKgO4z2UIuaTiPkkit404WiI4v2yB/8AkcGLOzEFEiSLMrC2RfEeVumanEfaKZG3LcEZN15vtCr095DOcZHwGrNNclRBbupGtUqkcl1EytwIsSSqVWTUwxUE+1lRbTJlr3VT9lo3/Z44fOwz/3XsxlHmU1fSuF/dQtItsebNF2k+y4G7TkdPJJl6+TY6J3gxhRSiRTrxWUH6vxolb7uCHNd462dGv0vmzm7qLhGpu8T4Sj1aNP1l8Rm6YgLSiMFokkDPYENGve3lO+g9u99x27VWW87qXfcZkyiHgtjaBfx/KOHh+5fQcbKZCfPn4tptIZYHgf42Av1tWPyw+v1XDDOuYRrDtl5GIhM6x+kkvV9HNdlEdJuZ2hu//de+uqeKxJi+rN38q2PrCl8/fEwGfLQQqr6ukinTHjrGxh3Nbv228F8RofFijekXXnnc+ob7J3Ka9zBbFi75xmuO5oaewAmcwAn8p/CdTYS+Xr4rbrrpJurr60kmk+zcuZNJkyYde+yFF15g/fr1x/5ev349uq5/Y3nhhReOG/OSSy6hurqaVCpFVVUVF1100b+4F/59+K8oOgP9TKRz08QSZj4e8gFXf/ojzLkxxDSoVp2HznqDh856g9ffOYOTvryUWT9bz6BnK+j6KpfWzgxOnlyFLoN+dTcX3PwpYkqApETmAaPYGvZ4BY6fNOOuVwkMT7Pq89GEBis4m+CHf5jH+U+uQ0rq9PteLc07C7n2phWYvQli2QKxbIGUB/oXdJIxpAdbcZhIMZj9Er/K3YA5YEhEI0UmxvymAu3RXJKZOgUbUwhAItuCb28cZ4Mx1tBzagj1Fei3/EaS+WmEzz1E8wS6/C5yflpH07nZNJ2bTWCQzsbFlYSLZWquWMKhN/uTtTeCYoNovkg8V8fSmzD6YDeW0n1SBv2WpQgXmUlkSjROMzFs0R7aT/MS6G8l6TZcTZNeE8O2XkbxOwF0SaT6ZhfWtgjWtogRGzLSju+rMI79nZx19XUIsTj5mxPECmykBxbhPZQmf2MIOZTkrY8nEHgojfeg0Vta9nYcXQZtt4dIgUSyX8IIi+9QyNmpY+tMYm+OIiXBXZ/CXZ9CsULvCA+dK4rRXs/G2aaS9IgIG7/CWR8HATSTgKDqRlHZBxJZItzdhatRp/xPNxrOvm06mllH6jYz/udzEVOQcUjnrPKDZFapPPyTq4ieGWHXEyPZ9cRIYnlweI6T6OAU8QlRXC0Kc++/DU0WiPtkTOs8dPy+jJaL0ogpyLq4CTmhE/eJbJz4RxZe+Twn311Bn5UBXG+5UawQ+Cgfxx4r4TOjWEtDRApMtEzTsfWo2HpULD0C5rBGpAgGzK36+tiXGX3KYRYsuwL/2Qn6rE4R6QNiSkdUINBfIFwsYQnoFI9s5Ue33kEqw0TcZ+TouQ8FKVqfxHsA8td2ULwCdFkg2M9YTn9oHhcX7qJtokhGVYhonkjpe2FcTWnszVEcByxsfvxk0g4RS62VYLlA5yiZlMsYX5zdTfs4mVCJRCJTxtWYQNnvoc+aMPGhCbpH2Mn8UgZdRzdJ6CaJeLaZtuWlaKpAvH82ui9J5o4uVn68HDmu0TbejGoRDTZrejbxHIvBrnQm0Uwi9oYwpu4I1q4klgA4qnvIrFKxtkWI5pmJFFvpHuE0pOM9Qbx7A3j3Bkj1zSbtEMHjRhg3HCmpYwlpFHwoI0cVitfGCfWRMYfSaGaZSH8vogqxwXmY20IEyx0oHiu+HQGkYBxXo8qkm25gyrSH2LJwCZaOKLMmX8SAZ+NMu/gqpl18Fcl8N6RVYjkG81x/novMre1M9/7oG46d/07MmnIxkb4O7K0J3Hu68O0IYK9qp/ATyN6VMFyvY0mULCfZm7tQLAKqGdonutAsxtfGaw/MRIoqyNE0PYNlbF065pCCHIgR/V2UtvFmLEENS1CjZbKImFJI5zjJ2aWgep0cvNOGKZSkcYaRd+vb1oOrJkjSayLpljCHdFw1QXqG2VDsAuoeDz1DrXSf1QdzbwJTIIFrfze6RaZnsEzB2m6C/QTqLhGJDPQSGeglNbDw2Gd2v7rluH1wlPkURw+h5e2hZK6rZdaZl5Dy6rx6zyMcuXMo+24aRu76TqRoGs8hgZL5MQRVx9KTom2iyWDis5yEB2Vi7pQxd8rH2NXpo+5G6PizztT9yhZWrl7GZyt/yt7bK1m83TB+8O4NAAZrfLSQmj7qbuousFJ9gxtRgfZxEm/0juOMi3ayZeESbD06UhJsvSq2XpVoEYz/+Vy81TrefSLmjz3ceOUK3LUiBRtV8rYrSEkdzSxjqv+ms+7fgzRiMGJaY8ADi4+x26vanmRV25PAnyWfU66/nk/X/Iy12nI+Xv9L4lnSt34PAGGrh9KiLmouO74X9NCCeaz5wXhmTp/zjdcoHZ3fWHcCJ3ACJ/DvxP+EvPZ/E/4ris6yi2vxfmHC94ad4X+oQO40Ufqwjr1Tx3NQZNGvf8CiX/8AexsI72Sx9q5JVF9XiasBbHtstEXdmM7rojvo4PlPJ+OuBXOnTO1sE4M+vxJns86hHSWknSJ1s55Bz0hj7pRRrWDx6/zprhkE+4lUfdaPJ2c/TYYUIxUzsecnlez5SSVSElo+LKGn0UsyacLaDa56mPrbO3nyZiPoO5GJkQeng9kv0Hq6mXCZhrUjQedYG1/+qhJvjcJvi9/F3gZmv0jmThPmEGgWyHnPQt3L/ZHjIMdBdauMefhmUh445RdzyaoyIkzENOii0c8YGOSk5vIlyEODxH0C9XON/rlhP9qHrV1g66KTyTyYxHsgRuaOLgRFo/ls8Lzh4pNPR6JaJAYujVJ9s4vqm10UbkiQtS9B7wgn7dMKMPfEiQ3LR4qmufT+VcRzLAiazsyXN9I4w0Pp+wm8c5NoZpGcbUHcD7eQKEmR8moEhqm4vrTiqU+R8EoIKoT62mg73U3ep120j7fQPt6Cb1Qnik3A2aoZPYoDJXbcW0n7HRNRXCY65iSI5chk3taApcdgoBUb1DblYAloyGERKQ6iqqNkqHgPGCxiuEwnni2wdssIcn9yBMUmYP/MiSmqY4rqZO9WsXYJ9H9aQTjkIH6zn56JaYIDdCJFAnJMp/fKCEK3heCUOF0fFBPNE/EPU5mydD733f9DUk7omJhBRlUIXTJySL01KmqLnYJFJiPfVdRZvPgJFi9+AlsXJN0ichyO/GEQgmYcf1Ur+uNshoxPbHSNtKCWx+geaaJ9vIC9zWBIp926CemBLMp+UYUmC0hJYyru4HUekhkmtv1uCdFBWfQMkWkbL5P7RZrcL9KYwzqPfnoOvl1w+DI3+Z90o9lkGmdIoOvkbUsQLhbompEg4dPI2qdhigICeA6KyM9lUfxxgnBfzWBGFeM5Hae4KK/USLmNAvmjP71IOsNixPPoIMd1+iyT8Q8wUfYcpPLcDHusAk0WsHWDKWLcSGftT9F7VQTzwRZD/idAvNBJcFgmH/3pRTxH0ug2w0E6XO5GM4ElpJLwgaUnRdUDBUTK3ETK3EiRNN69AXS7he6RLnw7AkQKRewdSaIFVkyBBG/8dCFySw+6WUJKapj9Kez1ATrOyDVeK3wd52CSEDRwflrNp2t+xrSCkbSe6aVlZi6qVabmcis1l1sJlFuouS7LKCS6dExhIBJFyHDTb2nDf+ya2XtSNimXQDLbAtEYmllGD4ZxNkQxH2oz5KIlWYT62UnlurC3J8ncG8HVqPJZ5VPM+eVqvHsDxPOshPs6ERUIlQnHJgJa9+XhPaQZxk4aHJ6zhFS2nUiRhWCJTKzYTllhF4rTjLNZNyJwBmXSfI6XDX98CmdTAlNco/o2J56LWth5VyXKwBiBk1IEywViRQ6EeIpYeSZIBjt6ZI6PkpVR7PUylltasdzSStvtqX+6L7SvDnDggnvRc7NQ3Vb6P9/DnAfn0++VTta8/RKqx0Yqy4olpNEwO59osYPgr6J4anWszUFjwkQSyP5KJfsrlbLl/mO9o3pu1jG2UxoxmGkXXcWM/j/lpHsqOLnc+P8eZUXLlvtJDSw8Jq2Vo4ZU1tmi4amFNZ+OYeW+YUz9wTVIKR1zENqvTNB+pXHuBfoLmK5uN6J6Lmhha6CM4ACVdEUv2i1dCDpIXQFDyj/y20m31T1VmHrjlKyIsqrmYcBgjP+aNQ6UyYy4YzFTxdkcbCpATny3qf4+rzVQfyQX3WowpEeL2QEPLGbl6mXfiEgBjsmST+AETuAE/lP431x01tbWcsstt3D22WczdepUbr31Vmpr/88UWP8VRWftu/0Inh4n3Eck94sk50zdSej+OCmXgC5D+2SV9skq4VK46icrUCq6ARh0VRWxIo3Ojwvp3Z2NkpSx9IgEB+qkMjTsLRI7JjzN1oeWMOTkOlyNSQY9W0HmJgsMimDt0XFc0UrrJEiNiSLHYd7uS9kfK8TUamHi7ouZuPti0k5wN2rIAQmLJc2FV20g47ImElnwy/k30v/VuaRdYAnodA+T8e1T0QdEMeXH6BznQFBgzAMVtM5JM/3dn5BxYQvvX7OQcAmkHYaLZ/u5KdbftZjsnVGyd0aRQhKKzYgSOWfeJoJlJsSv2T7FDll7oPNUlRG/r8D3vAPf3jT9/qCgWAW+WjaMotU9XLZgJd3DLQQG2klnO4n0dZBxQGLN7x+l/KUeukdaiPR1MejxIIMe/7qBWQBPbZLk1CBNvwJUneBAJ689MBP3ni50UWB58xiKPo4CEB5TQMdJEoHBbroe6kvGTjMF63Wydkqok4O0TTCjWgXcB4OYIxreg2lUp5WCjTEKNsYQn/OR80WItqkqugQXztnI+TXTKD3/CEmPjH2TgyEV+2l8q4y8bVFcVTLq5CBZmyxIKR17O+yeX4kpqjHg6TjdpyoEhqmUrEhh8YOzzjhFEl6R0VftoW2iQNtEgc4xEs4WneYz7eTuUNk66k0KVkk4GwRuuHoFlpBOwSIT5cuiFCwzkbMzTmCISvZ2EXs7hEsEvLUKugg1l7s599LNJL3gqAtxeM4SeofaiRbq5BYG+PlVN/Dzq25gx72VRIoF0CBcJGIJ6GgmgfjgBKoFVDNkHFHot1jF3q6TP7KdwEgFW7vA5vmnUHupief7bMTelqB7pIg5omEKiVi7jRtzKaGR8uqYAxAqNREqNRHsJ+A5KCGldChIkM52IgcTlKxUaZ/oIZ5tJnu3Qt57Fko/VA0X4G1RLL1pYvkQLhZJZJkZ+GwQXYCOUxwIqpFlK0WSZO9VkFJGT62lwY+lwU88W8S3I4C1PfZ15q2ZlEemaF3YkLzaoHO0CSmh0XS2iYJFJvTcLPo900TbrUkSmTKWgMqohyuQ4grh/h5iPgnFJmLvUpESGiXPHUaTBfo9p2JvjmFvjiGmFGIlLoSOHlxNRoxG1v4UsTwLlqCC0N7NRU/Op+XCYkJ9bVh6k7SeboNQhEgfI8i+9nITReuTx/IWY5MGMn3U3YQunwAY/5+uMTYGPh9l4PNRcj9uxVIWYub0OWTu7CbaT0EtzYNEEizm/9g1M54t4NsRIOmWOHR7KUOfOkDn94fQM9JFYmghrr2dmDvDmCMabafakBs60c0Sts4kM6fP4cOKM6i/wEu4j0SoRGThLU+TylGwdRpMaeY+cLQksHUmsXUmOeneCsLFZjqmpsn/qB1nbQjlkTwiRRbSTgH/8Ax0EeK5OoOerSCZaUYXQTCr+N8rZNCmK9FbbSDo+HZr6CIcuj4H1Sqi2Ux0nGRFTMG5z66n+KMQ4aSVcNLKvvGvfcNo5m8ZskwfdTeN94pEi+w8uvJ58ta2s+LTt5hadS7NZ7loPc1wZFatECyTMD+dSdcEFcVrR1A1Yjkiv33kKX77iOGS3PL20GNjr951H6dcuQj/8AxC/ezEBmWjmSF0W95x8tsZyzYfK5IzNzVz88UrEBOicQ7NCiKVRDE70tRdaKZrtIC9SyPlt5LyW8GdxlUPqVfyiOYJhN8oZP+fBmPOidMVcNLS4WXj40uxv5qg7IPr0XYf+IfHx9F9JI0YfKynd3HVOQDHlAFHnWwBnC0qhe80sVZbzi+bLjjG3n5b1N5YQtmfVHI3HM+QHlowj5nT53wjMgU4xraewAmcwAn8p/A/0dP5fyPWrFnDkCFD2L59OyNGjGDYsGFs27aNoUOHfsMV97vgWxWdF1100XdeOjv/56Qv5rBO5sdWkhMipDwyn780lsSHOfhHqoTHJnDUyTjqZIrGtrB44zR8tihDllYw0NlB3oAudAmUTAViMunBMeSowODhjbgajdnaso+uJbCohNqrRSy9oJkg3Won78Yj9K4oBAGkfQ7mzPmUM/oc5t0PJ1I2oYGOQz46Dvlw1UH7+UncR8D5rpvP7ziFHFsEcXSQtlMFnA0CD855iVNv/gJdgEiBhP1zB1Q7kad1873rN5A6K4jrcxu1s5dyek4tFzw1H7UwgemsHlzNGlMHVuMUrTzxxh954o0/YusQSI+Ksvf2SpZ/cDqJLAh9P4y9U8fZrNM1Vse7SyarWqHxXGj9YQrVZsJ/ZhxNhu5xmSy7bwbuBhWrXyXtNOHZ1YWzTWXqz24nOCwTZVIQe1sC/8hM/CMz6RlqJZ5tJlJkwbzOg+stN4ksGXddnGieQKLUiy6A804rqQwzciRF20SRfi+2466NIaR1HG0qaYdoFCWfeTgwtxJTxPg/OL9oRDMboe/hEhvhEhuevT3UXeBGDMpIKZ11C0+j6Y0yqraUEewnYu/S2Lp6GF/9vJLWSQ5MZ/fgfsNFNB9aJkugQ9lbN1Lyy4N0THBx6pAa1sxaROULj6GZIJ6vs6OqlOBpcT7dNQRHk4CjSeD9Hy4kWGb0T0YKJEb8vgLVYlxolv16Bs0zVPyD7GgmEV0SCPex0meVTvT8ENZeFWuPwfDZuzRKV6ZoSWTgrdY4MtvLkKUVJLxg7h8itSKbuvOt1J1v5bRbb8TaA9G+Ktm7U8SzBMIl0OcNGemcHnTRGLP5TBfRQoHeiB13lUzf7x0hXGxi3hmrGfx0BYlsC6pNp+XSFIUbUqi/6uGUX8ylYYaJ0g/i2Lp17J0a9k6N3C8U0lOCBMpF+jwvkXbKBB5KYzvcTVZVEktAIVwkk7YLiCkNUTGSkUOlFgo3pAgNTeOq7kXxWIllS+Rui+JoiVO4IYWQTKPe1oWj3bjR1u0WdLsFR5tK81QvOU80kf95GkvAkAYeut5G72ATpqhhwNQyyUzOFzq6JJLId9J7WhHeV12YYhopt4Qc51hIvWY2elATGRJyJE3DteVISZW0x0T7qS7aT3Uh9IbI/lkdySHFfPrcMwT7mvEPMONsjBHLMRE+tYzilT0UfOJHsQoEyx1YAtA7pZScnSqmTz0UrBVZ9/JzZO7spnewhJTQjhWgyiRjYkaxQdMCnaYFOrGB2fS5yyhwV3zyJs5DMlIgRu2Py9Ed314G+V2RvyFI18le/IMEBoxroDnmZcc9lZRce5j6c2UO3pJLrCyDRIaImIbY8EJCfW3HingxrZF3egtiyijEHq6fTk6xH1NUJ1JoIeUWaD/FjuKQURwy/mGGzFbuMNFxZh5N073okoDncBRPvUKkSCCRIUJhglRxkrRDRI6piB0WwuPjqPUOhp5Ux5FznqN1qgY6eKvA1p4gdG8M7dQgzhadt342jboL3HR2GsugZytYqy0/rv/v7xmyvDP2KbpGS1x2/3xiA308HuhD+MUipATY26FtooBqBXMIREXH1iiTdpqoP8+FoEGeFCFPMkys8v9gTBioe6oY8MBi3K9uwf3qFuLZAokMiVA/HeVh43g46gb75FuzKPqtiNDRw6q6Rbz2wEyyvzQmI20fuNGOOMnxhJHDIhkHIXhpGHeVjLtKRvSbCPYHW5eCLhkTUNZendxXbZQ9quPZamXQsxUcebU/WP55v+Vf7qNtL99ByaoYt3nr/+ZzpoqzcTTHjv2+99P+VN/h+JZHooFDC+Zx5FKJzE3N3zBhap3iPWZYdBQnnFxP4ARO4H8C/1M9nf+34ec//znz5s1j27ZtLFq0iMWLF7Nt2zZuv/12fvazn/3L436rovPdd9/FbDbj8Xi+1bJixQoikb8fyv3vRuCMBN1jNJxrnXSOlTCFdZIZ4PtCRI/JKGMiKGMidK0tJGOPzIHWPNJO46jwf56Lu14HReDI+U+hKSLZE9pYMWAVN939JqcumofZkabn6gjEJGZf8ymps4PMOPUrzvRVExqdpPaSpYgq7Az0YeWe4Sh2nfiiQrwHRLwHRBAga52VRLaxvT1DLGz5cgCxVie+XYak9dePX8UH609GTIOtW8Mc0smo0VE1kZe3TkTY6gGM3plNd47H1gW2A1YsL3tRzQLrNo5k3K/mMn3DLUzfcAuCAq5PHFzZMBlBhaRXJ3uJHUE1+vzsrUaUSdou8tCUP2Hf6KB5ihXnF4YpxZV3rsQcUFEtAsEymeazZXRZYuLd23A2JUjbBSxrPaS8ZjomqXRMUhl+5X4CPwgTyxWQUpBRFcI/SEBMKuTuSCDFFSzdCQ791Io5lEYXRfq/0EN4WDbnPrceczCFKaph7VUwh8DdYLBVrsYEqSw7mGSiORKyP0rnOJ3OcTptZ2WTvUtj+qQv8VXUYw2o+PbG8VbppMdGjJvTUzop++B6vIdULK94sVzXhjosiuozsjcRdaqeGoI5pNP2y36sjg5hdXQIsTw4dOUSBFkn9wMLOZ9LvHj7Yl68fTG/75jKsLNriBYY7rfeGsNYxN5psB7jBtWhSxDPtaLc0IOrIU7nGJl3xj5F6wVp3I0KaYdIz1CReLaJhocGYus0InOkhCF/VvZ7sHVraFZjieaJ+IerSBGR+vNktElB8rYrxG/2s3TYKzg6NJqmyqQyIOOQhustN1ICDm4oI9RX4E93zcAUhjF370TJMLINw8Um0kvy0UwCvq+ge4QdZ0saQdMRNJ1oroyw1UPWfoVEpkz3MBn3XTaq5uUw/OHdxHJN5G4LYQ5rWJr8fFb5FE3nOAiWCwRLzRStkkjluegebiNnmx9dEkjkWOkZaiZZ5KFzWz5pp4x3b4BEvpNEvpO0Q6TgszBVTw1Bs4hYGwMEy0xIYYMFsXeo+PZo7LhmMdaeNMkFflIuibRdQBfBWe0nUijiajRC6u2tcfLfPIzjkMGWpDxmXI06wXIHjgNd2Lp1bN06qxr/QNWK/qx79Tlq0hEW/7ySnB0Rms524mhPEc+U6Dw1i2Sug94zEmR+Wk/g5BSXLViJqBju1wgwbsFckvlusg6o1J8rs2XhEvyDBPIfs9AzWEYzQeEjJgofMZHIlBEUjZZJZqZedg0FG8Po7V3HYir+U+iY4CF0ThR3nc6hL0poX9iPk++qYPem/thbRdw1RvSSOaLjrVHpHmEiVCpw5KcSGx9fimYSadtcSCILEjka3e8Wc27RPiPfOFc4mrZC3YUSdRdK2JtFqOik77sxdtxbiXVyN9E8I7sTDPfi4AAdTREQe8xEigT8AyxYugXkOhveamh7sS/jfz6XI+c9jev2JmL5Aj0jHLS3Z5CzxIbncIKWy1Jk7dM4Z0gV5wypovo6wxTsH7Figmxi9a77GFjcSt52BdulbQiK8QlCpcYxJcd0HM0CmQd0MmoSdI6Syf0iBZJA/udpeiekuOnwHG46PAf/8AzWLnsBMAqzQwvmsVZbTvu7Q9j100pSLoHbpq+k5+3iY9La1bvuo98zTTT/UmNV25NMmfYQHVPTjL/jC7b/Zgm6ICCmoX1nPnnbFcxhHfPHHkLD04SGpylap3HFzPV0jDPhaNUJDdDomKTQcZLEkUvs7Hp8Hvmfp4kWQN9Xv9uxMuTde2mYYWfQsxVMFWej7qk6VgQeLeYlf4ze04pYqy2nbLmf/k8p/3DMvy4ap4+6m0FPhlEKs1i9677jit78p3d9Y6LghJPrCfy78Ne93idwAicAVVVVXHfddd9Yf+2113LgwD9WyvwjfGt57WOPPcbzzz//rRaz+T8nC/tbcG+0IWUabImgQfdJGvE+CqG+AsgG84Ju9MyF++rQZCNzP6xpHcyBikri2QKCIlD+pxsZUdLChmHvUvbeDVzoaCY5IUL1qS8Tb3Zha5V477Ep+J5z8NnysSx9ZRberRZGP1hB33PqyLZGyNxmpuydOB1jZcwXdGK+oBNLSCPYTyA2IEmkQDBmoLeIyBGJZIaAf5hKcJBK3/cSmKf0sOnRpVzx05WYojqpzzMpXCPg26+QckHaZhioWEIaBRtjaLLA5T83ctdiOQL575nJf8+Ms10jWK5z+InBuOt0fLtB+mk7clIn//M0vn0KpoiRl/jrFy4DAVSbjpiCL+6vZPHHM/D9qp6eoSKhfhpF6xSaZ/r4/Dfjj/XO7byrEn9/mcwdxmKT0vied+CuV7F3qXRM8KCZdXqHuQDQLBJiSsH+lY3OsXbaT3PRPN2Hs8b/tSlJClu9H/vOevLXdfLA759CSoIUS6M4JZLlOUbhIosUr9MoXqdhCehIKY1PPhhL1da+tEwy2DhrQMO8zUnXGAHXrx0UrBVpvSRFxzgB5Y95ZL5vx3bYTHRgGnt+BF0S8A+Bda8+x/IF07kloxFHKwx4eS6OajNpm4CgwoVrf8yFa3/MnsUj2L2xP44W0CSI+wwJautUlaQXdu4op3eECrpO4oMcgv1sZB7QOO+l+WStt9A0W6HjFKM/s216mpYpAnUXmOgd7sbRppNZrVGwKY1iFVhz7iLWnLuIlAcun7iZzP1QO3sp0U4HTbMVvL8yc/WS24wez16B4nUpYjkiUkon5QZHK1h7DIY+a1+anfePRZA1+j2u0TtCp+v7BksRzxJwtqrEfxJAtQioFgHFAbtvfxL9x10EykWsfggOdOKqlfhg40mIik7XSW42PvEUSraLCfPnGr1uPeDoVNn4+FL8AyxIKUjmOpBDSSw9Sbw1CoEyC33WxAj2lZixbDPJDIlkhoSrMUHXyS7858QJ9JO4acUK7J0aZW/G8BxRSGaIdFyY5KwFt9MzzEL6xTwcrQlEVcfZECWd48Rdr5J2SkiRJIlsC7hdtE7zIcc15LiKtVfBElJpuiCfl377CC/99hFKX3qIgo0xhm29jCvuvpNf3X49zT9TsQRg3cvPwUXd7LinkkSmTNnTOomhhZwz5ACrLj4Fe2OYLQuX0DNUxBTXaT7TQtNMnUGVhpFM9XWVdI2yEu2ncGBuJYlsC4lsC1sWLqHr1GwqLloFGDfvK6s2ABxjSP8TcJ7fhnmnA0tAQ1AFzLe2olrhlu+twOKH++c9TzRfZOPjSwkXSuRtTxAvT+FcbzBYTVNt6CIkM3U8ZQHuu+1FRtvrsQQNub9vXwo5AXkbBfI2CpjDMCKzFftD7QyprCD1sQ/VDJ8+9wxpu8gvf/MiANlrLYgpgawDCrFc8B4yene7T9YIzYhiDmsM3HgVna+UkvDp7LinEnOzmc65cTpOtpG33EL7RNj28ii2vTyKxwN9/um+CFx2EmD0KjZPkeCPOTSdbRhhaSYjtujK+SvJ/zyMsylFx8lWTBEI9TXT8D2ovxg8O8wc2VfIkX1/Ni46ytgdLbB8T9oZvu0yooXw1AuzyNobB4yCc/qou5m+ag8HLriXU65cRCzHxJFznmXzYycz9bJrCJeCPDSIvQOazhYJ/CBMuPTPExPd10aJqWayd6mYwxpvXvAo/V9UsHWAu1Zg8qyH0U2GMiNY+t2+n5X9Hm6+eAXV11WyVluONGIw0ojBgFHMr9WW03BJLvbONNNH3c3qXfcRz/lmCPpf4m8VjZrNxJo3X/zG+qZX+n6n7T2BE/guONr68J/ACUb+/30YzOV37en8/3ur/8+RnZ3Nrl27vrF+165d5OT86/3036ro/PTTT8nMzPzWg65atYrCwsJ//sR/E/xDNVS/mUgxWPxw5MKnOHLe08hDg1jrTbjWOHGtcRIalaTmsiWs/P4jhPoKtNVkM+rhCsJjEpgKYgwfU0dH1MWkm2/A7Iszbuk8lp38NGXv3kD2DgHx68nbBY8+jykCzmYd//gk0XyoX9mXdfsGI8d04jkWxDT0Ruz0Ruy0TwDfHo2cT8w42nUSXoH2MxQ8NRAcoLJw2utk7JeI51owvZ7J3OYJvPbATGI5IooD2k4XCRcaMRWKXUAeGkSTBA5fZty0vrRoFohg7YVQqUioVCRSYDCZgQECwVlRcq+roz3kJpEh0jhTInJTgJd++wjOZh3f1311xWvTIMD0C6/E3iYSU0wUbEqT84VA12gT5ghE80SczZDMEDj57go++slCIn0MV9i9vx9B6qZeeoZLmANfu7buAd9mQ2rdNdJC/fleitb6sfbqFHziJ//zKK1TfcSzBVqneElnO0GW6To1m5uWVBifYZCLUB+ZaJ4Z3SQRGpxJ2m4wMe4jMewNYVz1On0+SlG4XiFQbiLURyJ1SoRDVy6hfaIDxS5g3W9Djhpurh2TVPqsDpHzmYxlrYfeyQmU3BSDnq0gliNxyi/nEhik4akRiA5KIapGrIqp24Sp20TvhTGEsijvL3iY6MgE4T4CmklgwLMpvAc1bCVhpMwkXaMlHB2a0XuZLSJHIZEpkL3OgrcKYiUK5iYLeVvA1ibSdYqGlNJJeEVaTzPhHyww452fMOOdn+A9pLHswEkkMgWGPVGBvUHGu8lKzVUusnel6RolIiXBP9DIkAyViNg7dBKZkHbCnLtXkfRKbPjjUxSsMFFzuY3crXBG6WEyD8TI3xIh6RFJv5lDNE8kmieiCzDg9ZtILMsjs1oja2+MjKoQvr0psr8QcNVGsV/cxllXX0ci24J3dy+hUgFLQEdMaZx6x41YezXSTkMO236aF8VhwtKdwNajEuxno/DdFlbNmYirNoqrNkrvYBuuJgX3Bju2bp3F119GzzARKZai42TZcKXuNeOpjWPt1fEPEmi5M40lqNM11kXXaCu2dsP5ONbHhWoRiQ7KMqKMXCLBfhaDtR0qExmg8KNb5vGjW+ZBUqT1jjS2FR68VREm/Ho7wjYPXy6opCYdwfxyJhPmz6XtNGg93UbKLfPxhpG0TPPReqaXKdf9CEsAVJNAujSBs0bm4NysY9ep0KgUGXtkJt10A6ESiVCJxKl33Ei4ROCZF2eQyDJTe1UuE+bP5aXfPkLnuP/cN9eGYe+S8OmESiQyDup0v1fMvFuXs+jzc5BSOndVnU+on8bIhyuI50DTWVYWnbaMCys+BeDAdU9SfV0l7lqBnwxYy8+fv5pZ9gSKDUovraVnmBkxBR3jjcU/PsnHK8byTvlHpDJ0Tr9sJ4FxSX7aMZrv/3oVO2N9MYUEdAk8h2H90qdx1+loZgF/uUzmLolkwELLGQI5b1gJnBWjYIPG6N9WcMa0XfCFh9ydCcwhFfdhEcuMLiwzurglo/FYNMrfwilXLkL9fg9TxdlYexUOz1nChj8+hZqdZsU1k0nmpyl+T+DVB2cSK7CT+kUvmhnsnTpiWkeMSpS/akTvFH6qU/ipjr0zzczpc1i96z5m5N98XIGV80cbcgz08UEixdZjTOHqXfexas5EADLX1eLdG6Dfx9cQLBdA0ynckEJRJM66ahu2dpFEvRt3rYBvk4xvk4zVrPDRMxOJ3eBHMwlc+/A8ogUWTDEdOaYjRxUcNX4KXz5IwvfPb7T/UuKas1Pl/blnMmG+EVR+lOn8y+eUvFh37HNMH3W34QL9HREtMowA/xqZLzn/bu4pnLixP4H/e/HfyMj/bzvf/rcaCV1//fXccMMNPPTQQ2zcuJFNmzbx4IMPcuONN3LDDTf8y+N+q2+GyZMnI8vytx70tNNOw2L5xzOd/070HdTKkQufIu3RCA1PM+jZCsreuwFpk4cDFZXHIif6FPYw4vcVTPv0Vn5w4XqQdGK54NlqxbbRyZm+arr3ZNM1UiLVYSNRpJAtpfHukYj7BKID0vSMVVnwwI8wRXV6ZsTxbrVgikL6pAiWJjOJLAH/QIl4HwX7eif29U7KhrWQdIskMwSSGQK2Hg1Tr4zigIwqifmfzMHaq9E6XUW8spP1a0ZhCaokPbD72sdwNArsuLeSnF0J+s2tBiBSKGBrlRixqIKkB/I2G/EOrrM7cJ3dgbNVQ0wJyFEwb3Oyd08p8To35rCOpUcktsXHzOV38sQDj4EOgcEa3cPMZBxO0zbRSaxEofv5Egbcvx9BBXuHjrNFITBCIVIEOTvieA/G+cG1t5HMT5PMT2OKavTuyKHkgyBSPI27MY1iM7IKdVmg6I16nM26IXnsTBMa6KHlDAcFa7sxh3S8B9NIcYXeKaVs/80S8rfEyV3VSObnrXgPpXA2JRBiKfwDRextSextSWIFNiJlbkwxHV0Q0MwisdOihMo1dF3g1Hk3ImiGI621GxSHjjIliL1eNnq/zkyBDkKnhczNFlSrjimi4x8Ml03+nKvuWEHR+xKhEgEpCVn7dLL26Tg/dqIfcTDp7TsZVtKKb4/Khiefoneog64xAuJGD/Yv7Fh6QY6pqGbDPKffjCPkbYuTdgikHQIZu43zKukSuXDORopX69xx/2uIaR3JaEekYEgHBUM6CJWI9HlOQnFA7nbD6MgS1DAFROI5Mq4x3URKVcQ0FJ3ehDkMwXJjssQcMtgVKalz6h03EiwTKf1AoXeQyOGfDSbQ30aswIY1oKHYDcfbSB+d3B1xRp9yGCkJsRyRjnF2Gs7zYOmM0j5FoXmqC+3pXCzNIcS0TmChirtex9at0jXShGefn1CJiKNNQzPrhPvq+AeY6DzJSaRQwtWYIl2cyZHZXnpGuegZ5cLRoWIOpvHUpugeBdECM32XddJytpfcLxSC5QL5GwVa70jTPRIEFaQNHjpOFokWwK6fVtI11kGkSCDpMYxu/OUyjk4V4ftGhm0iU8DVpFOwVqTtVIm2UyUK1wgo+z146pMcucjFmucm4j2kUvbRtfQ3OXE0J+iclqT/q3F8+1R6hohouUlSGcZ7nvzbnShWCPYTcH5lRVQg+wuBCfPnMvb+Cgb/phdRgdYzBJJeSHqNvryq6ytRbeC/PMwHVy3EFNW4/cwrGDP2P5fTefLdFbjqBaQze4nlCSSy4KEXZ7NjxqOol/QyOqcZ3aoS6ashG0Q4P33zKl7cN55Bz1ZQ/sGNAMRz4eEl30dQYdTDFUQLdao+60fKA9+7ZT26U0V3qmRst2BvNwyj8rbqrNw7HLnNwvuHh/HEu7NY9uYZWHuga0qK7glp+r86l66Tjd5DxzmdXH3bCkYNbMS7XyTQX8K9wU7nWAl3o8LuR0fi26sQKrXQNsGMKaIzv/8a5vdfQ7/lNx6LRvlb2PbyHWTOOoScm8Onzz5D/9fmMv2CKxB7TcTybXj2mUhmSPQMh94hEu078xFThgO4KaZj9gsE5kcIDtToHCvROVbi4xefJZnrYPqou4/JeqePuht57Q4+fvFZMquMIjVySQilXwFTxdnHHGunTHuIVW1Pksx1cPvYj8n9QiGea6F7qBnvew4+WDsO3z4Fc0Aga38c7eJetIt76a3zYp7VhXuxGwRjkqlzrIDncAJNFjB3RkjluljVtQRb5z+XFB6V/QKkHSKmQOI486CjWaJH0TulFEtH9NgN6baX7/jWx+JUcTYrVy/D3pbA2fLNiRbXV63HRc/8Nf4bb+xP4AT+b8X/tvNN/xeX/9dx1113cffdd/P4448zefJkJk2axBNPPMG9997LggUL/uVx/2X32s7OTvbt28eePXuOW74LKisrGTFiBG63G7fbzYQJE1i1atV33pb4cwWc8su5bLjwEV6c8gzV11XS922V8Mgko3d8n3CxRLhYot3vRkpDwYcmPlw8GSwaSn6SjAtbMIV1Xnl4JuagMdveb3Arcq9MoeTCekEHu++s5MUpz5BT0kv3yRrS97vQ0iKhMh3VAqkuG7YOQ95bMq0e3zYJxYqRI/lmMQmfsa2mqd3oooAUF4jmG+tcB2VSbpGy1zQ+H/E21h5oOUNi362VWAQT4REpytZcR+33TTQsHohlrYdEjk4qQydarJHKgJRLYNovN9C5N4fOvTkIKmTU6DhbdJ768eNISQHvAQHxSoN1zDisIUcEfrDsNjpOkrG1isTzIFxk5AUOejKMrUdlXc1ANBNISegZKtPnA9DMupF1ONxGMkOm/wtp+r+QxtoZx1ut03Cuh+6RLlJuCatfY/TvKhDSGkpJDp4jRkRKMsMw/unzQTe61XzsLBXqWrB3phn3q7lIsTR6houOaYWk3DKaRaJ1mg93vU7aYyLtMSFoOnJcwz9ApGuUmZRTpCynm6zdAmKVA0Ez9nlgsEa0wPj/WD/0YIqCOQi5a830jk3jajDiCSx+AdUCeVs13n/xdJ5/ctaxzLnS2bXccc/r3HHP6yg28IzoxlMW4OzsKmI5EoM2XYmzOY2YEgypa0Bn9/xKovkyoXId326Vzqf60jHWhu+rKDnbwjhbVXy7NawBjdWPn07PUJm7Xr4CZ2sad73hJNtSlUtLVS66CLFcE2IKWiabSbnBElCxdUE0T8D6rBfvPiNeJvHHQqSEjrtOJ+2E4JgkUgpiuRLWboVkpo6lO07R+gT155lxtqbpHSjh7y9h79QoWZWmZFUa2R9j554y/IME3I0KKQ94D2poVhnZL+M5opHIFEnnOhEVncjHOYT7CIhpHVeTjuK142zR6ZgA+Zs1EKBgRSuONhXbzE5MgTihvlYyxnbhbFNwtikMXrCXhM+MbhKxtwqIqk7n6TmYQ0akkBQHV10UYZsHXQZns2H4VHJyE33fDjBz+hwyDqdxNuv0jDBYqWixzo8XvoHrIRfmiIb+tUlm/u219PkoRZ+PUlh70/R7pRPFLiH0jSKoEOwr8eKk5+j/2dW0nm6neLlM0zQHgqajmcFUb0WX4KRfV/DOulPQZUCAyOgEC256jaRHIFIokJwaJFXgQbGB5lJJ+DQSPg3nu27KX5/Le9cuJLfSxpV33UnnWImW8/K5tWDdd74Wflv0nJ4k5QH140xSHiOTVhfhlDd+gqYLHH5gCJZ2E4IqsPf2SsxBQ6JdmO1HceqYO2Vq0hGSWSpyAuQEiGnj2uBshNSAOMvePAMxKCMGZb5cUMnOu4x4qJZzVHLXmVDykzjXOXHXGhJwd5NC6Wsi3i9NWHoEsr6SSNsELE9k8uqDM9ldV4QmG/2fggKFn6X4bMnTdExSCZbJpJyGjDd2Xoj7/ngF9/3xCspfjx/7zH9vhj50xQQSI/owYf5cyt6Mof4ugKBBNF8ikWVcW2suX0JGjUbedhUpZRScvYNEkv0TSG9kIcUNdjtdmmDY1suI5ZjwD89g+qi7j/VBtr87hD8GigBDspr5gpOGGXakEYNZves+tr18B9ZDHQBYD3Xwp19NJ1Qi0z07hrNNo+PsNPY2gVi2hGaCjrE2Il9lEfkqi37Lk8jPZdE51kzMZ6hcvFUQLrUSnxUk9WSCttuSnHLlImw96nc6Vv66gDxqfPSX+zXlFli5ehnSiMEcme39h8zkX2Ottpyzrr6O3sF2Mj9r/MbjDYvd6LlZx63738a2nMAJnMD/P/jfynQKgsC8efNobm4mGAwSDAZpbm7mtttuQxD+9c/3nYvOnTt3MmzYMPLz8xkxYgSjRo1i9OjRx35+FxQVFfHggw+yY8cOduzYwZlnnsn555/P/v37v9M4vYNFzGGdQsnFjS8Y8pzegWaEgInYvkzSDiNaRDjgJNRPY9NjSwkMEMj40kTRezLyfZk4WxW6JqWx9hjFSO3hfNTCBKMersC/JZeyt29g7nMVaMt9YFPYPPItrAet3DrL6Kd0NEjYuzSi+VDb5aNnSpKJ39/FxO/vIpEFb9+0kMyqFNIbWSSyBFSbjpSC4ACNzKo0qgU0SWDKtT8iPiGK9wCcOu9Gpl9wBc79Zvq+BtZ2CVNMI2d7iPLXw5SsSmEKifh2G3l4y189A1NEwBQRiPtErL0qPSOgYtEtjBp/mKKraumozqbk/QDhQhFtcJTC9WnMASPbs+hTQ15r7YXWM72IaZ3SZ0R8a+qw/bAVaw+0nyIjpgS8u3oQVVDNAh3j7HSMsxPp4yBcIuDbq3L1vBXY2xLIMZWC1R0Imo4USaJaDcfYuE/EURdCt5qJF9ixd6v4B5qIndqfWI4JV1OaRI6NeLGLlNOQ9cZyTJhDOg/fvwRdFIziPWHEJzibdSKlKmm7QN2mEqQkZO8y/vYscmHpETnv/K1kVAn0jNKI9NHJ+SqJrVvBccSEJsLVt61ATIGrKU3r6SLo8OWCShS7ccPdGnbz6+cu49fPXYaYhvRaH44XMnh0zUy8NUlyX7ESz5Gxt4OnTsUc0hn/87lEigX07CSpG3pJZgi4mzQSORY6JrpIZoi0TFMJF4r0nJ4kc0obnlodOZzG1q2gWiFnu0DOduMkD5cIuBs1RAViBRrRPJmED3z7FVrOMnqWk16Qkl8/59wQB657kuxPzTjaNeSYjmoTMYUE2id66BlipegTld6BZrw1Gt4alUihiGKXUOwSqWwn6IbErvkMiexdKgU/rqX+PBdlb8eQUjrhM434m0SWjKNNJ29rEkHTsQRVuofbAHDXiFzyu9UIikDdlQXEsyWEF3wICQX7la1ENmXT8D1o+B588fwommZqWLripDzgqovhakyRvd2PKaSQeVDl4A1WokUavl3gbkjhrhVIPZpP10kZNE33knVXPeU/rqLoEwVNBnurwGxnkLwHj5ByimRWpcmoDtMRcyKHU8jhFIksM70nZdM9TCYVNmPv0vDWKPziZzeSsc7OuZdupmuEjL0V2sfJ3HzxCjQTeKuN3kLVraJaIJmnMKi4nXtevoxYPux/cB6xLgdHrhdQzTCgMkHheihcD7ZOBVGB6388j2CpmVi2wBnTdrFn0Tyuf/WbUsN/FxwHLAhfm5ha/DDolv0IY4JMPm0voZCN8PUh8jenQYcz9p+PajHYs/atBVg7BKqvq2Tm8jspWC/w2YLF2Ds0Ynlg6RYJDNWg3UqiJI1m0tFMOi+Ecih77wZGLqxAtKh0nJXm8dNeQ0rohrTdDl0jZerPE419mKUTLoW0y5CXdp6RJn+VyYjhKRSRkzptE82MfLiCgnUioZMTBE9KEh0X58UxL5B1IE3WgTTaA72MvnnxcZ/9rwsi9ysG67dl4RIO/8DG2sEfUvp+And9mrztCq4mhSsbJtM9wrgmWPw6ilXAc0THsceKoIKjRUBXRXRVpGR+jG0v30H44hAzlm0GjMKq4B54b0gWvVdFcLTCJb9bzaEF845jDFfVGRmYtT8qxn9FhLQDcl+1YQ6qDHwiYUjE3QK+PRp33/wK1h7j2tQ51kbaKWAOgaNdw35mF5pZQErp5Fba0B7MZd/413A2JegdfHwsyV/jGyY/3h8Bhqz2KMP5l9u8VluOOaQj5h3CPzyDQwvmoTq/m9LJuqeRRBb0Tv5mD678mee49zv6nidwAidwAv9x/G+lOv8CLpcLl8v1bxnrOxed11xzDQMGDGDz5s0cOXKEurq6435+F5x33nnMnDmTAQMGMGDAAB544AGcTidbt279TuOYg7Dx8aWUvXMD+VtSDH66AlNMp3ithqMZbrxqBTdetYJkjkrmHpGRCytQCpLIcZ14pkSwn5W6S6CoqIdIEVimdJO9RWLAH5LkbwwhR8DeLGEOQs8oHc+XFs66+joEFZa8Ngs5ZpgU9Q4S8Y3qpCSrF5sjyf4Hh7P/weEIClx/8zx6B5pJOwTsHRruWgFPrU7WboHGH6g42jQiRSYUu0jm+3acLQrOpgQ9w10Ureim7nyJws8S2Ku7CJc56R3mIlBmIX+rgrvaj63HiOEYOrWGoVNrCAzSiOVIFH2iGBENT5XT/FI/XIdFHnv3abyHVXKXWWk6R2b3TytJZhqSMf8Qo2hxN6gE+pkQVI3am8roXVGIo0PBW63T9x3D6t/VmMZTEyFrf5qs/WnSTgF7u04kX2L1jBH0DLejWkXqL80lWmileaoXQdNJek3kbuhGs8o89vZSegfIiCkd374klp4U/kECJn8S2+ZDWNuiXxuSGM6jmiTwu/Nm46gL4agLYYoo6LKAuy5B2TsKnvrUse2LFEjISZ1AmYWqGypZWTeYSDH8dtqfyKgWOHIVJL0SJdPqSWXAs0/NInlylLbxZnK360TKNEY+UkH8tAjemhTBL7PZd2sl+26t5JqbVxAdFyfQT2TyxL0cuUIglmOwt8WXGoVNuEhENQvk7lCw77XS2eTF3aggpjV6hspkHkjxyq8fQdAE7F06OTkhNgx7l55hAvE8K+3jTfj2aoT6CoT6CnhrVFwNOvb2FHmntZCzXaBrosqBGyvpGCMjxUXytmhk1KrEfZJhpPWFh8m3VBC7IIQmGc/vHShT8oGf3C8i5G4NEiyTydqfpGuUiL+/ETUSy5aIZUskskwUr9HRJQHVqhPsK1H7en+81TqH59joHCPx4+HraZtgJdhXpOc8Q4vZcZKZ1tNk8jb5sQZUvlxQydLq0wDI35zCEjSKYv+YLBrassjar+KskXHWyCQ9IKREGma6SJckaDvVScotEy9wcsPTb2NvjmJ2GX22iUyB1lPNmKI6lzy4hrQLsqoUmivL2bZ+CNbmIKaojuKA0269kW0bBtMxNQ1A62Q34uPZiOEkYjjJJfetoWNqGu8hjbqZzyAnNMJFMrbOFJaQxtsfTSDjsEZkaoTsXRp/WDsDVz2k7QL91l2DGJWw9IJvq0T7G6UcqKhEjsE5E+5nzfTF1F3+C5wtOv6hThIZIokMkZYpMrYOgeyf1fH6rxZiisG6TSMZ8MBiDi2Y952ug98FjsldZFarKE7j9wM9ufiec7Cvcjjuz20IKzNpOlvG0SJwXZ/PcTXpRhSH1ZB9zz5yNqpFp2e4yKm/n0fCa/Qra8MjmP0i7loByZ7G0STiaBJ59NAU3AclFDvIFhWp28RD86/CFDW+nQXFYLDliETGERV7iyFn9x5M0/B9jcIVMm3T02Tt1zDFQLqsE1ejsU0t56gI3RYyfRFqzngBt5Dik+ef4ZPnn8F8WZJogdG7ebRQWb3rPsSRQ47bH7EcEwDWTpEp1/6I1tNtyFGF9nEymknk5ZLPyKjRCRcbLKMpquE5FMPiB02GL3/2JH1fNZxho0OyebT6bFxvuXntgZnHFUhrWneTs9ROcmqQJ9+exYz8m4/1nE4fdTcDHlhsRJTctRnbCg9Fn4SREhptp5ponezGf3oCd4OKnND43SNXoIvGddvVpOFsSSPHdRzNcTJ+bcPaq6FJAppsTFhNv/BKvrfkEwo/i9Hwm4l/99j4y+1V91SxsmoD/uEZJM8b943n/mW/7Iz8m9n28h1MmfYQ8sGm73Q89p7dD99+5Rv5nlPF2bgbVAY8sPgbrznBdp7ACZzAfxz/Csv5/yjTOWbMGPx+41579OjRjBkz5u8u/yq+faPm16irq+Ptt9+mvLz8X37TvwVVVVm+fDnRaJQJE76bm1jSCyfdU4FP1WmYKeJsgMBAKLm2jpat5cdudj3VksEK7VNwN5pouzBB8esmw4H1SxOdHhfWHlBX+BAEne7700iCjul9SJ4TJLHHQ/E6jXCRyOTfb2bTTePoGmMnODrFwMfjNM70ILzoo7VExFOvoX1tEhjP1zBtTiPoMnJcJ5IvotohNjAFIRP9ntFZu2wpk26+gbaJIjk7daSkyuHvW/F9qXNwbib9lqdoOtsK5FO4IYVnZzdqtptwqR2h0E3SLZGzuoHodkPHW1Cu497TCZJIXsJNpMiC1a+hWAXOf3Y+zkwdR7tC/1ejTH33h1hGgpTQ6Pt+kiMXmlHNEvZ2kJIqOTtVHE1RDl3lpM9qhXA/F7bOFGZ/Cl2WMIWNm/jM7jhiJIFuMVH7o2L6vdJJ+5k5lL7RTrLEi71No3eojaQH0o4sUm6BfElGcYKg6fQMthAcrjDoST+oKhTlksh3kLslSDLXgXdXD9UVXkQ1i+2/WQLA2VdeS8tkidxthlvr4AV72bdwBJ7DURLZVmw/aeGZ8jcY88BPeX3+IubsvIMFqy7FVAJF+b0oV0tU7+6DSYLrbljBm7+Yjr8/RPJEho08QvOBMoTNThJZGqpFZ+iTBvvkaNXxpSBcDId/O5RzFuzl8wOjiGeLxP9Uhk3RsPUaTHD7ONmQ8m6QiOYKxH2QHhUlUWdjxtt34m4QAJ2uHhdj769AL9PRTAJpp06wr4i7zrgxb70khanGRsplxfl4LrakgrXVxOSKG3D4IGwWsNzSSm1zDoRlPAclTBGdM+76nHULT8Pao5Cx24Rig+ob3JS9pdB+ipN9t1ZS/vpc9JwE3tUWQqUC3oMGDabYBbpHGtvvrjUkw1a/Rs8QibJhzagP5/GHkrMorFIxRVSiLXbCJRArVTB3ylRf7yG7rIcZ511OVqGdZIZOsNSMlNKJFwm4mjUytljRRRV3vfGecZ+IOSRhCepcdunHvPHBDFJugWSGicqGydRf46D88SSBAQKVdz/KzXffSjxb4MU/zMKa1Gn4nk7mlwJqUYLG83zsu7WSKxsmsyc4hIyDOq/OeZQZgZ+Q/YWO2Z+i8ULjnHn1kekceWAJnANjflOB2auz865Khj1mGFopLpXPFz/F8D9U0DFOx3NIIOWBLbcsZuYtt2IKp+m4KU58q4dkpiG5tWoQLHdw3W13MPauH7Bl4U6GPVaBb5/hSiZoEv6hOgff6c9F0nxiExOUFXQh3pXFpO0L2fDe/H/havrPsXXUm0xwzsUcgtgn2WQcVpn8wOeUWzu4Z/P5lPxJwBSR6TxNYfFjs9Eyjd5Zc0BAAA693R+zF5JFaXTRhK0LnC06+TM7aH/PgS6CdNhOxuGvpZy1GWiyRu/sKLZNLqw9Oh0nS9jbwGxPE8u3kje+lcf7L2N2+g7koUFOL6rjo/zheHaaaD0/gWe7FV3QEFRoP5iNfopG6bsaiWwTI0+rofqD/pz6yo1ICf2YkY1zcPJvfn5t958t36WMDLwfHmBUdgWlqzqJDsjE1mk49Gbt0yiYd5ipl12DKVPD3qER9xlfm9EiG67mNL2DTZw6/ybiQ40bjWuuX8WqccV4ywLHMXR1szMY9GwFfWLxY46wt/3yzxLqqeJsyhgMC6Dl7aHk/yHO6ndfYdyCuWTv0rC3xGnXHVi7YtRcbeL/Y++/4+wq67V//L3q7n3P3tNreiMQIAm9hY4gGEQRsEtEVLCcox6xHfUcGzZOUJAioCgiopRA6D0Q0nsyve6Z2b2v+vtjkQgCHsHH5/n5Ndfrdb8mmT1zr7XXrH3vfd2f63NdLQ870naAQH+JqcODxLZW0CIqntESoSuGmPhNJ6UWBf+oTSRVdsyK/NDzk33wxTe+N/b3be7HmXOPY1326tdViPf/3OmLryH64iR2MsYKcSWVi5fjjoYh/eZ9mH+JySNtEi9KeLeVDsy7/6s1MYszT78I/qKN6GC18yAO4iD+0Xg7uZv/rO6155577gE/nnPPPffvktG+Gd5ypfPkk09m8+bN/8dOYOvWrfj9flwuF5dffjn33HMP8+bNe8OfrdfrFAqF1wwAbwoyi03KzQJyaxndB4n1NhtfmkFivU1bJEdbJIfhgtIMnWpMYux4WNI5zMgJEuLJGdSCTSJcxDdhYargKth4fx4h6SvinTRRHw0x56Re8l0y2UUmj3zlWLJzvMgVaH5AJn1IkNh2E+9HRtn6qdWMn2YcqGaA8wEmMGKi+wQ8GRupCm13SzQ9A4NnuTnl4g8ydrxA8kWb1BEChQ4XM35TI7y7Qvsa0+k3zED3XVlKLQr5JY3UEm5CO/OYbhH/cA2zJUZhdojC7BC+0SrluXH2fs1H70oF34RGYG8e3S+gz6kyvczEUgSGVwRRpspE9uggOi6VttvCP2IT21YmM89HJSlhqRLd92jYsoBUt6gmVATLouuHe8jO8ZCd48FyyeQXxdEjHuJbLIyYH8+Uhe11oU5VGD/aQ3ifhqVCdF2KxgdHOfvyK2lfUyIzR8WWnF5SW5HouyiO7VZRM3XEQpXcDJXJo+IE+pyexTNPv4gzT7+IXI+LlidN1IKJb1+WPV+eT75LJPeVKhNLZcxrEpy+/mMUum0uuuFqwsem6PqjjjmjwqLoGKVHEnTfo9H5pyLX3X0W48skp8et36TwX20oZSd/cWI5hPcIaAsqaAsqBIZ1dK9Aw1aDiaUSSVeBcrvl9LUp8Oy1PyN9eo1qA2hdNbrP6yV1koF3ysSVB//jPrKzRNyTAjPftZdaTCCxxsXLX15Nwwabpk/14h92DF5syRlynwe5CuUWGD4LAl8cptZsoPlF4hvzCCaMP9qGNKmiZiXkCrz4zeu55zfH4s6ZiKZNdGedcrsFik3qCBdaGBb+cBXh3QLSmItKg8C7LnyKiWNg4hiHaMQ3W/jGbdSCY7BUD4moeZh4qI2Bc0Rm/I9JrkdiarGKO2tii+AZkoltt4htFpFvi9F43RBK0aTYLpBeqjN1nIGah+ZP9OLOWpQbJUZPMxk9zaR+ah5X3mb6ULjlJ2dRD4lYkkC1AYTvNNB3/s/RwirZuXDhUx+jHhaoLi9TWlGi0CXg61PILjRpvF8lMOwYJ239zTxMr+PmfPmHPkXLYxapkwwe/u2tyGWQyxDZWeGIL6+i+56Pklw5SGhvhRm/vhy55lSSwtskDvnOKh698rsYCZ1SGwQHLN6x80J8gyWiXx0kckeA5Ev1A+Y7hRk2rav2UeiU+NOTR9Bz18eQ6vDEz2/giZ/fQG62gJWss+Uzqwn1WYSfc7N27n3IU0Xqwb8ug/x7sOKi92O6oNwEpQ4LpWzy4A+PJWd6uWrpWh7/xY1MHwatXVP4zx3HdDkyTi1iU5hrYKrQtrZKNFngJ++9gXCvgeEWkEWL1DEWF3/hAXZ9aDXKqgmUVRNMLxJJLxCJ+Kp4pmxM1amc6z4QNgfwjYG5Osl7V1+NZwqqwwGeGJyBmpEozLRQ3AaBUZPcTJGffuEn+IZFXJMSlaTMnktXs/npmZRm6KSOENB9Itk5Atk5AnpARp6fJ3jH829YGVt6yQ/IvGMua7I3sunzq+l9X4Jcj0w9Am2P1vAPVSl8Mkm1QcXwiGRnKUiaQ2p7PrOTiWUKTU8XCO8qYqpgqrD692dAd9sB99r92PXB1bjT0PdON1dccD/3zYscqHSCQ6KGvuq8X7Scv51Hn/gi3b//KNEdZeSahZyr0PxUgfRCL7NvqJGZJ9L4Yp3GF+sIdZ1aBOShKQbPEzD9LszPxgiMGKhFxxFXD7sRUmmkUh0jNfmm98Z+wrcfgtvFCnHlm0pc12z6OpkjE9STTpyOFhSwJ6eB10uZ3wwzPvUCkU1p8LhfQzgP3Kuv5IIexN+Og5XggziIg3gr+MpXvoLX6wXgq1/9Kl/5ylfedLxdvGXSeeONN3LTTTfxta99jbvvvps//vGPrxlvFbNnz2bTpk288MILrFq1issuu+xNg0e//e1vEwqFDoy2tjYAwr0aoV0SgUGbwBo/l198P7YE/kGB9HyROcEUc4IpGk4dJbJJIb0IgvtEdkwl2Xvx9eQmA1QaBPIPNjF+hs6sd+0F25ES3jvzIcaPFel4dy/bn+uhMMPC3yvhSjsSQXfGJNcjYksC1ZjEcDrCrFtXEdqoEurTCPVpeFIise0WpiognTlN6bwC9SjIFQulYhHZAdWkQsNLAuPHQPvDBuu+fT2mR2L0RB/jRymUWlxs/txqBs+JEByo4x8s49s5TbkzgOEWUHI16nE3wd15grvzWIqIa7rOlYueoONBGyyodATwTFtYUy6a14q4MhotT5bJ/cDClgWEL07S9kiN6AaZ8L4a1UY3kgbxl4uYbhnTJaHkdcaPlpg8XKCW9LDtu4uIbS4S21yk0OUh0FsEAZSSyeQSL4JlU+4KMnZShOR6DUsWaNhsojcG0TpjyGWTgbP9GB7wTNtYHoXU0iDdd05TbfYi5arsujJBZFed7AKLUJ9JbGPuwD3hS5lMHCkjWDZC3WDsGIXa4irCHXGnIrLUQ+I6D03PWiRPGEX8eZxHbruJ02buZM2ThxHf6vTypRcG2PWh1diKTWKDSWa2xMD5jhPp5NEmXz/9LgwPSLKFJFtML1IJ99XJzpTRW+r8+sHjiG8QMDzgPnUKAP8LHiqz64TWudn2YjcNT8uMHu/0hVkKxLZbVBstRq/rodRmU2oR6H74g0wcbdP765nIp01jNNcRTIf87frQauphaHlKI7JJpv+P3Uh5Cd0vMHRGCM8UBPstpJrALe/7CZVG6Hn0A/jGbIZPkRg7WqX/AzZKQST5lCN1RHCkp9n5FmpWoLa8zFP/tpzOe3U679V5/rvXI+k2tZiAUrHZc4lTYRZeyb1NvCgiT+YxPY6s1VRFfOMGiQ06Us3GP6ojGLD3e/MYP8pF6yNlGh+XCSeKIML6nZ08+4OfoXtBrEiIFYnA3UHCu4rEtkB2sUHx1BKR3VUieyzqYZnjtp3H0DngSgs0/VFF90Lkfi8ndOyjYbNJLW7T8qiTq1ptEBg72aTr/F6UAlhtVfrPl6hFJDAElnxjFbEdGrEdGuU2D3oAOh6w2buxnb0fVWh61qIww8SdMYns1XBnbU77788Re0Yl+ZKFJQt8reeP7P5AgB0PzuTpn/yMWlxBi1rkZtuImrNTKNUhsl2gd+XPcGdsln/ucpZ/7nL2XLoaUbZY8o1VBPpLSDWbUy7+IL2XJileUHjL6+nfipGTPFSSjrOxe9LJ/pU0WP3rs7j2sdPp/tNHOP3YDYynQ4xtbcTwQKnDxjsqYIs2tYRN6ggP57Vv4T++8WEys2Uyy3Re7m2n/QGbn/7hLBZeu4rCXS0U7mpB1MEzBVPbGjDcAu6cBckaouHcS3LFRjRx+r0bnD7myB98KEWIbRLwP+In1yNRazT58MZLUYvgHwZJszn8q6swvRah7Qr+YQH/B0cObCSk58oY20Oste56XUzI6YuvYd1tVxNb28+8P3yV5Z+7nJanNOQyJDZqKJkqgmUzflwI33CVQqdA82/2EtyRwTNt8NKa+YT22eTmBBg/JoikOa8JeX7+AEF7tQHOac2HcPfV36Xv01dz37wI0qK5ZE7peU3Frv3SwQOEq/OG76JmJSYP92OLsGtVDEuRiO6okVngp+u2MWpRhVpUIXtIlPaf76SwtI3kUxLyVJFyh49iq4xv3MROxlByNexk7HXk8Y3w6nOyk7E/E8BXZMB/SSajL0/jSpVZa91FfPVzjH1g/hvO+2ZESE4mQJIOlAheffyzVz9xIBf0f5vnIP6Mg5XggziIvx//qkZC3d3dpN9ArZLL5eju7n7b875l0vncc8/xzDPP8LWvfY2VK1dy3nnnHRjvfOc73/IJqKrKjBkzOPzww/n2t7/NIYccwo9+9KM3/NkvfOELB1yU8vk8w8NO30h6jupkWLoFnvn6j7kyPESlQcSVs1FK8Mcdi/jjjkVMPtqCJ21Bcw3p1DTu+0Is/eLl+HYpFGeaFBfoRJ9zsen5GbR/dg9aEA75ziriGyDz/U4aNlm87/hnEA3I/VsZ+0NTlJskAsM28YsHyfdA4GE/lsfCO2Xh+sI4ri+M0/xUhalDBbKzRLgnjvJYCFcWanGZfJeMFhIIbcsi12x67qoxdozCcR//KOVG1bHHz0MlKXDyZR+ic8UAmbluzG/nqMyMoZRN1JKFEXQhGDa1Jj+1Jj/KSJbxo3zc9r0zmZ4vU/l8ntHjJSTNovVRC99IFWVgikK3F/V/oqjpOva3Eoh1k9jWCspUCe9YFc+0ga1KqOkKrqkK6uA0Pb8tEtorYEkCoU1TGH4Vw68SfXmaWsKL4ZYQLIjtqDOxXESumDQ+XyLfqTL0XhNLgnrMhVzU0MIyDZss2n8zzPShUGr3Eho0GD4rjme0DCI0PwWiYRHcJ1JuksCyGDg3wsC5ER5ffT2hfTapw1UKhyTwTELPDw3qIQFXziFCWlCiGpPI3tvC2AkCp154GTv/fSFW2GDet7Yy9e4KnD/NYf+5yjHYOVIi3GvR8JxMdJeFOilzw2cvQCnZSJv8SJv8hPcZRL42hA1En3HReNi4Y061z6L+QAOzfrkKV9bmoRN/TPGYCv4hJyJFKQpEd9ZovmCA7MVFlIJIvltEqgnYIrj7XYR2SdgSlNfF6bxDIHWUReooi3nXr6Jr6RCDZyjUV+Rx5cE3JqCUbDzT4B8z8X14lMQGk4+svhLPJNia87cI9AnIVTh73jb8Q5A6ysKzJEN4F/hGIbJN5MaP/YTQGi/jH6rTf55C/3kKs25dhVIyKPRYpM6ps+j7qyi2C8gVG8+UTbFdYPz0ZtQcFNplRk+1OP/7DzO9QKHYKjF0hkRupkg9LBIYsnno9790enbvjpA5xOSkRTuZe8Mq1AKoWRE1K/L8d68nszBAuVGgoTWLbQvk/q1MOSkyeZjI2LZGArtl3BkotYq48mBJ8PDW+RhugfhmSB0hkusRqcWg7QGRj7c8RnDYRJItWtdCcKCOOunIJFOHq6QOV1n51YcIDJlU4hLuSYHu222mF0nENklMLFMYPlklOw/0k/JML9NJHSFieAS+/MmP0PVHA6kGR37pciYPE5n5yyJq1snUvav7EQIjBokH+5m3ehXunMV3vnE93/nG9Rz1mY9h1iXUgk3/eUGml5kUOlVantJouMH7ltfTvxW+UVBKoAct/KM2pscmvVDAP2pz9JLdINs8c/sSFrSM075ojFCvTaDfcXZuflSi+SkLPQC3PHIChS7n3vJGKizpGeJD37uHyE6b2mEVqgknKii8z8KdtlFKAt5pk9SRAsHnPLhyzmOR7SVMxXmjVoqQOkUnvcCJljnhynUERp11MLRLwtoYohoHtWRRahYJ9WlIVRFThXoYpv7Uhm/cxjduHyCB8HoX1v3ky5xK0/5VR9ot6hYNt2/EvWOMsZMimC4JuQymTyY4YGM3xrE8LjyDOWJbTaIfGUT3C0iaE5uz6fOraTnfMcLbXx1ceskPDhzzvJc/yhlNVyAtmkvq6MiBc9r/M2apdOD/wZ0yLU9pND2cwtufZ87qtLOhl60S6qszcVozpRaRUovzNj7ygbnYAkSfH8dWJYLbpmn83R6843928BVSaU5ffM3relrfDPuddfef31rrrgM5na+GrcpkF4ad3/H7abln+DXXeD/ejAjVFrUjpPNgvV6Xdue3TqdvZeRvOt+D+MfhINE/iH9J7O/RfKvjnxwDAwOY5uudzuv1OiMjI2973rdMOj/5yU9yySWXMD4+jmVZrxlvdIJvFbZtU6+/cR+Oy+U6EK+yfwAgQsP6HIUemHf3J1h47SpKbTblRsH5oPK8m9DzbvyjTo+c70UPpaoLwwf1sEC5wyS0WyKwVaEaBzNq8PJoG6IO9RiU2gRKzU7e34M/PBZRh9L6OM8u+j2hXh13zqR4XRvutON8mnwe6kER85oE5jUJpg/x0r5GI7zPwjdhODEZWzTUnIk3ZSHVoTQjjKWA5XIMixBANG2kmkV8q058q4Zc1Ol9uhO1YJO/oxXdL5Ge55BNOVdl+DLDqfhZNpmjm6k2OW/goT4L8aY4XffW8G0Zx5XVySzwwe3O47pPpJZ0k1riYuIoH4UuD9NL4+Rn+NACElKpTqk7SGZhkIkz29h9pYvI7iqjF+hgWWRnu8nOdlOeGaHYpqCUnX41SxaZ8esC7i1DSFMFGjYUmP3fZeSKhWA6FQLdK+LvL5I/soXoVsel1jOQwz9qY7kVLI8L91QdwbDQ/RDq1cgviKAWQS3CGe/6AJGdJVofKzG1WKTxuQJ6yEWlCUQNxpfJyHWbzOEGyReKCLpAodvDyEkqycdkHr9nCcEHfVSfiZNbWqdhs4lvHErNIlNHGSQ+3k9su0V2psz0cTrJE0ZJnjDK9EKZXN1Nuc12Pgh+J0Zsm+2YdghgtVYRDZt33Pw5rEk3xU7bMSApwMSRbu6f9SDJ1R7UPGgLKtQbdQQbxEPyIED+ECfyIzdDRYrWkaJ1lALkbm0j0CdgmBKl04sUuy2e/+71eKYtNL+I+d9JsrMkRAPWf201il+jFhMotdnccPlPuG/nAmwZ+t75c87v2EyhWyA736LcCu994ONMH69hDvjwjoh4R0S++M67MTwSwV4RZa9DErR5VWpRgcnTnNdpqQ1KnTaVRufl+LNfnkV5nobpAt+gQzbBkQgveOG9KCVwvXcC16TEk88vwH/4NLU4eCad0XPXx5g82nD60L7uwfuMj9D3AuRnWxjNddS8QOyMUeSaTWFxnfwsm2KHwBGz+5k6TECu2kQXTdH0fM3pS65ZXHXDR6g0SOg5FyMnCWhhhZanNF7+8mqkulOJ/PGGE/GO19D9As3PVCg3qrQ+XsNUYcnpOxB1p7rK+hDxdQqtS0YJDmpk5siUmxT0AGQWOlmuublBWp6sErg7yIKfrELJ6+z+bCdtj1Y47mvP4RU1vKLGxAkWTWsUUseZRA+fpO1+sGSBSkIh/qWBv3s9fTN4p0y8UxZNz8DUcoOOBzUs1UY0YPSrM+n6tY1/1GLyf7oo/aqZyAeHMFVofaxIvkuk0CFR76wT3/jnuJXQbwNMf7uL/751JcV2AWWH94C7KkA9JND0rEbqcAn3lEDs/GFMFxTeXWTimABK1XIqmKM2faf9ArkCYk+J3z+xlFKjjDtnETlnFKUMgWEb97SBO2MzdYjKby+6lq2fXs3HL7yf5LrygWzmYpf1muf96grd/n+vGVrP0FcdSe7EEW7E9hbM1ji1GES/NUx8SwlBtxANGz3sRsqWKM6JogVFpm/uwDdhknw2y5lzj+PMuccx+vv5r5GH7s+3BEj8j4faonbWbPo6auHPBGs/+SxcvJy11l0E73ielvtTCIaNrUogilR6Ik6mcFVD3TNO8tksat5GzdtIdZu2OwcIbUxBXUPIl7FlEau9Eal/HD3qodriB8NEqOuIE9N/031ibtnJ6YuvIXjH8weu1xvlngqpNOtuu9p5nt1tPNj/gzea7k3henEvBHyQf311P7pmz/9qqnWQEP3jcbByehD/itjf0/lWxz8rXq1afeihh16jZL3nnnv4xje+QVdX19ue/y2TznQ6zVVXXUUymXzbB92PL37xizz99NMMDAywdetWvvSlL/HEE09w8cUXv6V5InsNsgtDmM01bNFGKTuSrVrS5p0ff5wN/7GaDf+xmvwMAS0InikbreAit1BHC8AHj3sKuQJKBcf98jkF1/OOpKnxRYPqnJpjiz9uI9dscodrSDXHDdPwSeS6ZKZWVqnFoRqXyc0Q8aVM5HwNOV8jtrXKxFIX0+dXMV0ilaSE4ZMQDRulYtHwYo5PfffXSHWbUotKYmMd3SuS7xQx3SJKUSffqTJ4lo/YdovcTAFfyiS4M4tcAddkGcvnouMGEdd4Edd4Ec+0QesjBp60hekSyMwTyXe7qcxrRM6UMV3AB1UCg1UC+8qImoUrC4kNNaLrp/BOm7hzJpJuU5gVwpXVib2YpnBslZ6bbc752WPM+B8TK+QltqVMbEsZ3SeSWJdFMCwmD1NQChp6yE1pWRelhUmwbYqzwtRiMmpOI7S3RLC3Qm5uEM+URnhvleCQSX5BFG9Kp+98D4jQ+0GJWsKNN2Vj+CSkuo1vwsI3YTGxzEvfBX72rFKIbbModfoxPCKtj9XxpZxg+2pMIv6CRK3JQ+cDGuWkwLlnvEBqhUa12UQt2cR2mFCUOfkbTyNXIDBssvWMn9L3hx5cWdPpPSvIuGQDl2wQHLDp29aCXBEorSgxcI5MLerkc4oGMO0iuaof02MT3SJiRTUAXDlwZ+HED32Y0eNU5CqEHvfi61f46GX3Y2wPkV9SR1BNOj64jz994TtEH/IQfchDca4ju46/c5jgAz6C9/kJ9Iks/s4qvvXdn6EFBCaXqJhux1Fz/v+sQtjjQ/eDKydwyd2fIPiSE/Gw4IX38vBXjkMpgVQTCO+xOXfZy/i3uYjNn0Y5LoNyXIaf/uACDLfIBR9+HO8ElFvBs8FDcY7BYd3DuDIQ3WHT8LJDPLyDMh+79H7CL6mOu2wDVBoEPNMm04e9sgnSazG+uZF6k4F/QGR6LESl3cQ7aeGdtHClRZoek5hcajN6vJ/Olb2kjnQhagLeXS7kEoRcNfI9Asm1Kr5hATUHm8ebaVurU2oR8X8/SL7HhW/CiZWpNllYCqiRGt6WIqVmCf9/jLDgJ3+OJYk846YedzlKhlkeJM1m5EQ3oX6dsWtm4E47hCjxso5asrB+nKSSUEi+VEOu2nQ8UKThZXDlnc23WoOL4gUFtl25mtxMD7NXT/LQ727lpY8dymeuvILPXHkF3b81GTvFpOt3Fs8dcjeHfWUDpXaHhOy+Z+bfvca+GcpNEkrJiRtq/5PA1CEuxLqA7hUwvCKFTpVSi8jEmRrunIVwpR9X3uasW54ivM9CKUFwk4tq/JUdXRvcaYPMbBkt6LwGojstRM3Z/NF8IpE9GuNHq5gzKihlKN3UCjY0f0fGM2Xz9Z/cQG6OxfShsPQLl9P+cAn/I35m3lZArtmoBZPH5v8R8xjHkbiSVMAGT9pme72ZhT9cxU3Xn0Wh28v0QpnphTKJFwWSqz0HnverK29rNn2dFeJKzjjrPbR/1aJtbZXYLoP8wji1hJvILpvMl9qxRYFaXCX8zBDTh3jIHZ7EN1wmti5NNS7wn9f+HKFcI3v2PLJnz6P9qxZyMnGACK3Z9HXWWnchJxPIa9cfyOKMPj/+mr/JCnHlAfIphUJkjkw4BmtHxCjODFFqlKm0eMCyqM9uRsgUCA5oBAectcVqjKI3hbAao2jdCWyXQqnTT3lpF+p4Ae+WUeyWBNT+ek/nqyEtmoswOonc0Xaguvlqx9oDCAY4o+vqA5XRt5LTCUBHM+gGdqX6uocyp896a3MdxEEcxP91/H924+dfLDJlv3JVEAQuu+yy16hZL7roItauXcv3v//9tz3/Wyad559/Po8//vjbPuCrkUqluOSSS5g9ezYnn3wy69atY82aNaxYseItzaN9LMP0YvBtcRPeIaEWbfSogVIQuOvmE/lTxcufKl5cWQjtA90v0PCUjCDZ+Mbh7htPdMxP0hZ7Ll2NXLMdSeKwzcSlNTp/KVLoBk/axD9cI/ySSmyHiS07rqvJl0qEHvKiFJ3qZHDARikYVJv9VJv92IpI29oCjXe4KDWJWIoT56EU6rim6oydGOYCX5Gnrvu5Y8QiOCYYHJnHcItkvlQhc5RGcr1JoUOk+6YhLFkge0iUl76xmsyiEEOn+Sm1qdiqjK3K/OznP2TkZBlXRsMzbdDyRA21ZJOboYAgEN9SxfZ7EAybgXMDSDWLyN4ahlui0h0hO1Nm9DiJkXfpeKY0srPc1FuCzPyvGpYq8sD7jiG9yIdYrLH3Ejd7L3HIDEButpemZ6sIuoVc1DBVATWnU5gZILCvgG9Cw3TL1GNuagkX/tE69YiCYJhINYt6SMQ1UcSVEdhzmRfvbhXNL+JJW2RmS0weJjF+osX4iRaLV26n+RmTlj8piIYT5J6eJ2OpIlPvrhDb4ch5JQ3kkskjt93E1qtW8+CvlhHc5CK6RUItmAifSKEmq9y87mgML2RnSZz6uU85GxqzFAwvCIbA7l2t7N7VSrlJwDsqohRAftlP27wJcot06mGBzGE6sU0CUz/pwkjoSJpN0xqF/DyDYqdjBFRsV/CNQS0K3kmT2HaT3/7H6QgmNK1RIKOy8/6ZnHzL56lFBGoRAfeYzKxbVzH8XBulVoFKg4Ara/OLT/+IVT9fReXEEkoR4ltMfBM23uXThHptIrscwuUdcfoc00foVFJ+nrru59iiEyJfbRB4ZGg2egAm+6JYjzpDsMDwCNx944mYbtDCNluuXk1ou8zAzTOpNMEPv/5Tim0ihtemvrDCtY+egS1CJQlGxMBSodguEdsksG3Zr8jMF4nsgOgGmVKbTWSjglQUSS90hr2oyMRxNmK8TqjfYvSWbsrdBoF+Ac8k1OKw+5EetO4axQ5nI0mpQuh+H7YiYknQ/w4FSxIYOd1E0pxNqOyyOsaYF/G5EADbNnUiarD8ok0sv2gT8Y1FTFWk1OqsBVLdwpYgN1Oh2KYQ6jdJHWUxsUzBO64xdozkVJFbXRzzxRcY+KyAJTuOxQCSZlHJOhLZxFMpvDcXWPKNVZTavRRbZYqtMoJu4YtWmDxU5ZSLP8jGrx5G96+m+dGuU/BN/OPeuTzTzvPT/CLZWTJKETrvr+CdssjOlMjPtGl8rkhggxu1YJCfH0Gqwy0/PgtTFSh2OuZKkb0GxXYBV96pOBYXavQsHSSxQaPYLmK6wXSDXLPJzlJpfaxK4g9ukk+nCQxWKXbbVJo9HPKpzXzj0suYfUOWpmcswnsqDJztx1IgfUiQ6MvTmC6BBT9dReNP3AimjSVDdh5EtxT48bcuxJuyCfUbtK7aR8dvxuj4zRjrbrua1KrqG0Zu7Ie10fEQGF7h4Z3/tZbxY8FwOTLv7Bw3hW4vkm6TOruT5IslvON1hr9koyf8tP1umCuuWwWFIpGtOSJbcwipNA+OX0fh4uWv+SC2n+hpHXHOaLqCzPImgNdUEPf/O3v2PLJzBNRUkci2Eu6pOg3PTeMbLGPG/KjpCnY0iDpZRJ0s4s7qiCOTKKkiwr5h0gs9mF4F97SGd1+OwW+7GF3ZydhJEbSuhtf1t74af0kYez89Gzvkf8NK1/7nlzkywYP9PzhAOPebCv2tMLfshLp2QF786vmzc4Q3JLGvvrYHq3AHcRD/b/H/1dfgv1pP537lant7O5OTk69Rs9brdXbv3s3ZZ5/9tud/y5Eps2bN4gtf+ALPPPMMCxcuRFGU1zz+yU9+8m+e6xe/+MVbPfwbovxogmTaxnDD1PE6QlHGNeGcV/2oEp984r0AKI0QztvkTqhi5lz4IlVKbSqxbRaWDNWYyNyfr0LqAKkCbR/dR2kywdQhKkbEoJyQcCuOdNOWJfLzDTrusZk6zKmKNr1QRwvKFHtETvnci6z/0CEA1Bo8mAkPuR4ZPQhNzzo7/qMnBDBdTlVg+ecuJzdTwDwK9LiOYBl0fEem1AnC72LM2l5i4JwAoX02Y+e1s/HfV7Pghfdy2DdXoTVBZI+FJ6UjVJ34kgt+8jk619eYWuzBnbHRggqJdXm8ozJa0o9rtEBmSRxX3iS23UKqGvSd72XGr/NU2gPUYtB5Xw3DK1NpkohvLFJu9yIaHs770VruuepU4huLaE1BZv/C+aAweFYI0QhiyQK1BheCpWLJApm5Ip07y3gFKHcG8O/O0H9hgvix4/g/60aPeBAsyM71IxrQsD5P4Po0gdVRfKMike1F8rP92AL4x2wMj8AZ5ziB7nesW060ScY/blAPSshVm9CAzWeuu51P//79jJxkk3jRqba58hKzn74UcacPSYLwLgNLFrAFmHqyGVwgBi2qDaB31lALbmzRITWJly3GTrDpuNeR69ViouME7IP4Ro1MpZmICcUuG8+wAthMLxK5aulari2fzvwFQ0xs7qDrmEGmftWBaNpk5oNUF6jGJV76+moWfd+J5sjMEWl62sJSLNILRTzTDvkozrCccPq042rqnbQxXQIf/OGn0BPgc2v4R0xno2HCYuLROC9+88cs3/A+5CeiyHVw5SzcYwqeFByyZxXvuOxZ7tpxGLEH3ZRsSGxwSHqhw3ltGR6YXmrgGVKQNAjuFTjuio+iBm0qSYHAAOyqNyNY0LABjB1e3BdOMOqN4RpRCLXmmLVwipeG2rEtkXmrV2HLoPsEDB+4sgJ6ACyXjfkKx7KG/XhTAurRVTJzPZx27ovc+8ISYhcM0zucQJBs/OEK9oYoriwYLih0gm9UwBYlDD80PgcTR1uEtyr89Js/4t1/uBJqEu5JEd+4Y9ok2FBtstn9tQUA5I+QKXbZxDfajJwk0DJ3EvHZJtS8jTdlUEnKtD1kkp0Fve+Vib0kUG6GxqfzPPrTo2gadfoQEUTqYQHfhMnMGw0Wb11FQ1OV+7t/z8LATGJba+hep/pWaXbhWushOKCT73YR3VkBReJTcx7hujlvTpT+XpiqI+F1FSzqYZFSO+RneYhvcvqgPWMVyu0+PFPOH0WpWEzPl2l+tsrQqR70zhqBu6pYqkxU8VKLiNQabHpusah5mxk6WyK+0aYWcd58/cM1ouuLlGdFD5yDLYm0PmJQ6FIY+nAHQyu9+Ee8SDUbLehs4lmKs0moJfxU4xLecRtbFhBNm2pcINgHlksm9nKG9OFRJg+Tydwzk9hsZx08o+tq2kN+1my6itMXX/OGPYZHvv8HvHjL1ZzRdAV37jqdOc+MgMfNmadfhHCEc+6pIzy0/WaQzPHtRJ8dI7m6AcMvMvzhNnp+mQKvByHl6IjtZMwhSQvDrzPAOXdHmvsuqoHb9bpMylcjsjXH89+9k7NuOAtJ0xGrjlu4WNOotgXx7pzE9rkQNGenzxYFiITQkgHEuJ/Y1irK0DRWPATDY3RcFcEujkFrI0K+RPCJYbjtb7tXIrvsAzLbV1+/v5QQn9F0BQ+OXwfAeT9ay33z3lofph0NIkWDr/neWusuTlXfC/NmvO77B3EQB3EQ/1fwT1y5fLvo7+//h8z7ttxr/X4/Tz75JD/96U+59tprD4wf/vCH/4BT/N9RSzhfp0+pE3tORSmI/NdFv6TeUUevyQdcMUUdvvkfN9IWz+KalDC2hIjudBxkg30VcsdXaXzJoB6xUUuw+w8zufnQW1FLTuC7f8yg3CRxxQX3Y7ihdY1AsV0hu7ROYn2JUotKvlPCN26z7hNLKLf7nA9uIwXHHdYGVxYmD1ORy1BttlDK0LDFIDtHILbdIrzHRg1qKOE640f7CG3OUIsKjJ7oxAp4p51IikO/vYrIHQEsBco9OkrZYnKJi8lj40weG0ctQrHdRcPGCnOu3E5g2ECPuJFKNVy7J9h9eYzIhjQ3XHct7mmDWtLNjDsLVFv9GG6B0D4bqVSnGpdxTxuUO3xYigA23HvFKUhVwyHNHpmpI0JMHREitt3EVAUST6ZQCwaujI53ok7z03VKM8MUOly4pzVsl0J8m8nExkZqTX5KrS6UgoGrYOEqmIwfFyL1392Ihk2wv4blloiun2JkhROEHt5X5+Frj+Hha49h1g1VGl52+oCmljj9cOMnWvz40ncj1QXUrEglIWBLMHFpDUUxSGw0kWrOBzXRsHn8phtxZR132DXnfZ/AoCO1lKs204shtt2panf+yUIp6SglnckjbQJDOv4xi/QCFWyndzyyA1ZddD/pRTD3+F5u7l2OmpXYk0qQeFEgfVs7vkmTYL9GbItDZgXLpvvej2IdnUcP2hheh0x2XbmbyC6bSkKgkhAI7JVof8jGPwSRvRZy3Ymc8E7ZhPbamI86fWbVpItawk18q8Z573g/9kNRXHnn/LJzROSyE+Xhmba5966jcW/0oFRshBdCDL/DotQs0f3OXrrf2cvGL6xGycjoARvdD74Jk6mLK6z79vWUZuq0XtbL155+B96TpqgkBUw3XNj2MuqYQsMmE/uPcWb4pgg+5kMY8lCPW3hSUJhp4T5uGlF7JRLGb+IbEfCNCFhRR75eKHjQohZPjs0guFeidySBMuZCGnOxYclv2PmR1ZSbQbSc11V+ieOOHNtmMX2IyC1n/wxbhPf8+lPsu/BnoAu4p8AzpTv30U6RT5yxBsMtYrhFtJPz+AcFqnGB2b/Io9/aSK3ZwJZAKRt4UwZTh8rUGmyiG2RiW0s0P11n4Nww1QawFYHGFyroXgH/iIWgW5humeCAyaNPfJEzT7+IlieKFDs8hPdWCe+tIpgQGDZQ8zqJNQPs/ZCC5ZY5o+1TDpn5B6HcKFBucuS0ogldfygw484i0XUpLFlAqGnYokBquUWhQ8Vwi3gnbXS/TOefisz5SobCrBD52X6mF0jE1+dQswLDp7ipRWX8gyLBgTrJlyokX6pQanNTWBDHtzWFUrEozQyTneOmFpMJ9usYQScbLL6xSLC/hlqwaH20gOF27g/BsgkM69iigO6XqMQlmp8pEd5XZ+97PdSbAoR6a9SjFu6MjVwxkSsOIdsvC32z2I3IVsdoiGCAwGAVanXKMx3CpAeg1Oam7Q/jIIlEnx9n8D0tiJpFeo5M911ZrKAHanUGVzcwuLqBoa+KrNn0dYJ3PP86I6H7T5yLkC9BrY6QcdatVxO5A8R17wALXngv9Z4kmeVNTC+NoScCZA+JohR1tM4YZsgLogCigOulfRhRH9UGFalQRclUsOIhbFlEaG3CDvkx5+zfSfrr3gt/eZ0i9+14Q5L36u8J6Ty9n5jBCnGlU+k9su2vHuMvIXe0IVS1A2ZEr8aaofX/a2TK/2elfQdxEAfx/xT/apXOV6NcLvPAAw9w/fXX8+Mf//g14+3iLZPO/v7+Nx19fX1v+0T+HrStreEfrpFY4yK2qUjLkxpf+8mldP5KIPK0m4b1Ag3rBTxTcPl9H6J6SzNyCVqeqoMNtbDEwNl+Wu9UGDxdpOUpk87378U7aXPxbz6Jb9xgeonF0KkyasHm3itOoeXJErWISGR3jeRalbFj/QQGaphux8a/1uDCldFxZXQsn4v0XJnG50vUoo4BTmDEwt1exDPt9MK1PVqj0CHiSZvct+x/kHb4sBSYOClOdI9BdKeBLcLIxTqiDuG9OrkekaanC7TfJyLVLGLbdALDzgj1aURfSrPvEpUNv1nA5KEy1bjC9JFRtJ4kPXdWQZHoUbykjnQqkv3vDOEZKeFNaeRnCOy52k2wt4IlCwS3OcYThU4X9YhMqc3psSy2KiSemSbxzDSlFgnNL2K7ZEyXSKnVRbnFhemVcE/VKbcIlNrd1Jr8CKZN171VtKDT2zpxZQ1Jsym0ydRikO+U8UzqpI70MHKil773JBAMgfCeKtW4I6UVDai0eUktDVJol4ltdkLGmx4TKTe7Sb5koPXUKHU65F5Z78fYHsK+YhLRALlsYosCs5++FFOBE7afy6r3f5LggIZn2iLYX8OW4N++90tyPSK5HgUtoqJFVHp+V0cLStQDIpHdOr5xC3fWJtRb54ZfnIWac+S12rNRAgPge8pHehEIFtTCIumFLoIDdTS/QD0sMOMOx5QnvMtxX9X9AoPXzmbyCJtSt0mp2yTcbzJ0voVcs0nPfyW78pUNl+jWEqEznR4x32gN92SN/osE9r43QGSPRmahRX62RWDQxpuyCc/KkJslUEs45i3j76xjKZB8TKYw02Lk1h5Gbu1hwU9WEdoj4MoJKEWYWFlHH/Jz6Pp3E96qsP35bg6fO4B2XwPlJVX0U/Lc3LscrU1j/CiRlvf2cdfeQymdUsIWbb5/5u1UE9C78mek+6Lc9vFrie408fQp5Geb5GebxJ52UWtwnlegV6S2PsqXr7gd/xYX6tw8lupsutxUSOIfgVK7jXFkkaPn7MOVt8j1iOjNdT657d2UDq+CALNvWYUrLVHohny3Ssevhgj1aTz0nmVUYyLVmMjqxb9CNCCxwekrE00bsSzhHzWwJQHDJ1FrNphxRw532iK90E//B2y2f/Q6ao0WhltEsMCWncig3vdLaCGZp/7n51zw3Mcx/S70kIvsHAEtqKAFFSaWC2TmyEilOtovJfrOuBGx5hhxGQ2Bf9ia2fJEgcTLdVx5i+bHsgAIukm9I0Jg6xR9F8UJ7Mow57os8RczhF4cJdRbJztbodzqxWwI4hut4s4YJDcY6BEP3kmbrj8UiD4/TnSnjqWIWIqEpTiVefe0htEURikYeEcrKCUb/0gd0yOS7/E4GcQdPmxJxHQJjB8bpOm5KuUWG9PtSOZtEUTDJv5iGlsSkcoGs28u4u7PoPtkmp+0CYzoWKqIpYoYLbHXyFbfCNbGHawQV2KPp1CmStQWtaNmNdZs+jotfxjFO6mDrkOpAnWNjlv7kWoGrQ+9QhBlkd3/1kP7pYO0XzpI4O4gK8SVFC5efiCWZYW4EjmZwE7G0NtiZE7pAY+b0yMfPnAeK8SV2DVnHRBmdjpGY+kK0ScGaXh4CDlfA0APKKgjOQTDdAikYSLEo4wd48WbqrP73/0IuolYqKKHXJDOUk/6kHvHEFJpRld2/tV74y+vl+B2vYY4vxoH5LUndNB9V5bCxcvJnNLDmsLNf/UYf4mJM9uwPY4D+qvnBTj5sg+9rmL8lz9zsPJ5EAdxEP8Q/Iv1dO7Hxo0bmTFjBu95z3v4xCc+wX/+53/y6U9/mi9+8Yt/V4HxLZPOLVu2vOljf/jDH972ifw9UMcLWN9IE9mUxnJLpI5U8U5a6AGZ5OMpoptyRDflaH5oksbnQPcKNK6rUmxV8Uzr+EfqeFLgGS0T3yhgiwKT3+kitK9K4wsm40dLzLkuS/MzlmPLH1Uw3TK+lEmxw4VnSqcWd6Ry8a0G1genUUomfeer9J2vkjrCj6RBsctLwyaTwLCBJQn4/xDEFkDNaliKSNvZA0h1m9P+dDXeMWh+tkpw0MBwiWTmynzp07fjfdGDf8xk5BQZ/6jNxFFBUodL/PqmHyHXTKYXKUwvUhg9TkVv8CGWJMJ9JsmXdCxZoNQmkJ7vZvJIH5WOACsu+wj6IWWwbQIDMHZCGKlq0La2StMfVQy/wuA5IrtXxZk8t04lKWB4RCzZiXExvJBdHCO7OIZStolvKpBeEsWWBNxpg+xskUqDBIJA+31ZNL+IktdQCwblZjdy1cI3WiNwdxA1q5FYX0SqgemC1BEuggMWHffniW2zOGxJL6V2N6HtWeZcuZ05V25HKZm48jbBIYNih4BSEBk/1aTaIGKqIh5fnbZHLIdkVpysQOlHDqOpR2QysyQCj/rwTtmU72xGqpsMfsDJXjW8Mu0PGfzX5y/FM+X0+c358jbmfHkbplvCvCyNpNloIYmJ5QJyzaYWV4lv04nssVALJg1bDSJ7qqhFJ7PRlbOoRQX8oxZySWPqKBOl6NzH8Zt9FE8t4U3pFE8qM3m4QHSLSGyjRGyjhPrJMQKbVbJzBDoerODOGIT32tQvyjJ9aIDJdU3YIqQXeMnN9hLeoOAdExB1m9BukUC/Q1RdeQvz4RiJDSaiJhAYMRBEuPzi+1EqFi2P20iaMwwvZBdaKEXwHj+F263j7ixQGAhTj0DDIZNseWomG/5jNVZWxdwUorwline3ihk1qBgK0Tt9KC/5keoCVz3xHkK9NnN+sYr/Pu1Ozn/0E8SuGsCWwDUt4ZqWKLUBM8rYlsCsd+/B8MB1AyciWPDeGevxjAus+OALrO49jnyPjemxsEyR5/Z1U4uKBActqMhYT0Q5ZdZu3nfmEwgmdNxfZM9lq/FOWfR/oJ30fJXC7BCFHptCj80V160iurPG9ELHrMWT0um8X0MLSvReoJJeIBLaJrPrY0FMl0Butk3XrQKnXvLhA9dMCyo0PZYm360w80aDWsRZYjNf6yAz14srVabjgTKWKmCpAtFtUGuwMb0qa+fex/LPXc4Da+4kYeWHIwABAABJREFUc0IHcir/D1szd3/Ui2BauDIalkdh8sgg2YVh5LyG1hyi6QUDBIHJY+LocR9mMkyp1UXLbbsptjl5iuNH+fCMlhhf5hDChjUDTC8OgijinqziGitQblYpN6tMLQE1VQRBoNKogiAQGKohF+uIhk10awFhdBJsUPI1DI9AZVmZelSl++4KtiQglw0iu6vkemTKPWEGz/CSXuijlvAyfJ5jnewdq5E6XKUekqmHHDK/9JIfHDDz+UvsJy7GisNZU7iZWnsYuWwwebiXM9o/DZKIK1XG9nnB7cKOhbDjYaRUHqFYwZZFxKrOjDtLZM9dSPbchUSfGWGtdRfrbrua7MLwgWNYjXGEVJrJJV78wzUqM6LQ0fwa8iSEgwfO97wfrUXIl5he0YHW4xj31aIC2dkKaBqTRwSgVHaGJNJ+xwBSRafjdofk17qijB6ngqIcMC8CDmwy/DW8uvpqJ2Osu+3qN6w27r+m0ScG6VvpRMB4J3XOaLrifz3Gq9H4uz2YXhUj7H2NbBdALht/9dgHcRAHcRAH8X8WV111Feeccw6ZTAaPx8MLL7zA4OAgS5Ys4Xvf+97bnvctk87TTjvtDSuad99991t2nf0/hfLMKMVbW0GSKLe6Ce+18A/VGD3VYvgdSdKHhkkfGsZWZaS6jWDD+FEe4i9MIZg2tZjixFy4FUL7angmqnzie7+l1OamkpRoedxgenmMUrNEqL9OvktELtZJz3Hms1winfdVMFwCQ+8yqf8xQaFDpeuPOl1/1Cm3gjdlUWwVmVgmMXKyRKVBILSvimjAvg/JDJ4lk/5FB1LNJLJdwlW0sEWB0RMkAr0FGp+v8m9/upjQoEk1KiGXBAy301Mmz89z6vc/x8RSx8xHMCHxsolg2VhhA2yHGJVaBZLrDeJbKvjGLWoRCcGwcb3ko9QiYbqd6qHplhn/tIYrZ2C6RZLPiXTeq6Nu99C4roYrb2J4BKK7DPyjJnLVQq5aVBIClkvGdAtkZkuIpo0WtEkvtik3u5hcGsGWYPpQH30fdDIWS00yul+h3CTQ/w4vA+cEkGqOU7AegHd//UH63xlCLZpM/KAbpWzxwMO/ZejLsxj68ixyM1SqDc5GQWy7RdOzOjNu1gnvcxwdW74rk5kloxYt4ltrJF8oUvl4luCQQb5LpJa08U0Y6H4BwXYq1HKfh/h2g9xMhUpCdiTBEiSu6OeJhxbzxEOLqSQUhDviTB1lUmwR6bmrguEWwHbMmmphkbFjFKYXyBS63LhyJsLcItWYRHlJlWKbSLHbj7dfJrPIYug0L0NnCjTfqDK1WEXe6qPjwTq1uNPXZinQu6sZ75TNWWeto9TmyBgrZxeobogS6tcI77HxpE3qEfBOGBx52SY+8qH7GTvORW6eI8uuNEKhU0Ip2jx13c+Ry47sO7bGzbVPnI7hFhg7XsQ/puEf07jigvuxRSdSpPJEA+LToVc2CETkElTuS2ILMOPXl9P8pBOJ4R13zGP6zriR/i2tmC6BapONXAX3mEKpVUAPWfz7gxeBIbC5r5Xmp+tEd1pEd1q402CkPDTfo7Dj3lnsuXQ1/QMJ/GMWG/LtHLtyA/c+uIxcwQsCLFwwiLrBhzTqJj/TptApEtjnyOkfefoQbn/gBHyjsO8iPz13fYxaRKRho0Fkt05wd562R3TaHtF57qprkUo6kb0aw9+QGTnJRbFVpdAhMnDFZ8CC0lEVGl4UnY2DIYFHbrsJ0y3hymjYgiPDnVweQwvB4BleGp6a4KxDV/D4Q/9Gw0tZxk6MYEsCWkBCC0gEBzX8gwIP/f6XnHzZh5BrNsde+TEnq3RZ4z9szYyvk6jFVZRcDVtwNh7Cu4pIuTLq4DTenZOYfjeJdVlKLS6kVI7IpjREQsR2aEwsD9B25wB62E33r6aRaiY7v9SOf0wnf2jC8YyXJHxjGr4xjZl3FKFYRirVsWQBKV1CquiI5TrewSJisYY+t41AXxExlSW2IUfyLg/ZWRKDZ/lQszWGT3ZjqRKx7RqiCd13ZQkNaHjGSjQ9U2b8aAXLLREYsnHlDVx5p2KcneNInd5IgrmfuDz+0L8x65vXItZNCl0eAkMmmePbscI+hKqGnvCDIMDQOHrYDdUqmWNaESdzoJus+cPtpI4zSR1nYuf+HPux7rarD0SMiBPTZE7pIbZD4x3XP4Z7rEx2Yfg15Gnw2iDrbruapZf8gJ/fchZGc5T4Q/0oG/YilKo037mHxPoKWDbxzRXMzkbMzka0ZIDeyzsRdBMlr4Fh4t45Ts+P95A5sRPkV+wb/D4YHPurOZ1vJlWVFs1lhbgSadHc11UeMyd00HPL+IFrWVvU/lfn+kvYTQ0gCsj9r3f0lUpvHKF2EAdxEAfxj4fwNsc/NzZt2sRnPvMZJElCkiTq9TptbW185zvf4Ytf/OLbnvctk85Vq1Zx8sknMz7+5zeH3/zmN1x66aXccsstb/tE/h488ONbsAWotAfQPQL+wTKGT2bOD3M0bNbQggJaUCC9JIKk20R3VEiu1yjPjDK+3Km2JV8sUejy0LvSxfjRfn787+9GzZskns+SXqAgWKCUbMc1UYFa0ktkn4klC0zPlxk6zYt32qT7Vig3gy9lML7MxfgyF8mXLFw5E9+4jeGzaH3UcdTMzvZgeAQoSyRfAG9KRzAt3BkLWwBLFYlvBCPoxvDKhHcLVBokajEI9dmIpk10h82th92C4YaGTTqVJptKk83EMonMfC/uIQU1bxDoLYMN0wtkDK+MYMH0oTi9pgb856duptQGxWVVej8gUBkNMD1fZWqxjFo06btIIjhgU48quCerRHbXEU2QKxaeKQ3PlIZv3CFtwQGd4JDF6HEq7Q8bBHpFPJM6oQGN2ml5sMG/wU10p46raJE6QiG6y6DtUceht35kGcMtENllc/t/n0lig0luhnLgQ+SiH6wiM0clM0clvrGMZ8om3yVhyVCLyVQa3Qx90EApmciZMr4Jm3pYZOjjJvWom+B/+yl0yChlaH7awteX5/APbcIWnB7PUK+NJUGpw4nISR3pQTBh4mfd+EbBN+rEQ9TDzsJi+KH3Qi9S3Wb6EInY1iqm28mcVI7JUOgQOOwrG0je4kEtWwSf86AUoZwUnfiJokjDRpPAXolCp4pv3CbYbzOx1I1ShGqDMwJ7nc2Ke547nHpYxPP+MYztIepRi/dfdy+TJxik58rUYjbFDoV9X5jLz285C98oKAUJ37gTdbHp86vJLIKZv7ocBHj8hhvAhvA2iYmjbUy/wcCZKgNnqvzooTNxTYlUkhDuNfnZlT9BCwokXjYodVqE+w2MsEli3hRjpzj9ra68TXSnxZH/cTlYEP/IAImXbOpR2zHtUkAuibQ/bBHdIJN4VGVyiYvUOXVS59RxZW3iGwRGT3LiMOZft4r4OgXDI9D7q5ls++Yh6BETM+siuh12PdtNcMDClm1EHWpxm3oE6hFIvmjTeX+V7KEGob0CnnGRRR/ZwujxEoZHJLswfCDb9oKVHyV9SICBs2Vavi3ScX+ZqRN0fOM2879wLYlNBjO+q6OWnP7WegTOPPVCXFNVbAFcWR0trBDb7qwvbWureG8tOb1uR/0n9aQP4/g8ekDBP1rHP1rH1Z8msb7Iiovez+ShKsFNKQy3wJmnX/QPXTM9acvp0dZN5Okiruk6pkcGQcBsCFOen0BOl7AVidDuEmYyjBnyYisyUtWg+fEsencSdfsQZsRLsd1N+xpHbr/gc1uotviwFQnX9iFc24cQU1nsWAhhYprI1hxW0INYqGL53QilKtTrWC4JcTiF2RoH08Q7XqPpuQo9d0w5ldFBcO8apx6SGV8m89sHbkEpaJg+FbGu03PjMIZHohZ1TJIqCYXswvCBjMe11l1/lQQ1vmjQe7FC9NkxqjGJdbddjeFXQdNQdw6D24UQCqAOZRh/1wz8wzXssB9EOOuYc5l7zQBzrxmAjmbO6HKiT/Yfb4W4EiM1SfCO53EPZHnwoqPQEt7XHH+tdRct528HHLLatiaLtGMAfF4Erwc0HTsedXptz+hA1EzEUg2xVEOqm/T8YhRbkZCKVYxEEDvgwyqVidy3g1p3DFwqRm8/gvpa87+/xJtdJ3vf4GsI8qslt66CCYazPp/RdAXp+eqBuf4W9K2MHCCXr/6dtdZdZN/AkOkvcbCn8yAO4iD+IfgXldcqioIgOJ9xk8kkQ0NDAIRCoQP/fjt4y6Tzmmuu4R3veAennHIKmUyGX/3qV3zgAx/gl7/8JStX/r9Z+N91/rsotQooV43jyViINYPR41Rq7SEsWcA7ZeGdsggMao4UTBZAgHyXTMuTFWpRmZGT/ZRaBLr/4PTn1aIStiJQbfYT2WsS7Ksh123Gl3toer6GXDUZfqdTIWt5qkLDRpPpD5fJd7toXGeQnufIX/2jNkrJPHAT9tytoftFbBlqMYFKI7Q9bFNsEZE0i2rSjekS8EwZ9F8k4B+uUexwTDZ84wbxDUXsZXkE03GgVCo2n/z3K7ElSM9TCAwIzhiE7DE1kuudSmfqqABKGT562f1oQZlqXESsC0wvVPGPW3z+lg9gtVZp+Y2CKFs0PQuVVhvvOHjGqjQ8L6EFBOpB55bRwjKlj+cY+4BGvttNvttNtUEg3yUx9uE69ZCIqEFmrkJ4r87Echfp+SrKYyGCwwbx7U4lUnA+p6AUDYZPVknPl9CrMpYKpgoIkJ4ncdQHXmZqoYvpBTJayJHfmi7ILPCh+wUCwxZaQMRVMBlbYcGIB6lu0fu+BNOHQj0E6ss+cjMU0gs9yBVn/uxMicxhUXZ/bQG2CMVWmUM/vplqTKLr3hqVhIiaB6Vs45k2EGwnfmP8aIXcYTqoFtVWHcGEcqOIqEPve1SiO3XcGRvXnREEC575nyMZv6ROqdnp+9MCYHgdF9fmp3UycyU8UzaiYZM6yqIeFpCrTs5lyxNVWp6oImngydjYbgupZjMwFicwAA0vC/zsPy7A06fQeNowsa0Q21wmPd+FXIVLr74fwQSpblNttLl44AQst4V/wMnnnLd6FblZTsVT1EV67rQIzsoSnJUlvsExUnJloZKUeN/vP0GlEcaXy0R2iEwvkHGPS+h3J4ivc4ht4P0jZOaKeCdNPnXGA4z/ugu1ZGG6LdwZnXrMIjAIpSYZz5RJ6ngDtQhdNwp03egQ/3KTgL9f4siPb6A6q04tCvWQwPNf+jHjF2oknxHBZ1A4u4Q4q0h6gYgt20g1gevPvwFLtal11ym2ioiaSXydhDtjYamw6ZZFeCYdmbipwsSRLiaOdCHlqkR2V/COiphuGQSIPqfiH60T2+HcqOUOP6NnmngnbNrWltl3SZQ1f7id7FwPguVEJdmSQ3pqcZXKh8P03DiMVNFw96UJ3B3EM1pCLmrIRQ2KJcQ9wwyv8NB+6z6QJaoNApWOAOF7Nv/D1kypahJ+fgShroFtO/LVQp3CwjiWW8I7WERPBhCHUxgBFSlbwXRJCPkiSv8ketiN6ZbQFnQgj0wT2ltCzWokn0gxtELBNV1HD7ucCtsrQ8gVna+pNMLAGOQLGD4FFBk7HMC1ZRA7HkXaN4owmaH3XR5EzYSaxtRhfhpeSIPHjVK1iOyyefey86lHXVSaPaQPDTlmOS7n9RV9epjo08MHci9PX3zN62Sb+5G/1Ik2qQclZt5cx0yGWP+11ZzmuQS1fxI0HW1uG/Z0BrtYAl2n6d5B5Jd2wfAEQlXDTmexyxXscgVzy07skP/A8f6SEFmDw2QXhhHr5oHzgz+TuBXiSs5ouoKhr4oIsYhTYfV5QXf8AZSN+0g+PAyAoJnOMCxwKdiCAJKE4VcwI16wbISAn0pCwZpKIy2aizGzFWvzjr96f/zldTqj62qEGY4R0ZpNX2fNpq8fOPcV4kqUvI4xOOz0pSZjNN+8/a3cjuz50lUI5Rq9n5jxuseij/S+ocHQXzvfgziIgziI/yP4FyWdhx56KOvXrwfgxBNP5JprruGOO+7g05/+NAsXLnzb875l0gnwox/9iMMOO4xly5bxkY98hF//+tdccMEFb/sk/l7sucJL29oi2o+bEUybcmeAzvucGA/RtBEMh9ykF7hoW1tFsEHJ1mm+d4jhFV6ycxyTlGs+/CvUiSKpMzR8KYPaqiy1mIxnoootiXgnNNrWZBHrJqUWFz232MS2lCl0edCCIon/8RAY0Sm2ysS3GQT76wT763h60yBAYKiGFnRkTpHdOvGtOp4pGHqHTWSfSb7LxfQip18yM0+l4y7HOVEtWJgexyxo/LgAngdCTm/PfIt6SGByiYBUdyI03BlnBAd0Gta6GDzfZuQkD5HdOpE9Gnd//jRE08YzZRLdYRPuNSm2iUR2W8j9HqSaRdvtMqJm075GJ31cnbHjAwSGNXQfyHWbaqMHSxYwHooTv8fziuushXfSptxiE7vbS2xzGT1kU4tBpVFGqsGHPno/asmmnHR6jgqdCtW4iFIGSxHxD0N4r0XsaReGx8nWnFpuEtlt8eJ1h+GdcnoMTzhtE4mNdRIb60S3lakHwZXRSS8xqcQlkk9JJNfZ9K2UaXlSo/VxE6UM/lGbcpuTwZo7uUq4z0QpOXJkUxGoNEN+ps2WaxfhmzQZPsVDLerkavpHHOOgWgxqMWhcZ6COKXh3qwiKhX9IoJp0ekajmySGzpCIfmSQfLeALYJ3ysD7jI9yq012vpP/aIuQnyGQnqfgTuOQWgt67tJx5WyiOzROPutl+j4i0PcRxxnWkmDmLTr+MZ34kyoNL0wjmI45kX/UZvreNnSfwPRiH4YbfBMWv/vC6cS3WGTfV6LlKZOdt8+lYZ2If8wktUJDn1chscEk1KfjGxLId7nIDYXIDYWohx0S6M7aeKYsIjsF6h11Djl2L5nDdCrNFghQDzvmR6YqULqpFXteicjnB/nh2jPwTppMHClz4hHbGT3OhSclopRsMsfXqCQklLQMNgimoySYXixQabbwTdis2TWPxCMqLM/jH7VY+MtPEnjGg1S3ablXRno5QL3oouUpjUCfiFSHf//2R/EPCbT+QUYPgB5QCe2r4RtzzJJycywan68S7KtQOadAdKdJdKeJEfNiiwLNT5epx1RsWUTSbaoNKuWEjOEWGTkZem41CIzo2JJAZAeseO8HiG0uk5vhRt02SL7bTfSFCXKXlLAFAa0jzppNX2fwu16iL0wg5EtoUTda1A0eN9Pnz6XpOR2rUMR2KSRfKOMdLKId/eYSyL8Xtihgh1/pCazWUMZyCHWdwNodyOkKetiNsmcU3C5cu8ewPSrq+j2gquD38ugTX8Tdn0HdNoidySEW6yh7x7BVCSERRypUMd0SBHzOeIVYmpPTIEkIAT+2piPqFuQKWDv2UVvciZDOOo6s4RCzbsogFuvYQS/JZ7NYbpVaZwRLFgjvKjokqmYRfLqX+PocALrvlU2x7gRad4Izmq5ghbjyNT2dryaBK8SVhH+1nrXWXUQf6UWqaJguiRUXvR+rXqM2uxG7XseWBbJnzUVQFHZe04odDSLM7ERf3IM9Oc3Y++djLujBXNBD4eLlr+mJ3C+vlZMJjBWHk1u5hOgjvSib+w4Y9qwQV7LutquRk44zmJ2M0fRDFdvvwZYlKFcwp9NIpRp0tWJNTiPWDCozolRmRLE27MDc14+4dwj6hnGlyojrdyDGItgBH9FnRhCbkph+F/Jo+i3dK0KmgDE4fMBZd4W48nUmPsrmPqRFc51rPDiGmX/r/ci2qtDzs8HXfd9ITb6GnB/EQRzEQfxfgy28vfFPjm9961s0NTlZ0t/4xjeIxWKsWrWKyclJfvazn73tef8m0vnHP/7xdeO8886jVqvxnve8B0EQDnz//wVm3FCnHnWT75KwJQHBgnrUhS0LTC9Q0X0Cus/JWxw8y83wyR6GzgrQ/4F24ltMGtcZhPfq/Pf330utNUTXzQKekRL1NQ0oZQs9oKLkqqQOd6NHPViq06uUnu+m0uQmP0PAnbWoxWR0v0Rg1EAuG+hBGT0oM/DuRkrNCrYk4B2tIFcs8l0KtizgHzPw9itYEmQWgX8YCmeUkWqQmaPgmdJx5XTyHQ5RCwxb1KKOY2d0q0ipTcB020T2msg1JzuxkhQYPFtEKVk0rZVoX1NGqpnovleuj2kzsdzJEpTLjuxX9wk0bLLIzlGxZYGRMy0sWaDrNoHIPpPBM1S8UzaujEG5UUYpOrEjml9k9ASB0RMcF9bwbgE1Z1JtdOMbcRyDXTmLegR+cu9ZAFTPKTi9pHmL7EKTwLDF+HKVUhuMnWJRaYTibCdGRvAYTBwtYHgFJo+00YMWL/98MYOnqwyerlJu86AHoe9dMl2/d6IAdL9AboZIdKPzfE1VQK7aFNsFvOMC4b0ayd+7SM+VaNhYcjL/YhKJDQZySUALigydKpLYYBLeZ+EfgfR8N/n3Fqk0WVSaLKYXyMS3WPhHbaRJ9RVHYSdGxTdhEtkqklvdjmg4UmjTJeLK2SRftOm5q44eBFfOycCTa+CdNBk9QaYeFug7X0UtWYwer7L+x4fRfI9C8z3OPSIaUG51Y6kihS6BzGEx6hGB3Fyb4ECdcK+BVLfJz7Zxp0HzCxTaJTyXjyI+F2J4hfhK1dak3CTRdo+Mb50XwbQxvCKhARNfyqDvnT+n750/BwGqcYlgX4VnfvwzKklo+aNMly+NL1ZBzYkk1+lUmp2tvcD7R3AVTJK3e0hf20lkm8j4USLdd2V5/v5FaGELNQf5boHEGhehPo3AgJN3ml7gIb3Ag1IQEA0BtWSRuN9FuVGgvidIqVV05M0pi59+70foPpHIbpP4UwrNX9+H4YLGdXWqcYjs0ZB0G9MNuZkqpkdm/CgP3nHo+V2dzHwPliJSKXiwJQFbEhBrJtnZHqRcFVdWR6roeKdMxk43CQ5qPPOjn9Fxn4Xhk9GCMnpIJbY+jZKpYgQUGp5OYbckiD8xQq07hvJICBSJd1z/GKd5LnEWK1ni/ufvQ83UUDM17ICX8J4q3hf70I6Zz9BXReRsGXPLTtRn/3o16u+B96U+7L0DEAlhlyuONDJXwFzYgzCdQcnVsHJ5qGsgSwjTOYSAHyyTiRMTjslOrY5tGCBJMJZyyGSxApUqls+Fd+sYZtiLGfZSnB8HwDxmIXp3ErMxgq1pVJrcWLkCUmMC9alt4HFjFUpYg8MImQJCXUNI5zH9LkTNwDVSwPvwFkdW2tmKXNGd6mmhAkD0xUmafrsHdTSPOuoQn7+sNv6lfBMcSShul3P9X9qFunsUOZnAlSrTd9VcXP2OUZ3ZkWTOdUVK3UGEcg3p2a1kz11Iy5/GkPvHD/Qk7jfS2V+13A/3liGi66cwUpP0fW7+a85jhbiSB8evY6111wHjHCGVRsgXsZMxxPmzQNMRyjXEgB/6R/CMlvCMlpDmzEDq7gTLQkjEoW8Ysa0FbUYjKBKYFoVDGyl0eUid+rfHmZhbdmJHg6y17jqQwflqrBBXOsS5OUl2YZjTF1+DXau/Tg77v8lfVxz1n5ghD5nj21/3mJxMvMbl9yAO4iAO4v8WbPvtjX92HH744Zx44okANDQ08MADD1AoFNiwYQOLFy9+2/P+TaTzvPPOe91YuXIlIyMj3HTTTQe+9853vvNtn8jfA8G28fZmMNwweZiEtzdHvlvBPV6m8fkyob4aob4akd0W4d3Q+acCnb+dxphbxr/P+WCSm6kQGNQpNcuU2lQGzwkT6jfId0pkZ6vsfV+IxMY6pkui2OFi9HgXse01PJN1XFmnSqOULNILRIYuMLEUkdHjJUaPl4jssRyDjS4X5XYfUt3GlzKZ+lCZelAiusui0C7R9ojJmVc+he8xP4IFiXOGGT9Kpf8dLrxTNkpBJ7VUILFBwz3tuJBGdlt4x0U0v4hg2ZQOrVE6tEbT0458sB4SqSVcDJ/ixpIFykmJQruMb9TZiUkdoZJeKCDq4Jmso5RsSs0yrnEFyyUysUxFrFvENoM7YzK9UCU32zFPSh+h40mb9NxVo+euGuUmcOUtBi6A8WNE6mHwTlqMHS8iVyC2zSa6tYjvniATy12oOZP4epF8l9PbmHzJouVhkWqTia9PJvGyyd3HrSa+EQIjJp4Jx4F1eql+QEYs1S2YU0IuSNTDMoVugfVfWY2ogztnMbVYwZIFsnNB1MA3bjF2jMrHvnk35R4dPaCS7xIpdsLEkTLPfPh7WBIgQDkhUegUKbU6UtzAbwO4J0VnpJ2/uRZwZKm5eRburIn782Ms/epLeN49jiULBIZsyo0ChXaRyaUW+S6R9AIPiQ0mkb11tKBAYYaNd6KOb1hAMKH1URvNL6I163gmDephkXpYJNRvUW4SKDWJlJNOXqxaslALNsF9AnpABtNGqdg0PWNTagNPxiLcZ5C/vRVbhK4/aEwdbiPqNloAvENFojt1phbLlJMSpWYJ3Sdy+FdXcfhXVwFgugS0sNOjpRZhYpnI2puXwfoQahaKHQqfOv0BpFPTlG5qZewomVyPROaSEvlZNu4pgUp7AO84BPpF5r5vJ1IdphfDxDKV7CITtQi+cRPfuEktYXH8UVuJfmaAWkREfsVHpDDTRA/CxHKBj3/hU5guAaVkYikCz2ybSajfItfjAtExMwEI9jky7Owshcgek9wRGlOLPSTW5em9UEWQLAodIoUOkZ/cfb3T67w8RnamG1sWcaWqNDwlI5g2Z55+EZ6xEsVWhWrUie8wQ16mjowgF3UG3t2ILYvUZySQqgYNL5cRyjX+ePlJaMctoOU7MtTqnHXoigNrlx73I6cr2C0J3FuGaPqRCyPmx1hxOPnzDvmHrZnmjBYESQLDpO+z89E6G8DtQly/AzseRShVEVQVPB7sfBE7FHAMacpVGteOQ7VGbW4TleNmQ0czQlPiQDUTrwdx5wB43EjDU0jDUwRecuSg6mgeedsAYlVHDPjx/n4d/f9xGOg6gtsFgkj11EWIzU1Y6SxD72oGXUceSFFP+hAKJcSWJmyXQr01hLi9D7tQxA56EfIl7IlJaos7MUNezJAXIzX5prLa/ZAaYhipSTLHtNL2nl6sQ2dTPrLTuU5+F5FdNmY0gDCdQxpNI5SqBHamoVRm7/eXEH12DOoatUXt1Ba1E7zjeYzU5IFjGqnJA1+txrjjkNvRRs+tqde4su4nnvurn+poznmgrmGE3AjjU5TnJah1xygc0w1draCboJtOfErViVTRWiPQ1gyyhNo3Cf0jIIkE9jrvdcn7/xz6/b+RQWnRXPSwmzO6rub0xddw+uJrDlRu95/zmk1fJ3VCnMjWHOaWnYjtLa+b542qzK/BC5sRLIvI1tzrHhpc3YAQDr7u+wf7OA/inxUH791/IvyLymvfDBs2bODss89+27//N5FOy7L+pmGafz10+h8FS5aodURofrZK8iWD8VPi5Oab9L47SHael8w8D5l5HgCi28tUWn3YbpnYn7ykjoki1Zyez2pCJtRXR9JsIrsd99joXsPJbOwVqDYomC6B6EtpOu/NYbokTI9MdIfG9EKZekii1lWn404RsW7SdW+Nrnud/EnTLdLw/DQrv7kG0y2S65ZwPxQidYxFboZIYMSi2CzzyLePcWSMu6v0DiWJ7rII73ZC3LWISvNTJlOLVcpz64S2TCPVbbCcsPdSs4RrnxvXPjcTZ2lMLrUJDOpUGmQiu2wml4h40ha27EhKpRq0PFmh5Qmd1DIn+F2qO/2PAIU2iVqDxeQShVKLgGe8ghYG97RDXqWCjO4VmVjmZWKZl+hOG80vcO0Jv0YuCUR3WViSQNc9NQJDTqWx7/MS1agAh+cptjvmMIIJ6fOqyDWLYpuImpXwj9hIus37NnyQzFwBS4Jqo0V4n0Hn7x0y6520KDfJyBv9tDypU7k0R/vDVRb+aBXBQQs1b2DJgA0nH7+ZSovtSEFjFj+8diWzb6xTSSoYXtASBkoBTvjxZxEsiHRn8V84hjdlY/icFaTaIOKZBs+0IzOuh0XUoo0RN2h+wmboTIGRBzv4/RNLsa9PUGpxiL1ShsheA9+gRGWWhidtMXqh5kitbbAlm4llXhIbKtSjkDpcwpZg5g0aSI5LseEWmFgu4B+1sY7LU4sK3PCLszA/No3hdvI6R4+TUAs64Q2OK7NnCuohkZHjJXwTBrGdBnpARq4ITC9QiOy1qDX7Md0i/hEb3Qf+URNRs/n1l77Lr7/0XdS8I9WuXplj6RcupxYD75hAPeJI1jd/fjXxjUV+9c0zcd0ewXhvmsZ1Jv4xC+99QazGOo0vVJ0+x0aI7NLpu24O7gxEtzt9rYnnROohmFguMrFcxDcssuXni6j8WyP1iCNFl6oCUkVE0GHeEf2YLkf2+9gtv6AehtB2BUl3jJ+anq2x4qj/JDNTxvA62aiWAtUPZ2l4QiGyRyezKIgU1Zj5Ex1X1saVtXnn9Z/jpW+sxpcysWWQMmW0mJvAkEZ6gYsH1tzJ0FdF1n9tNYFhnfRcGSlfwT+qU27zEN5nYQRcDJ3qZLkKts3OL8ewXBLuDf0UerxoXU5cT63BQ63BgzpRABGqLX7MzkbUVBF5qoh7yxCRR3v/cYumbSP4vFCt0fSc7rjY+j0IHg9W0A3lCkI0jJkMOYRyeIzo4wPYiRjUNWxNw71pAO9g0TECyuapzUrCyATkX3Fv1Q2QJWdIIqbX2bjInjXXIUuREHJHG913ZcGlUj1qFhRL+HZOY01OIXa20v6L3c7phgK4xgrY1Sp2agpBM3BtHQJJwlw0A8ujgCAiBPy4UmWmjggwdYSTc7q/6vaXstD9MMYnAIhszVE9dRFy7xi+F/qwkzEKXR4iW3NI+Qp2MkZ9VhNmQxBbFtFmt9D9ew3b4wLDwPXCblwv7KZw8fIDxyxcvPw1x9ISXtx7UmgdcYy9va8hw/uzPM0tO1lr3cXQBY60yW5JOFJnjxvPY9twb+gnN0Oi3BXEjHgxI160lhB2yIc9qwN1+xCIYKemweNG8LidXlQg+uQQRnviwDHfjIy/+joNr3DeO80tOzG37HzNddz/tfH3+1iz6evO85ZEx+H3DfBmx5N7upDSpTc8jx3nfRVq9dd9/2Af50H8s+LgvftPhH9Bee3atWv53Oc+xxe/+MUDaSW7du3ivPPO44gjjsB4xTTu7eBt9XT+/xtqCaeKZ4sCpSYZd8YmtEsiut0x32l4MUfDiznyPSIjJ/qZPFSiMDuIUnGkqqZbJDNHRK7aDJztQtRsPJM6SslA94oE+sv4x0zqIdGJBpkRppb0YboEim0K04tUYjtM6mEBb7DG2Ac0Bs9yM3q8h9HjPaTnS7gyOtlDY9zx7TN5cvXPsWUwPNCwTiS6y2T0NJPMoSZjJztGOHpQYcYvDLwTGr5xA9+kiVw1UYomyZdqxJ5xMXpGAmyI7TBoO2eAz3zyNyRf1Em+qBN8yU3DSwLVhEz62DpTSxz5Z6nFkXkmXirjnTLpO99Der5Cz+803JNVgr0VaodW8Q+Dd8qi+/caog7VeTXOvO1p/EMQGHIqZUrRyeKM7DWJ7DUdB8+8xWceeB+hPpvJJQLLrn6J4RUeUsc55ivJX7uJ7dDQdoccGXLVJjhoEb/HQ6lRxvBAxwNlahGnF1LbG8STAlsS8A2JuNJ18l0KtahILSoiajbVuTUMn0T9hSjFdjf20jyunEF6nkrDZgPDK/Dyzxbj7iyg+R0DGsMLey71YEtO5EziGYn4DoOGzTryOVOUqi7snyT48hd/SXKdjQAEhp3YEVsEw+XIuG0JEk/K5GZKxF8SaXqugi3a1CISDZs0ih0CnmkLwYboboOmNTLFVpHOm5xqZcPGErGNjvR76FQvsa0mvjGYPsym0ONFKeh4MhaejEXj8zbVBoHm7yrEt2gINpi/SpBZooMA/kGBycO8VLujuHI6ouaYTQUGBSaWKhguEXeqyoxfOj3GulegFpbwDhWxRYf8Ty6RKLRLfPCqq/ngVVfjyZjYskB9TQOBEY3YNgs9CIFBG1GDUy7+IHsv9hHeVaQSF7H/EMd0C1QaHPmvMuAGwdmw8I2DVLfIzxCIbywg6iBXQS1aeKdssARn2E6VuvddXkLHTDq9nhY0bLCJb9Op/XsSpWwj1Z14kfh248BzDfbXyM525Mf+cYvgoEl4j050t4Hv+jCRnSXcI3kyCyD6sJs9l3qd19e4QcvjZRb8ZBXuiQrhfRpW0IOaqyOXNZrXpDj13e8ncHeQs457J9k5Cu13j4Pl3JueKQ1LFlDH8sz41g4sScD8VpaWP8lUEgoTF84mO0dA3TmC2ZFErpnINROtMYjtVpGrJmKhRq0thD3pRGvcv3HtP2zNlAZT2NGwUw3Lag5JsECIhJFG02DbVBY2I1Z1KJSw5ndjhwJUZoRB16GrlcKxPfStjKC1RSEcxL1jDJoaHJltIo41MoadyWFncqAoWKoI5QqugomQyUEmhxUPIYxOsvPrjXjW7WPn9zuxp9KYS2ZDueo4rs5oAUWi2h6icvRMzIU9WH2DIAgIfh9Sqc7Iv5lgW9i1GqZXxZsy8aZMpEVzD/RXvrpCtx8rxJWIh87DOu5QzrjzOXLdMpNnzyBz6kwAvJM61WY/tipRmBXCNZZH0E0Yn8JWRNSxPIyMY9c1p/LY1Ur0kd4DFczI1txrjulKlZ2K8lMbkXu6Xif7tZOxAz/ftibL+LtmIORL4Pdh+7xoR8/DzBdoeayIf/sUcu8Ycu8YUknHiHgR00VQFYRsESEexfaojuuu1+u4J7c3IFW0t3SvtDylQa3+murmq/+9QlzJ5NkzOH3xNazZ9HVO//2LKLnaWzpGrTuG7XMf2JjYf10KFy/Hmpj1OmnvX5L5gziIgziIg/j7ceutt3Laaadx880381//9V8sW7aM22+/nSOPPJJIJMLmzZtZs2bN257/byKdP/7xj6nV/vY3keuvv55isfi2T+qtQguKTB+iUG1QCQzr+IdreCcduWO5UabcEaDcESC606Bhk05gyEYwwT1VJ7rLwpWu4x+GYotIbAvofpG+S52dCtGwKbf5UPMGggn975DJ9chUEzLDpwuEeus0PlfGVAViW6u0fFem4Xcemp41adhs0LDZwJ124k/UkkV6EZx2/qW0rs0j6mB4BAodEv59ClJJxDWh0P1bk+n5Tn7l1GI3xVaZYosEls340S7y3S7qIadSWW6UKCdlJm/v5CsPXMifbvgpf7rhp6gFG7lqM7ncIrDJRdMzFuVm5zlVEgK5OV5WfnMN4Z0ClVabUquLseODjJzsgzG3U511CfR/1CbUZxF73MXNPz0L0bTxj9TR/QKtT9TQwhLpuc6oxgVKrRLBXpHcTIHEyzbP/vQI/EMQ2SgT3VFm9AKd3EzV6WUs6oR6aw7xq1hUG8Cbshk6zYcedExBotvBVbBR8wbR3QaCZRPq04ltKRPbUkbSwL3bzYXfeJDoTpNCp0Dg7iD9l9jEzhplYqlMeiEUTythbQxRTUD8Gac/sukpAePCDA2/8NH2sX2Mv7/GYzffiH5/A65n/ZSbZP7t1ssotYqE9xnErhogvE8nvE9HrtukV9QodAlMHulkS1qyQOoIL7YIcs2m5Wv7CPVZ+AfKTBzpxNR4UxpaGLSAjCdtMXKSn1pMoO3RKsbsCoF9BdSCTcvjNpYM/R9/RWrrFym1inimbGpxFUmzCA44RF9JK07MiwzBYRPDK1KLqbgKFuE9VWJbq7Q+XkMpmRgBlUp3mOhupwpsKU7UUGBYJ/lylcAAr5gcOZs4cslEqln4xi0Mj4RnSiOyx6LQKWCpUE0oND4PA+cE0YMQ31hk7GSn0pl42aTz3iLqWB4t4qaagFpMoeXJGpZLRjRtXFnw9xXwTBr0/K5Az+8KuHJgydD5gEb1wQR6QCK2zUT3iZRaFEodTuUlvDWLXDbJ9TjX1pUz2HehC++UyegJHspNIrpX5PGbbsQzXMS3fRIt4mbihDgzb05jKgIzb6+hlHSUkk610U3LE2WEmkZ6vkpqaRBbdLJnjbgfSxUJ7yyQWZrElbXJLGuk2hFyepwzVUL7ygy/I4nQlMAzWSd7ZxtyxaTUItD42CSBAagtamfPx12ImoWoWUwsdbPvPQHEJzZQmh3BNV6Ejmb8wzWO+uzl/7A1s3xkJ8J0ht6PtDtr0+5RBMsCSaSyoBlsG09fDi3mBcUxPxOKZQrtjhNtpSNAYF+BnusHUPeOQ7EMXg9CvuzIbH1uhJ5OhKYkQlMSO53F8DmRLL6tTv8nfp8TpVKvM/eaCQRFYe6389DRjPjcFqc6F/QjD6QA8IyW8O3NIo9MI3g82MkYZlMMTJOW7yngdkGygelDfTRe3Ufj1X2MnRRh1jevPUBi9ktX96Nw8XLsrXsRn9rImjMW0bCxSuKRYaIvTNC3MoJ78yCewTyCZhJcu5PM4Q0YfhW7qwV1rIjWFKR80jz0RV3oUQ961Lk391c6Xy2fBRwX1lyBtdZdGMkQotf7uqrhftSTPhqfmAZBdCrThRKu8SL1UxajR1xQqUI4COEg00sCKCNZZ0NAVTDbGrBdMgPnRsgc20bm+HZ0n8ToZ3Uml0b+1/tjP6k0t+x0iHIwcEBa+0Y/W4v9+bmuOXYGwujk/3qMV8Pdl0aYmGb088Zrjh+843nOOvLM1/3d1t129ZtWrg/iIA7iIP5PYX9iwVsd/6y49tpr+da3vsX09DR33nkn09PTXHvttWzcuJGbb76ZBQsW/F3z/02k86qrrnpLJPLzn/88U1NTb/uk3ioKHQJyBdxZHVsWKLW5UUoW2OCbMNB9IrpPpNgmM360k7mp5g3KrW68Y1VGT/Chli1ECzxTBrpPIPmIQrVBwTtWpdQiOnmWpk3HGovAsIVgQmiHhOkSEXWT8WNALmnUEm7KTSL1kESpSabUJBPeV2fwdIWh0wXa1upUm9yklofILtbJHKajFmzim3UuO/0JtFYNwyvhTdmUm2VEHRo2lPFOWgyv8OAbc6o+Xe/oQ6nYeKYtQgMaxQ4I9Aoc+19Xcex/XYWpCEwcJZB8RkQ0odjuGBGZCnz+Q3cxebTBjTeehR6ArnvrKBUL/6hFZI9FfCNUGgRqUYH4Q05EgXfKxJW3KXQJmC6R/GEaYs3Ek9IJDloEBy1ceaf3sfn8AeJbLOpBkcCwTvDCUd7/yfvZ90mJ+CMuAiMmod4q40d5MD0y6QUiqSNkAsM27qyFaECl2aQWETFcAp5pk5av7UMum4yc7MhBpw71MXWoj9QKjYZNBr+/+lRqUQnPJFQbBOZ8v0L1lmYiu2xiWyF5hwelDEoJAkM64T7HdTX2n26mF8j0/momXd82OfnSD9GwoUxw2CI3y0Y81HFNVUoGhW+0UehQKHQoVKMCnTeJeCfgxCO2E385h+mG4KBJ4iUBSbMZu2YGnpTO9GGBV8icQPazZeQqIEAtItL8dIVKkyNtbr9JJrswTGqZTfjqQZSyjbzbS3BAIzigEd2hOdmggoAtCHgn6nhThjOfDYmNNbIzJDwTDsEsdIqkF3pIL/SQneVm5BSZelih1CgzeZhMsUNAKdskPtdHPSJR6HIT2V0hutvJOFVKJtWEQmauimBB6nDZifqo2jQ9r1OPOEQ7uLtA1+/StD5cpJ700LpGIjtTJPWeGtOHBdCTAUy3SMvjZQyvs/ExtdiLf7BK4sUy4PSFml5n+CcMQtsylBtVJ9rGJTB6ooAtgXfKxD9Uw5OqYysSomFT7HJeE3JJp/v3OvkuiabnNZqezFFuFFj+ucupNfmpzGpAqhr4R502gORDQ4ye6EOLOHJYpez0YmcXx0i8XCW2rYpUrCHnazx09y/pu1Ci2BPAN6ERezlDeFcR70AOV96iMDtENemmfmQZvcGP6ZFIrMsif3aCtjVZhs5NEBjRAZj10zrSln1IW/aRfKnO3vdej7HicNS8AaKIkC+h9k0SWTfxD1szPWMV7HiU7l9NM3GVBsGAI2FUZDxjJbS5bVQ7QoiGBYqCWNUZvKSDpjXj2NUq3qd2YwRdoGlOr2fAh9YUpLKwGQDLoyBoOvbkNPbkNEI0jGe0hJ0vQLUKQb9jRBQNOjJfAEWB6SxCpsDI55cihINO5TcUQA87eZ726IQTwWJZCPkSYrWOUDeQh6ac3y2WiW6vMv3NTqa/2Ylx3J/NhIDXuMoCr+khzBzTirK5j6F3t6G1OFE3diyCnvBje1Ss2e1ENqVRdw4jVDSGvqmgTJXxP9eLqFkMn+xm+GQ3dq1+wOn1L3sZo4/0Os8T57VjVSqvqRpmF4YP9HXKZcekyfa7oVQmd0w7mDaewTzuvVPONZzOwnSWyM4quFVHyqyqSEOTCJpJ6xNVsnMEIltzuDIa8lMhkg8N/c2VQmnRXOgbJnNkAiGVZuklP3gN2dtPBtvvSSF3vGJQ5PeCx/2a5/2/wfa5sdqSdHwy97rqr9Eap2/l64nyqyuuB8nnQRzEQfxD8C/W09nb28u73/1uAN71rnchSRI/+MEP6Onp+T8y/99EOv9/7L13uF1lnfb/WXX3fnpvKSe9VyC0EEITpClSpCno2EedeS2jOKNjBSuOXaSKAgpCQidAaAnpPTm9n7N733uV3x9PciQGFBjxfX8zua9rXcnZZ5Vnr732Out+7u/3vm3b5rTTTmPBggVvasnn83+Xwb1ZuA9PqkZnOIh3CAUFCSZmqxguGS1noeUsIrsKWKpQoaIzdBIdMut/fxvOCYhNF2WnStHCGbcwXBJyySYxzYO/x2DoBB1blshHFBLtMqEdCYph0JMlDI9Gw5M24wsD9J9lU70xTaZBwjNq4hkVD7jBfRJN62yS7TrFgOitrF+v4O7VGF9uIps26790Eved8kP61kiUAhKBgwX8fQaJaW5c4yUiuyx8fWUcKYsDT7ah5myGzinTfZGMt0+oXLXPxKh9JkblpgStfyzimjAwdah8NU/gkEVpaYYf3XQRDesUPKvFiet+l4NYp4LhlEg3yqz97DM4UqAnbVzjBuNzVCTDxtKg6lWTsYU6nj06Qyd6SHQ4GDndZOR0E0fSQinBwAMtKCURbzK8XEf9cohbHluLVVDxDJdRc6Yg/ikohhTh+tojXGcd8TL+bpumdTaGAz776TtJtqnsvGsGplPGcEGqRUEtiP49ZVxnYraKlimjlGyqXoiSbrUYWxYidl6OdJOEnrboP10iPaeIumaCXLWIaikGZPrXeHCN20K1q/eQatFJTnWTD8u4RiUqfuFBMm0OXaEwPkfH11fG11fm/Ks3MLTCQWh/kZf+MIf47ACOuMjZNPXDDspBlVJAxTdoUPKBnizjuD1ExQ6D8XkKlgZqPEfb/XkydRpquoylQv3TkPn3BpSSRWDJOErRRCmadF8i4R61sBShnB9B3YYiqTaJfKWOIwmGV6P3LIXQfpPQ/hK5GqFeRraDb18cLWdTvckgsssCoO+nHTijBkrJZmKOm8wNSRJTNRJTNWRD9M/6t41Rsd0kX+sm1aQQ7dQI7bPRMxb9a4OUKzzEZ3pJtKtYGlTsNHC+4CG8O0fZq1Hyyowt9JAPSwytdKJlbUphB8kpbnJNPhxJC+0ro2hfGcVSID4vIiY+zk4S3NBLy0Mmla8kJzMlkSR+/cefUgqo2LLN1z77U4oVDrJ1OrXPZ3CMZhleFSSyq8z4YhulIL6Hkg3pBgXT7yI7t070E09XiU4XZbD6WIbgnhRjC12UQjpGyI3p1pn/tRtpv6OMf2cMR28cgHydm+SsCIWgzMQc8XnU3u4gX6kzutCB6dExv1FD18UhqraUcPXE0ZJFoVK11ENLPaZDZvV73o9jNItjKEk57MKsCQnFKpF6x+6Z2SYPaAqFpgB139TITo9ge11gmNiqjD4Qx9IltF4xeShlCzhjYIa8UF3JwHUz0fomyC9oJfxsP7Ysox8YxjmWx44nsRWZUkMIc0YL5owWbIfO0KkhbMsSiuR4DLsiCL1D2OWy6BMN+yHgw85kafrRLoz6CMWASqEpgJI73EPSVAuGgeQWiqI0EhX5mZmsIDqlEvpwCtkQ38WmK3tp/sLGNzwP67behG2UUaurcI8JM6PGBydQNu6EQhGpWEIpGOTrvMjZIlhgttYiJdM0fzqHFEtgV4VRh2O0/7iH9h/30PXPMzFGx5j6HzdPHmcyMmV0DBy6yAUN60c5666WLz6KBPeudWPpKuWwGzsSIrAzJtx880VQFWxdBUUGRUYbz9J3XiW5WbUgS1h1FZBKI5k27b8cAkApmhQikJ1bh/+OF97UdSKNRpGqK3GPlXlk+IfHbLdu602T4//TCw+Jcd/sF+PizfeuSaNR5FQeisf2biq50l/9DI/jOI7jON4x/C/r6cxms3g8HgBkWcbpdNLY+OYdz/8W1Dez0r/927+9pZ2+613vIhwOv60BvR34u0sYNTrjc2Uqt1mij627QOV2J4WgMGUR0Gj7fRrTqwE6esLkhI99kEK7MNbRMha2KuHrzpFudTN0sgS2TfPDNg1PFbAViUydTs3LBRIzA4R3W4wv8JJqs6ndaJGrhbrHZUphB8WwTWy6OLB3QPSLji1Qqdgu+jK9Xxhg/MctwvnWUimELeLTZS7ccCNVLwtyNXyCi6b7J3CE3PSd4ab+2SKxaQ4cSZv6Zwpg2XT8zGboBAeVl/Uydk8zclGYZ2Q+ksR60IlvwMA+IcmwI0Bon0XNb5xkqyS8g0UOjQYIZ6HhCZOJOSqmQxj2PPGFEzGbQSmCpUnkOsokkxrJaRZt95dRCoff17CFlrWIZ8TPxYCMUoBwV4nYdB1fnyCXE3PcBPfC4qu3s3H3PML7DMbmu6l5WZzTvtMdVL1qUfZIWJqMUrIZXKXg7YVv//t7qdmdpuddPs655kWe+9xyJMMmOkOoBa4RQV5LIZ342TmCu3Xafl+kGNaRHvWQq4WyW6bxcYv+NSq5jRUELAtTk6h7bIzknArUvIUjKdO3RsJdl8T9oJ/YHIvgXplCSMEVMwlt0jCc0LdWvNfnP74U13TI1uhoKXBFTdL1KtXX9aF+pYHyZ6Jkiw4cd4cYmy7R+FgeZAn3SIn+04QKULE9T99XNCp/oqFnLNShKIVlolfY01uk5HNj3VtB3xniBjb91hSxWT5Ce3OUfTpKusDIGjctf0zh7xaTKKEDFkrBJLJNw1LBcCs0PF0kPsUhvgKmjVKywbLx9mbpPdtH1WYDx1AK8COXVQoPRCZvDNFZMtWbDBKLqnAPl7BlCVCp2FnCdMkMnCajHi6CMJ0SSl58j9INKuEDBulmF4EDGUyXm9COFP1nhvD32AQOZjGdKr6MQa5Wxz1SYvw2ET5f0ZPAmBHA329j3e1j6MIAnmELW3ZjuGSSbS6cMZvLL/snNLdJ++8MvvOri/GkxnEFPRRq3NiSk8otBXrPcjD1p1GSs8PCjMuv4esz6T3LQ+CQzY6P38oJH/sgAGrBwgi6UceSVG3S0EZS5Nsj6Ikitc/EkXJFCq1higGVskciuC9HucVFoQJ8PUK5Grm4SMc3y2hZN7Yika9Qaf5TllS7G7nsRx9MUoz82WRFKVqUAxqWQ0HNGnRdoBPe7iR9Tgdt39jx97lBvg48vRnSUwLC0dQ0SSxx49lRpNwYRjIsCHnxbh4gu6AB12AWeSxBpgGcMQ+B7XkaHouDrOAczmB73KBIlKbVoY+koTKCOp4m1xHGFT88ATk0Sv0dMezWBmxFwY74kQfGoKYSDFMQvGQGO5FCqq3C1lWUeI5Azyi4XVgBN3Y0LlxM3S5ys+twb+qBUECUBRdLcJjQ2i4dZ9fhLMpIiMdSvwTe2HxGbW7kke7vAEK5k0oG9pKZ2BlBgEynmMgs1vlxdsdQehMQClBoDKDFvSj9oxTmNOHcL8qAm7+wEWVOJ/s/94mjjnPExdaeiAlXWM+xxOy15bjNf8py9m0bWHfBYsqVXrSRFGRzEApALo+kKhAOipU1hboNWQyfRrnKh5IrU+psQJvITu5XndJO/QYduWwhz31zGbB2dQQpmWH0xjxnhq573Z7Yx6x7ObPiA5y9/BwKU7+OcVIA9Ndvx3kjRbIwpwnngXEK81p4av2tk68/Zt3LGs+Vk/mlb4TjxizHcRzH8Y7g7SiX/z9WOgHWr19PICAcRS3L4oknnmDnzp1HrXPeeee9rX2/I6TzH43eS6B5nYVRZZK8JEf4N14KVU5sWSK4O0mmVRAx0yExeKqP0AETS5EYXKXR8FQJydaxdFDKNsWgilyyiM6UqX/SxHBJjC7SkMuiLNcZszEdCpItohuqX7Gwu2Uk0yZwyMYRNyh7FdSsRLZNzM6XvSq+XkFs41MUwvsg85UGHKooIc01mkiGgq/Xxt/twNdbJFerUwjLjHxDxnzCjb/HRjJFDIZagA/85D5+O7aYif9oIdBtYX2uEl/EINMgyJh+WxjDaTOyVKXm5x5GlkLJJ+HvNvAgciclpUCmCbwjoKUBCSp2luk9WyayFQphCfc4NN8noWYLeEY01HSJ0AGVVJPK8GqTqqdVWh4UZYOJDh1bgbEFOp4RG0uXsBQJwyXG/OKd88hMMQnvg3y1zcQc8fDd9GiRQoVGySuRrdVwjRlEdqjoaRPXSIH9H3YQfhH+9NMTCZiihLpihzDD6F2rETogCJ+yS2ViIUjnTRD8hoSWNVELGlrWwjWSp+pFD7/5yjf5wAc/jmvcoNgQIF8hE1tg0viwjZpWcDwWEIrpKxKxmTa1z5vo0SJup0zRLxF+9LA6uMZBzUuCVDsTNslmUQrd/WAb5lKo+GEVlaMFlFwCSw2Sr3Kgp00y9Srtt41SrvYxPs9N4LcOElNkAj0G2Tl1VOwqEZ+qi97LagVn3EJLC9LZfb6f2ufLHLrQQ2QnqBkH9RuKlP0OtKxNw1NZYjM9jF1i0fLdPLFON5YmiGcxBFWvWuTag2g5C3d3AiPkpuHJAoUKnfi8CEpR9PG6Jky8h0RZonNKAKlsoWXg0PtlGv6o4O8ThkqFkELtBovBc0vIRRPDJXJkEx0quRpwTchkayX0tAs9ZYBtU/NSASVbJj7Ti5a18XWl0d0KhkcltFcQlNEVIWRxSSFZov/adyjFgX92UHufQnhPGUsTPaeu3gQH3l9Jw1MqSsBJdKYT2QAtY+PuSxPY76RQ70fNWTjiNkqXiZI3yEe8hHZnOPP8yzGniPNb+7FuUp+pZ3R1HckOm/bfWTgm8tiqjJQrYgUOl4FKomxfyZXQEzq+PonwS6Ok5lbR+iMJw6szMVsj0CWjFG2UXAn3mIbePQ6mhWsww8OP/haAs1ZfTGJqBVrGxt9tMP2rXdiFIul/nsnD+557B+6WAn1nBWi5axg0FdPvpO7JOEPn1hPeW0KPlTF8Onpcx92VEBs4dBofywuXW1lGGh7HrhVOvFK+CNkseq6IFfIi58WEmJo3kaLiOqKqAtuli5LokRj2RAxj/jTUAyLOI3VCG95HtiNXVUA6S/zkZgIHsyhlA8plsY0iQ6GINR7F6XEIhVRT6T+3gqZf7hfj6R2i3FaFfqSnUFUmidERVfEvHWMlVdwzzwxdh6RrIvJlc78guH4f6r5upGmtk/2a2SUteHaN4exPYg+PgdeDc88wj/TdAoiMTps/l56+tqRXnjuDvn+TqH/3LtzVi5j6HzfT/IWNQgltbsQOeAFBEs/ZHeeeL64lYI4Jt9y6APrBPMVaP459GSgbGJWHo0QkUNIF8rVOnNv7sKsj6Dt6IRwUpcEnzSfW6CT8+CHKU+qwtr1xBuxfniM7Gqfp6hRmMjlpFnQER9az2hoohxw8tf6zrJ32L8LA6HXwl5mpRzB6Y558fy3TfzB+1PFXyxdz8LvLmP7Vrr86xuM4juM4jncE/wtJ51VXXXXUzx/84AeP+lmSpLedVvI/wr1WiWpYmkzF8yrSiwFiV2SQixaDJ8kYPgeFkEIhpOCImyAJ0xq1YBPcb1P2qdgS+HohH1YoeSWGVzrx9oOWNVFKENlt4u+zyNXbuMfNSTJVsU2o6HIZclUqhZDE2AKdkk8mvNdCH1PRx1QqtlmCyGwuU7G9jFy0KIZU4bgagsAehWyTib+7iGTCyDInetqi/qksrjuCuMdt9JQwcml8okSqVeJr372MTXtbKPkV5LJNfLoLwylTsTlBxeYEiQ5ZKHR7bWwZGh8vUHZLDJ7kBBtR3lsdR0tDbKpK2QMlH5QCCtUvyDiSFvWPRpEMm0ytSv9pTtSiTd+ZfrS0gZ6xCb+k4R0q0btWo3etRtmD6E/dXkYyIXmJKDMO7zcoe6AQgeYHLYoBhdqNNvlKqNhWoBjWKPoF8ZqYC3qqfFh5Nuk+30314xrVT4xQDIk81WinSiEiFrkkIZdFrEfVqwaFCFR9zEDvGSfRoVP2iizPQqUTW4J3//DTqDmDYlClGFCxVGh6UDimzj3xAHrKJlclEdifo/4pg49++24KVQ6y1TLTPrBn0r228YkSzvEizrECcsmianOG8J4cnmGbumfzFAMy2UYXB98XJD5D9BuPz9WQy1CqC1AO6IT3llAKNv5eA0uR0NIGSsGk+oUkctmk+tkocsmmcV2cxnVx2u6JomXKtP82Q+TJPkyPiuPgGI69g/hf6GV8vhelCKGH3CJPtgVSHTZln7hGXaNFlKKFHitCPInaO4Y+ECc6U8YZMzAdYpJGKVpEF4SILgihZS3KXgXDKVO7/vAclS16KC0F8hUKked0SkEdR9zGPVwgsrNE5VYL2bDx91gUwgpqskQ55ELNlFAyBcI708imzdjSAPlKlXxYYXSJm9Elbiq2ZVELNpUvThDvBD1jY7l0Gm9X8e2NoRTFzU7NlBhbWUnHnXGiM8U9wDtkIp03AQgjFqVkgyQRn6riGMsyNt8Jpk3oQIFMswfTpVHyS5T8Eon/00Cq1YWWsem4O83AaV7iM33YkkS2s4KhVX6Gl+n4ujJka1VMjy5K+QEz6ME1WiA6y0UxrGM6wHDJ+LrS5Ou9JDrEQ7gd9iPFUpx15ns468z3gKLgGjfxDJWQTAu7OoI1rYn6DSWWf/qdMxJquXMI0hlsSULe00N8dpC6+3pwDKSQxxNoE1kys6vpPb9CKJ/FEvquPiynKpRFWUYqlpGGxyGXw4zFKTWFkYej2NE4pZbDpOdw+ee+G6uQBseQyiZ2JICxfAbqoUGoCIFp4X++GzkcothRhZ3NEX66F9OhCIUz4MWOBIQzrsuF3FSPVDaRFAUpl6fh+1swW2uhbxia69C7xynNbKI0swmcDpQ5nZNE5y8JpzKnE9sQMxzxc2aA20V2Ti1nbRmmMKcJe3gUa8E06B1C23wAq38Iz/ZhUYocTyJ53KK017Y5Zc3XOWXN1zFGxzC372Hd1psmI0Zei+brRzBWL8Ixmp0knACPdH+H+OzgZPTID39/NpkGGdvtQD8wguVQMFqqcQwksAsFKJcZX+BhfIEHdTRJqdKLb9soZnO16CkN+rF1BalQRtvWhSNlgtNB3xr3X702jiFzbY3QXDeZyXkER3I7AbLNHpzb+wBED+9rjISO9IH+NTR/NEHVyxK9X3dOmjAdGUvVy9KkSvxGYzze03kcx3Ec7wj+l/V0vtPxmP8jSKel24zPkymEJPI1Nr+c/2t6LpRxTkiUghrZBsg2QCEs+uiKn4jh6c1i6RLxKTKOJHgHy+SqJbJ1EnUbckgmDC/TRfSEYaPmLLy9gmzqiTLOhDjphZBMoLtEeGcWf7+JmheKKUB4t0V4t0WmQabkk0i0iz65sk9By5gU/RJqFpxRm+oXZAZOcVL2SLhHbMZnq8Q73XgGRW6oZyCHljEoVKiTvXhTfi368HwHUzhjFoWgzNCqIEOrglRvKpNuEGqhd/cEqVYHvgGDlvujOGIlLEVC/n4lFdvL1D+ZoP5pUYalZS08Q0JFjM8NE52h4esvE9llkWxWqX8mR//pTpxxE++QUHKVhhxKQw7foEWmXqLsVXBGDSp/7iG03yRXoeDrtwjttXnqFz9DNm1GF4tLr/sCHS1toBZsstUqrlEJW5Hw9eYpRFT8ByXGF9vs/bcgFTtMtKxNtkmYACGBaxQy9ZpwGa5RKQVs9nysilJzBb4BA2fMQk+WSTeqyAboaShUCtXR0iRqn5zAOVZkYrZC96+n4JowqN2YIdvoIlOv8fWvvo/BVQqhAyVG/rUNuWghFy0S7Trj89xYukIxoBKb6SEx1YVs2kzMcR3ub7Vp/UMewysyX5t+N0R4U5RiWMMxUcA5kBTOsF1JJBv0Q6PYioScLdK3xo2tq/ifPYTl0rBcGrYuHI1RJMZXNzG8zHG4j62MXRGk5o/dFIMSgQM58tUajY8XsDSb5geTND2cRi4aOLf1oo4msRrEwz3FEq0/68a9bQDXhEHvOUHi0zS8Q2W8Q2XKXgX/riiyYeMeKjJwjgWShOEWGaWVm1PoWWHaNb7IJtPoRM2JKJzYDIV0g0zZI/G5396O4VGRs0ViCyuQE1k8fVnSLTaOpEXZK1GxrUjFtiKpNjdazmJwTQXtv8/i7y6gjiZx9acwwh70wSSSaVMKOfEOlhk6NURkZ4lCRMW3O0rVh/K4R8ukGzXC2xIUQwpaFgrVHqq2FBg5wYdkWIwsl7B0GX9PGX9Pmcfu/hWeoRKOlOhdbXoojiNpIdk2iXYV96hN/TN58nVuIq9EUUeTOIZShF8cQekfJdbpRk/ZaFmTumfzBA5kKFS7KYRVqu/cRam5AoBSRzW2pmBrCtJEAt/+BI6dvcgH+gWR23GQ87/7mCAJ7xRMEzuXR7IsJJeT8AvDYjIhGsf2uJHG47jXbaP5B7uwZZns/HrsqghKLAtlA7s6ArEEOBzg9aBURNB394NhUF4wRfw/4BOxJ9k80752ALsmAn3DwgCoZIFlw0QcK5/HjMbB50Efz4Ftg2GgJQqCXE4kAJDiaWFClEwLghkU5T9yVQXKWAo7l0NKpMV3QpZAlkS57mj0DV1Xze17JpXO0I4EdjqDZ/sw69bOwfHyASSHA7V3DCnoR6qMIAd8k5mRxRmNwtm3rhoMg1yVRq5Ko/crK44hTq+FEY3i3N4nnGx5fcIUnx2k/bZR6h6bEDEi5TLOPcMomSJYFsaMZuygj5qnJqh5agIUBce+IeGIu78fu1AUJctpcT7Nma1oyTJ2NI5n6M/HeTNkbe3dGycNjo5ss1q++CgC6juQxC4UmfofN2Mmk9j1fy6Hfek3n/zbqqQiE9oWo/nTuWPMnoL3bX1d46O/NBw6juM4juP4u+N/WU/nO43/EaRTrihg+CxyC/L4OuJc+4OP4u5W8Q7YjC5Wec+FT/OeC5+mEJIxHZB6sprec33IJZvgQYtEp0VsmoaxJE3txiKlkH5YIYGec3TihyMZvAMW+QqFXLUDWxblsr4BA8MtMzHfw+gihcjOIqYmoeaFCY9r3KBu/QSyAYFeg+RUEy1nka1VRYbojiKekRKFiESgy0bLCiMaLQOyCZYq4xorkZzixXQoJFvlSSMiNZ6jGJDJNfsohGQcaQs9A3pGlCVqWRs1b5FYWIVcFvtDlolPc6FdNUI+olCIqOQavRQrHDQ8GsdzII7plNGTBt6BInUbUuQrVMbnS3hGTJLtLiq3mMglG8dEkat/9AD6Jg/6Jg/pehnlcARcrFO4pHo+OoArJhxpLRVO/uD1uEbLtP4hi2scgnvkyexGZ8IivM9EOyRm9UteGWfCwtMn43/ZiVyymFhVwvaYZGpkMjUy3hELz6hBMaTiHShjaTDlzjzFiE50hopasDh4qZNAV5nw5gkS0y30lEHJp5Cpl8m2B8k2ujA8oOVsTKfM6FIvWsZEsm1ytRKVr8LgSToTc5yMLtYZXawTOlDE32dQ9qm4xor4+su4x0xSl6YJ9BjEOxT0RJnRJW5a7xfl3P3vrsMMufH0ZpDzZWxVZnileOBVsyboGlosD6ZF6/1JyiGncKIcTaKMJpGzRdxbepF3dVG5voeGp3KQL2DbYhKi2FlHzbMiUsLbV0AfStL4uIXcP4qcFw/KdqkM2RzygHiQRtewJqLgduHa3k/rncPUPTaBsz+Jsz+JtycDhSK+7aMgSXR+M4F3xyienjTOaJmhVaJ0VTZtpv80SfClYaKz3SgFC/ewTfWmHJWbs3z0lhsxXDKlah++3ryI1Cib1D9t4H2lD++QiR7NoUdz+PoKuIfyVG4pYrpUcjUOctMrQRa3q1J9AFdPEsm2GZ+n4Rm26FsjVGTL6yQzt5ZUs07FyzEmFgUZPsEmsiOHayjDyFIn2Qabsk9jyu0ptGQRV38KV79QH/VoDtdQDueIKKvtP8tGSeap3ZAkuDeNvquPol8h3xIkNa9aGNk4NXDoVN29g/DTvcglC31PP8pIHPe2AVHSOLcNfd8gez/sE2Rr9yGx+L1IqSxmWx17vzYFJuLIdTU88LHVKAXrHbtn2gEvks+LEfGCJEFGRJ2g6yJDU1GQg36RhVk2cIwXQVOwJ2LCyGd4fLKf0I7GhctqSSiGyvPbBfFLpgXxDPiEY+vgGFJlRFzvfePC5VRVkGQZJRKCeBJ77yFQZOxsDmlkAroHMJqqoHdIjFMVsSuS20VsSZXIAU2kIJ1BDvhFf2i+gLZpP9qm/ZTnth2T8fiGZKV7gPKsFgYvaATDxEwmoTIsXGJtW8S82DaFOU1IsRRKXrjLoonXw48fIvz4Idq+tQt6h1hb++GjykTluTNEWeuKeUeZ8hxZ58x5XyQ+XTysvPSbT0IqLTJch6Lg82IHPEijUWxdQ5vIYu/rRkqmkZJp7OFR7HQGuWQiOXSkUBDcLrBssjOrUA8MoHeNQVsj1c/HX//9v8E5WnfGLOLTJQbvmzm5zV+6AUvFMlJFmLZ74xirF00S6jeL2AkNmH4XpESD+NraD0+OQ5rScoyB0fHy2uM4juM4jv//4X8E6ZzbMIS3W4ZxB9mcgynnHyRfa5Fqk3DE4L6fn8x9Pz+ZxGyTUpUhSt+m5iiGJGwVJAtyDTbFtIN0syArnuEypYCNvyOOZAuTECQwXMJcxXDITMwTJbh6ooyWET2dsmGRrZXIR2RKfoWSX2H49AqcUYuyWwaXxchilbJXYmKuRrLFQcmn4u81cUVNvINFwnvzJOaWcUVNHr/jF2QaHHgHikzM1rFUiM0zcY2XyTf6SUyViE9RydVKuK8fnJT2c1UajoRJ2auQrZVJN0mUXTJjy0JkmiQyD9RiyyIapeRTiM5Uic0L0vXeSsbnaxj/HCU+1UlqipfRZTa+bgnTIVEMSeQrFIauLRLvdPPt711K3fM56p7P4RmxqdxWJHFZGlfUxpmwyN1cj3OsgGu8hKUJpXhsgYOhEzxIppgQGlzlpNBcQi4LZ9bMokYmZjuIz7KYmC2TrwIkGLyqhDboAMUiXwP5Ghg/P0/fmRI3f+MH5D6SoHIzIgInZSAZkGhXaVonzoPl1mm7r0SmTmPonDKucZtUi4ozViYwewLXhIGaNSl7IF+pohQhuF+UYutJqNqUI7LLJLLLRIsXiM5QKYQVSkENybAxHTKNXzQpBhQa7+zC0mW8AxaJDp3BVRqeYZtiWGd0WYCui0PkmwM0P5wlNT1EMahSrg8xeGoI26lheHXUdIn0kkYGzq9n4Px6cq0homvaSZ4zi9LUWgy3SnRVA/3Xd9LzrhATsx3kmnxYY+Mou7rJtYdxdydBlik0BERvYmUENE30wzkdkMkieT2iXzHgm3S8JJWBVIZS2Ikd8FKuD6FNZCCdFf1aloWjL0bD7QfIVqtYigRlk+TiWkJ78hgumcpHe0GSkMsmtU9M4NufQN87gJoSBFgaHMO1cT/IMt5N/Ui5IlKuiJosIKcKSLZNMaThO5TG/Woftiqj7uhGH05hBlw4euM0/34UX0+OpkcNXGNFpEIJPWVQ9dQQyDKVTw7R/IiNNpoG06TpvlGm3JYgOkNDOtDHugduRzIsJMMi1+wjPjuInMiiDArX1s6vjYJpEZ3npxR2Uu5sJLQjgXt/FO8j24WiZoEd9GHMbceOBND39DN+7hRBkPxeem+tRN6wBWN0jM7/GELb3o3s9yH7fdi6SnJJA6ZDofOLPVARwuofxPHyAZGP+E7CsuCF7ViptHAFTqQwGiqw4glB8IN+8brbgbKrC2lkAklTMQeHsVKZyfgSyenATqWRAj7sUhmlugprPIpdKoFhiMU0MWe0YPX2Y41NYI0fNvpx6IK4GSZYFnJVJZgWtmliGwZoKsrOQ+IYGXE+7HQGymVCf9qDXSqLa9qhY1cJgminM6IktK0RLVE4Js/xL8nKkfJaye1CSxSofjGLHQmgVldh+p2QLwjCWyyCruM8NAGWhdo9DLqGlMphF0vETm8ndno76+I/Y138Zzwy/MPJYx85pjKnEyVTnCRVryWc67beRPMXNorIlNB1Ymzb9jFydpMg+8Pj4PchZXPkWoMYSzuFgVKxhBQMkDntsDmQbUM2iz0yBrIkJi/CQXFeEMrl38Jrz9EjfbfQ/oOD7D7/S8Cflc7XnlfboYFhsG7rTcKJ+U2o9K81Mwo/fggllcdorwf+bLqkzOmkWO35q+M7juM4juN4p/CPyun80Y9+RGtrK06nk4ULF/Lss8++4brDw8NcdtllTJs2DVmW+fjHP37MOr/61a+QJOmYpVB4fZO3fxTeMum86aabyOVyx7yez+e56aabXmeLdx6frFuP/+xhbBmunbWRHYO12IpNMWLhPH0cwyXIopyX8e8WqqWZEgRu+GQL02NSO2eEztYhUq0ShYhNsk3HEZcwng7jSNgUAzJDp1uoOYjssshXyIR3gqnLDJzqQs9YBA4VsHRRrmspwujEliXyVWBpkKsSfafCcMjGPWLjGTUwXBLOsQKJNpXop3IMnOJCzitg2rT/7oOML4Rku+NwDqSNp0fBUkRpYGjeOKUVaSwNMr9ooOyFshc8QyXGFqqMXFpAMqHz7APEZsq4JkwuvOBZ0ifkKfkk2t59iEyDmF1PN0kUag0KVRbDE0FsBcbnylS/JGE6mXRb1LI2vke9xObapDpsWm/ZT+st+0lMlUi0Oai+1YXhkMjWyBTCCqZTZXCVk8Q0m4HToRSAfL1Fqt0mPtfE12fj3a2TqVdJX5VidJFKdkme0JQYagHevWYj+RMyuF0ljMYCjl4HtmxjyzbaTg+rFu7hG4NrSWZdpJskSgGIznQQPGQilyFbK9xGB04PMLzSiWSBZ5eDsZMNjFOSdF0J8r0Res6Xic3Qqd5UohgQ58Q9WsQ1VqT2+SxyvoR7IId7IEe63UfNK0XCm6O4B7JoyQKSZVMOu7FUMBsrKflVEh2iDFU2xDWQr1CI7MwTOGTj6k8z+GmDWKdMrlom2e4k2GWQnBVicJULw6Mjl21cMbHoqTK2DHLJJtnqINmmC7OnURv3CJPmO32fXsTYJTNBkcg3+kXf4KYuTJdKudpHYXotAJZbx2qoQpLE52qrMkRCkMmRm9NAbk4Dzj3DGAEn2r4BLI8DnA5B0hIZbI8Tu6aC6ieG0BMlpGwO/6N70Hsn8Dy6A7M2Qq7GgTwcRTLFuSEUEKpIriiULsCqDmEHvZhBD2ZQqDkTyyIoeQPXaBHJsDBaqilWusmcPA0sm2KlQyhiYxNYmoxr15Ag/+kc2pZDogSybIKm4t4xBMUixTo/pVo/mCb1T6ewTZOzl58zSbA9u8dxj5UxqgNYdRXIIzEyc2tILKjE119GSxZRozmk0SiFlhCSroMm8igBtJ5xYZzj81L57JhQkbt6afqSRep9y5FUjVJLJZKmYqUzWIdJQGBjL/KGLeJc9A1SWjXnHb9nSiUDu1BErQgj+7yiHNM04cVtYoXDkRc014kS1KoK7GIJO18AScYqFoS6OREFr0dEoZQN7KLI+pTra5H8PkHWJEmU8r68CzngR5rSglwZwRoexU6KyQDbMMS65bIYhySBaSGpKlIkhJVIiddsG6mqQpx3twvJ6RAKaTYnynBLZbHdaBRpNEqx2kPqfcuP6eV8LZJXLme1fDF2Nkd8dlBcWyDKhgejWCPjkMkJ0qvIWCNjgpgH/SK2JJvF7BRqnP+OFzhlzddft4dUHpmArn6K1R6M0TFkx59djF8bPfKYdS/r4j8DrwfZ66FiWw5J17CmNEIyhe1x4xoUWa543WIpl/HtGMPSFXC7sWsrKS2aCtk8zj3DWH2D2KUS5vY9PPCx1X/z+njtOVrjuZLCnKbJ/s0jSudrFc9cs09MsgBdF4cYWfK3PQpfa2a056YWpEIZdTx11Hlbt/UmTOexjynHeziP4++J49fTcbwh/gE9nffccw8f//jH+dznPseWLVs48cQTWbt2LX19fa+7frFYpLKyks997nPMnTv3Dffr9/sZHh4+anE6nW+4/j8Cb5l0fvnLXyaTyRzzei6X48tf/vLfZVBvFZ/cdwljCR81U8f548AczDEX/3TKY8yb30U676T1zG5az+ymYfYwhguKEZvT5+/CEQdbsfHt0xjcU82+zc2YDggclEhMtynPypJtEoqp4ZaQcwqJTpvRJYKEnfCxl4WhiMsm3aAwcKoLS5FITBMZcYZLEktbHkfKQrJESW7ZK8pvx1eYxDtUDJdEYqobwwX2o2HcI6CmZXrPkal+AVr+VCLTIOGcgEJIuMHGp+kMnCqRf7wSa58PZV4S6fJxOi/ZR+cl+zAdMnIJ6PFguGDz9jby9QbDF5Z44usruW7Oc6TmFxnLeYmcPoThgsAhi3nTe7GCZX689DdkmkDNwdhiW2QnzrIoRGBiPiSn2FihMp4+ifXbZrJ+20z0BMRPLJBu0IjPtshXQT4i0X+6A/cQeHsl9Oo8kg1KTgbZ5kMnPoGesnCdMk58WZHilhCl+jK2DaltFSx913bueWUJRklBkmzsnMqJa7dhOWwsh01xep49sWo2H2qilNVRliYwKsvc9NFfk7k2iS2DZ1j0nhoe8X4+9YW7yM4qEqlJcsuce4hUpIl3glyUKLtFvqZ30MSRNLFlCdOhoKTy7LvBi+VUsJwKrrES43MdjC+vwFZlxpYGKLtlSkENR9ISBjVeibIfSn4J2RCk0HBJxGa4KLslsq1+gnf5cCyOoeaES2umThhbNT6RoxRQmZitko9I5CMSpZAuSkg1keOamCEcWb2DZYzDRk1yycIZg2IYyi4Zd1ccNBVjmshZsmWJTINGbsUUjICTfdd6GX/XVCxNYWJJmNGTKjFrwmhZAy1rgCwhmTZmay22qgiV07SgXEaKpbAP9pKZVYU6kcYO+pAqI5RaK5E9bpTBcfz7koJ06Cr6QJxirR97aFQoaLEEkteDvW0flseBEkujxNKgKFQ+NYScKaBkikiZPEq6gGvXEO7hPGRzeF4dgIyY/NK6BcFzP7Wb4pRqJKcDqzaCVDawXTr5zlqQJLR4ES1RwHI5kIpl5HAInA56fxCm9wdhKBQxnTLK9oOMLfELsxzTJvjqOFqyiDISxwy5sbM5nK92IwV8Qi02LSib2D7PpHqMZUG+gBwKAuC/4wVkpwN10z7sQhFJ05A0TZSNaiqK14sxNoFcVTGpcNoHe9+xe2ahUfRD2vkCdi6PXSximyZqeyuSw4E1NgETcTE+wBoYniyNlf1eZJcLSdewSyWsoREkTcM+XBoJYPX2i884GhdRJz4vSsBPeVqDmAxQVeTGekEcPW4kVRUKnaYh+byCvCqyKMstFEWpb1OtIKTFEnYuL8psK8KCsFqWUE1NE8nnxUqlsVJpHKNZwo8fmiRMr4fAbS/wmHUvtmXh787jebkHaTSKcagbO5NFrqmk3NkoJmdkBTkURA6HMA92i/fi86JGMxTPXULx3CXIRfOYHsTV8sUYo2NIwQD684JsyTWVk+M6ooYqXu+fDXoSKaivQtndg11biTIwgX04p1NKpIXymskJQlwdgUIRdTAK+TxSIo1jZy94XMK8SteRXE6UQIAnfv3zt3StyJEw6mObJhXHI+N77YP6xNVZrP5Bzpz3Rdpv2cfsUw68pWNM+eDLkMtjVAeOUaY9L/cccz7/2iTCcRzHW8Vx5Vzg+Hfp/w6+853vcO2113LdddfR2dnJLbfcQmNjI7feeuvrrt/S0sJ3v/tdrrzyyslok9eDJEnU1NQctfzfxlsmnbZtI0nHNslu27btH5rN+ZfYsPxWLml8lbAzx42nPs62VCOqZHF+x3Zu67iP2zruo28wwpSzDuFpS/LC7+cSW1lE0i3SUw3sYJnAAQmlCOkWCE6NYY65sAMGLSf1kmkAK1TG0m1hdgP88cklhC7vJ7gXlAJ88X13E52lE94BSPCVL/2Mr3zpZ0Qec5JoFTESWs6m7BGqV+2TMrIFal5kJxYjNp5hC9MBrnGofEXGcEpEZziwNJBNWxghNZfINNmEd8i4x8SUirE9QObxKnb+YRo7/zCNbJ2KMwaVW4XpzoNn30Jgt4ok21hXRPnJqyfSdcYvWFLZS+Z3dahzk4ycZLPviXbIqFy/4f14BsA7aOPtkbE0MN0Wah6ksvj8qypTZBttPrz8KT68/Cmkk+PYlky+SsLXnKRQa2CrEJw3TqECkotKwjl3QQyzKU+wI84v9y3D/9F+YnsqWD6li3JHHk84h77XhVyCKyo3MmdaH449Lhr8SbrO/SkvDLZw3alPcd2pT2HmNNqDE0Qq0rQ0jJMe9vH+xc8zWA6RiHnI1cC8f9+CeWmUcsCifGKKzz5xKbNbBlEkmw88dB2prRU0LBhCqc1Tf0Yfo0skMvUKJZ8MXxzHdMrE50Vou9tEMkV0TdmnEt5TPuxmrBDeJeI+tIwBEqRaHZQ9EjUvmbRcdhAsiE+X0LI28eVFlCLEpou+0u/NuofEmhz6FSN4Rkxy70ly4DLHZEZqaWWa0so0I4tV3GNlRpdDus1CS8gU352g5xyVQhjkElxwy2MED5aQDEg3yUwsq6TvkkZiM9yMLHUytsCFZMLQSqFEeLsUoVBPd5OvkIjsyFIKO0i1Okm1OsnMq0MyTJTuYeSiIZxMJYnylDpwOZErI3h3jkFOvH9rcFhkNUoSBHxI0aRQPJtFbJGWKmJNaxF9gS4naBpKXTXy7m7sRBI7kcSqjWAnkqIHMF8SStihPlHKatugih46CkVBhCRJlBg6HDh2HjZR2XlQZBlGkzhf3I9VEUDafQhpcIxCrQvT6xQkJZGi/lsa9d8S5YGeA3GkYICa9UPCVGZfFIpF5IIBhomytxfJ4aCwoBU76INMFmtsHMvnwAy5BSEKBynVBzFqw6IccnAMxesV5Opw76PkcYslEhKkzONGDYv/m9v3YCaTgpS+Q3Bu7xMqoaIIAlhThXXYIAdAdruxi0WkSAi1sgI56Mcej4KqYmfzSIqClc0hHzbzkaoqsApFJF3H6O1HcrvFZxwJCaUyGqewoBVt38CkMmxPxLDTGYwx4TZMMEC5pRJ0DaWxQayTTGEXitjhIFK+hF0dwQ54MKc3Y2eygvB6PdDagB1PQr6ANTaOHA4hh0PEZwcn3WSP4LUPmK8lfVYuh7xlH6XOBnDoyLqOXS5j1EdQd/dOKop2JoudSiN7vTA0ijU8CvkCnu3DeLYP88TT/wf/HS+IMtl5XzxKETQGBsG0hGtuwDvpbnvk912fncO6rTeJXklZQhqPI3k9jK4MgUNH8nkhnQHDxK6txGyvw2yvQ8oVMRsqRJmybYsx5guCxMdSSE4HuZVTwDQ5e+5pfzX38rUPnub2PaDIGKsX8dT6z7Javlhcn9v3HHUefb/3Y5VKrNt6E4NXTCN3oXTU+f1bUCMR7Iogaix71Pk6M3QdRnsd4ccPveEYjxOG4ziOvw+Of5eOhcTbKK89vG0qlTpqKRaLx+y/VCqxefNmzjjjjKNeP+OMM9i48W+3Qvw1ZDIZmpubaWho4JxzzmHLli1veR+lUomBgQH6+vqOWt4u3jTpDIVChMNhJEli6tSphMPhySUQCLB69WouueSStz2Q/w4+1f4YXxo5jUfHO5kZGObh4ZkEtTwnhA/wqYoX+OTAGj45sIZrFmyk1pXkso5XsJakmNY8gstbRPWVOHfmDpLTbD596X1gQ2pHhLUrtvD5ZQ+xr7cG39woclxDq8mh5kC2oHHuECMpP9JFE8RnW3zjlvcw7+JdpNokYrPgFyMn8YuRkyiEhNKlZqEQlFEKMHJmGT1jYcnguW6QskdGLom+0WyDTXpBgZe+9mOQIDnT4LJ3PY3hkpDLUFOTwBGTiC4ySLZJlEMmxQqTTJuJlgUtC/JFEySnWQyvMXCP2lx426e49gN/wv2Sm+wzldT9QWfaL2/koacWE59h4XrQz9dPuwd9QRzfIQX3fp34/DITCy2a3tXN56+5GyWjkG0to5TAPzNGayBG3awRfvvt1fz226sxLRl1RBfK6Ush1KSIghnvDSOZ4AnkKRdUCkUN25aI9QfZtfwOete38M13/YY9d3TyzwsfI5/VseZkKAdsrtlwNcmSk49c/kf2vNpC+70f5Edz7+TX95/Gr+8/DdlpMJz1Yz9QQc9gJc4RhQf7Z/Htrafj3udg3qr9/OmxJcwIj2JpNrYt8f6Vz7Kjq4GxQxHsYJly0KRnfw3aFg/pohOzsky6xWZinkTvy430XWTiHiuTq9HoPctD71keMrUqSsEi2qmQrXdiORTSF6YYWuEg3Siid864fiPxDoUtu1pE6ev0DGNLLOSoTrYB5p2/m/zyLO9/7mp+ueRXXNb4CgNroLA9RNe7f8LoYomGxgmmVo4ztXIcS4PuCxQ8vTLO+gzFxjKeOwN4+mXozFCYnWdjvIMp/7GbbLNFeorJ+CllPKeMk6tBqPi9JsWgxMyVh4h+KoczbuPvgmS7KEvuPs9DqkUn2SaRbJMYn6uSafYwfNEUCpUuEvMiTKysEj2SqbQwMoolBNnSFIzF04ktrIDD/Yql9mqUeAbPS90iw3FgDLlQAkkW5Xiqgu1xQ1ujKKVUVeRMASkSwpzRIohOJofs9UChKNxTj6hqLie220FsVZMgoQ4dDKF0ociiL9EwkCrCyD3DgiDJIppGnUiLElzLohjRKUZ07EJRlCGG/cIcB4Syq6pIo1FhuNRQg10VEaRtNAqmJRTTV/cib9knevwsC3XTPpSdh0CWiJ05VewrmwNNxSoWMIaGMYaGRbyEXxByKy2UQrVZqNLFJVPeuZtm0C/GY1nYRZF9qVZVCJVWlrEyWUFCiyWwbLEuiPJWTRU9l8Uidr4gPptUGqWhTpTsRiKiHDSTxRwcwRwcAUVGe3ILdtk4bDwkC1UyHEStr50s51W3HqTcGMHsH0AKBjDmdUBdNeVqL/bImCibLZRR+8aEEdLi6eIzHhwD28ZKJJErKzAaKzEaKwk/NwCIh6m/JEBHCJSkaihzOjFWL0JuqEPbtJ/0gnoklwvZ50XZ1S2U2EJRHKtGmFpJTgeSQxdqdrmM0duP0ds/SYiUOZ3iGuHPJEmtrhLbZQuTPx8Z22r5Ytq+vv3PfZK1lWSXtoIiU7N+CAyDzMIG4cob8onJmFd2icWhoozEJ0uV7Wxe9LVmsljVQtH3vNzDutQvKcxrOSaC5LV4LelT5nRiJ1Koj21ijeuKY8pqj4w19NBulIBQKet/sw+jVZTw/6UT7RsiHKRU6Ra5p/yZrJrJJKlW1zFmUMcfjo/jOI7jH4L/hnttY2MjgUBgcvna1752zO4nJiYwTZPq6uqjXq+urmZkZORtD3v69On86le/4o9//CN33XUXTqeTlStXcuDAm6tCOXDgACeeeCIul4vm5mZaW1tpbW2lpaWF1tbWtz2uv914cRi33HILtm1zzTXX8OUvf/koSVfXdVpaWli+/Fhb838E6tU4fgrs6a3lCyc9yH9Wb+OnyVruG5nPR4J9PPOKMCz4ybt/yGm7LuJH9S/x4/jJ7B8UQdzLFu1j/SOLWHn6Lv79xbP5ykX3cN/YQh5+ZR4PG/Npmj7CeMqLpdpcPv0V7thzCtdftI4fPLOacFOCT0x5gq8+9R4SM0xe2DADSQappsCrT08DoNxhIvnLKEMOSiEI7gV5QqcQhNzSHBMPNJKcayGXJYZOs3BW5Inc72XBizdi+sE5pPL7rnmUqqBQa2A8W0Wuo4ykm4RWRIm9VE0pZOEekLn4hicAuOOu01DnZShldRquPUR0cxs3P30mfqC8KEOq7EU2oFRl4DmkkZhm841vXIYZgWKNjXdGnMKBEEpBoice5stPvwdNgtmL+timN1A+EOaVSjfSiJPGy0QJXmwsTPW8URJ/qiPfZqElZSpnRYlnXeRLHsxxD1JZwhcRD9eN9WN03HMDngJ84pn34jolzS2/Pw8rYrK2czN/yM5lbtsAO19u47vPn8e0k3vY/3Iz73/wg9iNwiZXVmxiOTep5SVIqxTDFrMDMW7s/B3XmVeRKjuoWzjEC70tSL4yq1v28WD/LO48+b/4Qtf5DD3ZSNPpvZxWuY9bnzid82oP8PtXV7LitJ0s8PcyXApy7+MrMJ0mkgV1z4rjlgIq8ekOal4pkmrWSTXp+H6vkauEshvKPqh3xDnr8o18pOI5Vv/kM5TGXPx07c8ZN/18/uFLeOGFTqyAgaSZXPHwDXS9+yfc1hKFFpj66xvRMzDxfC0DLeKYks+ic0Y/HUsmWPfoIk4/fRtPOqdiFRWCzhKpvhAvxaYg2eDtksk12FglieyuSorz8gQDOQoDYVKLC+x6vh1/N6TPSqM/78N02Pi7JeIzLBIzRP8zgH92lMrVCXb01VH2uQgetAgfSJNvC2PpEdzdSTIdQWTDxhErYXhUXBMGVsBNocZNol2lJl2k9321tDwQx64IQv8IhIKTbrRSNA5ut3jABtwHE0iZFKqmitLJiDBXkkai2CNjWPk8SiSMVR1C7h8lnMpRmFmPc/cQtm0h6Rr502bheblHlLJ6PaRP7MCzfjvWvKno/XFIpLANAyubw7Pr8AO40yGI0GgUW5GRNA3rYC+Sy4VtmkjT25GKZUhnwefFGhxG9vsE4V0wHbl3FCybwbVV1D9QJj2vBt8L3YR2aGL7KS1I2QLFlVPw7BZGRWSy4HQItUySRe5jwIva3or8/J973v7esB0aNNZhH+o57DxbEsTycF+eHPRjF4oYg0MonVNFlEq+gF0qYxtlUYrb1CAiUTRN9GwaJrLfK9x5vR6kmgrkw6TLzuVBFWqy5PdBvoDkcIgJiyO9mraN5Hahbj2IVF2FPTaBlslil8ropTJ2Uy0UyoIIA3Yiib6vLHpNSyXkqkrKTWG0Vw+gHiax1riISzmiJr5WVTzy8xEjIef2Pg79Uwdt9+q4xg7PSDt0JFXFzmQF8awMCyWxXBbk8wiy+cnJAjsg/q68lnAdOZYxOoYSCGAc6sY6aT5PbP3hUWN67TgVrxdPIoidTIsy4qAPPVEms6Id73MHwetGWiwcZaXuYTHRU0iBaYqJgFgKDBM5nhGfq/Mwmav2HGXi89dgbt9D6n3LiU+XaLs3PjnGI+/pyFiHPrOCmpcLPPHkv3Km/2qUXIm/ZSU06eYLFFpCOPuTDF49c/K4IEivlrU403815uG2ntfL6Py/QUKPO+gex3H8L8Dbyd08vH5/fz9+v3/yZYfD8Yab/GUF6RtVlb5ZLFu2jGXLlk3+vHLlShYsWMD3v/99vve97/3N7d///vejqioPPfQQtbW1/62xvBZvmnReddVVALS2trJixQq0d7D0663iio3XMnVKimdP/S5hWQdUbutbxtzIILNevAxPgyA5l3WfyRfaH+KxvMqi6T1M8Y7xwME5LAt2EVqb46neKYQrMnxx/cWcsHgP/roUH5jyHBOGj7Ud27m3bQnLPQdouDTGz3tXgmwTG/fx+YOXoLug690/4V9G5/LAgTl4nSVWrhF/UJ/+3UKybTZaUSJ40Kb1g/uJdTdRDLqQetwUw6DHZZCg5JDQn/ORrYFcnY1dWaQikuajHU/y5b2XIrsMpMVZGPUQesnJWIcD99wE6gtBsq0mv3rwNAD2/dOtdP70Rhwz0+x6qgOr0qSmJcrVp2zku785n2yjjRUuEXzFQWJemapnVVJtUApa+A/IJJ1BpOoCxoSD6eEoi9/9Kr94dQXJohOHp0QpqXHhzG2Mtvt4dp9QZLQhndFRJ1de8zS/2rIco6wztqcSV2sKo6qId4uL7KwiEwcj+FsTlE2F5tmDnLF6Dz999HQc232cfNXL5E2djSOt+IJ57u94lOkjVyBv9+JWy1i1ReQRBzjEI03AnyPWHcLTJzPr/H28dKCF7U9M5Xdnp1E0i317G9AnFN573gb+8MuTeNI3hVzawTKHyqG9dURWTPDZ5kf44dBp2F6Ttf7tzLxogC88925eHJiFLYPZVGTkijLs9eId0AFwxixKPuhdo1OxXeR/lvwS6Wkm/r0Klg7feW4Nq+ftwi0pLD5rFy/1N5OzHfwpOgepqog17kDKKGh1Repqo1xw8AxGuiPUt4/ztYtu5zuHVjPcE8EdEKWrxXE/+4ermD5tlM+++z6+tuVMvrb4fj773EVc1rqJaZ3D3DexkGde7aTinAGemPEgXxibxZ0bVmKXZRJJNx/61Hru6llEQ8sg20LNyL1eSgsK2CWFfFuBmRUT7NrThOkRD+KWLbFtWytSoETZC2MLZIZX+nCOSTgSYGpBkq0KhUoIHFTRshaxGTK+Ho2yDzLNNnIpiHfAJt3hJz5doemPEqRzgjSOJ0SfmsuJZ8/hMktFxqqvJNHpJ6TKUDbJNwaQGgI4hzPIZRPLrSPlSuD1QCaLc98IdsgnSlKLJdzP7hflkj4fxZYwStFCamlE7RoGwGyvQxmKITfXwWFiZKUzyLXVQsUqFrGTaWSPW5QomiZMJEBTyc9uQM0ZaJJEucaP1juOMpoQYzEMar7/Epau43teqIbm9j30fmUFbffGMSOCkHDYRAi3W5QU5/MoPh9YFlJSxNTILtc7cbsEED2BkgRuN5LbBbqOHU+I3wV8GH0DqA31KACj49jVlUi2DYMj2EYZtbkRozaM2j+OncliptKoU9sEma+NYO/rFuXQR/7Iaip2voA0rRUGx0SkCoCiCDLnFMZQSJJQqvMFpIBfkD2PGzPiRd7TI7bTdWFoVSzB4ZJlSVPZ86VKpn1wJ3hcmDUhAOy+gWOI5hG8tkRTSmawqyOTxErJFEX5cTYvyJ2ukTqxHf9zXViZLLLPC7EEVlPN4SzR+GQ25ZHS0zND17Eu/rNJZ1oAJRDA7GxBHY5hbNhyDOFUq6sE4ZzTibl9DwogVUSEq3QqjdZbRpneLNTOaBylbBx+A5JwuD0SK+Nxif8fJt+2xwkDI5i9/Tirq/6q0vmXbruhP+zAf0cGDmePvna8ypxOHrPuZW3LJyhMq5kky9Kg2P/SK74j4l9eB681ElIf2wTVVVS/6D7q+Ob2PWjVizAzmWM+u9f7TP+ROE44j+M4/hfgv0E6/X7/UaTz9VBRUYGiKMeommNjY8eon/8dyLLM4sWL37TSuXXrVjZv3sz06dP/bmOAt9HTuWrVKhRFYf/+/Tz33HNs2LDhqOX/BppqosTybi7ccTXX9Kzl67EO4lkXpwd2kY26aQnFaAnFmOEb5u6Jpdw+toKP1j1OQM1xXefz/Gj3Sfyg7mVeWPozEkk3Hzj1CQ4mKygbCkvdXbjlEh/YcTlbYvUMGSHe7x/j1Jr9uAZUlk7txo6UCK0cZepvbuDBrllMqRonGfcQUnOE1BzZNgN/VYZShcnYUouXtnQQetpFvkoYGZk6OOfGKVUaXLTiFWrf1UspCJ0Le3D7irw0/3f8tO9ElI40Kzq68DhK7Dn3R1gqfOrcB8mknFStHuCbp99Fx4oeOlb0cKCcwZyW5cSmLop1ZYINSUYOVnB9YJhr3rcOPSFBQSG1tIAkwfgSi2LYwjEhk5xh4hqVMfMq4Y4Y+55p49frT0aZ0Ak585QGPWg1Of7UNYMXn5yJ64CO64DOvvffiuUxiZa8+IM5gvskqCowtWIcM6FTecYgvzjxlyh5meSQn97hCCVT5fZfr8ZWbLb8y608eccS1m+bSabg4MSGQ/zT0BKsQ15OPG8rW1/o4NLZm1Fasui7XOi7XHyt8z7UrEy2s8T2kVpk1WL6KYeodySQDnh48uxvU/ZbfLlyF63vPsT0yjHstEbHk+/nxfO+w3tbNnHDpsu5t+1xFHeZG392I4pkQ1Gm2FAmsGCcJ0/5LntP+A17r7uV+CyL+CyLoXPK5Ofk0doyyJePcctnbmXq5fuQ/SXKJ6UoRixWzDrAhkfn8lC2iVdHGmivmsAn58mUHfx02a+xPCa21+QDM54jlnOjyhZ6RZ4qd4ZfDJ7AyHgAW7FZXNfP4rp+mhYMMqdhiA3D7fQWK/jKwj9y86HTWTVjPz965WQ+8eCVXF65kfZpQ0xkvLQ+dD0rfftZtOAgp3Tuw4rr/OT3ZxLtCbGtqxGpKONsTSEPO/inZU/gdpboTwbx1aZxBIo4AkUsSxKRQSWF1hN6KdeU0ZsyKMsSZBtgaJWEsTyNY1qS9BkZRpeD4RVNDeE9ZXxdoiQ8XylR8snUPl+gVO2l79JG8rVukksaKC6eQmJ5PYWWMIWWMNkpYXrPCRB+tp98o5+L//AsqVYN0ykjlcRDdinkZP91ETKzqtjzhXaMugjjSyPkpldhNlSQPm062bPng0NHH0njem6vICmKApaFMpHGDvspVnsoTaunNK0e2eshtqyG8ROqyMyrw5zZSuyMKUKddegMvbuV2AkNlHwKB9+rU6oPMD7fRXphPbnOGsq1AeyAF6W2hv6Pz4eAn8KcJlgxj+Y/ZZFiKQyfzsgSleLMJoozm8hPq8Kc3izcbXVdqIkBL1YihZXPv2P3zMK0GsjkkBQFc2wCSiUkVSW/fAqYluixy+VET6YkI6WzWL0DSE4HSkcr5PKoBwaEo6tponjcwljHtpGGx5FrKpF93sneVUwLSVGEMZGqiBgSj0soeLaFHQlgDgm1l1xeqJmaJrJkATbvhoZaQThV9XB/qQKmiRVPYGVzdH5pHNnvxc7mUUYTYvF6UeZ0HmN8A0eXkcZOaGDd1ptE/2Mmz8Pr7hbE7IiLb8CPf5foacW2wenALhvIw1HoGwave7LX0Vi9SCiKzXWsli+eJJxqdZXI/nxxG0ZvP6MfX/GGn89k/qXDAZYpqgIsWxD0Tbsg4Be/O+IOXFOBVBFGqq4QarnHLcqBQwFxLnuHkJxOlDmdk2o2vL5xyBH1chJtjajVVceUyj5m3fvn1wwT53bR51NcPn3SmfqNCOdfQlI10ScuS8eUQjv3j4px/8Wxj+M4juPvj+NmQkfjnY5M0XWdhQsX8thjjx31+mOPPcaKFW/8N+KtwrZttm7dSm1t7Ztaf8aMGUxMTPzdjn8Eb5l0vvjii3R0dNDZ2clJJ53EySefPLmccsopf/cBvhl8qOkZpofHWFrVy6sDDbTp47SGY6yLz+EHp9xOztDIGRpPjU5hT6yGTUONXL/5Cm4/sIQfvHwq35t/Nykrz+6yRmvNBHcdWsTcyCCaanLbxEp+snMlS2v6uHvaPfzH9rX8KefkytBL/OSaH5Ip6/j8eUb3VGErkIu5CDtyTGkc5a4nTuCuJ07gubXfYUblKIGGJHJR5qKVrzCxrMyqtVuxHDbytDTZrBMMicF8gK6xCkohk939tfzn7Ps4bfe5vL/xBZ5b9l/Eim4+N/VhLu9eS/XFvXxr3bmcPG0/1zQ+z8FiNapkoUoWax76JN9e9DvSZScdrSOk94TwNaaY+psb+GSoG2lOCj1U4KlV30N2GHx3ze04YjLupVEuWLYJc0EazyGNeMJD3fJBrJoizjGJHRvbcdRnKRdViv1ekCB44ijBE0eZetuNNLSO89JYM9W+NLFlJZhwcFnNi+hRhd6RCB+86wZuOHcdgV0qzbVRTEtGOSHO59Y8QNv9H2D6u/dz0qz95KJuHt42h/5siP1X3Uq85EbNSfiUAg7NQF8aQ18a44PPXYncloGSzEdmPM20hlGiBTe/2LGCT134AKfd/8/YTou5L7+Xm5r+wL1tjyP5hYq37E+f4PYu4Yw7f9OluF0lynOzfP7ld/Gd1XdAWUJVLM762WdYse1C3tt9KtVTJqieMsH75r2MNOykJpAiV9I4UKrm5U1TmNkwzNXTXuRLZ/6eXeM12DL829MX0BKKsWdvAzf3n8GBiQo+sfMSJFNiWtsQP3h6NbYtsflAM/8yZz1btrdyQuQQtinR1jrKWeHtnBXezqzgCIPpANHeEH/sncUyZx+SZOOQDVobxzh46Y/5Ru+ZvL9hIxe3bcFfleFMV4lXDrTw1KaZ1LRHuffK77Bq4R7mtvVje02eWfJTpKYc60dnUDYUUgN+CruCGCUFo6SQinmwNZuLF25m78E6ApVpFtf3UShqdJ50iP9cezd+d4HsmAf1VS8ES2gpiWQHDJ6kkVpcwPBAeI9BdA7kq3SGlzmo3FYm2aZiOiXSjRpll0SyXSfZrlP2yBSay4yc3cTIMo3vff9CbBn6V0O+OcjYiRX0rdEI7ZZINalUbpboW+tBLdgMr9RItXsYvThPrkqh1FFDfF6E3KpOiq0Req5upfvGqaTm15Br8hGfotO/2kX/ahd7vthGMSgR74SST2F4uZtcpUS+3kv/JU1U7CiImKUT4Zur72JwlZNsHSRbFfIfTSCXLfL1XoymSip2GPR908XwCp31v/s16++7jf5Lm3n8N7+gVGUweLKDwZMdZGtVMk1OwpvGKXc2igf2sslZW4aROprfsXumc1svZiKBbRiiJNbvxUwkcO8aFkSrMgxlQ5T9HjbQsfJ50cs5OCJKaB06KDJSdaXoe9V1UepqmkI5rakQhjaZLDTWIDfUiuxO26Y0s0k402oqNNQI4traJI7ncWO11GLUBAVxKpZQGupAFuWyyBJSJCzIU9nALhtC4bZMYW7VUCuIoW1DW+OkMc/rlWWuli9GUjVCD+3mzHlfpNRWBcUSay66Sqx/+BiUy9gj45h1FUhtjZDLY7c3iHJsRSF2QgNqcyNqcyOO0azoFR2NHnXMI6W1ypxOlDmd1D0ZP2o8j1n3YoyOTZI+NRIRPc9BnyCPDh3btoSBUSqN1VxDqUMsDIwIcplIYUcC0D8kSsD7BsHtwsrmsKJx4rODUCoddczXw2tJXrFauDKvrbzhmPWO5I0WZtZjV0d4zLpXuC+b1lu6Hs0VsyhUOEQeNn8m3Y9Z92LUR6B36ChX2+MPxsdxHO8Mjk/o/OPxyU9+kp/97Gf84he/YM+ePXziE5+gr6+PG24Q99x//dd/5corrzxqm61bt7J161YymQzj4+Ns3bqV3bv/XD3y5S9/mfXr19PV1cXWrVu59tpr2bp16+Q+/xa+/vWv85nPfIann36aaDR6jCnS28VbJp033HADixYtYufOncRiMeLx+OQSi8Xe9kD+O1jrzjGW9+JX8+w78TaeSU2jypVmf7KSnOXglKoDnFJ1gA2zHqAzPEJbJMqX5j7Eyvpulk87xG1jK7mxby2Xb7yOGYFRmoNxzg5tI5tzMN/bi6aZDOf9LHrkY+xd+RueSXfyh/RsPrX3Etq8USq9GU5duZ3aOSPceuptPLuvg0vrNmG5LSy3xakbP8znGx7iazPvp31eP797fjGOQJENfe2cs/JVihmdmkiSS1a+xPkVWyhHnWw+/xYCwRxnuwuMJP1syrQQkT0Mp/38y7YLqHSkqXJmeO6ib/HMgSl8YeMF/PxPp7NvrIp9Y1W0Thvm8zvfRc7QiOY8mNUlPtO5HqOyzIz/upFc1E1dKMmpv/s0kmLzg75TKDaUifUEqXfEKfd5Kc3NovS4iD5Sj6xabPjot5GLEpYlQVxnytx+ShUG0bSHaNqDUoSBoTCmLXGgt4YvrngQPSbz6fXvpXFFP5fPeZmqRSP89N4z8Z4zQjzvIplz0hyMM00fxjGhcCBeQXc6jB4o0rX2Z+x+uZWZP7qRrYP1FKtMHh+dxnVTn2dB9QALqgeQEhqlCRePnXUzNwQG2bO7kY+1PoHHU2BjsgOlIPHiWTeTSTu5bMu1dP70Rjrqx6iKpMGSCDrzOFxlkjEPXmeRikCG9rpxvrT7XObO6CVTcFBoLTG+s5JNfY1Iko0k2WRMB1Wzx3CoBrkdYf4wOo8Tluxhx8FGHhmZwX/uXEMq7ubX7/0+/3rSQ5QshWnTBvFqolesWNKQXAb7uupwVOcIeXL805In+Un3iTiqc6wf6YScSndfFb8aWsGvhlbw0aqnGB0Kcv2JT3Pr7Dv4Q3o2piWzoa+d7oFKWh++jgN9NfzbQ5fw0MAsLmzbStuj1/D+hRuR/GWGB0I8lJ7LM/umsO1QI+HqFBHZg6JaZEoOnFqZ9ulD6DOSLG3rYWlbDw31US5Yuok/HpyF6i3z7zP+wLObOnE5S+x8qY3PPn4pY11hwvVJ/KvG0N1limELuQRKCWTVJtNgE7s6S8OcYWKXZtGykGzVyDTZxM/LYssSxaCEnrLQUxbFoIycVghf1I8eBy1jgw2RLQqDqzQkC7z9EplGUMoilkcuiygiyYKRM8pIhzzEZ1gcvFLF11dgbL7KyGKHMLQahMGzTPouMpENcI8cXoYU9LPHccQlxs4souaFy/T4PI1SAIZOdBKdC5Ih8W8/v5zaEwdwd8bRMpDYVMnQiV4GV2kcfI+TTK1KdtiLZxAWfuVG2h68Hn+vxfJP34AaU3FOgHNCxOhkamSMsIeDV+jYEjz8xL389OdnM7449M7dNJ0OlGBQkMBSGXtsAiVy2H28WMLq7kfyeZGrKgCwUmlBREwLqboSIxrFSiSF8pZKi55Kv0+omtWVWL39k66pktOBvb9blAzX1WAbhsiYzGSxdRX7QA/FOj+k0sgtDdgOFXkkhq3J2OEg6Jroa8yXkCvCIouzVMIan6A4rQ7Z7xUOtqqKHfSJ/NhYQvSLcqyD6us6nzbXAaAlCoyc14KSKYrtXM5Jp+Ty3DaUkRjSeFz0tkoSBP3gdOC/4wXsRAo7kZosr31k+IfHkiPThN4hoaoOjh3zgKfM6Ty6VzAUELEs4zExjoYa0WOaLyAdGkDf2Yu+sxfJ6RTxKOWyyCsF7JExMTmQLyDJElJbE6EdCQrzWv7qpXHEYOkIHC8fwI7GiZ059Zj3c8TgJ9GhU6z2iN8fjtl5KygHNHwvdKPt7Dnq9TPnfRFlxyHMZPIoZfoYNfY4juM4juOdwD8gp/PSSy/llltu4aabbmLevHls2LCBhx9+mOZmMfE8PDx8jGPs/PnzmT9/Pps3b+bOO+9k/vz5nHXWWZO/TyQSfOADH6Czs5MzzjiDwcFBNmzYwJIlS97UmE4//XRefPFFTjvtNKqqqgiFQoRCIYLBIKHQ2382edM9nUdw4MABfve739HR0fG2D/r3xpwnr2DD2f+FIsG5+y/mwanr+NjQYlr9UX7SfyKtPkGGPzS4lIFsEK9WomBpjBe96LLJ1uFaZlSPEgpmkSWLnniY0eoAJ7Ud5K6hxby7fRu/3beA2oY4M194H5dPfQVNMrmgaStt+jh9uRAbetv5/oK7WORIYZcUnoh1cu/aHwDwTHYal26+jrsW/Jz10//EB7wrUCWTJ7un8sxgO7Jmkco7GSwE+c/qJK8sf4VvTizDo5dYuuUifjj/Tq559mrmD7VSNhQcusETXdOoCqbZWgqzasoBSpZKy/wo013iD/5vRxbTHXMTrB5kZ1cLXRf/F20PXk/XuT/ljnSY9/litD14PdPm99H9XDO9Spj/OvlX3PjgtcxwDtIwe5hmb5znjTbS9TKtlTEW3v8JZI+N11GmHCzRGw2jZBTkw4ZM9uw072rdw0NPLUapK3DT0+9C1wGPyaF9dSQKLk6o6eLJeU68epEGb5KXtnZQ3djNutQcOlZ1c2C8klTKRTCQ48fJeuSSRL6thM9RRq8xuL7pOUxbYiAbBECyJNqmDdJTDnDZjgt4z4oX+T+vXkA5p1HZlMEImix78qN0rf4FbQ98AFqKqLLFnMgQJVNhNOVj1/I7mPL0+3EoBh2BCVpdE/xiZAVtjRMYlkxtXS9Pd3Vg2xLva3oZgO+sP4ffXXALFzz6ER69/Juc8djHkUoytm6RLLiYUT1KJqxz8+AaepIhvjjtIb568CwsW2JpfS8rAwd5NjGV3dFqHJqBacn8fO8K7l74M/5PzwWULIWvn34P8xyD/EvfBQDcNHgWK2Yc5JHhGfzrrP1c8cLJ6I4yu1fcztV9J7I7Ws2y6l5kyWKRt5uX0+2cMWMPv3p1BdjQ0TaCWy6hu8r4PAUub3mF93afyp4VtwPQdt8HiBoSixcf4IX97QB8ftlDPJ/sYHljD79sehaAf67NEnTnyRIk0JDkZ3N+w8/HTyRZdpF6phM6izgPOfjUjfdy9/Bi9o02UyhojFteFtf38VLFTAyXjX9qnMIrYco+kA0mVY58BTTPGmLoyUZkTTjrXnb+09zXPRezO4CehnSzTGifTfxdWcyygtztIrrQwDGqEghnyPeHkcsSzlCe7vN86AnIthk4QgUqKqKcG+nm/p+eDOdFkf8o8idNHWI7KnAY4NvsJNMIgYOQrYdibRlXr0Zwr0Ryqo1chp9PvZPTnvko9ooiwUiWQlHD/5iP2PIyF370OX7+xCmkm+Erl97JZ5+6hFy1TGKmyXdW38HBosjL+smDZ+CcgFJAR84qpFpVVr/n/dQVMxjud65vvjilGkdfAlmRIRTAHhkHl8jEzCxvw7tthPy0Klw7BwVZDAWFMU2FyDNNvW85wXs3g88LxRLyYVMonA6ksShSUwP2RExkmQJSewvEEmAoSG435ZZK1FQRaSKB5HYLYgMwMi6IrCKj7uhG0jUI+Eme1EJgZwxcOlIiI3JYK4I4tveIklyHLmJzTBM78edZWGk0+ob9i5N9i5URjO17UJsbscajVAN0DyCFg5MOx3ahiD6YoNRWhb53ACubQ+keEr2VZQO1rnbS4AiONtt5LcxMBjUSYW3thyddjV+7Tep9y/FvF2RLCvpFDmciCcHApEJpTGlA2d8nMm4PE2tJVUTJcUMN5IrY0ThyQy2SqkAihdTegpTNQbGEM1vA+BvXx2t7KpVAACkSIrQjAYdJ8V++t4otWZRcSRhAFYoiGuYtwPNyD3Y2J5T+156v7XtQ5nTSe/HRDznHTXyO4++J4xMYbx//47+L/42ezreCD33oQ3zoQx963d/96le/OvYQ9l8/yM0338zNN9/81gdyGE899dTb3vav4S0rnUuXLuXgwYPvxFjeNr6+7Pe8d/dV3JZYCMB7u09lw1Ab50e20D1SwfP9rTzf38q67bM50F1LvODikYnZ7Byq5ZyKrfyfWY/Qnwryyvx7+VDFM7j0Mo1alF3RWnonwtx3aC7vmb6Z5dXd3L3wZ9y+fzF39S7mD/1zMQ+fwsunvUKLmuDr48s5YfZ+hrN+vtp/Nl/tP5tdmTpawzF+m1gMQFDL8/ihabhdJbYuvpuPzHsa05L4TfMzfCvWznMjbdyzYyHPzr6f8a4Ivx47gXtX/ZgaX5rz2naSiHrxeQr8aNqdrE/M5vm+NtrcE8x0D6JLJrpk8ocp6/FHsmwbq+Mn5/yMk3aeT7g+yZq9ZzNcDjFj4+V84sT1nFRxEKZnMA2Fj2+9FNNjcqarRG9vJc9ung4SLO7opXc0ArKNXJ+jVFaRVZtC0knngh6qThii6oQhzIM+1j20BD0u8dF5T6KPqdhTsgQiGRo6xojHPdxcu5nPdK7HrZbxaQXwGEx1jwLQlwgR8WX55PzH6YyMkjRd1C4Z4j0LXgGguN/P5146n0djszg0XMmh4Uo+csY6ZMnm+sevoWwpDBcCfGLOE5BXWOPfzndOvRMKCl8Ym8Uly19Cd5XJlzWG836uanuRmkCKq3pPYlGzmEV6rq+Nuw8txO0uUqWn6E8G2RWrwchprO7Yy/ZMI9szjVjhEldtvRqpJHHmA5+ioTHKWUu2Mr19iMtaX6HVEyVVdLI/VsFVrS/ycrad5+fcx8EdDTy5o5OvrjufrWN1/FPH0wA0eJMEPXnevfFGpvrH6B6L8NmNF3Hp9mvZ2t3A1u4Grqx6npe6W/jGlN8BYI862brs17T9/oMM5fy8NP93pAwnWdPBrwZWIEsWg7kA89v7mNYyTLbk4NVUE42RONFRP784sJzhnI9Fr15C2/prRal0R5wdD05Dki0k2eLfXzgHw1J4rqudBZsvZdnWiyhmdP6l7RHMsMFv5/6c/xo7mccOdPLiK9PwnDjOPy9+lCWXbePLm85Bxuba05/EyGg8seS/GCt4MZ027fP7ye4I4182zruu3kCuFoxLYxiXxsh3lOgMjvKr674HK5I0n9DH7bsXI68PiZ7jdhk1L1zJtVe8eF5y4RwH14CKpdsUXg1TqjQ4cNmPySdcqDmJttXdyE4Ds8fDwZFK7rnnZAD8jgKmQ8TJ2DMy3HrRT/nEtfeRXZrD0m1MB0hzUygug0KNSfmsBGpWovbsPk574J+RVQv3PgfFkkqpoJLotHB4SzToMSzVpvWEXj7zzMVUPaeQmGEiB0r884NX8It7zuAX95yBUV0iXy3Kgi2HhamB6VIZWe6l51z9HbtnFoMapboAdiQEpkX8nBmU26pJr2yj6JcZO72ebI1KYUYdViYrymBtG3tsAjuTJbQjgTS9HXt0HEolzGhc5KMaBqgKVt+A+H++IJahUVFOa1pgWWgHhoRLcDqDmUwheT2U57eLHkWXU5S11lZBKIDZ3Ufw6S5hQDQg7hWFGjdSNCnMezwu4QKsKtg+tzAjqq1Gqq1m8L0dk+ohHPtwuVq+GGN4ZPKhSW6sQyqW6frMbLGCotB/5VRRzqupaNu7RUmyxy3KXavC0HSYcKoKqAqp9y0/pgfxCIbunwkVIsIk9NDuo8ajzOnEf8cLor83maFUH8T2eQT5dejgciIl0qhdQ0ihIHYqgxQMIAUD4PMiaZo4p7G4KHFOZxhcWyUyTg0TJAmjvU58Lm8BZjIpyHfv0FEK6FFj39UFXf0Yo2PYpRLKGwSWv9HD/aF/6kCqrsR0ysesJ41GafvWrqPWf63SeZwwHMd/F/9d0vS/+Rr8H004eed7Ov9fxapVq/7q8nbxppTO7du3T/7/Ix/5CJ/61KcYGRlh9uzZx7jYzpkz520P5u3irrGlNPkS/GbfEgoZnYa6GB+b+hRf3nsOsxuHCDuyAEydNspYyU9IzfHk6FQumLqNFm2Cbw2cyQfanmXWi5fRGErgcxToKVXy85m38fGDl7Aw3I9lS6wO7OQ38RW8d8pmPl+xl5SV58wdl/PR9idZH5vFVmc9BzJVzA/0s2uiBkUWfS3/0vAw+0vV/GpgBXN7ZvPVWffjmF7mjz3iweau3kWc0NDNBwZW8OSBqZw5bTc3z9nIF8bmUdMxQat7gmu3XUmlN8Pdm5ewcFoPqZJwhZQlm+s6N/Lw8Ey+PHMX8zddKo6ZcHPo9F/y/UQTT6c7ubB+KwElR1DJsiXXwpTKcb7z/BmcOmcPV01/ifnuHj707BWcNGs/8155DyfP2sfLg03c0PksPz+wAt1h8JnTHubfN59FRThFpuggdyjMbq2WW1bcA8CnNl2FNDPN1dNfIGm4qVk+xHjKy4Ut27jn0AJ8/jyXdp2GUymz/YV2lJYs5FV+sXc5N87YQEMwgVct8YeRufTFQlS1pTEtmVjJw9SKce5dchdLt1wk3HIPtwz9umsp3595F99Sz2RbVyO5kM5jEzPAhqfTnVRoGZyRPDlLx6sUWd7Uw3PdbQT9OZ6UprOu837e07WWeMHFRfWv8qGZAzyQ9XBLz+mscB/kbmkhTrXMqhn7eXm0GZcm+kEfPvn7TNc8sAS+HutgU6KFdXtncPDUX/GJ4YU8M9SBbcMv5/yaH4+dwtnhbbQ9ei34DBTdZNVs8fD2k54TOatuF7sztWQKDsykzpZoAw2RBKmik0Z/goHD19G3+tbQUBXngcRCsv6dHLz0x5y7/xzwlekZj3CV6yRiRTddUaHcLY70oSsme0ar+crcP/Dp5y9m49zf0/bg9QDMqx6kKxXB7yiQdjuxXWXqfUlmvmcveVN8ry8Lv8j77vko/3r+fYSVDLMdo6y965/5xJZLuGLRC5y18cO4XSXqK+NEXR5yJZ1vb16NndaQCzKLZ/fyQN8c1szdxYonPoZdllBUm+6Xm7jhXev44eNnMBAJ4psTxTqcbVVfF+MHdS/T+vCNSBLEHgpgnZInsahEcLPOmdc/z90bl1EKyJRDBnJBRjIkYcwk2ximhK8mw5RnrkJ2GBSrJfofaIVWEUvke8pNvgqyK7PUaWWKZwsik+yN8IGNV6JoJtgSclkiXw3L6vt4cd0sjGk5PI4SmbYCB7prUQoS8xoHMeplth1o4unVN3PGSx9CAgZKYZbOP8ju8Wp8ezXab9zDiy1Pk7eLvOfgeWzfL+I1pLyC6bZQigquYYXkohLXfOgxPhLsY8nn3lzvxdtFdJYTpeDEcMNNH/s1n3jpEirWyRQDIldYefcE6d9VUBkOAggn2VBA9GgiyAAeN1Y8gVJdKXoJh8fB7UKa3i5KScdF36LkdIDLiTU4LMiRxy1U0aAfeXQCqyaMNpEVjrf5AnZVBCkvStGVumrhImsYh7Mn87h3Dov4lWhSvK7rh0twCxQWd6Bv2CmupbvSR2U8/mW+5BHysrb2w9jV4ntDVz/1G4KCaNk2jQ8MC7dhwyn6SYMBSKYpzKzHMZqlFHGjUzWZvem/4wWKqxcx+JUVR0WgAHh/52fPJ006vzqM5HSQOufPMWPrtt7EmfO+SGhHgke6v8Ma5/uwJRmppQFblUFxCgXW5xVmS0212F394vweju6xkmnkoB95ijB7qv9tN1Y0TnlpJ/qhUZRd3fR9cCZ13+h/w+vitdEyR8hzbHaQ8OOHeCz+Bg+Ytj0ZaZJ49zzCL49BMvmG+/5LtP9ATGR7Xs4e40x75rwvTuadHsH/C+61x3EcR/A/5Rr8H69avh28JnfzLW3zPwCJRIKf//zn7NmzB0mSmDFjBtdcc81RkZlvFW9K6Zw3bx7z589n3rx5XHjhhezZs4drrrmGxYsXH/W7+fPnv+2B/HdQMFQijgyza4bpOuMXNPviPBHvJB7zcH/Ho5wa3MOpwT3c27OAgVyIZyfa6RmsYLzk5b7EIqqdaS7wdlMbSHFa5T6cisFsZz8/GV9Fuujk85UvMd01xBf2voul3oNUa0k+MLCCeX/6KB9ue4qBUpgGZ5yLvUlm+IYZKgZQZIvTq/dyevVevjG4lkdjs6hxp7FsiSY1ziMDM7is4xU+MbyQSneGF4ZaqHUksS2JiJblt5kq9mZqmBMZojcf5rTG/TR543x+5YMkik7afRPsLdXw7ZpX2ZxsJpr1cMHBM7h97i+5fe4v+dLSB/nUyAJOdu9jqBhghnOAei3G+Z4svzs4jz9MWU9DUxSvUuK5aDv3Tizm3Fnb+UzdOqZEJvhR4+OUihpOqUyprFLMaXQXK5GGnLT7o7y68B7KzQUun/syH994KR/feCnhuePkE07+a/uJ3H1wAaYlk4u5+NWOZVw3dSOKbLPj0al8teFPNM4fYmljL+9ashmAFxNt7NnXgCqb1LjS7FpxG7PdA1S5M0RLHrYP1nFp12lUeTLU1sRxBQq4AgVkyeY3Eyv5fcc6zpq1g55kiH3jlahphTs3rOR7r56CZUk80jWDgJpjX7wKI63j0sr0JEKcvOMSKh0ZLqp/lUfGZtH24PV889Aa8iWNjbkOqrwZatxprqp6jrtm/2Lymrtl9HQ+MbyQDw0u5Wc7VvJqdyPPrPo+Z+9fy1WR5ymWVHzOInfHl7J1op5n09O49+RbiVSL0r9M2ckzz89maDTIHQcW0ZWM4NLLzJ7RSzTrYSAa5Lr258mUdWIxL7GYlx+0/5ZM0cGuZC0PJebxL6NzafTEoaDgcRexkNh1qAGfq8iu5Xdw/8E5bHu5nd0rbmdXvgGXr0jrn67DV53h86v+SJMrxmB3BX076ygXVBTVosKZocM5ilcp4lWKfGTPe/HPivKfW9dwvifL6sc+TmBWlBs6n+M3W5ZSX5HAtCQCeoGOyARt4Sh2Quei5a9QN2uEX29dRjztpj8XRFIsVs/dTd2MUZBsfnNoCXJZYstYA9FhP4lBsZxTv4M1e8/mysUvICdUklPAMhRx3hpgtOjjp2t+TjkiCKfltLBcFnJGwXNIw7dXpbgziPNVN2ZexXtQxZagoiPKqtO34b5oGMMNxoSTP019hOtbnuP6lufoOvenTG0Y5ZNzn8AedmLUFvEumuD5p2cx/dRDVIXSpJ6tYmrDKPUNURrnDzGU8bPj1VaCVWlW3/4ZSlEnF0/ZwhL3Ieb4B0iNedn+qVs5GK/gY0OL+bexZex/qo3us39G99k/ExeT2+S6Cx5l1pn7qaxM8pN9JwAwvuKtKVJvBeOX5kgsLhI9sUSuBu4cFXleJb9Eus1GO2+c6M4Kym6JkfPbKLZVkTtpGrbPjeR2Y7k0zJYakc/pcGBnc1i6Ci4ndjqD5VRFGWxjNVZjNVgW2DZyKCh6PHN50W9o2UguJ3L/KFI6J8pDXU6RC+p1Yk9ED8ekaEjVlYLYZbJgmOByweF8UaPxsGtrsYRz1yD23CnYc6eAYb6uuvnaf0GY/KzbehOm14EUCaE/v5veWyuF8mpaWC21jJ/RhKSqWCEvVnMNzp440sgEjv3DIr81lYZUGmP1IhwvH6D5Cxsn9/+YdS/G6kUEdybp/MY4dsBLaVo9L/3mk0f1nEqj0UnyKTkcwnxpPIrdPYA0PE7+pE5wu8Dtwj7YK3pRm+uwAz7sYgk56Be9rb0DouxYkpB0DX0sIxyCk0kaf/TnSeQ3wmvLawGC922dLFN+raHP5LgdDtTqKpRAgNCOBFbvn0ntm1GB7EIRvB7scvnYWJvDCurrje84juM4/n44/r16HfwDejr/X8SmTZtob2/n5ptvJhaLMTExwXe+8x3a29t59dVX3/Z+35TS2d3d/bYP8I/A8sghPlO7n/kbr+OV+jJLAt3sydZy6ZzN/CnnZFNGhM7nixrzAv3sTNexZvFT+OQCT8Y6+VHzQyx68sN4/QVuzyxmVf0h5us290gWsmRzW6qDgVKYj3Y8Rczwcn1gmFfSrcyc1s+X/nAJtfNGuKLxJZ4uSEx3DfFksZP/nH4f3xs4HQDDlpnhG8ayJeZOGeDW8ZP5984HaFdjXNH3fhTZYl71IM+MdfDhBU/zfKyDTtcQ51e+yo97VtERmGDDcDtBZx5FaqdgaBxIVXJacDdjZoazKrbz0sHzuH/Jo5y66z0AaIrJRbWv8tXBs5npG+Kmg+eiKwabqvdRF0yycvu7Ge6q4F3127kk/BK7i/VsTrewPjOT21r/xOVd57CouY8Jw8e9i39CTznMbSMr6VjUR8HUeKVY5tI5m7lz12KmN4l8oX9peoSnGjq5r3suqQkv9cEkD53xPc59+ON874k12KrNvdd8lxMf/zj7zvgv5r14FYWcTlvdOC/sb8NXnSGg5Tk7tI3vx9swkRjJ+ugITGDbsP2Jqay/+hv868C5jKiiV6xnqIJni218SinxwYpnaHPN4JmJqYzPLTKR9LD/pNu48NBqBtMBfrR1FV9f8nv+wziLD7U8w03bz+a85p0kym6+/fjZhNriLJzZjVspUbJU/mvTSSgOk141TME8HcuW6AwJVWx/spIZoRHW7xUh69MbR/hk7/lUOTN86uDF7Fp+B1f0rmKhp4f1xnRq9ATfGjyTVMZFKJTl1b5G/FPiJBNuplQIW+powY1fL9AUjLN3qIYbAoPcPbAISRZ3sGv3vY9lNT1McY3x/cfWYIdLyKpNZWOc8a4ILyRdUJSZ2FPBP1UtwamXKbos5m+6lGTcg5TQkAIGUyvGeTXTzAz3EKvm7aXSkWG04MNC4qk903je3YaqihzUCl+W8ZQXh8Og47cfZPGig3i1Am2OMYKRLPPDA/xx9yL2bw/gXBAj2RukpmOC3myYaneGDac/wPTnrmDPvgZQbR5/ZRbt04dQsxK5nWE8Y9C4OEFi3Ds5BfbrvcsojLoZGG7i9PO28mTXFE5vO8jLw034G6I8tWkmT7mnI2kWBy76CRcfWsOWrW1YkRJ5VcP2G1CSwVZREypXvv9RfrTpZHIpD8/m21jR2M36q+5hyctXs+jVS8huFgrXV9oK2KbMt15twjkhEZiZYLirgvef9TS3716Mx11EXx5jKBlgUW0fL/1hDtPWHmREqqTel2RXtY9AZZr/U7GVWc9cj7rfhdxeoHPj5SibfFx9w+18eO9l6PMSfCcu7knI0HXmz/m38ZnIks2qukN8o3oL7+s5mer6xDt1y6Q87AaHBU4TzxD0pYNYRYXTr3uR+59Yyg1tG7hp7BwuOPMF7ty3iFS7B0dMglkV+HvC5KpliiHwf7EG9w+CqDkTbeshoXIqCko0I0jmhFC77HwByeUUJbYA1REoGdgj/x977x3exnnlbd8z6J0A2HsvoiiK6r1Xy0WyLdfIVe5xXOIkTrLJep0eO3Yc97j3bstFXZbVu0RJFHvvBSTROzDz/cFYGyfZ3djv5tv33ei+rrkuYAg+MwQHD54z55zfz4FgtyIbtMSNGhTuAL7JmSgDcbR1fWDQw7ATkmzICgHB6R3zQ43Hkc16BJUSgiGUTT1IOamIkoRsNaEYHcvGRsqzUZ3628H7ny+wtkvvsTLlNijIIJ6SgJxhJ/uBMIFJOSiDcVQOH0m7x+Y50RcaU7T1+cGWQMxmoG+2Hmvz2HU0NFkB88rPen7CWPCkTUkeC6zUKvoWWcl4r4PiXzxKzp8Cuz8Phj1Xz8QWHOvhlHPHSn5jJh26NheBQju6Ph/yuAIi1rGKF23bCHJWKoSiyKNOhMKcsUDVYh7zYY1LyI3tY5nLryn00/WASMbF/27f87cWpqMrikl47zhbQ2+MKe/mZH0l8PyviLvdKP50Y+LP+dLv9C/58wzyuYXyOc5xjn8U36Rc9n9Dee0999zDhRdeyHPPPYfyTzZbsViM9evXc/fdd39ji8y/K9OZk5Pzd2//E5xyZ7O+cznfHb+DN0dn8EbHNLr9VvYOFnDXoSvY3lnC9s4S9NoIeRoHqVoP3SEb3zJ3YNf4eMFVwf1Tt/DUhDe4suAYde4Ultddyqf1FRQkDHPYnc+W7jJe6JrNY/ULWdu2BLUYIxRXcu2KXeSYnNxk6ecX7av46da1nHKM+Xmuz9jL+oy9VCb0UudN46Qrkyn6NtI1bu4/czFPOBZyTe4hfl38Ibn6ERSCxOtt01CLcTKUTiZru8k3j3Bf2lZ+WrKRqKRgJGwgWe/DpgmyYXgSN7Zeyi5XCZsWPM57PgujAT2jAT3N7Wk83TKPCeYeNnRNYGDUjF4Z5dBoHouSm7g1bw//suhj6v2pTNNIbBicyOGBbKo92ewIWlEKEr6omn0jBfy853x+37mE53I/QxRk3srbyQeuKSw21bJ37hN0jlrpHLVy64u3oRLi/GTcRvTWAO8Vv8+POsZEcJQpQVZMPcUVB25GjopMObqOJ6veRG8Mk2108tMZn3FD0UGcEQMbnZV80l/BK80zeLL0TY70ZvOryR+x5qL9vOuZSIszkd8WfsBvCz/g5kl7EUWZ6tFMaiPp6MUIgZiKoVEToihzY/dsHsj6hGGnkbKMAS4xeLm5cB+fDldyVfExPmyrZChsQjbFKLENMcnSxZFt5SRrvCg0cZSqODOzOqiuy6XJkYROjKITo3TVpuGNapmW30lhxhDr0g9x9HQBZ0ZSCUbVLG9Yxf7GAn604UpSTV6e2L0UpRhnXdkRlmY0Eo+IRPfYUHZpOdWaxQJ7E92diZiUIeq60tDrx0oLJ9u7keMCclygqzeRvT0F7BktQlKARh9lcWEjbr8OlVfkyvHHsOc4MRS4UQlxpqZ2I4bFfw84YwJaY5hS4wAbqyfwSvsMmlxJbGwbx4F94zjUnodSG2NVYS2hoJpQUM3wF+kERnVE6i3ocrwcPV3Akb4cPhyeTIrRy8cnq9BnewnnRvB6dCDIOI8k0+1NoG5LEaX71pFk8fHa0mcRNTHEhAjtpzKZd/5JomkRIhZoHbEjulQsHF/PwvH1JFu8aIYVBLJjbD1djtEQZtfOSjwjBpx+PSq3gtun7iIj1cnSuos4s7cQWSNhtgZAAEEhIRqjCBJkV/Xy4nvLxsqt5zyBLAu0ehKpfP0ujk17hRyLk8M3PsLhGx/BtkuLOKoibokRTJPGlI0dCpJVHqoye3H3mzkx+R1icZEvTpTzqxteoW4wheIJ3dSeyaayqAuLLsS4nTcjdOoIJ8a5tvIQW6c9jcYNN9WuY/+ED1GrYrhjetwxPRn5DqZXX0pQUnNqWwnhuJLiV25DLcbJMrv+YXNm69pnEfQx1PoongKZ/vZEVIYox4aziFtjPLh9DfiVXGI5jiwJSClh5q05gRj905fwolE0o+DotOHNVNF6pZLQ9CL6Ls0HuxVEESnNjmQ3I9nNDF09AdmkJ5JjHyvT7XeMBZwpiWP9ooCith16hzA2jqJtdpztPey+rmSs5Latm0hxGrJOjWzUARBJt0BWOpGKHMQhF1hMRJJNY6WxoTCj5TribvdZn86/FaQoEhLGso0JFpTdDhTuIApngL5FVoZvDqBuGUQIRpB1aqTMZOIJeqLpCcipicTNWlxFOrJfbcF0sB3TwXaytgfJf89J/HT92aylYkLZWPmuKIJKQdrTJ4hn2Gn68T1fKRP9815QWadG1qnHvEB7BlF4g0gGzdiCJhpHMeREPRJEPRIkbjeOBWxe35gQE2Mlt7LPDzodoVwrgkGHMDiCYNAjVo77u6+V7AckRI0WhcVy9j1cKq79SobWtq2ZraE3AIgtnTImgPQn/qOg8C/PIe71Ititf5UZlfwB/iPOqdie4387567v/2H+iTOdP/jBD84GnABKpZLvf//7HDt27BuP+7XVaz/55JO/uV8QBLRaLYWFheTl5X3jE/omnJd4itdHF/JJpJLptg4icQXXZewnIGmI5iiYpWsDwCVp2OCazPGRLLKMLu7qXUi718bRwWyyLS7eCE5jV/nHnHDlMDWhg7fDk/lx+iZKVQZKu9exMKMZr0XLVFM7+eohfuFcRXvQzlDQyI3dsxlnGcSZr2d/1Rs8NFLBg7XTAShMdTDT3k6xdoCfNK9mTeZJSuwOjjqyOUo2eZZCREHm6swjNAVTWWc7yG/7VlBgcBCRlFyw69uIo2oqJrcRk0R+mL2Rh3pWkq72ISIz0dRNTTid+w9fTGnW2N34i3Jr2OfI54gzF0+tjXUrd7N7qJDPx31K5ZErUSgkXH1mJpe3U7rzJr43ZRv1xnSGwiY+HJ6MWhGjYTgZrSrKg+Ufc0/TZVxYfwVJOj+Lai8kHFfyRV8RqUYvRu3YHfnzLtvFhq4JLEzXsSC7hRmHbiIaVSCaIygUEjvbi4mHFJiTfURjCm47cTWXFVXT4k/iFyfO47ziWk70ZFKaOoheGeWJCW/xtnM6VkOQx9oXY1BFuCr9CAZ1hNtrrwIgHFPy4sRX+GnHRRSohlhfdw2rcmtpbU6jvLyXL46WE5qkIi3RzUjQwPcHq4hKIqc+KUN5kUS40cJhpw7RqeJkfwaBmJplq46xq6eQmF+FMiFOq8eOaIyiVEhsbCwHoGJSOzFZxBPVsKHkQ+ZWrwMJrsk7zPNNs1ErY+RmDGPJDzHB0svieY08dXghR03ZLMhtYW5JCwc0eWQlOulzWdgzWsTyylpOjWSgM4bJt41wSetScg0jvDDnZQBuO3E1OnWULk8C9vxRojEFO9uKqMrs5ZSQzlufz+EH533M72qWsGnLNKJmiXWL9pKjGeaEL4ci3RBP1szj7R1zKKzqIV3vocLUy0FnPtUDBr5ftZ3Tvkw+2TENST/WR2pb0I8xpuTQqvcpeuNWhNQwAa+G3X1liFEBRUoI34geQSmhM0SIN+sI2ySeLXudB00XcPJAIblzu7jppTuwz3DgGLJgyPOw8/OJlM9spzaShaLFjJQUPft5zjON8sYNr7P0ue8TscoEzGpiCTHUPWr8ERF1kY9N/eWMHEilaFEb1okOvEENnhEDSQUjXJB1hpdPz6D+pqcp3beOReefYMvxCVzd8C0eqvqAH5y8mJwpPVQeuIGIX83E1jGvQUMiyIlh0pI8jHgMeMJawqkxUpUu/iXzMy7ffi+F79yKNsfDzlW/4wddq1lTeJpfJp9mlbCSU3U5ZOY7EBUy/3rxO1xtGmX8H27jlXEzSF89QG+/laLd12I4rGfwKjMAqQYvdYMpfFhbycwVdbT4Evne6g2EZBWLDA3AU/+QObP8qdu4cM0RPj49EXOBC0+3md9M+oB7d1yNGBIx5rsIhVVcsPFutMkBlPo4W45VImRJxLUi8VM2IpURRI+SuAbmT2zgaNd4okboOS8J7aiM/egIwayx3o+U3Q485XYiRoGEcC5CTELRN4ysUSFplSja+yHJTtxuRDHoBlkCs4lQrpWsjQ7iSQko1GrU/R58pXa0gyEU/jCqET/CqAtlZw9yTiayToWmrnvMygRIfvEEYkoy/Mkz82+V2kaXTUGQoXeelfz3nAzMtWLpiJG+00m/ZCVUpkPTP2bZEsizYGgaRRGOEk+xoHAHsR+PEMtLw/zwWAax9Qsd+Q+3feU4W04+yFJxLQqTCcFswnlpFbaD/azMu5fN7Y+cPZcv1WvNbxxEyEgfEyfS6/BNz8F0opdQXgL6dhdCOEIsMxGFcywgiyUbQRAI5eViONiK8GUvrUaNbNCg9EYRTEZiGXaUzT1Ip+rOHvO/yhRGE7SI4RDbg6/9xy/S686+t4GrZ6L5O8SKvjwHAIXRSNwfIN7T99c3BezWr/Tlwl/35p7jHP9b+SaZ/HMVAOf4P8VsNtPV1UVp6VcVxbu7uzGZTN943K+tXrt69WrWrFnD6tWr/2pbvnw5hYWFzJ8/H6fT+V8P9t/EfF0Pd+TsYsBv4l8SG7g87ziXGLy83D2ThmA6OUolOUol97dcwp6BQnaVf8xMSyuzzC2sSTtJJK7ArvbjCui4uWcWlyYf4+Wm6aQZPXTHLNzeO52GOa9xk30v1Y4M9rmKeKZ/IRlGN62eRC5KPYUkiwyFTZTYhrihcyk2pY9Zue3Mym3HqIpws/Uo7w1OYW/FR7zTMZlMvZPvFOzkOwU7GQ4ZSFT72O8upMadzlXVN5KgClLvTaPFZeffZnyCOstHTBJ5LO99ftW1ig8KtnOp9Siv5OyhSDPA71qWYrYEGAkYGAkYKNb2Mz2xE5vGz28vfo2dg8X0uSw8OFzGqtxatkx8AXOqF70iwrT8TrrCdnJ0w5hUIQ735KBTRHm+8lXcAR0/alvDnvEb2FX+MT/L+hiAV8e9yojLSIl5iJvz93Jz/l72D+fj6LFS406nRD9A3azXaZ7/ClNzu6ib9TpGfRiVIcoVBcd5b+of0Wmi9IctDAWNzMprI13r5Kby/TyW9z6OgJGbjq3DFdWjVo5ZioRjSv710IV0O2wk6IIk6ILIssDlm+6gc8TGVYfW8/2yrXzcWsHU8W2cbM+koKyPpbY6nH4dA212alxpfNFThGGug8eztiIrZKqKuiif0o7d5CdJ6+PTMxWEo0qeX/gS0W4j/SMWVJoY/rZ/b56eZu2g12/BFdJRvv027Ho/F808TlRW4HXqGfEZeLnkDe7N3MZI1MAzp+aSlzOIxRCkN2AhReslP2WYh4reJzKqpboul7X2I+yf8CFzMtvJNw7jDOm4PXEPN2y6iRs23UQkoEKW4aclGwmE1Nj0AVKsXlK1HiKjWiSdxJNN8/nO+C9Im9aHYImwz5HP693T6Q9aeOL0fGIhFUJ0rMl9KGTk6ep5VJ/Oo6C0j1stvWw+VUE8KYrCHkZhDzO8L43zMmuZdPxy4saxQFRwaFgx7RRiapB4RME1Uw8iuFUE28yE8sMogiIP9lzA8aYc4vYYhztzWL76CBfnnETfpCbg1SDk+6ltz0DhUTBvfg0JiT5ODGZyYjCTPU2FNEQS2LL+twgxMBlCNF7wNNG8EIZ2JYIg0zlgRyr3UdOeydCAhSSzD8GrZNRt5MVjs5FlgcKd1xHrMbCrswhdUgBXQMc9H19D0KWl15lAZFiHOKrilil7uGXKHmJTvYhDGgYGLcSjIoIgc9ec7fyqaSXfOnkDibP7KajsRj5u4fHh+Zz+vJgDjjymVq8lElewbNIZXi19DaU6xlPtCyh851bEGLwx9znmprSi6tWALJCzpo2dbUXsbCui+kghf6x6jVsn7uXAkVIGfSZ+ve1CAF4Znf0PmzMDuVEeTTsOAvi8Wu5btIlH2pciqyTSxg3i6TGjUcfIKHAQazYR7jYimiIo/SILVx8nZpBZN/kQsgC+XJkTb49HEQKVF8I2CKQIDM5LZPiWAMO3BBicm0TIKhIxCwxON+DLMyBbTcSNGjrPMxHPSaPnglQC6To8k9OI5CUha1XIokDMZkDh9DE6LRnJoEEMS3gK9HSvsIEgEJiUg5CfRcyuR4hJoFQSTrcQTrcgJtlp/Xbh2aDlLxdi26X36Fmk5outP6Dg1UGEP7gJJYJ6NMzQTCuxhW5kUUAIhImmJxDTihCLE82yEU7UEsxJwJ9vIZKgxvWjTFw/yqTpx/ew8mDH2WNtOfng2eOFZ5QQKUwlmCQQTzITKk4BxixStkvvYTs0cDbj6ZmRDUolg4vSMNUMgUaDGJGI2g0Mz00jlKSh47JEOi5LRN0+hDdPj7NQSaQiBznBjG9aDtGidKJ2A4Ik4Z+QhsIXRvL4vvL3/1eoO4eR5lWxMu2Ov/nzLzOeypRklCnJ2Ha0Eq3M/xpX45iVjMJiRtBovrJ/qbgWyeNlueGav9lL+ufenec4xznG+J/8TPytz+n/03wT5dr/BZnOyy+/nBtvvJF33nmH7u5uenp6ePvtt1m/fj1XXnnlNx73awed27dvZ+rUqWzfvh23243b7Wb79u1MmzaNzz77jD179jAyMsJ99933jU/q6/KhdxynA1n8uvRDqo5dzou1s5h9+mKm2rvQCFEubLiECxsu4erMI7gDWpY3rKJIM8Cva5ZToumneso71IymEQypudh+jLZIEhkJbnKMo7w+NIsS/SBrWpZxXe21fKfwC3wxDZXmHnp9FizqEK+0z8CsDJGqdVM3nMLdadt5tWMGPX4LPX4LleYemqNG0nRuHndlU2xzsK8/n80jFTzVMZ9PSjZQPZrJL9K3Umgc5tS0tyjR9/PttB04nQZ2Osu4ovg4V6Qd4budF1PXm8aalmX8vm8Z3x2YhEkIkWLwcl3BYV4rf5nXyl/m2c55bO8todWTSEs4lZ3lHzEuZZBs9QgbO8r51dB8npvwGhFJSZvLxvHRLJ7Yt4QnM/YQDqoIxlXcXncV36/YRp5xlAuaVnB773QuOXYzSTo/Tw3PQ6ePYFYEeadvCu/0TSFBHeKGmXu5IOU0j51YxEDcx3cHJjHX2sQlrUuZndpOosXHSXcWdeE0Ls45xY7qcuKSSLvXxjFXLs/VzkYEiqwOjPowd6fsoLMjiZ8UfUbvaAIqbQxZhmcL3+bZwrfxebXMrGpCEGQyEl083rqIXPsoS+x1yLLA9rLP+NRRSTik5uhFj+LwG9GrowyPmnjJXUbSeAe1A6nUtGcwMGomTzeMxeanKMnBzfuvoXJKK8omPRZ9CMke4b5J27lv0nay1cN09dlZlNqE2RpAq4jxSe0EDjoLyMsa4ltFR7j49I3ccPhatjSMY2puF8GomqikoK47jfcPT+GVone4fP/NIAkUFfVx097rKPziOhrcyWxsLSfH5GTpzrtQuxSoXQqUmjiBiJof1awmwRCkwtqP069jY2M5lWWdoI/h9egwKUL0OqwIIxo66tJ5u/RNTp7IR44LpKU6WbT4JB3HM/lW+iESbV5eXPkcIwED+dtuZFFFPRa7j1Sbh1Sbh4SZQ7zZMIW1udVUjOtE0aPFWjrC1vpx3D9xK7dP3k2Zrg8hJiCrZGx2H4sWnWRV4mnS0p1kZg0jtBrY2l7G8zWziU/2ojOGiY5omVvazLx5NVQPZeIaMuHpSMDTkQBONfc3XEwUgXnzanA6DYzbcyMHFzyOeeEg8ZiIQhUn3mFgZnErV0w6ymR7N4mFI8gSY/2cwKrSWkgJERrQk2rxcHfJTmbOqicnx8HO6c9w2YzDxI1xLIogFkWQ/MQRmq96BgCtPkJ3VyJPnFrAisx6FAqJcFTJgMdMOFGmyZNMKC1Gd7+NuCTS2pjOyeEMFn18HyGnFpdfR1lVB8FUmau23M6nb87mB5d8SHH6IPWH8pAGdGObNco2bwVPV89DsIe5Ivc4hRU9PPfCKpq9Sf+wObO0oI/CndehNkSQYiIP7V/BktRG7p65g2yTE2SwG/xMSexC5QMSwzwx803iGhl/TIMyy09/2IIqPUDMEsNbGSHzwg5UPsia2c39N7yDu2SskiDcaMGfCcic9WQdLRUJp5vpWKVH44L+OSYQIGwREaMSbWvU9C2y0XG+gqbr1PStSsPcHsSfbQQBBpdFMHdJtF1mQ1KLOCttDFfqCacYkM1GNHXdaOq6GZ2fTdOP7/lKKWjxL/7du2z6ukfIu/8A0kAxzeuT6fswF2ToWWzAlwlzM1vpn6VidGYasgDmeieuqSlEjUpGi5QEUpQYOrwMTVaR8MseEn7Zw4qJP2XzFbOAsQXY9HWPnD2eZtCPEJfI+KgbMRhFMzjWe7rl5IOszLv3bKnxpi1v45goEihOxDAYwzU1BSIR1K4w3Ut1RCwCWkcY+xkJ+xkJyW5G3xcmuTqEathP3KLDWDeMIhBF6YsQ1yrRt7jGymtL/vNKpD9fMCpTkiEQRHWi+ay6758vKpeKa9ly8kFkl2dMDAiQA0HUtV1/PfB/gsJiAVlGEL+6JNkuvfcnMST1VwLM/1WL2nOc438R/+tuBP2Tltc+/PDDXHzxxVxzzTXk5uaSk5PDddddx6WXXspvfvObbzzu1w4677rrLh555BEWL16MyWTCZDKxePFiHn74Yb73ve8xe/Zsfv/737N9+/ZvfFJfF7MiwAR9Nw+0XMjK7HqWFjRQYeunxp1Oub73rIrsJkcF15cc4sr0ozzRs5hVBbV85+QVPOXK5M78L9g76yke716MTeEnx+hkecIZDnfnUO9P49KUY0xP7sSm8KEUJMKSEosmxPqMvazOPs32jrG+0c1VL6AS4pg0obP9ldMNLdxTdxkxScGdCV34ohqqknqZb22kd9DKoprLyTK6uKrhKvb05VO85xq6wnZuPHYtJnOQQEzN4dFcXu2dyUhIz4y8dj4q3MY8WxNHHDk8NbCIuqN55KgdPNi3igf7VnFn7hfMS2tlXMIA9f5Ubu2ZQ6fbSkMwbcwqw5fIBDX8KutjvpV3BF9EQ2aeg0tbVrF2/An21xXhD2poCaVwbCiLDL2baaaxbOD6tN38LvUEK3PrOTyaS0wSiUki/QETh0dzeWj7+SQkBGiOGtnaUUpHOImYJLKpqZwnS9/ErvHxev8MBiJmKkq7+GXhhyxJbWRV4mka577Kj3vPZ4q5k7lprTREklGZInz70+vRaqJYTQFKMgY578htnHfkNqSwgoikJD3BTXd9KmW2QXpcCSzQN6PUxCh641aqT+YzMbuHq5ouY3TYyLNlr2O2BHi2fg7+sJrIgB4kAYM+zB8Pz8OoiXBF2hGU3Vqq27IwT3Fg1QX4txmfsNdZxF5nERIickRkY3c5CzObMarCpCR50CsiyLLAS3Uz+XnZBl6c/grZKaNMMPcw0GHn5sJ9XDL+JBl5wzw5MgOFUuKKmYcIRNWIqjiyJKAS45SnDpCmdaMcVJEzq4ucWV3EBnT4+4wEO02EY0o+PVLFK5Nepiqnh3anDcIKGFWz313E0qJ6TAVOJK3E4mM3kz++F2W7jv72RNp9Ns5ffJTf1i9npNnOze/eSpbZhT3JQ6snEZ9Ph+NoKo6jqQx02YgE1LzRPIXRkB45N8DSjEZmFLaz21nCZ/3jmajpQUqI8ZNlHwGw/eAEPnVUEoio6B9O4PbVmwn51dCjQ6mQUCok0Mc5uKuc/VsncEH2mL2FLtOLLtPL6rlHGelOYOUb36PHn0Bakpu4W82/9C8jEFERG9UyN6+Vb6/awqNZn/H+1ll4Y1oc3VYuG3+Ci6aeQHap6QpYuajsNLZcF11Ddh5+9RL21RZhUYc47+SNLDDXo/Aq+GPLHP7YModEjZ+wHCUjzUmBfYSs7GFumbCXN+um8MtxH1FqGyIYUiGpJfTKKII6TlbaKG6nHnRxgjuSUHlEhLCIXhuheSgJdZ4XY6uC7Xc+xBpjO8GoCmSBqVObmTq1mYREH/+WVIvsV6Js1/FS4wy2lm4kPM2P8k9WOf8IFic1InnUKEQJQZTR24K8fGImvRErVeZupk5spWckgS2fTSNlQS+SX8Xtu9ch6SSGQwYaZr/Gw+mfExnQU1rYx5rKapoP5eKeHKG1M4VNIxOQLLGzt34TZwwQOM9DTAdxFURKg3RcoMTaKOMeF2PtDV+w7oZthGwCrgIlioCIpygOagmtLYg/Q0b7qwFGyxR0XiAiuFR4s0SsDTJdF0hE9QJiBMSoRKAwgVBFNqGKbCJmAWmgGGdFAjAWrHypKrvCuh7rZ3W4r5nJssuuJZYcJaaDcGEIfT/ETBJbD07E2AXKkEzUokLSqjH0hnEVqRAlkBTgHmchfW+QwBVqAleoCacYWPn22DE8V89EPxQ9WzpLNI5qwINsMSK4vHQ9ILJi4k/PBsVdFyYx4d5Hmfm9W0me3k/XlXHEOIgRmUheEqEUHbmfevFlycgKAUVYQhGWiFm0dFyoJa5V4B5vJWLXEEkzjwVskRjqlgGC+QkMXFxIzKz5m9fEn/NlUDfmuxlFjkT+81+IxxFMRjb3P4mgVo/5p34NnOePG+tFVSj+6jykQGDMK/TP8Fw9k3Oc4xzn+IfzTxp0qtVqHnvsMZxOJydPnqS6uprR0VEeffRRNJr/+jvkP+JrB52tra2Yzea/2m82m2lrG+tjKSoqYnh4+Buf1Nelxp/JrxpX4I+oeevoNIJxFZfYj5Kld/FG33RMYgiTGCLXMMLu4SK6Inaezf+ANl8iPxq/mcPufJ7pnMdbnvFMsXbxaO1iWj12XHE9r019kYN9uXglHTs6i3l5YA7v5H+OM6ZnvLmPR9uX8G57FSG/muPTX+FXgwt5ZmghemWUdQVHWFdwhKGYmQ8nvMCOphI2+A0ssDdR50rhieYFJNq8VNj6GW/q5c3SN7HqgkhxkSPDOdwxfjfvVr6AL6bmuvQDjAb1XJJRzbyEJlY1reSpunlcmlnNUNAI6SG+e/AyRGREZBwxE/6YhqcyDvNC1n6ODWYhyQK32vext7EQb0TDtKPXsOTD+9jhKCPV4GVt5glq2jL5vK+YyqIu7CY/hxy5+AIaOv1WdrtKyDE5+UHdJVQeuZJcjQNvVMOSlEaWpDTS02vn0+IttF76LCMDZq79Yj03lRxge3cx3ogGyaHhhppr0SmiRCQFRbohvp35ObnKEO+1VrF1ZDxFu67jd5kbSVc5KdINcu+By5mb18qVC/Zj1IZxDJt4vfADcuyj5NhHSUj2ohZjKEWJy+cdpNNr5b6y7azYfSf3Vn6OvXyYpPwRMnVOBjxmVLoY19ZcRyCowaQL4Rk2ICREQJQJhtSIfgX9wxZ+UXMe0fQIgkuFUROmc8RGgiJAqtZDqtbDA4cvYO2U44iCzJb2Ms4MpeFwGakZTiPPPMLUrC52esr5Zed5JOn8fNY7Hlkp8dCxZWzrKqE4YYj3P56LIMrs7Cumt2Usq3X/lC307MvCGdJxypXBgoWnaWpJp6klHdkYJ61gmH9b+R6jI0YEY5RxKhmlIFFkH4aYQGLhCIf6c5hnacKiC2FPd/Pdsh34IxpiehmFJYJajLM+cS83FB3kvuWfETPGaRxKJlHvp6c2FY02QtQsETVLKMwRfjh9EwZNBH9EjdyvY2dfMSXGQWqG0+gesHFP21qUQyo+GJjEaLsVSSdR3ZSDuzOB5gUv89iRxSgH1Dxz8XPE4iI3Fh1AjgoIElgmO3ht/2zaVj5PrNZCrNbCx19Mw5zmJX1KH60ns7go4zRtFz5HuaEPX5MVdHE8UR3pKifrmi8nc1IfUUnBXXO3E5aUHBjIw54/il4ZYWtHKSMDZrKTR8ha2omojlPTkYHHreNf6lfTcuUz/KzsY35W9jERSUnFq98h2+Sk9lgerq1pPH1iHsuL6rl977dI07qxmgK0rfkjJw4VUZHXy4jPQGHmELdM3Y1h6RCSRmbJtDO4au1oNVHuHLeLYIrM3H13MByXWJDSTDw9RI/PQo/PgqvHQsHn16PtVRLNCRH0aaj69W3MyO7AqAz/w+bMFz5YRuX4DoJDBnCq+dH4zaSlunjv2GTq/amc6MoiPqgjVhSkoy+Rtgueo23FC2CIcXX6YebWrGHijm8jSNDYkcYXvUXErDFMCQHUpgi+qAYEuP68nVx/3k48QS3SGTMI4K2IYk3wo8/y4ioU0HUreemL+XzUU0l4hg9fnkQ0MYqskUhI9zAlo5t4ehijKsyZO5/GkukmpXgYX2EMd6GAoJJQXzyEu0jG+30vklKgd76a3vlqjv/kacYfuorM28Z8ILdL750NWLY4n4ecdA79+hkCqVralr9AfKqX3818F9WlQyjTA4hJIVbcuZfBqSJDk5REbBo8uVpMXXHiKpBUAq6Csa9R75RMvFMy0Qz6ee9HK5i+7hGsNS4AFBPK0HjiRFOMdF+UiuALEhiXRs5NA2w5+SBbTj5ILM3GpDW1nPz+0xx86BkyDB7EAQ0jpUqUYRlk6F4qEvi5DyEu4CrSMlKuZKRciWOilrT9Es5iFbIAklIgYlUTTNczMtlGPCsJ9WiYlP1OxOh/fjPjLzMVKw92jAWSzR1f+flXrF50WmTXnwJDnRZh1PO1rkfrxzXE3Z6zIkh/fi6CQjGWcf0zDr9271ee/68r6TvHOf4f4n/zZ+/rltZ+E7Xb/5vR6/VUVFQwYcIE9Hr9//F4XzvonDx5Mt/73vdwOBxn9zkcDr7//e8zdepUAJqbm8nMzPw/Prm/l+1tpbw24SVKbA6+N3sLh3tz2OMtxawK8WnxFnLUw+Soh5lk7CRD76ZM28eCQ7cxw9bG1aZR5iQ0oxAkNg2WY1P6+fa4XQx6TDzZuoCd/jK2Tn6OrrCdiwpqON6YS/62GzErQ/jiGv6t4BM+mvg8F40/Rfmum9jYWM6O5hJCcSUbeivZ0FvJMV8eC764i6l5Xfzo1Jia66PF76JVxvh0wsvs7iqgyZ/CJbXXcGXmUTTaKD/M38SukRJed02n3DLADw5cwry0Vl5vn8Zr3dNp6k9Go4rxfk8VTxW+TVqiiyn5XRiUYQzKMC+2zaI/aGbemdWUHfgW1+YfpjxxgPc9E7lgfA2CIGPRB1kxp5puTwL3ZmzlDxvPo235C1yUVUPNiTz2VnxElb2HhjmvoRQk2r029h8vIcng476y7TzXMpsrMo/xdutk3m6dTNvyF8j/9CamV1+KEBUx2f10hW1YdUH63WZkU4wi6zAf75lKTBIZjhr5dftK5u79NhpVjJgsclvlbuYdug2AgKRBoZKYbWnhw9ZKLsw8zerxp7is8TLGWQYYZxng6Yo30CqiLE5qZDBsYjSgp1TdT1baKEc9ucxMacd3IIkNJ6r44bgtRNwarLoAsgyOLhvIoG3UkpriZs/Mp8EawWQKMj2zE0SZ1BIH6QYP4T4Ddx+8nE8aK/iksQIE6A+ZSTN6uLTwJJGTCRj0YSoS+2l2J1EzNFZCuyCpieNHi6i096IaVaLo1ZJk9FE9lIk43kNJ8hCegJab5u3i9VnP88lgJctWHSPH5CRL76LemcLKqtOsrDqNpkfFupzD/OTAGpAFxCENE/fdxGDASPWxAgRTlARdEI0yxu+al9DVlEKGyc3G4QmEYkoSy4a5sPQ0Nk2AF0fm8HzTLD4eqETlURALK2hsS2f6tEZCHWZkhYyskGme/wrt4STyE0bZXvUi1yzZzQMln/DKqRm4hkxIYQUNLenEbDFqWzNBJaEaVZKWMYomzU/F4asw2/xYxo9QHczlprL9PHJgGZnZI7xy1eM4HBYyC4co3nMND135Cg9d+QqSWsbfnEBnRxLKLD+b+sspfPcWXmqZQdqEAfSWIAN+ExuGq2hqTaPfbeZAZx7P1s9hc9s4HIMWLs2pptAwNkcZ7QGGvCYKTMOMz+nDZAny9MzXGe1OoODz67lz2zXcue0aGkeTkHQSx7qzkaxRpNluVo8/xaba8QgKmXePTMMfVlP64m1o8j10uqwEvBrUYpwPOycyMGQhbouyo66UeFKUC3LO8EzTXJqvfoY5eW14ZDUjESPLy+ootw5Qbh1g7sQGZhW2EU6U0DZqIS5Qff/THO7O4cDO8f+wObNwfjvNw4kIxij3L/uELaMVBKMqLMk+Xsjaz5z8VmSVzHcm7gSgYMf1XN2xgLum7GSnq4wEzZiFhmyJYrH7yEsYZVnVGQRBRqWMI8kCGmOY56tn83z1bOwGP7IIy1ccQ2MJMdxnIdBlYu2FezHPdKD0iWQa3URGtWSUDlKYP8C9s7fhHDRxof0kih4ttY5U8jatp9A2wvDJZJKynFQsbWJ2cStDjYmct+g4jmY7vhvcrF+zjfVrtvHz4VKEQxZ6ni48GyT9ecCyacvbABj6QjRHfdw2bg+rDX7c+5JpmPMacZeGN/fMxtAL1gYJX6aKoSURDHf1YG+I4ZwbQu0BT56WkE0kZBNxViQQNYhc9eNNAPTOUxM/XY/m0yOIEQlrS5zOy9JRhCVa7y5BGigGoPl6DYd3lFPx+7H578jxIsgK4q8M4b7VQ8f5GsZN6GT0izRSjkp4cgViOojpQIiDY6IChLHyZWQZd64CWRRI3NXDaLmB5m9pCeSYiGu+mk38W/x5QLll2XjiPh8IwlfUa+On688Gp3JaEnGfbywz6Rj59wD070QKhVGkJCH5/F/Zv1Rci1hefLa098/3/63+znOc4xz//3Pus/e/g4svvhiPx3P28X+2fVO+dtD5wgsv0N7eTmZmJoWFhRQVFZGZmUlHRwfPPz9mdu7z+fjJT37yjU/q61KZ0cev+1aiFmO82zuZMzPe5KQrkxpXGuUHr0YhSCgEiT92zGVXZyEbhqsoSR7ipDubWacuoT2cRM+IlV6XhbusHdye0EOBfQRRkOkLWWmN6ukOWlEgMb+8Ea0xzPHRLI6PZPHMwAL8soLppjbKM/u5oLQGnT5CpsFFLK4gFlfQ5E2mKGuQdJ2LoiQHj+1byv2tF5Ok97HwyC0UJzlI17jpH0zgj21zCAXUHAvk8+vsjzjhzOIG2z6mFnby0ckqtlS+SmnCEKIoEwiquSjjNFfW3IA7oOPmtF00uFJocKUw2pHAiwUfohLjpFk8nPRm0em1Uu9PZaFlTDXQrg2w6ehEMkxuftK6GkWun/xtN/LFUBH2ohHyt95InTuVZ9wZRCQFTxa/hWCJ4g7pSFJ68Hp1vNk9lUBAQyCgYYPfQNsFz5FtcZFdOMi4pEFGIwacQR2lyUNcVHmSd/I/5+kLXqDANMInHRUoRYkEix9/SM2JEwX8sXYOsR4D1YEcXmqcgcUcoCGYTjwusrFvPJ80VtB5LJOugI2ugI2bTl7DC1n7eeH9ZexuKcKkDXN34+V0NaZwYjATlSgRr/Sh0MX40YGLKS7sIy6LxBw6lk2uQQwoCKbHseoCLHzme8h+FcGwmr6AGWQYGLJwRdJhWi99FtmrIh5UEg8qmZLXxaGOXIrNQ/ws+Qz3Xr6B+RmtZOtG8YU1ePpNPLf0Rd5smUpC0ShbasuJp4XJmdpNe38iBnWEoENPTUM2i3KbyFSPAtDQn0JXwMr+3eXsbCviu/nbaHQn0+hOhnIvaiGGHBdQaGL88sK3yE0a4crMo6SVDSEHlYTjY2I6ziY77616gtqeNE6cKCD+uY0y2yC/Sz3BnjPFHBzMI+DT0Oe2YK8aoixzAF1CkIGACcke4Z6FW7hn4RZ+NDSBbM0I7W4b0z64l1Z/Erdvvg4AOS5gTvQzq7wFtS2EqI0xu7IJKStIutGD1RBEo4oRCKrJtThpCyby9Kn5KA1RfGE1LzrmYU/ykGHwEBvR8qvmlfyqeSWZJYNIyWGEuEBkRMuVmUcRU4N8r3QbPW1JBPqM9HYkcvBUMffO3kaS2cfSggamZnQRi4msqDjDR10TeefTeSgVEvm2EZJNXvJ1DlqGE1mQ2cIzfQt5Z8WT6I1hple2ML2yBU+TlbKJnQiCjGJYRUmSg4+OTOaWyXsQBzUoXWOL9csv2EOk0UJlch+tS15iNKTHMWhB3anhobnvgixAUMGhkVx8Xi0Aj2du54mBxXx6uIqnMg6zrW4c2+rGsa+lgAytC9kYp3BZGyV5/cytWcPKgjqev+Lpf9ic2e1OIBZTkJLo4bXu6Ywz9lNkHUYUZebWrGHXmRLuW7CJd7sno9ZFsVgDdHmtuOM61iUe4NPiLTw0+12QBIJhNZ1uK9sOT8Dj0qNRxWgbsRPuN2BOCGBOCGBSh2m48WmCkgqNKobaoUKf7aXBl8pQu40pC+t5J/9zLOkeXih5nSKzg2fq5zKxuIvfty3m1SseJ8viorSgjzOfFyHnBAlE1DSNJhKIqVFmBKgezUARFAmFVTz9+RKe/nwJHzy/kJdvfwzvJR4MT41V30xf9wh9H5WP2ahccR0Tj17B1vdf4YXR2ay3NFN17HIi5WNBdUlpD6rUAO6yOBGjiLkzgnJAjTOoZ7RECaNqooaxQE9/ZT/6K/vJvK2FV3/5MI/uXc6Wkw+etUXxXD0ThS+MY6ICW2OcpJ93oCx3I6Y28Wj9MtRDShImO4j+SRjwD+e9Qm7yCOaEAK4eC3JaCHdES0wPvUtkNBNcIAACFF7ejHHKMIEUCCSJKAMSLHIyPF7ByPxMhqfHKXwrzMA0JS3X/Nei9V+KHykmlIFGjWJCGbEpJSgmlJ3NdP65GrAwODL2900oA1FASLB8LVsWRUYqxGII2r8u25KbO4j/ycv0P+Lcovcc5zjHP4R/ovJai8WCIIwJTZrNZiwWy3+4fVO+tmVKSUkJ9fX1bN26laamJmRZprS0lKVLlyL+SQRg9erV3/iEvgklxgGcSjvXJ+7jBy2XMLdmDTfm7OetvqkIgszPms8HQCFKrC2q5tPO8ZTYHeiVYQIRFdXOTO6q2MmngxMo3HkdWv1Y/0rAo+Uzx3h2mQsJBNUsrqpHJcapKOth6+A4epqTURZLXFV9I6emvYUhYy937buSirwe9nfl0zB7TGL+9t7pfDflc+7ruJjlSbUsSmxEJcR4rmU2QbeWM/50agLZzK5somE0GUGUSVO5WL7lHh5Z9CZXnb6BisR+bp+2ix/3L2SSqZN4rsDxgSw+7J5INKagONHB595yKqz9AFy2+DgnwhbaG9IwZXoptAzTO2DlrbLXuK/rIvr2ZRKcPkRCppuYLNLZb2d91X52DhUzEjAgy3DRhJMcHsrlze6pdHcmckHXnWRnDJOm93L7nnX8cvaHvNQzi0vKqgHojCQxvuYiEk1+rs48wil/FseGslme1YBREaYvbKF0/zpkSSDF6iEcVdI9auXMrFcp23sdslImOcFLVX4DvcEEgn1GwrYwH9VPZ+LMFgIxFTdUbeZhzRJOtGcBkGT3Mu7At5DK/Lw+7UWSFCHW1V7Lsmk1bD84gVZrIlK7gZZ1z5C/9Uaau1J5bu7LPKNbSH/QTHrpEMUJQ/T4EwhmxtEkBQj3G8jJaaVZSuXg4j8wZ//t3OnSQFzAZBuzKDjVl44UE9ncNo6wpKTRnUxbbxJSSMm0cW30qSM80HIhU9K62NtegFIbQxrQ0aNPQBBhkr2HgRELa8pO8duUau4frOS3dctYWtiAI2xi2eITbN5fxe/aljHoHFuFxlwafv3RxVjGObk47xRTtT38oDmdtyWRO/J28Yp6JhenVvOhsoqm4SxuqlmHHBdILxmi3z4majW3Zg2F+QMk63zkmJ1EJAW1/akMuw1cWnqSNLWL4SQTC/SNADQoU/nhkTVMzu3m1xd8wJ2nrgQFzCls5WhvNl6XjoBNzaqCWk470zEoIpRkDDLgN/Gdgp1sHqlgyGCkz2fGrvbzw0mb2TxcwUhQz1WJB6l2XMotaV8wGtYxO3GsPP/oaA4eS5BvTTrK2x2T2eMq5uKSU2xwTAIgtWCExWmNvFU7hd6wFYMqQrffiijIxKMKliWc4amMw1xkWM7pxmxyM1o540rlpaaZiKLMZ/XjkUIKNlsmkGsdpc4xpiCqcQpck36QH/WvRlZA7UAqtkw32wbKSK0YRBBkglEVb9ZORT8IB/aP46KIjv7msdLocFqU7+25HJPdj89jpvNIFnJGiKI3biVrYh89IwnMn1xP/ub1zC8fe38P9eTw7unJAHS5E/DX2rh05X7ePjmVT0en0nb3P2LGhCL7MDem7eFMMItndixBlV6DKMh43DqurTrMi8GZPHx4OVWFXUTjCtwBLUZNmFdPT+eyuccoefk+Zi06g6iOEwmocPbr0WX4CA7rMWtCGDVhbhy/lSzVCAAvDc5l3pnV7Bm/gXVxFfudBqIDRo67tShsYQ4dLWGtpEQQ4LwDdxAPKcnLGuJkYzZvLXmGuCxS25rJv8z+FO+ldfRHLDT7kjnVlcGQxohRF8akDuMrHyUUVo0FY0BonpfJajVpFg+O3+QxoeBR0mtcJJwZe8H2t18GoPSF22i48WlK962nYc5rvOhJoey526DcS6zbwAXzj1OyoJ+D7gI695cxNGQme2UfP83dyU9PX4g8L4xnQzoAlsvaWLHhu1gbRO6qvpJH044jpjYxNE3m4ENvM/7x21j7iy38fsdKxMTwn4SGVtDw0NOsaVnGiC6RdZ3zOd6bRaTTyMWLDvNBxzQ0uih7xm8gv+VmNIMKLAVBHHICAKf3FKEIQiQrRtQOklpNfJ+aSLJM2C1gaFXSu0BJ3kdufHkm+j4q/7uuE2dFArbdXfgqktFtOMwW6b0xwSMg1tn975nOFDvWGhfx0/WIOh2SYwSpp/fvvh5jnd0oLBaERNtX9n9Zxqvo7Pur/X/JOZuIc5zjHP/dfJNy2f9Xy2tfeumls49ffvnlf8gxvnamE8Y8OVesWMF3vvMd7rrrLpYvX3424Pyf4J2GSWw5MYHL99xCltFFb0sShzwFZBpc5Nic2LQBbNoAPf02WvzJzEpvp89vZkFCIz6vFqUgcdSTR2NXKjdN3Ec4rOKJiW+hNUa4bdIeJqT0EXerWWpo5e2mybzVMYW2gSRES4Qf52/kkvyTFHx+PX/oXMRDs9+lri+VByo/o+LwVVQcvgp/TMOBYC6zba3sdRbzSX8FH/VPZF56G/YUDyvH1bKwqo4TfZkoBBmdPsIhTwElxb1sc43HOWAiQ+dijqERUZBxx3V8cXIcobAKrTKK36/hxPECdvYV0+JLpMWXyPOts7n39GUk5Y1SnOjApAyhM4b5l97zEAUJZaWbMtsAdr2fzSWbmFfcwqa+cnqcCbh6LLh7LEw3tTHQZsfhMTJnfDNVBV3cn7+ZmCyi0kf50ReXIskCmzvHsblzHH/4fDmVqX10NaTy66MrOOHIwhPQsn8on6POHNxRHVp1lDXFp4hLIhG/mrzEEX4zUsq07E6yiwZRChInRjLZd7SMXRc9zKKiJr593hZqvihCq4jxi88uJiPBjSDKCKLMgrRmQm4tKwrqeWtkJg/2nsd5GbUU6odYMOMMDUPJKAp8XN2xAFuSF5DZ7y8mEFMxHDQw6DSxr6OA7lEranuQP1S9jSXHxb6NlaCQWXZiPReXnGLy+HYum3MIX6cZX6eZ8rQBRIVMpNvIln1VBKJqrDY/7ec/x9HGPKYldeIJajnQncfKwnpEUWbJrNPEogo02ghtPjvrJ+yjO2ClaNd1HBrOJRRSsaWunKbhJDYdrCKjbJDeHjszczqYmdOBrJTJm95FlsVFSFKxcOs9KMwRknV+3uibTr/HzAF3IVkGF5MmtZKXMMrBhY/T7xizfFGKEt6QhtauFOZamzlyrIhOt5WLi09xa8VetGKUxz89j1ePzmTNhrtZs+FuclXD2K0++gMm1m+4hVW5taTmjpCmdaPXRkhNcTPe3MfmtnH8uuBD2n021qYeJy6JbByZwCs5e+hzWwhEVIQlJQ+fWUogpmJpagMLtDLpRg837r+OYEzFp93j+bR7PHVdaSQZfSwx1qJXRzjQkk9/yEJ1ZyYLq+oYaLXz5umpSG41n7WXs7HkM76ftZl/zf6EV2e/wH2H17Kucz6FJgfXz9jH9o4S2ltTKU0a4tPJzzIprxtDq4pM9SjzE5vx9pvw9psIZsb4ed15NM9/BVJDRIZ0WLRBhrwm+oct9A8nEDiQSH6qg6e+8yRZlX2oFXFeXPVHfrr0I0RtnMklHUROJXDJvCPYKh3oTWEktUz3sBVlrYG6kRRsKR52Nxaxu7GI9AQ3xEQsSV6Mmgj3r/mQ7qCV2SUt3Lh05z9szrw17Qvurb6MT/oqMOW7eLtjMs3ORBJtPh4/uQBfl5nvz9gCwJDDjM0YYM/4DVRk93Hp8ZtovO5p6kdTmFnQjlIT47tLNqLXRlg34wDOoI7O9mT+df9FrD94LesPXsuBjjzmJ7eQt2n9WA+2NsrUilbalr1I3Klh9dyjaBVRsswu1pUfITXVxYDbjCXZx7drr+L3fcvIyxnkt6eX0RZM4p3jU+l0W8lMdjJ4MoUpyd3MsbfirbeyIKf5bHl4NKxkbdsSBjdlsevZ58j4qJstJx9EkGW6HhApeO8WALQjMOP+W7lj/G7yPruJG8yDSCq4sfQgn136OzY1j+Phgys4cKQU0kOoezR0dSfy8/rzMGrDuI4nseX+h9hy/0N0exJQBESO/+Rpdr847ex7rs/2clHzcs7c+TR3JnRhynGj0UYIJglnS35Pd2TQfNUzHDhSilkfYuqMJrZ2liKLMkGXjkPhGIY0H5Ianix+i6RZ/STN6kc/fpSiJW1cP2MfCJByYReyCixNAumXdhDXgrFbxp9tRBmSiNb+53eq/1zht/fSHPT9wbPPN7c/wub2R76a6fSH6HpApPNnsxDUasSEv9Z9+M9QmEwISiXy0PBXymaXimsRBkf+Skjoz8t/v3z9uYDzHOc4xzn+e1i0aBEul+uv9ns8HhYtWvSNx/27IsU//OEPhEKhs4//s+1/AlkWEMIiKm2MvdWliAkRDr8+kb27KujYkUvrcCKtw4m0LX2RQ3X57Ngxid5+K/+6bzUASlHCpvZTmddDgy+NS0pP0hFJQqGQeOrIgrFjKGQyFCZMuhDX5B3m11M/oCDdwR0nrsKiCLJ+4n7M6jBaIcrSwgZe6J6NKMiIgoxJFeKYN4+bE87QHzAx7DPSPWrlnuSdjAya6fTZ6PJZCQdV/K70XQDcUR3tDjtlhn6UxiiHHLlctel2Np6o5NXG6bRd8BwNc16jvT2FC0pr2H/x7zg/88xZn851eUcotA9j0we4IuUwnzRWEPRpWJ+8m8OHSim0D9PqSUSSBSoOX8XRviyuyT6EWhlHNEWYXdXIYW8+VRPakeIiC60NpGg9/KJlFfVDY6IOCyfWMT+phenpnUxP72RiVRsRScnsKQ2sHn+KUExJaFiH06+jpiOD13J34uk34YgYeWvcKyTYx/zintu9gG5fAgMuM+0dyTxQ+AnfXbKRH3RfhDui44nT81GNd9PoSKJ4SifJOh+Tc7qZnNNNd9AKColtHSWccaWyvzWff0ls4HNHCTpFlCSzjwsKahkOGZiU3AMCvHRkNgsTm5BlgTsn7EJxxkDIo2FyZjd1oUxcgyauuuQLCjKHuLloH3sHCzi1rwhHxEjWuAGyxg2wNLGOBIufi+YfoWJSOw6XEbdHT/4Ht6DQxWjyJDMptYdIr4GNDeXQZGR3VwEKhUQoqKbYPMS1lpO8lbeTidk9dHYmMS2nE4M5xJS0LsoqulAKEvZUNwd3lXNwVznTSttZk3YSpSjxTs1kUjJcxENKlGKc9lEbvyjfwNG+LKodGZQaB0hQB5i1+9tIMRGLPkTrYCKz0tuZXtzO2z1TkJUyzjYrb5+aQlswibebJhPTy7Sf9zymfBemfBfXvPodUo1jIlMzZ9bT7EvGE9SwoXkCo4NmflnyIbsGi0hLcHP5hjsZCRjoidj4tOJV9jcV8JgzF4BwREUgpiYSUlLfnk6VvgOAmzN2k5/uoG8ogVhcJBYX+ePsV+kesfLa6Cx661OQQgr2NhWSYvewq6mYi2YeR5ZE0Ma5b9wOij6/kesO3cAVR27iD31LARCROTqczcv75iJJAjfM3Ev1sQLOO3Ibc6wt+AuiTNJ28kLDTCrLO6gs70BhieDrNLO8YRVyXECbEiASV/L2pOdpWfgySnWMiFWmpSaT9SeuIUnnp34omZvfvZXf1S9Bjiio7sjENn2QcFyJ52ASszPakZUyYpueUH4Eh8NCOKpE2adB2aeh80w6SyrqqJ7yDs5dqTzWsIj9dUXsqy1i51DxP2zO/M7JK8iyOelpS8KoiaBTRSmyDlNiHUIKK1ClBTjszmckpEeljZFq8FK6fx1DASM3lBwEoMQ6RIfHxvXlhzjPWM/RqvcYiRpYmtXEpLIONOYwaYlu0hLdPDL1PYzKEIJKwq4OICpkjtTn8auRYg5c8Aif7JjG/embsWkCtPqTuC1/NxZ9CK1q7GZJhbmXQY+JguRhDgzmojZFcHv0jEsYQM4O8nlrMTuHilHl+8jXDbNi2ilWTDuFPKyhfiiZm27ciJjaxIrNp5EGiomfrqdu9QO0rn2W0hdv47u3vcfw8hCHXPmIuhizTl2CoRdOeTNZc/QWspNGqSruZO28Q5xXXEv5/BZkSWBr1QusyKyn4canuaRuHZfUrcM1auCFy5/mcVc2kcVuCt++FYCa6W9iVIXJ++wmJv/sNjwjBhR7LcTmuVm4/DdM/O1tWKxjlRSta5/FH1ZzqC6fQvsw2tQAtSue5KpNt1Mz/U2EQh9XnriRQaeZQaeZn4/7mA6njWZ/MqgkBj7LRlKBLwta9+QStksMT5IZ96MahiYqsdb/x7fiFy7/Dfnvjflsm984iHdyCNEXwnP1zK94dX7pLQowPDcV0wdm8h+uBSBWkP61rkchyQ4aNUJy4l8Fj19asfw5f15uey7YPMc5zvEP5Z+gtPYv2bVrF5G/oVoeCoXYu3fvNx737wo6H330Ufx+/9nH/9H2+9///hufyP8Jqno9siGO+rgBfY8C/Qkd3lwZpV9AUoFmjwnNHhNVv7wN1bCKqrlNKAc0COo4Km2Mk43ZfN5dTJLWRyiuYmt3KT87sopFWc2YbAH2NxSSmT1C0e5rGW61U+vL4Acbr+LmrD1k2Zx80l/Bc/vn0+a0oRZiLE6owxvWYtEHseiDbGkchyuq447uFXS1JfPYhLeJx0R+O7iEqcUdpOk9qBVxNA061m2+FX+vkaMd2SQYg9yZ0MW15Yfp6ElCVskImjiZVhePu7JZ2Xge987ZyvbOEmZ9ei9hScmU5C6mJHfxSX8Fp9qyqEjo5/6Pr6Z5/ivcPeVzNnomosz0U9uXikqMM+I3sL74AEG/hvZwEt5eE0/PfJ0DzfloxBg1vWlk2FzYFD7afXYuyzpOoslPvFfPnj0VVLuzzv4f6gdTqB1I5fCucdS5Uzkx+R0eWvw2GQluLFY/Ew59CzGg4GB3Hktf+z5aZYw59lYyihz0DidQlORAUMls94zHK2lZZKvn2+mfs7CgGZUyTjisotQ8yIHmfBxBA46ggeqtZdw1dSfxmMhUexcF6WPiMeelnOHoYDY/zt9IoyeZzhEbU03tCKLMzdP38Fn/eEZ9eh6rXsTKNYexJXspMQ7yZscU1OYIL52aiSekpSdiQ6WI863zdrG/M59gREUwoiIsqRhvH+CuxN1jljF+NVJMoGBcL1JMoNg8RF/AjDI1SFVuDxF7nEnpPeQljRAPKvnw2GTm7ruDBbUX4YuqeXj+OwwFjZh1Ic4Mp7EiuRaVIs442yA/ufg9fnLxexw7UUhv2Mo0awczCtqZl9aKoJD4afpG1uSf5vm+eVSl9XJl7jE++HAuO0+NY1FxE4Io4wloERUye3sKqHOkUGXrYeGUWmZNbSAvw4EjZOK1KS9yw/zd3NU3lUWZzSzKbMY6bYglSfV80j+BVfbT3JC2F5UyzuycNtIyRvlB/SU4PEamJ3Yye3o90ZiCTPUoDw4s4NIJ1TxTP4dAUI1JF+L0QBrLSupBlHm6ZyGTjl/O/TUXc3/uZsoyB3D/KcN+29GriQzr2NJehmyOMWd8M+aEAKO+MeW0w0O54FNgtvt5cOdqSrMGELq1xLsMHD9ShNEUYt/RMmJxBWgkIn41b7dM4ppFe7iwoIZtjjIUHiVr370bucHER4Xb+KhwG6fn/RFrgZOf5n7KtRMPMSe7jVBMyQWb76Jgx/VITSamzGxCn+0lPKTnxMEiAh4dy5Yc59qiw4geJdPzO4jFRQzKMFes3cWRwWySckdpvP5prq46jEITY3XeaZKqBkmqGkRSy7R67BTtvpawXSbd4kb0KHl64au0Nny9hfvXYVzKIJNt3VSO66RvMIG9FR/xYOYn7D1WRkKSj4Mzn+XkUDp35u4kGlRS3ZSDKMj091l5q2MK886spt1r49LMap7buwCA7w5M4owzjX0D+ZzuzuDwjOcosw5SZh3krn1XsHOohKKsQVLVLiZndGOwByjT9rKu6QpmzK3njqYrydaNctqRRlwe+2raUPEyExN76QlZmZ7RSUwSWZFRT3HKEDGfipPDmaTZ3ajVMdpPZjI5o5un9i7mQF8eB/ryWDm7mjMz3uSVR1dR/ItHuTOhi5nfGwsCx2144Oz78dsX16LRRrk7bTtLSxpYn7cPZ2WMg615GHVhMg1uPijYjl4RYVNTOdn6UVZNPM2M7Xfxxs65FO+5hr0VH7G34iPalr7ItXtv4M6ELmK1lrHvoYFiKh67jTdyd6FyKnjwuy+RnTVM8vnd1K1+gMHbgpz8/tPsqXrl7Dm9XPUyCr8CZ0hHNKKkfPttnDf9JIVfXMevqz7kVxM2MD+vhfl5LbztmE4sLnJk5zjum7GVsBXeu+F3yAqIJMVRZ/iR9HGeyTyIIgTGG3r+w2vji60/ONvTCWDdp2V4uh1nqUBscOisxcuXr1kqruXwL5/B2B1C0GqQo1G6F//9KodLxbVEMq1g0EM0Cnw1k0lO+l+p154LNM9xjnP8/8I/UU8nwOnTpzl9+jQAdXV1Z5+fPn2a6upqXnjhBTIyMr7x+H9XT2d7e/vffPx/C8HsKPoeLTEDxHQyWocAMogRCCXLfNngkzhjAO+ZFKr3FhOzxiAmIteZWLnyBGecaew4Oh6MMRSDahTZAT47MIkNF/6edaeup28wAVEpo0oNsO1YBRVVHXxv+5UIUQG1S0TID3FJ7inuPnk5MzI7MGlCtHaN9YrNL2tiX3s+8bCCkuJevJIOkzGERoxxftIp/m3TpSjSA8RtMkp7iKhXDTI4RkyUvnAbb697lL05BUiyQFttBtF0Bc83zUaSBR6tWwHGGAsn1/LG8RmoTWN3heMxBfdM284HvVWkTRhgXed8Kk09fN5bTKzbgGSL0taXhNEUwqb0sWXe49zecgU/W/I+t352I4IMzb5kHpz0Kf9y9CJ+cOQa7JOGeLZhDpGIEjE9QIrVizOkI0U7pnb1bxM+5fX+GUwoPElbIJHCndeRluRm0Gmiad6rFO68jtkz61hmO8P+nCJK9IN80DuRaUmd7IkVkGMc5UwsE70iQmfQzvPNc5iV14YrokOWYXFBE582jSc9xYVJPfZ3Pn7D73ig+0LU6hirLKcIxtWUHfgWsaiC707cwS1bb0BWS/x23nu82DsbtS5KXBaxawNU5vVRaeyiKZhKXsIoOweLybU4sSb14o7qOHK8iH2qfDSKGM6ontk5baRovAA81ziLLKuLE+FUWocTSUj2cnnecd5pn0xp9gCKPxX1T8zo5VhbNh+u/AOX7L2NF2a/zIISmYlHr6AiuZ/XcnZTun8dr4qzaO1MYXpJO2/l7WRx3QV01KaTPr2O39QuAyCjdJA3Tk1DbwpRYB9h46YZyPkR1p5cz/dKt/HGyWkohtUcFMrAJmFPd1PtyECOi7wy+SXWn7oGrSpKKKpia3sZkR4D2eX9hGIqqoetfC94KR3dSdhTPCRox8rpfla8AQmBR7uW8sDI+eQljfBu5QusPbme6wsP8dihJViSvGjFKDFZJNHo4wtnKfOtYz2Lnmwt26rH8+mcJ7mr60KcEQM2u49cwyjNjiRmZrXz/MB8ulxWikrHesBEQeauqTt4tncBM0ra+GP1XDS6KDeV7Wc4amI4YmRiYg/dfiu1XRbq29NZtvAU26rHg0Zicmo3Z5QxBgYTKMrrZyRgQKeKcso9pqg9KaGbjVduZmXjeTT1pDDx6BUAfKvgKB6flgfaL8QV0nFz/j52fTEBUiNIEQXqMjeHTxdiTvfw2LLXmaQZZN5H97Hp0ETUKUEkrcTBmiLsmS42dY4bs5mpsRFND7O47gK6TmSQNnGABaZ63muuAmDZlLHeY1ktgzXGe0UfMmnwZu7YfzXHL/o9cN8/ZM5sHknkeG0eV0w/TLctgfxtN0JI5DfL3+YHn1/OGu2VXJZXTXs4mVum7CEqKflRYh3f6ZuBWRni7X0zkNUyKblufrnkPZ4amUeTJ5krMo8RkNRoc6IcDpuoHU0F4L5p23i6cR4bJz+LTVRxyeA4wmEV7wxN58as/fxr9QUUpjgYiRootg0zGjcwMGjBNU5g25EKfrPsHX584iIOz3mG3zhmEpNFRF2MFIMXsyqEUpDo0BjY31TA/Kp6dp8qHfufTjhAxWN3Iq90U3Cbg5kNt3L4tXtZ+sZabindx4JbbqLh2acpfvU2mma+wY3dCzjWn832xlI0CSGi/XqcAjSQzNLA+fS7zcQjCqKygsGQGfxKrlu8i5mGZkr3rxube6MKEmx+Ct++Fdks0br2WW7tmYkYhbxN62lf9wzjDnyLLJuT9Zn7kAaKCThuYsbJS/F/kYS9IYajUkkgJ4qglAlE1WyY/RQ/7zmf6uFMGNBy/8mLuW3cXlLUY3NvmtrFgSOlaEq8PFK9hLxZ3Vy45w4wSqhsIfITR2jsyqHwrVtRzfQiLu5mKX9fD+RoRZytFzzCJt947vnxtj/1oI7xZR9l+Q8exZglo7HkElcJ5Gzy8Pe6zG6X3mO54RpIScY7aWwx82Um86w1i9n0V7+3Mu0ONvc/+Xce5f9+zvWknuMc//fxz9TTCTBx4kQEQUAQhL9ZRqvT6Xj88ce/8fjfuBEzEonQ2NhILBb7xgf/70LhU5I4Y4CoWUY7LBCd5kPtEogZAVEmkiARSZBw7UhFMyowe+EZMvKG0bWoodTH4Rcm0X86Fe2gAl2TBkWun7l5rei7RS5+/x48PWb0tVrio2pEUUZpCzEcNKD0KFBn+NFNGsVsCeCLa1Ap4+zrKODCtNMoNHEUmjjFhkEA7EleInEFL/fPRiFKfFxTya9fW4uQEiIWUYAoo1RK6HpUyCMa5JiIsWqE77asRS3GGfKa0Gd5WZ5WRyCoJhpVkFHowGb3sbe9gOum7kcUZURRZk5+K384tRCnX49GEWMgYGL3cBGRuAJTkRMEGZM5SKF9mOc65/CYYxFRScHPa85DkRKkYEIPbU4bIUnFvVU7SJvWx0B/AuFWM+l2N6Ioc3/BZrwRDVsPTGTrgYn8qnEFaToPeVoHC6yNmC1B1IoYMZeG/E9uYllJPYkaHz85sIZmTxIf9VWyZ/wG9vXn80DJp5xwZFGc38+7LVW0ehNJS3Sx92QpU62deL06ttWUEwspkWSBqdZOplo7WX3gNkRkzs+r5drNN3NgMJc1haexW/w8tO18RHOE9MxRPneNo7Epg1lZ7diUPkJxJfMsDfzy2ErePTmFkaCeRSlNHKnPo8mdxPHuTPRZXgrMI7T1JlE9moknqmM0YmA0YqAiZYDFSY081zuPoiQHeQmjfNg5kUhUQarOg13lZZqtE4s6iM4Y5jtNV2C1+rm39jIqj1zJzUX7uDXlCwreu4XJGd2UW/q5aepe9MowVb+8jWSdH1Oum12nS6lK66UqrZcJtj5U2hj+UT01NTkA6FrVeDst/GTnpaj1UeKmOJJOQlLJVCT24zuWCBGRG05eiyDIjDiN+DrNaDVRcsb30dGSejaD2DlgBwGWZjSyvewztpd9xs0HruH1oVnMLm0hMqin5Vg2LzlncXLq2zx2dDEfLn4Cf0DD202T6fJa+UXBR6RovRzx5LN5uIIJxm5sGW4uOH0dJtWYP6XLpefzriLqZr3Owe482t02/D4NP8zdxA9zN7G5ZBO37/sWLSOJHHPlsn/B44RGdGwfKuOQI5ffpO+g3WdnyG9k4bQzKHVRZlua0fYqUemipGvcZJrdJNh99DgTUCvijPr0dHkSqG7O5q1ds5l16hI2l2xiXFY/Py7bzI/LNvPU4YU0zXsVrSKGVhnjte7pkBMAGSxJPpKMPlJzRzBpw/yufSlzttyDPssLIlSm9aG0hjCl+Eg1ePF0m+lvSyRqjZOW6iLT4CapwsGe8RtY/8X1hF0awq4x/9tJk1v5l4Ufg0LiJ4OziUcVyD4Vk7fd+Q+bMz1OPUJI5PhoFqGIEo0xTNuFz/Fy3yzmTmzAqgnyct0Mnj4xj76QFWdMz+lIhP19eXzYXMm3F27nX+d9zEl/Nv9afQFbO0spswzSFbaTonSzcaiChzuXc1PuPm7K3cdjNYt4ZMK7nIqkMGHrtxlnGWBaTien+tP5dLiSRIuPe7O20epNpMNt5XLzaTYtfJzz995BUt4o39+9FlkSubXjAmrc6XSM2FCq4yywN3G4O4cUvY/pVS3IUZHDm8cjhESEkMi1R27Anx/jzIw36b2ykMOv3cvKtDvo+6icJz9YhTdDSdHrt2Log4lHr+DMcBqeISOfz/8DWk0UfbaX2yfu5nDV+4RiKvwjeuaXNlHvSqFpOAnMUTZ0TWCWNoBWHUWrjnJ+SQ3Kj6zMnllH89oxBeJnMg/izZMQ1XGubF+EVG/CFdSx1jjWq6geUqJ70krqim5yftTIuBXNJKe7MGV6UStjrD16M50eK/39VhSZAcpTB9g5XMKbNVN5s2YqXklLYUUPkZASKaTkyoyjqDq1SDoJRZ2BXrcFIQ7mYicC0PdR+Vd6Mr/ky+dfZhr7Pion6ajIzbfezXOvr2Tchgdwlgo4S/8kxPSnIOnm6zcSThDwZigxdvoRwtGvdT2KZhMjc9NRe2NfKeEFoL0Heeivfb//NwWccC57e45z/F/JP1mms729ndbWVmRZ5siRI7S3t5/dent78Xg83HDDDd94/K8ddAYCAW688Ub0ej3l5eV0dXUB8J3vfIdf//rXX2usX/3qV0ydOhWTyURycjKrV6+msbHx654ScWMM/yepKDP8hK0QDSmRlVC1sBFZBGuBE2uBk1BVkLBNpsWdSG9zErEJfmJ9epwzIsQsMUKpccKJMjmJo+zdOYG4FiSVjMqtQD/PgcIeRq41EXNq6e+zEk2MEo+JuDotRI7YeL+2Ck+PmcIUB0+emU9uyjC5KcO80zaJiVm9jHRaae9KptNtJbgvkfLcPiIWmYwkJ2pdlH9b+R7xJhPxcX50WV6KcgYYHTTTM2JlyG88+/e+sHEJU7K7oclINK7gV2UfEo8o2N5fStClJejSsruhmIlZvXw46TlEQSYqKeh2JxAMqzFqIiQnezBoIpxsz6SnJZkdmyfTdzwdocZELKCiuSkdd6+F3a4SHt55HpGYEpMtgKyEru5EogN6/tC1mMlJPciWKLIlSuCkjW31ZbzXP5ltI+W42xO4MK0G0RRldmUTW86M54gjB+WAmgvTTmPVBNkY0OLy6Xl/eCpDDUm0H8kmHFbRciYTh8eI0hri5dMzkOMC51WeBllgqCaZV2qn80rtdDSaKCe6sghLSuZOamCky8q7tZNwDJsw57moyO7DE9TijOgpKuojQRVk+/A4OketfOEuQ4qNBftdZ9J59+0FCCEFPSMJWE0BggE1xz4Yj65OS8+gleOn88/6oFb3ZnDKm8mgz0RMFjnZkUllUh86TZQGZwrHXLl4Yjp21IxlvOYnt7Aoo5mq5B6STV6agyncdOIa3r3ocar7Mnm7eip/PDSPdI0bd2mcwzUFeLosCDJU92dQ3Z/B5x3FvDP9j+i6VSALxCr8BIvCiCEB4hAJqMnNH0TpVoAuzu66YsKJcVBLrMqt5frCQyjVccTkEGZtiPdL38GU5uV3Ve+RlODDaApx3/St7OovYk3LMta0LCMrdZS+gJl9NcXIGok5c2upcaXz3YFJpKa4+KNjPpeWnqRu1uvcnb+Dh3pWcnAol3WJ+3kp9zPiiDw27m2UCokzI2n0ticiKGQCg0Yual5OvMmEy6dj89wnCMkqQrKK0n3r0Boi+F06ViWeZu6+O1APKvFFNFyZeZTLGi+jsSuVqSld2NUBYm4NHw5OImtBF0KjgROuLNqcNrxeHUGPloE+K7Uz32Bk2IRCG0fSSgSjSvI/upm2HXmsNbpZa3RTXtBD3qb1uMI6yqyDTLD1EfVo0BgjBOqseMJaHE4T6QYP/aMWhKjIU5Vvgj5Op8eKFBdJNo1lwjHGWTLlDMk5o3iCWvYdKWPUq+dxVzaiX4FmUIVmUEUkruDkwUKebZ2LrkXD3v4ClN1abpm3E6L/OHE2tT5K69pn+be8j1GIMotzmyjYcT0Np7Jp99po3FHA7eP3cMfkXWxsKGeqsZ2fdV9AosHPA5WfAfDg9jXsH8pHliEQ0PD+qUn0BhM44i2gMqGXQFTNxuEJbByewISMPm7Zey137b6KzKxhugI2Wl2JfHvcLrJ0TvoHrNxx4ipikohjyMLFp6/nuZG5FKQ7mJ/eiqCSiIcUnB5Io6k/mbBbS4rVw2NHFyHLAnWOFAYDRvTWIKHcyNne6/l5LbRd8ByPu7I5+f2nKf7Fo2zufxL/kIGmH99D6medfH7FQyy49giBeitmbQhDh5JLT99I5JiVlbn1PHF0EQ8OlzHsNaBJCOOK6Lgpex8eh4FlZfXY9X4Wnroamz6ATR+gwtCD5eoeXsnZg/inr9jSF25j3bx9yD4VdY4UjFUjXJ9/kPxtY1/eTT++h+AdTtrOZPBKzh5StB506iinpr2FL6whL3GECfY+Ti99ktigjkydi5gsYrX6sVr9vFQ/k9GgnqrcHpZV1PLHtrlEMiIIcYFQegyvW8fyhScInLSRYXURPTMmJKSYUPaV6+LLwKdvkRWArH+V8OYKdK4SiemhbvUDNP34Hpp+fA/T1z1C8S8eZcXEn/LSk6swXtRP6kctdP9YxvEb+WtZpmys3k4gRaDtKpHN/U+ePY+l4loErQbhL4SJ/tKn82/5dv7l60RRsQEAAQAASURBVM9xjnOc4+vyZabz627/r5KTk0Nubi6SJDFlyhRycnLObmlpaSgU/7XP83/G117V/PCHP+TUqVPs2rULrVZ7dv+SJUt45513vtZYu3fv5o477uDQoUNs376dWCzGsmXLzvaP/r2IQQX+eX5spgBCvh9Vu5ZwYpzDJ4pQO0U8Pi0enxa5X0vcHEOSBRS2CLQYkFQyCoeKPyx8E6VHgaSSaK7LIJYQZ+p5tayee5S4Vmak2c7PJn+CGAVzgwJkASEmEB/SgTGGcaYDoU9Lct4oIwEDVRm9tPUl0daXhKfPTHVHJkqvSHl+LzpVlLBN5kxLJrJKpqshFbnexHNdc4llhpC69YTDKtqOZyH4FMSjIp6gFo/DgL/fiCzAaFjHT9a+Q5bZxbffuQlRJdHTbcec6Mec6AcZGj4pYumOu+gcttHVlkxVSi/UG4nEFAwNmdEoYggODbo+BVGzhBgB9STn2FURFxAiAge3jUeIC7j3JyPsSmDmjHo05jCyOUqHw87uTRNR9mtQ9muIZEXAq6TfY6bVOWbm/X5PFYIANUNpKLQxevutZE7pZShipnEomftrLmZFQT0nBjIx5ruQtBLxkJKcsn6iXUZkWUAe0SBFFHy+cTKmGjVxexSxXY/YridWa0HRpqPFm8SJD8tJyxuGIS041XhbrTRtKyDQbeKX2R/T0pPMlvYymjcXEGk28/nGyaSmuBBEGVmUUXnBlOVB6tUT2ZqE2KNFFYBQkgxDWrQDCjZ9Op1Nn06HJiMHD5fiO2HHG9Ege1TsODae4X4Lg7XJnO5N5/BQDhWF3Thrknhj/yw+/XgG1UOZtDaks62jlNCIjqtfvYuylEEIKFC6lbx5eioKexhbphsxJmBJ8xDwaAl4tIjVJj7zTCSUEkeRGIIWA4JbRdwogU6CuIA/ouZHaz6gbcULIMBDy94CaUxh+PGTC7CZAiQl+Bh0m5j+2nfxdZm5743r6e+w4+s08/vTi0gxeMnWO8nWOwlGVGgVMYoL+5g2rg2DMkzdqRwsyiCDTYlsayzjQssJCnZcz2cjE0nTuekftvDtM1ey+NS3eLd7MtcevAF/WE2a0cNry57l11M+ROEX6XDamLGwlpnZHTwzMg+tEEUrRFmS30jQpcVkDfDLjy9Gr4vw3bUbuDSzmuZQKh1Ddh6a9R6HB3P4uKkCc6qXJK2P7i+yQYD2YTs6VYx4RGRJeT0oZBbVXojJEkRUSJw35RSSJII+zkWX7mdXSGBXSKB5bx5KfQxfWM2O6nJ6AwkoXQriMZGoPYZz1EBuyjBHW3JQKCRU9hB31V5Ogt2HVRfAbA4iyQJDfiO/nPUhwfhYUOl3azl/9gmqZz/P03XzmDe9jplLzjBzyRn6XBYKp3Th6LLxvW99gMenxV41xEjUBJF/XNAZdmtYWn8+V+28lUSjD3dUR+uSl0gqGqGnPYlLLt7LpsFy3umYjEYX5dHmxSSoA7T1JfFa3wz+WDsHRVDApg1g0IeJB5WU5PRzrC+LrZunUO3M5O78HZxoz+JEexausJYV5WdYMaGGlWl1eCIacsxO9jqLeffING6fsotVBbW0NqVTWdDNz0s38Hn3mJDS/sE8Lqs8jhwXWJbbyPnFZ7CnuimyOPj+9K0IokwwpKLvcAYpZi9XVB3Fqgli1QTZ01EAwJ0JXTzuyib/PSfjNjxA2wXPIQ0U03pLDtlKE4+mHefJtc8Rl0Qeu/GPRGMKgjlRPjgzEYIKXj44h0hQhSDInGzK5oe7LiUn14FN7cfhNzLkMNPtsNHtsPHUoxfTu2es133e7TfzmDOXhhuf5oQzC0OHApUyzuiwifMMDRAY63CRBooR3khEjArM/N6t7PpwMt0DNgreuwWtMoYrpGNHUwlTDtwEMuzuLaDYNES5fYBy+wB6bQS1Io4kCxiUYdKMHgiLrJu1nyVVtUzM62FzfTmPX/UcrfXpJB+PA3yld/NLxm14AJ1jbNXkrEhArBzLxjbcOJa1nb7uEaavewRnqUDTj+9BGBwhagT35jTkUBj9Z2aOVr2HdKruK6W4f85Sce1XAt6qX91GxAKPz38d+PdMa+fPZoEoEuvs/qsx/jwz+KVv6H/E180ingtS/2vOvUfn+KfgnyzT+ZfU1dWxZcsWPvnkk69s35SvvarZsGEDTzzxBHPmzDlrIgowbtw4Wltbv9ZYW7Zs4brrrqO8vJzKykpeeuklurq6OH78+NcaR5fpQ1FrwHEmiYhHTVwro0kJIFrDSKV+aDeMbUDCaRX97YkgyETtMdBKSClh7tpxNXJ2EEOnAs2wAoVPZF9LAR/VTESIg6lV5KcfXkEwM4YvV8ae6sbQrkTlEsGnxL8/CVW+j6H+BEJRJUcPFZOS6CEl0QP6GKoWHVmTe2k8lMew10DcHAcZlBkBUMokTHagECRwqYlbY8RdGsTsALJW4qM5Y1/2gkoaG8sn0Ouy8NPDF3G8JQfThBH0h/UIKglJFpBkAeWgmvXXbqaysJuSlCHyCgaoHswgkhTH0WZncmEXXUM2dIMCwfQ45laRqEXC15KAwqFCMEfROkRkJag8ImIEPBMjHP18HOEBPaJKwrzdgKyAC5Yf5oLlhxGV8bP+eL4zNnSZXgZGzSibdRg0EdZX7OeWKXtQiBKDETNmfYgpad18emYC95Vtx6CJQFIYIiLtHcmQHGJ8Rj8qrwiSQDg1Rlw9Nr6klJGUMoowRBLitG3PI5gu46hOAUFGERJQuwTECMybVsfqJ77H9RMPEvRpECRQBMdEpjx7k0neqkEzLOIuieP3j/3cXSQhK8BdIqEMCuh7BUJJ/96lFM8PIqllYiYZ5/Y0MMRBH0PXqQKFjNyhZ7ghkZq2TK457wsSahXISvC0WEEfZ3zKAMYmJVGLRPXJfEwZXuJaCVkS0R/WM9JvxtwiEDliQ3CrENwq1NNHefHYbJBAr4ugDIK+V8TcqICIiOhSsjijiZ8fP4/8bTei0ke5f8PV6BKCBOsTkCWB0WPJDJ9KRqgxjd1AyXUhSGBK9SEmh9g86ylONWfzeVcRn3cV4WizIwoyIwEDR6sL2XS8koyyQV4+OhtFShBBhFeHZyMoZPY0F7K/L4/JOd08XP4+w6MmeluTkHwqvG4d/T4zd9RcxU9OXcTPLnwHv1/DycEMkjQ+RiMGXhqcy0uDc5lpakFljJBvG2HjFQ+jVUXxSloer17Ar1PG5obX+2fyy9KPsJoC+Lxa1iUeYPnqI1DiI+TR4PTrqMzvod1rw2AJ0t6ZjChKSF16tjSOQ9yRwNzSZjwxLffVreW+urWsWnWYteNO4O61oLaFaBu1s+vKh9Bpo2TlOsayg/XpXDfpIKEBPdGACo9Hh1ETprk3GYM6wvTEToZ6rfy2cRk/Td+Ia8RAZvooO9+bQtWLd7Myv46fZ2xiT1Mhe5oKkaWxz6uuV8G/7bmIl6a9jMuv4/1DU9Gkfb2bb18HbZ8KvXKsBPLzcZ+y/3Qx+R/eTJltELUtxBunptE5bCMui9w5bheOHit72gqRYyIN3akY9WFithj3ZG1jemoXKCVaD+dwedEJIkkxukat/K5lGVPyupiS10WR2cGWo5Uc6Mvj3fYqfpr3GU2jiRxtzyY5y8mTBxbx8Z6pzKxsIk3n5pGuZfgDGlr7khgcMfNhYyVpmU62tJaxs7uYfMsoxwey+EPtQlbm1yHLAprxLvqcFj5+fzan2jM51Z5Jw5zXmHHyUsY/fhtv/uI8nBUJ1K1+gOXplVT8/jaafnwPXTEvH/hNPNy5nO5T6Tw/MB9/m4X2855H9qjQp4ypbJstAWRZQKGLoXQr6K5LZXtvCQszmmlb+iKLCxtZXNjIoz94mox53eRvXk/PBRJPf7gSgKbBZGrufhp3vQ1Vr5okhRokEFObyP/0JvypAu9d+nte/eXDCFG4e/LnbFn9O0a9elINXhJtPm4r34sp143HpccV1VM3mkLdaApen5ZYXKTHY6HBk8Kp5myWTKrlterpAJyoz+X8sjNEZCUY43hyx+5W/62gsG71A2ctXMxvHESxz8LkiW3MOnXJmGjQa/dy+LV7afrxPQA4Fxdg6JNRe2HosnLW3beJyt/eBnB2nL9ku/TeVxRoX/ruo0Sscfb7xm40hFPGvq8Lnmih6b4Ctvad+srv/6Ww0H8350pd/2vOvUfn+KfgnzTobGtro7KykvHjx7Nq1SpWr17N6tWrWbNmDWvWrPnG437toNPhcJCc/NcTvt/v/0oQ+k1w/8mLy2az/c2fh8NhPB7PVzaAGWkdaKeOosjxo7OGELIDhAf0yCMa4j16oolRoolR5OQw/tl+0MUwGUOglhBdShS9WjSDSuQBLeFEmXBOBLVbQNGrRVRKiBEB9/gYcb2E0qVEMyIQ35RI2CYTSYyjG1AgxCHeZIKwiKfHjCIzwFB9EkP1STw06z3ipX58YQ2xpChynQnNgBJjYgC5yYg2KYDzZBL9ezNRuUUEdRxDqo+7KnYiC7Dmo3sQakwoNHEEhYwgQcCjQ46LZGcM4z9mZ/3NG1F3aQiFVIRCKnSlLp7asJJOt5X6g/n0HM7E02NGO6BgzeyjnGjLIh5QEkkAZHBVRJGVMnNn16LI8aPq1BLIjWKuHEblg/AkP9bDamJ5QdROBbrTOkbmhokZZD7aP5WP9k8FhxZT69hiJmaJE24xYzEFCdvjJOt9vPzBUl6qm0n3iJVdX0zApAkRk0X05iD/uv8i+tsS0Z3SYc90kZUzjORVc+ZQPpGMCOYGJbpuJf7CKLZk79kShvErmlC5FYSSJVQeAWUANFk+JI1MuHDM5mdPcyH+kijvvL4QfaMGZRDCKXHiOplAdgx3vkBcB+qUIPGgElOHgNotYugeu56jJgl/zljAGU6OE06Oj/X3hkW0QwKCBPomNcYaDWoPaAdElAEBWSGjbVfz4t55hOwQM0qo3ALqXjV1HxeDAIqAiBgT8LdZ0KQGEEdUBGf4ycweIaYFlQ80wyKaYZH4HhtKhwpFQMRqCBBMixPIkAgmg3ZAgWSN8vbJKUzN7ULfpGZCRh9xcxylQkIRAdmjIq6RiRnjKAKgdoO31UpMB94BI/GAihVv38fkkg606hhadQxMUWpqc/CdsKNKDqKxB3EHdBAWkWIi8ajIzvZiZElA06zF7TBRO5TCTZvWMz67j1vm7USVEEb2qRgaMSEIMrGYyBt90xEECEeUbO0s5UBnHgc6xrafnV7FpaUnOdWaxb90X0SxzcErzTOQIgqmHL8SenVclHySp3oX4TmchCDCtVtuZsPxSUQcOtLSnaRYvFybtp+23iQS9EEUuhjuPjOmUidKdRz3lDEhqqOD2ShFCaUosfW96eweKMTYoiDD6uLUtLeY++l3icYUDB1KQ6eJohtQ8PL+uZizPKj0UUSFhNOvR4qLlFkHefvgDJIznLhGDNzecgX4lWQZXfz+5j8S08t8dHoi51evJz3FRXqKi6tKj9HcnkYwLY7SpeDnneeTZXPSdtEfkRr/Wjzlv4twbpi2UTt6a4A1LctQW0N8f/FGjvZlEXHokMMKYt0G9OoIvz28HF1igARzgNmlLXx38nb8ITWWJB8Ptl6AI2xEoZIQ833sHCxGNEUJDhgwaULUOVKoc6Swc+skHlv2OnaDn48mPs93zlxOKKJCcqkpsw0yr6KRmdMbeCN3F/t784lJIgnmAJJbTVnGAOl2Nxdk1pCc4GVVbi1TEzrwDJgIjuqwqgLEvGoWZLawuug0kXFBKvN6qMzroTnqw+nVc+bOp88GS19S++t7yH3uIS594Hv0Ra1IssCmtQ8zHNITN8co3beOhGw3Z2a8ydwJjbi7LeQljmBN8CNpZBD4/9h773DLyvL8/7P67u3s03uf3nuBoQxFQBFFbNgLY5fElB9fu8YkJiLBOIhiByVSFCzUGRiY3uuZU+b0Xncvq/7+WDDKN5gI0W8s3Ne1rtl7z7ve1dd5n/d+nvtmtifGsdlq2p95G7sGm9k12Mw/D17BqytP0Hvlt+i98lsEBmHL+9/LP624n8X734xUn+UrN3yHy069kd7r7mTle2/Ff05mz8dv5bqnP8B7PnIzS17bwdd/chWv/s4nqI/PYjsCWyq7+UbHJt7duoevbLiXZ3ubmemPMtMfxefVaYlOMzkS5XMNP+PLF9zLSC7MW5Yd4GyiDLHg/qn/ct/lrG7pJ7PKFQp7saBwyc23nlf2vfrMHF/54J0c7q7HdgQmPraBti/eStsXb2WreD1XLPsU+354M6kmgXBfkUOf3c6tz1yOdUHyP/X7X+HDf/VR5IzEjtE21t74FWYWurOLvxr7d7Q5gctf//YXtC8sqXuFafsTxCvX7BW8gj8NfPSjH6WxsZGJiQl8Ph+nT59m165drFq1iqeeeupl9/uSg87Vq1fzi1/84vz35wPNb37zm6xfv/5l74jjONx8881s2rSJRYsWvWibL33pS4TD4fNLba2bwvTscCNvbTqA4wgEvQWsCS+I8OpNh7BCJoItINgCdkHCHvXx+qVHyeY0tCEVqSCgJUBvLIAAZoUODuQrLQQLhDEPtGTBELB9FqbfJl9poQdBMAXwWNgK5BYWEGxQpyW0KYk3tB9h8apeFq/q5W9//hakTj+zKT8URfQSi2Kdjnk8jJyDMxt+iJSHQpmFXmLh6BK5wSBfPXEx0coUdsRg5RVnkHp82AUJPeIgJGXISwx1VFCMW3z9J1fhXTrL1pazbG05S7YvjBE3SfVEsTWHq6/cT7A6TaFB55H71+I940EN6RhhCyVewBMtgOyw58lFWIN+tEUJtDGFuTk/tgxmUmNurY7c66V+4yDO2iTkXBXbunnj1M0bp6RtmvQindR0AM+4hKc1xdycn1hDgu5fNiMVgK4AVr8fW4XB/bUc+fkC1CfCkJVREhLFKMx1lpB+qBJ/v4SWcO+vYgSKMQdlWqGwpwRHAkeCnh+2YYRsHM12r5cD+dEAgiHgP+7BVkEe9OA4kK+ykbOQXFHENySB4CDmJMo2j7qBdVJDnVTIVYARskm1WWjTIgggp0W0WRF1WkKdlhAsActvkauzyCzUMb1QKAV56wy5eTp62EGuyFNo0t3z3qwj5kVsBYy6AsaaDIUSqFo1ii071CweQx/1Y3tsbFNk4kgFggOFOBSqTQrVJjigTQvYmkOJJ0e4U0JNuKmfhUYddVQlfFjj8K52sOHwqUYamiZIz/gx/Q7atIR/VACfRXZREd8lUzii487KeS1Ej0nlsnGOHG4m0Rkj0RljYcOoy9y2Z9FTKqbupgNuXN7JyoYhSuJp7N4A4oRGMW6zuGUIv6bTvmiIk6fq+ebxTRgJDW95Fn+gSHI6QGkkQ8fhBpwxD5pquvunS5RF05RF3ZrIsUIYb6jAwe4G9j07n3xBIRzPsKGyD6Uhw+f2XENAKSItS1IWSyGGdSS/gRQrki2qvLrqBDc/+0YE0WF0IoLc50VOyKTORaAjAFmZvQMNpPeXMpf2MZf24dk4w9holHS7SV93Jcv/cRuOaqMPBtDjFuWBNNaSDFJWJObLURZJUx5N41N1RNnmqZ1LqGuZIJ33ICYU+sbiSLEie0638LE734eaEBCSCsnZAILgIAgO93avAFPgn7bei6M69O2u59yJGhbfto0lF3a/7PfpfwdJtcj3hzAtiWNn61EPBAiKeWTJZuWSXiprZ7EjJiWeHMFIng/Of5p0zsOBwXq+euJiSkMZbmg6wujeao721GFmZW5oP8Ki6Bh2QmXtsh4Cio5PdZe1l5zmjuEL6T9dxSUP/jWJySACUNkyzbN9TTT7p9h9aB4t995ELq+yIDxBVSDF+uVddAxXIOJw/8AydFPipz1L+EHPGj606Qn88Sx9uThvW7sHgJ/+fAMN5dMsCo2yKDTKG//xE3Ru/j7NT76TS7b8w6+P/7nUzt5rvsmBL9zBrtk2vtv2I67Y9WHeXbsbQbExxnzkj8RoffrtaJKJVp4j7smyIDZBtGmOc6//BmtWddPXWYk15uXMhh9yZsMP6dzfwB13X8XtiTpWfXYbR77xccbXyFzrz1IRSqFP+Pjbb72T4YE48+7axsHPb6diX56A6EHWTL5y29fQbZnyDaOsu+IUIbXIyaEqdo620hyf5u7+NaRtL29eeJCa1klqWidRFZMDu+chekyu37GNJeoYa2P93H1sDcNdZQTrk3yx4hmeWvgzGv3TNFa6wjwvFgQc+5vtnLn2MwDcdedVXOK1mN84yrL4CO9+3y+InnWInnXo/sYaeq+PcsWyT+GdgNENGps/9D6qnhAR94Rf0v0o2G7WSvJgKdGTCap2uD6hWzd9EUuDqWUvtGC59rbHkcIvbRuv4H8fr7Cjr+BPDX9pNZ3PY+/evXzuc5+jtLQUURQRRZFNmzbxpS99iY985CMvu9+XHHR+6Utf4pZbbmHbtm2Ypsltt93G1q1b+e53v8sXv/jFl70jH/rQhzhx4gQ/+tGPfmubv//7vyeZTJ5fhobcOg+7N8jXntqKIDjMnSwFAdTSPA/vWu0GDOEicriIZ1QBBwxbpDKWRG8sYEQsjAA4aQUlJaD1a5CVkTMituI8l8pYBMUhWp5GKghIWRHLC4INyrhKMWZjFyVw3N8uuuoIPz6zirM7mzm7060psmWw5jSUOQk8No4lUKgxyDfrND3wPvJ1BmJRJHJaQp6WccIm9PspHIjhmAIHh+swfQ5SRkYsCjghEzlWQEmJqGV5iqUW+YLKwck6Dk7W8fote1FmZBzJwfJbPHhsOfbeCEJaptBeQF4/h5FTkFMSTr+PgLdIqCJNqA9i86fJDIawZXBmNPKVFoEuGXVIxfQ7RNQC9rEwgt9AO+pnemcV0zuryOwtxd+hEizJUmjSEZ8J4zvhIXkmhpaADa89jmiAGTNxRAdHctDDDnoE1DkRqz6PVHTFm4olYPogP69A8LhKtMtBnROQ85BtNPC3JvC3JphdYRI/KhDsknEkCG+cRE2IlC6a4sGPfJl8i442B75+BTkjkqtyCJ7QmP+qbqSciB3TmftlFYW4gxLQkdrSyDl3f4SgQTFu07Z4CCNsEV49hel33ABuVgTBQU5KqKMqRn0BI2YyMxomFM1il+hwzo9jCGSzGtqAilOfx6jWESc19CkviA6jRyrBbzK9swrfsEhj+xj1FTMggKWCHrOQEzJyQqZQCtbaNNq0yNHjjWSrHWwZsAXEOQW9SidbDfFlkwhrk2jjMsNHqhAyv7438+uzfHTtk5BSmDkdR02I2B4HdUgjEChg2SI/fvXX8EwLeKYFup9tBMXGMkRkv4mkWOS6w+ztbeTwgVaXrarNE543S8OCUU4da2BqMEbfM/XIJQWkfi++0iy5GR/pGT/V1bOMjUVR6zPYXpv0nI+183pxEioXVvRwYUUPtiXw9Nk29KJCSVkKqSGLPepjQXyCX55ajO0ILGwa4fBILdmUh+kj5QijHrQOL4x4KegKdz5wBUJaxsopvHXpAUyfw9oLz2D5LUQDXrv+EI4jICxNsa6un3V1/cyMhlFHVKKVKTylOdL1Dt5IAUdyCNck+WjdE5iGRMXiCYYPVzPWUYZli8x0l1BTNoflcRgcirO8ahhBF7CTKu9atBfZb5KrskGEUGMCshLDQ3GGh+LEAjmkoMHf/eqNPP26f6F63TCNi0fIVdn0JV484+P3AccS2LjuDNe3HuVjGx9H3DzHsBHjrc0HOTdXwufbfoqYkDFtkY/O28H3+9cR8eeJBHLQ78crG/xsaAl6tcHS5iG2Lj3D46Pz2DfewEe3PMbBA218uPoJ9i+/j/3L7+OZjlbOTcb5xyt/zD9e+WN4Lq34c60/gyEvD/Yv4aI1p3Akh5JwloNTdXROlpEzVZqrpugfKmVp6QhfW/AjSoJZsn1h7htcwasaOxjOhunNxdk12sRP3/YvbC49x9OTLTw92YKluSI+zKrYmpuFYY+3YUQ8XNl4M00Pv5cPjKylPxnl6i//DR9c8RSnc9XUVc2wbnUn3hWzmFmFvKVgGhKt/kkuip7l0Ir/4C39W9gQ7aGiaYaeN3yD5YduYPmhGzArdP7ubT/hrjuv4tCnXfGi5+shz3VWcdGaUyy+phM5YOAbg6bH3s3jP/4uy/55G44jcP2jH+To/hamUgEGMxH+pvpXHLlwO8msl/FskHc07uW27otJGD4sW8SyRdJZDw0rhvnX9f9BQ+0Ulz32Mb6zfyN/veYxrll3lGOrf8zVHTfQ/MQ7uffQah6f74pB/bYg4Dc9TAFSRQ8HJur49/uvYu+X72Dvl+9g3r+nKTtscfmP9jG3Vsf0QcXNvUwvEck0W//tPfibKbPTiyUWXNyDsDBN7/XR87WmI39tUIzZHPk/21+w7k8/upUr9/af//4CX89X8ApewSv4feEvNL3WsiwCAVfANB6PMzo6CrhCQy9H8PV5vOSgc8OGDezevZtcLkdzczOPPfYY5eXl7N27l5UrV76snfjwhz/MQw89xM6dO6mpqfmt7TRNIxQKvWAB+PSr76WyeRpz1IfltZHK8ugTXhav7EWek7EMCcuQqNs8iJwXeOjZVQx2lSNMayhJCSNsIwQNqjYOs+DiHqSCAPU5rBITR3Uw9sUQ/QaprihKymXeTJ+DZwZs1UE0BPy9Co2bBnAkeOTAUqw5FXlJEnlJEjtq4F00h6Pa1KwdBsENcORZGSEj46nIIaVkAgMCuQowyw3IugOkyIZJBENEEBxK5k1jRw20WRATCpYuoUctnJ6Am6p5IkDidAmJ0yX8x4E1SHlQEm66rqdfpe3qHnAgGM4jCK6Akq06fP8NXyNzKE7ubITCVUlyT5WizYiYFTpOxCDcKdF6bY+7Ldnh4NEW9JiNNKaRL3MoLMxTWJjHCEBucQHjcATPOZV0s0Xw4knMiEW2Ck7NVFKoNAmXZnA0G21WAAFybUX0MhNxyItggRgrYszLoUdtAkc8FEohWyEg6eCdACklk5oOkJoOoMzIZGoEcpUOZluOwsNlLkv5dBlX/PivEWcURBPyVW4NrVgUMIJw+vFWjLCFr1MjPc9ESQs4jgAngxTKHBwR4iVppLzAWCqEUlpgYjjKOy59indc+hSVm4fxn1Mw4obLTidVtHEZKSOR6QvTWD2FVZdH8plsbOpFXpJEUU28ParLkIsOvlEBI2biWALFqEO23WDgZDV5Q8UMWPg3TwEgtaSRWtKY5Tr6QADPmlnq2icwK3SMEhMhYGBrNpLXxPTbTHaUUigo2AooLWmI6gimgLI8gTXi47ZntiJGi1x04QmKFSYrV3djeh2yXREmzpZyw1M3ka+wyVfYGHUFKEh4fDrCkAdz0A81eTwnvdg+i2JnGLsoMTsQYeBkNaIhgCm4olJ9foyQTchbRJmT8MdyAHh6VQxdRggakJcYzoRxPBY7RtvYMdpGfeksyqiK47isd2kog63a3N3wFNKkgpGXOd1Rh3M6iNanYXkc1m3qoBi3sQIWa2sGaNw0gFgU2LS4i7uf2Izts9jd0YLsNymW2lSqSRTF4qqmMzx7YD7PHpiPNikjzk/jV3UKkz6+cM29FGa8OGGTgKbzgafehqxYTB6uwCzTESvyTCUCKDVZDEvCCbgD7UNDddStGOFTl/yUbx7ehNjnxfZbFKpM8sdi1LROImQkhIzE6GQESxcpbZ3hPT030ByaYUF4gqOv+yqzk3+49FqPT2c8H+SpiVa+uudSNNnkB11reHKqncbILO9/+D0sXt3LcCpM2vLSHp1ksj9GW2yKI2+9lZWxIcr9aTAEJnMBsqaKbknUhhLcN7ycygUTvOux9/Cmvot5U9/FOKaIZYms8IxQq8wQr06gT3j5wI/fh1ifI5fXmMwHec9FO5nqKaE9Ook+4UUWbeaFJhGTCk/3tHLDLz7IM4sf5G2XPM34YIxdY80MTsc4PV1BKuHjVQ/dzAWBs4xNRxibjlCMwvXXPIMTNrn2tscBt4Zy/OM6s5tqePSKW/l69X54ME4xAvcPLacnW8bgaAmD6SiJgTA1tTN4JQNr2oOIw+eeuJYNx1/HwV3z+eqhS5jsjHNV15UkR8IkR8KQlrkxOM2xv9nOys9vQ16YRKzoAqD32ju5q3Y3HdNlLKkd4Ruf+Dd8nSorP7cNOQubGnsBWLiqj0vruxiYKOEro5ez7OcfwevRqfCn+cnICq6qPc0TA23Mi04wLzrBpU2dLAhP8LOZFXy7/W42LOxBUG3uH13OY/3tLD90AyPTER658Ha0SJENx18HvDjTufWN76DQ7/5tDQ5aND30Xr7S9h8U98WofbJwvl3v9VFm35bhO3deiTSlEOqFj1Q9AfMy+Csy59u1ffHWF70Hb0/Unf9cecEw52ZLMHvce94ed2s7cynvi64b+/QA/37/Vee/P3Lsc68Enn8A/Cmnw/4p7/sr+CPCX2jQuWjRIk6cOAHA2rVr+ed//md2797N5z73OZqaml52vy9LHnHx4sV873vf49SpU5w5c4Yf/vCHLF68+CX34zgOH/rQh3jggQfYsWMHjY2NL2d3uL33YhJZL44Att/C0iW8lVlOHm7iXVfsIBAsEAgW6OqtxLN4DttvsX55F2pSwG7M4anO4On00NtbwdGTjbSsGkQ95gfBwQpY5OpNbEPCjhkY8/PIeYE3bn0W84Ik2qyAmhQw/TCb86GXmUhZkbLGWexDYexDYcRZheqwW+MycLIa8hLWhBfBEhB1AbsjiJwXyNY62ApoA269oBkxST1ThpQRMQoyU+MRyElkWqzz/P3Xr/guZm0BdVqiGHMFdKSC4DK68/PYKiDA+qtOsD7aixM20Y9EKRyN0bJ6ENtn85aHPkixzpXXz0z5ybQblGwYR0jLfHbdQwSvGeP4oWa84xJSXqC0aQbBEFDnBGyvDRMemPAg2BA46qHywmH0eXmQHCbPlCI4oNcX8atFsAXM/VHCp2UyrQZKWgBbAMVxmeWlaZQeL779PpSkSKbRJr5ygkyLib0mhXzNFJ4pgeBJheBJBTNsYYQc5JwAjkCmFojpbHn9YaywRdXicVItFsFuiZPvut2dYLCgUK9T0zpJYPMUQlGkfMMocpcPe1EGO2zgXTrL1EAM0+cQ8+Wwh3yo0zLf/9UWvv+rLXhkE8+mGeSEjKPYyDkBM+DgmRSwwyaDx6qx0yrisIfuRCnG6TB6QSZfYWFW6AQrMrzv/Q/Td9W38MXylC6a4l2rn0WqyjHeWwIBi6nxCGJBpDjtpTjtRZBslIxAQCsysbsKpyghBgycpEpJ/RwVsRSRhgSLV/VSEs5iVOrYjsAdG3+Ad36CbEYD0eErl9yDnVDpmCsHQ+D4U23YJTpWwMaWHcQ5BSdo4gRNKsqSoNkYhoxVVcSOGVhzGrlq27VtiZoIaRlHcXDiRaScgGdKxB/JE1s6hVKWZ2I6hOm3mVc66QZnS9M4Yx4Uj4mj2hiWhDqlkNpXSmpfKb2nq9HLDEKhPJQXmE778VTkaL7v/VQvH0MQHVBtTK/D//emn6DOCRx+ZAGeSZHVi3o59IuF9O6txy4vcnCoDqEqj5iX8EXyWLqEWBT4+qEL2bZgF4/+YN15SyVbceB4kLFT5YSqU9zee5Fb9y1bbK08i+Q1McZ8GFU6S1uGkDr9SLJNccrHTNqPoFq0NoxjD/oYmY1wR+8FaP0a//SG76NOKPRe803MkM3wUBwpJyLlRJY1DCMmFPK6Qs94KUcma9g12sSyn3+EkvLUy3of/i4oD6VJFrxsKe/m3WufJaerzC+bZGNJLyldo3zeFMfP1JOYDPKz0SU809lKsEvi1FQFy566ift/sZFTxxtYvrCfsb44B3bPI5PTeE/VLqaSAUZGYoh5ic7ZUjpnS3nrmr1YKZV3nn0rCdvHTH8UR3WIr5zAnPDi9ej0zpRwKl3F0eu+ytNnWxGLIscGaxAFm7dc/Axen06sIcFrui/n8Fwdr1p5nETWSyiQdwW65lQEBz537hoU1URRTbRlc/xH5wp6L7uL+//28hcEJnpIoL12lLV/fxMHP7edko3jzO4rp2DJBMN5nln8IJtXnqUuOMeOrjaWLu3j7q5ViFGdcn8aM2gRjWWxgxZdo+W0to/Q2j7CN678NgDrP3EThz+5nVPr7uGSi78EwPw9b6Vl5zuwbJHeuRg3/OqDsDrJ0a9/HN+URUL30nvNNzk3U8LD+5djZxQO7mtDSkkk5/x07mvkqspT/GDPRvIJL/tH6tk/Us+jO1fweH87RyequXzvB3lVyQk2t/XQN1zK5rpe/nnh/VzU3M2+QgO1sTkKD/92IZ5MrYeeN94BQKJFAsVhtaZgq/D4Pd9hw/HXseH461h8UTcPrrwT77QDtXkOfXY72765DW1vgKCneL6/6NkXH4H9fIFry7JVvJ7PND3MT5Z9i9JlE+dZYYDASZVIQ4KmX73nBet+pOoJFl/U/QIhpBdT4n0F/zP8KafD/inv+58C/lKC+r/U9Nr/83/+D7btapl84QtfYGBggM2bN/PLX/6Sf/u3f3vZ/f7OQef/LeDz25aXgg9+8IP88Ic/5J577iEYDDI+Ps74+Dj5fP4l9TPZFyM340PUBQTZpql6ikJWxfJbPDvTTDrhJZ3wIohuHZXkNUnoXlZfcRrHFigWFHL1JoLkIBZEzu2rx/KCmJIRLIFYdRKes1+wihJma557nt6IdSJMvtzG9DnoJRbZogqazftf9TgToxHEVUnEVUmqFo1zprua2CEFuSaLd1RCnRNxGnJYUZOKdaPocRNHdjCDFmZbDluzUSdl9CCULZlE7X5uxldywGOh1GRR+zx84JkbcRJueqdQUThf6yilJKReL2bERBAddh5eyB1PXIo67KYD48B4OgiqjRM2CJxSMf026rRMW9MYM3sqkMryfPaXr2d4pARt+jmGN2C7QjJA66vOgegGfHJOwJYdLC/0DZRjmyIV9bNULprAkRzUQY1zw2UIQQM9CKk1BbxDCtdet5uVbQMAyHmBfMKDaECxxK2rJKozcaYMNVbA6A6R2VuKEYT0YoP0YgOtLIc27Srb+v0FzLCF1uPhid52hILI8FgMqaRIuslm+e0fRsqL/N2NP8HTp/Lhhp0kcx4+csmj7rG15aAjgCDbbKzqAwGUsjz9w6VYHpvosinMgI0ZsOk4V00i4SPYPoegi5hhC7tEJ1djoQZ07LIiYkjn0ouPkjdkjLoCHp+OGNGJlGRITfu5s3sTi/e/mXzag+0IfOf4esoiacoaZ1nePIiYloi0zSLmJcS8hCC5b7KRs+WEVk+hhIoIox6kosBMbwxVMkl3Rjk7WcbEcBR5UsXsDfD5c1fTFp+irCQNtsBX+y9F1EXmsm7tsx62Uf0GeCyEkIGtuqyp5DUZ64uzfn4P5rgXuyixuGkYPBZSicteotjIWRFPLM+rF5xALzfJ15hIok1eV5AkG3FcQ7AEjhxpZi7tozjpQ8kICGcCCJLDP867n5o1w5g+N6Xa9thUVs8xNx5EO+NFOBGkOOKqWTYGZ7FTKogOVtCmr1iKsCzl1tQuzPOm8n3ka02MCh0nJ6PPelBVE9trkRsNIKRk7PIi5GSemmkn3Wgzdy7K3LkotgJ6iY3tt0iNB7m5+XHmN45iF2UeHlqElVARLAHHEDg5WMXmK49jDvspq59Fz6j83apHiHuyOIrr9VsbSvCtt/07PxjfwIotnTT+/L1EWuZ4w4qD5/8w1XgTlLVPc2ldJ44lMDfr5+iqe/nAph2ksx7+UBhLhJmaCRJXMnRkKsgmPZwer+DbhzYyMhdhYWwcFIeySlcRVvYYpJfq+FSD9c19mLUFLll3krOTZWilOSqXjiOIDh/Z+yYubzpLRWUCrS5NMuEjmfDx4zOr+OimxxmdirB9+CK8FVkCpVnGp8JIZXk+ueAXnF5/NxlDY+vxG/nG5u9TtmCK7i3f5Wd7V/L9A+uxLBHTEin3phlIROlIlFNMaswMRnFKioSbErx9yy7CWoH8nIf8nIeQt0Dn5u8DoM3o54+/6rWnOfzJ7Wzd8AVu+8zXaLr//YydLie2boKAUiQzGKLx5++lYCkAhMJ5TvRX0xSfwS7IHD9TT0XTDD5VZ+OCbr677tt0d1bT3VnNvw1dCsA/f/4Oruq6kmcKbrDW9LP38YEFu7hm3knsQ2H+ZeF99F57J6fW3QPArq/fyeCPmml96h20xqdRZyS0cZmaJWPITRmEhIJdk+fJqXkEK9M0N4xTyKsU8irU5Hn//GfJ5jQ2NZzj9nMXc3yyin2X/BtPnJnHhw+/idOzFXzm0dfx+Pyfc9tfu4Hd/+3TCW4wvvLzrvrsyY9t51MbXXn8YoWJWNHFnqX3s2fp/Zzc2cpr7/gEe798B8FAgQ+MrCVfYXP8r7czm/aReour8bD/Bze/KAv5fHpt6i3rWaUV+fLEZdQEklzy9nefb3Pir7ZzTd0pGuomX7DuZg+c3Nl6Xgjpki3/8Gc5CP5zPKZX8MeJl3qv/cUE9X+hTOfll1/OddddB0BTUxNnzpxhenqayclJLr744pfd7+8cdEYiEaLR6G9dnv//l4Lt27eTTCbZsmULlZWV55eX6veplueoqZ+GmjyibNMamuLy+WcQFJvO4XJKS1OUlqagINESm8HWJTrO1PJMZwu2LlEZTyJlJSSvie2zUNpSOBJEW2fRxmQM02WUduxeAnkJZ1ID3Bo5J2BhlhoocxLCvjDSlML2Jy9FEMA4GcY4GWb4XBliTiK5OU8xqSGuSiLYIPb5EDISI8cq8ZVlXfYjL2JmFCKnJZifwZEcLq/qoFBuub59moUg2ciSDQvTYLnMZnlpCisv845XP8k7Xv0klt9Gj5toEzK+kx4EQ8ARgfYMclakWK+zpHSMYIlry5BptJAr8hgRi/69dRRjNtaEF7U2Q2lZEiPk0HRpHwsXDlIaymDFDY511qFEi5heB9Pr4MhgLs9ATqKsNMV4T5yR8SglFSn0Wh0hqWAXJIy4AXMqSgp+vH8tJ3e0IqZkbMVBnVQwFmXdgDOmIw950OrT6LMepALkGgyMqIUWLqCFC9hdQdQ0vH7NIQonooi6gGiA0BFw0x3zEltbzoJi46xO4Z2ETz9+HdaiLH/75A2ossXtR7cw2FWOPelBrysiSA6/PLWYlYt7sca8VFbOQdBkciJMsDpNsDrNpYvPgABzE0Ea28bAY0NKQcqL6NNeguE8tiHxxLl20ueiNFRN87y4c66gImUkUiMhbFvAEyiSKag4lsBIbynZosrxgWo2ru0glfFQPn+S8vmTqJqJpUGgLsXURBhzyoutuanAcmkeWbQRanIYgwGq6maIL53EihsMj5ZQMGUmuuLINVlGjla6DH7CCzbIGRF9xoOoWDgZhYb2cWKhLLFQFkyBM9PlIIAnUmAkHUYZV7FmVYyoxYfW7kAvM7B6gvxs/0qkkI4cMEgPhEknvG56quxauziSg2+332Xla3Wsdvfe+7uzr2M6E6BixRgVK8YQTIGpRAChKJKvc4/Z9to4ssNTJ9tpbh9FG1AR/AY7JtooDrr2L6Jk84mD16NOyoiaxQc27cCRHZwTIeQ5GSFsoFTkaKqeIliV5vR4BdqsSKA+RaA+xaWbjuMdE5HnZDwleT7x2JvoOFuDNCczd7YEPBbv3roDBHBmNXbsWYztsZnqLoG8xL88eC17j7RhKw7dndVcEOviHQ98gNNPtbD/VDNiXmR2MsiDv9qAEbQxgjYPH1iOIDg89OQa1wu200Pjz9/L/f+4Fdv+n6mB/1coTPhQBjwMFmPsOd3C/IYx8nMeEKCQUfGKBmqwSOJ4KbFgDlF0UIdVJk6XcVH0LHZB5omTC6gIpzCKMsOjJRhDAZyUwsGpOsb7S5AlmxsWH+aGxYexZjT+7fBF1JXNcrKnlssazlLIq1w5/zQ+r84Px9bT9Pi76J+LMTUd4u87rqPUl6HpZ++DgIVgCRTGfUR8eTaEekhOBphIBZHnZBpbx/Cd9JBKeVnp68Mn67Q1j9HWPMbIeJSmR9/NTcPrefS+7/HIsc+x/AO3MvGxDQBYXpnbRi9DKgj0vPEOpo6Wc3iklkVL+2lsnOBgZyP7D7STzWlsnXeWWl8CJeBmKkx0xxnuLuPYeDXvPvx2PnvxA3z24gfoONLAiJVmswd+0fYrNnvg+t5L6X3NnXyvdx0Pn13Mhdce4QP3vo/2Z97m1pwCTQ+/F/2yJN1bvsvyyBC33PAT5CVJhk9XsKJqmAVLB7CzCh2dNdiOQEgtIisWsmIRDBS4bedlbG05y44T89ladZZM2sOPUovAEjm76Qc8sfhHxJtnWH30em7puRZ4cXbw8Ce3M7fMBOCm4fW8IzTJX42vQBuXabz9X7k9UcftiTr0MpNci2u7s7xsmEdOLyLcJfJwzodlSoTu3nu+z/+Khdz/g5tZ+NCHsRyBg52NPPm9u1j/iZsA2HD8ddzXs4wdC1/oC9f85Ds5++7t54PZTK3nj2oQ/FIH8L+t/e/7mH7TpuYVvILfxB/T8/PHhL9UpvP73/8+Z86cecFvsViMYrHI97///Zfd7+8cdO7cuZMdO3awY8cOnnzySTRN4wc/+MH5357//5cCx3FedHnHO97xkvrRZ72MTkRg2IutSzyyfylPPr4CfySPbUokMj4SGR8bl3cymIzQUDMFXgvHFFFHVBTRAhusooSYk9hc20uxwmB6LIwetcn1hF0/PZ+FFNGRciLEXMVSAE+filSAYomDYLl1ivK0jB610aM2iA7lba5a4Kp5/ayoGIYlaSyvg6PZrNl4lsJACCNsYSsOnlEF9eophNMBHNXhgb6lqPE82qRE8JhGbeUsxskwel4hWJLF8tlMTIVY3jbAd35xCd/5xSUIQYNIdQoWpslV24TOuZdan/TStqEPb4/KoZ8vRNwRgYLkKvwKDo5qgwC+ujTBPpFoIMd0TwmCKTCYiNL7eCPjsyGkGQUpJaOnVFc11oLla9yaUSGik9cVV+3Va5DJaZB1FV/FnES8Kol/UCS9yADZwQjbeOpTCKaAUalTG5/DURwkxaJs1TimKSFlJLwrZ/nEhkeI1SWwO4PYnUGiy6aIvnaYBzuWopdYxNtmKMQdinU6X7ngRwg+k75MCag2SyrG8F46RXXrFI4Dakke73PMjVqWd/1IZRt7VkMdUjl8uhFHgFfXnADLTVl+3gf18eMLwIF4ZZL+01VUV87iHRdxKgvgsSjoCmJCJuAvos4J9J+uIjvhR5AcZMnCVh20shwrqoaxbYHCYBAtoCNHC4S8BWTVYs/uBVgTXsbGooyNRYn487Su66d4whWE8tekcYImdkzHsUXOjZa6PoLVOcbGI4wPxnAsAXFW4ezBBsrbprFtAStoc+tF99DaMI6UF137B8VB1iya20cZmooxdyrO3Kk4y5f1khyIEG+ewewNMDsTwGnK4q3Mgsfi649fRqQsTdXKUaRoEVF0sE0BoaSIkFLwjMjuRM7mWZR4geRCk7J1YyjjKvaYD3nSFTRKTfsZPVXB6KkKpGgR6ZwPsSjy5Yt/jBmwuWBxJ45iowR1+k5WozcVcFIKk89WIVXlkBcm2dLUgyjZiPPTxKMZvn12PYLkUIjbtK/vg1kVzgbonyyhxJ9FOhagGLfIZjSyGY2RXBhl0yze+QkkycZRXebfipps3ngaIS1zz/cvwRfNu/eC16akbg6iLoNmhC1EQ0SK6ly04jT/fupCtl54FGVREs+ojO21kSdU7MYcarm74MBYb5w3X/YM9UtGmHdFD5+84CH2/vMddF3w8l/s/y38Jo0bB3hw7ypWze+na7QcQXLwhgoISYWnhlswdQnL4zA+EEOf9KKXWNiazRf2XE3gnMxFizroO1uJbUiowyqO6FDRNMNkZ5z1S7tIDwf55cACfjmwgJr2CTa09hLWCjTWT/Dw2cVc3X6SvkwJX1z4U2YKPspKU1xZf4YL27vZWNHHVC6AEivw12seRZ6TIWCiSSaf23MNakjnLa2HiC2YZmRvDflleWxT5NNnX82+c430nKyh52QNxy/ejjqs8uywW7rR9sVbiZzTKT2SZ95d27jmazt4T8XTKBmBjSeuw4gb/MPSB5FFm081P0ywJMvrL9xHeTTFuXQJh6dqMHUJ3ZSINs3R+9o7aYrNoI/7+PTu1/Dp3a/B9lv8zdA1bP7Q+1j/iZvY2nE1HZNlrDh8A88s/wHPXvA13lBygO+96XbEs/7z6aSPXnErIW+Bpl+9h7s7VvPF41diWiJr13RyarLy19fOEsimPAymIuhzHvQ5D5srewnXJTk4WYdgCzzYu4SSaIZ7+lch+Qw2HH8dr+++ln+afz/pI3F2Lfop8J8Fg57Ho1e4dZh31Ozl8bzMQ52L6Xjvds5d/w2233sV2++9irqfw/cu+hYtP76Jr9c8xeLGYY7csp0Nnile1Xb6Bf29GNPZ9MivGU0c6E/HKK1IMO+ubUwvd3/es/R+rmjsOF+D+jw+tvJJbk/UnQ9m5+b94SZoXg5e6gD+/9WA/8WY7f+XeIW5/ePFK9fmt+AvlOl8xzvewdq1a7n//vtf8HsymeSd73zny+73dw46L7zwwvPLli1bkCSJdevWveD3Cy+88GXvyP8EjuTgGBJSfZa17W4AUbd2CFFw0AJFyqMpyqMpjozWkC2qDJyppLF2EtlvEFg2Q9GSaV0xiKhY2BEDTTR52+q91NZN46nJYEZM7ILEwvYhrIKEGbBZUDuGGbaQvCbF1oJrY2IJSC1pHAGsyuL5gaUcLjIxFULp8XL60VaOTVTD8SCW3wIBDo/Uos0IIIJQUqRtSy+Tk2GK9TpydY7UcAh9xovZmqfhtb2MHq5Cr9MJhXOkxwOuAm1G4fjBZmpXDVO7ahgno2DsilGY9eKdECmUgBM2kHIivTMlvPWNTyKuSlK8IH3+PBYTbjrfW65+iqpwkkyDzXhnKdHmOdQUFIoKxZiD3OnDVhy0hjRywMBRHRzV4dR4BfqEj63tZ0nP+LHas+6gaPbXaYKCLZA8VUJhVRZRM1GDRVasPEexoOI0Z/F1qvSdrUQwBOQOP7YjYGQV/MMC6bSXf7vvGsBNvTVCNjOJAJmihm0JCKZAKufB9toIKZmbH38LTlZhIhOkuX4C0xGZGImgSiZmzlWjNG2Ro2PVRPx59DKT8niKj178KLYCmAK23+Jbj1yK4jcQVQvLcgVRQqVZNK/BzHAEpTLH8FAJNZcMYs9pSJqFnlVQarLMjYUoVFg4HjeAcSyB/FAQrSyHVzP4Vt0T6NNeAo1JitNezILC2EAJHs3AjJisW93pikplJSxb4ExXDcKCNLbXxqMaCBmZ1roJJNnCmdGIhHIY0x4uW9CBmJMor05g+y0aVgyTe6QMxxYRiwI3P/GW5x4eEA0InVSw+v30jccJBvPcdPWj3HT1oxw93giqzdzJUoS6HILgoJ5wrws5iaoFE6T6IgydqWBN/QBGTkGY0ZAGvIjxIsWWAuqUjPFsjHg4gxQ0GO4qQ9ShcckwZsBmxfpuBFNwWUnZYVH1GMVyA6e0yN/ufx1yRZ6Dv1qIoNjY417sqIGTk5EzEoVyC+m0H9sW2dHdhjXsozjqRxJtbEfAEywiZ0RO91dDVMdpz6IoFv2jcSzN9TeVe73IvV7OPdlIsi+Cbkpkh4PUN0xRsmqCxS1DPLN3IaIhsOp1p9zAvjwPusj0aJh1LX1oZTkidUlsxcae0tBEE7s3wFA2ivpomH+68XuIHhP/GHg8BkZRxijKXLHmOGppnsuCJxnZV0NALtJTKGfR17bR+sOb/mDvzJqqWToHKlm5tJfDJ5qw5lTElEwhp6JVZ8mfjWAnVWzNRpmRQcLNllAd6mqnUdKw65nFiKaA/7SK1ZTH0Wx3okOAY2PVVDTPIIoOougwOhVhX38Dp3c3MzQdpTKe5Gf7V9I7XcJHfvF2hvpLUWWT/9i3lqdOtfPIY6sYH4whdPn5l32XY1UXcHSJ7oEKHEvA1CW+eXgTM3MBmJehtWoScU4hpBVwEirBhiTBhiRLHvwoxQqDTTV9iBVddN3ycSaXq4i7jgKw/YEr+buz13Hm/duZy/hoax7j5ifeQtd0Ke//0U3kchoP9y5Ck0ymMgGmx8IsqR9BzyvMTgaZt/tGTg1W4RsREWQbQbaRExLHx6uYWi4Re+8Aj8//ObHvBxB+GWPzZz7G+3tfx7uffQcVUh5pcZJFt2/DHm/jjf/4CdaV9XPZ4tN0bv4+8ysm+NySh5EFm6g/R6Lo5f0bd9I6b4Terd9meixMXdMkdU2T5G2F1RVDvK/pWcKVKbyageWIBLQi188/Smtkio7eKvZmW2Her0V+nrdG+U1c33sprYqrWrjgjm28/7F3ccvyX9H8xDsRK7ooNBYpNBZ56hvf5O273kXvx25m49G38KbKA9yeqOMfJjeTt5Xz/V2x7FMvznQavx5+vH79QVaWDPPAku8AUPeoy6CuPno944UQc/tfWIP64cggH44Mnv8uL0z+SQya/xT28Q+JV9i0P168cm1+C/5Cg06Az372s9x444185jOf+b31+bKEhP7YUFKRorF+An3OQ1jJ49EMUgUPi8rGsC0RWbCRBZvLGs6id4ZpWTzM6pJBrJyCKlk4jkBG13BmNdSAjijYPDI8n4m5EPnRAP5eBdlvcrq/GlGzcFSbjpEKl9FUTZqqp7DDJkJrBlW2EGzwBoouOzDpRZRcFqlYp2P6oXAmQuMl/SizMmJWwtQllyXVXQXVE121CEmZcEkGPaUhmAKe0hyyatI9VYoZsPjQ6h2kU16UaJHY0ikcxUbKC4w+W8PoszV8/fLvYqzOoEUL5OpMwqtdJVQEUBWTb+24iHxWpZhXEAMGSlKkpXkMQRf56cASJjMBxLyIUFJElSyMVRlKQlmcsiI33/BTqudPIO4L8Z7Fu6lfNEr9olHKw2kcr8WOJ5chTynIsg22QLjKZTHtqOHWfVYVod+PrUtURZMc6mjAccCa8FJcmsNfnUEwBdovOcf0kXK84QLtr+9CGPJQsX6U5sjM+RQGe0pj9kwcxxIRYkX0goyYc701cQCPqyg6PBfh8HFXcWvoYA2S1/W9TCR8NMZmmRyLoESKKJLF93rXUrVylOVL+hB0kZIF03g0g/J4isKEj8KED1Uxyc16EXwmsuxuYzwVoqJlGkG0oSDhdAQRFJv2+cMAVK8bxk6pOB4bPa+QOR1jxfaPoCQkUqMhsARuXL4PbIF0yovkMTk2Vg0SIEHBUEB0aIlP449nmRmMEm5M0D1UjqaaxFtmyBcVHM2mOxWnfvEI010liHkRj2SSmm8SDOZd9VxToPdILas2dlG/apjUMh2pIUsknCPRH+HbP7yCb//wCmraJhG8Jgs3nMOc8qL5DPLzC+h5BQImQ4NxHNVBSYkMZSLU1UzjxIuElk5jpRQUrwntGRwRRodi+P0FShrnMFrz9J6uRp2ROL6zzc08UF128VhXHZ5RBccS+daG7yFJNoVyi9UtA9hBC3lUQ56TMct18Jtoq2YpTPmwZzUECwJ9IsmcBz2rYjsCZmURxWsgzKosqR5FOBZEHdQIr5zizLbt6KUWeqnF1tccwj8oYvYFEGwY7Cxn5mA5J/tqkKpyvGrLYfYP16MP+zELCqIF/pIce860YJkiimihJiScoMnukSZCvXBmqJKbPvpTPvvlt3PDksO88X2Pkx0L4D/uwX/cwyOnFqEpJu898jbqNwyy59kFPDXWSmjzJE51gT8ULFtkfds5ju9ucRnqgkjr0iGcnMx75u/G1hzktMTGFZ20bezDMyrhKA6C5DB2sIrUxufYXtnBs2WaFXVDOKI7ASiWFinkVCa64iQGwiQGwoiDXqrjCczKIj6vzmQiiGAIcDqIpyZDfeMkyZwXKS3ysfVPoJcZYAmYPsd95xoS37rk2yxuGkYJ6nh8OrLHxMopGAWZ2Zw7Efba6mPMXzxIJuMhk/G4PszjCgsDIyz/4K1cEX0P73n7r7AvWI6ShtoNQxxc7g62FleMUeVL8cb1+2iIzvKNN7liOrkZH7Jok+uI0tQwwYn+asrjKUTNwtQlFI+Jd/M0q5oGWdU0yFWXHuR7K75LsB86j7kKrV/8yp0c/uR2HvjkP/Oz1kcRpjTe2/0mfrji2wQHHebdtY3Dn9zOsdlqDnx3GZtPvpYtJV185uTV7D7XRKaoEVCLPDvdQs5Q+cL0PK5adoK87opQBeUCr4oe519PXkpNKElQLbKmfIA3Vh9iohhkIBPFMUVeHzrCBfXnzqf0vhjO/aj1fF2lknEnGz7/6GtpqJpm2YdvpbIiQWVFAoD+t/0dt3ZchlcxeGNgjh8PrmLHd9fyxJGFgMukDn5GfIHgz/Poveab5z/vnmjk4XMLqZaCyAuTDL7T4opln2Kus4TZoo9PvPGBF6w7765tbH2TO9t+xbJPcebazzDw+Q0v40n43fH7SE19ZWD/Cl7BK/hTwVvf+lZ27NjBN77xDV7/+te/ZL2dF8OfRdCZmHONo1cu7OPZ4UbyBZVU3kPnbBn2gI/BiRiDEzEeOr0EpS1F32QJPzu3GAoiswfLmM348Ck6apXLgjx8djGGLREK5JFKisx/VTfOhIdQNIs47EHwmdhFiUhNkmJWpfdUNVq4gCg4FA2ZVSt7yM35KG2cpbRxFgA9qeEYIoINay46Q8fZGsywBaKDYwmUzZvC9lk4log6pRBuSpDJeMEQcMIGxZxCMatSzCkgwv65JoRRD7YlMjkeRvabLLqgB7M9h9me44M/fyd6TsWr6aC5jKDqM3AqC2S63Nrb5qopJMXCHyhiaQ7nBsqpbJomMRqiqCs0rxvALspMJQLoKY2crlBfMcOXnng16YJGoczhzicvobevnN6+csYTIcKlGcyYia06FJIePKU5MhkvocYEjiHgaDbykAerqgAijD9bjSdawE4rxFpmEQZ8ZEcDeJtSnN7bBM1ZigWFI3tbMaIWg8Nxjj/VhmC6zKZYWkSsy+ENFrATKrbuqgzXzh9HjOhgCyRm/RSnfMgZEX+Ja9thTXsoiWbw+HQ6jjYgzcrcOP8AU6kAAa3I2GyY02MVfHzLIxQMGUFwmJwL4qvM4KvMMJfwU98whV2UyA8F3dRE1XCVWgsygt9Ej1mIswoRtYAUNJjN+VwhKEPAycmYZTr5CotXXX6ApQv78ZTn+PHZlWilOWTVIhAsUOwPIoZ0xJBOLqfRWD/J6d5q8lkNb1mWVFeU9a295DsiTM8GUWQLoSAysrcGn2xQ0jqDWJHnVHcNFfWzJCaDaE0pgrUpnMoCh59pJ6DoYAnoGZWZ8RD+mjSxi8aIXTTG8Lky4iVpjp9qwNFsLEtEmNZwLAFvoMi1y4+iTUgEV0wzl/Ux0FfGhpZeCrpCpDqFNeBHH/eRazBRZhSy3RG8ikFr1SSCKaDX6Yjz0ygjGm9ct583rttPoFuh4YIByEt88Ns3URFOUdc2wcnH2xBUC8vnKqotbxmEjEwq6QPRnVoUbIH82qzL2AWKCIKDOKNizGnI1TmOHG5GDzlIC1LMi00y765tCLqAoAvsn6ynGIV1mzrcSZWojhm0Uf069pCPx36xik11vTgiCEkZuSpH8VyIQJdCLJRjNhmgWGGweX43piUyu9agqWqKr959LYYf7v/lRr71yKW8a9Mu5AtnkS+cxTFEct1hClM+ujuree2l+5k6WUplIEVJJPN/v+p+bxjrjbOvp5HwohmslIoVMukecWvPv7bjMrS6NPGlk+zubOb0mTr0iIOUFdm8oAsjZlJTNscFF5xEsATm5vwc2deKMiPzvrW7kFUTJyuzYuU55IyEnJGwPQ6JnBdxRgXAmPGgVOWQFiexHQHdlEmPBFEyAv925CKaGibwVWQIts7RWDGNY8P7997IyY46vB6DzbW9SLKN5DOIRrNM9cd4zbrDFG2FMz3VqJqJqpksWNPH2XdvZ5lnEMGE3AXtfDTaz9QKLyc/tp2IVmDjze9nX9Gkc6YUgMdH2hEFh2opjWWIhMoyLI6McfHFx7AckfLSFBPdcVTNpDKe5OzGH3D/4m8zlI4wlI7wf8p38frHP0C6AXre8A1um2vgbz55E+NWhuN6OXenY1x38X7OdVSxTPWgpdxJq6aH34vjCOQqYfxkOV//6ZUU8wpWXmZdRT95U+F0XzUBtch3jq2nQk0R92eJ+7Mcnqnl9oGLsUyJsUyIoakYG0PdrPP2MpoLMzQeY1HbEJc/9RGe6m3hg6/7xW+9N47csp1cmctUHv/r7fhq0zgem2urjqNfkuTyqg4ur3IDsOZ//QrbH7iSMl+Gf5ltZqy7lPdvewhUm9Rb1lP3GZtT6+45L/jzm2j58a+Z/ImZEMWURreRoXy7l+4t3wUgMCiQNVQmjPAL1j377u08/iOXFRUmZrhi2aeo/+Sel/Ek/Gf8Njbyfzs19c8Nf+ms7yv404DwMpc/dQjPiZCsW7eO/fv309PTw4YNG+jv7/8f9fs/Cjqf36n/bYijXkTBYSQdJpf0Yo25FhNzvVHMiIUz67KYW9q7qIoksUd9FKd84LewNQc9q9J9tpqAt0g24WV+9TiJmQB+Vac0mubowRaCzXOUBjKYEQvNZ+BYAoWigqS6AjxhXwHbEWiMz3B8rIorlpwk5CkQ8hTQcwqeUdlNRRWhe66UWE0SLLAVB0F0sB0BQXYQJBujQicxFKYslkIMGGxsP4djiUiKTW3lLG/csI8zU+XYHofa0llCJVlsU+DsZBn2pAd70kO0eY5XLTpJQVcQZJtgMI8+6XXZv+o88ZYZuk/XUFOSIJ30ItbnQHKYOlZOXdMkfm+R4UQEyWvi9xURshK5vEp1IImcE0glfYiGgBM20cJFtHCR4pyHdNILms3/96oHqamdpiSYxbEhfzzmescZAqu2dCDKDmVlSfTWPHpOIVaTZGYyBA1ZvGMSVzWcxtuexBr2EQgUXJGovIg3VEAsutY4tt9iRd0Q9qAPXZcRbIGyiiRXLDrF0JkKasrmKK1IIM6qOJKDGTcJeYs4DTnkrEgq7yE36UepzbBp82m+/cwF5Ga9jJ2owNRdn9TtZy5AFB3SKS+i6BD154n63dmeobEY6qSCEzGQPCZ+tYiTlSGtYOdlalonaV85QMrQsHSJVH8ENAu1PM/qhb1ooSIETH52YCWn9jVhmiJfWfkfvKrpDFc0d6DJJnJtFr+/iN9fZF1jH/3nyvGEirx6wQnySQ9CZYF9B9vxzk9gFySKuoK3MstPbvwKHcMVhDwFHFtEixSZzfjwhAvkpvykh4NoHgOzokjPTNxlfosi4dIM2ZSHVN5dQlUpiqYMHgux4Fr3yBkB1W/QVjrFz7sW4SzMMHe2hJaSaWrqp9nT3USxKDM3FkJtTNM4fxR1UsascFV9q/0pOnurUNICZWVJ8rNejLDF/b/YyP2/2EjFZUP07q0Hn0m+Rae/q4LBc25Krig5br1vEU4/3YI6KyGOucJeBA3MCp1YKIc44sHsD7Ciahh/cxIxYGDkZRzVwSo1KGRVnuloRS81z6diZosqUnuaWu8cIxNRJNnG8luYQ37UxjSFKoOiJaMkRZyIQTHhwYqZZOss5o6WoqgmYlZi/+MLqYvN4YvmeG3lMUQdihFQ0uBpTvG9x7aQGAmRGAkhmAJW0EKdlnAkh/uOrkBtTnNifzOfav/5H+6dGdJRPCYzEyH32uYkrlt4HH9jkivWHSM/GmDqZCkXzutGDOvYfgupIcszXS1IaYmRo5XsPLQQYjqOIyBYoCXg26fWo4/4kUIGE7kAcksauSWNFTDJnY1QtXicbGcENBu7J4DeFcbsDRD25BF1kWJLAccWyOoa5aE0b2w6TMFU8A4rrG/uY+3icxSKCjufWIYxEMAqyCRTPpR4gWfHm/j6gS0oMzJ6wV2+3nQfq49ez2YPmH7Y9bNPIFZ0YV2Y5PreSzn7UCupWpEfzmzg2Oof8/TR+cS8ObqfbOKyxz7G4xf9G5m0h4e6FzGWDzE2G6Y+NIdYWsDBrfHe2nE1dydXMt5fwnh/Ca+65a/ou+pb3H3jbTQ99i7AFcu54Ief4FC2kSPZBhKGF6mkyKJ9b0b/wCzywiS913yTkdkIem0RqS6Lf8ksr1lwgiVtQ7T7xmkLT7GydYCJTJB5deP8cmQhETVPRM0zngixJDrKmoZ+AKpL57j93MW8v+OtfLrhIZqqpohoeUrjKd63aPd5j8sXq7W8/PVvZ+JiV0jo9kQdm2vOEapI89XHryR4f4hPxTv4VLyDbiPDL9/wL+drUv86dg7PtMjX73o1H1rr6jtc/qN95z1K/2+UHv71Z2da48Rl/87fDFyH4XffvY8c+xyZBpvEo5U88NVL/tP6z/t8zl7azCPHPvd7YxFfYSNfPsyJyf++0XN45Ty/gj8J/IWm1zrOrw+irq6OPXv20NDQwNatW/9H/f7OQed11133gqVQKHDTTTf9p9//N1C9fITeU9VMnyhFVCzk6hyR6hSO7KaDOX4Lx2/x9J7FTKaDOJIDooM4q2DGTchJ3LhxN4mUD2lKoXO8DHISQ+MxxgdirF17luRMgFTBA5qNIlt4ogU4EcQqylgjPixHoCqSpHu0DD2rsmuomaGZKEMzUSrKkph+hxsX7Se+dJLpuQCJrhhKaQFkB49PZ3IsgmMK2AUJfzSPGs8zk/bz0RU72H2mFS1YxEorDI3HmNX9+FQdrSrL4GSMD7Y9TbwkjWWLOAELJ2AxOxPgVx0LKU76kEY81IfnWLq4H81rEAwUmBqKcuK1t5EzVBxD5JKmTkriaT786l9gWBKZvEbhXAjbFMh1RJHK8phFmemCHzNoI45ptG3ow9FFqqJJqqJJtEkZzWdQUZ7gHw5fych4lLHT5QDoZSZ7T7TiKA57u5qxTYHJibA7YBUdqoNJKIrYpkjVRUPc9/Q6LFtg44YzpPvdWe6qeZMUciqmH7SSPFpJnkOHWwjPn0U548MRYHIgxiOHluKIMD4XYnI0ghU0ET0mgmKReqYMTTO4+OJjrKoeQgwYmLrM08fmUdowixZ2B3ya10AQHSTJ5kOtT+GYIu9YsI/JRJDJRBDbkJhXN44RtSArEw7mGZmLoMXztC4cRkpLDI+U0HWgno5z1fhCeVdIaU5Fn/BiI2CZImRkli/qQ6jLYekSf3Xkeh44soKHTy1haiLM8uoRMgMhMgMhdnc2g2YjijYP7V6FoNg4Ex4ESyA9FEJQHC5v7EAQHP6u/zocR3Dv9wkPAW+R2tgcEX8eZVoG2aGQVQnHsmRH3PotKWSQmvGDKZLuD5PuDxP0FPnHRQ/gCRVxAiZCQqF2wxB6RuX0aAWxUBY9o4LkcKyrjpFx1y+xNJJBsCE/5SOraxghGyEp49giR4ZdRVg9YjM1HUTQXZEmoS2D0JZh+NlaatYMI86pCEnZTQcGPBtnsNIK+CycBRkCy2bQ4xZWiQm2QElpGtljMN0Zx64pULJgmj2nWkkPhXAcgXA0h+A33NrtooSYkpFSEiGvK94U9BTx7Azykyc24DvlZhEoszKO5JBPa+AI7H9iIe0X9OLt1vCMyVAUcVQHuylHYdpLy+Jh9DKTzq5qsjM+/vXQVhwRjIYC1so0mSk/N172NGg2aLYrmhTWOfvu7QiSQzieodgfJNg+x//37y+/WP+/w2sWnCAWzIEI8rjKkuV9PDHcxrb2Xezoa8Nfk8YqMzg4WovS4wWv5U5a5WQsv41oCNS1TSBMasRiGSyPQ3Z+ESuh4YQNYuEMQ/2lFKa9FKa9SCmZ0qWTjE5GuPGKpymrTGCWGXjnJXAUKPNkcEqKKIMeSuJpLEdg4EQ1d+zdwmhnGY4Ahx5bwIHeer6w/GeYfhs5L+DrV7BmVYROP8m0awFUunSS29b9mNvW/RjDgY+1PEnrPTdReqzAFcs+xa0dl2GcCpP9QBwjCEYYonKOlp3vwJFtesdL0dvyCAWJy5/5EILg8JFFO1kZHSQSyHFooM5lh02RtsgUU5kA9/auoK5lgrqWCebmC8y7axsrVRVfqECLNg5A19u3YzsCO4Zb2fHsEi5p7kJVLFJ5DfN0mKYH3od62E/d/RLFpEbUm+PYbDWnjjdw+4ktnJ6twLRFEoNhusdLKfFmWRfpZV2kl6pIkrylsjI8wLV1J7i68hSOA2+t38+tI5czMhehe66U/cvvI2l6qVo3Ary4quzjz95C75XfAqBJneTr1ftZXDbG1RuPsPfLd5xv16oEeNXuDwFQ55ul6YH3ka81yDRa/PuTlxG6ey/bH7jyRVNrgfN9rb3xK0g5kbf3XcOpA01c/w+PuOmz4vU0Lh4h02ghXjf1n9a/91NXuim484QXDZ7/t/D/Yl9+X6zu7xty+W/3f/1jxW+qLP8h8Ipi8J82/lLVaz/96U8TCATOf/f5fDz44IN8/OMf54ILLnjZ/cq/a8Nw+IXpLW9961tf9kZ/3yj3ZRgKmthxC7XT69qLlOLWEcqCqyiLq7CaHg0imgJKrIDuePBFcxQLKj84thbyErID5dE0Mx1+Nq06xUguzKGhOkTVYkHJBE/3lpAuBHjjuv08yBIkQ0KpK9IUnuXYSDV2UgWfify8rQlg2QKWz+ZHXSspjvuQciJW3EDs8+NpSZNLujK4ks/Eyslkkx5kj4njCNy6fyuIDoWEh5aWMWJansc75xGO5CjMeEGz+Ma5zRimRNiXZyrjpq8JooM86EFoy6BbPo6fqQdDRIjo7F97FytPfJybRy/GcQBb4JFTi0AX+ersJSyqG6UhNMspuZJsb5jFG3s4MVyFOKPSWaimtH6WqaEo1b4kZUvTnJiuAmD9pafY/fRCxsNeqhunGTtTTqRtlmRHjNj8WfSdJagXzWCYEtmshtKvYgYdgs1zJHUPYtDANiRSBQ8lrTMkj5Sy35RwRJhXOUFILbCiZJiHzSXYg+7D4Kg2qYwHu8QGnwV5ETleoL50lkTBS9ifJ5n1UkxqCKaIuSxDQDV57NQClDEVsTmL31ckIzgsio9hl4js6m7B49OpjiQ511/BF/ZejWMJ7JpuYWvzWQCOTteQNVQ8pTnqS2bpGSuDEQ9Wqc6AHUNrSLOqaoiDwTqsKR+WLWJHDATJxuvXOT5chWVIvGb9YX51bgHmtAfHY2NKDoIpsLBlmMFEFN2WaFrkDhDPDZexsb2H3Sfa2Ly6gxNTlaSnNcSqHHUlCYZnIvz0yHJkv8ngXBSvv4gk2cTbpxEFh7gnR/9EHLk9jViQ0byuWIdUEHEmPCi1Gay0QkPTBP2jcQBGJyP8ffG1zC+f4KRZSWPDOAMzMWSvgTPko2LNBMmcl6IhIig2Hr9OISczNhYFAaSQzuR4mJr5E4zNhBEEB7MoEZ03SyanEQvmmB4twyzTMUbdNHnR69DbV47gtRDzEnbIxBfNMTsYoa51gsHOci5fdJY9Ew0IuoCUUDDLdbJ5DWfQh5oVKPgUJs0QgmojZiSkQQ8p0cHJyzQ0j9M3Uoo8oWD6HYZHYwAETmpkFllgQaFExJpVcWImgmJDRkZNSTgSnN3dhF5vIAcMSKoIqo0k2ZCWGNhdB2UmzS1jjDxVi2dNmuxyi6ubO/h5xyIE2ea7T19AqC4JQP5UFEmHprn3ceGKDp7uaGPxqj5OnmhArvjD/eV6aMcapPosZCVsBY6faqCkfo4fDKyFzgDZqAU+i9ykH8pNHF1ECJiIkk0kkiOdKEESbGzVIXsgjhN3vV6taRVfZZ6Zs3EEvwXKc8dQXmBsNEpD7RQ/HVhCYsaPmJRZv7ifJzLtPHN0Hngs9CqdmYEoDa3jzJYW0TwmZtZHsdTCURzkCY2/P/Ba/IMi+UqH2Mop9EQA0/DCiA88NuOTYe4JrQPgyOPzMX0O3W+5g6bge+l/7+fcVMwTezj7jTVETsDcUpOio3DNvJMs9Q9xZ99mbqg7zEej/Xxppo1DiXq+07cexxF4f/Mz7Es1YdoSzx6czx6hESOjEilLM9jlTrD903X38K89W2n8xXvwl+S4ylfAHm8DoExNockWSZ/FE88spee5utHF+7bx+Rvu5f87fC2zlg/RU2BJdJSfHVxB88IRRhNhxsYjzAV8vGXjHp4ca+d0XzWn+6rd96ApMBoJcy5WwtBs1PXJTXj5SuJSVtYPUUhrxEMZ3j5wAXt2L6D7zXcA//Ki98baG7/C0o+u584KuMpXYMGet7JtwS7G9Ajz7tqGXvOc36ku0nifzbL92whdNUbvdXdydzrGF06+iqb4DCbQdcvHf2sQNu+ubdSzh/0/uBl7vI0LTl2LmhD4xtlN6GUmj9s/YcnNt/LZbT/hxuA0VyxzhyuPHPsca265iektNnv+47mg+ZZf97tVvP5/lUV7PpBfe+NXXjSt+PeBP3T96u8Tz6fR/rEym8/7yf6h8Epa9p84Xg5z+WcSdL4YPvvZz/6P+v2dg87vfOc7/6MN/SFx4rF2mA/2rIYtA7aAPvOcamu7axIPuClywwFEE8yiREPzBGGtwPG+GoLhPCnDD/U5ptN+zKDDzl1LkBsyWKM+cOCpXDvzFg3ROxlnWg/g2AIBfwFJdDjY3YBjCcg5kerWGQb6ygiVuTVZUwMxHNVmVfUQJ+Qq0kkvqtfAUGxKAznGpr04ItimiKhZNFVPMZkOku0PuUxoRY7iqJ9zg+WkypI4ukS+qBCuTJHJekgkfNSUz5HIefEEiwAUxn0YlTpCQXbrKJMSTlWBzc3nWL7rJrT5SQYyUabGI6jhIiWhLKm8Rm00wcm+ancAaUisX9+BLNhY0x7U2iz6tJfpmSB4LR47swB/OE923A0AR7056lcN0ztSyujZMhAd5s5FKV04zdRMEKfVhP4IiKCUFGBeBjunkhwJk/L7cGwRiiLTPSUsWtZPst1LJJBnaszHqe4a3rp6H8cSNWAL2Kob0Ne1TzB0poLy9mnGhqPUtE4xPFhCT7qCdQt63ZTnqm7GCmH2DTagT3qZxUtD+ziFSoXxcyUk8YDssPPoAtcaxWNjewV6+iqIlqfJ5jRMXWI4EeFsjxtgNzVO0H+minBjgqlsAIY9lC2ZZKy/BMdrkh8NcFqroDDj5Z8uuZd/7dnK5LQXLV7kqqYzPHh2CWuaBvjZseVUVM0hR9IMd5Uh+ECL5xnPBgl5Cxw70sQFa12vpD4pTqLoRSiIHJuoJtsfwt+YJJfTGJ6JoCgWjt/ESinIgby735bEWxc8y5NT8zj8bBtOdQFZsomWpBkfj6B7TJyKArYlcklDF49b8+gfLIPn2EUpXCSX1zg61EiwOk13fwWIEIpmSZdLnDnQSOXScUbyMk5KoQAosxJSu3vPAjTUTjGWDGGlVKSkRNXSCUa63Psj79FxZDfzwFHdN7VjCjQ1TjB8oAb/4lkSg2EKOZVIbZK5rA8hbPBo3zwC3iKO6iCmgIJEUVZQdAF7YQbJlGDYQ2ThDNNmGCdu4fEY5A0Jj2xCRkaPWuC3kKbciRolB2hunbVtu4JMnlEFSwVtFrL1rl+kEzKJxjN8vO1Jvvgf1yPnYeGr+jmjlJPvD1HXNMnIrlouuOYYZ5Nl2E/EWLRkmIfyywh0y5h+SDvPTeIFbZysiL9P4khVDWQlzu5uQrVAryv+oV6ZOALoKY1IXZK58SBauEjqWByjpgi1OtK0ghWwUKJF6PETXT7FxGgEqygxawWQZIfR3TVQblJs1llcP0qDf5bxyhAHTzRD1MATKmL0u+8GK24jeSwKpkKqO4onIRDfNMYTzy7F9tqosxKOKGHETN6yYQ8/fmITjtdG6PVgV7ipnjesPsCjQ/OQJZuZVgVHdpjN+LB0idalQwQUnWN9NZTG0663LFCsL9J72bdpvecmet98B2tvlEh/RkTetQFfaZKKHQZHbvkJzfe9n3Ov/wbzv7mNzVce56mZNm47dDHeQJGW+DTzYpMcGa3hYLqRpzrbcCyR0uYZ/KrOkBUjV1DB69ZmPplYwL5l97HkyW2c+Kt7aL37JrRZgWyzwdOX3cr9ynL85VksS2TeXdto+FmaKjnL029o5/LWDr524QGafvY+8pYKmk3Rkl21aFxbq/t7lnHL4l/yqa7rec/FOwE4lqzl4Kkm0n4P75m/h68f2EJD3SRpXWO26CUYzTGZCHJReTebrvrZ+fvgxQKj/T+4mU3X/Qs84NaZSkGDO793FblFBSKjAO7z0nXLx1l24laqds5hvNq9Rl/efgNVVw/T0VdFK6P/5T0YPes+71vF6/HtugxFtLA8oE/58ZW6Hr6mBz77i9fz6ahB/7G/Pb/ugS/eQdNjv7Zcafjml+l/7yeAP57g5vnz+r8dBP9v4/lj/3M+D3/Ox/YK+LMIIn8XPPTQQ1x55ZUoisJDDz30W9sJgsA111zzsrbxZyEkpKRBTMlcse4YetRGzoiIOREj5OBYojvCcgSM7hCiLmD6bZRzXvqHSjneXcfC+lFMS0QLF7EMybWuCJuULpyiMpzC1mwqF04geiy6R8pQj/h55ldLURSLxHiI7OESrll8AlG1cCoLDHVUsKBtmKpwkqpwEnwWgXiOZ0+1ks1qiNMqCyvHcVIKY+NRFs4bIlKRQhnWEKY0RuYiZFIeLL9FRfMMhbTmeoRqFlPjEdfKwxLZWtuFNafh9euMTEVxHIFoIEc0kEOM6ijjKk5G4eZNj2L5beyMwv7hegKBPF7NYGg2iugx0ZMa46NRCv0hRpNhBMlBzyn4/QUOjdTy9NH5yKV5KiIp8JmoPgOeM67PpjzEahPEahN0n6lm4GAtSxpGIKZzyfqTAEwORXGyMlo8jxTTkZMS5oSX+vgsly7ocK0YbAFJtVCmZbTqLP1zMb656gfMnoy7TGfLKHfvW89IOkxN7TQ1bZPUtE0ycqwSojozKT+C7LCiZNhNkfWZHDjcyqG+OnaOtrKnrwkjL/OuC3ahluUJawWCWgGtIke4Lok8J7uTELVJd/JAshFzEomZAKYusaWti+xIgFBpllBpltFEGKUiR3LOx+x0gLIlk2SLqnufzWk0zh9lti9Kc8sYn/rRm5lJBMBjY+gyD59biG2KHNrbhjeSZ6Ir7rJtAQt7WqMw5yGd9bAwOo63Nk3nXBmdc2UsbxhmMhvACZqkE17kqhyZlAdn3OMq//YHsac0YjVJ0ikvjiMwv3yC2/deQt90CUbUwtYlcmmNJSWjROIZnHEvomyjeEwee3IF5rgXQbHOP1tmTkFPakixIlF/jsa6Sda3nSM5FWBJwwhWiYHjCCgek4tWnyYYziO2p8nPerF9FmvqB2gMzeD36DgC2JVFd0IiYPLeC54ilfARmT+DpNqocTetPNCSoH80jtOUJTEURggZ2KZIsjdCwOOKAxWTGtXBJFKkiCPhBi05Gac1S1kkjTPuwdYcZvqi1NdPsaBuDKMrhJCS6RioJFKXdC2KJJvYgmliC6bJlYOoWYgJBSXpvjcEC9T2JNn5OlJBxDMpIqRlcgWVTz95nauWu2mKwwdayUz7cEqKDJ2pwFqQZUdvK6+rPsryN53i9ruuRSyIWF4olroK14INjt/CDNhsvP4YZcE0r1l/GEtzsBUQEsqLve5+L3AUh8b6CdJno64S8Igfs64AGRkxISMWBbzBAopiYvkcJoeiVNXMIqgW4rRK6eIp3nbdk9Q0TSFIDic663j40DIOnqvnitXHiZRmKI74n3/1og5rWLMqU6dLsUImW645wsh4FCdeRE5I0JbBbsiD6XpMWnEddU6iWK/jK83iL8ty35nlJMbdVGnBZ+KL5tEzKhfN6ySja/TMxPH4dS6u6iIxHiIxHuItyw4w765tzzF7kH5dijPXfgbrwiSn1t3D5fcdBMBbk2bB9m0YYZsb43t4a+Veerd+G0FwODVYRVzNYBgSHXPlxEoyyF6DdeUDDHRUwnP3o+I3UPwGu+9bxuJbt3Hir7bT9PB7ab4vS7TL5p1rdnPhox9nYk8VLSXTGIaEvDBJ90cViqUaX6s6wI7+NubdtY2PX/gI18SO8rOLvsZUKkBTfIbm2gnyGY1CVuV0vgaxrMA9Pau4p2cV5xIlRKpT+BSdu86uh4JE/2AZM0MRhvbWkh4Ocn3bUeJKmu8OrGfFF1z12hdj4i66/J/Y9fU7AVdh1uPV3YBzt4e5tfoL2l74rgMYEQ99Y3Fa7r2JY3+znTJvlvevfvp8m99k/n6T9Xw+vfZx+yeElAL9p6t486ufQp2UkXeF2Spej1xwn5PwEfUF2xUruui97C7AVch9PuCElyZQ81La/q5pkv93n3+oYORPTYjnf3Ie/tiP9X874PxjPz9/yvhLSq+99tprmZubO//5v1peLv4sgk7nOQ+5Y19Zhjr33CFJDuqcgDStEDkpETkpoabAipk4HhvL4+CYIvKUQn1glmJ/EOVQAOZUGponUII6kzNBxp+tRggaTKUChMI5rLxMpsVAXJSi0B1ybTvmZXn40DIcW8Tr03FEh6yhUu+fo94/51oL2AJiTnK97zw2ETWHo9pogSL9czGSs36Mah07rqOpBqrPQIsUeWv9fupqplnUMow1o4Hk1oJVxxLcd2wFSxf1Y5gSwpCH9JyPsf4SxvpLEEY8GFU6Fy7v4KGxJQSr0jiiw0cW7uRXy+9itifm+hn2exFUi3BpGjtskh4L4gsUwRbIZDwUs+4MvqVLiDgsbhihKT6DYwqQlagoS7I4Psbi+BjVrVM49TlEwcEuSDx5tp22JUMIBZGG5gk8qoFjw8oLOnFCJl29lTw72AQOqD4DM6Ow5MJu9KEAUX+Ot//yfdSuHKFh3hjdx+poaJ4g2Rth9GQFU6kAU6kAV1980A2SkxpCWuYXZxeiZ1XKYmmkrIggOExNhQmHcrTWTfC902vRcwqnhqvo6qnC7A0Q0HRozNI/WEZNKIngQC6t4UgOy5sHicUybgqx1yY1GSA1GaAw60Wf8uKkFCTNYnQ4xrtb9yD4DW679G5SRQ+lTTOcGyzHnpfFcaCsLInjQGHOg2OJWD6bfEbDCZl4Ajo1VTME65MIusjiqjGefGqpaz0xFWJiKkRK11yWWYDlzUMYYz6cpIodNnHCBpWLJihtnWFmNAwCWEWZY511eCIF9/7zmixvHURUbPaONpAYCCNXZymNZDBmPJhhi0vWn0SY0vCU5PGU5Kmrnaa2wa2nGugvZS7vY8+ZFrQxhRNHG1EDOlMHK5Akm52n5pNOetEUE0G2ETWLI6M17DzbzsxYCMESWFA7RqA+BRmZe3tXQELFskUayqfRx33o4z5SoyFE2cFxBDcIDRbcdHPVYV50ArXTC7bgMvKqScO6QfS4iVAUMTIqS2KjiFU5pJyIlBcZOlPBVC6A4LjB1jULT1IdTCKnRJyUwtRgjKnBGHrURhrwYvktLI/DmgW95OsMdF1GCxURdYFCuY1cnsejGsixAsVKk7mjpVBeAEfgzUsOolTm0NMqH1uygx/2rWHfI4tY+fpTKAmR0LopBEtArsohV+VQRxWkosBjRxZxrqeSXz6yxrUWUpzzZQF/CKhzIqLgINhgaw5iUUAc15AzIkJZEb3UpDgYJDcRcOttfSZrSgdY3TKAUJVnvL+EZ2eamdpTiaKZCIZrjfSqhacYyMawLJFNazpwyoo4ZUVoy1DXPoG/NYHsN3jq4RU4psjVC07hyA7i6QB+fwFvZRbTlMAR+Pwb7uHRi24jl/RSEnCZL0G2KRoyjiFxRWMHql9n5+GFzGV8XFl/hvJQGtsR8I7IeEdkfj64kLPv3n7eIkTeFabti7dScasbxNz27Fb2FU0MQ+bMtu3YssM/DLyKv/nlm1l37PWUBTOEIzl++egajJSGYUlMj0SwLZGH9y9nxYpztFRMIeYkPJqBRzMQTfi799zLsn/eRskBmf6rAzxz+zf4VLyDmzc+hmDDsa46rJxCdsaH56wHf/ccC+7YhnQgiKi7PpRp28tNZ9+C2R+gc6Qcw5ZwdJFrFp7kga6lOJZANuUhm/Jg2QIe2WRwMoapy6ileWpqp1GiRcR5aRDh7j0b+H7fWtoiU7zzQ79dvTZXppwX/1n9yW3URBLYRYlkm0P54wpOWxanLcv7Dr2d4XyUoa1eFI+JWutm9pyarOBbj1wKwK0dl50PNPf/4OYX1JCu+rR7TbaK19M5V0aoMcE9D20BoFDiDuSz1Q5qY5pUi/2CfXzdng+c/3zm2s9w0eX/dP77SwkAXkrb3zVN8jeZvd8nXmow++cUiPyhg7o/9XP1Us7PH1P9858E/h8JCX3961+nsbERj8fDypUreeaZZ35r27GxMd785jfT3t6OKIp87GMfe9F2999/PwsWLEDTNBYsWMCDDz74X+6DbduUlZWd//zbFsuy/st+/iv8WQSdnlmH2CnIVItoCfCNQ7hTxPSBf0QgPGASHjCJnzDxdypETiis3nwWOajjXzDLL/ctwy7RyTSbOAL0D5ViZlWcWQ1t5SxOUoGOAJmzUZf9KEgUhwKIhjsotG0BTzyPL5Qn3x9CLIqMTEXZ2dfKzr5WFJ9BbsaHtzbNRStPg89i59EFrhDMmSCWJRIpySJINhfN6ySd9mLqbkrwv+y6kqEzFQwlIwhh1wKEnMR4MgRFkVP7mgh4i9gKIMBr1xzmtWsOU7t8lMsWnOGpE/NIFTykh0LEq5L881NXsf7Rj2H7LLw1aaz6PKrfIDkYRvbryNEChimhjKl4fTp1VTOIsoXHrzOT9XOiu5aJTJDy6gSCKfCa2uMMZ8MMZ8NMJoKsqB3m2NEmpJTMqxee4NwBV2Gwv7ecXF7DzigcOFeP7DGIlqexbQFvbZpiwgOCw/FnWqG0wPDpCt6yaQ+DR6qRRBuxKDA0FcORHDyNKSxTxDJFfnp0ues9WRQJ1rt1co4lMJPyo7Sl3BpbHBI9Ubp6qvjq6nvxBHTKYimkjETDymFUySQazCEoFmeGKmltHUV4zoLjxFA12bzG9GiY+c0jCIp9fpFiRdSEhJlWUQI6tx68FMcW+cjON5MvKnhVAzIy5riX5sopJgejqJrJkrYhyEsgQEk8TUlZiuKon7GZMKlZP47iEFLyRBbOwLTGO5ft5Z3L9lLjTyDKDrHSNKfHK4i3zPD+i57EG8njCxUYnYjgU3TKquewk89ZhugiVk8QPadSXT7HsSNNWAkNv6YjGiKi6JDKe/CWZ1m9qJfjU1XULBmjMOWjMOVjdCbM+GyIhrIZJJ9Jqd/1UDUb8yDA/IoJhHlu/ermBV2I0yohbwFEh+ryOUxDwnNOI1yWAY/FmeP1bv1jyCDTG8EJmmRzGn0naqhum6S6bRIlVqAynsAZ92LbAiFvAbOgEKlJ8tT+RdgqRCpSeM5pFIYDnBstddPHRRCfY2zFHj9Saxq7vEjTItev1Ii4L8rH+tsZSYfxzUugzkisXdLD2iU9OCGT4JIZ7rz4u+DAmalyGusnod/v7oPfRowXsca8FE5E0TQTR3JoWD+I1O9l0+Iu7v3VZpyeAJLH5Nbjl5A4XopnGvY8uQi5AK+qOY2nOoM16Mca9KNHbKy4gSM7+PpllCyISQU5J/CN13yTPxSKJRa9o6WYPgcpJ2KGLdSkgFVVxDZdCxkAf5+E7bHxnfSwd6KR6YKPWCiLNulOQulNBXcSSnHQZItHd67g7PE6irrMMx2tOAkVJ6EiSTaDg6WkRkOEg3n09jxCRqZoy1ghCxanqQ4nKeRUKqIpHFPgb5+4gSsf+GtCsawrjGMLBGM5V/hKsjmTrEAUHUrrZ3GA+04vZ2C8hPvOLKfYUqDYUsCnGlzfeynxEzbdRoZCiZvWOf5xnfWfuImWH+i8+en38a3V32fl57ehTUuc21fP1RuP8JaGAwwdq8J4NobcnkLyGcymfPzH1q9TXpJCnZU48XQrvXvrsX0WpiViWiLFKFzhH+KKt+/hnk99mcUXdTPvrm2s+vQ2bj14KYXGIjggJmW+cMEDxM7anHtLKfFNY/gmHRov6ef63kv5zH+8kbHRKGbIoqo0wcjxStSQTt5W0PMKdeWz+IIFfMECH2jdxcR0iPKSFLYlIIk2sxkfS6pH2VLfjW9YwlOWY7Ynxr7heu7s3ATwoiI/+39wM7eddYPGg5/fTrLgRfKaCLbLTsYe8hF7yMfQm8rpmCyj4oCJJNo4p4M0/+T9BD1F1mxwa98/Pv8xHjn2Odq+eOt/2s7bP+4GvgOf30DiUCnJgQhGyKbrlo9Tu2EIAKkgUCwotCwefsG6nQ+2suX97wVcpvPJ7931n/r/3x5c/74DpZfa3+97+3/qgdl/hd/lXP1vHv//dNu/uf6LiYf9Ibf9Cv573HvvvXzsYx/jlltu4ejRo2zevJkrr7ySwcHBF21fLBYpLS3llltuYenSpS/aZu/evdxwww3ceOONHD9+nBtvvJE3vOEN7N+//7/dH8MwuOiii+jqenHl8f8J/iyCzlypgGCDf9xByQAC5MvAP+aaWw9vkRjeIqGHJLzTDvk4nLtjHpEnfThPxvCNSq6HpuX2Iyg2gmrhyA6phA9EsFUwoyaCKeCpzKLWZHFkB09JHqnbRyHpYXH5OOqsgJwXsNIK1phr36KnVeSUhN4ZZjATxTFEUBwcXcJoLFASdP0WHUNk1zOLcaY15C4vdk8AvBZOyCTbGaG2ctb1eZQcrM4gFy0/g9SQZXYihBUyWd92jofOLuGhs0vo76ngyXNtSD6DubSPi9acIp31UFY/ixrUkbwmxXMh1E4vEX+etct7MAsyRkpDn/ZiBmxaSqYZPVSFlVNYVjlCOulFkBzSJ0oo96cRbIFvHL4AUXAQBYcPLNrFkaEanJCBVJPjlz0LsTUHqdxVmlUU0zWQtwWMlEY67cXr0fGoJggOoseiatUoggirV3dzz66NmBGLc11VWFUF7GnNFTcZDWDOeDBnPGjhIsmxELHGOQxTQtYsvJEC5oSXQlalsX3MPd+lRQTN4vvjGylkVEaHYziyQ9/xGvp6KqgJJdnQ2os4rtHdVYVtSDiajZmVMQwJKS3T0VlDa/04rfXjyF6D0kgGqynPl7bcRzycRZxWKSlNESlPUxbMMDYbBsVGq84ykgjjiee5sukMJ3traG4bpbV9hJmpIHFflljzLH5fEfISkYoUO0/PI5HyYUdM7u5axd1dq9jZ0U4gkCegFdFnXEuTu05tcL39pvwIMxozWT9T0yG85VmKWRWxKOJfMIujiwyPxbB9FoLfIKcrBJvnkCSbbNJLIadydLia6Zkg43Mh1JI8aomrWGxNeDnXXUkolGc8FSJUmQbgk5c9yOnRCopJD/sGG8hZKnaJTtGQkaZURjrKwRHYcs0RUjN+Llt4Bq06S+pcxH1wbUBwMMe92F6L8ZPljJ8spyySZvxoBWJRQNNMhgdLULwGRV3GESC4YppUXwR9nmtdY+UUYounQbWRe700rh7i7rfeRkt8GmnEw2gijB0yEQsieC1kySaV9JJOeSldO87BfW0c3NeG/4zKbH+ED9z/HsyYSaE7xOhcGByY7YjjBE2kHi/OcwSkdSTMzRseI6IWuGjrMfY9Mx8jbmBUFbHyMta0B8vrYMvQ+c7tKGn47uEN5JMerJiBFTMQTQFxzrU10pdm0YNu33pdkZv//X1/sHemkpTQujxIBQGnwfWuZXnK/TehIpoCl206SqHMDUorLh9i7mgpZd4s0z0lOPMznBmq5HWLj9FxqhYtUmCyN4bUkKVh4SimLiHNKoTrE4TrE+RmvSA4qCV5EkkftiHihEy6U/Hzs8KdR+txLIGhMxUIioM2KeGZFEiNhvj52CIkxSIzGAJAHtfo7K0in/Aw1R9z63tnNK6cfxp7VkNWLWTVIrmrnL5EjPH1Apfv/Chdt3yc2CNdlH3dS822HpTpLIsbR7j5i9vI1IDRkkc04P9n783DLKvqc//PWns886l56nkegG4GmRQUoQWnxGgwRINeQ1TQ6wCJeo03hpCoj/EKeE2EqESNkRCJ0cQBEJRRGoSGhp7Hqu6qrnk487CHtX5/rKqiaRpkCEn0/tbznKe7ztln77XXXnuf9X7f9/t+f3L3qdw1sRZ/RYn6SXXqZQ9VcInHE7zrHz7MyKE2wpwi7A7RlsbyI+rjKerjKc7atJ1X/PBKNl99BiudNLcuu4ueB0Me/YsbEFLjDXicsHII3dHks//we9i1mJ4HQ4aGW5k4L2DyG4vZsnUZIga0QNYlI5N5VGeTcDzBzx46kVS2wUzdSOi1Fvzr6MnoWPDq7v10tBnvgtpMkq2bV6C0pO+Cwyxum0ZnIhoFn/S/mHFs2VZ4xtxQo6u4b3rV/N/j0xmY8EiMGFfeP7n6O/zJ1d/hx/f/gO1n3kzHJ/pJeCHnv3ELTkkivtVOLTJM8nkXfp4zLr2WvZ+68hnH+eaXTdmWZbfOkJiEleuHkA3B0i9/kZ+dZ8BwvLyO54fcsebpzGyQh2qnzarPXEe0I8cbLrqEizZ++mkS3mMX1/9Vi+eXS+77bNu+XOf5fIDZ9AXLX5Zj/3do/5US2ufLaj/btX8pfT/6u/8vAtD/DHnttddey2WXXcYf/dEfsXbtWq6//noWLlzIDTfccNztlyxZwpe+9CXe9a53PcPkda5df/31bNq0iU9+8pOsWbOGT37yk5x//vlcf/31v7I/juOwffv2l6Us5m8E6EyPauyGRguwG5r0kEKGRqJT74S++2L67ovRElLDEbkDmsgXFFZptAV2DVq3OOR2mvpgG5cMoWs2TsGCGWPmoBfXEE2JXZZE+zIEDZu4t4mUimBxk2RLjYe3LafZrmg9dRwCiXI0ytFIL0Z3NwjbIg4/tBBvzIZAYpcs/D0+o1M5VHuAjiRxW4jQEKU0UVeA5caIko3qbjI6k8Xr98jssZGry9z9yHrYm8aetBFS8/jwApzdCZzdxtVWxwI14xEVPB4aWoJSgsld7URNC1VxSK0qUF8YMTGV4bGhBchp48S5fu1h7K462x9ehgzBG7fZMrQQITWiZBMnNDsGe1C5ECE0+wa72DfYxZfufR2uG5HMNogCi7Dikl1ewLaVcVOsesi6REiN31pHxYLy/hbCyMLLGdOUauCSTDYZr6d4zRnbsWoSv6NGd2cRbWkWLZw0lXdnwTcaZMWi2nCpFxL05os0h1NGpjptFj7+sAPjPnLa5Ze7lpLJ1xGBxO2totubJIZsdox2s/ngUpIri2S6K2xYPsiKpaP82Tk/REUSb3EZbWmGZvIMzeSJJn1GR/P0dczwp3e8nZX5CV57zpNYQpNwIvoHOgkbNlbZ4sr1P6M+mEEpwX0jy+noLHLgUBf7+nuQtiKILabGs/hOxJ1vupaTOkaMoVTXJEIqPCfCcyLOWb2fILQ5PNQOAoLhFKu7x4ljiTtuE2ci1nWMgYbaVBKqNqo1xLYUwlZQtUm310hmmpSmjclPGFks6ZsgnWmgB5OIKQ/PjVjaMcXSjimE1Dg9NfAUtbpLaTKF1oILVu3hL+/4HaK6Mw8ct25egbA0y/LTKFejcyH5XJXbH94AAn76yImmbmtbQDLTwF9qQI62NSIV4S4t4y4tc2SkhSitiBOK6lAGJxPQ11rAsWMQmuL2NkRbEzHio22NM20zfqANkYgIc4p9h7r5ve99mINTbSw+fZDaVJKVS0bZ8IoD2H5IZ6aM7RrWs9zwiDMxcSamcXKdleuOwELDnFsNQTDjoxfXiVMKa9IhyioWrxsBoOWVY3xl57n8cvdS7jm0gvz6KYSlsSZcrJJtpPeOorQ+ZM3Xr6B4QgR1CycVgjTmSYtPPELX2nEsL0aNJoh6mjhFgSg4BNmX75kZdEQ0OhViYQ09nMBtq1ObScx/rgXcuX8NH37DT0ivmmFoJk/YHrF1pJfv/faX8Dan0ZHkXx4/hQWrxsnN1q6NmhZHpvPYboxyNFFsEcUWViKis7uI9UQaMezT010wpl0Y46yVHROIGJxhzwT/LEWzM6a2IoRExNCObsKyi2wYN/KFpw2hFbR0lUn1VmgEDm5B8pNHNqL9eF4JEbsghGb/JTcy8K5P8PqVH+fAR1dj3/ko3zv7K/z45//Cji1LqS6AU16zB3sgAWsqxJ0Be+5bxum9h1CRxE8H2FWJOyNpLgxwWxpGkly2SK2dwTqc4OBvf5WDv21yIb2OGsOvkpzw0DtYccvl3H3T11n7tSvovMOjuThgYKYVOeLRvj3m7pu+bj5fYgJk5UWC1KCFsiHXYQI8esxHRxJ/QuJNSoKdptyU60S4TsRpLYehafFEoY/xQ0YRIv2I1KoCdzyxnv5fLmLvkwtBak5YMUSUMGN/+OrjLwHObTUR7opqkHgyQWpA8r0PfwGAP//7P+DP//4PkN172fjXV7Bl+1Kmp9LcsXcduy+7AeeyUfZOdABw9x2foPy20vHn4GyA5fat1/C2P7qbqVqK9JoZ+j/0x5z8QcOM7nvNN4l2Zzn7ibdxxqXXzjOz6VkCYNmtM/OA9vat1zxDwnt0+88ADXLDupd03P+IbZ/t/f8MwNB614H5/1+08dMvmW3+j+rzS+nHrwPQmrvmL3aOn3Hptc/rPP+rc1f/S9rLLK8NgoAtW7bwute97mnvv+51r+PBB198aaTNmzc/Y58XXnjh897nu971Lm666ZkKkpfafiNAZ61dEHuC2BXUOgTNvCRzWJM7qGnZo4g8SeRJKr0C5QqaeYE/o8j2CzoeN2BHS4g9yB6QbL9/hTE9WVpDJWPcEQeOJMDReJMCpMbdm8Df7dM4nME/4FGveMh0iHY1E4U0TlniTVp4kxbpTAPGfE5afZgVrxwgWNhEZgJiTxPkNCoyRiwiFsiiYXOUq/EHXOy9SRM9nvQIKi6xB+XVEeKJDFZrQOxrcidMgaU5a2E/fa8epO/VgyBAVR2czjpWNiTpB7TnqsS5CF039QmDx1pwJ4yM97dXbQMBb9/wKIPFvHHsnW3+iTPYW9NISyPamrgFQT5fQ5ZsvvrKf0BOu8hpF3faIlaS5oEslhMj3JhSKUEYWmxavAddtbG764gpD7k1QzZXY+mGIaJYEsz4WE7M5JE85UKSw1v7+PkT6+hdN8Ylq7ZQqCawqpLRQhYZSNavGWT9mkGk1GhHs7FnGCsRMTyT47de+Si+Y6TS/UMdxKurKFeh8iGvOmEfzSfzdC6ZJjychoLLm9/2IJlkg7huU614hJHF1j2LOLC/h68ePAetDJvgpAPevGw7b1623dRYrVocmWgBV1EKff539x2MHckzMtRCW08Ja8Kl88Rxfj69FhkKPDeisKONtNfESYXsuehG3rhmB+Nlk6c5PtjCm355OXtmOlndN8a+3X3oqkNbqkpbqsrDd62nNp1AlJ4ynd5/71KipoVbBMs35knegIc3bhtXWDTTB1rp6CrSuWSaP1r1ILWyR0dnESFA7EhTDVzKIxmiXIzKhdT25thzsJc9B3vRscBxYkTZZn3PKCISWJbizj1rsGqSXFvF1LtsCM581S6YdnnkIcOSnLFygKn+FrSvSOXrfPK1P+TISAu67Jj5oAUfecXP6V42hRCaILAJAhsQWK1NMkuKJHor6MNJRopZGk+0sP6Ew8jQXI+4PYRkjFxWwWoInMM+TnfN5LNmYoL+DFm3ibAVhyZb2TnWhZSawS19xIdT/MFJvyTphk/Jpe2YeuQQl1xkPqDZG+Lkm1j7E/zhK+9DeZq2lVMcHm8laIkZ32YCC046ICi7TI1nQUJyZZHOdRP4fRWEEixYNEW8ssbvnv4o3oRFwg94+4YtvH3DFgYnWwhiCzGQNPf8AY+eVx5BxIJode1le2aKUIDQvHHlDvKrpwlqDvaMDdMuos2MvRr3+cnYCRTGM5zWN4gW4DkRF3/3o9TPMOkAwlEcGW1hcjqDbAmIGzbsSRPUHXQupDqdpDqdRBVc6nd1EJxQI7NmhrFdnWBp+vd3ExxJsfuBZcS+guUmEJTONLCqkuVLRhFFhzgVI0JJ3BnSvXqC/v3d5DorlHe14Lshwa4c2oJUd4WlS8bJZutks3XsGrj/YErinHHptdy276/puy/gjuEnUKPGtCd7QJAYhyd+uhot4dQFgzhHPFafe5C7f3kCuZYq4SzL3lwYIMo2QcU16RVKUN3RSuxr1tx0BWtuuoLHbzkB67EMTknw/jUPsP+SGznnf74Puwbq9ycRliLYmcOuCabWmkDnpkv+B4emWxAC4hOqNNo1r970BMWZFCqhsOoCHQuabYpmuyJa1KByME8zcGgGDt/ZfDavWH+QXYd6EKkQ7SlU2aE8kEN6MVEmpnXFNIn9HvvvXcoj15gI+s63XD0/J+YWnV8uLOLvdhv5bVr6ANROr5OcTTkQsXkt+doX2PrxG8jushHTLqlUg9fu+C3GftHLjrO+M7/fzPeeGT3ZJC/GLT/1908+92qmD+epP9nKkq99gS1/Zvp37gffh72mhLq5g5ZthXlmVttQf2NxHmDOnJh/KbfDf1hTT+x81s+OXtT/V0h//zMAQzQ2Pv//+Mldv1LKeXQgAV4+E6YXKil9vn14voB0k7z4Zbv+z1YH9/m2TfLil620z29CeylMZ6lUetqr2XymI/3k5CRxHNPV1fW097u6uhgdHX3R/R4dHX1J+wyCgBtuuIFTTz2V97///Vx11VVPe73Y9hsBOjODhsUM09C2O0JbYDU0yoJKj6S8yLwyg4pqlylZICNNclzRbHXQApyaJjOkSExqnBJYFQvviSSJQQd/EhIjgmS/TW2ByS90S+AVIDEi0Ra0POCTeShBbqeFOJwgdQRkYF7lkQztT8Ch7y9j95OLsMdchKXpWzUOAsSEBwdSyExA6pBEW5r0IYk3A1bTvNq3YliSKrjjNs0ORdy0oLdBaWs71GzufnQ9wz9fyPDPTUSbQBJO++hxj6nBPKMjeZwJhzNOOECci5EhBJ0h6UyD7913Ov6Y4N+/fzalwSxaauLWyJjq7GqhtqqJZcd4uxM4JdA/asOqSq747vvmzzPMKILRJFZDEDUcxISHuzeBGvf5t20bsUoWuj+F8hRdrz6CbSkGHl9Ao+ZiZUKimkOqvcobTthG3B7wvrPu5YSWEf5p76nUKx4LTx5mZccEa08eYNdQN7uGulnZMcH7XvsztjywirjgEtQcDlbamdrZjtNbY1HfFFHBAyX4wCvu4cF9ywgWBJzbcwDVFuB017h12ylMTWRZvHgCIaExkTQLcE8xfrCVTK5ONtlgbc8Yt95/JrfefyZnL+5HhpK47JDtLlOLHF7/yOXG/EXA9JEccVvI2K5OHtq5DNXZpDSZQnmaQ6NtLGibYc2/fJB/37qB5t4sfr9L8pCN+4sM0b91cPCeJeR3WKQGLPp399C/uwdlgzvh4E1J8k/YtD8miBKazGM+VhOymw0w92bAqkOq3yb/iIdVF4wP5+lIVvjSPa/DS4ZMDLZQ7M8TtCimi2mWrhwxQKRhcfqrdpNtr5Btr9DdVaQ6nUS2N3n84EKsbMjMWAbHi4i6AooTGZyCpGvjGL94chWivYlKKlpXTLN7qoO3v/JhNq46TLNp87l734STiMBT/ODxk6mPpPjSI69l9ECbAZFFl7joGjZ9Nu+0NV1DLKoRHUzjnVRgx+6FBAsCxLjH0gXjZgFfd8itnUY5Gs+NcI64CEfRc9IoTzyy3Cy6A4tm2aNZ9NALG+ieBiclDzN6qBVqFtQs3rhiB0MHO4w79IyLM2VjWYpmd8TPx1bh9VWZONyK7cTkFhWxFldRFQd1JAmhxMs00aFASsXUY53UphPIQDC8p5NNK3bz4385k2Z7TCNw+O7W0/ju1tMQB1O0JmpEvU1SAxaNTsX43X24M4Kbz/z6y/bMPOvkvbQuKfCjvScwPZGho6OEu6yM9hRx1aE0laJ79QR79vThZZv8YvcK2nqLXL7qfuTiGlHTRjVsVNNC1w1YTacbuJmAKK1Z0DONKDno2IAl2RJQXhYTz3gUZ5Is3zCItGMWLJtAp2Os1WVkS0BYcZGjHqXxNH0bRzg43IHTU0OEkuWrhxFFm4mtXSZotqWFxLig/LipKRtmFGFoXFu70mW60mWq65qMvCFk419fwcPfvop1P7iau+8wpTdOeOgd7L7sBv7iym9R7YVGV0y0oMHmLasRMRz612WIfEB1Zyti0CfOxbjDLt6URBYdwrbIlNfxNMpVuEVwi6BsE8CM0pp/+OIb2XTJ/2D41YLqsojpne04h30yG6aIVtWJE6Ze5c/u+VPUEzmkFxFO+XhTgrvvOQlRcLBLlkn7sDTaU+hUjKrbxOmIxmSCxmSC9WsP88j2ZVC2yTzm4447OFM2vWvG0bHE7ahTKCVxXjGDVT/+nJhbXP/t9944D0ZP+cwVoMDem+CiR99Py25N4+QajZNrDLz3Yyz/2Xt4z/tuAyUozSSpf7OXRl/Isjv/kEN/dfYzFsRzi+071a1kB0LkhnVctPHTTPxWw6g3+gJOXd8/v31hmYWUmvHzQm7fes08eNjyZzew/cyb50HLzBpx3LzRZ2tHA4A5V9oXwmgdDRrkhnUc+qtnr5t5vDqVLwUEASz+sxfPgBzd+t664xnvvRRmz+7qnP//85FlPvztq54GeH7dHG6fb3/vVLf+yut/dP9fyLk8/O2rXhD4PV7fjv53rr0YYPzrwAq/4PYSmM6FCxeSy+XmX5/73Oee9TDHSlm11i9Z3vpS9rl9+3ZOOeUUstkse/fu5fHHH59/bd269UX36TcCdDbaLBJTMW07AmJXkDsQUFoiSY2EZI7E2HWw62AF4JUVHU9EoCFKCOxqjFMDq6mpdUjsusIrQm6vwCsamWtyTOFWwG5A8ojEnzJAUEYgmxDkFVHCRF8jHxKjgjDF/CLEH7WYWS2oLtC0bJcoT+NuTzJ9XzdOWdC2bTZ6POoT+5DutwhyELvgG+NQptcKvAlTnD7MKpySxE0HuDsThC0x2lb44xaxZxY8cto1LJGtDJjwFH62SdQV8MtHV+KN2EZufNBB3pUn3S9BggwhOWThlCSZ7Q52DfxJaLvfQ+wyTpZRCoqrzF0V9TZJHxakDwushsCfkCgHEvuMtFVLaH9MIIo21pIqYXsI2pg1TR7Jo22NLjokk01kyaZWTHDbrvVYruJrj57DPYdW0ix7rF40ynQtyY4DCzg41cb6hSOsXzjC4WKefxvcwO9e+CAkYxKZJtsP93LuOdsI6zZDT/aAH5PorfDt/acjbQ2h5N/3ncDaxSOEo0m0Mk68k+UUKhJGNhcKkrtd3IJFeSrF+IE2dmxZSnZJgeySApvvOgHVEkIsePPi7ew73IVtKbQEIUDOSigzy2dM/lbJwc8bSZ4qu/Tv60HEAqtiYdWFMYLSZvwB/GkzJ9JDmuw+i+w+C38KMv1gV83ccEsKb0aQHNdm/iho36ZptJvv2zWIHWOmZZVtRm5eiogFjakEsmqhBaQXl4jLDoOPLsCbsEgdsnj44BJK0ylK0ynGd3WArZADCc5ZtR+tzflFgynscRdZtGksCBk63IaVCVEFF2IIQpviRIbvbj2NqUYSNZwETxHWHU5dPQBKsHr9EDqUXHTGE+iyg0yHyHQILQHupMXong4myykWts+YPGklcKZsFvVN0r52kkNjbaxaOMbqRaNUGy6qr0HlUNbcD6Fk5Ilus1gf9pG2gkCS7agST7voKY8/ufsSE/SJBCIS/Nt9r0BWLeyShVuwUC40Kh5Cw8D+bpQS9CydpLelSPhQK90tJc7ZsAdrQQ3RkLxuyR5EILGkxl5TQgQSubCG31fhrgOrCfIap71h7otQokNJlFLs29uLnHZxzpnGKUtEDMqBKZV62Z6Zv/zFGoo7WxEDSawph4mJLGJL1uhqpcaadBjd2wFKEAUWdiJk5kALN+w5F3UoSb61ilAg3Zh8bwmrq45tKcKKS/vqSYb2d9KxYmo+6usnAqzWALejjj3kMzSTh0mP4Z1dWMmQoGlSBJCaOKUQCo480cOKvnGiwRSkIg5u70NnI6wVZbSrUSdWCE6vINcY6buWEMcSbMVwMcdwMce/vuYr2MMe5VMbqNFV7HzL1az6zHUs++F72X7mzZz8OeOguvDsQVI9FXRokRyRJDdO45aNC3fc24DFNbA1ze6QRm+E1VsDL0YlFKotwJ22qKwLqKwLcKoQraqx99IbKS+BN91wD4lRycE3fw1vRvDKTU/SeLCNZLLJ2Rc9SdAbctKV19G2XdHSUqX35+aedksCGQjitpBmV4g75JLZY+MNOQg3BoE5b0uz83AP7riNUIJGO8RJzYpXDtCZrEDNQm5L421LEjzWQpSEM/70cuDZF5ZzYLHWDSe8ZQ8yhGWtUyy4Yj9vWrWdN63azklXXcc3X/kNPtIyQJyN0IHFmVc9Qsdmm0zOINvy20q0bCvML0aPLp/SaLVRT+zk4MUtRkmkgIbFrcvuYvmt7wfgyT++gepImgX/bs0DzE3yYi4+aIyOWrYVmFkj2PupK18QELtT3forWbVjPz+6ZMrceWySF6Oe2Mni//3g08by6O8+GzA5FpC/kAX7ob98dpD7UtvLwYgeb5/HO9/nAjq/anyeq9//1aZSz6cd3f/n65R8vO+CGavjMaBHb3fsmBz794sJjMzdV79R4PMlgM7BwUGKxeL865Of/OQzdt/e3o5lWc9gIMfHx5/BVL6Q1t3d/ZL2effddz/r6+c///mL7tdvBOhUNqA0jXYbGWmUI/CnzN+VHgsRgYjMQr20WBImJPU2izAhaLTboKDWJUmOK4K0JH0kIjGp8Ioaf1wQJiVBGoIcuCVwquDPKJyqJjGt6btH4xU0lUUat2KKzDtV8Asav6BJjJni8rm9gtiHlm0SGUJ9eUCY0tQ6BE4FrLog169QDrgzBrBV+8BqGNAcpTVOGZJDEhlA580J6t0xaPBGHRqLA2RogIsMwLkvi3AVVlMgpx2CoRTJfS7pAUlzUUCUgEaHptkKyjNjGWyo4tQMWC4vj6kujQjTUFkE3sYZhIJmqym1kBwF3bQonV2ndHYdZUNjWZPkiDl/EUP6tEkmzo5RqRj34TSZXQ4iE2JNmxqEsiFBQHksjfI0OhLowCIuuMhphw+vvxvvsMveJxeitaCzp0BjKM22/gVs619AdVcLllT80yOng4Zm3SGZbvLg4FLOXNHPgpNGSOUadGXLpP0mmYwpBdIseezatoh1Jx3CHnORDYl6PIfb72OXJWF3SJQ0oN8bchC5ANUaUJxOU5xOc9YF29GRQGRCbvnpq+joKFEp+YhQYCUiVGDKybx96eMUmgkSoxby8QwtuyF10MKdsmh7EuyKxJ808yM7oPBKmiANjVZwy5pqj6C0IaC0ITAPMmEMs8Is1DotkmOaZs4YYEWzKXlOBZRl5oHdmJ0/VUEzB5kDJv/TnxC0PilRm/OIhsSuG+Mt5YAquuQeMy+3aIxuwt4mv3hwHeKIj6hYuIsrKN+UMbBTIcLWZDJ13ClT7qK+J4eViLD8iPGHelDJGOnEEAsef3Q5VjJk/5ZFnHvCXraMLyS/sIhq2qimbdgxC9yCpCtXpn+wkzCrCXfliFOKw0famNreTm9HgX1bF7Fn5wLCpo2qOFh1CcrkIWtp3C+jfEQ8lsDKhCTckNYlhdmx1IimhNYAWgMWrB9FtQcsP/MQQUeEiEBHAmfSRgQCuS3NyEAbg5MtNNbVqQcOw7Us4XgCt6/Kj35xCj3LJ5keyxL0ZxCZEN2f4j2rH6IlU0PbIPamaBY9/BEbf8Q2+cmhAZondw6hBYQZcx2+OPA6Xq4Wt0ZE+Zi4rwG9DUTRobY8NAEqDXFCI0LB0lUjxhRp9hzLUylUd5Pynha0rVnWN0HhUI54PEFpWxtaw9T2dpyCxeSe9nlQ1DicQStoFjyyGyYJ92ZRCUWcjonKLqpkHG5TO120o41Ld2+DWuiCpdGBhJaAjs4iG3uG0RKCqkMcWTSPpIg6QtyiJK45CKE5pXuIU7qHePs/XYlcUWHNwlFO+cwVXLTx0+y+7AZSnaYES3hekRPdMcZ/tJCUF5Dc59Bo06iftbL6vbsQoURMeMj9Kexxh/ReB3yFOpxElByssoWQmihrcqap2lR7IZ1qcO4H3kf6MHzjq69n+4dMzc5wQ5WH/+0kaotMrd3HvnMSViJi6yduYPhcaAQOG//X49TbBJxcImqJTGCkYREsCKgu0MbsyFE4kw5tPSXaekq4B33UqipWTaJWVFH5kN1PLuLx/YuQgcQ6pUgzb9I5nAqc95HNwPEXlvb6ImObTPRr4V0NHt63hPqKgAO3LQPgtp+czm0/OZ3S2oj/8Yv3cOo1Brj3v+lr3Pm906l3CMrDGZZ9d4adb7kaMTaFddLapy1EH/72VfNS2Zbdmrg9wKpKUv1GbvzRTbexSV7MudvfQmrAovyHxXlWrPTOs9j145WceP0VxE/uwp8y+zx24f18mc9nW+Afu7/jbfds7NXzYY2OlTT+R4C9X6fF/vHO97mAzrHbvxBZ6XPtN/udzc97P8/Wjge0XgjQPTYX+KUy4XeqW3n421c9Q758dL+ODpy82GMej509ltX9dW8vRV6bzWaf9vI87xn7d12XU089lTvvvPNp7995552cffaLDy6dddZZz9jnT3/605e0z/+I9hsBOrOHI4KshVuMCdKSyZMcrADKfZLUaDzP/sWuoGVvjLaN4VBmOCJMCZQLiQmNsgVawshZNlHS/F/E4NQULfsiWncohIb0iFmQCgWxIwhTkuRYRNuThiiIEoYFtRoaq6ER2oDG1FhMvR2sQJMZVCZnsy6oLVSI2LCnExskuX5FvUejPPBmoLIkNmC3KLAaoJ1ZtssTeJMW2b0W3hQ4oy5trxql7VWjOGVBmAHniEfQG5AYF3Q/YGqaBjlwRlySo5AcNoC31qtodGjsHSlTeiICp2TKesSeAfbNrS3EvgEw7tIyhRON66y/I4G/I4HQYE24ICBKAgIKO9qQNYvsTof6aTUa7ZB9xAcF2Z02UTbG66rhjtnYBYv0fgetgdi4AP/fW36LoEUhQ0H4WJ6pHe10rZqcl0QuOGWYkV2duBMOzoiLcyBBdSxFHEkeemQ1R57ooV5zGX6oj7HdHZRKCaxHMsZIqCjZd/9SsgcFMjJMtVhfJrFhmkS+Tri0gfO6SaKkRjVsnESEtGOkHfPAfevxsk10zSZOK6b2tpNIN5GBREiFmwmYeqyT7/3N+RTv7MafNMxjYaWYZ8ibOUFy2Fx3ZUO9XdJoEaRHNF1bIqo9gvZtIS2PuLQ84pIeVqSHI9xyTGLCsOqRL7Br2lyvMkyeJGjdHZPvj7Ca4JU0mcEQfwq8kmH7lW2YUG2Z4ERySOJPGOOtKAH+mIVdM/1VNrhTktQOj+SwQPkaf0Ji/TKDPyqJ0zEMJchudVFKEOYUWBoZChKPJUhvThJlNHbRwt2bwB1xcKelmTNjgl88tI6p3e2U9reArcBWiKJD2Nek2RUxtL0Hd8g18u22CIDULg8kDB5uR3TXSQ5auHsSiKYk7m0YiaivUW2BuYcDiVuQxDMulQc6qG9uQ7sab9QhMSJJP+aTfsxn9OFeOu522X2gl86FM8StEamDDt4MtK+YIsxp/CM2PW1FXD+icWcHI8WsYecOpLEagvFtnVjJkDihUIGFXlTnXz9zAZMH2rB6awgF2Z0OHWeN0HHWCMkhOasQEDz51ZOwVpbpeMUoQsGBwc7nfO69lHbyqkMk22qIaZcVPeO4fVWwNL/1ykfpWTADgPIVBw8aJ1nyAc3RJIQCXXI45cx9CKk5MNANjsn1jlMKWbVwllVgeRXZFPQumKZ3gZE+t7RUSbfXqD3YzivP2w6uAiXwW+vgxbA3TXVZbFIDkjHxjMvw7k7oaOKNOIgpj4lDrTzSv4j0ohJogeXEtK+YgoZFsyuEpkSXHTYfXsLmw0vIHYDE/Wl+vOo2Sss1otrgrI9djvvTHOdd9kd8/9Sv8o+FV2C9dprF2Rnazx9G9Nbp+e1DPHzfWuyihUoq7KphFRsn17DGHaJcTPvyKeJMjA4s3nzOo8jAMJNRWlM8nOO+r3yVyqbKvHohu8vm/5z6L+TOHSOz10YWHAonhnjbTLTowMV/R+NAlgPlduwGhHuzOOkAFKQWlJFujNU00m/7YAKrAdMDeaYH8ngnz5DwQ6KeJtGkjzXpIEITMFKeonY4Q3pIYC+o0mzT3PH3Zz8r07T9zJtZ9D2zNFj+hd2kdnosWjBJfV2D6gfa53M6kwM2m1bvZsunb8AbcVh2+2XkDigqK0Pjnt01y9Q7DrdvveYZLMvkaXnAsJXZxz10X4Nanyltc/Nn3sCd6lbuO+EHVNc1ySUa8999+NtX0fFkRHV5ROmdZ/HktcZI6FiQufdTV84v5o8HAF7owvhopvPY9lzyWnjpIOL5tl8laX2uc3i52vMBX89XWnps4OI/opXeedYL2v7Y/m2SFx8XaL2Qa/5cucDHthcCZo+VL2+SFz+jX8djSX/VmBwtk3+2/fzGtJfZSAjgqquu4utf/zp///d/z65du7jyyis5fPgwl19uFCmf/OQnede73vW072zdupWtW7dSqVSYmJhg69at7Nz51Dz6yEc+wk9/+lM+//nPs3v3bj7/+c9z1113PWtNz+O1Rx55hI9//ONccsklvPWtb33a68W23wjQ6c40sRuaIGsRpM0pOVWFP61p5iXJSUVyUhH7gkaLYRmtQFNaZFNYrbHrGuVApddonRMTkJiKabQIWg5E1DskExsNixq7UO2yqHVJlA1BFkqLBeWFDkFa4FS1YR4LCivUWKGmmTMAtbzAIjUMQVZQ6ZWkjkDugCZzwOTh1fti0oNQXihJjAlSRzR2A/I7LRptBlQEecO+pUY1hRWS1l1G0tlsheQITD3QzdQDBuTMgWZ73EXZMLlBoo1/CIkJqHcZVswtGtmwCM0x7Lpx/ZUh8+yaW4JmqyIxbuS2PJYlu9PGLtjUuxX1boW9pEKcjWm0Q3VZCApELEiMCkprItztSdSiOmEKkquKlDcEaEcRHk6jbUBqqktirJKNVRcE7RFWDfwxiTsjiBIQJxXln3fiTVh4ExaHt/di99ZQjpHXKRtkzSKa9LErkvSqGXL3J4hSGlmXWAMJan2KxLBF0BvgTUO1B7Lrp2kubtKYSlBvutTLPsy4WELjTQusmiSsOqiiiyq6nP+aJ8zk82NkLkDlQ16zaD8qEROPJWB3ms7HFGgD9NyKyRnO79UkJjXJMWjZa9hUK9S4ZcgNRLTsj5CBxq7FyAiCrEVqNCY1GqMcgbYEpUU2QkHLfpNzm5iOSY9EJCdiuh5R2NUYYvPUa7QIyosc/ClNcbmZ69kBw9T7BUWcMEEMKzCBjOxByPY/9cSUkQGnc3JfERkpsAwBCdndhjVFQPRwC25Bkhw06oIgB/6URjYE7owgTGvk7HG8GYgT4I8JdGeT9ICg66cuXT918aYlXr9HoqOGPyawayADAVqgPE0zD1EuIrvDwdqXxDl7migJ3pSkta2CVbZxCwI54xJmY5yikaWnD5p+ASQPmXtRW+AVjFIhOQKFVYLUAYfiIx3Igk11WYiIoP7zDrxpk8s9uqUH+6FZ681HclhVSdRuygFFLRFCaDqXTuNMOERVm9JSyYHf/TuCsou2oNEOQ3s7GdrbSZQCb0oQZKG6AOyHM0z8sptoYYMTlx552Z6ZO0a6adQMw7/nUI9xSE43uXd4BaOjeWRgJMdWKkTaMelsA9IRiY4aIhfS4tYQjgKh8drqvOvEhxGtTdZsOEwq0SSc9okyMYVqYt4ErFL3WNY6RX1ZyBMTvSSyDRKtdZoTSc5Y3U+wsMkZJ+1HSKN4WLt+EJ2OTd6sp/EXlyARoyJJpeSjI0Gz4KEQnLj+EGhh8j4zIXEsiWNJYbWm960DbPzrK9h/yY3s/nAXD3/7Kv7Hh37M3Td9nbd89WP869+dR96v88RwL4cH24kqDgd+uYg/f8t3yZ44hdtep7YoRnmaeMojysdYZYuJiRxWNoBA8m+PnILuaJqX1JAxE+2MhYdwqprlt76f0tqIj9z5B3SnypRXxqh8yHvO+AUdT0asucmwhWe/cieDdyzmbe+9m3e98W7kvhSiYtOou2YcbI0MJUFnhL2hiFWTWDVJ/GCLKScTSbA1cUeASsXE2QgULDvhCNUe8NyI373wQWqvMkzv+a/57HFr+A1eYH5Hb1ywmT/+w+8x+bNeErt9Zk7Ms+t9N7DrfTew/UM30P+RlZzzP99HsNC4r4+dBaev6ed7p32VWqeDGl3Fof+bn9//3LHiJ3cxeW4wf8x6F1gDCVq2Sz6w/xJm1pjf4hW3XI4z7DH8WM+8G+pFGz/N+Eabg2/+GjNrBGdcei1f2n0Bez915TMAwdxi/tkAwCZ58fMGYs8leVz8v58u7X19zwef1z7/o9vzkfUe3Y786/oXfaxjwc+zgcXnA77uVLc+Dcg839IfzyZpfq7+vNQ214fj5eo+X0D4bNs9n+8fPZ7HMqRz338ppVPuVLf+Svb3PyuI8v9K+73f+z2uv/56rrnmGjZu3Mh9993HT37yExYvXgzAyMjIM2p2nnzyyZx88sls2bKFm2++mZNPPpk3vOEN85+fffbZ3HLLLXzjG9/gpJNO4pvf/Cb//M//zBlnnPG8+nTLLbfwyle+kp07d/L973+fMAzZuXMnP//5z5+1TMvzab8RoHPsdMPOzayWJCdj0kfMot2alRdGviDyBf6UIkwLxOyCvGVvk+7NBthpAR1PBDg1jVs0+9USmjmL9FBMahimTrBIDyvsBqSHY9ySwqkwn4NVXqpptApSo4pmVlJcYlFcYkwggpwBqM0Wwxw65jefWqcgSoFfULQ+bnIyrYbZX5AV+NMmvwkgMaURoQGwzZwgMQHNrCQ1qozUVRqGMUoaU6W5Rb7VMMezGuCWDWh0KmBXTF9gNg80FDTboLzIANEwrUGbhXa9U5Ppl9S6DBARCmp9ZhxFKBChwP1FBi01YUaZXNFQ4BUgfkWZbGcFocDblUDbIKXCP+iS2eOgfEWUUoRtEclDFqeevg8Ae8ameWrVGB7lTa4XSlBdGuOfPG1eY4JmwcddVsZqmlwmbWnczjoygvrWVqq9EKdjwvYQZ20R2dGgviBCOIroVSViD6aHckaylm/CnjR+v4tTkEzubaNxQt0s7qad+SjWXb/YQDCRwD3soSc9/H6XewdXkBiyye2VtD+pCJOC2IX0UIyyDMBBQJgWWIGRgbfuiWZrzJoSHu50kygpKC12SY0o3GKEWzKv7ME6yoLMkZjMoRB/vD4LBDWyqUiMNUiONGi02diNmJY9NfxphdXUNPOCll2QHIuIfHDKMc2cpG17hD9pWE4Zmv4Jzbw6IHXEzBWh56TiBgQKDUFOY9fM3BbKmBe5RYh9I70WMUyeDLn9JsihZyW/VnN2mzEzH739PnbDBIqcqiIzoEkPGXm4tsz2dhWssjTsmNRkdzkox3ymf95KlFLEHkwdbsEpC1M+qSJIjFgkRyD2NNo2wZPYMyA6Oa5wyuacEVBcpYiTCrtughf5vYLsdgepnpLLWAHEKUWUhMpSRexjHGv3OyDAnraRlqZ6bweLTx9ENCWyCau+dQV2KiR1BNwCuAWTN6rXVp4K/Ayb51V6CFrv9dl7z7KX7ZkZhRZaCeK+Bk4ipLovT3goTbE/b8o5Lapy7hk78fyQzrYyYWTxgdPuNS7dlubu/pUIoZEzDloJvvHQq1jcPcWuXQuZmsiCF9O6uDB/vHdceD8JL2TXSBfWjE1leyvNwxkaJY9TTzzIL7esZGnfBI8f6aO1s4yTDhi4cwnULOxcE3t5hVf0DqJDiZcO0LHEyzdZtXyEYjnB9q1LsFIhJ+RHjDqjbhPXbbxpweAPl9Dx5kHOf/dlLPtek/Nf+zlu/swbOOGhd7DrL68kMaUYerQP59E0suggHIXyNH/+6G8xM52iM1c2z7j2JqRiZF3ilAVULGzHMLO53hIrFoyzYsE4Z5++m8+e9X2W3XEZ9z+2hoc/dyNyDoxKzeP7FuFMW/jpgNs+/2ru/trXCBYGvOb972W4lgUB37rjNfxiapmZmy0B8bRrgi6+Rrc18UZtGgeyRK0RUWtkgnXV2Z/zSOCnA7StSQ445BcXTZmnroD6rjzf3X4Krhtxp7qVwU2JZyxET/vzK1j2fQMIL/qdS7lu9/nU1jQ59c07qPQ9ZUKx6fffw5GPRUQJSXKvC1Ijm4Ld31vF713/J7RsK/CGtefy3tVPAbK5Y71p5wzLvv3UMb0pE4gKcnBgfw/2+iJqdBUXnfsYYV+T3lNGeP0tD/L6Wx7k9q3X0OhU3N8wUuDkeMi/j5zEeRd+/nmzLUcDnDkw+UJBynPlrj2fepXPVWfzxdbgfKEA6HhGQs/3eL+KMXuh7Wi55/Pd19EBg2O/82L783zmwbP18VjJ6rF9PHq74x3nhYC5TfJi+i/OH3e/L+bcjw2UPJ/58+uQK/tSm9D6Rb1eaPvABz7AwMAAzWaTLVu2cO65585/9s1vfpN77rnnadtrrZ/xGhgYeNo2v/u7v8vu3bsJgoBdu3a9IIbys5/9LNdddx0/+tGPcF2XL33pS+zatYu3v/3tLFq06AWf31z7jQCduf4YLSA7oLEaisRERGWBQ5gQOJUYf9q8hNJkDseUlhjmo7DCw64r3FJM7AmqvQ6VPolXUtTaTW5J5AvqbRYi1rglKCyTeMWY4lKLSq+F0JAcMxMsc0hg16GZlyjXsIX1TlCuWXA7VWi2mG2VYxbeiQmTY1Ppk/OMIsL8AGsBpUXGmMefgul1Ahkb8OrUZpmsDiPLrHWbnNW27Zq27eaz5Iggc1gTJTVBBlLDGruOAWEe1BbHVJZoogQELSZfNHUEUsMmxzC/R5Dqt8gdVGQGBMp+yixJKGOYpC1wSwK3NMuA1YwJUa0v5vTX7qS6LCLanyHYmqfWF8PJJbSEcjlBuLZOZVmMO2WhXW1Mbjo1h0otyMU11MI6izumqS1QxB0hOhuSHJZoR1GcSlOcStN+/jDOlE2sJMHSBipnygV4bkRzQUjQFhMsDDj3xD1YZZtwd458tmZqQwpNbTqBXYP0fpv8vQnS96SwK5AYf4qF9rcnyPSb823dZtG6zaL9cej45Ww+5pTEqkPm1ixuCfwZTewJooTAL2iq3RZuWVFrlyhb4JYMG+6WIpxyRH5nBW86xJ0JiFIO2YN18nuNEYcVKMKURZiyaLR7eMWIxGgDb7qBiDXZQyHaEgitqfX4xJ5FZqBG7EpkaHKU3ZKi65cVUqMRSEGuPyLIWaRGIuyaIjUW07YjIr/fBF0SE8YBWlvgVhStuyPsuiYxZVyiM4cVmUFF+pCZE8lR8Kc1LfsiZGjGzqlp2p+Mye8SJKZiEtMxHY9D9pDGqWrSR0xOdPZQRPagma+xZ0ofyRiSEzG5/ojsgLk/3DK07IaWRx0y/SY3ei74UVmk6XkAoowifcAy+dP9mvxeE0CKXcjtE8gAaj2Q329ykiNf4FQhOR6THI9pf1yw6CcRdtVIi7UEOZsrm5jU80C8a7MBpe2PC1JDkB4QBHlNekDgFQTeg2m0Bf3b+sjut0iOaxIT4D2ZpNEKqddM0L5V0b5VkflpmvyBkMygIkybY06dFrLkPfsMIH6ZWntLBWfIM0ZOy3ehBbz2nCeRHQ1kXRIOJ9k13UV9OM30lk70jgxfeeQ1ULXZuGiIMxcNoCIJlubcJQew0yEDw+0QmZxQQkml5tE8nKF5OMM/PnE61ZpHe64KCtInTKNaArQSbNm6DJ2K6T/SQTSUYnoyg9yfovXcUbA06VSTYCjFExO92AWLcDANAjLJBvv29KGVRGciVCj54T2vMLWVZ1uQNcZngw8uJMja3HnLN7nz5m+w+Qs3Eu3Icd2u17Hhj7ea+ZAClYiN6ZQSqEiiiy4Tm3vQnmJp9yQ6lMiOJtGqOn53Da0FsmpR3dnKvn297NvXy6M/Xcef/uKtEEjys7Wff/LKvyF10CG3zWbRwkmUp4kiSZgUnPGnlzPwrk9wz999jcMPLTTBnwV1Dt2zmChhnlNogT9skTokcQ/6yBDihCJ1wDGvAeNsjtCmRFGiiV2wabZoyntaEE9k8Ptd0oOQ2pLA/elTkerzX/NZwCwgz7j0WrQNIx+erZvcl4B78/j9Hr/YvI5tH72B9V+5gvVfuYJPf+Mb1IbThElBtl/hjjnk9gmCvLlPZk7Mc/vM17ly7U/nj3XSVUYCe9slZ+PMNEyu56s+Y5zgJzTKhUU/AvmLHLJ7L/d+91TsUZfDR9q47ZKzue2Ss1nytS+w4GeK937nCuz7cvzsWzchP9WKUwrm9z/Xfnvn1HHn/9GL+/jJXZxx6bXPYLDm/n80O3t0e67ctecj/TweQLxo46efc79z7z+badLrez74jJy9l9KeD3h5NuAxJ3c+Xl7hsd87+vMXWqv0hQQL5IZ1v9Lo5lcdf+4aHf33r9rH8WqWPt/zfC7W8liG/aW020b+9ml/Hw9AH32d1v3g6md14f11yi3+le0/QV7737EdOHCAN77xjQB4nke1WkUIwZVXXslXv/rVF73f3wjQGaYllV4LGZrFfLPFQkZGvtdotah22VS7jCSx3m7RsjukmTPmHZEvqfTYJMdj7IZGRNBoldgNjdVkfvKEaUGYNAvd0kKb7ECMP6OM5NYX2A2Nsk0OnV3TZAYjkmOGzUmOGEAnYvAnhSmDEkBhjSLICBptZhGrZxmXOZlo0AJWaBwEZWDYkCBrvltZICisNvtVtgFIQsH4GZrxMzS1k+uUV8SUlwiSIwIklJYKqr1moV7tA5EOkU0Tue55QM0yohqvqEGYsjIAdk2RnDDsTnrQlKVxygZEdzz2lEsvApyiJF5eJ3PAYusP1pHdaaMtA3wToxZqW5Zmu5GdqgkPb8Ii6A3xO2oQSPxJwVQhTTLRRCvBgT29uDMSUbTx+z0jR61a+AMu/oDLxD29RPmYoOpgjXjY4y7ZnjLVqoc94ZAcsli9ZIT7H1+D8hRhPqb8eDud99tkHkpgF2yCvAHeczLo7CHTP6dqzt+qm7kko6dq1DVaBBOnGwl3fr/GnwarocgORAQpQa1TkByPqXUI8gdCYk/Qsi+kdWeNlq1TZA81sWohUcomzHsoV9Lo8PDGq8hGjFUOSA82sGoh/kQTf6KJlmBXQkSsELFGORZ2NcQphTgzDbQlsMsBzXZTW09bksxwROpwjfKSJN5UA3e6ibYEydEAq6mJkhLZVFgNQ6d7MxEi1uT6I3L9BpRGvsRqapRlghi1TokVKJLjitSoQltg1zWVHpv8/hCrYcaq0mvhlRRCGRCeHA2JXcMausUIr2DY3fRQQGLSMLJWUxMmTZ50M2/hleJ5CWyYEngFk//c8XiI1TTBmM5HNM2sJN0vkRHkBmKCtEBGGiuEjicD3IqR0CbGjErAqSq8YkxqNKLSZ1Ppsxl/VUSzxabZytNqCLbsMaqGbL8mPaSodUnatxlZu7YMwE4NGrCcOmKeG3YN2rcI3KJmer1hh73CLLD5l3aqvZJqryTIwPRqx+RnFzUyhNYtDtvuXmkM0l6m9qlVP0F5hnm77a7TcCqCux5bTzzjobIR2taMj+aQLU3CrKK5uElPzwwiFOybasezIqwJF9kUbJ/qIS45SEchY3jziU+yYe0hgmmfOB0RpyO0kvS1Fhjf2cGm8x4n4ZhER6tsg61xjzjYXoRKxbR1lAh6Qo4MteF11ijtb8FZUKW0twWrIdCWcdedmshglySWE6MjiT3qgaWx6gJr2sGadohTZn7uvuwG0Jrz330ZZ33sck58+B0sv3GALz2wiTv3r2HdOQcIs9qUytqVQPbW6OkqYFckWgKBZHDzQkQgiKc84oJLs+4QVFxUKibKxlhFy7zqZgyskkXxzAYbP38Fv/9XH8MtQeWVNQ4NdBC3h+jBJKXlcPIHTM3QFbdcTvupY7glyP8sSb0vwqkI5BEfpywNqz6bY2/XoOd+8bQyLcqB/KMuuW0OjfvacYomx/RVr9ph1DYtmiBv7iXnzcYWfdmtM9x5yzcBs8jMfmczXb+YIdqR46KNn+b+v/kq/gUTKBv67olZc9MVtG2Padsec8XWdyADiVPTjJyniJMafzqm0ReSPayZWSt49Rv/mnU/uJrXr/w4Z1x6LVYT/vfBJ/jJ7bcggoh6X5qDv5PArSpkZM7l8NsU+YMxZ1x6LbU+hb28Qtv9Hs2uFM2uFKIhcYsRPb8IURZs+D9XcPBtSeLPzuC/buJp8/yHv3/O87of5kDisYDihZiizIGsTfLiF1S+BV44QHw299rbRv72Ofd1PInw8QDC82VaL9r46Wc93rJbTW74sXmFx/vei8nPPF5e4XP1ec5p+KUa3Rx7vseyls/GYN6+9ZqnBReObsfKZOfasfPxV+UOw9Ov8arPXPe0efls7dkCB3LDuqcd/+j75Hj1d38T20sxEvp1bq2trZTLZiHU19fH9u3bASgUCtRqL76G+G8G6EyYhb0/HWOFimq3yV2s9mncssatmpfQhrWoddukRmMjQ00JUuMxjVZjRCQ0yGAWdIXmh7zRZhbR6WFN6+4Qt2xyRZs5syApLzEOoqkRs3APsibvLn1EGUanrgkzZrv0EcP0WE1N92Zjb58YmzUl8qDeDuVlisJpAf6UYWmcKlQWPiUzbLbMymOrBsBGSaj2CGNydFCSPijJPJwgOWghA5PvGabM+bfsVib/RkD+Qd9IH6cUQVYasAnI0OTNuSWNPwVhShJ5RioLJqc1OxjhlTT1NkHrzoDWnQG5A4rUMLTd5uPNaOpdmlqvxqkIY9ITm3FMHpGoioNTljRbFU46oDmcgrRxyuWIT/ORVro6SngTFk55LqfPMLHpAUmQ1QRZI+9sfcwitdMj0y/Qtqa2swVvZ4LkiDnmkR8txi5adD9gys54k2A3NYkpRfKIIHNIkBqG9IgmTIK2DIPbzEmUZSSPiUmNN6PxCmoe+HQ/YLYrrBSzzKCgtNgmNR7TujOkvMDIsZ1yiJYCGSli36bZm0XEmmabjzfZwCkHuJM10vuLaMdCJWyE1thTFWLfJk6aV3K0iSw1EJEizHogBVYtxC43ibIe2d0FtCPxhysEORu0JjFQQNab5HYWEJHCKtRJDteRocKumoW/dkzgwWqavsrIAM3IN/VfkqMNvKLJUZOxpuOJhimXEWmaOUlyXBsQOhEjI41bMaDYqWoiX+AWDVMqI0Vi0tSHrXU7lBcah9laj0tpkaTRal5RAio9AhFDYZlNlIDyYoE/o6m3SWo9gDR/ZwZDvJkYr6RIjyhy/cZMrP3JGt6MyYVt5mzsmiIxZe55uwbT6yVjr7ApLrNptJl7vPN+m+ELYhodBvxV+wAN5UUW0+sslC0orJLzLKmyTGmkICtITiqshgHGfkFjV410XmiTJ13vMKqHjscUjVZBebGmvFhT74TyKgNqpzZogpwp3xQsbM4rKF6O9ifffTeqq4kombJI9klFrGyI3WoMW2TeyCv1tGfk/XWb4aFWnN4alQN5HvjhBlRXE5Rg8olOOhfNEBc8lK/44SMns+2RZTjTNt6Igzfi0NFR5PB4K3EuYsv4QkYnchBLVCI2rKKnsW2FaEhKlYRxMFYQNm2chRWaUwmUpwj6QnQmMlL7pilrEx9KkRiycUvG/GjBKcN0rJ+gY/0E3oQkzGiW/fC91Nssfvatm6j0Cdyf5hj9rSUkO6p0/pvP9oeX8U9v+TL+8hKNNQ2iosfML7pQi+oELTH5JQUj2S5bkA2xahKmXagZoImlsZdVsJeZNIIHvn4aVkPQ+wOXzGBMbiCgcFJE57/5JAccuu5yUK5mwd0hNy7YzFkfu5xXnrWT2o+6aFxUpLSpSstWGxGZGtFukXlzr5Y92rhWZyROVc8Hx/wp83xttpjApV0zyplH/u0EwqymdTtkDhlZf2FLBwC7P5jhrI9dPs9gNN98OoevlsQJze1br+HC3g2EP+6gbbti8PWC9ifV/KKq7Vtpln2/gd3QtP/SpushjbYE3XfbxA607NJMvLdGbSLFj+//Ac2cIPfWI5zjw49rPo3eDCNnO5x05gEqvUbpY9c0Hfc41DotNn/hRoSG7q/5FFfC8Ktchl/lcuDiv2PotT6lJQ6VJeae92YEf7zkpzy08V+eNs9fiEELGHYWfrVs8NjPS+88i72fMoZGh/7y7Pn/z7VnY/uea5/HfnY0aFj8Zw++IDbp2Y596C/PPq6RzK9iWufa0ezv8UpuzL13NAg/npnNi2nH7uOF5IHOjecmeTGtdx14wTLR45XbmRvj53I9nuvz3L9zxz12ns5JXY8+x4s2fvp5MZtHf2fvp66cn4tH93GuzV2Xo0sZwVPmSnP9+tLuC+a/M3f95uoeH9vmQP3cvstvP/1X9vm/dft/lOk855xz5t1v3/72t/ORj3yE9773vfz+7/8+559//ove728E6EwfMdJEqxFjVSPjtuoIeh8wTIqINSLWhCmJVzCATwaK/IEIGUOtw0I5UG+3SY0qlGMAjlNTJMdj8gcUiQlFaiRkeo2DUBp/xsgSZYipUdk0x6y3moWock2O6cxqSbXbuIO27jDgUNmCZs7knYkYwtRTTqHagtSgpONeh1q3ycNstGuy/bOS3IRh3uyqAZ6xPycrngWlsy+nokmNmPcTY0b6mx40fey6yyG3f7aAuQ9BVhI7glqXIMgKmnmzAAhTguRERLVHEmRMGRplmUV2tdPGK8Z4BU2jzabRZjOz2izIqz2Ceqcg0y/IHjCOu/64MZuRwWzpmXHb9G1UInelsKqSloddvGlwyoJGp6J2eycyNICgbZuR/Na6IXM4JjUkSA2ZcZ+T0DlVTd+9Jv82O2BAtj+Fqbu6H2bWSFJDRjKppUA5gjBnxqrWA8VlguxhRZgw198KNfmDEckJRbnPuM7WOiW1TmncJZOSZk7Qti0myAJ6Vnoca7AE6RFFarhJ7Fkkx0Pc8SreaBmrERN7Ft5EA6tYR0SKKOMRtiYRYYwzbqJL2nNwCg1kM559RaisjwhjvJESVqVJnHQI2pLEnkWjN0OUcqj3pY28N+VQXt2CSvtoKSmszRL0Zqj1JowkN9Ikxhq4MwFCa8KMTaPdY2KDT5A1c6G8wKLR4VFa7Brpa6AJsjZhQhKmJU5NEflmLBotkighzbimBJNnxlT6BMVlHkJpqr0e5YU2YVKgjOqQardNcZm5jvV2Qb3dsILVRYpK76yJUGTykGudYp7dHDrPSN5nVjuEaQsr0ARpiVOKiF0YPifJ1AkO5T6LarckyEpqnTblVRHTr27gT5r91Dsg3FAl3FBler1h0bU1u2ivmHuu2QLVJRFWoGm0m9JIM2sEcQLq3ZowDYXlkmbeSMzLfcIEbcpQ6zAu2G7ZLPin10qsJmT6zf2hXI1VlYy8yjjsighm1oI96r2s0dLY1+hY4PTUCPKaZtPG257A25Iyeb0jPu6wi1WVWFWJzAZ4uaZh9lzDaOqSQ5w0Oefj/a04MxZeex3ZNAGDsCcgaI0JWmOmZtL42xIIRzFdSiEmZpPJlckHjzKKxmQCnY2IRhNYSRMQsfsTxJH5mepbOYGVDMm1VUxZnBHb1NQNBM0VDWrLQ7SEoS19+HaIb4eceOFe3vzqRwBY+u59nP/uywgz0H7Dg3Q8VoFHcogIlKt5///5MNWiz7s3PER6n01jaYAY8ckvLlLb2kqc0EQ9TeSki+psItqa5BcVidsiXrH+II3pBI3pBOkjmur5FfwpuP/Lf0dm1ww/+9ZNtC0oEKQl9b6YICNwC5Kp9Q7nXfZHpAcbfGvxfSBh+5k30/Ntj/Yna7TuMiW0nLKRrbftbND6RJHMoTptO2p4JYVXUsbNOgFh0pQ+yu9XuCXI9SuTvzwiKKw2KRqVRbD4NhOpXvvpAa655uuMbQpZ94OrmXxPFf+2HE7RBKKabz6dKAFjb27Sd6fJBa91WNQ6LAY3mUDbxEZJmDIKoeELFKnRALupSY6HdH/ZZ831Be5pCL7ysS/PM9x/tuu3abTZsKbCE48up3VnSOpIQGo8ZmYtNPOz0yMbUe226XpEkT1tguxpE9w/95s2FCFDQewKZBM+9PA7GI8r8wtnNboKgN/ZNfmcoMI6ae38AnmOyXkuUFR651lP+7z0zrOeZrwyx/Ad3Y7H9s21o0tWzLFmx7Klx7r/wguToc4d+1iJsL2++IxtX6i89eg+Htvm3jsahD/fGpRzwPB4wGauzY3dsYzgsdus+sx185/PjXPpnWdx6C/PRne1vSQQfMal1/7Kerdz7bwLPz/fX3jqehzLdN428rdPc5B9Lkb52PF5Njb1jEuvnZ+nc/s++vqv+8HVPPztq7ho46fJfmczqz5zHaV3nsW6H1zNv1/+2vntjgaUO99y9bMeb+6a3/VXNx23378u7f9VpvNv/uZvuOSSSwDjnvsnf/InjI2N8da3vpWbbnrx1/RlFHD95zXlSurdPjLS1Jd6pI8YqWuYlEZyWDcMZGGZJDlhwIi2BJVem9RYTJi0sOtzLJ+RDCIEU2st2ndEhK5ZbDfabJIThm1JjSpGzxJYTRP9tZqaWqdFdYHJx8wOxJQXmZV1akTRaJUECUFqVFNeKMgMakpLBfn9iihhpJjVbpumMAuFqXWS3D7N9AlGwlrrMGybiE3um9CQPvKUY6tyjBy3dac519IiSXrEMLb+jDYywIqiuMzkmGnbAFcZG+mjU9OEGVMntNZh6jYqR9BoNWxpYkoZZteG5Lgm8qDSbc/LCwEyhzVB1gADp2LAZW4gwp+Ws7mggtJSQcsuw4BZoflemDLAFIy0NcgIrIYkMxgzs9qibYcmTBg2MXdAE6bl/E0d5CAzqAkrxixqZqVtzs8Cb0ZTWWDOya4pYs+ejaaDPxlS63aQhtDBmzagvbzASDSBWbMfk3fVst8sttIjJpAhIyMnlRHEvilzo2evXZiWszJcE/nXtsAbKRNnfKxyA3uyim0JlO+ibYlohoiEjVU3+ZnYEhFEaEug0j5hxtQ0FZFGBhH1vgx2IyZ2JY02G6eqqHabudxoMTmcsQPF5S5eUVFckULOmmfFjkRLQWWhjzcTYddjiss83JImSggmTzZgq9pnxiDuDLDvdQiywnxvAeQOagorBU4J7Lphsas9BnAVV9qkjhhWLzFom3I8XQKwqLebaxj7kBgXlJdpEqNmcVtYq/CmDLhodGh0OsYKJVZoysGUlwhEZIIwpRUaf0wws0rglI3bc5Q0IHLklZ5hhOpGqVDvmC0TlBfUF0S0/9LGrUjqrcZIyGpKxIAp7eAV1awzr8ktyx0wNRIrvQ4ispleC0JptDB1LGNHkNsnqL6ujN6ZIfYhWBhA1cIfs5heFuONW2QPauyGuRftusn7thuz98z9xlW71iHnZZP2OTOUD7Rwxf/6V+A/pizAsU21BXgDHmHOQSVjGE5SXRriTDpYEw4yEgQdEdrS2KkQVXRhIgGtMSRjagtAOwp70iHoDHHHTJkb62AaHBCLalhDSewlFXO8PRkaHU/9Euv2JolUQL2QgNBCuwohQM7W77UOJ1AZRby8Rlz0yPeWmHqwG9bUqFR9rNYmUSt0t5Y4MtoCZQe7ZOHOgrMjykzgI8DowzGLA8XBrStpXlGk7wbThzd+415u/swbqLdLWp+EmdfWsQT80/dfg9+AM1b388tHV1Lsz5MqgFsUVDzbyMP3+ygbygkfvyR4YngVVtKcX71doPemETGc9bHLSS4I2fSO92D3eQQZyO2ysGsaf0LgljVR0iLxwG5OvP4KLAGn/NUVdExW0bYg01/BbiRJHKkQp1ycySqUq0itIZUkM20c6dJ7zbOi3p3AnQkIcw75/SFRysKfighTFolJSW5fhWarhz1R5ku7L0AtOIc/3f07HHydWUQsu+2P6Dwc4tRsXt/zQeTGxbTuicge8tASDr1FsOhH5gHZ8s8hsWcZmasnCdKSrvssygstWh8vUl6ZxS1FjFzQzuVb/gAVC6Jpn98WF9J8qJVsJcLekiY/rEn2zxBnEnhDNVLLuhAxLPvpH7LwB5JGCziVmCOjeQDeffD9LD4YMnWCQ3a/CexkjiiidIKzR/6E/V+4ETV6Iyc+/A62Dd/MgajC31z9asAsvOcA2HkXfp677/gE8ZO7aDnx+CUi5kDDzBrB4mf5rPK7JbLfMYv2Pl6YVPaMS6/l4aPA0rofXM3OY8BT6Q+e2bdnk9fCcwOUY1vfW3eAMqDnhbLCR4/l0cc8GkAdzZqu+sx17P3Ulc8Jnna+5er5v5+v6+0Zl15LdjYvN/udzfN5nnPHfS4QvfdTV3LRrb+a5ZzLBT3ePp9LGvzwt696Gita63TMOMye27GM4tEt+53NnMHTx7j0zrOeAe6OHrOLNn6agxe3AIa1Pzq/9+FvX8UZmDmb3158xnd3vuVqVn3mOnbffgsX9m5g76eu5IxLryXzvSwjHy3xgRufyo9edusMSzq/wMB7P8btW68xc+GY6zV3PX9n1YnAwWcdo//27cUwl78BoLO1tXX+/1JKPv7xj/Pxj3/8Je/3N4Lp9CebhGlJkDHlUJyqwgoMW4HWWIF5+dP6qbzPnEXrzhpojVsxcjorNCCh2mVTWmjhVMx246eY/DA0BGmzcC4vkuT3CjKHoJmRRL7JN8vtw5RYaLeQ4ezCt80wgFYTIk+QHDPMa2LMlDERMcyssg2r0gqlxXK2dqOgdTuESUFmUJlc0NAsisO02Vej1bBKYBZbcxLF7KBhivxpTZAx21T6LKzA/ICGCQyjoYyMNjEZYdchyEgSU2peKqwsA0iVbf5NjSqUDanRCK+ksOuaao+k2iMprBJkD0Wkxkyuq9WEaqeN3TSA1S0ZZ1K/YGSYYVKYcW8YttIrKkqLJHbd5KcGGYlTNmBVWwYkyMiA9sSkngfhXiHGappFfaMNql2CIG3ybPP7FfU2SZh+KrAQJSDI2aa8SmDks8o119+f1vgzmsSkMnLr2EgmnYo5xpwTspaC5ERM9lCEDM31FBqCjGGw0ZA6XMOZrGKXQxACqxqAEOBYiHqArDZRvgtCYM/UkKU62rGIswlUwqHZkyVO2PjjdfzxOsqVVBeljUw8ZdNodwiTkuJSG3/amF85FU1xqamvWm83c88rxUYOakOjzaa8QFJvFRSXOhz4XQcZwegrjfGRag1AmPqtyWGBc8Sl3mnmV3mxieAVl4n5HN7YM+MJZn4nxs01EBHzLrdewbB3MgKnBmFemRqyRYE1C/pTQxK3ZOSAiTFBx/1GdTAH1lJHZp1qpWEJ08Pm2nJU0KXZYgCzyXMz0vrcAU3bdkXLbsWiH83O25mYxJTCK0SkRiJkpJGRxiuaEjRWqIl8sEJFo80mPRrRuSUktx+W/HuDrl9qeu9TtG8P8WcUfTe4pgTObk12q4s7ZZEe1LQ9atG2XeHUTHCouESSGjGGTImJiMSEOZ4W4BWNyVdmKCJ8sJX8bsEX//5tL9sz0xvwaC5pEqditK1RrsIu2FhNyK2bhuVV1q8eJJFvYO1LImsW4ZIGuYVFrCkH2ZB4Iw5hR4hwFGGPmTdxLkbZmrDigobwcJrwcBq1tI62NV4qgKEEuuhSH02ZerutAVbZxs81UK5GpWPiRXWQGutAEnvapvpkKyKCeNJHj/no0QRtuSpTD3Tj9Xv4Y2bOOyWjHPEnzatlt6bWYWFXI1IjMe3fSFHrdJCJBPdNr2L8dE3+QIBbVfjbE0QVhzCnaLTDY/euRjYFiVFJmIXy6hC0cUVuLG8SLAhgQZ2gRSPWluddkKtn1FGeJvGGcWSsKS9wmFrvU1glKKxV86y+W559HvmC24t/z7aP3oByoHNLxTybY02UckgcqSArTZyRIpSrEEWomQLU6qA1aI2IFLLaJP3EKHYlwB83Od5OKUTZxqArMRHRbPexazHTp3fynuxe5BenKW1t549HTwFgyaJxKn022f46Y7+1gnq7TXKgSHbXNLVOiWhKvMkm3qTJDR97hUt5oUOUEGQON2h9aJTWJ0vIQpkgI5hZ7RJmgD1p4rEEdtFi55EekmPglCPjXl2IoVrHmqmgPZvkuMKfUST2e7jFiOyhJiLW+AMO/oBDYkziTTfIDCnSIzF2A/ypiMSYYXTnWvRkDoDldprtZ948z3wu+YfPo0ZX8bNvGaD9pp0ztGwrAE+xoyd/8Cnm8+FvX0XffcH8fufYopk1goe/fRW9v2McYOcW8HML/TMuvfZpgGOOHXp9zwc549Jr55mlo9vRIGC+HbWAndvHsUZCc/sDAzCOlkMe+93RH6x7mrHNRRs//QzQ82xy3KMBzxwrdnSLxsZRT+x8htT09T0ffIbk+Nh29LnPjduzsZyb5MXzx3j421c9AwzOgdFN8uLjjgXAOz71E+ApSfVcP489T2A+F/R47eixOrbfxxpbtWwrPI1dnCsN9Gz1ZMtvK83///at1zCz1mx/vEDEeRd+ntu3XjM/zg9/+yp+cvst/OT2WzjvQjPn5+Zb0JmcP9c5Zv19j76bvZ+6khMeegdjHzVBjex3NhMmBZeu+iUfyA/Nn+vtW69h6b+YNeKqz1zHw9++ivMu/DzrfnA1anQVanQV28+8+bjj9evW/l9iOkul0vN+vdj2GwE6o5RNeqhJdl/VXHAF3nREvV3iT0UEaROFtZuG/ZwrC1FYmcCpxtQ6DXgzC3NBZjAkNabwC5rJDbDg7ojiMtvIbgUkJzTJccOmisg4fFrNWaZ01uVWS8MOJqYU6eEYp6JJTBj3T6tpvuOVFdkDZgHSvi0ADZl+w4y27IvxCwq3YoBVvUMSZAXTawTKNWwimIV9vcvU7UyOm2Olh2NiR5A/oKi3mfIcXtFIbZNjitw+YyiRmFK4ZcMKHrrYmCg5VUWYlNQ6zNRw6prUSGTGxxM0sya/s7TEod4ukbEpCZIeiskdMOA0TBrA5xU1dlMzs8LGapj8vyghKC42Y+kVDbsmlKbRbgDzXG1Vb8bk8WUHY5MjqEzfANJDer68hl03OaexJ0iPRPQ+GJE/GM8aLhkwkRuIEEoTpo3UMTGpqLdLlCWwG2b8U6MmT9OwcsZQxymHpAaruBWNXY1ITMckJiMSkxFOKULZgiBj5KT1dov0/hJtT5ZJTEZkd89gVRqEbSlkPUTUA4hjUIow76OTHs0FWaxyHZVwQAi076Atk0cZtCep9jooRyLCGBHGVBf4NFoklT6bWodFYiLEqZt52GiVuFXDmmkJUUKQmDRBj0aLZaTTGQPea90QZsw90POAkXr3PKBxapq2BzzShw04dGrQuSUmMW4AfuYQ5PcqMoc1sT/Lys2W4xHaAEYtDOOoZ4Fg7kCIFWj67otRrgkcdD0EqRFNekiTHopxC4bh9qcN6E+OG3bTmzZuuC27avgzivz+mNSYIjmmSIwbwJYdiLECaN+mSB/R5A7FpMYi/JkYu65xyzF2Q+FUYux6PAtMZheQAtxCOC9RrHc4+JMR/niDzKDJw00fbhBkJJU+h5a9dWLfwqkq3LKpyylDjV0MZksbmb637lI0WwRBzjwHwpTAKyl6NjfwSjGxa+5h5QoKyy28QkxiKiY5rqj0GfWAsows/OVqwdIG9rBH4rCDVbHQniL2Nc3OmMLeVoKyy45DvYT9aeKkJk7HvGbVXuoNl41n7Qep0aurZkFcsaFuoZsWomlUCJ09BeL2EJVQqITit9Y8ifYVWgvjfD0m0a6CTIhWRo4b7c9gNQS5bTaq6NL+qCR9CHIHTG3i5Bj44xJ3RpI6LGjcYfISU8PQukuR7Tfz0GqaQEiUMIDOCjQjHw0oLbG4798+BsDI+0+m+oF2PnzBHQxeFjL1thrpIU3LVtuYoXngzoBQs4vC0JgeycAEEN3DHtknXPRoAhkIosCeP9fkownyuwXje9opLpOUlgnadjToejQi3W/cyIWC5HhEYiome8BIXU/7iyvIH4iR9RCr0sSerpq8ayHQiVlHXs8FZZQ4ulqDRhMaTbQtTW1ez0HbkijpYDVj7HITtxCQ3VsyAdapJs6UYUdP+c5H6b97CT2bI76/bSMzqjbrlAu1Hp9H/+IG/EKMiBRiskB6OCa/WyKbIbIZEiUsvBlTnmvu948wRISzaS3KKA4S46YcUHJYkhoGOZAgMRXjTFbNb1ApgjCEegMRxiTGQ9JDTTKHNHYlwJ5p4E7XyR3U5A6aYK01UyM5GphgUUHhTtbIHjIu0JcPncW529+COwMPNQ0ru+YXl3LqX17BP3z2/3DwdTdx2l+Yuqhf2n0B3/zyG/nJ7bew9MtfZONfX4EaXcWWPzN0+Pmv+Sznv+az8wD1pKuum5eH7r7MbDO3QJ9bkP/k9lvmF/ild541D/ZOPM+UArtt5G95+NtX0XVD4nndq9nvbJ4HCB9824/n3z9afvvwt696Gui97ZJnMqH2+iJ3DD9B91t2GgAzC7aOxyg+/O2rjmuGdLxtz7j02nm269laNDY+//9N8uJ5p+S5NgfS5o45N8bRjtzTjjPX7lS3PgOwH+/vO9Wt3PTVNx73XG7+zBtY9ZnrniaNvujuPb+SKZ7r//GOOwdMaxNGPfPktU8H2rdvvYb3rn5w/lzmAKJ6YudxjzsH3OZkuB9+6w85fLUk+4+bUaOruGP4iflt777jE6z7wdVPO9fz330Z57/7MsauqHPCQ++Y7//bvnTHfFmfI/+6Hrurk12fOZFN8mK2n3kzXdeboMbBm0/m0b+4gU+07ufHNZ/NX7iRpV/+Imp0FXff8QnU6Kr5++DIuS614lNzet0N5h57wyPTzzqW/3/779Xy+TwtLS3P+Zrb5sU2ofWLKCjz36SVSiVyuRwXLPsIsrWFKGWkWVHSRsaaareDPxNTXGJUxK17ApRlpKmNVovswRrNFo9mq42yQMZmsRe7ArdiSqk02mz8yYh6p9mHV1Tm81JMlJQ0s2YRoiV4JVOiQluGpdSzeWvp4YhGi4WyoZkVpMYUQUbO9tW82nbElBYb58NaD3Q/HDJ+soNXZN6gBAHpYUXkm4VQ7JrjpMaNrFLET8n2Il8QpgWxZ6zog4zALyiKS410NjFp2Jdap5H8hilBtj9g8iQXf0rPslgCu67xisrUdczbNPMSf9qUmAlTEhnq+fMU8WwNyibz73sFAxRlbIBQo0WSmDR5s5Fn2K8oKUhMKhotkjAN/pTpW5A1eZT+tNneahoTDAOMzRhUeiU9D1ap9fjzx5GRJkyY8W3mZmWLDY1d16SHmpSW+kY6awmS4yGVXofkWIQ32SDKuMhIoaXArgQox0J5FjJUiCAmTpp5EKVsvIkGjZ4E/ngDEUQo30HWQpMf2ZLAHS4aIOlYyFIdlUsimiFojUr7VBYlyewvIRoR2rdBSogUIo4JutKEaZvYFSTGDUgK8g7KMm7NiYmIerttwFU1RrkSoTRBxjKsxmQ0+7eNtgQyNIAmOxBiBYoga+NNhyjHjJNyBIUVDp2PVonSDlHSXFR/rAFS0GxzsSsx5cUusSNw6prYAbdirpU/HYE2Na1Ki1yzqCzHxEmL2DVAwylFiFgR+xZuoUmzI4FVM6WMoqTJcQWIU87sgraJCGOaHUmCjEVqqE6csNG2IMhYT92f0xHKlchAUW+3sQJIjDeJEhZ2zSw6rXoEkWFGUSArdYgi8D2iFrNAKC1PErvQ/sg0CEGc8ohmpc1aCpxigFCKOOEgmzHKM+waGgorfTKDISLWVPocnKpGKI0VahJHqjQ7kmY8DxdRWR/lmvFVtkTbAqcU0GzxiH2JPxUgYs3UCUkeu/G52YEX25b87RdBaGQoidMRVsU2uZoNSZyNEIEEJWhbNs3kkTwiEshcwNLuSQ4c6kJ6MWraw++pUp/xSbQ0aEwmcNsahA0bXXKQTWkMwDA5ibFn6ujGKYVdMs9JqzZbdqloghSZQTOftDDPNuXMBtWGTEBv7vmthdlexsb4TbnmWRX5gijxVP3h6pKYzofMs3Guld9WYvGfVDnwnl52X3YDy376h1CzSRyx0CeXkY9mCLOmnmp4WoXE/Wlj8tZlnHGVY/pbWxzjTlomsJCL8cfNNRXK9K3jcSPJT0yr2X4J2raWiXIuhWUeLXvqREkbtxBgjxcpndyNW4yYOsGl78djqKRHnHGxygGy1kRlE8jJImpiCmKFTKfAdZ5+YT0PPAeVdFG+gz1dRTvWbCArhTdo8g3ry9sYOs/Bn4De+8scuDjNpRfey3f/6TU4FfNcXvHOvez84SoW/f1eyJvFf3NBHnfCSKYnT2+l/dEClWVZrKYicaRCZXnWmHZtG6Kxrg+rERl1h9Y02k2ecnmBRddDJeREgaivDdmMkIdGIeFDEEAui56ahgXdoBRiYgZsG51NA9BYmCWxZwyEJFzQYty+D4+CZaE6W7nttn8C4NRrrmD6tJDXnLCHt7Zv4W8On8eBrQv557f+X37/oT9i77n/AMD6r1zB7158HxdkdvDqJXtZ/7+uI31EU+8QrPxdAxS33b2S3ZfdgOzeO8+GLvvhe+m6z6JlW4Gf3H4LABf2buBOdStqdBUXH7yAW5fdBTD/vWU/vYyBd30CMID3I2vumpchqtFVfLmwiB98ZBMAP/vWTay45XL2X3Ijsnvv/CW+btfr+EjLwPx7m+TFlN55Fg9/+yrU6Kr599f/r+vY9tEbuLB3A9ZJa7l96zVc/OAVFF41Od/Hsz52+TMkoHMs4RyTefvWa1j12evY+6dPPYs2yYsZ++jZNNqMmmAOeM3t53jmPTMn5mnZVviVslk1uoo3XHTJ/Ji+4aJLuH3rNU+T3677wdX0XO/ys3v+9Bnfn5PxHr2/o8dqDqid8NA72PmWqzn/tZ9j5MNNtp95Mxv/+goSE5rNXzBjPnecykJ/fjwOXtwyv/9Vn7nuafNibjznznn3BzOkOqtsO+Pm+WNuP/NmNl3yP5g4JUHvz2eoLc4A8LbP38FH1tw1399N8mLetHNm/r25Jrv38pU953F57ghfKSzg/37vzb+SRZ4buzMuvZby20r0vXUHh/7y7Plrp0ZXsfIfL4cFDfa95ptc2LuB4Y+fzaI3DPDjVbfxnsPnUIm8+fl8wkPv4P1rHuD6LefT/85Psv5/XcfrLnmYe4dX8Ogp350/7rIfvhdVbzD4kU9TLBbJZn99HG/n8MWpb/8Mluu/oO/GQYMt3/3Ur90533vvvc9721e/+tUv6hi/ETmd2rGRhSpuxULbEhkksGoB3hhGXts0P1axJ3HKEVY9xK7ZREnH5MbVJWFSGuasFlPtcUiMh6bUQ7eNUAbIZfsbxJ5FM+fgFYwE1G5o3FJMkDV1O9PDMdUuy4CcymwenWuMehLTMSI2eaZWU5OcjKl027TsDqn0OSQmNY0WU5ZCOQKnZmR3hRXmPbekCFPGor60WJr6hRXNzEpTIiY7EM/n7tU6bOyGxqkayWdyPCZMG9muXQd/MiLMWHRsDakscGh7skplUYL2bSFBxiym5YwpHVLrspB5SWo8Jp7NxazP1jF1y4aFBKi3WaSPxEQJgVcwY2IcTi0SExHStsgOhGhboANBmLRwSsYNMXaZdYUVpIcD6h0OlIycOJ5dUDqVGKdiFvaOb6ZuYkIQ5FySIw0QAhHNri47EnhjdcTiFP5kQOxJ7FqEXWrS+kgVlfKIMi5CaXIH6uYcLIFTqIMy4ClOuli1AKsGQVsStzLLKADJ8QIq6ZI4XEHOAkmkRNYMBe02Q7AkccZHRDFhdxarHlE8oYVMfw0RRKQG68haML9AVJ6FFgIZxli1CKseU+vxKS8yK2i/EIMlSA2HTK91SR+JEZFm6kSP1LCRHmtpAJ4MFc28g1ONUZZh1ZzZ+djM2SRHGjTbvdm6obOM4t6QoNUj8k2NT8DkNC7ySR+qE2Yc0kMhjXab9IABgLEn8SfqaCEMWExaZAaNpM8pNbEaFrIRIhoR9cU5/PEGdlkTpz2cogHTc8eKZ4NG7lgZmU+iXAsZa5xigD/cpLYki1OKcKabOEVBs9U3pYpmAafViHGqkuSRGqIR4NQDVCaJnCkZZiiXRlQCc62EgIRvcr4njFSkpRaYHNswhkIJqzWPVQsQQQSxImpPE/smL1jbAuVK/INT1Fa107a1jHYtghaX/J4ayrdwJqvE2QRR2sWbqBk5ddpHz7oOm4nkIMsR2rVwiwFxaDN6RoLEhCY9Ev3HPCCP01p2mHIttQUKMVtmxK5YOCWouhZ2VRJlYqYG8whHo6VGHk5QbvEhlNiZAF2SBHEKpyGJswF2wSbOWljDHu0nTTC2t91MIIx8HYzjtluwqPfG5HZbhCkTrModVEyvlQRpQZg2LO9cyRhlP5U3Lhyjpqj1GvYMadQYgSWIHYE/o6j6kpa95jmQOyhIH66gpeTIxyMy38sS7cgRHdzB8q/HrG9cAb0xvfcIlKMJRzMIrakrQWJC492ZZvqVAXLKwakI/EkjH3fL4Oyw8AuaRqsgnrHmn4PNFlj8kyoiVgy/JkVjUZnWf0iTPjJbJqYW0f5EYNyrp5sQa3Z9opO1Vw8Q93VQXeBAsUSwZDHeWBXl2mjPnp23EplIgDUrUlJq/prq1jyiXDWfSYlVqpv5XGsS9uaxGhE66SEqdaKkBD1b49qSJEYF39z8Knr6TSqEU9HUIscY+rhPsatOsYG2zLPfKyhEMyQ5UjdqjEodobIm0KM13pESoloDaYElkY20KZyuTPBNF8vYtm2AcyoBSqODEBEEEBt2lWQC3QwMgzp7rj5AFKMrJRx7NuJpWehaHTlZ4H1DZ/P4VzdgKY2fb7A+Pcwt46eTdZs8ePH/odNKIw6mOC39diqPt7H0/EP8RYeRyO4Z7GXbR9Oc9bHL2frxG1j+s/cAsOS+AC4zh7qwdwN3DD/BwHs/xhK+wOYv3DIPkuYAzZdmlvC9s78CwLm//QUm33M128+EZLY+DyCu++VlfGSNYfPUmatYee+7+fCGu+dZ1S8XFvG5N/0Tlw+dxQNfvg5eYaQPaztXsKvaw42Y/dwxDF+aKQJXGaDLKk79yyuobow44aF30McOhl/bghpdxdjn30vho6vmgU32OxfDt5/+bDAsrQFp5RVZvrT7Avru3cRF3zXAcQ5Ubf34DfNgbo6p7DtqP8cC17l29N9HS2SPbrdvvQY1akDnzIl5ln75i/R/6Or5z3e+5WrO/cYXnnacubb8b/azNPtFDlz8dwC85v3v5Z6/ewp4zv3bc/1nWcfVqHNy+D/zueBv/5AHvnUdG7/7UZbf+n76P2T297N7/hQ1uoozLoV03mf3ZTew7gdFtp95M/b6d7AvrPA/X/UZ7vgX2PyFG4FZlvjiFrru02z+ws3sC02gZvuZN3PWxy5nZpMwDOFRKXKyey9L/uHzHHwds9f1CU546B18KG/6vu5T17H9Qzew6jPX4Z14Ljfek6fr+gfZqwzgPP+1nyP/V4PcuuyueRB86jWGbYz6cvAWE3Db+Zar2cTFTwOqZ33scpwVgj/acO/8sZf96xlc1Gnui/5yK21+jaYOWXvH5dy/6Xr6rAwfOv8bLP9nRbzc5dLWB/li92NPu443v+4G/nHwZG7g17v9usplX2h7sUDyhbTfCNApohgcB6IYEcXYc4vNSh1ihReZHyuhlPkRjjXSlmjHQjsWiWZMZrpKc0EOLU0xezDsRtvWIkF70kTRkzZaQHIsQkRGGioDTaXXITUeoYqG/csOhDTajKTUHBiSkzEyUCQiTZg00s7IM1LIWpeNX1CgNTI0gLW0yOS2BGlJyx4TKbfrinq7RZQ07CSYBVnL3oipE+x5kxMwErPMYEjsSaymxC3F2E1TT7LabRMlJV4hopm3yR1ooBxJYjLEKTTxJjRxyiX2LWJfkh6ODJvgGLlhtc8jOR5jhSZfag7MOHUjZ1WOpN5hk5iKCTKSbH+dKOVg12LcmYBGl0/sCtJHzDiFKUF2ICL2JU5t7ppCZsy4qjbzDm45MrUobYm2rflou3YslD9rPlKqI5oR2rbwwxhZrJFphmhbYrk2shYgwhjt2sh6E8uWyDAmzHq4k1WU56A8BxnG1DsTJPdPozI+cdLBrhlgKWaFAXHWR1abiEodnUmiUh7E2iwOAWKNUAp7woCRMJs1NUD3VQzrmfFRriRqSZnyKNNV6ovzeFOGba32uSYwMVyfB7pR0kaEGqsZ0/5Enel1CXL9ivzeELseE2ZsnIoJmohI4RYjtC0IMxZuMSJdjtG2yU8Ocq5xc54xc73eYeNPx9jlEKcExeUmsmfXHTP3U45ZVAtIH6qb/qQtkofKBB1JUGDXQrOwlQIZxPPXRNs2WIJk/wzac4iyHnaxgUo4iDAmTrlEaZvEIbOoqi/KzZeZEVobUyXXJrV3Gu05EMYIxyIxZMDk3OcohXO4DkKiMwkIQmS5BrYNloUYm4JEAhyb5oI83lCBuDWD3ZjVsYYxslAm7mlFurYBm6Mz6LYWRBBgjxWJWzPEKQdnuICVS6LSCZxKBJbAnq7OBz2cqSZx2kdWA6woMgtoKZHlKjL5lARJTsVgSaKunGFG67Fxuj0cmDn3MrbsoMJuSGTkEmQNWIrSkNtrjJmivCZ5yMafespYqnFHB/kIIE2QgdgXJNfOUDmYx6uA9ViC2IPGjzrJaeYwJzKEzGFzvxeXC9ofkQRZyA6YoFWQFfjTzJe08QqK0mJJYlKTHhTUO4yCQgsj0UZLkzceaNxiTGIspNnqYAUKb0bgFczYVfpMwCbKOEQ7UrRsmyE96GN3dQKw5B8Hqa3tJrl3giO/1UtyzPgBeEVNo0XilRW9PzIljMKUUap0PxJR7bSwm+CWY8KkTWrY5AgDszV0JcKWrLi5SnFlltRPtjD1B6eS26+xi01EEJGoBYhak8aydlZ+KwDHxhqeZMmPHFS5gr91AFrzSKWIM765p6RA97QjxqefCp4A+B6iPptz0QyQVaNwEOPTkEphlQOwJcq1saIIq65ITFh4xQirUCc3kCBK26SO1JGhhxaw88nFpMeAMCRe0I7cN4i0JNSNIsEK0ujRCaxyEpIJgoWtyKbCnqyi81nEdAGUQtcbYNtYlSpoTaqaN/vIZdATU4iET7y0h9izcJ7sh1od0d4KhRI0A4TnosMQXTPPHRGEIAXCtmGmaCTHroOIIuKeNopBQHGlpvNRTerODDcMX0BuaYGUG/DHQ2+k1akhQ/jAinvpWFPmh9MbWfbTy3ASIXvO+QdO/twV/NbH7wPgD076JQDtXzKO4ku//EX0N08FTG3VgffuRY1+jdffYuSIG//6Cs58x7u5ccEAJ111HVs/fgP3GOzDlwuL+JN1hilaefPlJJeVOOmq64gWaZbdcRkHL7yJz0+vmL8/P5Q/DMDF6c2sSW5k95k38+XCIq775SauPP3O+e0u7N3A2EfO5iOfWMX3zjaA6vG/NUzqh/KH2fjRK9j6cbPs/+5XruOdey956hnwQMcsCN4w/96am65g92UG0N7/N3DqX17Blm/dwMUHL+D1rQ8yZ2725cIiKFww2893zIOZ0R+Y/MQ5IDhnbDPXjgagBqTB0YZp525/C59prOKKre9APJTDyUP/hwwbt+amK/jg237MR9bcRccn+ln3g6vZ/oWbgas449JraX1giNtG/hYANfp33N+A1B8Pze97+Pvr5/8/ByZPeOQKCidGnP7uXaSlz/5LTJ/e9+iTRDs2os40YzHzliqbX2kQ+pzsdXHrDCudNHf8y7fYtOtN3Ln2R5x01XXsvfZK1Ogqlt/6fvaFFVY66fnjTm6E9q2asz52OWPnxjz5xi+z8bsfZf8lqzj4Olh+13s4cMEqLuzdwPbhm5Hde1l2/bWk6+Ze/+G7vmD2dzpceP2G+SDGz37+FAt/6nuv411/vIgtnzbX/Wim3Mhyn3ZJ2PyFG1l34xXsqPRSye1ie2jz92/4Gq/xNese/AMaY0lGO+s82mdx8KKbCLX5DdsaNPD9EO2F3Dh+Hj/dvo4/PO0XAAw1WjhQbsMLKvxat9m8+Rf8nd+AVigUuOmmm9i1axdCCNatW8cf/uEfksvlfvWXn6X9ZshrF/9PbOGAbxYYKukiawGUKobN8GbD7I5FnHSxZ6pEbWmsmRo64Rj30GIVfA+dcBG1JiqfMovOo37YVcJBNiKinG+YCq0h1qiEQ3lZkszBGs12D7sez8rxzCIgzHl4I2VU0kVbEhnE1PqSyEDjlkKCFhctQYSa8kKHxFRMMy9JTsREnnF+zfTXCPLGQdAKTB1EGShQmjhlGylhPcYpmGM2OhPYs9JF5RqQC7NutY7ELQZG6ph0sMuG4dGWiY7Puaw2uzNGZusb84ig1cOdahoW0DcyR5SRZsKs66cj5vsXpgwQtusxWoC2JHYtRFZmZZTZBEJp87eCqC2JVY9MX4RA+RbuSAntWEQtSZyJCkFnGqGMcQ+AVZ5lOMOYoDONO1YGIYhakohQzQcagryH1VSIWCEbMUGrhzdWA0uYXKjZJuKYODsLCrRGuRbOeIW4JUmUsnFmzPjqWcAatPhYjcjIN5UiaEvijZQQQUzQk8UuNokzLrIRIxsByneRQWTKnPgSdyZAuRJnukbYniLyLeKEJDHaIMy6uFMNwhYzrxutNjLU+JMBdrFJ2JqY7YNr9uNZhGkz5k4xpN7p/X/s/Xe4rUlZJozfVW9aa+14YudIk3NOgqAiYhxsZ/Qz4QCD4CACPybgOINxGB1+iuCMKAo4fgw6DoJhUBCUJIzY0I1IaBu66XT6hH3OTiu9qer746mn6qla6zR0S6unta5rX3vvtd633norPvdzPwHFzCDfJ9a+3ShR7LXoBxny/Ra66cjcF4AdlFDzBtDag1wA0LMW6I039ct35+SXemgFujWUtmVtQEK2sagPFCh3O9QHC+RTg2KvQbNZImsM5cucuUi7xzs/b9oVTYG/nPxscwqSVUwouI91c1D3gK4N6gO5y/1J66w8sQ9Yi35t6MfFKgXVkz+azTX6lRK6NdDjOeyghB7P0VywjmJrgvoCMm9SxmJ+oEC53zsLCfLTtFohm7Xkm9t0QNcDdUPCLkBmurM57JEDUE7RZTdWyI93dx9YW/WmvJjXfm7RnlQAsxkxScMBkOcwG2SKq+Yd/vjTP3PXN8Yvozzgx3+B/Os664OuNWsK84Pkk5s1lHt4dJKtNQAo8nlevZ2CgLGP7u4VGps3GNgMLhAYMZPjixRW7oAbY6DaCdFdh1vk792saLenWWQtMDzVkpUDnE9vTyl6Vm6doFsp0a5RvtT5wQwrxzvXNnJ36Aca5U6DbpTT2gag5w0wnmLvCZdidPsMe/cZYeOGMbLTY9iTW8AVF5P5qSVFm2oN6sMViv0OJx85wMHraX3MD+XIarJu6Uva80bHXbRpraBag2qLQJG+9QTs0YPo14fIT+wCTYszX30pDn58C2alov1ufwKzswt1n0vRbQywe9UQK8daMjs/OMDocydpzgA0bzJNc6co6IzaHQNtRyb5PAflvFKKGEKtoAYDx1b2NMe2ttHf50JMLxph9XPbUKe3YS45D7v3X8OBvzyJfmMEW2Y486ARyrHB5rVbsFrD3nwb9IFNwLGLzWWHkf/l54C+h6oq4OILYNYqZFv7gNboN1eQbe0B0ym1RSmo9VXYMztAlqF9+JXIxw3QW/TrFa3l43vA9i7s0YOkHLzjNLrLzkO+TdYpAKBPbNPefOQA9B2niOU8cgBmkANK4QvfsQKbAwf+mnyptx6uoWvAVEB9tEM2zpBPFL79W/4cN08P4m+2j+DgcIrDgwneevn78aa98/D5+Xn4z0f/Ct93M2n+//wT98eDH3wLGpNh3FQ4OJjiD+73xwCAh/7Fd0N/iISwT76CwNkXdw/g9G2bePSDb/ImiS+749H4zO75uOHW83DjM960sCaf9ulvQ6YM3vegPwAAXPn2H8S/etqf4Y3vfzp0q3D/R92Mm999Of76h3/ZAZM3+3vv875/iSveotD8uzP4d1f+Mb5pRGfslX/0fNz/ymP4/PEjuOGrfwPH+zGe8luvQD80+LYnfhy/cMHHAQD3fesLccDFEvp3P/pW/NaJx/t2A2QmeeO3vNGbBzO4uf+Hvh9F0aH4sw1c+8qYzyLw+sv+N5ueskkqEMDQL37u6/BH3/UkbD90Ex/9r2/Afd/6QtzwPW/AQ/7vd+Pq+3wSv/37T8Wf/8vX4LVbj8e/O3wNvvHFL8GHf/cVAGLzWW7Xb+4fxus+/3T85SNjM9+HvvZF+NRL43Y+6JdfBJsDP3D1n+DX/uqr8D+f+EY8tqI96Hm3PhkPXj2Gd9z2CJwZjzA9PcJrnv5byGDw45/5Flz32N/CMz/3Tbjh5vNx4zOJoT5tJvjeG/45nn/xh3D1yj5effp+eOUhat8bdy/AL/7GP8Nfv+SX8fqdS/Hrv/pN2HtEjRu/PsyH/+emr8HbrvhTvH7nUnz3+mfx7M98Dz74kHfi402Dgerw4GIUtf9kP8bRbBWv37nUKyru+z9fiBu++w3+mn9+49fhP1/yTg9+n3frk/F/f/9h+OxPvQx/cOPD8O/+6tvR3LCOr//aT2A1q3Ht9sV4wSUfxB+efgRu2j+IM+++EPUB4EFP+QJOTNZwxcZpvPXy9+Mntx6I/3T4s3j4x/4fNF2GZlbgyCFS0DzqyK04Xa/ijUfegwP3u/GcMzVlfPGY7/hp5MVdM6/t2jmu+d8/ds69syzXXHMNnvnMZ2I4HOJxj3scrLW45pprMJvN8J73vAePetSj7la99wqm0xxYhbUFCW5ae/85DQB1C7XnNC15hnwXMIc3kJ+ZEDM6C0KgHRTEmqwMoJqO/mZzu7ZD1nTo14bIpiR8Wk1saX5qD5vjOWyZY3DKoq8y5HXv/baqY8TIwADZvIFVCqufPgU7qmCLDMWegslJaB9taeRTg3xmUIw7lJYY1vrwAPmYfNeyaQc9bTG5cg3D43MUZ2YoAPQrJawDyMNb99Cv0ULJWgNVZehGGYpTU/Tnkw/b7PwhVm7ag3Wsju6tF2JUZyIwpXqLEkA/zB0wVBRdcZAhnxO70w0yFBMCCdleDd2Q0G8qAkLK9sjOkPmXzYgNaw4OUHYG8/NHGH3+DPqNIbWlM+hWV1FfvIFia0agOidwZlYqzI/Su1UAVGugOwPdGs84tmsFinEHNekBDQxu30O/MQKMheopqIxqOswvWUe5U/v0Jt2gQrEzhylzmEEG3fToD4zQrhUY3LKL7jD1XbvqfLEmLXTT073rQ1QnxmgPr0L11H+2IH9QM8gAXcJqDZMVyGYd8omBzTTa9QLZvERxYgx7PgGgbrUgwWqYIx+T4Lt56y6a89aQTTv06xWqO/aA3qA4pdAdXEF102lUSqG9YAPFradRHitgqxz1BetQlsxU8+0piklNjElVApkiQXhGz+hXKmTOzBSAB/k2086PsUC3UaJZy7H6+R3sPuQABlsdBT1yaXHKDfJz270sw+aNCvVmBquA4ZkezUYGqxUBx/0e7Yr2gvzoOLXBFAqjxqBdzWGcaXo2N8inHQHdeQc979FuVqhupwAp/SblKi1OT2ArYnr1vEF7ZBX57hzZfgNbZYCBN0ksb9tBd3gN1R0uL2qZI5v10LMGNssoWMrmEPl+TSaOZU4Kp0kDPcuJrZnXBAqGAwKcALFOO2P6/OAm9fXqAGpaE1DNM88WocgBWwG9ITPEuoGal1BNC7P65QUauTulOkOBbNoVyq9a7Vk0q5TuyRSUZqkvyY+83iC/y8FpiwPX95gfzNCNKHDU9DCBzPlBjWJioXvyaVQGOPzXPeoNmkcbXzSozjSYXFRh9Zgls3xLUUeVscjHLUyZIRvX0O2A9h2A9oJmQHtn3SObdWgOlBidogBZB/5qF2ZUQLUGpTFQbQ/d0D5OC6kHBhXW/ux6tA+7AgAFoenO20CuNSwANW2gpjPoCY1DvjNFvz7A+X/Rez9l3ZE/KUWV1ch23B7SGWSThpQ3rXtmWRLDuHYR2gs3UdyyhYMfvg12Zw96Yw2YzGCPHoLuOpgiQzfKMdg25Nd8gkzo0bZA28J2HdRsTmBRK2BeE5s+GgJnduJBzTICn3kOuz+GGg5gZ3PYfgpVu+BZzFKe2MEwz8gcd0BAeOX2EtAK2cltoOuxun4xTOmemWVQwyHs3j7NWQDlbRnscAA0LWzfQ504hawNrJYZ5siahvqjKEjBYg3U5gbMoQ2n0NGwAwL9pshgVgdQVYF+haJ6q0Mb6Ic5lB155aA5uglb5uSPnh2F+uIxqDN7UEc20BweodpWmJ1nKCp5ThGN8yn5+WYzans+BX7vCw+F1haTvQF2jx3G+U8jU8Lnrp/AfT72jfg/6w/GdEpA91899f3Yale9+eCjPv6d+PcnHo4T9Tr56z2e3vn1O5fid658Lx55zXci385xy+4m7vcbL8LPfcf/wE47wg03n4+nPOAGvOC2J+Ejt12Op136eQDAK897H+63cQqtyfCFjuSV73nKn+N3b3k4iqMz/MpjfxNPG1icvmqC+7z3xVDa4j7v+5e46LxtfPAh78QXvvbNwNcSCHxoeQrAGh76iy/CjT/iANb9gZ/ceiDe8oknAus9/sXjPobnHfxzPOv6f4E/uv+7cMkjjuFPv+f3AQBX/fYLceB+IT3Gc25+KlaOUvAplRlcf+uF+PZPvACfevz/xPVPIb/Y33lIYD7Y/JjMN1+G5uh/hT7/b/AXvwkXSfab8Izv+gH8yW+9xd/zw5u34Eeuey9ecM1zcOXvvQAPfuwX8YzPfjOuffz/wGOv+V58zTd8Al/3iedi98wKrnjCKfzMz/8qnvXBW3HibZfjmlcFsHmf3/lBfOGf/wrec+bBHnDWtsXT/+q78JGHvx14YojO9iezHO/aeTiyBuhywFiNwbDB7+w8Dg888lGs6gHG7QDvPvEgPPjAcZwYrmF+MMf9ipN4aDnEP3vsb+HfnyCG+Nsedh1q2+JXd++D95++P373fu/AI//8+XjlF1bx1u96He77gefTOD/yj/Gaf/Xr/p1/+N/+Mt6wK42Sgbdd8ad49en74dnr1+KQXsEHH/JOAMBrbv8GfOPhv8J/O3kf/ItDH8PTBhbvm2W4b0Hr4lPji/HT3Qg/dvhz+Klv/W0AwM9v0573O1e+F0+47gdwyfoOfufK9+IpGzfg/UcfAnP8fvimEfB1j/8NPO6DL8F7b7o/nnrZF9D2GX711qfippOHkOcGK+5c+NQtF6IcdLjygi389NYD8KFT98FbytP4vqs+hl/96yfD7hbYUgRs/7y5Eg84fBLmXM8fYvGPMmXKy172Mnzrt34r3vjGN5IbBICu6/D85z8fL33pS/HBD37wbtV7r2A6n/pV/wlD5ycGAHruzNJ6ModkYbC59CAJCfsN9M4+CSSjIVDXsOsrdKgVGUyZQxkCS9nejBiLLEN/YETsKINTrYFMQc0a2LKAalrMrziEwY1b6I+sk/8XgGzSIjtDgq1ZG7l7WmJZW2JVmvNWyS9t0qBbLaEbAirFmRnQ9kCugykVADPIofdrqKZFd2Qd2bTB+Mp1rH7BbaqdgS1z9GslVGeRTRuYYUE+g7OW2lC3dHgPCuQn9wio78/Rb4zIR6dzQSAck2hWKgKg8w795hB63sJUhTc5tUohG8/RHSAAYHKFfNYjm1BwnW6lQDbtvG8OAztYIN+voSc1ukOr5Gd4xx7s0OWxnLWwo5KA6qyFGZVeqGTA3K8Qm0gTANDzDmpviu7CA9CTBs1R0g7mk46Ca5Q52s0Biu0Zus0hmo0C2ZzAKwDomvwphydrZNMW3WqJfNLCFKRI6Ic5mQPvt8R6KwVTZpgfHSCf9Kju2PPgsxvlGN7oIrhlmhj4qgKsAZqWBMXNDZi1AdoDA1S3E7trc41urSI2AICaOb+mnT1SDjQtCaPGEJPRkO8TtCbGoyjoM6UIABUhSi4H92iOrCLfb9AcGsAqoBi3qA9VlCIIxCwWOzVsmQHGwgwy7F9SYXiqw/T8HLAUdMRq8m82eQhmNd+kSNHDrZ4CSQ00stqg2GtRHyyd3zL5YppMoXQ+nt3I5SU1lgB7qaHnPWyhkU2aYE49rh3TpAFjYKoC0MqDRs90FhlUZ1z0YJCiSSugyGG1hmKwYAxZOMxaWm9uLiBTUOM5zKG1cN2AgI6e1X5dMlBF1wN5RnNz1sBWOdTpXWBlRH/vTkIAGKUIhPaG7muIbYZWgM7wRze+5svdDu9SefJ3vMalATJo13Po2mD/khKF851u1jPMDyiYDNi8kdICdaMM+ZSCN5mM/MnrDYXhaYPZQYoO3hfA6jEyje6G2ufizaa9V8ToSY3Z5ZsY3DGBqXLoaQs7yAl0OoWemjW+r6G1N1u3eQ6zUkJ1PVlp7M6h5h1U05K/4q5TMPKYZg7gZxrm4qPo1ioU2zOo20/S2qlrMr/mNbMyos9GQ0pJUhL4MaMKtspgtXYgyKLYmngmXZ3a9n1ra4pU3TzmfrCZQnl6Cr09AWYzmL19mocPvgp6dwpblaRosxbzIwMMj03QrVcobzwJc2Ad+rYTYa5UFc2PQUXvtbfvrXvQtFQvQOt8XsO6/cW2Hc0nt+ahFIHIAxswp05D5TnUaEh15TlQNzCnz0BdeQnaQysob96i+WkMfTclFlllGbWj7+kZ1tD/APShgzCHNqBPnqHPZMAjrdEfXIEpKUhbNqVzxmo4v3xDyr4hjXl9qMLw+IwiewPQNVkgmUEBPWugbzlB4HxQwZx/GLsPWsfkPI2D17cwhcL0SI5qj3Jlq572qmJicfqhCv2IzLurUxr9I8Y4urGPJx25CR/bugy3nd4EXA7f7kiLr37w9fixC/8Iv3Dya/Cuax4Om1n8r6/7Zbx566vwtZufwf8++Vj84AV/hk/NL8HXrHzOM1JjM8eqHuBBb3gRmvvNYK3C1Q+5DteeuQh/8sA/pPX4V9+OO24+BFQ9jh4lH/NMGxy//gj00Tmefp8b8L7/+1CoQ6QkN3slss0ag0GL1z/8bXjawOI5Nz8V//K8D+EH//L7MKha/NLD3oa3bj0Rt083cHgwwWdOn4fLN7bxl5++Emg0brz6V9DaDk/55Hfh5IkNvOzxZLL7P256POZNgc3RDN980acw7gb47Xd/Ff7m+wJzBhCbZi+Y4xce/7/wLSOaEw/8yPfis0/6f8NQn/83ePz3/Tw++l/f4AMrMWsKAH8wpT76ltEUN7Rj/Ptbno1LR2fw/MMfworq8YunnobvPfRRPLoso2dzQJ4nXPcdOPOpw3jgE27C79333dSuDzwHz33IR/H01c/gSDbHsW4Vl+X7uDRfwzsnK/hnKxNfz+Ov/Q707zyC6flA9dgz2N1axePvfxOmXYH7r5/Eu29+AJ528efRQ+GGvSModY/OapS6xysv/T9YUS3eP70/fnjzFvxl3eIP9x6Bp65+Dse6A/jq4U04ZSr8zzNPwOWDLQDAx3avwAWDPbzrpgd6E90nffJqfNX5N+KS6jS+YeUz+PZPvAAvecCf4Z0nHoHzh3v4xImLUWY9Ll7fxR3jdfzs/d+OD4wfgB87/Dn85NYDcf34PLz18vfjnZMVtDbH6X4VL9y4Hc/47DdjlJMs/Hv3fbc3/33j7gXY6tbwe7c+DBvfeANedMPn8R/+6p8ArpBeAAEAAElEQVSh+NA65gcBe/8JRsMau8fWoWoNu9bhgnfnqDc09q4E2sMtHnbfWzHIOlyxsoVP7V6IUd7ims9ejsHtBepDdG5mc4X+SAszrnHrv37VOcf6Mb547LPvHtP5l+84t5nO4XCIa6+9Fg94wAOizz/zmc/gMY95DKbuHLir5V7BdJa3noEarkINcmR7lJ9MjWcwm2tQ0xp2hSZMcXqKbnNIuf0212CGBbJJDQwozDwK1qyTryCbbhoGYW3vzW/tsES/WkFPW7QXbUI3BropUN2+B7O5guzUHtSaM5Nz7CmUIqEUZJ7ZrZbk39N1qG7fIx+wM2PowQFkuzPoxrUrJ4GImNccet4g25uiO38T0CPkZ8gfcfX6bboW5L8K4wD23AVmcHV1mwNkk5ZA5qyBnrdoL9iA6ixUnlH/rJRQvdNurJIJLgxghyWy3iKbNCTwKxD4BqAnFKQlmxL7x6Zequ+JEXZBXFTTkY+dNsi3W9/m7tAq+eTODboja8j2ZpgfXcXgFCkNmDmuD1QYnAhBVmyRoTg1BuqWBJ+yhB2WaC49SKzgWkm5EE9NYaoCzdFVMpvdq9GvVlAtscpsZqzaHmZQYHiyhp73Tggy/vkAoDuD4sQczSFio4rbtjF9+PlY+ZttAhvb+yjP7AFFjiInsy9MnRmlMQQ0jSUWrO2AU6ehdwtUtyvqm7qBKguU2xWZxQGw4wkxM0rD9vS9HU+gqhLYHxOgdf6LyHPK53fp+RTAxgF1b+LtFAbExmqU23OYKke+V0O3xgt8+facTJRbN4a7LTYag24lR7lvKQhR3ZO/qaFcpbo1mB0psf7FhoJxVQQ2Kd2Edbk7WxLYNUUjVZlCX7non9aiODPzbIcpdPS/njUeJNgip5QO8w7a+XfCWijjIgIbA9UCatbArA2g9+cO0GkCJsMyUubo3Sn934L80YYl1HgOu1KRWd+ggtkYIRtTZF0zKul5vSVQYgz5zg1KZPsz9/yeTG+bDmreUtCUySxsYD2NFaoSWF+leaIUzeV7qiiFamuG+QUrKHdatCs5Nm+YoT5UotqmPSqfUr+UO6RU6StN62TiFD6FRlZnxPhPc9o/eP1PnPUAA/JB7qweCtg8R3mmpr1iXNM8PD2GX13GgRfjFAVdR+xe10FVJSkM8xzZrgOUTgurTp4B1teAyTQE2HH+f/bQAeibjyO/+Dzaty89j8Z72hBb3bn9atdZpdQNgbc9epZuO1IO5Bl0U0Lvzfw6VnVDc6WhflNVCdgcxV7tQTNMD2yuA3v7UFUFdWqX/BD3J8ibFpjXGM02CGyvV2iuOOoiZjdQqyNgPPUKQJozvQd4AIh99M8yNMd70DWt22usA6VKw3Yd7AkShG1dw3YdNKdimc7IZHZrB2VNzKk5dQJ6bRX2oqNQt9zh71OslBxU1CalqO6dXWgG86B22JLyE6NtYaqcrHs6cmFo13NkM1L6mTz3e63qKUBa66KKA4Bq6BztDwygmoxY1qokxnZ7D1Zv4MAXOgy/SJGioYbQjUG7ojA4Q21UxmLlGLWhr4jh3z4zwO2zHP/rjoPYPDhBNylRuC7Lt3LctH8Q/+m2b8b5gz0UZ3K0awYv/5t/gSce/SJuqo/imw9fhx//wrfi6867Hke0wXXNHL+/90gcm2/impOXoFuxKL4whCmAa86/BLf89YV4wOnvQzMtoXZzlLsa/SDD6ZKYorW1GarTGnZnhPedeijMsEd+xwD9ikE20bDTISYrJf5ieh+88fjF+Pz2Yfzs/Fno6gzf9+A/xw9+4nthPruGR37N9dhrBzh16wHszwbQ0wymMPiPJx+Cz+xfgJM3HsT9H3wbWheGfn86wH2ObOGO/XX875sfiUHe4Xnf9F489C++G+ev72FnPsQjj9yOqx59C274xKV4/94D8OobrsCLrvwAfu1R/wPf88WvwVsvf38IfvNf34Ab2jGuu+ViPPiPXoRv+/aP+UA3DFaf8qlnY/sD5+PF3/f7+IMTD8MfVg/DO255BIqsx8dPX4Knn3cDapPj+v3zsFnOcMVBUuL+8H3+FB89ehV+6cKP4Ws+/a04NJzihq/+Dfz/jj8Kv33m8fjAsavwwEMn8LUHP4vnrp9AlkSEOXViA0daC2UV9m7ahALw1yfPhzEKnzt+Hq467xTedcODsLk+xdaxDQwOzFGPKzz8ylvx6lu+CU84eBM+tn05NrMpdvoR/vi2B+JD5X1w0eou/jy7Lyrd4Q8+/VAcPUKKhO+//P+itTkuO7iNJ33yavyH+/4fPOLwbfjY1qX4uLoYn16/CIOyxUMHt+KXdp6Gm88cwHnr+zi5v4YnHrgRf9bfD7995nHoTIY/mI5wRXUKV298HMAIbz72VXjlpf8Hm9kEL7jtSfjCrUe9AuMv6xZvvO/bAKzhwmIbH9y5H/7vI/43vu0Dz8Q7tjbwLVf+Nd77nidhuAXsr40w7kYYbTu3stMlyr2WLJKOacy6Ap/qLwUs8LHsCqwfmWB/Z4TqjgLlDpDNac3bDKjzAkY1OKfLP1Kmc319HbfccssC6Lz11luxtrZ2t+u9VzCdX3f4uch6TQdPlpFZUt9ToAEg8nMhMzZF3yt3IK6twmyswIwK6KYngcJaoOuJ3egMzKhAtjejw9PlRJtfuILhbfseuGS7U/QHV2A1sV4cTEX1hnwfBxn5xCmFflRCz1q0B4coj+8DGphesYmVvz4BDEoKaONYE7M+pINy3sAMKyhjoDqD9uAQ+R4xLWrawKwPfI40Fk5U2wN9T2bBE8fKtD3sIA+sYWfI9G9eR75DAICmoYiCWUaa8Kahvssy8pddGXnQyHW3R1dRnJkS0zoqUBzfI+EtI/NB23VQqyskqB1ch9qdwK6tkBn0vIbt+yBUaQW0HVRZkua+N1Bl4QVNW9dBC681rHsHPRzCWkNmXRzRViuKkNj3pNm3lu5zrKDdXIUdlN6E2FQ5VNOjWyvRruckfIvlogyBb316z9XvzNva1rcDAPlU9T1s3UAVufe3UnlOc7GhSLeqqoKpGputNU0sXDL7DHgzN5VloY7h0M1z578FEAM0p7lkVypi30aVD3aErofZXIGeOH9VpTwbja53rGoDOxqSgF0WpHDIKNquZ7pzTdF4h4U3V1adCcqHjsyf9d4M7XlrZEJpLfS0Qbc5gub8ftOGTAzdPTbXUPtT2NHQz1u0Hb3/bA4oHYCkUrBOcaJ6Syxn05FCosiB8YSA+mhIc1wrP5fQ995nDsZA7U9pbrFftzMPRJ4TyB9UxIS57+2wDD6fszl9byz5bDI75cYJvfF7k9negT54gFhqY2DPPwy1P4PZWMG7r/2JL70R3o3y9U/4Sej9GigytJsDMiEelsh2yXy93aiQj1uo1pCJ9xZZByBT0JOaGOKmJWuEMvfAW80a2JyUJhEw4r23pfe3KyMKCFM31J/M+mtN/dP1fh/nOQ4gMJKu75A7sMVrPNO0FnjOlQWwtor+8Brm5w1Rb2TUTgvkUzL5zXfnsFpTlOPewM5mZIZfld4vEgCtT54j7hp0ToE2GsKOnQnieUdgjh2HPnIIKAqYlQG6jQr9IMPgmi9QYB2loIdDtA+6FMXn74CdzamtRUEs+RopOu3xU7Su85zW87x2sQRM8CkGCBB3BKjASigHAAlwujXa0l5u+x6KAaErtu+9IiyAW2b7NVRRQB05BHvGsbouqjv3D1rqC9s0tF/P5pTWBYA9eoj6Zkpna3P5EfI37RzQVVTP/FCJcrejmABrLtXLmsboREsxDNy4Z1NSlqne0H7QGbJI2J9i8rALyPT6f38Sem0VOLBBFj3Dgq61Ft3GAO1qAWQKs4MZqt0ee5fmqA9S0KtuBGRzYLjlXjWjYFqmsOjXDIozGXQL1Ed6YKVHfkcJfdUY9U6FbNThwiM76PoMJ8+swbQa6DVW/qbA8KRFXynsXwaMjlPOZJtbFGOF4QkS1PevYMsh4PB1FGSr3gR6R7Zkc/fTAM0G0GxarNx3B3u3r+OBD7oVn/vkpTj//qdwxw1HoFunGM4shsc1mg0LXZO5vCkt+pEBVjqsH5jCOqf62fUb6DZ6qJosVVYv3cP9Dp/Cp45dgNVRjfse2MJnt45i/4sbMCs9rrjsJL54+2GozOJJV92Io9U+Lih38YqDXwAAvH2yhqcM7sAT/vDlyDZrPOfBf4E3ffzJeOL9v4Brj10MALj6quvwx7c/EKe31qC3C5ihAXpySdIHGihtYe8YwlQGBy/bwUVru3jsgZvx/37usbjv0VO44dQRfM4F+XnUx78T87rAoGqxs7UKGAUoi3K1Qb1X4aorjgMAyqzHTe+7HKMTNN4mo5zT0wuA+qBBcXSG7sQQ2jF25e0l+tLCFoAZ9UDVY7Q+R2802jrHBYd3cezEJophh3aWoxh2UMqi3qug99xZfZTO2G6a44rLTmK/qbA/HaDZGqI6MkVbU47i/OAcfZuhGrYwRqEoejRNjvbMAGq1he0VHnz5Mdx05iAOrMzw4APH8VenL8TudIA8M5hMKtheQZ12cU4ONrjiolN49KHb8NGTl+P0/gqMURgNGzzy6G14//X3w4W/VyKfG2xflcPk5GKhevKZP3LNDmyRYXb+EJMLcowvAXQNyjwwoL7b+DylFDSl8mtmelRhVs1x48/86DnH+jG+eNy33T2m82O/d24znS95yUvwjne8A695zWvwpCc9CUopfPjDH8a/+Tf/BldffTVe+9rX3q167xVMp+06ICPTT9u2JDAUOR2YZUHCDADLmt5ZS8JQldO9e3tQe3vI3H2ssQYAdZrMgzJLQgh6A6UVUJYY3XYC2FhD7gKX9Bsjx7IAxdYY/RqxYNn2FJjNkQGw0ynU5gbyyRzd0XWUJ/dRX7yObN5jdMs+zKE1EuqKDEYDMAW61RLFmSm6gyvEsPQWajJH0feOwRlB7Y2RtZ3oFEvmg2UONZ4h3ydNuV0dQnU91LgHdvegTpJQYmraDFm4s4YAm21IS2UBYDYjQSXLYOY1mS+2XTDrcj5F5c4eacGbFrkTcNgMi82s7N4+gc/9MbEh4wmstb4u6xgOapQiIc9aAo1tK77TQTDVCtZQHcYJgwziImGqdxEVufDn2ztQgyowkxceJhZ1dw5lnOk2Cz6a0rPo/TkJWx4ka6BwJscsGHaOfcsyJ6hZEo77HuhsACHcVmsJGDnQ6IVeRX3Pmn07d0qEDFRH2wFDEODRygNUVTMQmtHc1Zo+yzSBsbaF3nVmnrv7UOurIeCNMNtTbUd1liXUyhAYzzw4Rt0QS55l0Htj2I016D1nxjqbA6srQNNA742BskRxbIfed20FaFpKW8JCsFZA6wAKr7feQLWO8WKA6Rh82J762QnkxLoYet/VFZo3sxkwIcBouw6YTAl4OiUUAAIwnLIhz4G2C/PEzTd16EDoG/5dE/OqGBBUDojO5n5e2J092j+UAhpL/QFQO8qS9oXhEMgzqL0J7PoKzDDJwfgVLDbPoHb2gNUVYuRXhtBjGi89qTHY2iefX8f8q8kUqiloTIaUBsgOCqia2Ftb5TSn2haqdcyescH8czYngN51tD72xoGZY5Ni7ufW0lpyZqAe3BgL29Th85z2G+v2PZVlBBKBsCdtHEJ92QFksx7GRW7uC4Vqt4duLZoDJQUlUwp6G+SP69LpNBeso/z0LV7JZnmPZEDGQG5ew7YtARw4Jc9VlwE7+0CeoT04RLuWYXTbxK9hatsasklDUVx390gx5cCiBmjetC105ebwdBZAN68Btz9jUNH6533RgXE1HFIfuX5RThGigMCKun1bZRlQZuEzNx5kOmthmwb22PHwDAD6wKZXsKmyDApJS/ua2d+H3qBItvbwJs2ByRT5bg1kiuIQaLJmUL1FXwL9UEPXZPZa7jbQXeFSQbHljFByzVtvDaTaHmhaVKdqwFbQR49Q+28/DrW6gqyq/H6Um3UUJ3qYtQHySQXVGWz2wHQ/QzG1mB3W0C2Q1fSsfEb+p6ZQqOsM1TZFiLd5BrOXoToDzG5ZAdY75F8Y4vgtQ3TnNchPFaj2KberroFqj/b/fqBdYC1FbgYuN2q+b9GcdkJ7DpTjHlmrYJWG2iHzdcpFDRRji3IfmHQK42wTKAw++9eXoNrROH3NedAjC9UBq7cqNBsKg1Nk8mgyoB8BgAKgkW9V2N8rYHN61+G+QjbPoRsCFOOb1/HJeYGuzrF9fITbH9BgOiuhW4XBTQVu2b8QWlnAKHx064Ho1zpsHBljq13F9fvn4R1XvQc/e+YRuPHbfhUvuO1J+I0/fhreePUb8WvHvxrKMY9/Mz4Pl67vYHzNYeiW0szlM/IjbGcDMokeK3SdxpnTqzBW4fNbh3FwbYp5n6PvNB718e/Ev73/e7BzbB1qrlEDGJzRsAroRhb9kIJI3fbhSwAA7brF5nFgsG3Q1pQTXPcWUBr5WKOejpB3CsU+YPco2GOxq2ALoC9ztJsas10nz2y0uP34AWCco90ukU01mjWKJ1GdzGDYm+LGIfrSIsuBm24+ClUYYJJjeCzDNBtCNQr5foa2KIlNVxXMBTW6W1egeqDoFMy+hhlYfPqLFyKretwxLXHs5CZ0btBvV0CvkNUKtrQo9mgu1QcUbt/exGrR4PjpDfTzDKow6MsOf/qpB6I6XqA6UyObdxidytBVwPA0xaroK0WK9arCUCmYYoTGpd/TPdBVNJNGpzoU+/TOACmvm7USw/3F8+efyj/88prXvAZKKXz/938/OrfnF0WBF73oRfgv/+W/3O167xWg04MREHtpNZk6qTwnQYEPKwZAgGeb+G8AdOi7wxKAAypBG8y+OtYYEha1Bk41nnHSTUvBGxwIyRjASaa162DP7ECVBfKdPdi+x2DWwGySYKHHc9QXbiCbk++jMpRqRU1rFDUJywzC1JC06+r0Nr3X7h6UE3zQtCSUOWYRZUFCyeltAnlFHgTqvie2yBoCbfy+fe3ZQWjyB7JtF4SupoWZzAI46ueBzWFTrdalGuFKGwL8VggusJbawiySA5+WlQdd59tHz4FoZwCcXBh8KvQwrROmrIFtbbhO1Cc/t2MyYVZaQ91wC/TKCCgKFN06uvUQcEcxQ8qmkL2hKKXTWSSoQbCWno1w/aCqyoN6z8pz0WQC5xleOOWKYzGtY4w8y+lM27xpnbGOuXbP7DryV3OBRtD3gS1kk98pmQliezdqiu06EpZ7x/o0Ltdl29L7FhT0CFbT/LSW0iVwMYaYbg49XhNbTlE/25jJBfx6InNkGxhuViQo1wbjwHvXEajsDfVj04Q2jSfu/axjjZ1paE9spCpLmImLdHpgk95JjpUlJYfSmu7f2SPB3eVlxbwmlmo48IDc7uz6eY6+h9pYhzp6mNrV9TQuu+4kHlRQXQ8znjhW3gXuGg6gmw73VMlPj70igOaDIRDZGS/UKxell8BISW1nf8H1FRLyZ3MyEx1TqhrqMzevMhUCJhkHwnkuGAusjmiMAed/3IbxlPuDsVBwe7cDRLZpgD6AGzQtjMvvqA8doIA3AND1yHcb2DKjfL2DjHL2TnpoZ94NpSgOQFUBZ3bIQqLvUeyQySYmUz9f0RtYQxYLtu28cgJdR+sfIOVnkXlfYd32KCbkw26vugj6hlth5zXU7j503aC/6BCyC8+H3ToD1DWU62u7Q0GymK1UVRmAIq9h3ovdHEae09xtnRUHK24qV6d1+xZbkfB+wfuuNN8FaGzZFxSgfhcWRLZtycx/XpMyAk5B2XWktLQWZo9M//Xmuo+4q/enaC86QP1vLeMflGODfNyjH1E09nY1R18olPMeyuXzVdaS335D6a9U28PmmmIT3LGF/NZTUPYw7NoQ/UqFvO9hdnbDXCoLOldmc+jJFEW9BmiNbL9BsUsxEKqdggLi1e6ZvUFel6jXM+heodinaMt9pdAPgGoHaDYVdFNgeIo6oZ6VqHaIOSXQRP7SsBajEznKsUEx1S4dGrHvxbhHXzq3liEFUCv2Layi6MnzgxlFem4squ0e3VBDdxqqV+hWKFes7oByB5gfVoAFVm/v0Z92LjAdYHIFOwbmBwG9p9BsANWWRrNJY1ydAUxJbKrNgH5bYz5egV4xUI3Cbdefh+EJjWbdIpsDgxOKAlDNgfogUOwV2MUq3j55JB5x6W34XDvBSw58Gv9nuoFfvfgjuOW73o2n/emPQG+XMBX178dnl0BlFkVHDHBPFuqwGij3iAnP50C7otDUFXZ3CuRjjTvOG0LlBrow2D6xhh899h1Y+XwO3RFgLvcIvCurMK9zoLRw7pUodxTWjnUo9si9IHe+xcU4R72ZQVlK05Q1QDegccznFt1AQffAvNEwBTHSbUdWB1mtULox7yYZ+hIYnEGIzK5d/+dANy2gOurrwWnAqhzdCFg5BsybAvmE+qDtKN5CuUdjYgpSVNRdCQti3zXFPEMxITCY1dRXhdsGze0lmsMaN+aH0O+UgAGqkyWmDzQY3F6g2AfySQM9bTA8WaBZz1Dsu+wHhVNoz2tkxmBQZZhvDlHtGcBS1HNlgMGp2ruk8T44OHIQrZApz8nyjzRlSlmW+MVf/EW8+tWvxhe+8AVYa3HVVVdhNBp96ZvvpNw7QKcxgLJQGTFUqqfNEmVBh7gTYi2DIWfWCBfEx/sPAWHBNC184AV3wFtjKAADa3L54Adpu1VReAEWQGCIGHyySVPXRaDLnjoNtbPrtfrV3tj7IgFAxgyYEwysEyQi0MjfucNVZRnQqACypQ+ZVlS/NSQosnkihPBvLH3f8//O7A02sHHGkJDoGAAG1SrLSCDLNAnrbQfrKwJtYAyk+vC5NZaABQKotE1D7ZP3o49ApjXWg0jPdrqidPCN82BU3LtYN2h83FjYnT167zPbyAcVmdgBxORyRMa+pz44vU1AqiK2xCspxpPAJgwqDypIACqJQeD5IFmGpiUFAf/vUhPYriOQ2/ewaIMQWOT+WUowFd4XbF47H7nez0HVOVbXMSw+AqacBz2lw6A54QBt3QR2lcdQsoIpiHamzH4NZBn5CDslipwLyrE2qiypXx3QtNMZ3S9NWpl958fwOuXPeA07FtpyYKXeQI2GMPvjIFDv7UOtrjiftmBy65VVWQY7m1EdvD6c76xvJybhmS6Ai8rd/lKQ4sd2nVcy2MnUsaEaZmeP5nBZAie3oN1cu0eKUsDEBdQYVMTOndkLIE5nQOP8JbUN4LRugNURXTsaEqvLJsh955UKAOJ5BARrB6WAQenAveuH+dybwiu2XnCm9YCbG8zy8Z7bNGGu8RgywHBmofbkKWT7Y6prNETOOVKLjPZ/Z5KJ3sDu7vk5aPbG0Ptj2LKgtV66nDEAYC3MeOKVdWpQEdt5/KR/Vb0yIkB2ukN+2x1Qhw8B2sUHWF8HmtMw+/uwu3vQsxkwHBJ4a3LaD7bO+PdRQwSAx1YY3L+SdeU5z+tQ+ncCNBcNsYLkwmBIOeDcE5ipZDN1X4rcmxIrMSe5Dtu0dNayoJWCWA2vGKY9gxRi+fYUZlDCDCiitckUin0C2PnYohs6v05joVpDihIAZm3g8vtSpG01maM/sEqR4fsednsHejwhxe5oSNYXF11AyrQjB2Fuvo2UC+48JwZZQQEosgx2XiPfWIcPxgYAmUZxQmM4qshX3PX5+o0kC9hc48DfEGObzWlc6gMUnC6bk887AFRbM6i6RXW68P1jOEiWBrJJg3InjqWgpw10M6K4EV1FwLIxqE7PUQGoDw9RTDS6oUY7VOiGwMZNLaq9HN1AYeWWKYHyknxGrabczbrRyOcW7Q6ZsSpDY756rEdfKZRjA5MrNKsagzPA7JCGzQDdKGQNsHYzYDMX5dpSnm4YAmT5pMDkco2vf+Sn8ZHZlfil8WV4/y1X4bUb5Iqit0us3KJgXPqddm2IfAqs3WYxPNmiXc0IXOXk868bmlsrxy3aFY3xBRr5HCgmBWAov/BwjyIUH/wbWsPdQCGfU2o5UyjMbi0oH/C2U6oZoDo1Q7a1j7wqyfIjy1BsrCA/uoa+KDHYMS6wWo580iNrDUymyEpiM0dfKvSlQjcgM2YoYHiK9jhTgBQm+8YHVOsdYGzWFNqRQjm2pBg41qLaLdANKEdwua8c4wvMjhATXu1amiMtWWuUu5S2CkZBG2LBiwl8ne1I+aB++VRh1uWod9axebNjdTuLneEIq7dSSqtsewrs7WPQGeijqyh25hSV25DVnDmzAzWbo7QW61WG8vQMpswxLDVMmSG/dYssNbgYg9UDI8xG92Bsgr+Doiz93NV77i1lNBphc3MTSqm/NeAE7iWgkwRCAn7eJ6jtSOPaG29+BQaLQPDtG1TB/NYFrpBR+bx/HAvyDAqs9f48fD8EgPKCNX+eZd73jc2mFB/u1hLAgGNH2I+vd/daB/T4PRgs8iGeZbGZIPeJe08I4AWlgymoiX16Fvx4GIxyHb3YPAxpmyG1WJlgG9mMloVxX0cC8ATjuIythEramFznAWbC0kbfLSkEfhRs15KJbnKtUg5UiL61sznNMcCDCtuPoQ8fJAFGsoi9A+RN4wU1lecEaLLMmXETSx6EPSHoWetYBh0AqWCIbdtCD4cwsxnNKykkdh2Zo6nMMyVqMHBmoGGMlXL+b2KMlVKBpaWOcMyiU8L0CH5bzHDzd1qDI1lqZnGFUidSVjBTrJJ3BmAnM8+sU1t7b+6trCUQkLmgPYAHmfy/Xy+8PnrjFR2qLEkJ4taAPv+oV9TYyQzKsdSKGVWOztm0NGaOQWdljcoyz+z4PcUx62o4hG1bmOOnSPFVFqSQYKYUILNxgFihvgaUhpmTf7UqhAn4V7iYqoDue2Ly+p6ivvKYNI2LrmwpkuvMAWZnJu7ZSf7N0XcZXGfa71t+aNsOKiN3B1vXfs6yv7hS2rtG8BgqlcG6+aKY3VTKK7J84Cze+9yaRm/C/tf36GvqVz2bk3KldcofIFi8eCDbBquHpgnrna0ulIJxoI7OCe0VloavBbxPtOU98/QZoMipz+c1tV9pqEKRoohzbvYm3hPZQkXrYJrr4hagqug3ECwAxJwN5uoMPjWg3H7gmE7La1f6h1sT9h3j2tAbWGtpD2NTc3fWskWRLWkeqw6w2gC9DcofBnccMGteQ43nyNoeqqUo5dmsQ314gGKvpQi1AAnyTo9rHUDRezOg66HLnBQH+2PoQUF5Pc8/ApzcclY4LfRoSH3Eimaed26dqtUVarsbO56x9sw2zTNWhLnoqbp0kb9Zybu5ToHWtCZ/9Vwj3yJ6Sc/IGkBPa+SjCsgUBSqrG2fe7Fj2siRzdxdHgudTtuuA6WxOAZ0yjaEx6IcF8jGl6bFVgXzSQpncAVwNMyZQNDrRol3JKKBZR0EQ84qCE+rOBbuaGWS1RtYY6J76t9zvYCcK+aQj331LZ3k+15RXNweyucXoZAdTKrQrGroht6JBpqEs0I0UVKvwx1sPwShv8JEbroRtM9zS5jC9Rj5RKPecOSuASaYw3AIG2z2qrRmypoJViiLWt7Qv2YICTxVjjXZUQreUQ7kYA92EQKTNgcFJFwCypDmF3sKWGXRTwBQFBts0ptm0R7bn9oQzdCbYvocaT1AaoDpwANWZFvl+DWUGpABwPsFQCrobwmoKsGYzBZuT4oQDsdmcUuFxWjUAsJmGqTLotsBgG8hmlDqqOjGBboY+6F4+yXyOdWUzZLVFtcPWQXRP1joLu5bMkFUH5HODfNJTvvSNHPmc5lI+09C9hskVqj1LeY0rjXJXodoxUD1ZpdjpDCrLkLvYDxhPgnzcOSuv8QTl1gj6zD50SWmWzOqA7p/NvMxm+x7F1gRmRZzx52KxwD/GQEJd1+EnfuIn8LrXvQ7jMSkTVldX8cM//MN41atehYIVcnex3CtApweDfMAZJ+i6wAb+c2uDSSP72PGhC4QDkrXpZeGYHgZwAnSxsOwOeQXQbwYdQiMPwANY23WIliADT76HA+Kwrw63h0EbQGCR/2bTU9kmIAhiWggiXIz29dm2I2GKfQ35Hu5H1lQDgbkUJsrIJDhwgpYy0f+h/7RnGq2xIqBFzEDyd8xoMiCMGEpRlt2fXu/rFBFBl4Fa/zxvettRPdznUmnhhC+zdSZWDPBYMoPXNAQqOidYdgH0eOUIQIDO1aPynIRwx4xSRTYIncaQ8M5Aj9k9FjCF0gVKwc7n3kwRphfKAEMqdmbL2RSXUxxIAKodMGBlDIM3B7C98kUrYoK0Cowrm6gYsYPz+EiASx8Ec3lmGY2NzL1t34Sx04oAJ/e5XCsJcw4nWMAa9Ce3aGxdG9WgovHmPmeAzCUyPe+D/zfPO57nXilkPJhkk3sfKIqtHxjMzmYEiI2BymJG754ounYKrnkNDIc0ftOZByGKI4+ygM1m0AxsWOHmQBSNP/lm2ukMam2VgmW5ecTAxQsw/G5yTadKo9r5bxaUg1lJ9oz7SO55Yo9TOhd10ro385rMsJUGZLj3ZA/wVhZyH3EKKNu20INqQdHB1jTuxlhBZ2mt2tmc0iVB7E0qJz9jby3i2pLnNHece4LfA/ic05oYzRXH3LoIuuxTDiCsaS5VSYBHsdUKKcLYpNla18edA+3sZsJniVJxYDP2W8+FgtU45UPdh7nCn7dtWJ+DioR9rAIDl+7KWpRnakpFNKmhpxrtgSGKHReR3kckdhGHD65CTWd0po5n1PYihxoMYDngnHMxsKfPEHCczGg+9S6Y3HBAAa34LHJ9Z1tKNeOVpwxKmyZY8gDA6TMUUbwsoYyBzjIYZ82RzTcoHVvbEVjtDZk3s4LBjReZ+wqmmJWMPM4uujGyDFnTQq8MoCZk1m4rIN+eQs8r2FyjOm1g84z6qzUodl2AwOkMaprT+wwqlwubrG3yiUI2bpHVpETMxy3QW4p6by2sWkFW9+gHOYq1HO2KRjE1FA1+UEC1BXRnKS+3JXDeDTL0pcYnPncZVKuhZxrVnoLqC8wu6jHYA6o9YhHdhEe1Y5BPOujdKfLeKbW1puBshlLAcXC4wSb9Xe0SY2dyYhB1q5Fv09q2ZU79xIy+WcVqrpBN6ZnFXh32o+nUz2E7m0Of3sZgawXFqQnU7j4KewD61C585PmiQOHcH/oNWoNWE5tcnAl7iy2yECEboCBkgwIwIwKuNUXHV1s7KJuOUtB1HfIyh80yZwVQEit7xvmUd05pixFFDBd7TTbvKJjfSgWbKR9pvFAKypBvdF4blDstumGGwRkKHqS7sKfavX3o9VWau1Nn2ePcndD3sNMZ9LEt2t9XRrCzGXS3CWyswZzZFme+AaYzaGlBdw6Wf6xM54tf/GK84x3vwM/93M/hiU98IgDgox/9KH78x38cW1tbeMMb3vAlalhe7hWgE0B8MAPhENQKdu785lhbzsISXycFVvmbCwtfzHCyFlketKlwzcwPEJ7lNMUxAyAYTwFSIrMpDnDE7A0XpTxzEwkIQBDKJCPJ17CM5cGde1TfEyhJ2U33vyqC2Sw0ou+WjkcC8FQWhC0W5CQATVNEKK2IDdCCeWWBnpkAZpUSljS6Xn6W1O3ro0YiNbf17ZKmxkDC3oo+1/CRZAH48fbCCs814UfLz1cFCaDKjQV6Q2bbnKOs7yOAuZB/j8GgA0u6LIEycwKTm/PsiybXAN/HYFgFs0YAXjHhfRv5+WxKzeaVXIRptjQL9+8MMddEGgf/t69Hw5ouAmkSiHmFRWsXxpf9fvk+P4e8ksO1w1jAOGAqFT3cJlFSVpxNvxUyr6iQB66ZJUyls1zgwzt8Tv3iWRXBJt1jhfPDAgTE8jyMvbUEHNn0tXcpKZgxahwgnwt/TTc3vSm0i+RquUst+dJ5YV3sMZYDCRknHLJQns5rbgv3MSsLeW+UhfdPjaDcUhawbt1nRQwMZZvSz/gVXORXb7XC62ABQCdClnLRfeXH4kzgs4XYYPc+xoQgbvwca6CyMpxzMgqyj9ANYqfZz1wJs+TZPFgC8bNdHUppD0p9DAReq3xmauUVY7L4edObEE3YK5QcE9r3IZgRQP7lFfk/a9MD+Sb1kYtmS2eWQXli30XF7nyuVg4opPfnZI7NyuPeAQK5jlzwJXVwk+ZV20JdcJQUZkPykVXskqBDBHzVU/RddeQQvcfpbaqzLICqIl90INpHAyhuCbSePOXGX5PCQSpz5fxvA3sWFHOIz3R3nqvxBDhpvaJSr6/BTqbQgEvVQ/ERfN7Vjtald/txynY9HKA8xmuOrBiGW8J0zhqvgCzHFMQv0xmK9RGM8znVJ3egywLZ/oCiP88bKOP8mlGh2lUYnSwITM4Nyu0G+W6N3QdvoB0Cw5MNitO0T+SzFcq523SwW2eg90UqK8fssyJMGYOVIgM7EOc7U3QHRuhWC0rzNp35IG7WuTPYtkN2coTqQZeHgFR1632hVVVBra/RGegiLBenJsCxE5Qf9vovwvA4cKyFrTPO3aNwlgMaOe+jzJo6SxivVHLxIsq9Tfq/bvx6snecJP9pZ+qt8xzZxjqySUXBI49vBbmyaTGYHojTPXGU+bpBtraKQbPhY1CgN8jHQ/SjAnrWIdvaQzEoAWyg2HNppdZGUPMa/dZpZLv7lKKtdtkElKZzs+9hZjNgXkMPKpgz26RUMRbq/CNhP3DFnNgCBovWaudUYZn+rt5zjpe3ve1t+K3f+i0861nP8p897GEPw6WXXorv+q7v+kcOOq2Fpw8d8+N9FDndB3/HrKJnRRnQKG9Ca/ng8uasAkRK0MAHjmAoI/MmAYL9BsR+OGw25trhhUzBunqWRQffTPL7aT2YlAEuIpbSv5MKANCqoGlm4GisBwleo2/0wm+v+U7NbF37osIsiCgLoGAZIBR+mREY5Gsl4JSAgAEgEIDrMkCZ3icZTGY9tYKyKq5ziYmvjKgZvTdfKwSFwNQJds6bbwu2zs0ZxWZ2PGZs+sfpDzLt/Wnlhuh9lPKccqNKny9m+DgSZQIg5XrwfpOgw5IYwNbd13mhOJjzcR/3vg/93OG+cObPDP5ClGTrD7MFZYEbL+mXy/fIebOMAffMtxjziGWXrLoAC9F3YnwkUE2vD20XChyp0BFzUhVOqcAM2RLFjff5TP1iv5LFWV3Qn9oLo1DWm33buibTcJ4v0jQ/E3tr5uam3A/TgDRAMOW0lsw2WQkIhL2S91ynXKFu00tTfPjv5B5urf8cgPfxpfbYeF+SbKlvs2DJk73Yt8OKerKMTNIley0tQUT7Iwbe7S9eKcUWFTaYBksTcgZ1djJ1c8MpQJmBk+mh2OWiLMnkzbaewfPmpSzEe5cPHQf4YvNarotdB7xVDMI5ytdzeq0iD3657sxQILNd3waee44B1LtTn3Ko3xiS6evURaN21hoc4IqskEoC187E2vvY1+7a3Fkh8Dh1fYh2nGugIGZQNR2wugLl0thgUNHvunFr0635A5v0PO4rFHEfcOG0NTTwEbiMGFIJPGGjPcIX3huEwtPKWAwA7NZpf3lk2rizS0HS5nWsYBXz0zYNfLA+gPqAx0emkXLASOU5VNMgK3Lq19kcaBrKKT0cAPtjFL2BWR+iqDJkM4XRcUNpbVpD7Om0xtpNU0wuGlKwxG0yRS56S4xz18E0jX8XVsSy0pb3mPz0GLZ0lhdndpBbC9UOidlzfsZmMqV5tDKEPjiE2ToDXXdQHAxwVsPsj0nZcMF5MKsV9Jl9KGthdnahhwNgNITZ3aM6uxboXT+61HEAnZE2lRH8eZf0vQ8uFnzV032D7zF9D7U/hh6NgDyD2d1HcPnpodmiiOcTK6Gsoe8lCVM30JMpmZtbS1YGdYHqhLNEEanjAMDuj2l+OCWp0nwWBxnMzEKcEDubQ53eXrBMs30Pew8GxPu7KP9Ymc7BYIDLL7984fPLL78cJRMhd6PcO0BnngNGmPGwKRcLG5Il0YmgwYUFdxc9kkGmdRpNJe2XpckQIAQQ7c1wlMgn5wEns4QSkLjFLlmwiOmQjKerywtL0nwqW/I+8mBjH1AWqiRwlCCBN0gJuuBAAh+CkkUFYuDFm+xS9jNsmK6Tln/PTFkKPKV5cdR2yf5q0T4dfy4L1yVZXQkyWQBNr0/fVYJd2WdunG1k/hwA5tIchgApEpJ5Fc8FGw4W+TwGSk0LIJh8en9RIATAcsDAm/E2bQBCqckiK2n43Z1JdsSWCLYmnueIwKJnjQXg9mx2sWQck8ICkhKU8/LAUKDDcdkcZjbSJoeo60OVITAXWRZ/p0JfR2k6Flg2TfXwu6o8gCAHXjx7zWCN+96ZcN7jmlId1iiZMQvmXOuQc5EZTABW2RAUyRhvli8ZUh/8iYEZzyXeV8XeG8yRbegD34cqmJcCwSSSmWueyxCgTpq0yzERwCoCqUAAfZKtZL9Rfu/Ed5gUd+5d+HfyTA96hMJwQSGo4QFnpADjZvfBvFyCbrIwcO3gHMnzDrYls2YfYMidFYqbxetO67DuC+frmOegCHzuXa07A4314+OVpkjqc3ODAakVVhLSTJ6YfgMM3fnMY2YtsLcPlW8CdQs90QFgMkDl9GdACFrEQJJNr93zyCxTgRR2LAPwHqxgCzcvewuzOoDOMkp3NKiI/czI5x6ZDuwqANgiCOa5DaxqWRAAY+bYOgVFGEmaU1UlxjpZ31I5KguPnfxcRnKX5yUrMly8AO+bLhUZfE7w/sVtEdcAiIL98dlr2zakTZOK8ywja4mmhbIWWimC5L2lVDZNB3VmD3Yyhalr5PoijHINPWu9TzubvVvBVPt91oFjZAYAgW41njhWkOISqLqFrgqo8ZwY9FNbQSmZZUBRkMnotAmRwwHowwcJVNc1+Xxb66PGY07mpeg6D6QYTAI9JKj0itO88L6PYfhDvAnf/+Ia2/H+wgrYMPC2adDN61iB6fqn53y5XMSys7MZpbTzyl0KlKYHlTPdz2F296DZ8qEkZtlOp9CjEcx0Gilc+dl+P1ryPml7GKzaOumPc604sfku33OOl3/9r/81fuqnfgpvfvObUbm4JHVd42d+5mfw4he/+G7Xe+8AnV0H5IXQorNwyKyT2EzZR4mLDEGfgjZnvir/j7SaDBzYD9NFsI187NwzrTA58uZgrAlnkwwJfLmtLFixcCSEHDb3ZcCqhJDntdQ2bJaRELjM4kEeZkY8Q/YdEMAnX7dMmKKbF0yJIlDGbWNGSQINqSXmIp+1hN2KfASlSbIA7h7cC+DPdUdmer2J7vdCqDTjzTJYI4JUyXf0YCLWdkfvx58xE82Bafg5iWKEgny4edhjibAhvuffzBYJlt0LDYB/VhR4B4hMTZcKzYJdlX1ue5FSQQrSvfiflSDcb0A8bySQF/NCZYgYaQgNLD8vgF5xSLPAy2ykUlDlILC/aeG9YBlDweZVwodPMcPJpooykqdUsmRZYJcSNsx3K5vby769J4psAwA7cwo5+ch0HlkLTikVAi2pYA4OhPRMkg0D4n1EgFTeG1mAlFHCJcj0ikChLFm2PgCE+xH3oWcsBWCVJs3p/hp8zmMh2z8rFbxFW2wnLAKSNRkxthIQpZKKsbCmC/OJlYxsxcA+1XCgXGsygTYWADPPzo9wKnKhSoWRZ+p5jWXBPUCeh8YEywmZC1QqdHtScsl923YdVDUI/t7s+yvNpbUWa4583DCZ0TxidquuQ/sbp5RhxUmeE+PLlj7GuuBWfbDcYBPEoqDIt6sD6Jqig5syhz64TiDJKYtNVUCVQk7IhTJOK2B/QtGb2d9ZKRLc2Y0id5HYXZ5wPv/9mSLPBDeu3mUiBZ4IgCQ6o7loBag8nGnO2oXHWGWU01lakkSWJZKVCw8Mf/Y9Wf+4dqRnr+26wAIDBDx3dgGlKJJ0WcIYCuKmqgr29uMoZnPY7d1gdoow+31bpCWQnyvOxNOxj/4cyDKoAUXSthU9R5UltW1lhd7x6EFSuDgB2pw4CX3eUWc6ngGVMw3fXIU6tU0saJZRrtvZDNaEfosi5LsAY35PYA8CN9ZRGjrJAso+F25EEthRtgQsnRMRg5qAQIDO8BT7SHYSAPoTpwhM5gXUoIIZjyOXq2g8nGuJqqpovH37mexJQfc/lXOyXHvttXjf+96Hiy++GA9/+MMBAJ/85CfRNA2+9mu/Ft/+7d/ur/3d3/3dL7veewfolIeBQTgs/efikGdtZMT0MTNmwt+JsBiZ5UpTWhaSrIvUJ8zCIvMwb+KF8DcLXVJ7LIVQKcRIQUCCGyFARABLFj6UZL0LdXCfxdp2/721go1LQKRk26S5INfjhaWEGeY+YC26AIORz+LS9xEAU/7vD14BpHj4VQicZHsTXRtFvHT1SUAi2ZzQL4iBagSIBRCxoi/OBvrZf06a0yVmyiFPp5i7DHbZ5Fa+b28oyAULz1IQZwFYmvZoiDHSvg5IASCdGxrxHPJgz9VfZN6vU2kdgkyxAiBVCFFj3Hzhd0wEYP/sAGolCPDV5TksKwzcPR4IMAAXbYgibfY9MTRlSeCnEOOTmL164ZQFYckWS3/vFICIZ0lTTP4sPfy/ooXTz7j0NJGCzivYFEUH5T3Pv5MFBiGarQwuE1lpyP1IMvtJjkklA98IJR5/Finw3DMARP3sxzEBdQwYozFOzwfAK/14H4rAJX8vSwJAI5ZziRmwrAsIa1B+5ucYv0fh2iLa5s8kZqqkvzeb/7JVShZMWr3FA+/jHGG4Kkkgn9fkusHPVhq2D0H2gvLSLn7Gf3tmnBVkbq07wMnKG86n7a0zXCAq5aLTYjwlNnE2B4aDMFeEuwl0HrPnzPx2YpyMpfnd9wQMNflWqkwD60MK1FJlyIxBv+oicStAT9vwTnyWKwXICLPDAczagILL8F7pc94GXz7aGzO/ftTaKrA/diAU1GanbAQQLETCRIG3nOB3Z2WujGugFO31rDxwFgc8PyLXBfe/KonVpRRQwiJnSWyFs0WCX/ic9wIHMllpoHivtZRNwG6diVIqxaxhAoJdnIXofx/FnZ5v9veheI1vrFHKs7Kgtm/vkGnwoIJZKQicwu0jpgfWnR+qcz1R+5TGS59Hkc1VRbmcVVlAdWIvEQoX74aQWPBE7V7Sb8vcQAjcSdryLMpHaUUkweYSABqVJeNs+x52PD7L5Wrh7wVXFev2syL3LO6CIuMcLf9YzWs3Nzdx9dVXR59dcsklf+t67x2gM8sCkGTNMQul0tSHfU2cCYeqqnAQi8Av3l9OCoyJiZMvLLhLIWSZ0JGaLSYMZaRhZ1ZNXq8R2CoZEIg17SzspQKVYNMi4ShlrVKTWa6DzXaZiXLaN6sk4yo059YQwGUwQi9Ev6Tg7hkUG8xoBFNJ42IWmcJU4EnZ1xSgSLDH0XWjQBcMjnIv3C4AIQ/SxfOQgHwJzoEYRKdtkP975pLBmDt8NBbZd2kenZgA+iLbwJpW9hFMgBvPp4ilWcKw+jHk7+TYcknfG1hUtgjgLoPBpCaP/N4RoDc2Fsp5vbOCQN4v5mOUrzMTIEdaEch3UGFs9GhEgjNbMrRtbMngoqp6IWRQBbN+yQABQWBeAo7jyNJC6XBPlkEF7zssit/jHDjx84MBj3s3O5n6fdYDGpciyPsAyveUIDwtUrnHwqoAv9zPngnlNso+zlwEcaWCIA34uR3VwQpELNmrlzGRS9hUP4eZeRV7a2p1kjKw0udzIc2PCntcuo5kxNwIbKb9mOc015sW7FsZRYLm/YfPDOOUnyJStbVmcV7yvi39/VzbFxh7juCrKFCUcuZ7qiwooFDu5o7SZJoLwI4nUJsbgLbBdLXriMUcVHHeV/YxbVtiNfkzwJ0DWXjf3oGYys2rpiU/w1kNM8h8nkwAyKYtzLCgiKKzoPRTXQurNQFiABgOKCqpPPvkvC0LKGcsEs1XNz8jJY4DgNaS8tpKxaY4O23bBXDTNAROlYqALr2Es5hhP1UgKCe5f5RL4cSyk5kJmUAoN0SchTtLQRaNvWPTbF1HCqagACkihnNZkSxs9H8CSoN7Rg4znlA/naJo8Ri6NGFtR981DfTaalj7ZUnBc5o+VnCUJTGfWkGtrtCelFGgKePGRGkDNjdFlkGNhhTNlceNz0r2AV3yflF/uneIzJ7hztM+9MNSplv2/ZcCnNynqVWZcK1K2yWVG0FOYiWpUAQYS6bznAbsXgA4AcTEyl255xwvb37zm++ReperZM+1wgcnEACK1+hm8KHHjQ2RYbOMTA1Ym+mERZ/WgutjIFeIEPbu0PCAVbJGwmyLfwAsBUu8wURCijDXkilcJGiQplBeoOEgHUJoSRm2CCTJ60xokwc0rC13Rbk+iYAAA/yuC0Im9wF/50yK+D09EDPWm7AuKAqkaW8WDl3fDikEavG9BIjiPf24sPlVHw58xQwcC4x8eCd9vmDqKwCwP7jFPPDAWpoiujbIcYuAqBOWoiLHlAM5ubENvp86CMHi3VLAt2AK24X55+cBr520RKbB3FYd3j0B7pGPstaecUqFfEopEgBAZOYn2usjerJppZwTRRHfK6wVrANJSusANvs+sCeZJl80Do4hmLYomq0EPzzmAJlrlWXI+wiENSAtI4o8sHe85/D4pAAzc/vVPcl0Qow7+67zO3KbuH3cJ5LhNcabOXpmMw28xv3Je3CqIOHC64PBOjOeKVBltkiCYNk+92wOOqLkXiTBvxVzXACBBdcFpWKTdN9x1n/n7zmLopH3lmUKrYjlzPNoX+A15NvC5wqzvrweuS8AHznWciRXIbhGYNftvd5XkvdvNy8J0BiXN9Us+hjbZI9QKmbKHdDz48c5id1cUS4wDCD2W9575rVLvSPWBM+7oqAfjpbN67Pt/PwkJVEAzjI9jM+52rTQ4znUrIHNNKX6aA103cEUYs3xPuJkCTWro/PA5poAittrIhNgVlwPBwRc+DO358i9ihlAVRRhPnqwqMO5wK4Ymfamql75wDKOTlw03Pjr0Yj2KA8WnMJJ5rmVRcYfEACFwdKdgRvL7O6SQGgqzyno0Jezt7FrxbJAfly4X5SCHg6oT3hvcAp6VeTQBzehyhL9yS30W6fRb50m1jXPaR6Vbm/nPUWT0gZakTvFoPKKjMivnP+Ximw+43ls7uQ9JHiL5ALuy7PtmWftsgDEv4yLw/VLvoOLqyHHG+69Ftqa+hyncSzO5WLv5s+9oHRdh/e+9734lV/5Fezv7wMAjh075vN23p1y72A6gSDIAQ7AIACNXjA/QNCMs2ay4I1XsCJ82LCdei9YTBGpMWU8vLAsBBUfsCU1mWOtdVGQoJTHgC4kpBcsqBBKIn8l5/cTsa1CSy8BSMQOLvjuEKMXsUkC+EWMEgt0kdaMQRQWn8ftSkBe6A8GbJ03xVKl8LVSKu5jUac0S1WDMmKgIv9ZaeKpYlOZ9BmpKZnURHptsgRzkhUR/Rbl9OtNZGLKYE0KuMzInbXI+SkBCzPWS6IHL8yDTM4BIeCIa6PPuI/Y1E+k05Dtkv2mHNhgP0Vv5ss5IN0hzyxIxD4lII+fzwJ+5PMnwJFyQE+VBcCBNIBIgQHlBAnPhAjNdzQfWRESFAp+TzA2/gxCKcF7Cn8nFTgCsAOA9/91oMPWdWBLl4H/r2CRwdEi003HFJJmWwRQY9M+/06ir3jOLWPJgdjUlgEoj41gMf330q+d5woQ6ub/+bnimogFZfCf1iOVPq6+aM+UvxOWm7+L/ED5HOB3RVj38rpl/rpR0DlmlsV+hSV7nvTn9OPnimebsyz4WWkFdCJtDZujch9wP7mUTQvpYATI98GdpDJBsvtS6cIpcHg/d8GGmNlLAy1ZZ8YI9vOcC0ZMmv4rFcAmKyyMA5llEfaf1LfYyQQ2z6H6GXTN5y0AA2TzBs2REbJJh24w8DkfkWmK+CmVSwBF3DUmmKhbG/KkSuYV8Cw7nfPG5/ZmJg3WsbFCGeTHkEumoYZD6qdBFSxs3HnDke2Vov5TlQClLjgUgVs3L5uWzEYHVchF2rZ0fvP0cm4M3ufwzhgspaHKMJ/9msoyqMHAK6f12irM/tiPj09p5S19VLyX+NgAbIJPwX28nz/LbloB1qW+4b4ZDWl8BhUyvRHaqjWlTeF5O6+J7TROccF5XvOcGFMgMYF341lV5Cu8skL5YDm1y2gIxfmAvUmrjO6aMI7c/oQhJYuk0PdWRIY/mwlrJKtwv4ox8vXKPVym/AIQspmF3OW8f6jRyDHpyV5vbMht29w7fDsVFl/zy7nnXC8333wzvuEbvgG33HIL6rrGM57xDKytreHnfu7nMJ/P73bKlL9XNcQHP/hBfMu3fAsuvPBCKKXwzne+8+5VxIf0ElMpGBfqO3eh3nmR9b3I2ZmYK0iBhzW2fNiyZt2ZzCgnkLAJzYJ/GGKBAIBnX/x1iXlWeC8dARbPFEqGjeuUWncn4Hi2dZlJGxA0p6K9njH0mq2gleX+8Aek6ztVUDQ0D4QYpAp/qkjYEuwda/gXtLsIQJrrihg0pwiQZme+MMPMLJtjM5eao8KBDT74e+NNrH2/Sa19pn2f2K4DRyJdaF+izfbKABmAJgtmdwuMimQIBbOwDAzz2Pjx5yAr8h0zF8SmD+yjfx83lr69fL8QfDl5uTRNjRhKmdycmSMH6GTgIg8IuG/LGFj6+wvO9+eu537jPIDJs8Dzjz/rheDm54WJgKCPlOgEdCusGoJipo/7WoIZ/l6a+rJCi9OLSJDuAY51OQO7AJq4P+V1Utj8Shf5fq6fKQiMdtYJRayEyzKXCzHsn7GiT+xxDDa8Ukot9hvPgXQsBWCL9nSpZJGAUNaTtiG1MgFikCQtVWThNSbXhLtP7qULfqLJM71/qJtDvHYiM1v5TkAcuE6+o9sbUh/TiNHlucZgEEHws61jGKUg6licqP/Z+iE1jxZlgeWQoDVVHjDrxc9ipUtOa0QJRk0pHfx1JZOrVMhn6Kw0vO+mcUwt7zEcQCgtYt+FsdDjGdB2yPZnsFpBNz1soWGLDLruoYxBNuugZg3UrBGpfAyZ2PL51XTUNsn0cjv4/7Zb3K9dqhf/mRzn4SBmA9nKaFB581oG7ygLAsI6qR8gUCrnt7HRXkxsZFAUqaqkH38e6wDe5XjIkmVnZ8vkNUKRwmNH72JiQCTGKN7jBXh1bGYctZyu8fPJWjK95n70CsMyMOYMyFku5P4CgmWKS02kRsPw/qwodn1srQkpgIaD2KLM73vuPdlKJ2UjE7JCfi9lBl8Xv7Y0O072P5LJlvevYsuq8EF8gaiL+9qnBxsOqD/cuPs2JJYV9xrzWl5Dd/XnHC8/8iM/gsc85jHY3t7GcDj0nz/72c/G+973vrtd798r6JxMJnj4wx+OX/qlX/rbVbTMtIkFQDhA5n68gCrNl/jQZ0GEBRk+DJz5kRfEUjNVwG+qEYhyufYkGPSbeMqQQQA/AUgjoQeIhXsBTPxCXyIQMKDwv6Uw6Z6d+qtG5qziGmTB39S6jS0Cw7xpStMaAVD4f/kuCwF7xN9RH4n6GfzJa/n9PFMpgJgHp8lmCiAy9fIgTM4pwVj65zErasKB4+uVrB5rqxMBNgViDIxl8eakQrCUQaj8u6TtTf6PBFwfCCBmEP2BJxQK0fqQyg7HQqRAPxJI2CRJAg6uCwhgUgan8v3tQJsUkHoBKrIszEMWiJyyIBJw0nnO7wcEIcaBLM8+MmAVyhOvfWbhR0aylQIBt9EpPUIgDR0AbXIYqaoKigxumxfez6Is+koUBsI+aqgzq2Qtf2rRkLJ+LMzJOSABaFKH3w9SAMhKEv4fcAJTEdfJAJznjZhX1lurBAVCZHYq28l7Cj/XmJCCQ17HQJb/VsICghkrnp/pHBdFrtGIyec1xkCf+0ZapHCR4JSfnWXhLEiZWTmPrCWhmPdZAdzsvI7ADpI9wz+LLXlS9p2ZY2YllxWvpDDBaojbncm5YwPzyeuLASTv93kW+28qRXX59xVs+LyGj2Ir2+uUS3Z7B2hbqP0p+lEOmynYTMEUGaVr4bHg0rQEUuoayDMyrbWWfnPJMmqPMe75ZtGaoyqBtdVwVnMEYG47v0+WEVuXaQJp2skT2u2LPkqwCco0tq7RwX8ycpEpwv7on8vzjvvNWgIVrHxihSmDUOFn6u/lqKZiD/fnGT+DGVw+g5Ry7+fW6RLGj8kCLw9J9wgxbyKXFY7FwX3J8867URRhHrHiTwJAVhhYp9jIcwKuTrbjvtKjUVCOy5zKReHWZu7TkNAez5YHJSnpOcAT72HCKsdfDwanMThU3F+J2bO8huteKhMCS++LrpHxOOT48FzLs3C++cBJTj5YXSGTcgae57iJLQcSuqs/53r58Ic/jB/7sR9byMl52WWX4fbbb7/b9f69mtc+61nPwrOe9ay/fUXWAqYXggQd8MxEeXMgwbZF2mFZ+LDhw1UwiEoCTalRZOHJA7EiqkP6AHhGVLaBBZ5ljA+/n3uObdvYrCnVoIo2eZaUA8mwtr6PzXw9MHNmV9G9zhwy+j8Lpl7LoqJK5lFlIVWH7ysBvP1mzmPHQgYQTN7c+wW2sAgCHIPLPgQYSU2Qo4AcffAJjMZCAhzuQx4vPhh5LiTX+H6UwJ2BtxPMUrY3GkMA3qRRKgWWlNQ8L2I+mF1htg0CuDNAFsKyZFp4Di8Ac+5rHi/5t3tGlCLFjRuto8AMqmTj8r5XrL1WTpDi71ITVMnMaQ2V5SFwh47Nk1nIjszVqWL4wEACgHrfTV6P7F9sbAQqfd+yECxMbH2qEx5XrUK9/L0Umngc65reX5jfW9dWdTZB/itR6iYyw+QAPt7Pl+c9Axkx7qpwwWDcu0UAIr3e7UtmNgvmgGm0bsloyN8MavinLMCBceSzIr82bktqzsxReJWK28Gm0rzvu3VOgCYPCgbeH/jMkKlv+H0ZGHMOSlYOJQohD/BSppfXnVSQ8R6R5yG4DpdlLHx6hrCSRJxR0bkkmWR+f/FOkR/zkj0yap9ruy+87/G7tR1skQPTmVhLygMM23VekRmZmPM7S5aW+1+ee2URAgwZQ6CBWWU2xeW2GMofir0xyhMrMCsVslkHq5yvZm+h5qK/ncmuN7OcNTDrQwompBXQGZov81owaA6QN21gFF38Cdu7iKd9D4Wc6nCpPPw86jqouZtP/E5ynlZV6KMsI0Vg17m0MM4Pl02VraXPAapjOKC2pooj126fFoP3+CyDtdLHPQ/rONNQaXAabpNSxIpZS6ljrFMOGuPMf+mdKbBQFsCOM5n1eyBbAjFYK909bu5AKw8olVI09m0bACdcP2aI9xd+h0FFDLYzxbXDEkoqRVhp2bawnQI21qA6MjP1c3A2A1ZGgSXVCnpzA3Y+h95Yd3PYmZz2PZk1s9LT9ZmX1xJQSjKXe44bZ2/iCzhwqJ1lkVMkcaAogM4fxEp9f5+IsOuVB5mOTHz1cADDgan6HihLqM11YHff+WureN8DvAm1go5yiJ5zxbqfu3rPOV6MMeiXWI3cdtttWFtbu9v1nlMqiLqusbe3F/0AiLW9LBgLoU2VZST0ejNLKfA4TXCUI1MyUZIt4melApO1EeC0bRuCMPA97lr2bUy10hJkkVbQ/c+HCh8gUqhzbfDmectAYt/HER1diQCXNLeVgEse7g68RKysZDgFAPWBlFJBRQoKsm7ZlwzSxL0ekDlBImX6ZDAXD8KWtJPH2jN2DKIF6yrNc/lwkqyjNynldstxYuFPMAEyQItnKFiw5OiDkVY5gET//gzKjDC5ZvCd5749yj2fAXbEsArzPVWWMbDhPuV35vazkMB9yO/G78vaa2dGpgaVO3ilQEyBeViw9EGjdDDfigJvcbATfneuj5O2c6RNJ0BiQSNM4NObhvVsUskMQB+sH8QYo+9JSOTgKm3nTWcjc0h+L1ZIae2YnD6Y1PG8YCsJZhP5PiPawvOuaWITx3uqDAex4iqLzfuVZKO4j1zxSjyeOwxs+H26zoN1z1YURcivmOchB2MKyOT6SecZEEyi0/2+aQWzZGJTV6k84N/8LA5mJphQAPR/G7+3Z6sY8PB+wkxfL57NdfD78DvIM4PrZgaFx4L3C15/EsTzXpWa0Euzc543DJp9H2o/92yTmITyWKR7NY8Bv6McL6mEknsgF7dPeiDvgKffZ51JLQCKXyDyFHMEVM/ggUCGrWsa67aNQbcEv5KlNILhlX3uAsPx+aantC71vAEUoNo+lo48k+j21rIg09q28wDJr/m6gXUpidD3BEJ5fOoGqOuwz/IZUhQBkPCc4XeRbK00e5Zjw+NRlq5vTMijKgNB5U5Z17ROVnD74yBYXLDLgKoqWrvOtFRlZNoaFJc0nzzz5oL2AKB7nbmuB9uZOAedMtOf4zIQpFQ4MmgUQfQCC6/93oWiCFFngcD0dh2BSW9pIdZkJRShkxnV0TTevcJqTeB1XlN0W+5DY73yAa1jwIsCWF2hvlldcax4Q1HCq4r6d1AB3CdAFDhKZRlZjwCerWVAznuVDzpUFBHA9ylL+P3g9l3uH1awOnbUs4+KzITD+AlmUsponsBRQVFpDb3baAg9HDrmXIf9p+s8QxxZNPxTOWfKM57xDLz2ta/1/yulMB6P8apXvQrf+I3feLfrPadmw6tf/WpsbGz4n7PmjJGCIR/E1i6wXgAWTEwWQJYo0il+QRjiw5efL4Ad1x/5fUrmiIUXCWRZQOEihShurxAYI/ZQMm4CJHofI2YppfmbDhsU+xguXM/1WhuzZ2fpXwkMPWDk95ICCtfDh6gMhCMYzqhPZH/xGMgQ8TwOPKb8KO4rBnieOXPpa5ywF7XVsRieudSJbyi/Y2oS6NuthWmRjjXuUkjkcWBQLJkmbod8BwFkbd97n2PPzEuQKtOEJPNUlWUQcHgOyfFgZi4VcuRYCFbTsmkb4MEg5ZnldjsA2VPgD9t1IbIkFyF4eIUPA9HU7JRBh1y3DvjZpllkq3j9ShYtAfiLbJsNZmkMsGX/KEVtSJVRfR+tH18396MSJtpdF1+zRMv4FSvMwmohKCwE7zD0TgwCk/kXCfMpuGJrE6lIkEBJmijz+wJhDJeBIBmYgtsplXcSsMo1w1FalQrPNcY/25uZpvNavptUSvH7LPstxy8LeyqbWUeKG8nY8nPkmPM+IUEFr+V0bnjWSexZ3F9cj2AKyTROnGHcZ8yOSZDG9bESQvY1v4tsq+gvb73CRe77bFILREon9vW0de0VWnwesQllZK7O64fHkMEbC/dKzCPef1yxfQ91Zg+q6QhkWEssRa7Rrw/jiLnMVgJQsxqqCyDUjyf7zGdZeC7vpzwPnBWFnc1oHa6OQt/yfsXKAmem6PuXz6BMB19O4e9IFgFlAKqp3yMzawz8mOVv2/BcBnHChcWbkGoF7cCVXl3xAFCVBbTz+wrpfIS1AQNgeX4AC4CS54U3FS4JTKqi8EqJiN2EkNm4Lq8Mc33NSi6Xk9bPXangL4sQwZyDRloLWxYB0BY5Aa3hgFhj7hMH0lG7zAidmwcu4i1WRqFtznRWlWWUjoUVST76rmub/BysQM00mUALIJqasFqn7GBTVzJ3FS4cfB+DRb8Paw9gPfvp97RwVtiNVZiVKqwrNhnmPcStfY4sfy4XZe3d+jnXyy/8wi/gAx/4AB70oAdhPp/ju7/7u3H55Zfj9ttvx8/+7M/e7Xr/Xs1r72p55StfiZe//OX+/729vQA8pRZZCk5AMA+SwiYQ/k4Pe7lwkLCBXJ/XlAtBTAocgg1ESVp+ZjvI3KeNTDEjra30cXJCa/BFEwc6AGZUI4Air5NFqYUNIPX984DHafOlKXDqF3lW8Co+Y7Dp+1C2nQ9B/k5q9rlkzlyG2V02M+P+MCZERxWaeG8KLVlUBv4CCMvxjHzqJKOnFCVPl30lg0DwPdznLBSwkMcChBwPpXyE0GgeuroWxjstmTPH5TklGHZO7eOZflmHVI64tsrokVKb7wv36aAiRoIPfGMX7+V+73ugRwxwXbFN6+esZYGMhSGA6m3roHBgM1oxjgtziOvh9+J2pxYNLDwzWwUxVyVocc9W/Axr4xQqvN7477Ru97dfL3IPyTKfpy7yfZKKhnsScLp38nuQGzM7m8fzTu5pcv5yEBOp+Wfhn6+VijX+TBYW/Nsu1McCuhaWJfJeOS/5/tSMlllNBlxCKQVrY/aPA4hkwbTdj7XrG59iC6AohmLcvdtCqsxhZkcC9iwLz5X9l55Fco/kOZqJeSzPDH6WLAzEfZt0UIakrLJBrEjyzLAJ1/M+djYliHwWEK7nd+SIx/xMebaysor3c763DGBxmb+/UkngslTBCwQ2Srv3sAawtMew/5wFsa3oOmB7F5kL7KWrAlZr6HkbAhZxYCxOzzaZAHUexlpEebZ1yN+pOrhn9CEGAr8Hm9FOZ6GetdXAolkTgncteY5vS6aozzphbeB9ta1jNl2dpQDQy3KdAvS/UmHfVQrIQSa7wurFcjRcdmPQKgTSYeWEMa7+PDpP/f7g5q8ejWCmLkBT5s4SDriWuzMyE2tCayhn/mutoQjCvBfkGdRw6MxrRWAuDhDH57IDkVZrqOkssHNdDzWtCUjx3K8KYjuZ1a5rei7gzj0T5vRwAJ+KyMkkls/Q+YyeW5J/cGTq33WOFXd7hVRmFS7VjOsXtTIKVjHs98pm6UCw+hJrmk19/T6vFNT6KrA3pnuyjMagR1AiC2sRykPtLMB29glMr60QEN8fu7kvAgxOZwR2x+d4FFvjfu7qPed4ufDCC3HdddfhbW97Gz7xiU/AGIPnPe95+J7v+Z4osNBdLecU6KyqClVVLX5hjD/XAMSCtTT7cWyQFy758HM/kQZZHKTB/FSAFAYlAjhF96Uaet5o3fdpEJYFP1MGSfJzIAKr/J2SQoPUiru2y+Azy0rqxwcg8kGMTFQleONULwKERj6PdokfpgTry5glIP6eWUI+wLpFAciPp2SOkzHwfSV8lKK+FeNojUhQD/j0GlGRgrVs37JrXPoO30b3PD9urq98PzCgTYU8FqB5vrKgIdvg3t+3PxWG3fv49snPZLRUCQoYePc9HXbzOZQRAZs4+AcLjbJPlzC1fq0xUOgR9x0LJzy23M8spKfKFWmGKt/XaYm5TyKQxf6fvQhakzBPco1GKTP4WgYGztc3Ulq4sZeKJV+H8HH264n3JR4nHXzR75HSizQ+gGeRJEiGFGQkELcWuPISqNtPBuaPr2Ugx8BPmG8rFlR7A05p5dPUpGvQCdCqLINJcinmDO+pEvxLk2vpX2mET7UEw5IdEntVWpZq6lNFoNxbeV4AYS7wOcTKEamg4rkg2wc4EO3eIQnMtlDkOuPninQl1M5kfXLbuY1L5u8CWJX79BIFlvfFU5pAnYwYLerzqVu4DYpMbW0hcm2yEoGfw8yLNQTmAAIirIyUikINAoxubfl9jE113d5lbQjWhtmclJydgR1o6P0mgDFjCBzy8w0xsT5FC6cbsUHx5k2HpWKaTXqdks7nEtWuTdLig8Eozw2pmLWsoHbMnHHgrBoEcNx2BLyKIswfn0vXMZ3enUjH84rniNbijBH/A97M0zrTWZ5/UUosICiKef6c7dw3FoANzKe1dNawgCvnojP7tdNZiAnB7ZBBktjP1+RAU4cATNwXeQbFrCS3Ic8C28r7X2/I39OlpLHDCur0Nl3LfVMULldrRt+78bNVThGQAXr+bA6UQsFZZKRg8OvEpSZpQKwZWyVYC+v7nSydpAuGGgzI2ovn2nAQ+p0VOYmyDW1H5r6dY0atgZ0I03ZmUJkN5TOzN1C1M0VmcA04JYkJf6+tUqqcc7jcHeby3sB0AsBwOMRzn/tcPPe5z/2K1XlOgc6zFqkdThmuhF3ygiMLjylzJjXjqdDOwksKTvkwVcGEdkEwliBLHtTcNNaAs+Dk3kGCH28mKQDlwkEhwS/XwQymYMS8szqDRwFmvblHwihJ82Af1CgN1gJEINkXCQZlX8hxk2wcv08bA4q0DzyoSMfQXc9CiJIC3zJAwyZSWtOGIcGFHD85n1g7z+8lD1ojhCHOASuvSQXZVAhdNn/5ABSMUAQ4ZZ+z0AnEDKSYz7ZtaQ6wkGzEM33amtILgUpp2OkMMjqi7XugYSFVCNayyHeW4LPvgc6GNbeMSeH5IbTGkVAshRn3O7IiSJUTQrCXCpNwUSIUMcAXzJJf4+5donXFAqxQwHimU+Z5lM/TOuT1kiB0Gdj5ShUtzLDlunFzlANPRCwVN/niC2CUIqZm2kWAIOrvqoqZKhF4hNkwH2SM57kcDy3MKIEAfjtLdTM7IFhrX5fcQ8Q7LgRGS9jeaN/mucDs57K9HwhzMAWakrmUY+lBZcL6sGDngIll/7JesMqZBjgnrLXL91me51LRpXUIpsRMrjMRtHJ8pZKW60sVUFzkfuQH2QalASsNAXoHBg1KBZ/s9LzmcWYQit4rM5QwIfT+2jxvmE1jNrDvA0NWN4tsKJulNn2w2mDrg9URFixl5rVnFr1JqUnuy3OgCWwm75GcqioN4AetoDj/pBFplaRSEAjKbQ7+E7GSwndcqQAyea2ka8Naqqdugq+n1ojMW0dDAiE+2r9T1LLZdZ5T/7AcIUxd/X4tQTIQfFa5jXJeAi7gkZjXbl0oTqnFiiS2HmBFlADwbCpqyyIwlFLhye1shOxgHLCs2yB/5Rm9v8h/Grl1dB3UvqvfmzvDrRUK7qRaty91PVQXxsvmWQDlVQCTajSiflld8coPaH5GOLek6a0qC9jZjKy8hgNiQ+V481wAYMsMqiEFuAeZHP3YB3Ikn107r8OeqZ2CYeDMfnPnKtM0Ibp0WVK/1Q0x3eNJmEt1DbuxBhzHuVucLuQu33OOlz/90z/F7/7u7+KLX/wilFK48sorcfXVV+OpT33q36rev1fQOR6P8fnPf97/f9NNN+G6667DwYMHcemll961yqTAz4dhJjZtWdisg4NS8GHFh6I8uLmwAClNTmR97h7Fz5fmZ2zawQcB18eMJZtysQmnZE+5Do5MxxswEDOkfK0EFPyejg3yDGkiGCsG4wKMepNWCV51HHXX/xYC+VJBiJ8pTdEEYwogMJBSwy5BGreV/2cmgw+hZJzCgSTAB/eHAKP+OwmCJciUP6nG39oYqPEhy3UxIF5mTrdQTwI4lVqcR1wS82JpVhObIkoQKdkN7bSppf9fKe3AJQL7BMBqp4nPEKJNcpt8fkodlA+SleQ+ZLbX+3mKNkp2UQq3ibKI61sQ3OQ6dP2guL5UcJSAl1m0tg1rO2VNU2WRvE7UK9ffQmRlMYc908kAlOtM9xO+954EncJvKspza8iM2EfTlUon1zf9578IpRWM0sFEsAe8Sa67J1JGaQX01kcppb5N1ipfZxPlhFgjnqHiQDhuzKJ5IRUHiZJIWRub0roSWY/Ifhf7mVfOybUkFYlWzG+5v/D/8tyRyiIgnD9OiOaUO97nkZ/lhWABBgFw/uRop+B9QbbD9Qkrm6JctRzlVyoR+Jm8zwBiX0mAqmTHPbMo+kma3Epzan5nflenlLAuyFik4OI5Yiys6YKPZ9tBcd5KeZZ3HQnnfR89W2WZn88MZolxVMDeGOrgeggSxFOh7wPwFQpoZaivbNP63NJS+LdNCw6Q44EohBIkKwDYGDQCAZzxWHKuUk7pJOdb0xCbx+tqOAAmU/iUK1IZwwyqVG72fYiey0GcJOjlNnQEFD3Ly2d4Z+ndGVCxqSj3eduFQD0yfgCXjPwc2bc/klW09r6dYDDn2qMY+HGRLDf7WboAb3Z1SOebVPr4wTX+ty0qKFaM9X2QI5sG4DnCZslKEUh0oM+bohsTzR1WFKiuB0wPZGTCrXrHdAJuHSnYtRWopoXq+8BSVy7402xO41TX9MzxxO9BdnUIxebYRUHKAykXZJZAb9d5MOnTyFgL6AxYKaGbNo7oDlA9LogURT5vodZW4/m5MqLI1EPBbK6uQHVJvIZzrZxFpv2S95zD5YUvfCF+9Vd/FQcOHMD97nc/WGvxkY98BL/0S7+EH/qhH8LrX//6u1333yvovOaaa/D0pz/d/8/+ms95znPwlre85a5Vxod/6s8jD3YnuHiTLT4YpIDKwoK1zsQhMXGTmnUjDkm+nzXMWqRqkRpZCSaZWWPNqQQSkoEDFk2dnPYzYjtZqAFi81BpBsXaeleHF5KXgeol7FjkjyqBPl+XMAYyjQlrv2XKjoiR4veVArjoN39fXQdmiAWXVNsuWUx+l5QNl/dFLKWJA2CkzIVsmxSYl/UDgz85RnyNFES1DiCO+5nNhSROVZT3K5ig6ZhtEDkrLQtcfGvuTNdYcPVAGLDWXWs6+JQlAF1TiNx57K/VMpPkBEQJMOT7yz6Qmm05TqxMkQqDdFykgCQBrayvaQPryOMGRIqSlH30f7tnSVDh53nKFPDf7nfKfPpnirntQZEE4nI+iHUUXXtPFWazRV97v6wsi9cm95W3sAhzbSEKtBPSSIkhzIjZrFFG3+R+kGuIu8F9buWcABb2F+5b335DyhDZdj+mTsGo+JnAgsJswYVAmgqmwJaLW8NRdHA5RxPh2rdfzt08BpBSIefXKpvbGmGWLBimKJWM6PNorcugZoqAkB8H2dcGYT9iwOjq9CmnrAGg4mtckBwPmKV5rUH8m/sj/Yz7jNOacJtE6guv7DCWzmhmohtulyjcd9YCcPOb/Tq1Ee8CYh6Vgi1cLIHM9W+7WB8rIbgw4KTvwzpm4OzNbRlEtx0FNZLvJk1erQ3MbdMKZZ0OvqYAgR82/azrEFGVWUqpgAcCY8h7Vu3MTo2InsvP5/bN5uF6DkiT50DtoqyyKT0DU97zjQEgFPl83i1Z12o0jNJM+XbkOQEsPnusccA8YSRdfaruyAfTMXbs16qY4eS5M2+Ckov9JYeDYDba92R2O3c+uhxkh62C+F1WR86sFPG5VWZhnGTwu9EQmDcEbL2CRoHzv6rOBP9Sa0NqHA4eJcZQ5S6ADyuQywJoaB5ZpbzSUM1bYjt768baCn9e9xnnwl0Z0nsaG1m0ochp/2zbIBezQoHbymPHZTJFvHjOvXJ38m6ey3k63/GOd+DNb34z3vSmN+E5z3kO2OrDGIO3vOUteNGLXoRnPOMZ+NZv/da7Vf89qEr/0uVpT3sarLULP3cZcAJhg2RNkwSUXJyQ6tMkeME/0QQ7kLIAOFmzzgc9RztjExcpJCq1kKoFQAAuy5hUKXhL9vLOCm/kUmPOJmkMDPk9l4G5lAWTgssyQM7CdMpSSa0rsJhIHPD+p0vN+qQgLoGoKzL9jDcJZU0yP2sJIIAQMFPTvejvFFCmjBcfgjLSm2x72sfy3fm7FFQVYo46QV36TdDhLOphNoOjcXI9MvqfCQKzb6KLFmubBt6My40HlPJRWS0ffs6UjQJ25LQOmBEBIj/PyM9HCtKcK4znMX8vTR/lOHE/SzZaKGb4Xf3ck+MmlDQeTPK85s9ce6P0R1LRAyyACu6fs/n6eaCSsGaRYoWVQ1L5wePG81POUZ4ybIFxTxWpBe9NUCqInKjWKRM41Q0AWGN9vjjbdjGwZmG/7RYFfxY0U8AJxIohV6yz4ojSkDjg5JnSFLiJteej5qZjx2ly0rknFRqsuJPg0a+1LL6W6xAAIwjnQdHnr+fxBsL+LtMLyQjCfg24vdKl7vHsn0s5Qgyd9t/5IGfK+dcJKwvlU0Y4ICDNM9P151mS0B9RiiU3D9i8kqOLyiBQ/rs8p32I9yret7idRWizPBsin3vOneuYTT9n+T5Oa8TAl/sGoL8jRkzsl9xOLk0DvTejPskzH3SHWVX/nK4P9bLlAAN5NgeWTKl1Ppys1HEg3b+HtcFs1Nqw/vkskmcqz+0so+BDyu2FnIaD93IOEiTWNeZ12DNZcZcqj8vS7et9ACjWBh9Sx1aq4ZDqqNz1xgZgxCmueN3LdSAtg4R1l4+A6q4La9DAjqqwppzvIpmeZg70GNgqD/Lb6tAp1ty71jX97txPwUAqC6wmy3xdH6L7ssKiKuLPhgPYMnM+mhT13o6qoEBuO9hBsTyqObed6+X53rgxGVQ+aq6t8mC1MKhCSpyyoP7Som+LgkCqViTdc18MnD88v3vlUuv4CNYEgG2mwGln/LrNMno/9pVlpW7XwZbus9mcgiXxfNKanjGovrQM+w+9yL34rvzcxfLf//t/xxVXXIHBYIBHP/rR+NCHPnSn13/gAx/Aox/9aAwGA1x55ZV4wxveEH3/lre8BUqphZ/5fH6n9b75zW/Gy1/+cvzAD/yAB5wAoLXGc5/7XLz0pS/Fr//6r9/l9/P13O07/6EVrw3WMXiU4FMyW7wJMFMkTZB4k5EauQggikMQCBuhFIbl5JMAUzIbEojwoS8EKZ9aJQWoXCTrKQ8RbjcDaglK5eYPCOCdPMODLCE4SdZnGQsjBHifM1IK3+6ayLxFCljcHvmd+8yH3k7fk58hixQU07Z7zbpglpy22mutPQvg5hLXxWCvF4c/15cymPIQl4oG2cZWCL9SUHf94QNOiDHx10iNMxCZfCqZFLoXaQjcAeUFMyD2PxLP4hyWVh6Yrj7/ThIYSsVE38emk1IIl4ygVJCI915YK/y+ck3K9qR9K1l4qVzhOh1rvhCoJxUK+GMZMXKZUoHbkd7rAKkHQHI/kH9LgIKgnIkUH1/pMhMHjzWAMG30fpRsBikEVhminxkjftdgjpYFE2l+T6noAxbXp/yfQXov5qRcCxKsy72A2ywKp9qIAKK71s8dWZQ6+/+8r/A85t/p/OX17vbV2MxYLyqxeE9hK4UqCN4s9KUgngENAxk+/6w1wUKCAboDeZ59jPpULwD+SBnolGHSn9LnUgTomV0HNk317RHjkEah9XXwszgQGQM2VpppoXzjsRXmsZxKxfe9qNvvf+z7CQTzWJEKQokxCKazFtjZI9PFyYx+jPF+qMyS+vdwe3UIkuXGgZlorXwqDB43Bp6+fby3cfoqEQAt2h+iua5dqpMWMH3IO2ltMJUFYqaT11lVhmiuRREAWJE7V4jejz8BbR1ACxDYQnlWchCfxkWN5eu1Dn6hcq1Hpu86VuiygoPXettRRFntWMnhIIA2VmIMBxQEKs+I5Zs1sTJgNIwVx0LB5AMCcc5ePmd4vfd9AOtsZWRtSJvjTNDVjPMwU91q1sBWCfHgUxmxXEr7pB0N6XeuA4Czjrm1NrzzaEh/ZxmxrCsrBEDZlN0aMj1uwppTTUe5Z3nO+ki5IpZD18MOiPH0Z57Ycy37oA4qYvV7A9X2BGiHAyBTsBsrYh6VcdC8fypnLb/927+Nl770pfgP/+E/4Nprr8VTnvIUPOtZz8Itt9yy9PqbbroJ3/iN34inPOUpuPbaa/GjP/qjeMlLXoK3v/3t0XXr6+u44447op/Blwjs9IlPfALPfvazz/r91VdfjY9//ON3/SVduXcEEuJJzezFkuA2AAKg5L2ONxTefLwQZIIwcLYgClKIAWITj7MIr5GvaWr2mwrVWgc/RwlqpdDDbEjKmPK7AstTB0jTUa4zAXILGn1mkLhIMOqZkTZo+uW1Wi8CQ37PLIv9mVJQkzJX6ftxf8jncV/K/jIigqUSqVMEePTBhiSA4vc3JvZLlH0n+4PbljKsacAT+Q4m+FdKk7rIfI7fVeeifc78rO9hhYmt5YAXVemFJJ8eAPCsBAlJ8HVFpRfChfCbjaKyyXkugaIUvL3mP2F6+f5lLDOPWfp/aqKuBIMo25Vq7flZoniT20S5sfTelKFz9UVscqpAEddHQqXsWyD4c/PakZEe78nCYITHnQXuhN0KEUQN2LxMSdDB78GmyjI6rwD20mfbWyxkiRkvkCiIdBI5k/vPhOA30diFayIG29oQOEiYWktwtdAOnmPpfD8Lsxq9q/eVzgJ7Hs15FdrqFZeOkctzSrmhFVQm0msoDatsWPMCWFETAmhlZdRZde19WDver7EsAnjkdjQNVFYGYJTuEW4f8YWDjaWmpGzuz/NemOjKuvx7anE/v6sSSodMAMgyC5GzxfgvMO2AZxa9tceyM1qOjbTu4H6e126s3frp4YGr6sP91P90r/c3zTQA6/dd8k1ugUGx2A56iRDspm1jhbqUHTgFSlpYZuHvKuE6I10y8jwEzwFEzleWaWzoXxqkcCYw4Gs7kqs0z2tX16ASTO0SeYeLezalOunCWLG81DTA+mrIgWkM0DsgaS35R3IdfefZQVuVdGYxwydNk12/2EyREq3uFoNvMahlBTyD/aYBBiKqrjsjbJGLqK6GghpJ6x4vk2hn7irkRk1tUp2BGRZQM0vv0VtAA2pSEysKkG8mtyXT/v1tkUNlmszD687vK7bIgM4Et6SyILNhrk+7AIqWcpOq1RVKg5JlUKU7x4whZlMpqLEKfqptT5+3Pb0r+3T2BtBZrAA5B4sy9HNX77kr5ed//ufxvOc9D89//vMBAK997Wvx7ne/G7/8y7+MV7/61QvXv+ENb8Cll16K1772tQCABz7wgbjmmmvwmte8BldffXVoh1I4//zz71Jbtra2cNFFF531+4suuginT5++S3XKcu9gOnnzdJq7KOIhf6+CwLIgKEuBUvpnAk57b4KGTIJMqR1mMxthMhQJ2ioIBL4+Lgta4ISl4cKbTMouATEwk3XL/+X9KaMkmb2UDRRA2F8vGUfuAxa2JLMnmV1pPss/qTAuGWf+XAJM7gpnbmv5vfkaCfQcWIl8ayE03AjCsvcrlIJlYvrjmRzJWslnyTZzn3LdwuTN9ye312nJZfhzb1Ion8nmS/L5WkX9o/I8CGxdF6JEslDuDnLLbKQMSsH1SgUEz1U255LzRK4jfh85l3ldSYUNKwO4PyQrlCoXWOiXOd74HqkQ8cLsEkVTwuJw37Ovpm3bwLim6y29F4iZF8DnoY3ukesvvSdh3BbSEXmBb4kQ+ZUsUsOdJBZPAWXMtutwvSIBW5ofR+yx62dW5nhz1z7uZ38/j2ukQBJgghnXTMfjnygD5JixmW6U/ilVUFhhIi33GjlW0hIkXb/MSg4qymUnx9kx3V5pIxky9zsyz+TPlI7MRz04dIL+AnvI5qRu/UcKEWalBZvt25GFvSJiTgFv9ssBY5CH3IxemZLp+B7RRg+YnbmlZ/mcya1nA8U7SPPhaJ8WZrPyDKaAS4VnhL1bgArrKgKgrDCJzh0TlHxRJFbrx9IrANlclIvcS7mdYjzCGUZ7qfcdZRDPc5kZQXZF4DNDspqpcqbrnH+eCSBwOCAgZvqQYoitGmbzeB9iRapkS41bo6zA8Pu0DeuT53br8pjKs4MGOpwtfL6kUY7lGSJlkTwLPooiTYcHzoKhtKWzqOh6IHP7QkuAU/XEGCrn78usqNUU3daWFMgHjq1UHZ8/bkyYyU2Dccn373rnyxr2BjWZEiNY0+eqaYOPK4N0fm7TR9Ycqg97gGro3FYOyKnOEEB0yg7VdpReaFgSwHTBqpRjSn1bm5Z+OuMj+pqVgfPpdeMk/E5V40xmtSaz7bIIJrK9gZq3BKpHI7pv3hCgZcDJ+6/bp+3w3AacAMKY39UfAHt7e9FPXS8GVWqaBh//+Mfx9V//9dHnX//1X4+PfOQjS5v00Y9+dOH6Zz7zmbjmmmvQijN4PB7jsssuw8UXX4xv/uZvxrXXXvslX7dpGpSpW6AoeZ6jkTnL72K5VzCdkVkMHybs4MxmHwA4cpk3OYJgfDicvAl1WmUTtkwIOnzu9D1p1GRuMdbS2wQIASEFSAoMGVSw8CNZUblhy8h8UlhLmUT+XLJx/B5SMJO+FVwE+IuiNUoGSjIBEtTKZ/N1/FuymGnhetLvUnbL/Y5MdJeY2fkgMPJaBgriXj4GF5gO7gcg5CZMzUS4PZIRS9sKuEPYHcrMlvI4c1+zaYxk3+XYye94TvcmzCcH8iN2VAr3y+aI1DanLI64xjZNlN80apNkzeV8k4dzWlIWm0t6vWThWUjicVBq0XQnZSNlnfx+XJdkoWRhgLQMVDCAsos5HdPAQZIFW4i2mygsovQo/P0yAP2VLOlc9p+J/mJFBQBYE++BxgZBD/D9xObEACLfPq4zMv0UygMPMCSDzEoVH/U49OnCO1gb/ALld5JpXnJP9Fk6x9I5mjCbsFYE57Jx0vk+rBVvqiatF2RgH/9uyp8jHOCGASQzhT4IjTAbBRD8A1PrCMeMhiB11gM+P0ami+oI+wd8u2FtUIpFSgOxZrN4/1CF8xMXa0WxSaczZ2VW0zO1gk3l8VTIAGWjfU25vJJK6Uhwj8Cum68+8BGbWidgE7kO67QLEWutNVDFwLUB4fPM+a26lDbetJjZT9EfPnKtUrA2Uchp927c/qaN+7Bp6byQ1gNS2csKmEyHCKRAAKy8162vUUAXmUcXIDDBoAmA9+UrVGiTEWtRgkPPGpvg/+j6P9rHrWO7jIwzIRRIEsAOHVgeVDFzO69h2wkFbioLKGNhRxWUA3YcfMcWGVA45jMDgTpW1GbhfGNGNJ2vpGQxsDw2HQI7Ks9ZNuvlvatgJT3l/FScjkQSDVx6AzssCVT2hphJfgbvd9ZCdXLvFWcar59ME/CEu19roG4pP2hnQv96BY0CLM01PZnHsoW1vg2q7el+BupFDoxdFGR2F1gdQo1n1M+rA2I6iwxWuTNVRKxWbIVwLhcL3N2UKZdcckn08ate9Sr8+I//ePTZ1tYW+r7HeeedF31+3nnn4fjx5blmjh8/vvT6ruuwtbWFCy64AA94wAPwlre8BQ996EOxt7eHX/zFX8STn/xkfPKTn8R973vfO23+f/yP/xGj0Wjpd9Pp9E7v/VLlXgE6remBzL0KR07suiiaHl3oTFsEkPMRFr3phAqHtwew2oPLKBofAG/Pz6W3i0CRSxokwrXXAw7+v+0Azo/HEdt8JMIs1O/NPwQATU3EZDuXgYAUGPn3oIMtMvFdBnLTd2RBzQutom+WAVaug5+fgoCzgdCknVE/MIMkhcQlrNUCiE3Bo2ub/072nzz4U9PZ1ERXAnPvx9EvB3pizEhLL/qwLIIixQviyfhlWRzoh99/4TkqCA2Ai/osIotye1loFiBMmiguKEFkOohl4FIqcSRYle1dwlz6Zy6sKR0EMdcPCwqEFGg44Y0ZSLVkDXjTzyyLTDN9lfL/lA0Vio5lQYhU8q7R3BP9u9RF4CtZJKMjzWkT8EhCgxZ7kF5cD6zskaxnAr79de63DCrmr0/3KGYeWAj2a9TC2+m4dRiNp1QwSYWXLGdrW/q97C8WIp0Zng9KJK+V/sy8Zh0Lx2DSu3n0BjaDX8+qyEPEVr6usyKfafAf9IpOALDKg5/oFQTryKmPIn/MZYoXAQqpftGX0oTV1ZlGcZXKCm+2K/f+TgSdcu+oTGBMmZmNzHmlv7mVbUPMfAPedNb7XvJc4BQfwgcVQBwJ1tVnu47ea8rpQ4SiwCnivBKGu0/2PfvnZsLXk9vWm9CGTDtA4IT86LzUsS+0PGfSOW2tM8HNACWUazw3ObKuBDBKkcktj5c0q800+TCyHx8rD1j+4DQsEhwyyNIqpEvhvtCK2sayzbIgaUwG8L0DB7DaFqpwbFumYJERaANgy5z+VgzWAmgEAJtnQKa8r6dK8n7HSjUnCzTiPOJ+lLJf10dr3vuaAgGQ8lzh/hFnBbfdgzIg5C3OaF9g4GpzTZFnhyX5ZFoLmymoWRud0ShyYFDSXOqEMosjas8TuZNlCN5DXb00dyxQ5YAB7IhSqFDqGQ07LImgYeV5b4lpdcCTouS6eB51Q9c3os/PwaKsjd2Kvsx7AODWW2/F+vq6/7y6E1NjlZw31tqFz77U9fLzJzzhCXjCE57gv3/yk5+MRz3qUXj961+P173udWet96lPfSquv/76s37P19zdcq8Anb5YC0AEF0g05EqJhNteuBbCFG+anikVm7oEfnwtCyEAIp8HdiJnwZMPHU6QHZkK2ljobgVYBmKAAcTAjw9UBs2+D7AcDMoDLWXj+LNlha9dxgrJ/pPPlbkkpXmmNFORYEXmMU3NKBNA6tlXrjs1z5PvLATShRyoEqxLcJ5+Jz9nAdmIdDOufZxbbwF0ATEQ5frSIkFa38OydtHXIeaJnLvcf1YoIbjPUjDH/3OIey5SScJzVPYLjzcLqXKMlvR3JNy4e23bEmMgAacE3ikwEHPZA0P5XNlOMa4pSF54DwnqjXhvObbM9C9jYpVayswtAK6zlBS8+mcKEBOZWt8TRWqfU3NauYdoJHNAjK28jgWtTCVgAeEaURjU+mBEmVpSl158vmw/j6NQACxTDsjnRwoJuRekAFr2Awv4PLeNDXuvtB5ZZvHB5unWhrMHCKCMmVIIRYfwZ/QmtpkI5uN8Cfme0NcOvPUiMqo1UCbcyzkV2cRWQUaarVw6Kh2AF+8Rzow08oU0llgxYzyY8u0TvsA+IAzviSAAGAX5sUv8PGGDr2HqT8rP4f5WCgyOw7ipWGEhgKAfZzlmUkkLwJuZcnu1O8O9VVKYU7DiOmuANigHGOAqI/q06QNY5fnufHZ9vXwtM2dsSst7O0eqNdZFUHUmcXkGGBVyOhpD3/U9oG2IkMsxK4wBrFD4cHsGFT2bgZSPptsHeaftYkDL84JNf5sGaIVMVTkWk3OQRmtREbgxLuULzyfez40lgFcGP0R0xpuTUqqRHirTpNDRivaV3gYgKnKf2iKHgokZx0Iqj93aZSKiyL3fYyTzFLkz5XX90jDjKPqJ62S/S95zWK5SisAk18E+3r31zKh1llmqc2xp5yywXFAfmB6qUyEar9axWw+3RyoKWGFb5OQDCtDazzJAW6img9lcgT5N88BmCshyqL1ZMJ3tyNeTmWfl1rgdisBW53JJFaxf7j2gQD4SdC4rhw8fRpZlC6zmyZMnF9hMLueff/7S6/M8x6FDh5beo7XGYx/7WNxwww132p73v//9d/r937ac47y3K0r5g80fzFk4FNCSgzjlLSzDwSNBJhcPYITAIcO5c7Fuw2WfA2ni47RjHAF04V7ZZgkS+HlcvzikoyhyQBDcpTaO2y+Zn2XsHf8vgRXXJe+Vn4v38qBKAm1ZUoZDmOv6vpBaOi3AU9o+PphE/3nQs0Rg9CCCfShcGyLma5mZKINT1tin/Ze+Q3KNF2YluJYgjIXpdJy4vSwA83tKIS6NZsjtlaZrrLxQKgYUgr3zWmquk7XoXOTc4v6VbfOdLPpEjiOzc5LFFUqBBUaF6+J70s+XKU6WXct9nYIHfpdldaX1nu1QsSECbQqoI2buSxxKbJqb/vj0QWK++IjE92TKFAkY5Xu5z2y6DwJh7sg5yHWx0GhtEPCzMH84VY0EfJ7NZQFLrhkgFuzYSoTbtKS/vRIh+lCsaetMKMW+wEGNpPJA3ivn2tL5y/N7mUk59yu/I+9zbIapgzmtZCM9C8j9yIC5owjXXuGxbO8FiF10YDUyXXV1KhdNk0xfLUVWVcSqeXNVK4LtiOBEcr5E5sw87r7Pu3gOpAyeCIQUmVO7s5RdYGzbBrNiTl8CBDZMnpfsL+cUA1EEZGnxJMeOI5KzUO7ZAh2YXGZFTbKPsB+uvEcwtZKJXnB7YJ/WpvUKFjY953QwkSvQ2iq872bLOTGdSeugDAF7GCD64Hx68XeRE/jz8yyMuZdrjPhfKuWlkk8CdaUgU20Q4+p+c1RbZnmsBdx882cgK2RGw+BnyvM2c+cIm55nikAkQBKsoR+ba4qkyr6kgPPX7Ok6rQNQzxzr6fYS5eQZqxSBWQdEPQAHgn+mV4xRoCB0vfPP7MHRi9WMcoTaIqNULW7u2pWKnstzzpMXzqw1MU+2mQpgUNM7QimoeRv2hSXyqx0In1ROycRrZ14HeYtlJR5jY4C6Dc+0lsAkt6Ul81k7KIgBdP60DIq9P6lvD2BGZ/cPPCeKhZ9jX/bPXcCoZVni0Y9+NP7kT/4k+vxP/uRP8KQnPWnpPU984hMXrn/Pe96DxzzmMShktH35GtbiuuuuwwUXXPDlN+4eKPcK0KmyjCL+AUFrzIcn4AVoL8RJ3xq3gSgW6uUh5hkrG/KZyQUemb0KjVYKTjhAAgcpYHZD+qRJMMLh46WAJDZSeicVwLA0Z2WwAASBSX7Gfy9jNRmESKZOslJp33CQG948+RrZ7hRMcbv4cw4Sw/+nDKME3gzo+G8eL/H+3n9qifAZFX4HfqY7YCPBV7KKSZ2pwCST0XsAdLbChzmPVyf6kVnOpgl1cGAJqWBYwkD6Ppf+J/KZzMxLwCFZYr4nFejl+wgFhE3MWn2/SDZSmtByXdxPco64vmQgt4w1jFhJ0bYo1Y1rW9RHaZv5VsFoyudwicxgeX6Jz1KQkwYVWvARFs/lH/9e8ncnBM57qkgBXPRTGvXV75FyHcgi1/6dAHtrTLxWeIyFMiYyx2VhM6pEgFtuK4+FA/ApqI8UTjzPmibqf65bSb84+TweF2aIgLD/yHXDfSHnKe9RUkHEgDGKEWCDtYQEninAFiDGpzvqY19PMg0lBnWBTVRhffu8mU0bA13ud3G2+dzWHD20D4DKA3Y3NqTcZfM7Vq4lygR3D4MrJVg0/y59yH1K/pY9CfisMGOAJc8OAZI9W85gK1GESP/NCJy64lNWSWUy7xGp8kvcb/s+rF1WLDAIYoWSNzdWEWuP3kAVefBn5f00BczGAFPnp8lMsEu1Y5n5ihS/bizndQDbKpx7PoItA5Ou82bHdjoNeznn/ZRzkhUpnPdRKQKIbRf62Lj+Yj/BPAtRTgHHxIr1yDkh5X7E0bPnLjCgdSlLNAj41B2Zd1Y5tYXBUpkBBiG9h2fiTQDh3L+FWyuDIgTrybOQ/9PaADwdcLWlq5PbWuQU9EcpqPGc3tOtGTVvQ9/x+e/9v+kamyiCLUeJrTti2pM9iqLKZlF9qhfnPa9VY6hfSpejUyocWc5ikqXtXN9qAvSjoatTyBqsjGp7x9JSnVa7lC9aA0UwhT5XC5vX3tWfu1Je/vKX49d+7dfwpje9CZ/97Gfxspe9DLfccgte+MIXAgBe+cpX4vu///v99S984Qtx88034+Uvfzk++9nP4k1vehN+/dd/Ha94xSv8NT/xEz+Bd7/73bjxxhtx3XXX4XnPex6uu+46X+ffV7lXmNeSMCPMlpZprf11orBTvde4C1MbFm60iF7mtVLGa2SDiaOs1yyCOreBRYXrgziMhQ9K6kcSXsSGz41om7xGMqF8jwRREsDwNdK0FAgAcBkQ5OtTALQMbHEdXA8fehLQpteldcnPIjakD+BRCoB8SCbs1EK7+HPxHQl0RfyeaT840Orv4vu7Lpj6CWC6FIDwGMh3cc/z9S5r97IiwXIC5oISQvSn7Hd+r2VMoewbcY8HbAze5fcpwJTvId+ZP2MhWLJgAvwy0IsUAqK+yERV/A0AsucikKkE08avmjBZKRhUee4VRakZJ5RaYNnO+r8Y07viK/oVLTL69hLgFgFChLFZ2EPP9i52yb6FMAYepEgLEVmn/O2KX0/OHHdhfMT+FkxG87g+pZany3CCV+SvLOeZUMT5/UEqKXlf47KMjZfX9WSK7xWPfNZI0ON8MGVuXpnX0vaB7Yh8C52ppjSpjfwkIwUg9b/ShQBsfB4ElpJBnw8ExcWZkS70qTwbuS5uGxCb7vJcke4prt9tW5Ppn9ECEAmTT75PWhxxnXJeWeG/amyc4xE0pxSymGkSYxGxsfI8kPt+qxb7AAgyRtN6c2pldDBJ5raxj2cvQHdZEACbzYDhEHY2B6yiFCyDygMrWAMoB066xNqm74MvKLeXfTkBAiBNG1v3JPukv5/NfX1U2ZwAJefnzLIQECnP4rnilWiZByfeL43XEkfF9Cx7H8x084zuZd/MPKNIq8aQ2W2Rk+nqeA7PpDaKmH9WGtZdAHGlUDJpRaykV2q7dCUAga1anNM8d5wrlO9vOdeqitpUFQT0XQRnNnvlCLtRYElj6N3qxpv1sj+nbQFlLAE7Zi+looIj5LKvcWrWa20weZYWOmy67cYabevMmXsAhQuw6aqQp6mhfcNUBTTPwVzMPwAWoHE616kti8Xz4su55y6U7/zO78Tp06fxkz/5k7jjjjvwkIc8BO9617tw2WWXAQDuuOOOKGfnFVdcgXe961142ctehv/23/4bLrzwQrzuda+L0qXs7OzgBS94AY4fP46NjQ088pGPxAc/+EE87nGPu2uN+woXZe1d7c1/OGVvbw8bGxv42s3vRw7HcKUAQ2ovtYoOYv+d9M3kki5arhsIn/GzJCBJzUbT+7heEZQi5EwU/3t2UYBcKQjJQ28Z6GPBKY28JtuaAjV+bz48ljGdDEwiDaR4v/T5EXtxFuB4Z21LAWj6uWOvogi13CcSeC8D7qwxT/uW65LvKusR7x0JoKKeiNUR/ZMCHwBxMA/WJC4BZQv9taxfuHCbl80ReZ9kZuWz07IM/C7rUzlfznZ9+l78tQQPsvBcXPa89H8uZ6k/zdUon7vs2TLQDYDlJpyyHdYuZUDvtDBQEu3itv7x3pvv/N67WZ516UthzmzHoFE8W7Z9wV/S2uW5kM82R2RhwN73MXsjAcJZwPYC0F92jVTacV1iXvr1J8y+g5JRavHjObwQcCdV1iVzAEC8xqSCTwQliVJ6pOzzMvcF7yvZx3swEKLFinPNu5vkLkqy9AvlZ0pm07HIPviPBF58Romz1Qftkz6naVsT5lTm8aT+YanWRmli5FgoGcI/E3ta79hcDtAj6xb+nNIHO1V0SN9i7wvLKWpE5NtoDzrb2ZnmeeR3kGeae0YU84H/13H0XQmUpa8u8gxYGZEPpFIB1LDiYDYTinJNbJsxjul0jHVZErjo+hiIWBsi5vK4OZNsAppZYPuAkF6k64LJr1yDTUvtc/1jq5LMWeXclpGfGXgy08n9znJTkXs2kNOAhGc1wndR9JVUWHDhyLcOsMJYyp3ZuTnugK1XlLC/olLhGb2YE+Kc9vk6nXUA+z96QMnzoBGKIwaSvMbcs8hk2AFOHgP+ze1Risx4dQYfcbgovF+lmjvTYCNYcQ9aezfePbzJNUDAvXKpZTRgyxx61hKAdwBd1bQubFkQqzkVKUHc/tWqDu+7/v+P3d3dL+nf+A+pML74mkf8e+TZXUv90vU1/vS6/3LOvfPfRTnXdRAAAMX+A6wx5t+8gLm4YAj+kGffTrn5sSY8NUNkhob/BxajaUYspWAfz4brlQqHidNyRkKB1OJacXjzs4Cw4fHms4wplCBKHoiSWeQ6+NDkd0hNa7UOzuk9b7zC1EiaVwKLUer4mfIZifAUXSffQ9bLzBybXsm6ZJ3pb75OqcC2pkKqNJuTny8ZR8UHSAKmFlJguL8ZhEowEwnKS8BtZAooBVo2O+X2yv5hIYKvE9dH9fSJwCHn1TLh3f0wU7VoPWDiNSEYS/ley0xmI/NUebBKk1vRBtkmzsfo1xzXza/r2GbJ0EowFflYyj7n57ifFIQujLm7Jq0zNamVY+vngWjXlwSqX4HinyGEzbOBuiiarRhHzz67flgwLxbj4Z/BgNNHtYwB54JZrJgzaZ7N1KT5rAGcls3TdI7z/FnCUkZKLanISfeP5D7/DBagtY7OJMs+hVLBKU1c07kmo5jzte6s8Ok5AJ+z0l/T9R7URIpYCeDE5wx42IfUj1nfB3N/FyfBrzf2wWOTUq6f16j0R+V25FlkAqhKAVrE3u2jzmfJmQB4s2gf6Ej4TrIJLwBih9ksF/BnL3/Hn/nizIojM/eUmeO/jYmZTW6jVGB4xZ6J+iMCoNKaxMkFPA5yTGAM7KnTAXAyEHKmqz7/sNYu0EwfTCq5f9ktoW2pvrqhnwSA+rHk8yTZe6PIt/yZdgoTOa/rllKdWOtzZIbr3ZxnwKm0sFhK9qTeQE1nAYTJdcxsMSuUGYBxTk0G3F1PJqS1C4TkGD/V2+DT6XJgUhTZLKRaccGA/HzwgZaCPKgkKeDYUMVA3TGaBPyLsEdyu/hdeJ72Fmo6iyPhcpBBrq+ug6ylXd0dmRt7k15us1YUZEqe/exDyybXvHdwDlQdzGhln1tnLq263gfetFVOLDHvJWl06HOtyHl6V37+qSwt9wrQaU2/cGhGjtpc+ABMfVGkYGltLLhk4uDm4sCWP2Sl1pwBrywSkMj2yXdwwoc/5IRGmZN+R/Xxb6915nczCfslhRnWyAqQxtfzQXE2YVfWy/0jg934F7Gx4JYezhJUpIe2bN8yTbIENP7AEyBWbsKyzWld/Lf38U2UB6kiQY7tsiL7TMwlb74pD2h+hHxmCu749zIwJkoahGbBtzEVZBJAbcV4eP/MFLxKkCjb5OqOgIF8H89EBD/NyGSLNdVLFA4LwG5ZnyXPjNqRAHYAfv2kprjLwOCCueaSdixr/9mY2gU/Q1Fnmtrl78S0Fgj73BKQHrVVFjdukbmzbGsyvy0LjD3PLTGmaXAXuf+yhv/LYJnZV3QZQx0pDWQ7xftAqbDfLFO0AIuKN2avpHIoBeupws/v/yb+XvYd9z/HIzB2sW8FC0b1WK+YVHnugSb7c0a+mu5a2/eBwXHPlYoA27Qx48jPl+/N+7d/H1c3B2fh25xPn21DmhIyDe7d587vzu0Ttm7CPsTjwdfKs1GCZvBeFs5vDroUmcs60O6fKeM/uD7y9/E7sWCeKiLkuZsCUfnDwNMDsVgJzu3y78b95/xhKTCbIcAk7rONAxKVi2g7cbkUx1NgOqP+YiVE7wBX4QL0sJ9t18HujcV40OfetaQX/cPv6M2TpV+tIaBTlo7ZE/7LWoW0K4BPabLAUHIpnDkxj5s8y3x0WxPW7LwO7KYxIW1Mw+ljFF0zm1O7xtOgGBmWQSZkZUDb+aBCqu0pTQmbiAqljwTONtcivoYOFgrGeFnQ5pk36/WKFgd8GfT7+SSUIoADsNKU2lj6uzdBccDWVl3nxkMTcDRhrD2g7014RrQ/agLp3CZ2JZm3ULMm+NIaA9V0UL3zXeS5mTmGd1JDTWohWy5XZJ4zxdzNn3tB+dCHPoTv/d7vxROf+ETcfvvtAIDf/M3fxIc//OG7Xee9AnSGA3m5sBpp5DiggASGaZ4vebBy4fDirIGVDtpSK7ssyl3apmUmjF4ASg4mINKILmzGqaaeD7v0eykkpQLPWYRr/13KXkpBiq9J65BsgNT6ne0ZS4BOJKhJwZDbtAycSuFgGcCVoIrbJsGw1NTy9akpsVJCq5gwEgnAXDD343vOpg1b9pkU8sR1USROIAYBUohls8lEcFJSCy+LBCMpc4sg6J+17Sm7y+C3bWNQtYwpde9xZ4xgGgAmBXsL7CG3LQF7KciK/FTT56RtSesQQHTxhWLQw6xs6hPK/bx0znylS3Yn61GUpcGO5PgCYW91Jcpr6yOw6lgpCMT7sJw7WATjsg3+YwGA5ThIpjl9xyjNjVRcLVE6RApJubZYebNsfxHtjf73bKIOQNcrSgVAtTb285RtAIgZ0Un7rSXW0YGtBcWmPI+0CgGThPmuZ9E4YJ68XyqjJBiUe6kcy0Tp6iPRdksAKRDAYWphkgB3GUnWBxtqWti6CcBRgHAfyIjfxYNDcZ5ypFy+V/5O98F0fad7l7QoSuZUdI7JPpXv7JnQftHiRSkBuE1QsGoNu7NLyr2VAfUjAxFjgMmM1l6eB3DjQJqtRWAsOS514/13PZiXCs1MB2DTG0DpkD6jbcN888GUNIEkBjqZkHPSc5DBrlJBWcVyV9eF330fgjsaS+80p4ixaBpqM3/m5YMsgLO+J39JsVYts8S5jgJm2UqkorHWg0ZOiUKRa2OZyuaxzKSaNjCzPIYMVPM8lgn4veT86nryDwUCsM2z2JLM2mDqnOc0Rk0b3KU44BT3GSuWWH61Yr1xvzCgd23jqLY2p0i/aFrqHwbpzMJygKZ7Wnn6d1D+LgIJ/UMsb3/72/HMZz4Tw+EQ1157LeqaTKf39/fxn//zf77b9d47QOeyiS0O2QhA+kheNhyOvMmxVsprtoWQxCxlykRJgOSu9+ZDXKQJ09kmo9z8JWBL/U1TQCvbwPfJ95XadRYUmL1j89L0Wnk/H4yy/pTJlWxp+lsCI3loS5Mj+S7ybwmKlgkkkslk7eoyAMxCmzzwJRiR7ZPXpYCShUYJftP+5rYvEzzTPl52fXqf7DMptEhgsoQ1laaozGh5ZQr3hw2s0jJQGZlZAf4zfy2DvNTsMgF4vj0QwNCNwUKaDHdtalop/5cMJX8X+SGm7KkDJd4ENylcl/T3PFsUWv+dUnE7+f3TIsc1fZekb85axz1ZRJvOxqinfRH1YzrOzFhxZOsvVc4y76P6kz3TKzHEuMnP/X1L7l0Yg/Qe8V2q1AGwuB65TilAS6UVfy/ZMgkkZUC4dE+UAezk9WkKi3ROWwoi5P0cHVvmgZa18ZnC9WrxHG4LM2qyyH7xCi7rmUFmO725KwMIOR58jvF53Cd7G/c1s0VyH+L6OZ2IUKhJ1xnuVyXdQbik779MYWZtCNoi/TL5R1rBcFuXMdz8P+91AjBGZYEJ7eM6l1mi7O6BWTVVt8BsHvZjBjV1TYCibojVbFqKxBwpaFV4L977Zf9wYeDLeSAdu6bajgCeZCmlWa8DmVYpUK5TCSzFGa4UsW3se9ob134xZ9n/0CT7i1xDDrjZtoWdzty4irnEc0PMK+XAtqo78qP07CylCQlrwwSiQqnI7JtBrGdymc2UZ7zMByrnmrWxfy2v71QRJJUPqVKFoxKzhV5V0k/dCKs9HeYRr1tWQMj9iMeefbnb1oNxNW998CJVN2EcaxEVl1PMnOtF7ut35eccLz/90z+NN7zhDXjjG98YpWF50pOehE984hN3u957RfRaAIvCmtTkygkghXOTfJYGd5CRY1NBWv4vAz6wFlZq9GUEP95gZWTatP18nTys2bSJn5sKVMsELPk83oxT0HK2w1YesBLc9T1gxaaVHp4pSyNBrtzsEuDinyvbIuvmeiTAls/ga9NDWda97Ltl/qSyXvk7FTJkm1OBgftqmYIg7Wv5vSwpc8sgPAWuZ2ELuc4ISPgxXAxcw/elwY9SYdzXKU39krFLA69EPnEMjNv2y2P1EkDtny8+V8k8SoGrB4p9CCazLHCOfGbkx5i0XYJf/7V8Z/lOci4sAZ5Rsvpl731PFBPvO2lwJf5M/l6ITsu/02A31sL7B3GRQdu+xHvJgEW810kWeyFIE78Dz0fBVtOzw1z175nul279Lig85Hrjz/y+n8UmlbLI/a7Iw/7FSi05l+T+KPuQ70mVfh7omXh/5LYoFdhSGaiOz6bO+JQ8bMobnVkS/Ebn5ZJ1IvvQ2mAGCVC9cMCwt6F9/DvT8edcWOj2+y/9HeW8due3yjJYuLnC0TW1orOd9xlWSKZ9yWcqP98DYiFwyyB8ss/5Xv5OFqmUlMrS9D45R5b1bdq/nNaN56TWFM3WvaM6s+P7naPk+rHoeth5jYUiZR/uGwaO7KOHDqqqfCoNPy+6LqQ8Y2BZNyGAD7e/KIK/o8v1SM9J9n72J+Top9s7tFaHg+BHXJXA/jj0Lwf0cX0AAKgb8g2WZthNE9KwSCVEgzCfmtYHTFKzJjZBbdqY1Xd96n9zihQGibzGZWoa7u9Mh9Q17E8KBJ9b3z4DH/gnz8lEWD6X62r6YLJbFDFRwX2SZ2GuWwufUxWAtwzwEad5XxLAmtuBHph3C9aBITIxgJafzfvIknl3LpW7AyLvBaDz+uuvx1Of+tSFz9fX17Gzs3O3610inZ+DJdUWcUmFIamNlouGPxMCcyqU8b0smPmAB6wl5udJPyU2OUrCxS9EJUz9RqUmzV8jNgnZ3hQcSE2LEdfcmd8Sg9oUIHKR7CELTukGnBYJCuRBu4SFWqapXwrAlgF0bkuqHU8FtWXM6rJ2SCFuGTDmwoJCyjZIoC7BRMqsSIZnmcIEWDTvEsKGvy5lyVLmKAUKol1RpFTJ7sl5lLIAoq0LAET8vRB4Rr6eBH3pO7t2pSa0S593tsMgASlQwT8wCu4jQErK9Pr3kL6rSkVALDLj5HYvY/eWAZaoQ+L3idIz3FNF7BMSZKVAU17jP3PCnGfDuch9kM1pZUlSWPjfS/pkmWlslN4mAWh+DJaB0xSk+n9UOBMkYBVA3M9/uX55v0lNbPlH7h3siiHXLK/DVEmVsmZALMwBAXTId5N7DRcB2Hx0VKX9b8kMRgyobA+3172H739ug1TCyT2LmUsWwL1/W7Kf9uJ9vTCc7JNc9zLlJNycYFPN9PyVz0pdCXgfTRWkZ1NayjYZE8BWek7Kevi8lM/kPpNKFflOZ3s27/9yXSR7vxV9bZuGTK4ZaC6bc1z4XMkEMGGmmIMbyjzRbet8J8Xcb13k2kHl2ETjFE8qAMmuD4BGvjPX0Tm2tA3sup9zbJra9cTUijVgu47Ad9uFdvv+dGuZGfu6Cew1g9DZPDCr3E+8BzAjyYoj1wafg5T9JNsOmDdUl16y9zFoBmJWf96E9uosAFRuf87t1PQ9W8LJiLzSn5R9MaWpbJYFBWC63zLAlSbpLI+2zse4S/YHr5xJ1hp/xqwnj+s9qTz9uyhSpr4rP+d4ueCCC/D5z39+4fMPf/jDuPLKK+92vfcO0JlOaukvcbbBT9OQAHcuFBZ50B5mOoSml5vLMqFKttFrjZLveKOMwsWLRZvWtwxs3hmIkfdLkCfBBZsQAfHB96VAoWzzMvAkAaEEf7Le1MQXWDQx4ZIKW3yNZ3J1DPy4Lt58z8YqcVsFWDvrb/nD96VsR8p8Sq02f8+CTypgS0Zb1skCbzIeS1N4iDGK8g5yf6Ssm5xHqfCZzEFvrpsKSMn6iXz70vtUEsQrqWcBHLDCRwC/qMg6BEhl8LuQxsSB7DSA0FIAmny3zKw2CurE7UkF23SNyp+kDff4Qb1EebAA9FlxJb9LTf2lm4L8vSyY2rL/eY6Kfj+bCbRvl/x/WT8pslZZGniKS6qAk/N+2brge9L9WO4xXF9qesp/y7GWe1S6T6Trmy0TWBiXIIz/936JJrRBnn/sU+nqYwAa9U1qAgxEoCli5BkkyD5YBtjkHsn3yrJsHLlflp1Z3Db5vQT1iflk9Ix03z7bGjMmCPLLyjKhcomSzPdnqiTkNkggnLqhpHuyrFeeNRLUsg9mcq0dT2An08W2LmNkuV8ZePUOwAIUoChKmZPsbV0ffD8Z6MgASEq5nJbuvRjszesAZtkEeDYPipK68e4zdj6nfZaj+rKSQ/aJO1si8sBaqjPTZAJaO+C8sxvv05yvtOuB8ZRMRwG6nv0iGXQDi7IcR47lAEdpGiH2N+V7jQD4ACJLEBENN4oOzPdaG9/L48D7zjJXMvblNE4RMJuH+Sx9wFMlD+89IviV90dP95E7Y+7PxWLu5s85Xn7wB38QP/IjP4K/+Iu/gFIKx44dw1vf+la84hWvwA/90A/d7XrvPea1y7S0DDxTIAEsChmpsO3rUm4SWVj04TMgXsz8P9cl05v461T42yUW9pOTBQVugw+E0At2UcWaQn6P9P1l24EYwPIhk25UrJle4uPkha1UeObrlz2br5cHnPwt/14m+EkzxWUgLDXX5DZ6tkW0zWvWzyIA8PWsoTSI58QyBkLel/4vhabUJJY/kwJPWge/n9C4+vbL/1NQKT+X7yyujb5fxsDx58s07gz+0vkjvjvr39YutHMhJYZrjwTJUaoTtjJI56OrwwM/yU7y76S9yyKepiUFnL7d3A7xPkt9BIEwhjwPU+WD7HsHlhbW2T1RkkBC0tc2An0+1yK/g1hjOrQbGmK9JPsisDhfpFJEgM1lcySaF1gCjpGMUQoo0v1drulkHi1NV+OuX8iny+MoLTnku/J+kwpg6d7hrrVGRA61ixYIEShfovjw7yn38aWgTi2ajTN7l7ZZguqzKQ5SgCetZc52DkjwJT9nBWK6RmRJzVPl371YO1KxJy1f0vf2/SK+683i2PH78vul98v9Xa5z2TfyHqmg4D6Wz1zW3+neLFLW+Dog1lI6jvI9zwYM0rkEEFPI67B0foIMcDxD2Ic5lGX0v9Yhf6cxUd7LyP90MnX9IZQHAKw1UIy32BqBcwSnQci47cKU37P63G+zeegvaYJalfCMPKdusZYA76ACJhO6ryzp/7alPU9nwTRVuhSw32meEQCVeyWnbvH7kgE6S3X1fahDizb2DsnI+CDL5Ew2je57YFAGJtYg5FblNcbtkUoduXdyPmFjqH8Y4GodlA98Lfv4SmWUlFv/qZxz5d/+23+L3d1dPP3pT8d8PsdTn/pUVFWFV7ziFXjxi198t+u9d4DOvgdUYl6ZMk9nK3xILjuUGIx5wMcbgFiY0ndBLkIheHmzuyi4UBauYc1f4ovir5PlzoR7WWR7z3YPvz9vFrzJcPlSz/GCNh9i/197bx7tWVXdiX/Ovd/xzfVqpACZZRQQEC0wrRhFTGwxmOiyWSALzUq1M6BLwXRQV5dTR5IFAWInDG1iN9hBxfwaDagoKoUxUOoyIioppqLKYqj5vfcd7j2/P87d5+6z77n3fV8NlO/V+az11vt+7/fcMw/7s/c+5zDS7VMC8MmSC2qyfjmk8ML/y98j1l4+a4O0VJCAyxdyrsmVBFjuPaJ0eN1w8sE1xpQ+n9x9RJzXDQlLPoIrBQZfGpQPuaDIPPJ6nE1I8bU//42HFf3HWhw9RBKAV9h3SIinPzoHybD3HYsh6y+zXYci4wRysiMJovO/TBDm7/kEVVmfXGj3KXP2FiQJZ4TdOSnb5z7FrzuRV1IAubZeCoMyHEu7cLiSsE7L333W6FKQ8oL6UmzuMXQsmdn/Qp9hc4fiY1bOiWXkRa4vQIGk0njg+6T5CbrWVT3Ln3ffMK9buZ5JIkjLQhzn++C0drcg8LkvFXOibw6QigDu5SCJGa3NvA4taUzcvHClHQefaygc1S0nmTxPfM2gvDtxRO6JnjJ/gog5Y9/nmePrl3I+on5Iv8lxL5UnMl4+v7O6Khym5lszqp7zOYiXiRSz9L9Wy0kUP80fyA/QiWJD0up184wdamOv+IlSkHLfkkU6oZiutiGlE7/SBmBpp45cpZw95bmMorW7JUDRHstGI7dikodHkkBlFmRL4vsZMUz6QJ8dyNRqwp4Y24Erm8S1vE5azdwCHLFyKGr7jLx2u2772zM64MpKSuWkULYfoc/GthxLUlFO8xzfx8q3apHygLsy17N9vElSvJu9P8v8/DuO3TmNdiGcXgsAa9aswcc+9jH84he/QJqmOOGEEzAyMrJHcS4M0hnHACJ3YgT8GmafNpKTEnqvwtLjaNOkxolr+elSahsvXEFO65zAag3HwiaFPYqb5wPI003EZOK4wSg3buf92H2Hl5XySBpI2iDPQfmmz/J96dpJCwN/TvXgbGZPi0SYC/o8/5RHqkuuMZbkgueRwkmNvCSn8j0qBw/HyaIUBjkh8RFdWZ9cCOGQQpZvYisTLKi8vnA+i4Mvzjh2Neu+tHwaU04qdW6pLLj98nHIF0AlXHmZsK9EXTkCvGh3L6nlljRBXi0BkwolWT4Jmh+sRl0QAPpM9cUtIlV9Y2+i5IA1h3D63vFZOiV4vfg+i7qTBxTZfbhJdvqp1o4SQVr47Psqt4yXEvZsnBaIm69ttS4QYt3r5VeOSMUKD8Mtorxfc6+HLC2fpwJ347Z7S1n+CmSc/y4VSFQmSez5fYpl5K7Eu8LGyUmkj1jT/EcnpadpHo5OtqW88bxwYkcEk48dPqb4OJMEn/LJwcceDy+VerIOZT543Dwe2Se4vCHem/UgNd9vPgLNyb3Mv6w7SSZ5v5RxUbko/ykjln2WNoGuNVGZdbHZMEQsivM7Nvt9oId8ixKByGa/D6208xxAUXFP72SuvPZ32resM6VmAjvudbfryjCZ27mKY6DbzS2vTP6y918DmettZhntaXFPZmrS7/bMAUGkPOFrh9b5gUV0pyhYv4liU19pkn9WERBp1/2Vy6Lc0ipP1pX7pqUykLetrRM2Z/H+4yi2RR8nzz0t5l6lgLqQGecbSA6Z6zsLBENDQzjjjDP2WnwLg3RKyIHFBQyf4O7TuFJ4Tgh53Pw74BJJ+q3ONmn7BDTfYu4QRe1+JvJHkw5QdHPgCx69x0/PpfxQOeQCKQVDXj5eHrkw8bohEKlzTvf11C29S/nim9l9eSqrO1vHsZsvDk7SfVYLSVakVp9PvAQueEmSKi1gVmkhiC0JQPxdep/XEY/fR3qqyKjv/dnIKyekUjCR2nD+rIyQ8XJIBQgRH9L+yzikwMvy7ZxcysN44BCJqnwyUlqwHEg3dKm4SsQizj/LBV4u7D7BeV/AIZFsjsoIs4qifEuBFr/LOOT8Q2DfCwdEZb87J9UCTp07btCcVGbfHaUEJ3BizvC6zFIcLFzZNTzW/ZUTHtlO5NbN+6aIA0DRtVvOhbxfCyWOPBXaq1DjpFZaMaR3A2D7Z8G9Wc59Pld9KVjLepbzAtWTXHM55BrMxwgHry9eT5Rf0a8KxJATXq6s5PtauQVVCudUPqaYsm3rWwsFvPOQGD9llv5CHXjeLSixiDTy8HHJWsnf4QoUZMqKnflJwqpeA9Jatu9SAyNDuRtrPwGG2lk+FNA1h/0UrJWRMm6z3V4uu/DfAXsVHb/STmWWTeekXqAYP+WfyRXWyqqZ6zxZ6/j6k8k7muLqdKGy60O0zlziqd90spNdaUxEUUay2T2ndM9oHAMpnY6cEcQkza2IltD13H7NySZZmmuRIafNWm6pTZNiv5KEUK5NfI2neYSPFXomZc4ocgkukN8NW7HEzgukGlB69nDynXmOmZkZXHfddbj33nuxefNmpGLu291rUxYG6dTadOwyzZ5PaKf3pMVKWiCkgOEjBmUESFoExH2gBeskh3RjI8h3+LUwfCLgYSVBlQJ/GZErIzZkOfVZPek/J1bSqmJ/V3CspJwoS7dlypNcjHg983rg7rp0bQBf+HkYKaRKa7SP1POySM25rA/A7Zs8PW5tlfmgfEqUXQXEFyP5O7dklGm1eThhTXKEP6tFzYVNK2z5tKmyL/F+4rEQ+NxiS+uf1Y/P5dInrDmn9kb+uymd8HJhlulL0iDylWdKEFMep7DiDLLndI/ASSSQ93lOOH0CttauEs3nSkvxizrwHngl2lmSPm7d9JK17L+XpPrynn0uhMt+L7jxkkufcHsFzJJj80jkSyJLU+5LLcQlrZDSikqKAQBaFS1mvrttc6FZrIm+9uKEU5JKTmJ5/DwNwJ3juHXT53In50aaT1h+Ch5LPkWYVOBwcEJHxFDmk+rCN18BxTaVc6BH8VWwjrPvZW3OlQm+uavwzKdQ42sK4Lr9ynqSfYDXtyQjUiEq1zl7Ym7PeCZkdzna/YeUdj8R7a6cOHRmJaUDG5WKjNutCG/uz83eYdZSlZ2YrZGAu9ZajxUpizRiaxUFYAhvHAFarD8kp/CrgJj1U/eyOytpDq1Fbp+VilWl3HvF+bUpiskkcoxweRWA3QMK5ASz03fHOW9D+awsDO/fQilXUBbRM0lEeZ7m+12dsi0GfWee49JLL8U999yDP/7jP8aZZ54JVcZV5oiFQTo57ID0CB7S7ZLC8UmWC9j8dxmnFcAEabWLFVu8+d4D+k7Cmtw3pT3v8TRtOUUYH1mRV7VI8ibj1zp31WDhyOUFUC5x5fUg8yhJBifjlCdamLhl00f2tOd3QsGKzOJnZFtjFusb31sr81BGfmR8ZVYC+u6x0Hi19zwNnyabW719LtFc2SCJJ18w6D8X4CThBIqCF68LWTdlwptUdlB5pPuaLDcXqqWSg8VfekiRj6xm+ZjVtU22oawXEr5k3ygbW2XEWQp8LySUgj3DnIS3LC/Onj/elpJo+sZ/VTlYX5BupD53Z0cZId63UUpSKseh+F52CrEsj3XXFn1lVgsUzycL5+xn9hFNX5yUbszKlsXtPemXuSVbyPFOcw+3fvnmGvlZlo9DpsEPlfHNKzysXJt5vnyWTmEBLsTnqRevhwFHFTEn4iqIoQ9lhLNMwZIHnoOgKudWH4gIcIulHLNUX7xuuesytR1vP7HOabLcZcoc7JoyckMCoJHlja5CUQqkPDH7KbM5va7s9Tc6STLrqc6tj7ZMoo7Y/k+tU+upSiDySmGzTDvvO/IRkxvsbQVZPuzdtxROkl3ytkmz3xu1ohK2xp4R+Am/VLd0ci0RNrl2Rsq9TikRY5iIoM8wwOdIn2zDjStSKRNHhoBzZQLvW2maX+WS6uL4nJfYDdKJuYb/3cP/+3//D3fddRfOPvvsvRrvPlSlv4DwaWs4aAHji4pvsfUJTETQ5ElpdhAyDaCMx3d1gJw0fZMFxcXfj0Q+fAd5kDCoRBiOqpPESMsmTrbM70JjwjontXGU/xHiyJ8Wd/flkzfllcrtW1QlOXeEKkbIfCSe8iMtpFRW2o8h64O7DsrfeBr8v8x7Wb+SC4RPQJbvcMHFd/BSmQZcPidBw+d25YuzTJmTCTUFQdkqC9j4KCOVvFz8Oyt/4SqSsjahvkmfqxYLyl9VnFIgcxb+yB1/BOnh4CNnVOf7a0H2lZePGw+xcyCFIHqH/86T8/QPSzAlqeTJMIWHs7eR9xcf+ZJ9SkKpgpuqvHfUd32Oz/3WPpdCHM8HL5N4z7lTlP3uvMe9MgD3v6wDPvZ4XyVQXsusl2Le5fmzbaDYlUc+cEstX3OTpEgoCZx4yrjov5yrfFso5N5KWS7KQ4nVHGVzjVL5tTXynQzUL2SdVd0L7M0rWD+YTeAVbefcL2tlg6i6rfgfzXllSgU5Z/vySOup1vaOSnv1T0bkzGuGLPLrTSzRFK60zjuJcW0FULwCiNxtGVQcQ9VqcA4XymQFygO57xbKI097FuD34Nq812KX9NF9m9S/pMwq5VN+5yiQy1Zc/iOZy6d8oD5ACgP6nafNvztzjaf/W2+ENO9L9JzXG48vUmav63wHm2Pn9DfPcfDBB2N0dHSvx7swSKeEj1jS90Fc1jh509olSjx+n8DF//tcY2kiFtbEAjGg9/nmcZkPitNHyghSmKCJzHcokWcfRSE8mCAoiZ4kdNLCK+uK509OrvSZiDaRU8qjJLmeAwac+Ox+A1ZGX33z932kWSogpKZQsd99JIt+l0I7Tdw+MkBl5e9FIk1J2Hi5+H/5jBZGTkIBVwgh4dUnEJLwSnFI4sxddWR98zh8ebflL9mT7dQ5yzdPX7ZN2TN6h8gqXRzOCWwZwbKCPWsX7t7N0+GLNS3e4mALwE/A9jY4kTcua1G+71KLQ3R8i2hZu3kUDD63Qk6SJPGyYVndWDdWOZ4gBPnZiHyJYMDjKL37FjmJKLNY+gihTGeg/MmyRJ4+J7YiWME+TR2C5Nzx6SOYIl4i4D4yrPv93ALuK6evfLI8PqslzUdlRIbSqCJQFI+vDUQfdQgkkLtMZgTRkmoRV0EpwOKd1S1eqeJYyPLG+5VzqJlv7pTjjtWvcyqzcwCQh7QT+BpA8WYH73BljO71ciIj1z7ZBrTG9vq5DMGVy0C+jxGw/duSSjqIUafm7k6dFsgf35fJ5YTC6bdsPlbKbLfhlkol7tNUdPUIJ8J04m4cG0ssfaa0pMxBayPgEkvArWu+FhBorVXKPROD5DEimyT/cSWmJJA+ksvzKPsW7wM+BQYPx+WQmjDAJCmQJkX5b74h1bv3N8/x+c9/Hh/5yEfw+OOP79V4F4Z7rdaupoUgBVH6LBc8LljRAOeuGNxVk4TEQbQZcqGVrqs8Pfqdp8WfUXw+wgbkkyoXgCku+Rt/P45MGQF/GLlnlOIm+Cyp0tIjyRx/pkQ+q1DlZiNJqXwOkS/apyHdcwvuunDL7hBrT7m1hnU/sQt/5O4dlW1MaaTsMxc0eDqS7PjaWf5W1U9nE4Dpd77YlxE8/l2eQMnL5BNUyvJIC7et/7QYf5n7HSfK3MWM50kSWR9h8fVpIG8X+j1JjUtWlGvyveWW4xgojPNZrwHZC/CejqpKTk71kRPZT2W7al10iS5kQueC8qCQgrYvzqr0eDxEygRZqNrT6rvKpdTSn/337uvTuUDrbW/ZTySZAsrnTEaQicQ41whxcsfjJuLqS5+HZWUuEG9pQfUJwjIcC29d6VndOcjcE+kKC2c/OUXjU2Dw+hdlcyy5PDsey3YZUafPMg6fC7kTJ6vP2aze3m0Gnjx4wfshlTdJXJIKWEIpybElnonn9GfRl1QcQ+vUHLiT5gf95OSsZB0sWY8pPkepl2oz1zLyaPeARgoqda2a1popLKLc/VYhzg9P4+6/cQSlVfG3zOKo6IRcWlNoTzOBHyjIrcl83zNZDaWiNhHf+TzA13xyiy1TBDhroViPtM4PMOLv8nf4Okj5IJmRPOR83mILgIAdiDjjjDMwMzODI488EkNDQ6gLq/Xzzz+/W/EuDNIJ+Pdr8sFNkIuR0CTKzev2u73KwzOAyiZ534TqkEJUEyn5TAp4XFD1pecjLVLoLyO0No+CEPN8cctbWRmkq6uUBXgafMEpI9Hys3yPH9Yk4+akj8cjyajMty8P9F3CUU5Eefy+55QfWoR9V8PQOzIfvOwEvkdWlpng01jK7779V9yKxC0aXJNLgh8JtbNB9kWfMOobW9LqKPMXKb8Cp8xq4IOvHxC4goP3PaXyY/vLBFOpANH5wRkmWAQND4nZ24hyokeClU0zjp1Dcrzki8CFIA9Jsi6FGbGSgjff40h58O11LFh8+H85L86mDJSaew/p5fdiUh44QeX91rkDlNcLT9LTnlQnPhI0UJl8CjIJTiJFe8i8Fuo51UDkyYssh+gjDrGl+LnA7CuvyAO/09c7BiM4xMhpExYft9xT/LzvVLlNynrnxEvWheMqLupIEtoyhVIZWeXlkURc+eZCrpRj7zvx+5Q9nj5L9Vog4+SiTGH5CbecBCbsUB9ff+XzrPQUobiZ1dJ5xg8ZSjXAu3SWLieVkqzb35gcYImpScSdZ1IzV3u34khZSCjyRMLsvYzwSy8hriDlips0za4m0UWFsjzVWs4Z0sBC4fh4l3Iez6NP+U3hazVAtJG1xs530qnTYtkGeWee4+1vfzs2bNiAT33qU1i+fDnCQUIc3F3AZ+n0hfdppkkTJyZHIwgOYL3gkARFPvcRKCnE+p7zvszzw61kgGtt8xFKLSaYqg7F3UmVyKNzHYqHDPmIqySDMp1Mi6j5GlFGOHma8nTgQhnEAuerax7el6ZtZ1VcDHlc0mIuLbvUlr53OGTb8Xw7/YKFc8qvcjIKFEm/Dcba2OcJQGTUp2Un8kkLZYnwNSu45ZKnzQUexyshLrdC2vAKiNhVP7ysdKpx1QJBrlpIvAJRoQ18ml6bX0/7KnfOsS5hPrfNvQleF/wQCKmhp7zL71KgKiNGzELD/9vPmUDrI7eONVHOtzz+CrJXCia8l512W+pq62RS55YiHjflhQl3kmA78fLylZVVli2OBisvVxBSVMJSS+V18kR9Olbe9vUe+MTa0xuOWyQlEaQ2IYsaXcHB88LKpPmpMUSQxVxQ6G92fRRzxiwEvoxsyjI6bTfAHOhV6jgRe4ggJ6K8DsFIKFMClrUHz58CyvMxWx9jbaYAIE3N3eTZqcU6SvM5FH4SyedPx2UWOTnk/31ztnMCcJK4pBQotLm1hLJ+ZfeG0njhsgtVIe8nNXF4oFRoZfXhhCHLpRwPXFEg5yTel/jhQpyw+ublvGD5OxKOocads2ycfJuSs60pBlRq1oxI5eWzv+8dorJfIdtz0HfmOe6//36sXbsWp5xyyl6Ndx+r018glFky5+KiRpOdWHx8+wgs5ATDB6O08Im0HGhdFF7lu1KzRROn3W/HJhs54ZBwErHfpJ89F6h9hIa/U2UhpLL7yJzPoinfz8L47vHy5hFU7Kjo3iEWMZ6eo+Hk5feRTx6f1TgWDysolK1Mq1uWjo88qWJZy8rkjZ/yzNuLY7bJUWpGrdDmGWdEXKosirxvlrle+satdIFVnv4A2D029NmNQxXaxqdBl3D2C9k86vzP9xsXQPhzT1va/4MogPYWbLqR+1lYRLyCqGxLZlXi38vgc0nlxFPubZOkxKYtLYODgs3ZBddWEsK5qyNX3FUIb4XwYGS7yqImhTw51srKV0HyC4qqsjgo33z/pmOJUW75Ae8BS07+AacdnTKxdArpaV0+T5U942OQl5UrCWXf8cEnHPM2538CpScm+whIFs7p2yVw+gxZ4oQipnTvcTYXz1rXkvhXKXB4+VPtkouy9aHP7uWsUhhn3x1SycI6+zOdLLlzPZfX5FzuyBRSrpB/Mo/8wEffPMC/yz7ilDlyD2rkbSz3ENPeWXkwFg/j6z+0DlO+uHcBh5UtmFzH34lEe5NiPOEGmNT1sCI507PWzlvweWYuf/Mcxx13HKanp/d6vAuDdAKulcRafCJ3ApDutmxwqVrNkhbaWO4ctS0FHt8CkzLhiw88Hl52xjILFy2kZRaFMnLmA/e75/FXveMTpqku6L9vMqmyYHrIZSnZq8qDJ6x3QYFfq2qf+bTbvgXHV4aysvsIiIxf1oMUnHx1I+IuzX+qc0LN0ytDmfDuE7Ck9tXnrkPPCT4BWr5vFz322WfdlK5knj4s3Qe9h0RU7DMsKBIGWTx87c3nBxrHkcoPzqhol0qCsjeRtbFRtqVWuJGnyhb6h2xTYc2cjQD6BGG5n5KH5UJ9wcpT1b985eXpet4j8qV9QhqFz+qNl6OMPBTciWdThFaQm4FgBdYikfaRH07uB73+paq8Tr55+1B9VVi+LQYVUssUbfSbVN6W1SkP57Muy34m+0xF23vTzKzeXpJfpmigskqSKNJ32qZMKcEV12mxPDbv8j0Jyg/7zad00b2euRYkO1BI9/vmc09c+dHLTrilOZIrIXxyBRgZpTmTDi2idBjh5YS2IC/4LINyfeHjUhJPQfYL8xL9Rtej+H7nY1aeocAPbnIMHTFKCSXPk0+h5ZOtbJrKXcuoreVd7zJdUkZIWXM+Q7bvoH/zHJ/5zGdwxRVX4Lvf/S6ee+45bN++3fnbXSwM91rAf3WJhLSW2I/M9zzVQGwmM2cvAn9PLmBicbD7utgz0D4uaUGUlg4pwMrBzy0ojOiWTiA+gszzJYmLtMb4SA1t6ufhKTn+G3+/zJLns3qyz7R3wyGPIi7H9SZzqTGWT3jzafcLSfcbX36oXHIPiQ++OmMWONsvfKRS1pMvrhIFgKwbZ38grzOeVtVC4CMZst9zl1oejvoaHQ9fRUQkwZREs0yLq8r7pi2/3cbmumWZKIruWqXI6sr2F5+1Uo5PWV5Z10JBpaW2mIo6F0+N3QW3crLL7UsFYQ4+hzJy5T18hYWTn8vcavl/xzo6xwXdvq/zOq8sl/OymDvFOPC1kXQ7nfWAorJ0JYHj+SgTMglRybu62FerSPisz32/+4TS7HulQsW3PnL45jA518p3y9acQTCbddQHXTF+q9qwSqHtS6PqN1/aZe9F/v4kFUjF8c1llbwdfPt7ubVfae3KauyOSp2m5vcyBb89PM5z9ySHdB9OU4DLclRHSrtzAe9PFIbv+yRloY1T5e9RVrI13siOuTeUcjac5mPC3qlLafN7PXn+aQsEfeegtZLvD/UomAr/e+zeUa0BsPcS7Y5jnzKVW3/7SXG80LN5TzqxG/PAPsnJC4rzzjsPAPD7v//7znOtNZRSSHZTOb5wSCeQdwwagD7feKAotBIZYaejlZIcOXj5AiLvUiqDJBQ+qyBfNCn9qj2YZQObLzRy0WGTrJek8TwxYduSNn6aXBnKCJREmdWnxK2G15NGUiC7TniPcFL6Oytr4dh1wCoiKomKJ1558l4lIeQknoUrEHr2uxNO5NFHyp10Af/JxXki2YLIBLgULmmk8VamAa2CHKu+fS3U9+W+YDFeZD3MRjS5cqm0TSP/Xm+bht3v6RnPLA4LLpiyC8fNwRvd8qs49hVIkJIoa0dPWEkUC+SKSEfq318mieUgFkG+R1KSXSeob38UK5+3nktIU+G5TwkpyjZrO/rm592xdFa1l++5bw2bLQ5fWWVdyDVShuNhZyOaBN/6MMh7ZemXEVgf2eHwtYtUBpdB1mFZfZcpFnxlqVKA+/I4WxuXfa/VoPi9rCV1XqZA4t9VVT4pr+KzTlPYg0x8B92Ved1wZQe1L/OKs/N/mh0ilO3zdA5AozkaALTyHzTE3iGZxCkS+27T0OaQLN3tGZkgNUTV6Y++sSnXWiq7YqSV8udT+pbNBXzs8rLJdZ3i8s3RZcqO+QxeL3N5Z57j3nvv3SfxLgzSmaaA8gl6cxDaMrdEvveATl8DkJNKadXgC04Gr/WkSntbZmmU2toyzS9/1wdPHotBmGXXF3/ENvhHuXuKLGvVAQFO+YjYeYhUFXyH9/j24XEX2lnTkIoGQQrLiF1VnD7LKM8nf7dwz5cI66DEEsvj8eVRnuTHLcI68dylJS3o8rtDQrMFj45+NxEXM8gXLEk05VjlLvCA/2ReJzwj93DrT+7f9fZ19k7ZXl1fPyp1b/eNdZ/CiAQolRFb/cJcl2LaULgeyznCJ2gIzGatdOLmnz2LMiePVQcLyXRLry1h7zi/SXLkyy/gJ1M+AX8AgjiQEqEqHllnZe3iI7CzEaHZ0pBxyDCe+pwVMqxU+s3mwQMU19QyLxL+vyzOiLVlFbkuUxD4vleRbvleGYnk4eQzCu8jCrLtByXE7F1+mjV/V0WRuUNzALIv90Y7lkXFTlMeRFbj+ZNeMZyIlZU3SVEgRZwgag2tXOWDc0pvFAG9BDqlcpSc90FWUSftPF/kueWsKU4+mEzTZ2TZNz/wetM634JSRTb5ussJvG/OpkPl+HpMSmGeHtWDnGf7fSB9gbaL7CukKVB6AmPVO/Mbr3rVq/ZJvAuDdHJXPL4vjODTFil2mEwUAWlSIDMmbGbhUOwUS7noCtiJqMr1x0c05Tv8PfmZv1dBRLyTL30XRMOx5nisbV6i5CSVC+3y0uYCsSqx5vHvPtJURvRKXSYFoawik1XkuYqk+N4pIzb0vLDP0FOf3u/SSlryrj1goYy8ijgd+PqYic38q7RYM22zUu7YJPeoMqLJ45DjquqUZI/iQfZBcm+qqo/Svb9ZmoPutSz1AuBE3SfMJyV39O0LDEoQZrHsDWyRnYW4SsIpfysjooV3xHxXasUsgdfKKgV5+XkASHI+6Mm4Tlpl5MZJSDwrIyS+/uez1lUR1yrCOyhms2D6wgzyTpkSaNZ1mJWlTMlQRQyrCKKEL3zVeu1734cq0lqVF/4KH1e+PPnqz9NvyvZG273EjIR6r97xvFNazxKynw/icqx1PkdLbxt6P0mgpQIijti1fSi+Q8h+N9fwJbkXplMmsUUE7F742eYgbmAou1vb536sdX5adGYMyLfQaCBKXdma3iXrd8rqQCpEFoDV70DEfffdV/n7f/pP/2m34l0YpBNgAyQtCrNyEaYJMUmMVin7rOIYaDaAnn+zd+Wx6WQdLdsrSGH4f/m8SmtYtmj64uPhvXnwaOJkXhgkoZO/yc/SyuQji1VWpTJiWWYplPny5YPnpSw9SUJ9eeRHsksC6yPdXmRtI8PZdOkQnNRN31cebpWX8UsUrH9ldVDSf+07lH/Fxgdf7KTwQWOTPvsjLz6T1v6yfg4PyczqxrkgPGJ3KvqykFbUCfOAEIHcvEeq0F62/7A9MDqbY5Tdp4Rc8BJ3RO5zMCFBxbERhGR9e4Qdn7Vx4ANm2HffHs7ZwO8PLBBO5SoIyk7JLeRZz3JvYyETuSA/iEtw1V5XDvu7b0ywuq8MM4ti1BfnrMREoooAVylCy34fRIlaFR9HmbJ2EIVvmRW5zNLJv/sE7DKy71MmyDTke4PkRb5LqGpX9pvjOVB26JpSsFfU+NqqhGgUlFQkW3k8Fnwu93J/NgCH2BV+p3KVbbXyPZdbPXxKBBmGQNeKlY1v2p9JByURGSarIbdQ2jWVpU9Z5ZZKCk8Kgn7frC+9vlu2OMqfxUwpHUc54cxIt+btSu8oBVWvZWQzV6paa7U1GDPvn6hkTMwn7A5xnu9lBvDqV7+68Izf1bm7ezpfIKnmBQBNAtyyAuSCMJ/gIzZZp9q4ihCSJL9omtx8BlmEswFWRhDKnjngi590R6XnHnchctXw5cfp/DxOpYrviDxKIsdJh7S0WXcRsV+uzLJHv/N0fNY5SeaqLJ0yvPyN/87TqiJjhTBZnyBrliQWRBq9bsZgbVXhFmzTEenLcshwchEoQLpyl4Txktuy+LjLTv5CUXCyrlqROyZ5WBl3Sb4HaS+TJ118t+LkWo5SN2pZhyLvlWOclGLZXkQrfJV4ZuxTSyfVu0cosKdvs7wUXpdC4CBzpFT+Zd+ryHWp5ddDWrlA77vWw7d/tEBa+TpR1T+FUOuMOVGflae9MsxFyTDQKbj8P30uK4skOCWKWm8aPoIzwDzjwEf+uJutTN+3XcX3Hn2Xv9P3EsLkuIHy+uDKNY4qy5svfKr99ezDbFZmmXaZpdv3e0mfKOzHFu6atn7II6xKYS7iL/OO8CmdfHvFvXmURLQMNAdzA0XZM7nFQ6ZZKADJVbPki7vs8n2y8nO2Vth7a5Mkr+es7+huD3p6BnqmY97pdKGnpvNTfPm1K90ee2YsmDpJXMLJ80XjiJ5nedH9PqxXHJNHtU7zPNr35j/5cuaAufzNc2zZssX527x5M775zW/iZS97Ge6+++7djnfhWDqBotZKLor0OWXPpIDQ6drnpZbNMs0kW8zKLGp8r6F0/XTLwhZGvsBSemyxdE5FlQsvzzefsORzZjGiZz6Ll+P2ke0H5JvkfQe0+FC215Aw6J67MqukL14faeOkmIf3WQ6dZ4xgS4un/A2AtYzy9711VOEmZi2b9Nznbl1Sdz4rnddyLfJZqEcuGCbI+2KZdSCPyP+8CkKLzq3mZX2LWzkr4ywTWkkhkJa7MCsVuQdFlKVF8XkWXitECRL8guzn5CCtNX0GcmFBlitrX2mpKzutkr7bcJlQNedDg2Tcop8N4pbnzY/PsjobwaIwg4DF5ZziOYsAXVofVeNrUJSRpipLnUx7rgJVVbyECi+GyvA+74MyzyEfEZVxZHPZnNye+bOy8NKqWeaW6vteZekc1OLpyzsJx6yPFtL1pUkWOZ6vqrby1M1sbvlVh3BVub8PpLiRrqYFeSjNyVnVeKN8cE8emS9fHMIwUqgLj4uq7nZBBx/Zpcfpzyn09Ez+jkDBw4MssgAcd2CeZ6m81tqQ2Uwp6Vg0kwTQfoux1vN9T6cG5noc7QIg2+Pj44Vnr3vd69BsNnHZZZfhwQcf3K14FxbpBFzi6VscJNjg1lzTxAkaUDxESL4POIvXwFZNG02FC5+0rJQJyzzfPkiBo8qVqcwFiX2vckVzhPQ4ntWqarKXWrJb5n5bafWT6TICWeUiLOMphPXVKbmkePJA1k7rrklunkIYsoRDauJluzALpuMCWtYHJHwCGs8TJ9SkCOF5l0fUU5njOCdfkXKVOYWFVvk/87KUCIu+Ni1TEDh9zScUSmuKzAt7TnVQlk6hb9K1OLxMPGzffHcIRpKYnbLavfj9BSGfwgKs+/38/kDuDSKsZmUCIb3LrYg2bL3uCFMDX+nAf+YCIgtbRXrLnpXF5VOecJdnJwzPM3/msTLOlqdBrpopO0ipEmV5YmVXcZzHU7VWyvYZxErn+202q6BP6cGtJj6Lpc991qdckp8JZS6isyliBikTj0++I9PyxSF/81mjeXyyzWeLM/vM+5/joir7mFTAV+Wh7DcfsfeM/7IxUSCXUhbhZZPurpIs8vR5PkvGioXPvdZXXgnhseEl+2UyJvVBfl8mt8wyZYAks84Yz8ilTrPtPGV9RH62llLk6VH9SI8TKmvKPAnnIao87KreWahYunQpHnnkkd1+f+GRTqBo8SwThO0Cl+ZuEFKg4O/JiYBbHZM01xhVEKcqN1MA+WRS5grks5yknsmhSlvP4/FYkpzv7M5RS8aklcdnyeFWIbEg+OpFHvzjpFdxwE8lZsmbzV/JATI+UmwXnWR28lqAT9jh+ZQCUpU1zveufFbWX6q00tIl1ZfnsnIQ+NgosagX0iQPBJl/+Nu81ELsGwsyX2WQ+WN1XbbHt/A+jyd7v9C/Svac79Z9jrsLWbY4zu9W9c2DYj7xHTRCpNtnjSxYRmaztAnrS4EksnekFbFgiS0ju2UeK4IMeNthtnViFoukT4AuOzSpiqD64NSZLx9iHfO2l68+fERH/j4IHKHUQy6BwSyfPtLoU5ZKC2fJ+me/l5FpGTb25LuKOBLK6mtQ4l42PiusmF5SW9ZPVX4dUSF9ijeKzJ2a9POgCrOyOWUQhRP7z5/PelgY9W/6b5W9HsJJKFvDeD7LyKsvbNm4GWTMELEEPP05k135Vo1s3PvIpHd7SRZGWkNLtyFQnL4tBby+Bp0PftfBFV5zeWee42c/+5nzXWuNjRs34jOf+QxOOeWU3Y534ZBOqe0BTOfnR0LLgWEXOLbfjNwHOEgg5gSTE04AqlZzTq31WWdMVMwlwbewcsKZxVVYIMvch6gM8jc5oXNiWyHc+07gLOxvKyM7LLwsPyeyBaTu1RRle+uqLMk2bJKH8xJqXhZyq/S512ZlU2mJ1TNiB9T4LM7SpbOYYYDfz5XF6bP4VSocqkim770yy6rMZxw5BwfIPNk2qtfyPR2yH1M6nvcRw2shlPHLsjh9i9dxLBRIPiHMt+DH4hmLs1KRUNGXbRkjBa3Y8fNVAk+Wv31+kFAmYDtW7l5+iJG9j9fjflrYH6mUq/mmYnBhqC803j7BEyiQQIeEeUhrQRCtOvBNwkc0y36X+ZXClRTwSsii75ReWQ4ezmulldn0xFlKAKoUkr6yVdVRSdkHRpUCrAo+CyY9932velcq2vZVvssslDIMYTalWRnxLBtXu9NG0jomSJi1lPnyX4YqhY0sC3te1sdtPnzzJb3PXYK5Rc6nWJG/+eLjKLOWVimhqvrBbAqMSLnXlfG8SZnWpwxg83RVnc55/anqn/MZWgNzda9dAOU+9dRToZSCFmV5xStegZtvvnm34104pLNM01S1x1MOiiS1hDO/6FdYTQvpZgKwZ/+j11LGLX8+Tat8Li2fkhjMRmh85YwFeUlzt5FSYlaWVwkPcSnsOSWiLq2gnPxK8irClRF6In5Vezm5u4QVrCP3e6EsVZou6Z5a1hb0jLdHmk1okgD63iuzePp+l0SxTAgjkkaEy2ft09kzxT4zRYhCbL8XLOGcuDFlQqF9IvcAI3t4Qtl+UiBPl/IGwDkarUpg5sLFLG3rII6gtLIWTN5XpNLJae8km0eoDuk95+JxltYA1rI9QhTnSozIEEYFmIvKwYRJIfTZ33zzqsdF01pA6Pmgwhwj3YW0hGCo5Jwt45JWKZu5TCkgiTXPRomrq1M2cqv21BWPo0A4xbw8V+u2dftF3l6OhYpbgQfpTz5hv+odn2LHZo593xPhs2x9K1sPSenK5ykJGV+VV0cV5mrdGaAOVKuZB5+eMcpsUtZIYmlfqkh7trbhaUvlvPjsjPtIAXEMVasB/X7Bqmb7Yja2CqctZ//t2J3NUlpRd6UWOQ6hDHPqoYwc8rrj131VjdHZFJw+klvWflZGEDJo6mlL/p+3hc8FmIdh80UZyXTmK5m3sjqYLcx8QpoCao5eRwvAvXb9+vXO9yiKsHTpUrRarT2Kd+GQzjL/ej5gaRAQ6SrRqDmDhb+rxCLGrJ2GzBgB0neojbWURQrQqnjZvdS0SmEajKTJRdJHVski61tgPKTGt2fOSavKalW1sPOw3AKcsoOKPNZcfmmyUzcVBIEL0fJKE0LZqbKyLrwWNB9mI/q+BZ/anv7LfkhzfxlxLROOKC5TUL8w5dPwz7aASK20tIrGkUv2Erj9hrchsz7nSXgO0opcbwFnLEgFCs/rbBYrHygKSer5M0rfxqNzYimVEqmGsw1WZ3Wko6ISyabHPDV28yjy3QJXJmTCiCWIlC9GbAg6TfN9mgzSmldAmaXQMw8X3HdlnDyuLN+WeHIhLxZzOI27LN4qoicFZp9lwBK+Cte/WU/79Vh0vOnI+iXhPdseUiY4kgKBzi3wEu1BiGZJngvCs0+BsidCaNW8J565e3CLa12pV8dsZNM3p88235TJFp76UDUjjql63TyQB3zJ9+WzsnTL6p/1HZ3mCncAdCMzVLNpDrHJwtmxqOL8yixuRSS325K5uGoM+/o2/1xGKCstcoNYMyvya9+jPA/iOizTknHL9uBhfMoBXgbqr4lnLNk5W8FuVWHkU1FctB5lcZTNMzZaahd+d2eZEtc7NgacT35XcYBaOg877LB9Eu/CIZ3St93nb0+TJh+4dlCqnIgCs2tr+ISQIidQungqqdZpfjBLyuLn1p8kLbjoykW24O5Kk0cqJiaeR/6fUEKiZt2zJjGbW5InnYIV07fgp9pYyyJVJJ8C3pNs0+JhQ1UoPWmYHbRTyCMHz5tSxqrim3y1iEeSRPkOCR1VQhF3J5Xvyvd4/D6LuQ9l1gxpjfdYsnk4fqqvBRcUkR/CUzgYqKxf87qr1YraXPqN7+HxLfgScQQ0GkC3Cyway+/trdUAOiEQfaDVBGY6OWlNUF6PKuvL0k2Z8sKVY1X3vO0t1NghVlbJEUHVakh7PSP4kkItZW0jrHyg9qEyKOEW7CMfEnMhO2VxUP51Lkh509HaVfSUCMalcVA2BLGu2qPptSSUCJqF/aie90uJL7Nw2udEvuIYqlGH3jVlBXhfuefyPA9QQYCqxtlcMaAF0skrzUvU5vzzIHH65ABfWTztyS3R/LNDpnh8fPwDxqsiju2BLwWS6pBrFOte5l+QOBlXYc6pueKhVTQplcsiqcg75Z+53RJZrdpPSPHz//JzmTs6Hw+l+zvL5Dk5n/B6GES5wFE2t8k0eNsMMj6UMsq9es09O8QX1HddGyHrSwBgt8DI+TqLw9fXvBZUguzL9HlQBVbA7wSuvfbagcO+//3v3600FgbpTNNcmSLJZplmPEndiSgpTsSlAjePjwvh2TO7L5BPsswC5yaR2oXQuqNoNoCZJYe7GWokZvLhJDYjwF7wvKfwWip1lJYTrCprGYfQJPP7LG1Zq8BJEFlDpWXO52bKLJSFPaMewlrIT0l4bjF1To7l6fN3rEWciLJN0P3uI4icvPk0a9TGsm0S0ehlljtJeMrartBGJYoUCDfkLE6HWMYo9Hvn1F6fBTtiJ+bKOvQt4LQo8zAmk+a/tH45Y4GNsWYdaDWhh5pAopEsWoqkHSOpR5heWkP7mT6ml9bQ2pKgvXHKZPW5HcD0tF+IlfmNIiP4MxdunaSutVAx0v5CLdhUh1EERClUo2EVPlAK6UzHBnXITWYdKQg6VG5RLrLkkAuoI3RXWEUcrTu30Glxn2JGrhBHiEiB1uk4fcInvEv4CF0hb0wAkySVWzMHtsLQd1Zubx2QdSqzhulMIeIltNo9bVMniWOd9lmk6fcywd+pAx9xsj96FEVE+sqUZ2XWR/4uPZPxyrJLZHWhVVZmupt7FtJZ2g+yecNbB4xUUH8tkCelikS0VgMa9byN49gov6ZnCn2iQFxUPl6hddGl2iq98rlQxXGuNKL1geVfZWPJ9kv6Ltf+KhlJktkosqd1V9Wzbwz43Nz58yqCSmXm5ZuV6Pn6kiSwNG9KhaZPMeqLt6zufOtYqt10AENE0zwvBU8PWSZSJEg5qooAyzqguY975viItK8/zEPoNIWeo3vtfD299q/+6q8GCqeUOsBJZxQBirmnSeJZtvD7NIO+MOR6IgUEpim3E7p0K6TP2Xd7qAg/RppcCeW+UCIkcZQL8vyqDuGu6rgaAkXyoJSbL5Y/J/883ywOR7gsswBWWCS9ZZT5rXJ/Ei64Fqwe+P7AQpppcV8hJ7Xymhd5Z6V3f6Ekwqku7h8j4uQTcHSujHD22FXUZUEo89VR9pvVtEurLS9Pqguk2neiL0/PeY/vrZSWBFa3Tn4lONHMEwUqZHav9pg++1yAOLmixXu4ZQQ7AHqoibRRQ3+8AdXX6I3WMLUsho4U6lMaW4+po75LI+qmiJ7dlpex2QA6XXit6xG3qmQukPV6PifRxd1RZAU6pSLoGE7f2+som8uUgqrX8t991gkiU9l9bUZgRT5PRgpKxZYQAQCYoKwyjb0CcquLT5hDLmBzou7bQ8nfc9qBTuRlZJPvMwPlAX6Sa/PA8qpYm/oO4PCuJb769v1GZLEsXq5MjSKXdPO4+DpI9VKrAbGG7sLs5Wo07J4ucq9UjTr0TLFuCvUAQQ485Jny44SRXdrml32P8/5A/Ux3u+76ppmLMCNwfI+gJXUZcbUuglxxp9y0+N5Ybz6pfeo1aznyWTF5W9l6oz5LvwHWIqiTxJRvqA3QXeGUHu2bFPHafdgkD2R7LHW/n3sdUHmz98j1lfKiarV8nYwpSWG1pHzUa1md91HwChPuuZaYMDd2p75lvQprP/8vP8t4KhU7PI2qtOmzlBcHsfBzyyhPh8mH3vc81vFCPDyc1i7hq9dgz1Ag2Q7IrZiAV6lrx0iUwpzix5Wworxa27mbrtWyebNtLPa78jkIAOb7PZ1aAweIe63cx7kvsDBIJ+DXuvALcIHBNUryNy44lmhW7aKTpEAtckkg09I6l/sCrgU0Ehfuct98wFrSzImfGfkkga/MMsnLwuMVkIcb+e4NpTw6v/G0mDusvT5BXnfiI5Yi395Fn5eH9mTx/IvrVeR3e0qqsNhx4qmRHyqke33HSisPIHL2JmbkCwnsIu+QPRaGHzAjSa0XTjt6CClL25aT16X4zvNvESmgp519iJUnEjJLaoG0RmJ8+Fx8oYvPpPWRW41943M2jXOjYT7z+YAISKsJRBF0vYZ0uIlk2FiN0noEKKA/FKM7EqG5LcHIUz30h2NsPSpGY7vJ+s5D6mivbwCdzAJIp1k7daSKz2g+4oJ8HEM3ss/Npj1YSCFy7oHd6+Au3gATEJgg1Df5LVhZ+N6vTBlnhFFW3lpsSA2bN+14qtUAlRas31oIbwXXUkakvIIm76dktaH2F0TPJ9DK/ZbO3kpG7nzElIeRh/m4AUVf5v2BPrNnNi4qQyZs2vKJPmatmfYU89T0+0jlitNeH2jUM4KSfQeghodM/6P0+n0TFyPs1sWSysasp17LqbTA2h/cOrbrDieoVnHFyD6rbyCfb22cXMlg85i6isAS93XbrloXSQNZBiktSkcSKrC+IK/q4fJEFt72b3JnT9KC0ly1W9BT05Yg8gMPnXpmxMPWI1c+cGJI1u5+P99L6pDNknUpNYRbz3RMWagesjoqtJvNoMrr1al0V67y7tuU6wN7z1EUsXDeeHxWWHnthySSFM6pAw8x85HLMkspUFTmSQ8Mypvsq7xvZ7DykpSpfJ+T1JVpohSF/Z9O5MrK1lqn+VkUBZmArSO8v82mEJgPSDWgPHVThXlKOstAJ9gqXz+fIxZAj4ArcEptm8+9lrRvSgxKpZgQlk3gPGwc5c/ocyZMFfzga7EZnNz1kVssKX6yHMWxnQhUHBtNKgCVabNM2Ci3iMAlPYXrNCK2AMkFilnUbN75exCEKIuL8sTTVtzaQeSj5oZTKsqfUZ6oXJ48a53m5Y/j4mSq3Hzzvag8X16XUM+ptnn9Kve7qBNblqyNbB8QbcHbE4DbXixeUh4471M/JJdrIvcVFk+qC9sWvJ/S/2wPn6rVoOpG4DSWD9OnVKNuwlBYqZFnde+Mhyy8ueeR3G3dunDySwoDj6XBqW9Ogigc9X/+LM7ul6xl1rl6HVg0DowOA80mMDpiLCXLJpEumUDvyOVIJkfRXzKK/uIhJEM16JqCrilE3QRJM0LU1xh+uov2xim0Nu1Ce9MMoh4wswRIWgppXSFZNITukcuMZSKOTPryUCPSONMBDJTHZiOvw3odqtk0hJOE5kbDPPdZGfcSdDMjJkREqP1rtTy/9HtsCGTUbjvuWY5FNIrsMyvENur5XxxbooOsr6hm0/TbRsN8ZpY7lZFdW3803sjCRM+pX7A5oeDqC+Tl4JYbEcbn8mv3iUlLIuBY+HhbKZ4/Wzeib1C/5YKZ9Kjhlkz+Hik0+dqk8rUIip0ETfXZbpt06jWoZiNz5WyYuWBs1JAeGvdxZNpxqA01MgzVauYWxUbDzh0qjqFaTTN38LR5WbP29u3rI6Jl51JZl6wOrbIjqzebVqPhktzM2udYs2mOzTwawIizDU/tTHmgfk5pUjvy9snmThrrKooQtdvmBFoiZLxvRtk66BPY48jMCzp1yVm2l08ND5l2aTZN/IeuMPWeWR/JSs33tltC22qaccP6kR1nQ21XmUrlornUWqjZWk2eGnLulgpCalcmKzntyvsu5Sty+62TBn+fzQuOBTh7XjgLgPIj695pz9ixIiOO83rj4423G1fC+ca4HPuUXpLkHgv8Xa7M8M4PtF43HLkHKJGVaA0mjyMuh5E8SvIFX++p3/tIY9aeTh1zsi7frbIWzwdonY3LufwtDNL5xS9+ES95yUvQbrfRbrdx8skn4x/+4R/2KM79TjpvuOEGHHHEEWi1Wjj99NPx/e9/f+6ROBMdmwQAMwE1G1BNpnXP9lERuVONTHMbKaP1pQGUWbusYM8OQ7GLLmm3+313giUXniibPNota7mwBDOOodrtzFXHHfxWc2WtiGzRtUKJMha57H5EWw64JMa6GLF8WeGwUTdlovLUamZhIyGUyqsiYKjtCg3Z/klLxNqtfJ8I5aGWCYEkKFD9ZwutJZ/IhQ2lsoW6nuUvUkY4qtfMM62he31T9kyLr6SQTnVHGn1+vQXlv93OBO04b984yoVjZJN0vW4EAla/zmJIcdCEzvtTs5H3DdqvUzd1jhpz9SLCZgXSbEFtNuGArBa12CVu1Ef4AkzPSIghoQTI8051QouE1PLzwyRIgcIFMqqHdisTaut5mKxebFzNhi2nThITlrwAPJO0445G+Wo2TNzUJrXYWC7bLfO/mQmWM13odtPkeagNtWsGSmvUf7sD0a4OVJoi3tFFbVcPjWem0HhmCvXnp9DeOIX6ti7qz08D/RRaKUQzPagUGHnS5LEzBmw8exRbjm1h6ogJ6LFhQxRJYG21gHYLyVErgZFhU++klBoeMmSY6qbRyOu+XgOaTejx4ax8zUKd7C30x1uOEI963ZSD2jGOgaEhU56hbI5qNaHGRgyJpDZQypShVjPlJlJZr+fKAOoLlFazaZ+pobaZa0aGTH9v5UQUjXr+PWtX+5kE9WzOV/WaVdBYAk/jguY668Ycu32cC6uNRv4dyElTu2VJi52n6mZ/ri0z9VEPaSLCbE8JzU6RBRH3LF92vWllZaib+diJP1Jm3RgZNoRxbNSEa7fM//FRYHw0Fw7HRqAnx4DRYaQTo+i9+GDTL1tNYGIMycGLoZoN6IlR6KWLoIayPjo5Ab1oFJgYRzQ8ZNoqjo0iZ3TE9Guqr0xAt3VUrxuSVa/Za0CsCywpvmhc1Gv5/M1IgyWlRBZte5n5wMZL/Y/mhCirt0zRo+IY0bKl9jkp2VQ29tT4WFaWeq7UyMgcKU1Qy+QEki8iBdVqmbK0mojGR/N5O9X5GqqU6Tvtlm1nVTdkQjXqWb+qQw0NQQ8PMQVLNobqddMu46Nmv/nEGPSySeiGkSmoj5ISASPDUCuXm/cpvypbH9stIwtl7aTaLTM31uu5dTyOclLZ6wHNhlU0OArMODJjc3jIjjFLXLjiIlOqWUVso56P3XrN/eNl94xNRNna3GDjnxR+QFaP7byNaOzQWCerpkyDf49zxQx5damakVFUq2XdqhXN9zErSzZe+TiwRJWTTzvnMqs8lSNSWRmNvAOST+kzV1qQkhgwYWsx1PCQHUtWxoyZfBOpPA5SCqT53m/VajrzkC1THOXK6mYzl5WY4YX6hDVSNNgYn8fQqd6tv7lirlzoe9/7Hk4//XS0Wi0ceeSR+Nu//dtCmDvuuAMnnHACms0mTjjhBHz1q18dOD/XXHMN/ut//a/4gz/4A3z5y1/G7bffjvPOOw+rV68eeO+nD/uVdN5+++344Ac/iI997GNYt24dfu/3fg9veMMb8MQTT8wpHtVomMkm06gbjY/R0Olu11ocSSurSAMJmEmdyAuzSqp220wcnPg1zURthWFyOajlwpJxj9Hmvjs+0fR6xrWBuXPoJDGnXwKOgKa7vZzM6jQXtEnoaLfNb72+WThoIWP5p/0tamjI5stYVBq51jLJXbj09Iwhchkx1pmblcoWY2ePFH+WETsiWrrbg9XGN+pmX1eqM82ezq1r5E5Sz0jl6Iix7tRNvLrTNYJPv5+RfeYuRYJHpOy9gnZhaTagxsZcbR+5RkVRTh5rMdDtmvxl+dD9fk6aSIFRrxuXM/rLyILuG9KrO1kcfGLt93Mrl1JGiEtS87xeBzpdU+d8zxtg8kzhKB6lgMWLzB+RVuqPQ+28zVIN9BNTb/3E/Dka8yxeKm8UmfRJs9vtuRpfepfXSSMjidL1OSH3uMjsR+IuedydiIQkEkT6fUaiaw7R1b1ersAggQ3IlUJE3ptNYNcU087XgE4P0ClUmgLNOnSTEec0her2EO0yp86qXl5PulFDtGUnalunTVfr96GbMXoTbSz78S5M/GoXJn8xg9o0oCOgNwRsP7yOZ8+cxNaXH+RohfVIG53JFp47awWe+f1DsG3VoZZsdA8as2XQ7WbeV0mI7ibQY0OF0yP3JqJO39QdCZJT01A7pkw/oHYioYIERbpDMLPEWjDh1hRAmX421DZpNDNlgO1vST62yHra62fCVSZgtVu5dS4ybW2/W0ViVl9ktWq1LFEnQV8NtYFeP1NykLBlBDydJEZIysioGh6yQplqNFzrEJBbnRoNE5b6ONf6k3AbRaZ/01qRjRFLZJpNk49uL28DnXmKtFrGfZEExEyAs8owmufTTAnTbJiyZ+QCAJLJUajJRVDtNvoHL8b0i8YwddQi9CdamFphDsvS7QbSoQb6ww2kBy81LrK0dgLQsUIy1kYyPgRMjJu2iiNgasr8UdmIQLVaZi0cHbFkyyqWsjJb8s4OziNFghXUM6iWCU8WPjsHZ+7AOknyOUlrqyjN17521tlNn3MUiWT5aRo3eTUxDjU6YvoYucdGkZnfO12oyQnrfaDqNRMuSQy5r5sDyKSXlO72oJYuzsZSnh8904EaGTbPR0fMOBkZQjrWMnGSLJCmZg2NFXrLRpAuGYdKUySjLfSH64CKTF3QWpWN2XSoYRQScQx0e/kaScoRUnq1W6YvtYRyixR37VZuVW+3mFLVjD2dzcWGINVzxTVZX0mJGUVQSybNmjfTyeeJJM3bv87WFlIy1pmljxT2NB6y8U9pWrflfqbUGBk28hZtgaD6595aXLGQKcyNq7iRP6zCl5S8mUxilfxxbOo/KSq0db9v5hYidEROMzIOra1yzOytTnPFGinzABOGezxobcpBayZgyppq6Klp6Olps/ZTm7aNAtR6TWUKGmvFrddNutn+buvOnbldc+82aG33C/Orl6x3GjcqkGzZFTLOfMScrZzZ3xwwVy60fv16/MEf/AF+7/d+D+vWrcNVV12F97///bjjjjtsmLVr1+Jtb3sbLrroIvz0pz/FRRddhLe+9a340Y9+NFCerrvuOtx444347Gc/ize96U04//zz8bnPfQ433HDDnE65lVCanHX3A17+8pfjtNNOw4033mifHX/88Xjzm9+MT3/607O+v337doyPj+O1K/8McSfNhfG+IWPpzl2IRkdyCyNzF9GdLtSiCWB62kyEma+6Gh4yi2+vZyx53a4Vwu1vnW6mrWTEpNvN9/HZfSVZflpNoNczA5m08ICZxBpZnkiII8GMSORMBxgZgt6x0+S313OtsaQpGxs1wjeBFq/RYeD5rXZx0dPTuVvmyLCJKyuLnpoyk+TwEPS27UZ4yOoTSgHjY2Yf2y4jlButLvPvJ4JGiwSR+1YT2L4jJxZjI/nVE1PTuUVnesZYPHZO5a5+3W7utjsyDL1th9HkU93GEfTW7VATY+a90RGg07F7YOwC2u8b7f62HUZQUMrkifrCyDD0zl3ZQpECw8PAjp3m/SQx7/Z6VkmgO10j5FDeiUDv2JkL7b2emaCnZ4w2eunivB6Yu56enjZ9k7TGpIhQyuTJWk4jU76p6bzdOTFJ07ze6TtZXro909eGh6Cf32KEppmOWTxpzxC5fdJ/wAgtnUxIonbPhBCbT+4CFMd5HpLExEXjJEnNe1rbdrX11GQCZ5Lm7V+LTbrcfZUWwiQ1/VEp08c6PUPW6JCNrhFQdaOO3tJh1HZ2ofop1M5pJJOjiLp9aKWgW9ni3e1DN2qIn99l3hsbAnoJkok2uhMNRH0NaCDuJHj2JW0MPZNiZjJCZwyIe8DSdTOIein6wzX0hmPsXBkj6gMqBWYmgfpOYPSpPtJ6hDRr0nhGozMRYcehCov/PcHoI1uQtptABMRbpvCNRz6DfYHfP+fTaDz+XN4uzbpxuU1T6HqM6PkdRokxOQa1YxrWlZDaemra/DaV9QFSULA21UsmoHZmfYY8L0g7v30n9MQYUMvaaaZj8kF9faaTkbw6MNw2ioQ0MX1h0Th0qw48vdkle60msHPK9GkiHHQoC5unTX6UnaN1dpiTGmrnc/vwMNBqAM9vtYKTGh5CumWrIZ1Edoj4dbr5Hlzqf9k4JAHUxl2r5eOR9tZ1e9aFEv0kL3unAz0943g8WAFvYhw68xxIh5uId0wDW7YBtRqmTjkE0EBju1EyPX/CMOKuRmdcAQoY2WDSHXl8FzpL2oin+4hn+uiNN1Hb1Ue807Rrd/EQ6ts7QD+FShKoLTvyuYGssTSnjQyZOXymkytOJ8ZMX5meMevK9LRria7FZv5uZdbvbJ1Er59Zrcx8okn5CORKmtERq2TSzz5n6q9WQ7p8EaInf4vkiIMQr9+I9NDliLZPm3mzFpv6rddMn6S5ZHoGiCMkyycQP/lM3j+oTZtN26dNhQOIAN2sm/4bRVDPbYNeNGrGRKrN3ERC+dgw1ObnrdCvR1pGydNoQDdMH+4vGkJt2wz6Y6at67/dgWR8CLoRY/sRbYxs6KD+zE50DhpDc/MuQxB2TJm5anwUatcU9OgQdKsBNdOF2tWBHm5CPb89t6wTCdm+E+mKSaiZvlG+PbPFrHGA6ZtxZOonjoCt282WBa4krdVMndVrZt1tt+wed93tWYuZaa9M2btzV77u8T33FB8pbyn9OIbeucuMvaG2IetDbdOXGnW7FtrvAJx90Y2GUY5kJwBjuG3W08yDQ09Nmb7YbuXKDBqXI8OmX2fWVVJ0oNPN5R6SZzpdZhXMFHAk3/C1GHDkStVomLGcKToQxyZPRMABk496zchwZB2NYnNiOiELr6enzTo90zHy4NR03n9ZWBsuk0vyA+Ai04Z0XRgZFNLUyCbbdphxONTOlWXtlokniqB3TRklEc1TmRderzuFb2/9IrZt24axsTHMFxC/eLX6I9RUffYXGPq6h+/qrw5c5rlyoY985CP4+te/jocfftg+W716NX76059i7dq1AIC3ve1t2L59O77xjW/YMOeddx4WLVqE//N//s+seWq1Wvj5z3+Oo48+2nn+61//Gi95yUswMzNT8mY19p0qfRZ0u108+OCD+OhHP+o8P/fcc3H//ffPLbJ63RCRJIWuxVC9PnS7AZVOQsexFYz6y8ZQ27QV/eXjqG14zgys0REjbDeb0K06kmYdUacH1UuQjLcRb9qCZOkE4p0zZnC1W8Zth4hTvQ491ITaMQ3VrEM3atBaQ2WueQCMBWXnNNCqA89sQXrIMqhuH2mrgajLBBWtoZt1RNumrEZeLxq1WuN0uIno2W3ovngl6s9PGWEws7wlYy1EjRiqk2mBh5pI23VEO2egJiegYwXVTYCxEWMB6ifQwy2o57cjXTJu0lw0iv54C/Xf7jCTR5qaety6E+nyRVC9BJ3DFiHqpag/twtquovuIYtQf3anKcJwEzqKEE91oTo9pCMtRNumoOs1q/1OJ4ahun2oXs2k326ZOqtFiHbMAJ0O0sNWINo+DT3URDLUQO2Z7VC1GP3JYcTDQ0iHGoh2Zh0+1VATY9BD2V6afmLaaKidWRcS6LFhI0jv7CB90XJEOzvQ7Qb0WBvRdA9qpmsm87ERqznWQ01gtJ1ZbbKFsVVHstJoruNdHXQXDyHqJEhaNSACatu6iIabUN0E6VgbvYkmmuufQ3rEQaZN4ghYOmq7bW+khvoOY2HsjdTQfmIbdC1Csmwc8c4ZdJeOoPnUViSLhuz7aT1CPDUMXYug0hTRVBdpqwFdj4z1SmsrEKm+WejSoQZ0HKG2ebtpi8WT0K061FQn17qOjyIZb1shJHp+h0lz8bjJ30Qb3Yk6ms92kDZi9IcyS3xNIWlEqO1K0B+O0d7cQW+sjtZTO9Efb5o8NyLUdvWg+hrdRQ2oRCNtRGhtnkbaiKHjCI0NW5FMDGdxRth1SBv1XQkaW7roHrkI9Z2mbPF0H9GuDnpLhqHjCFEvhUpSJO0a+u0Yrc3TAGLTnwCkI/kiOL1yGK1nZpBMtBB3jFZXtxqwl2THMZBopENNIDaWo2S8BR0rNJ+bwfQKQ2RmFtcw8nSCpKmg+kD7WQ2lgV0rDXHeuTJCfRdQm9bojiv0W0DrOSDqa2w/rIZ4BoiyNJO6Qm1aY+i3CklT4dkzF0NpIOoB06yv7G1sObaFJTPjJg9N0wbxdB9Rp4fpg0dQm2ijvr1j+uPiIfRGamhs7do2hAZmltQw+tg0knYNqq9R3zaDZKgOlQLQGtPLW6hNmzTimQS1HR2obh+dg0YBPYHmJkNg0pEW9MQQ+kN11LdOm7lzYhjRVBfJWAvxszswdfwyDD3yDPSKxdCtBrRSiDPviHSsjWS4gdq2GUPuhptQO8z8kC5fhGjaCMvJeNv0oS1T6B40hriTIH52B9BuQM30kI61jeW7lyAdbqI/0oBaMozadnNoilYKUbuFdHwIqVKIun1DOLI+q7aaeRC1GnS7aY7LihWSkRbibdNQ3R70sOmPaqqD/rIxxNunoaa7wFATnWXDaD6xFXrUzON6qAk13UXvhBeh8dSW3NrTaKC3fBRTK1p49pTcuj60aRTTy5Zh+Ckg7pq+136GXH2BziKFNAaa2zR0pNAbUfjtK8YwvDFBWq+jN1bHzGSMqF9He3MNivpou4beaB216QR6+QjqWzNFQz+FbsbQS0ehugmimS4QDSFdtgjx8zuQLhpBMlRHvGsIabOGqNOHjhYb9/aOUeamY21ErSbSYeMG2R9rIeokiHoJ0O1Dbd8FvXgcarqLZLFRguo4QrxtCslYG/2ROnrDMYbXDwNaY+OrF2Hy4S7q0UGYWjmEdnQwdh3aRn1qGI3nO4i6CdJmDd1FDdS395A0Y9Sm+khrY4inDOnuThwCHWfWStKV7EwQdxKoJEVvrIHecAyVakQ9jdquPnQ9gppoIWnXEM8kSOsROhM1tJ/tQiUa2w9vYeJXTfRGG0ibEVqbppCMTCKa7kG3auhONKESjahVQ9o0bdY5ZAL1rdPoDTVR35WiO1ZDWhtD1EsxvXIEUaIRTQ6htrNrFJRDDew8fATt386gv7iF/nCMoQ1TiKIJs2ZOddFdMozGMzuhV0xi+uARtJ/ehWhXB/2jVqK2ebup39FhpMNNo2ToJegccQTqO7qIds4Ykk3rYbuBdLiJtL4YtS1T0K1F0LUISDR0PULtmR3Q7cx610uA0RGkoy1T5noMtW2ncRsGoLSGrkVGebItI1TdXr6Ow3ifpYuGoWb6pl3i2Jzeu2wSGkA000V/0TDiqa4Zr/UYeskY4i07oUeHobo9o1yeyObVyTHoXgLUY6hnthjX8k5GrFON5PAVRlaZ6UNlyjhSFFjFRyfzDOn10DlqOZpPPJ/JDkOwB/TEytZZf9ko4p0dqF5i5MRdHaTjw4g2PYvk4KWIn605ynzdMspAVZuEmukhWTSCePMWYGzU1G0Wr27XASwCun1gdMjIgcND6K0whKf2/C4z3lo1qF4CNdVFunTCfJ7uIh1rQyuFtBWj/tQWJActBmJl5kdSBk6MmXE73LaKYD3cNJbamQ7UwSuMAoKstJOm36XoAQ95l6EA7B4XWrt2Lc4991zn2etf/3rcdNNN6PV6qNfrWLt2LS677LJCmL/+678eKF9HH300vvzlL+Oqq65ynt9+++045phjBorDh/1GOp999lkkSYLly5c7z5cvX45NmzZ53+l0OuiQuwSAbdu2AQBu+cFHdkuDcv74xbhz2xdLv88FF5y9BsnPH57T+xecvQb/9MOPzTmfg/5GvwPAndu+OGs8g4STuODsNfjygGWITzoWX/n2ZbOX52dfzMN/80Oz5rcqTV95Bqkz3++UJgDcMUt77EmbzQZfGhecvQb/t6IdLjh7DXpNIPn5v+9R2lV5+kaWp38W8Z8/fjESoNAG/zRAm/ji43jLMVfgn379eRv2/+6lsu1pG+1JHBecvQZfydpy+/bte5SHMox/4Xu2/qlf38G+f03k+y3HXIE7snomULvGJx0LAJX9zwdfP5Z9+it3f3DW93cH549fjK9m77558TvxteduKg0n++6guODsNfjKD/I6kXPW+eMXl84jvnzMdW6eLb5vDhAP74u7k0bZmKayAHDa+6t3XlGZFwD4yg8/ZvL1jfcPlIfm2ur2u+DsNfgqK2NZmS84ew3u+OHHcP74xbhLzF0A8LWsTF/dzfYpkwcuOHsN7hTPX/o/rse6D7/Hpj/IenbB2WsQbe6i8/NHbPiX/X+fxo/feCXOH78YZBOrarOy74S3HHMF+puf2SdrzN4AyRccr/nTv8F37luzz9bsC85eg6/8qHoMnT9+Me585NNOWjLNC85eg+THD+Gf2G+7m69B+0zZ3DwX/OH42wEA+9Gpco/Q1505u8v2YZQXcv1uNptoirMadocLbdq0yRu+3+/j2WefxUEHHVQapixOiU984hN429vehvvuuw9nn302lFL4wQ9+gG9/+9v48pe/PFAcXuj9hA0bNmgA+v7773ee//f//t/1scce633n6quv1gDCX/gLf+Ev/IW/8Bf+wl/4C3/z4O/JJ598IajFXsP09LResWLFbpd3ZGSk8Ozqq68upLM7XOiYY47Rn/rUp5xnP/jBDzQAvXHjRq211vV6Xf/v//2/nTD/+I//qJvNZmW5161bZz//27/9m77wwgv1aaedpl/60pfqCy+8UD/00EOV78+G/WbpXLJkCeI4LrDuzZs3F9g54corr8Tll19uv6dpiscffxynnnoqnnzyyXnlLx6we9i+fTsOPfTQ0N4HCEJ7H1gI7X3gIbT5gYXQ3gcWtNbYsWMHVq5cub+zMie0Wi2sX78eXToHYI7QWkPeaymtnMDucaEVK1Z4w9dqNSxevLgyTFmchNNOOw0vfelL8a53vQv/5b/8F/zjP/5jZfi5Yr+RzkajgdNPPx333HMP/uiP/sg+v+eee3D++ed73/GZpqPsAJOxsbEwgR1ACO19YCG094GF0N4HHkKbH1gI7X3gYHx8fH9nYbfQarXQarVmD7gH2B0utGrVKvzzP/+z8+zuu+/GGWecgXp2KOmqVatwzz33OPs67777bpx11lmV+fnhD3+Im2++GR/96EdxxRVX4C1veQsuvfRSnHPOObtbRAfR7EH2HS6//HL8/d//PW6++WY8/PDDuOyyy/DEE09g9erV+zNbAQEBAQEBAQEBAQEB+xSzcaErr7wSF1+cnymyevVqPP7447j88svx8MMP4+abb8ZNN92ED30oPwPlAx/4AO6++2589rOfxS9/+Ut89rOfxbe+9S188IMfrMzLqlWr8Hd/93fYtGkTbrzxRjz55JN47Wtfi6OOOgpr1qzBU089tUdl3W+WTsAc6fvcc8/hk5/8JDZu3IiTTjoJd911Fw477LD9ma2AgICAgICAgICAgIB9itm40MaNG507O4844gjcdddduOyyy3D99ddj5cqVuPbaa/GWt7zFhjnrrLNw22234c///M/x3/7bf8NRRx2F22+/HS9/+csHylO73cY73vEOvOMd78Cjjz6KW265BV/4whfw8Y9/HK973etw11137VZZ9+s9nXsDnU4Hn/70p3HllVd6/aUDFhZCex9YCO19YCG094GH0OYHFkJ7BwTMP+zcuRNf+tKXcNVVV2Hr1q1I6I7dOWLek86AgICAgICAgICAgICAvYfvfe97uPnmm3HHHXcgjmO89a1vxTvf+U684hWv2K34AukMCAgICAgICAgICAg4wPHkk0/i1ltvxa233or169fjrLPOwjvf+U689a1vxfDw8B7FvV/3dAYEBAQEBAQEBAQEBATsX7zuda/Dvffei6VLl+Liiy/GpZdeimOPPXavxR9IZ0BAQEBAQEBAQEBAwAGMdruNO+64A2984xsRx/Fej3+vXplyww034IgjjkCr1cLpp5+O73//+87vO3fuxHvf+14ccsghaLfbOP7443HjjTdWxvnYY4/hne98J4444gi0220cddRRuPrqqwsXtn7gAx/A6aefjmaziVNPPXWg/HY6Hbzvfe/DkiVLMDw8jDe96U2F44C3bNmCiy66COPj4xgfH8dFF12ErVu3DhT/gYCF2OZr1qzBWWedhaGhIUxMTAwU74GChdbeg6Z9oGKhtTcAvOlNb8KLXvQitFotHHTQQbjooovw9NNPDxT/QsdCbG8e9tRTT4VSCj/5yU8Giv9AwEJs88MPPxxKKefvox/96EDxBwQcyPj617+O888/f58QTgCA3ku47bbbdL1e13/3d3+nf/GLX+gPfOADenh4WD/++OM2zLve9S591FFH6XvvvVevX79ef+ELX9BxHOuvfe1rpfF+4xvf0Jdccon+l3/5F/3oo4/qO++8Uy9btkxfccUVTrj3ve99+m/+5m/0RRddpE855ZSB8rx69Wp98MEH63vuuUc/9NBD+pxzztGnnHKK7vf7Nsx5552nTzrpJH3//ffr+++/X5900kn6jW9849wqZ4Fiobb5X/zFX+hrrrlGX3755Xp8fHxOdbKQsRDbe9C0D0QsxPbWWutrrrlGr127Vj/22GP6hz/8oV61apVetWrV3CpnAWKhtjfh/e9/v37DG96gAeh169YNFP9Cx0Jt88MOO0x/8pOf1Bs3brR/O3bsmFvlBAQE7HXsNdJ55pln6tWrVzvPjjvuOP3Rj37Ufj/xxBP1Jz/5SSfMaaedpv/8z/98Tml97nOf00cccYT3t6uvvnqgyWvr1q26Xq/r2267zT7bsGGDjqJIf/Ob39Raa/2LX/xCA9APPPCADbN27VoNQP/yl7+cU54XIhZim3PccsstgXQyLPT2HiTtAwkHSnvfeeedWimlu93unPK80LCQ2/uuu+7Sxx13nP73f//3QDoZFmqbH3bYYfqv/uqv5pS/gICAfY+94l7b7Xbx4IMP4txzz3Wen3vuubj//vvt91e+8pX4+te/jg0bNkBrjXvvvRe/+tWv8PrXv35O6W3btg2Tk5N7lOcHH3wQvV7PyfPKlStx0kkn2TyvXbsW4+PjzmWqr3jFKzA+Pu6U60DEQm3zAD8OpPbeG2nPdxwo7f3888/jS1/6Es466yzU6/U9Sn8+YyG3929/+1v86Z/+Kf7hH/4BQ0NDe5TmQsJCbnMA+OxnP4vFixfj1FNPxZo1a8KWiYCA3wHsFdL57LPPIkkSLF++3Hm+fPlybNq0yX6/9tprccIJJ+CQQw5Bo9HAeeedhxtuuAGvfOUrB07r0UcfxXXXXYfVq1fvUZ43bdqERqOBRYsWleZ506ZNWLZsWeHdZcuWOeU6ELFQ2zzAjwOlvfdW2vMdC729P/KRj2B4eBiLFy/GE088gTvvvHOP0p7vWKjtrbXGJZdcgtWrV+OMM87Yo/QWGhZqmwNmr+htt92Ge++9F+9973vx13/913j3u9+9R2kHBATsOfbqQUJKKee71tp5du211+KBBx7A17/+dTz44IP4/Oc/j3e/+9341re+BQBYvXo1RkZG7J/E008/jfPOOw9/8id/gne96117M+uleZZl8oU5kLEQ2zygHAu5vV+ItOcbFmp7f/jDH8a6detw9913I45jXHzxxdDhyuoF197XXXcdtm/fjiuvvHKfpLUQsNDaHAAuu+wyvOpVr8LJJ5+Md73rXfjbv/1b3HTTTXjuuef2SfoBAQGDYa9cmbJkyRLEcVzQJm/evNlq0aanp3HVVVfhq1/9Kv7wD/8QAHDyySfjJz/5Cf7yL/8Sr33ta/HJT34SH/rQh7xpPP300zjnnHOwatUq/M//+T/3OM8rVqxAt9vFli1bHK3Z5s2bcdZZZ9kwv/3tbwvvPvPMMwXt4IGGhdrmAX4s9Pbe22nPdyz09l6yZAmWLFmCF7/4xTj++ONx6KGH4oEHHsCqVav2OB/zEQu1vb/zne/ggQceQLPZdN4944wzcOGFF+J//a//tcf5mK9YqG3uwyte8QoAwG9+8xssXrx4j/MREBCwe9grls5Go4HTTz8d99xzj/P8nnvusRNBr9dDr9dDFLlJxnGMNE0BGLfVo48+2v4RNmzYgFe/+tU47bTTcMsttxTi2B2cfvrpqNfrTp43btyIn//85zbPq1atwrZt2/Cv//qvNsyPfvQjbNu27YAnKQu1zQP8WMjtvS/Snu9YyO0tQRbOTqezx3mYr1io7X3ttdfipz/9KX7yk5/gJz/5Ce666y4AwO233441a9bscR7mMxZqm/uwbt06AMBBBx20x3kICAjYA+ytE4no6O2bbrpJ/+IXv9Af/OAH9fDwsH7sscdsmFe96lX6xBNP1Pfee6/+j//4D33LLbfoVqulb7jhhtJ4N2zYoI8++mj9mte8Rj/11FPOEdgcv/71r/W6dev0n/3Zn+kXv/jFet26dXrdunW60+mUxr169Wp9yCGH6G9961v6oYce0q95zWu8V6acfPLJeu3atXrt2rX6JS95SbgyJcNCbfPHH39cr1u3Tn/iE5/QIyMjNt4D/cj1hdjeg6Z9IGIhtvePfvQjfd111+l169bpxx57TH/nO9/Rr3zlK/VRRx2lZ2Zm9rDG5jcWYntLrF+/Ppxey7AQ2/z+++/X11xzjV63bp3+j//4D3377bfrlStX6je96U17WFsBAQF7ir1GOrXW+vrrr9eHHXaYbjQa+rTTTtPf+973nN83btyoL7nkEr1y5UrdarX0scceqz//+c/rNE1L47zllls0AO8fx6te9SpvmPXr15fGPT09rd/73vfqyclJ3W639Rvf+Eb9xBNPOGGee+45feGFF+rR0VE9OjqqL7zwQr1ly5Y5181CxUJs83e84x3eeO+99945189Cw0Jr70HTPlCx0Nr7Zz/7mT7nnHP05OSkbjab+vDDD9erV6/WTz311O5V0ALDQmtviUA6i1hobf7ggw/ql7/85Xp8fNzm9+qrr9a7du3avQoKCAjYa1Bah9MTAgICAgICAgICAgICAvYNwualgICAgICAgICAgICAgH2GQDoDAgICAgICAgICAgIC9hkC6QwICAgICAgICAgICAjYZwikMyAgICAgICAgICAgIGCfIZDOgICAgICAgICAgICAgH2GQDoDAgICAgICAgICAgIC9hkC6QwICAgICAgICAgICAjYZwikMyAgICAgICAgICAgIGCfIZDOgICAgIACPv7xj+PUU099wdP97ne/C6UUlFJ485vf/IKnTzj88MNtPrZu3brf8hEQEBAQELAQEEhnQEBAwAEGIlNlf5dccgk+9KEP4dvf/vZ+y+MjjzyCW2+91X5/9atfjQ9+8IOFcF/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+======= "image/png": 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", +>>>>>>> origin/main "text/plain": [ "
" ] @@ -2573,7 +3084,11 @@ "cbar = fig.colorbar(pcmeshATB)\n", "cbar.set_label(f'Attenuated Backscatter {wv}nm {t} {tel}')\n", "ax[0].set_ylabel('Height [km]')\n", +<<<<<<< HEAD + "ax[0].set_ylim(0, 5)\n", +======= "ax[0].set_ylim(0, 15)\n", +>>>>>>> origin/main "\n", "pcmeshVD = ax[1].pcolormesh(\n", " data_cube.retrievals_highres['time64'],\n", @@ -2586,7 +3101,79 @@ "cbar.set_label(f'Volume Depolarization Ratio {wv}nm {t} {tel}')\n", "ax[1].set_xlabel('Time [UTC]')\n", "ax[1].set_ylabel('Height [km]')\n", +<<<<<<< HEAD + "ax[1].set_ylim(0, 5)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "118e3241", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmeshATB = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'attBsc_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=1.5e-5\n", + ")\n", + "cbar = fig.colorbar(pcmeshATB)\n", + "cbar.set_label(f'Attenuated Backscatter {wv}nm {t} {tel}')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_ylim(0, 5)\n", + "ax.set_xlabel('Time [UTC]')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "0ed6880d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "pcmeshVD = ax.pcolormesh(\n", + " data_cube.retrievals_highres['time64'],\n", + " data_cube.retrievals_highres['height']/1000,\n", + " data_cube.retrievals_highres[f'voldepol_{wv}_{t}_{tel}'].T,\n", + " shading='nearest',\n", + " vmin=0, vmax=0.3\n", + ")\n", + "cbar = fig.colorbar(pcmeshVD)\n", + "cbar.set_label(f'Volume Depolarization Ratio {wv}nm {t} {tel}')\n", + "ax.set_xlabel('Time [UTC]')\n", + "ax.set_ylabel('Height [km]')\n", + "ax.set_ylim(0, 5)\n", +======= "ax[1].set_ylim(0, 15)\n", +>>>>>>> origin/main "fig.tight_layout()" ] },