From af9e6dd4f1f044e9316736034bb5b9d0061622a8 Mon Sep 17 00:00:00 2001 From: jkingslake <48723181+jkingslake@users.noreply.github.com> Date: Thu, 14 Nov 2024 15:19:08 +0000 Subject: [PATCH 1/3] delete unused NBs --- src/sections/radar/apres/apres-intro.md | 15 +- .../radar/apres/apres-range-frequency.ipynb | 76 -- src/sections/radar/apres/coarse-range.ipynb | 661 ------------------ src/sections/radar/apres/phase-frequency.md | 17 - src/sections/radar/apres/stacking.ipynb | 52 -- 5 files changed, 12 insertions(+), 809 deletions(-) delete mode 100644 src/sections/radar/apres/apres-range-frequency.ipynb delete mode 100644 src/sections/radar/apres/coarse-range.ipynb delete mode 100644 src/sections/radar/apres/phase-frequency.md delete mode 100644 src/sections/radar/apres/stacking.ipynb diff --git a/src/sections/radar/apres/apres-intro.md b/src/sections/radar/apres/apres-intro.md index 36687cd..303f681 100644 --- a/src/sections/radar/apres/apres-intro.md +++ b/src/sections/radar/apres/apres-intro.md @@ -1,10 +1,19 @@ (page:apres-intro)= # Autonomous phase-sensitive Radio Echo Sounder -The Autonomous phase-sensitive Radio-Echo Sounder (ApRES) is a frequency-modulated continuous-wave radar. Like all ground-penetrating radar, it emits radio waves and records radio waves which have travelled into the subsurface and been reflected back towards the radar. Reflection typically occurs at discrete locations in the subsurface, which are interpreted as interfaces between materials with different dielectric properties. We refer to these as reflectors. +The Autonomous phase-sensitive Radio-Echo Sounder (ApRES) is a frequency-modulated continuous-wave radar. Like all ground-penetrating radar systems, it emits radio waves in to the subsurface and records what is reflected back towards the radar. Reflection typically occurs at discrete locations in the subsurface, which are interpreted as interfaces between materials with different dielectric properties. We refer to these as reflectors. ApRES is primarily designed to measure changes over time in the separation of sub-surface reflectors. When ApRES is used on an ice sheet, these reflectors can be internal to the ice ('englacial') reflectors or the ice base. Englacial reflections originate from contrasts in the density or chemistry of the ice, while basal reflections originate from the strong contrast between the ice and the underlying sediment, bedrock, or water. -ApRES has most commonly been used to estimate melt rates at the base of ice shelves (the floating extensions of ice sheets), by tracking the separation between the ice-water interface and the englacial reflectors (REF). ApRES, along with it predecessor, pRES, have also been used to measure englacial deformation on grounded ice (REF) and constrain ice crystal fabric (REF). +ApRES has most commonly been used to estimate melt rates at the base of ice shelves (the floating extensions of ice sheets), by tracking the separation between the ice-water interface and the englacial reflectors {cite:p}`brennan_phase-sensitive_2014, Nicholls_Corr_Stewart_Lok_Brennan_Vaughan_2015, vavnkova2022ocean`. ApRES, along with it predecessor, pRES, have also been used to measure englacial deformation on grounded ice {cite:p}`kingslake2014full`) and constrain ice-crystal fabric {cite:p}`brisbourne2019constraining, young2021rapid`. -In the following pages we describe some the theory behind ApRES. +```{figure} figures/apres_at_JIRP.png +--- +name: fig:apres_on_JIRP +--- +ApRES being deployed on the Juneau Icefield as part of the Juneau Icefield Research Program (JIRP). Photo credit: Elizabeth Case. +``` + + + +The following page describes the theory behind one aspect of the ApRES system: how the so-called 'coarse' range is computed using fourier transform of the signal recorded by ApRES. The following page applies this theory to real data collected in Antarctica. \ No newline at end of file diff --git a/src/sections/radar/apres/apres-range-frequency.ipynb b/src/sections/radar/apres/apres-range-frequency.ipynb deleted file mode 100644 index 3dcd53a..0000000 --- a/src/sections/radar/apres/apres-range-frequency.ipynb +++ /dev/null @@ -1,76 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "208ac01a-26af-4c51-b33b-3284ed3ae494", - "metadata": {}, - "source": [ - "(frequency-and-range)=\n", - "# Frequency and range" - ] - }, - { - "cell_type": "markdown", - "id": "6780cd12-b423-4758-b78a-58654775f710", - "metadata": {}, - "source": [ - "Many of the material on this and following pages can be found in the [ApRES manual](https://github.com/ldeo-glaciology/phase-sensitive-radar-processing/blob/5cce6bd838cb70e290316195af9ceefe3d4a52ee/other%20documents/ApRES%20Manual%20V102.1.pdf), Brennan et al., ????, and Nicholls et al. 2015. \n", - "\n", - "ApRES emits 'chirps', which consist of continuous radio waves lasting 1 second. Due each chirp the frequency of the emmitted radio wave increased linearly with time from $f1$ to $f2$ where the bandwidth $B = f2-f1$. The signal is transmitted downwards into the ice sheet and is partly reflected back to the radar's receiving antenna where it is compared to the transmitted signal to determine the travel time of the signal and hence the range to sub-surface reflectors. Specifically, at every moment during a chirp the radar measures the difference between the frequency of the reveived signal and signal being tranmistted in that instant. Because the transmitted signal is always increasing in frequency and because the reveived signal was tranmitted a few milliseconds earlier than it is received, the recieved signal is always lower frequency than the transmitted signal. \n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "94be9e38-af6d-4a73-8f14-e3724a9d0365", - "metadata": {}, - "source": [ - "## TO do: add cartoon of chirp and delay in received signal" - ] - }, - { - "cell_type": "markdown", - "id": "5d099010-a5e2-4b33-851e-79dd8bb430c9", - "metadata": {}, - "source": [ - "## To do: derive Equation 1 from Brennan et al. " - ] - }, - { - "cell_type": "markdown", - "id": "14ab8fb3-07b7-496c-8fdb-c81fa032e669", - "metadata": {}, - "source": [ - "This expression describes how the frequency difference between the transmitted signal and the received signal relates to the range to a reflector detected by ApRES. What we havent discussed is how this frequency difference is calculated. Next, we discuss how combining and summing the received and transmitted signals, then filtering the result, allows this to be computed using the concept of *beat frequency*" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "vscode": { - "interpreter": { - "hash": "d30c63c32c419ea9f0bba7809c4e773ec43b1ff3bce90afeee7dfc4932bbd9b5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/sections/radar/apres/coarse-range.ipynb b/src/sections/radar/apres/coarse-range.ipynb deleted file mode 100644 index b8fa9c7..0000000 --- a/src/sections/radar/apres/coarse-range.ipynb +++ /dev/null @@ -1,661 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1999246-c910-4ad8-9028-217aae0b7be9", - "metadata": { - "tags": [] - }, - "source": [ - "# Discrete fourier transforms and phase" - ] - }, - { - "cell_type": "markdown", - "id": "9e7908f2-df90-4e87-bdfc-f7c0b198fa25", - "metadata": {}, - "source": [ - "(under construction)" - ] - }, - { - "cell_type": "markdown", - "id": "e7290dbc-7320-45f8-80a2-1a377b0ef772", - "metadata": {}, - "source": [ - "This page demonstrates the use of a fourier transform to compute the range to reflectors in ApRES data. \n", - "\n", - "The idealized sinusoidal signals used in these demonstrations are intended to be simplified version of the signals recorded by ApRES. Specifically, they represent the signal obtained when the received signal is differenced with the transmitted signal. In general this results in a signal with a wide range of different frequencies. However, we will construct our synthetic signal with only two frequencies." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "022c1a95-0a47-4289-b10a-aae791229a47", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.signal import argrelextrema" - ] - }, - { - "cell_type": "markdown", - "id": "3f775706-020b-4c97-8973-39c08bffd3a0", - "metadata": {}, - "source": [ - "## 1. Define some reflectors\n", - "Suppose that there are just two reflectors beneath you when you deploy ApRES. In reality there are hundreds, but we will assume there are just two.\n", - "\n", - "Let's use $R$ as the range, and define the range to the two reflectors as" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5311190e-d5ae-4903-9026-985c27fd2dd5", - "metadata": {}, - "outputs": [], - "source": [ - "R1 = 50.0 # units [m]\n", - "R2 = 120.0 # units [m]" - ] - }, - { - "cell_type": "markdown", - "id": "828d016b-2d92-47e0-8d3f-9642380ce49b", - "metadata": {}, - "source": [ - "## 2. Compute the two-way travel time to the reflectors\n", - "The two-way radio-wave travel time in ice $\\tau$ is given by\n", - "\n", - "\n", - "```{math}\n", - ":label: eq:tau1\n", - "\\tau = 2R\\frac{\\sqrt\\epsilon}{c},\n", - "```\n", - "\n", - "where $c$ is the speed of light in a vacuum and $\\epsilon$ is the dielactric constant of ice.\n", - "\n", - "Next, let's use constants from {cite}`brennan_phase-sensitive_2014`, to compute $\\tau$ for our two reflectors:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e35a69dc-17cf-42c0-ba5c-f353be141887", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tau1 is 5.873e-07 s.\n", - "tau2 is 1.410e-06 s.\n" - ] - } - ], - "source": [ - "ep = 3.1\n", - "c = 299792458\n", - "tau1 = 2*R1*np.sqrt(ep)/c\n", - "tau2 = 2*R2*np.sqrt(ep)/c\n", - "print(f'tau1 is {tau1:.3e} s.')\n", - "print(f'tau2 is {tau2:.3e} s.')" - ] - }, - { - "cell_type": "markdown", - "id": "c729fbdb-0082-4290-8b79-7cb3712d09c1", - "metadata": {}, - "source": [ - "## 3. Compute the beat frequencies we would receive from these reflectors\n", - "Equation 1 from Brennan et al. relates the difference in frequency between the transmitted and received signals (the beat frequency; $f_d$) to $\\tau$ using some configuration parameters related to how the frequency changes during each chirp:\n", - "\n", - "```{math}\n", - ":label: eq:f_d1\n", - "f_d = K\\tau = \\frac{2\\pi B \\tau}{T}, \n", - "```\n", - "\n", - "where $K$ is the rate of frequency increase in Hz s$^{-1}$, $B$ is the bandwidth (the difference between the lowest and highest frequency covered by each chirp), and $T$ is the chirp duration. \n", - "\n", - "Again using parameters from Brennan et al., let's compute the beat frequencies we expect to receive from our two reflectors:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dd8d55a5-8d77-4833-97d7-088c24b30f28", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "f1 = 117.46 Hz.\n", - "f2 = 281.90 Hz.\n" - ] - } - ], - "source": [ - "B = 200e6 # [Hz]\n", - "T = 1 # [s]\n", - "K = B/T # [Hz/s^2]\n", - "\n", - "f1 = K*tau1 # [Hz]\n", - "f2 = K*tau2 # [Hz]\n", - "\n", - "print(f'f1 = {f1:.2f} Hz.')\n", - "print(f'f2 = {f2:.2f} Hz.')" - ] - }, - { - "cell_type": "markdown", - "id": "d8c59352-e1d5-4ae7-bd79-6c900ef0c504", - "metadata": {}, - "source": [ - "## 4. Construct our signal\n", - "The signal ApRES recieves is the sum of two sinusoids. One has a frequency of `f1` and the other `f2`. \n", - "\n", - "Before computing the received signal, we need to define a sampling frequncy, which is controlled by the ApRES system (Brennan used 12 kHz)," - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "adb84c65-6c1c-4727-844d-d745fe2302fd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "sampling_frequency = 12e3 # [Hz]" - ] - }, - { - "cell_type": "markdown", - "id": "fde69399-7158-4102-9960-5887dd3196ad", - "metadata": {}, - "source": [ - "and define a sampling vector," - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1afaf96b-e463-49b9-bdfc-5359c118ce8b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "samplingInterval = 1 / sampling_frequency; # [s]\n", - "t = np.arange(0,T,samplingInterval) # [s]" - ] - }, - { - "cell_type": "markdown", - "id": "8e20fc20-9e11-4cf4-8a91-79fe4dae7681", - "metadata": {}, - "source": [ - "Now we can define the signal ApRES receives" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b020b1d9-5980-4ff3-8ad3-8d42fbfaee32", - "metadata": {}, - "outputs": [], - "source": [ - "s = np.exp(1j*2*np.pi*f1*t) + np.exp(1j*(2*np.pi*f2*t)) " - ] - }, - { - "cell_type": "markdown", - "id": "e3801fa7-21c4-44fb-a970-855b10adbf4c", - "metadata": {}, - "source": [ - "Plot the signal to convince ourselves that this is an oscillatory signal.\n", - " \n", - "The code below plots a small section of the real and imaginary parts of `s` on the left and both components against each other in the complex plane (on an Argand diagram) on the right." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "00c1945b-b1e9-4747-939d-759c7b250a3a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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08ek/gU//AZz/ENA0s9Ct0R2XP4TT73oXPS4/bv/qbFx89IRCN4lR5BBCKgH8F8BVHMe5khdLvCVlWkPcF5kwgR1zjIOTSJTD61t78f1HPit0U2RZu3cIa/cOZfz+V352PGa2VDERm8HIAboJDXrc2AGA47i/AfgbAJSNmcat2+fAUZPqJD/zppe24Muu5I/KjkPHVuPGr8xKu87EiROxcOFC/OpXv8KaNWtw+OGHAwBGRkawc+dOnHDCCbj33nvx3HPPAQAOHDiAnTt3MqGBwWDoS+8WYNUvAGICnv428KOPgVHWWVr1RTd6XLQqzv9bvQ0XLGhlM1YMWQghZtC+yGMcxz0rsUoHgPGi560AupJXEvdFFixYUITxlQxGdkSiHB76YC9uXbW10E0pKGfe857iOs/9aBHmjbczMYLB0IguQoNeN/aU7QJYvblHVmgoFDabDQD1aLjmmmvw/e9/P2H522+/jddffx0fffQRKioqsHjxYlY+ksFg6M+W56jIsPha4K1bgb4vgeb0QqnReO7zTkxptOGXp8/Ajx77HJ/vHy66ewKjOCB0FPBPAFs5jrtLZrUXAfyEEPIEgKMBOJXSOBkMo+IPRTDz+tU52/5/f7gIR0yszdn29SAa5fDfzzvw62e+yHgbX/3Lh7LLfnHadPz05KlMhGAwJMhaaMjljb3SWoo3t/Xihq8ke0tSlCIPcs0ZZ5yB66+/Ht/85jdRWVmJzs5OmM1mOJ1O1NbWoqKiAtu2bcPHH39c0HYycoh3CPAOAvVTR91MMsMAbH0JmHgsMP9bwFu30eejSGjwhyL4fL8Dlx0/GcdNa0CJieDt7X1MaGDIcSyASwBsIoRs4F+7FsAEAOA47n4ALwNYCmAXAC+A7+S/mQyGvrj8IcxZsUaXbb161QmY0VKly7b0guM4bO504YYXN2P9/uFCNyeBu17bgbte25Hy+qWL2nDjVw5lAgTjoEaPiIac3dgrLKVoH/TC6QuhptysQ1P15fTTT8fWrVtxzDHHAAAqKyvx6KOPYsmSJbj//vsxZ84czJgxAwsXLixwSxk5IRoF/n020LMJOOk64MRfF7pFjIMJ7xDQvw2YeyFQ1Qy0zAb2jy5Rc0uXC+Eoh8Mn2FFtNWNOaw3W7XMUulmMIoXjuPchnaopXocD8OP8tIjB0J/Vm7vxg0c/z/j96647FQ2VZTq2KDM4jsPOvhGc/sd3C92UnPDwh+14+MN22eVHttXiwUuPRJW1+MY3DIZe6FF1Imc39nJzCQIAtnQ5sWhKQ4Yt1Je2tjZs3rw59vxnP/sZfvazn6Ws98orr0i+v729PVdNY+SbL5+jIgMAfHgfsPAHQFlxzQIwRjHdG+n/MfP4/3OBbasAjhs10TUbDwwDAOaNtwMADh1TjRc3doHjODZLxGAwRj2f7XPgvL/Kh+3LsfJrs3HhUYU3Md3W48KSu5U9EPSkuboMPzl5Gk4/tBlNVWVZ3StCkSg2dTrx6Mf78OznnTq2Evi03YHZClEoG288vSgnWhkMtehadUJvyi280NDpKhqhgXHwMOQJorKsFJZSGeO5rS8BVWOBrz8EPHgGsP0VYM4F+W1kHnD7Q/AGI2iutha6KQwxMaFhbvz/+kcAZwdgHy//PgOxqdOJ5uqy2LF3yJhqPPbJfnQO+9BaW1Hg1jEYDIZ+RKMcJl/7sqb3VJaVYvNNZ+SoReqIRDlc8MBH+EzHaLM/Xzwfy+aM0W17mWIuMWH+hFrMn1CLuy6Yl9E2hjxBzL/ltYzeO/cmaSHia4ePwx8umMsEd0bRU9RCQ6mJoKXaiq3d+laWYDCU+ONrO3DPGzuxZFYL7r/kiNQVOA7Y+x4w9VSg9UigrBrY9+GoExqC4Sgu+vvH2Nbtxh8umItz5o0rdJMYAt0bgZoJQAXvVzD28Pjro0Ro2NM/gmlN8SihQ8ZUAwC2druZ0MBgMAxNIBzBjOvUGzV+9fBx+OM35uWuQSpY9UU3fvyfzNM2AODz609Dnc2iU4uKnzqbBe0rlyW8FolymKJRVBLz7PpOPLs+NcLi7m/Mw7mHs34ao3goaqEBACY12LB30FPoZjAOIhyeIB54dzcAYPWWHqxrH8KCtiTzuf5tgHcAmHQ8YCoBJiykQsMo48lP92NzJxX67nptB86eO5Yp6MXCwA6gaWb8eeOM+OujAI7jsKffg6/Oj3eaBIOynX1unHZoc6GaxmAwGJrRGrGw946lBb3f/unNnfj9mszuJ7tvX4oSE+sryFFiIiniQ6ZpMmKuenIDrnpyQ8JrvzxtOn7CqmIwCkTRCw1tDTa8uqWn0M1gHES89EUX/KEonv/xsfjGAx/h5U09qUKDELbeeiT9P2EhsHMN4HMA5cVd6kkLr23tw+RGG3568lT8/MmN+GTvEBZOri90sxgcBwztAdqOj79WVgXYmujro4CBkSDcgTAmN9hir1WWlaKh0oL9g94CtozBYDDU8Yc123Hfm7sU1ztrzhj86eL5eWiRNMPeIObdrD28v9BiyGjiiIm1CeKD1ogXOf7w2g78Iakqxlu/WoxJonsrg5Eril9oqK/AkCdYtJUnDlq61gP2ifGw7VHEx3sGMc5ejnnj7VjQVosPdw+krtT3JVBiAeom0+fNh9H//dup6DAK8AbD+HjPIC5ZOBGnH9oCE9mID3cNMKGhGHB3AyEvUD8l8fW6ycDQ3sK0SWf29I8AACY1Via8PqGuAvuY0MBgMIqQUCSKab+VNgMXc/y0BjzyvaNz1g6O49Dj8uPv7+7Fgx/k7p4w6ZrMwv8bq8rwj28twGHjaljkgwxlpSUJwoMvGMEhNygLD3/95nx0Dvtw66qtsuuc9Pu3E56fO28s7r7w8IzbymDIUfxCA6+4tQ94MJd3HmcUmHd/D7x5C3W7/94aoLTwZZL0guM4rN07hBOmNQIAFk1pwO9e3Y6BkUBiOai+bUD9NKCEF78a+RD2vq2jRmjYeMCJYDiK46Y2wFZWipkt1fi8yOpXH7QM0tSemNAlUD8F2P1W/tuTA9r5lLnJSbMuE+ttWLt3qBBNYjAYjBS+7HJh6b3KlRWSQ+Uzoc/lx2l/fBdOXyjrbRWSfncA5/z5A9Xrv/6LEzG1qVJ5xVFMuSVReHh1Sw++/8hnKev98LG4h0ZTVRnW/vZUAMDl/16H177sldz28xu68PyGrtjzxTMa8fB3jtKr6YyDmKIXGoTQnvbB4hEaFi1ahA8/zG0+fj4+IyOCXuCDe+jj7g3ApqeBw/+voE3Sk32DXgyMBHHkJBqpMX8CTYP4ssuFE6Y3xlfs3xpPmwCAmvGA2Ua9G0YJggnrrLHUgO+IibV49vMORKIcm4EoNEJ6REpEwyRgw2P0PLUY2yyx0+GDiQBjahKrnUyoq8DzGzoRCEdQVlpSoNYxGIyDmZc2duGnj69Pu86qK4/DrLE1mrbrDYZx6h/eQZfTn03zNHPzObNw+qEtaK7OvBykLxjB5/sdeOyTfXh5k74pz6fe9U7a5VtvXoJyy8F1PzhjVkuC8NC2fFXKOn3uQMLraiMk3t7en/C+J65YyKJZGRlR9ELDOHs5AKBrOL8X3XTkQwDQ6zPC4TBKS3X8mbf9Dwi4gG//D3ju+7Sk4ygSGrb1uAEAh/Lu9tObqYK+o9cdFxqCXmB4P3D4JfE3mkzUjK9PPlTNaGztdqHeZkFjFY3kmDfejkc+3oe9AyOYKqoEwCgAw/sAUkIFLjG1k/jl+xONIg1Ix7APLdVWlJYklpedWF8BjgM6HD5MaTy4Z7gYDEb++HjPIC7828dp11HrWfDfzzrwy6c36tU0XLfsEBzZVqcqSmD1VcdjZku1bp8tUG4pwbFTG3Ds1MzL0a/dO4QLHvhI8/vkBsyvXnVCzER4tCMWEa749zqskYheEMQDIdJB/B6O42RTYZKP+z23L4WJTTgxVFD0QoOtrBTV1lJ0O32FbkqMyspKjIyM4O2338aNN96I5uZmbNiwAV/72tcwe/Zs3HPPPfD5fHj++ecxZcoUvPTSS7j11lsRDAZRX1+Pxx57DM3Nzejv78fFF1+MwcFBHHnkkVi9ejU+++wzNDQ0JHzGihUr0NDQgM2bN+OII47Ao48+CkIIbr75Zrz00kvw+XxYtGgRHnjgARBCsHjxYixatAgffPABTj75ZDz88MPYsWMHzGYzXC4X5syZg507d8JszsDzYu87QHkd0HYcMP0MYOOTQDgwatIndvRSoWEaLzDUV5ahobIM23kBAgDgPED/C4M6gYZpo6ryxLYeNw4ZUx3rNAn7ZFcfExoKjrMDqB5HK56IqWml/10dhhcauoZ9GFdbnvK6ID53D/uZ0MBgMHJKn9uPo257I+066VIiepx+LLwj/fuV+NslR+D0WS2Sy658fD1e3NiVNh//suMm4bqzDs2qDfniqEl1iikmu/rcOPWud1Vt74y7U9crNRHsvO3MUW1i+bdvLYg9fnrdAfz6mS8SlosjHW7/6mxcfPQEEJJYCSMYjmL6ddJ+I+LqKe/8ejEm1jNjSYY0RS80AMBYezm6hotHaBCzceNGbN26FXV1dZg8eTIuu+wyrF27Fvfccw/uu+8+3H333TjuuOPw8ccfgxCCf/zjH7jzzjvxhz/8ATfddBNOPvlkXHPNNVi9ejX+9re/SX7G+vXrsWXLFowdOxbHHnssPvjgAxx33HH4yU9+ghtuuAEAcMkll+B///sfvvKVrwAAhoeH8c47NNSsvb0dq1atwrnnnosnnngC5513XmYiAwAcWAuMPxogBJh0IrDuQWqMOHZ0mMjs6HVjfF05KizxU2NGSyV29I3EV3Lso//tExLfXNtGU0nCQaDU2DWio1EOO3rd+L+FE2OvCYO6XeJ9wSgMzk6gRqJWdvXY+HKD0znsi6UuiRlTw0e5FZH4zGAwRg/pZnYFpAbD2YgKWqoAnPHHd7G91512ndFcDWJqU5WsGLG504mz7ns/7fvD0dTfN9fmnIXk6wvG4+sLaPTjvz5sx40vbklYfu1zm3Dtc5sAABtvOB01FXR8YCk1JeznRz5qx/UvJL4XAE783dvxbS2diStOmJKyDuPgxRBCw5gaq3TqxCvLgZ5N+n5Yy2zgzJWqVz/yyCMxZswYAMCUKVNw+umnAwBmz56Nt96ipmwdHR34xje+ge7ubgSDQUyaRGfC33//fTz33HMAgCVLlqC2Vros4lFHHYXWVjpTOW/ePLS3t+O4447DW2+9hTvvvBNerxdDQ0OYNWtWTGj4xje+EXv/ZZddhjvvvBPnnnsuHnroIfz973/XskfieIeAgR3A3Ivo85bZ9H/PplElNExPmq2f1lSFp9cdAMdx9MY9zAsNtRMT32yfCHBRGvGQnDtvMPrcAQTCUbTVx/P8bWWlGFtjZUJDMeA8AIyXMGqqGgOAAC5jCw2RKIcepx9j7akRDc01NHqqJ885zAwGY3Rz0d8+xkd7BmWXJ4eZz16xBiOBsKbP+Pz601Bn0z4R8aunN+KZzzpkl5eYCHbfvlTzdkcbh42rkRQh1mzpwRUSxokC7+0cSPE4GI2+D99e1IZvL2oDQH0vkvtzc29eE3ucvB8vOaYNlxxD3ysnxt3+8jbc/jL1KvvWMRNx8zmH6dh6hhExhNAw1l6ODQeGC90MScrK4ikDJpMp9txkMiEcpjegn/70p/jFL36Bs88+O5YKAdATVetnlJSUIBwOw+/340c/+hHWrVuH8ePHY8WKFfD74x1vmy2ujB977LFob2/HO++8g0gkgsMOy/DE795A/487gv6vnQRYKoGezZltr8iIRjm0D3ixeEZTwuvj6yrgCUbg8IZoB2F4H1BqBSqbEzcgCA/D+wwvNOzjHf8nJIXDTW2uwq5+JjQUlGgUcHXR1IlkSsxAVYvhIxr63QGEIpyk0FBWWoKGSgu6mdDAYDCyxB+KYOb18iUDhVx0fygiabaXjmyrTHy2bwjn/VXer+DCI8dj5XlzsvqMg4XTk4wTAeDAkBfH3ylfpSnZ9+GLFaej2pphNHAR8vovTow9ljq2hdfeu/okjK9LNJdOTrOQev+/P9qHf39EJ+buPH8OLlgwPmUdxujHMEKDwxuCLxhJXKAh8qCQOJ1OjBtHBwX/+te/Yq8fd9xxeOqpp/Cb3/wGa9asgcPhUL1NQVRoaGjAyMgInnnmGZx//vmy63/rW9/CRRddhOuvvz7DbwGgfzv933QI/W8yAc2HAT1fyL/HQPS5AwhGopiQdEFt5fPEOxxeKjQ49lETvuSwxNo2+l9IrTAw+4e8AJCyLyY32PD5Pkc8uoORfzx9QDQU92NIpnoc9WgwML0uen1rqbZKLm+psaKHpU4wGIwMOftP7+OLDqfksvsuOhxnHtaCqb99JSEXPR07bj0TllKT8ooKhCNRTP2tdF48AJwxqxkPXLJAdjlDPePrKlLEh2X3voctXS7J9eesWJPwfDSlpwj7wekNJUQ1AEgQY+TEM/HrR972OvrdgYTlVz/zBa7mfSLWXnsKmmTu7YzRhyGEBqGz2eMy5gzWihUr8PWvfx3jxo3DwoULsXfvXgDAjTfeiIsuughPPvkkTjzxRIwZMwZVVepM9ux2Oy6//HLMnj0bbW1tOPLII9Ou/81vfhPXXXcdLrroosy/SP92oLwWsInKPDbNBL58MfNtFhFyg+vxtfT5gSEf5rTaqRGfXUKZrRoDmMzx1AoDs3/ICxOJG+8JtNaWYyQQhtMXgr3C2D4UhkWIVpATGmrGAb2peZRGQuikNFVJm8y2VJejw+HNZ5MYDIbBUfJeWDCxFuv2ORTLVk5ptOGNXy7WtW0zr38F/lBUdnm20REMday68viE5499sg+/fU46ald8LD3yvaNw/LRGyfWMRE2FOXaspYtySCeyfPrbU1PWF3PU7XEfk9Ek1jCkMYTQIJTXGxgJIMFjnOOAaJiGC+eRkREaOr548WIsXrw49vrbb78deyxeds455+Ccc85J2U5NTQ1effVVlJaW4qOPPsJbb70VS5OQ+4w//elPsce33norbr311pTtitsh8P777+P888+H3W5X+S0l6N8ONM5MnMmvnQT4hgC/E7BqqxddbMgKDXV0sH1AGNiM9MajOsSYSqgZn6srp+3MB/uHvBhrL0+ZoWkViS5MaCgQI3x98uTUHYGqMcBu+XBQI9A/QoWGBhmhYUyNFZ+2D6UuCHqo2GdwM1YGg6EfTl8Ic29ao7jeun3SUaXnzhuLuy/U34cqEI5gxnXyaRtMXCgMwXAUH+4ewC+e2oghT1DVey7559qctOXub8zDSTObUFOe/5QN4fiTqrwiiCxi88h02wCkRQdhO9ctOwSXHT856zYzig9DCA0NlbzQ4A6gUmhxNAIM7ATCPjrYLbcXrH2Zsn//flxwwQWIRqOwWCyZmzQq8NOf/hSvvPIKXn5ZXQigLP3bgEO+kvhaHV/icWgvMHZedtsvMMIsfnJeeJXVDHuFGQeGvDQ/fqQ3/SDP3Z2H1uaWTocvJZoBSBRdZrcaW1gyLCN99L/cMWhrBAIuIOQHzMYMTxzgIxoaKqUFg8aqMjh9IQTD0bgYduBT4MEz6H75/jtAZZPkexkMxsHBa1/24vJ/r8vovbkc5D/zWQd+9fRGyWXP/miRZLUdRnZEoxye/uwAfvNfnQ3kc8xVT25Qve7kRhue/eEi3SeBmqqsslEOQprFfy4/GoumNKTdjrCNUCSKaUnpQbeu2hor0cqiHEYXxhAaquhJMzASQJudf9HnoCIDQEPZrTWpOfNFzrRp07B+ffoQPT247777st+Ib5hGLtRPTXy9lhcaHMYXGg4MeTGmJnUWH6ApA53DPnrcRcNphIYWWu7T4HQ7/ThqUl3K6/GIBha2XjAEocEmE6YpDLA9faklWA1C/0gANeVmlJVKO34L4vOgJxArd4k3bgK4CODuAt79PbD0znw1l8FgFBFXPr4eL27UFln48TWnoKUmt8JsOjNJFr2QPX0uf0JYfj45Y1YzXt3Sq7jeqYc04fWtfTlpw55+D+bd/Jrs8oWT6/DI946GuSRzLxE5weHiv38CQF1kgrkkXjbz2uc24T+f7E9YLkQ5/PPbC3DKITJ9bYZhMITQUFdhASFA/0gQsPMvegeA0nKgqhlwtNOQ2bLKNFthZIVcSUdxRIPB6Xb6MNYuYz5XbUWHwxcPW69KIzTsfjNHLcwP0SiHPrcfzRJmPTXlZlRbS+NpJIz84+mjXily6QE2XmgY6Teu0OAOxFLmpBAiHQbcQSo0uLqA9veAU24A+rYCm54CltxB05kYDMZBweE3r4HDG1K9fj4G9+l8Ia45cya+f6KxK1QVgs/3O/C1v3yoy7Zqys34cPnJsJXpOxxavbkHP3g0tZymIDL8+owZ+PFJU1OWqyEa5XDji1vwyMfa/MA+3jOUEkkg8O6vT8KE+grJZVII585dr+3AvW/sjL0uRCZcdtwkXHfWoYrbuf2rs3H7V2cDSBUvvveveDQSE+KMiyGEhtISE+oqLLxBWCm4SAgk5AMqW4CyagCEegQwoSEjVJXZdLTT/0JlBYGyKqCigUY0GJxeVwCzxlZLLmuutuLz/cM0bQJIH9EQcAGBEcMej4OeIEIRDmNkZnfG2svRw0oLFo6R3riYIIUQ6eDJzaxJPhgYCcimTQBAvZBO5+Gdrfe+R/9PPRWwTwQ2PU3L8QqleBkMxqhFS9nJfA1YolFOtmLFztvOzGpW+WCB4zj87IkNmqNTxOhVDUQrSw6Ll9N8eVM3fvTY5wnLf/fqdvzuVVrJbfNNZ6BSg9BhMhHccu5huOXc9KXq5cQOKU74nbSv0+7bl6LEJB8t/ovTpuMXp01PKcX6j/f34h/v78WPFk/B1UtmqmqDsL++/eBavLOjP2GZGhNKRnFiCKEBoKGyAyMBWK2VGOztRj3HgZRV0hkriw0IugvdREPCcRwGBwdhtSqEDAolG+0TU5fVtBreAJHjOPQ4/Th5pvQArrnaiiFPECHnMMxAeo8GgA4GDSo0CCKCXBhpS40Vva6A5DJGHhjpT+8/UMkLDSPGFRr63QHMbrXLLm8U+fYAAPa+C1jtQPNsWt4ToJFFTGhgMEYtp971Dnb1jaRdZ0JdBd69+qQ8tYgaCU6/TnrWmM3Kpmd3/whO+cM7mt/3+fWn0dLjRcrS2WNiv/13HlqLt7YnDqIPu/HV2GM9jxGx2JHM29v7cOlDnypuY0qSWFZbYcbn15+WMtg/YmId2lcuw75BD0783dux1//y9m785e3dePg7R2LxDHW+Sf/67lEAAG8wjENveDVhmRAdtPXmJSi3sIhFI2AcoaHKgoGRAFpbW9Gx4U30hznAWQYQE/UPCLiB/qjhfBqU4LjcfyWr1YrWVplSeQKOdtqRlzLdrB5n+IgGdyAMXygSK6WajPC6Z7CLZu+ki2gAqCFkvTFDIoUysnIRDc1VVnwpU2eaoS+RKJc6mzDSC4ybL/8mIdrB0y+/TpHT7w7ExAQp6vloh0HBEbzrc2D80YDJBNgagIbpQGfu/W8KgcsfQpXOYb4MhpG4c/U2/OXt3WnXyfegXsrgrlBtMQoPfbAXN72k3tPqL9+cj6Wzx+SwRbnnoe8cFXucrnzklzefgQpL7q7zi2c0SR6Xa7b04IpH5KMgHN5QSirQJ9eeEku1nVhvQ/vKZdjTP4KTRaKRIGqsu+7UmMeSEhWWUllPiENuoNVa1l9/GmqLWGRi6CQ0EEIeBHAWgD6O41JieQghiwG8AEAYjT7LcdzNWj6jobIMn+93wGw2Y9KufwFDu4EfU/MRbHkOePFS4Iq3gbH6lyAqFE5vCGfc/S7CUQ7//u5ROFQmrD8vDO9L9WcQqB4L7Hs/v+3RmV5+Fr+pWvoC2MwPugOOLsBSKR+tIEQ0uHt0b2O+6HFSk1U50aW5mkYXhSNRlLLwz5zx2pe9+P4j63D0pHo88r2j4vva0y8vdAG00kRZtWGFBm8wDE8wEjMBlsJWVopycwmNaAgHgYEdwPQl8RVa5gAHPslDa/PLB7sG8O0H10oatTIo+eiPMArDm9t68d2H5atIFGJAn86DgQkMibyzox/fflBdGciDwb9COD4GRwI44tbXE5YJM/m3nDMLlxzTlrc2nT4rNQqi1+XH0WlMNpOXvXzl8Th0bDXaVy5LiZxYwH9PrSkQcoLD4bdQ88sPl5+cUjGOURzoJZc9DOBPAP6dZp33OI47K9MPaKwsw4Cbn73q3wY0z4ovHDOP/u/aMKqEhvve3BmbXf7dq9sSlNC84+qKV5hIpnos9cgwsC+BkAqgFNEQdvWkH+SJIxoMSq8rABOJ58En01RtRZSjs8lShpGM7AmGo7jxhc2IcsBHewbxvy+6ce7h46jpbXBEvuKEgK3RsKkTwnU+XUQDEI9yw9BuWgmmSWQ81TIb2PwM4B0CKkbHoDwa5XD1M18gHOXw4e7BQjenmHkYOe6PMPJLOg+GD5afLFmKOR/ItYsJDJR0PhXJbL91iWyVodFOfWWZ7ED6+he24PoXtgAo3HHVXG1N+ewVL27Bwx+2S66/9N73Ep5/fv1puObZLxKqcgjinNbvJLefFq2kJuxrrz0FTaxfWlToIjRwHPcuIaRNj23J0VBVBl8oAo/HA5tjL3DYefGFtW2ApWpUlBUU4DgOz2/owtLZLTikpRp/eG0HuoZ9hVPsXF3AxEXSy4ScaHc3UDYtf23SEUHQkfMlaOYjHUwjvUB1GqGhrBowVxg6omFgJID6yjJZAyBBXOh1SVemYGTPp+1D6HL6cf//zcfv1+zAfz7ZT4UGQTxIJ3YB1MPBoBEN/SP0XExXdQIA6m1lNHWibw99oemQ+MIxc+j/nk3A5BNz0cy880WnE53DPvzxG3PxwDt7oM1v/OAhH/0RRu5J53UAFHYwzwQGeQ4MeXH8ndLGgmJ23XYmi4iUQDiG/vtZB3759MaEZcJxVwzH2YqzZ2HF2fEJ3+RUCTHzb5Evudm2fBV+u/QQXH5C+pKYyQj7YOk97+HL7ngqr1DeNNepJwz15PNXOIYQshFAF4BfcRy3RcubhZweZ8dW2Lgo0DgjvpAQoGEqMLBT5t3GY2u3GwMjAZw0owlzWu34w2s78P7OAVxw5Pj8NybkA/zD8dn6ZKrH0v+uTqDBmEJDLy80yA2ca8rNKDURWPwD6fPjCaH7ycDmmNTxX36QJ4guzBAyd7yxtQ9lpSacOL0J6/cP46EP2uELRlAeExoUTJVsDUD/jtw3NAf08waPSnmcDZVl6HB4gf7t1KtHfO0RohsGdowaoeG1L3tQYiI4aUYTuob9eFX5LQx5suqPMHLH2r1DuOCBj2SXF3KQ9db2PnxHwkCvGAZ+hURNucnHL1+IY6bU56lFxue8I1px3hGtkt4fxSQ4CExurExoz9ZuF868570074hz28tbcdvLWxUrXEjx8s+OBwAsuftdbOuJFwUQUk8y2SZDX/IlNHwOYCLHcSOEkKUAngcgOSIlhFwB4AoAmDAhXgNeKHXm697KvzA98Y0N04H2D3RuduH4YNcAAOD4aY1ori5DU1UZ3t3ZXxihQUgDqBorvTwmNBh3cN3r8qOm3AyrWTp0jxCCOpsFtuCA8mxy1RhDRzT0u9OXFhRHNDByw7s7+7Fwcj3KLSVYOLkeD7y7B+v3O7AopFZoaALajemb0j9CUyeaFCIaGiot2NgxTI1qq1uBUtH6lc3US2UwvWGckVi7dwhzW2tgr7Bg4eTRkQ5SIFT1R+T6Iozc8JX73semTqfs8kIOqiJRLsV9Hzi4S+0lVxeQopgGwkbFXGKSTRcoRsFB4JAx1QntuuCBj7B271Da9wjnWCbfZ/VVJwBI3UfZbJOhD3kRGjiOc4kev0wI+QshpIHjuAGJdf8G4G8AsGDBAk54XZjdCg3yAaO1bYlvrJ8GfPEkzWG22HT/Dvlmc5cT4+zlsVD+hZPr8Wl7+pM0Z7h4oaFaxu1XGPQYNFQboCUd5fwZBMZURGF1elQIDS1A5+fp1yliBkaCmNIk77VRb7PARIA+JjTkBLc/hN39IzhnLhXwFrTVghDg03YHFtXwOY5qUid8DiASAkrMOW6xvvS7AyAEiuXKGirLMOQJgnPsA0k2qiUEqJsMDO7KYUvzRzTK4csuF84/glYHmpOm9CcjPWr7I3J9EYa+pPNfAIBttyyRnQDIB1LtE8zuDjaU0lkANqDLJUYUHASe+v4xscdSZSvFtC1fhT9fPB/L5mivMGLkfTRayYvQQAhpAdDLcRxHCDkKgAmAJjcrIV+XOPfTMovWpIt8w1T6f3AXMGZu1m0uNNu63ZjZUhV7fti4ary4sQtDnmD+6wUrRTRYKoFSq6GFhl6XX7bihMCkcg/ghPqIhnzUJtUZjuMUSwuWlpjQUFlW2NQJjgPa36P/R0lovMCWLhc4DjistQYAUGU1Y0JdBXb0uoGSfgAEqGhIvxHBLNIzIC8QFin97gDqbRbF/N36SgsiUQ7c0F6QaadJrDAV6BodJS7bBz3wBCOYNZYeE2aW25wxevRHGNmjJDBMbrDhzV8tzk9jJNjU4cRX/pQaFZbLgQrHcdjVN4KL//FJLIXMCLDBW34x+mBaXLYSkL4W/Pg/n+PH/6GP37v6JIyvq9D0Gen20UOXHomTZipEhTJ0Q6/ylo8DWAyggRDSAeBGAGYA4DjufgDnA/ghISQMwAfgQo7jNM0QCINrs7sDsEuEMdbxRiKOdsMLDYFwBLv7R3DqofET4dAxtIO5pcuJ46cpOM7rjZASITdgIYQObDwpASqGodcVwPTmqrTrTLSM0AdVKiIawj5aiaPcrk8D84TLH0YwElXMj2+utqLXXcCIhs//Dbx0JX18+VvpfTMMxqYOGj48e1xN7LVpTVVUaKjuBSrqgRKFS3csyqjPcELDwEgA9TblOtsNlWUoQxAmT6906d36qcCXzwPhQGJahQHZ0kUn4WeNO/hmUbWSj/4II3OUBAag8AMlqTZm26ZgOIorH1+P1VuMm1Yph3h/PfejRTh8Qm0BW3PwkG4w/f0TJ+OaMw+RelvRoSQ6iM1FtXoutK9cllKC9jsPU5+VPbcvhYn5N+QcvapOXKSw/E+g5aYyxlxiQm2FGTZfV2JpS4Ea3rtg+EA2H1MU7OobQTjKYWZLvFM5iw/T+7LLlX+hwd0NmG20ooIctgbDRjREohz6RwKyFScExpTyRjOKpQVFqSQGExoGRugsipLjf3N1GTqHCyQ0RMLAe7+n6VIjfcDbK4FvPlWYtuSArd0utFRbE8Se6c2VeHt7H6Jj+mBS8mcA4sfgiPHOyWFvELU25XSPhsoytBL++yWn0gFA3SSAiwLODqDe2PXYd/aNwESAqWlSmhiUfPRHGNpRIzA884NjsKCtcP4j7+3sxyX/XJvwmhYTw0A4gnk3vQZfKJKL5gEAFs9oxE1nz8LEen1ThDscXty1ZgeeXd+Z1Xa+msYUctOK01FlNVYqnxGQEhweeGcPHnhnDzbfdAYqy4xTfUFOPBEQe6WoFf8IIWhfuSzFtHQy82/IC8Y5+gA02CyoGekG7GemLiyvpYNhZ0f+G6Yze/o9ABI7lbU2C1qqrdguclXNG+5uOkufLg3A1giM9MovL2IGRwKIRDnF2rtNJhrRELDYkXYYbuM7JZ4Bw1XhGODDNevTmEECQFO1Fev3D+ehRRIc+AQY3g98/WFg/8fAuodGjTcLAOzuH8G05sQB5YyWKoSjHALDvSi3KaRNAFT4A2hEg8EY8gQxoyV9dBFAzSBbCR9FJRXlVkP9DEaD0LBv0IOx9vKDts48w7jMuO4VBMJRxfUK3dnXEsUQjXKYc9MajATCWX/uFytOR3URDL6P+3/yJSnl9sNn+4Zw3l/lK4QkM3vFmpTXvnfcJFx/1qGqt8GQp33lMji9Icy9Ob6fD7vx1dgyI9G+chn+90UXfvIf+fRH4Zz9cPnJGGsvV9zm/Am1aF+5TDIC5IPlJ2Ocim0wtGMooWF8eQBWtz8evSCGEMA+HnAaP6Jh/5AXADAhKSdpcqMNewY8+W+QqzteWUIOWyPQa8wKYYLXQLPCLH4dLzQ4uCrIFPqkCBEPXuOlkji81PFfyQekucqKQU8QwXAUltI854vveZuWM5xyMvVr+eR+YO+7wAwJAdJgcByH3f2emOmfwKQGKqJEvENAfavUWxMRhAav8VLPh70h2CuUfWgaKsvQTHiD3CqJ9BCx0GBw2ge9aNN5BpPByCWPfNSO619Q7hO88rPjcciYwqUEufwhzEkaAItzwuUqTqhl521nFq2nSnJIuZhnf7QI8xVSII6YWJd2AHvXmu249830hrz/fH8v/vn+3sTXvr0ApxyikKLKkKSmwoz2lctw+b/X4bUv45N/bctX4Stzx+K+iw4vYOu0cdacsThrzljFaKhFK9+MPVYjqEhFTRzLb8NogowRMJTQMNE8TB/IDXprxtOZToOzf9CLhkoLbEnhTpMabHhpYxc4jstvSSV3FzB+Yfp1hNQJAxogDnrUzeLXwA0fZ8FAoCS90CAY9RnQs8LhDQEAahUGei01VJTpc/vRWqvNpCdr9rwFjDsCsNYAExcBplLgwNpRITT0ugIYCYQxuTFxUDme38clfgeN3lKirJruF2+BKtVkSDTKYdgXQm2F8gxfTbkZY4iDPpESGqrH0f+jQGjYN+jBstnG8tpgHJz0ufw46vY3VK1b6E691ADmL9+cn5ATroZfnT4dPznZONGL0SgXCxtPRs/f5Benz8AvTp+R8vqf39qF3726XfZ93/vXuoTnS2a14P5LjtCtXQcDf//WAgCJx/hLG7vw0sYuw5VllYpCkENYT43/QvvKZXj4g71Y8dKXCe9//sfHYt54e8btZSRiKKGhtZSvSiXVqQToDFbnZ/lrUI7YP+RNiWYAgMmNlXD5w3B4Q/mrPMFxtIKCkqGcrRGIBIGAiw4ADcSQR5jFTx/RUBl1w4HK2Pqy2IwrNAjfTUloEPwDBkaC+RUawkGgawOw8If0eWkZ0HgI0L0xf23IIbv7adTMlMbE1Al7hRmVZSUoC7nUCQ2E0PV8xhIa3P4wIlFO8fgDAJOJYHzpMEZK7agslVi/tIxWiDF4lNuwN4hhb4hFNDCKGrnZ8TU/PwGn//HdhNeKIVxebuDyo8fSl6b+8uYzUGExVNc5RroIhnyKPj8+aSp+fNLUhNfe3t6HSx/6VHL91Vt6En6v+//vCCw5LO10D4OnfeUyhCJRTPttvCypcAwUWujTQvvKZZLlVffesRR97gCOThI3BSHt1atOSJuKeemxk3DpsZMSjq9z//xB7DMZ2WOoq2WLic5ecVXNkNSp7ONpx9rg+dr7h7w4si11MDGZD5/e0z+COlueDJO8Q1RAkCttKSAup2dYoSH94KYi7MQ+rioWASFLaRmdUTZg6sSwNwir2YRyS/pc8HpeaBgcyXMJrv6tQDSUWFlmzBxg5xpDRtMk0z5IU6PaGhKvX4QQTLcDJmcEqFB57pfXAj6Hzi3MLULqjhqhAQDGlQzDUdIAWYvEmlbDRzTEUunq8xw5xGCoRGrQvmRWC/5wwVzM4nPEBQrdeVeaTRez6srjYiVljY6csFLo30Ng8YymhLakE0V+8GjihCKrHpAec4lJ1pvASNENltLU7zHpmpfx6lUnyJpInnE3FTnPmTcW91wonzbSvnIZNhwYjokMwrbe/81J+Y/aHWUYSmhoBO00ey2NkJQRanhDsOEDQNPMvLVLT4LhKLqcPkyoG5eyTBh8tA968+fM7OZLW1YpqMexWfx+wxmvDXmCMJcQVFvTnw5loWE4uEoMjihENAC0BKEhIxpCqFMxyKvnRZlBpegOven+gv4XCw0tc4ANj6mLvClyOhw+lJoIWiSMSadXhwEn1EU0AEB5neFSJ2JCg4qqEwDQTIYwQBog4dpDqWkF+rbq07gC0TXsAwBmVMUoOjZ3OnHWfe+nvN6+chl++9ymohAZpGZB5TDSoEstxS4wyCFUChBIJzyI00DWX38aavMV8WswpAbjk655Gd8/YTKuWWqMUphAairFGXe/C0upCTtuPTP2HZfc/S62iczzX9jQhRc2dMXeL8W88faUbQsmqcV+vhQzhhIa6iKDGOIq4Q0S2KSUBjvf3XR2GFZo6HP7wXGQdFAdw5dfFDqeecHNm8nIpasIxCIajFdOb8gTRG2FRbGDUeJ3wIk65dQJgAovBo1oUGPEJ/hZqBJd9KRnE2CpBOpEYpZwrg/uNLzQ0OnwYYzdKlkneoqN7mvOapeO6Eqmos5wnjVaIxrqo0PYFp0O2XmKmvHAztcMHe3SxZeRHaNQfpfByCfpqjQkL/vR4im4ekn++mQf7h7AxX//RHG90SgsCKx4cQse/rA95XWjDpiShQc5kevwW16Lr2Ow0o75Inkw/cC7e/DAu3sMdWy0r1yGXX0jOPWudwBQQbFt+arYd1h91QkApI8T4bvLfd/2lcvw7Ocd+MVTGxPeM5qvF7nEUGdgVXgAvVwtIt4QWqUm9WIu48bqXIvpcdJOZYtEp9JqLkFjVRk6HXkUGgThQKmknoGFhkFPUJXnBfENwWuerG5wbWs03CAPAIa86vZFhaUU5eaSAqRObAMaZwAmkYu3IDoM7gYmnZDf9uhMh8OLVrt0mF6rlV4bPCU18qkCYsprDedd4fCoMyMFAERCqIkOoyuSJrS5ehwQ8tIUErUpJ0VGj8uPslJT/nx5GIw03PzSl3jwg8QqAff/33wsOYyKvMkiw+7bl0oKp3ojN7CWYjQPGPyhCGZevzrldSMNItVw2Lia2HeSi3Y4TBRRw9IrEpHybmhbvgq7bjsTpUVaJSWZqU2VKaKJWGwA4sdJIBzBjOsSz4t0gsPX5rfia/NbU6I/mD+IdgwlNNgC/djG1YLIzShXjaFO68PGNf/qdgqzV9JhsuPs5ejMZ0RDTGhoTL+egSstDKkRGqJRwOdA0GJXly5QUQ90pjeVKkaGvSHVIdr1lZb8p04M7QUmJFVAqR4HlJQBQ3vy25Yc0DnswwnTpM+1JjPN1R+IVKgXGoyaOqFGaOCvNR0heaOnuPh8wLBCQ9ewD2NqrKN2YMQwBnKDOaGTLlXJINeD298+twmPfaJN0B9tA24xUlEmRjavVEtytIPUfhCOzalNlXj9FyfmrW3FjJR3w9TfvoLfLj0El58wuYAt04aU2JAsJpaVlqB95TLJ61g6wSF524I/yGi+juiNMWQrnrLAAPphj3VGUzCV0NKXBjb/6nXJRzQAwLjaPAsN3gE6iCtL05kHgFILYLUbMqLBoUZoCDgBLoqI1Y4hJTNIgE+dGKQh2wZClejCU19Zll+hIRygA8a6pBugyQTUthleaAiEI+h1BTCuVlroqS+hRpHdQZW5+hV1QNgHhPJ4vciSYW8IJgJUKfilAIilJnWHK+EPRaTXiQkNxr0ndDv9ssIzg5EPfvnUxpTO+dabl8Q6213DvryJDKs30woEbctXSYoMzdVlaF+5DNtuWZKybLQODt7b2Z8yuD6yrRbtK5eNepFBivaVy9C+chn23rE0ZdmuvpHY8RMMRwvQuuKjfeUyHDu1Pvb8tpe3qi4nWSwkn9uTrnkZ0Whq/1sQpaSOjbblq/Ddh1Mrn7SvXIZPrj0lZV2GOoxzBeI4lPqHMMhVweoNya9XM97Q5cy6nX6Um0tkjQlb7eV47cteRKNcfsLAPAM0mkHNbJqt0ZBCw6AnGDM3lEWYGS6vx6BbZepENAT4nUC5Pes25oNwJAqXP6Q+P95miQljecGxDwCXKjQA9LWhvamvG4huPhdfzuG4FrT0ZWdAZa5+OT+D73MAZmMMVIe81C9F1bWNv9YMcdVw+kKwmiUqpdSIfHsMSvewDwun1CuvyGDkgHReDADw0sYu/PTx9bHnDZUWrLvuNF3b4A2GcegNr8ouP3F6I/713aNizwdHAjji1tcT1hmtIoPS73MwI450+GyfA+f99cOE5YJJ6B1fm42LjpqQ9/YVE49dtlAylcJIx1Jy9MHka1/GtluWSPYNhGMjORLrzW19aFu+Cs/+aBHmT4jn6DdXWyUjJ+S2z4hjHKEh6AEJ+zHEVaNcLqIBoOkTnevy1y6d6XH604bJjrWXIxiOYsATQFNVHszBPP2ATWUn19ZouNSJUCQKpy+k7FLMCw0mWx2GutWkTohSSQwiNDh9IXAcUFuhzvG/3mbBl12uHLdKhBCxICk0TAL2vmNo078OR/rqAraoGyOcFd1umdn7ZITqFN4hGullAKgZqbrjD55BAMAQquDwBtEsUakDtgYakWVAvxSAhqP3uQOSVUgYjFzyzo5+fPvBtQmv7bj1TFhK44Gwlz60Fm9vj08uPPydI7F4RpNubVCaNZQaBO3pH8HJf3gn9vzioyfg9q/O1q1NxcL2HnesdJ8AK8UnzxETa2XNSq95dhOueXYTgPyKNOFIFAMjQfS6/NgzMIKdvSPoGvbB4Q3B5Q+h3FyCOpsFTVVWTG60YU5rDcbZy2GvsOTE90QqlcJoJojtK5clVJyYef1qfLHidFRbpfsVJhMVHFz+EOasWBN7/Wt/oaJUspCQvH9mXr8af/3mfJw529hG5LnEOEIDHybrNdsRSBfRUNVCKyUYdMDR4/JLd5h5hEFIp8OXJ6FhQNmfQcDWAAzsyG17dEZIw1GMaPBRoaGksgHuQBihSBTmdIY5gjjjHQAwVYeW5h4Hf16pLQ1VX1mGIU8QHMeB7PsQ2PAf4PBvAhMX5aaB6YQGwfTPP6y+/GOR0eGgHgytMqkTpQEn3KQSvW6VUSSCJ4HPOD4NDo/6iBohomGAq4mZSKZACBVZ3N06tTC/OLxBhKMcmqrKCt0UxkGEmlny5HXSdea14PSGMPfmNbLL0w0Ev+xyYem978WeP3jpApw8sznrNhUbLIohO4R99eLGLlwpisYBlCsSSBGORPHAu3vwu1e369dInZk1thr/uXwhasqVz9HkwfSka15OERmLmdVXnYA7V2/DX97eDQCYs2INNtxwWtqKatVWM9pXLsPqzd34waNxfzXBWFV8PLSvXIafP7kBz63vBAD88LHPU9ZhxDGO0MDPXgXL6uBPG9HQQvOSDRSyLqbfHcDhE+yyy4X87c5hHw6fkIcBlWeAuvyroaLecKkTQqnKOptCR56PaLBUNQBwweULob4yzXsMaI6ptbRgQ6UFwUgUbpcD1f/9Hh3MbV8FXLkhN+fe0B7AWiNt6lczjv53dhpWaOgc9sFE5P1Z4BuCp6QaPU6VlT7EqRMGweENYnydyhk57wA4UgIXKjCsdE8QyvQajH6+qktjPkRlxkGPlKHj279ajLaGxHriyQNdPWY8z/nzB9h4YFhy2dprT0GTQlTPli4nlt0bL2P32s9PwLRmBW8pgyFlZLfm5ydg+ij7nvni7LljcfbcsQiGo7E0CoFkweGpdQdw9TNf5L2NerGly4W5N8kLeAASBuPJYsP0617BJ9eeknYitJi4eslMTKirwHI+UmXeza+pEkOXHDYm5bsD9HhorS3H+785GQDwx2/Mw4qzZyXsU6OlmuQL4wgNfERD1FoXm3mVpIoPXxnpNazQ0JhmABsTGvJV4tLTTwUENVTUAb5hQ0WTxIUGpdQJKnSVVVOhYVhJaBCiQLwGEhrU7gseYb3g+qepyLD098DLvwLWPQgc/wv9Gzi0m0YzSB1b1bzpn6sTaDlM/8/OAx0OH8bUlMtHyvgcCJTWoF9tRIM4dcIgOLxBzGlNU65SjGcA0fJ6cD5T+ntCZTPQu1mfBuaZfrcgNLCIBkZueeazDvzq6cRyuFKd5uQOeLYd63TpEWq33TnsSxAZRmMKweZOJ8667/2E19igRh8spSbZtIpsTf+e//GxmDfentU2tPD29j5c+lCqoaEa5t38WtrlR9/+Bt745YmY0qiq7lXBufCoCbCUmvCLp+h1bc6KNdh+6xKUlSp7KkgdDx0OH9qWr8KWm86ArawUNeVmxfKaDCMJDfzMMGdrTD97VcmHybm71c/EFwmeQBi+UAQNaTqV1VYzqqyl+ak8EfTQ6BC1qRNWO8BFgIAbsFbntGl6IQgN9ZUqUidICSqq6wDsgdOXZmAD0DQSwJARDWpz5AWhpXTXasA+ETjyMmDj48D2l3MkNOwBxh0hvSwW0WBc079Ohy99aVGfAyHLWPWVPgyWOsFxHBxeLakTAyCVDcAQ5CsRAVR83vWGPo3MM0xoYOQDtaH4eokMyaZzYlZfdTxmtqjvPzi9IRy78s3Y83d/PfpEBpYqkRvaBzxY/Pu3M3rvXRfMxdfmt+rbIB1YPKNJ1bExMBLAgiTDVDWcwvufHDu1Ho9dtlBh7cLztfmtcPlCWPHSlwCAGdetxp7bl6o2029fuQzhSBRTRderWTe+Glsm/GdigzzGERr4GeUSWz0cwwphsoAhQ2Vjncp0M+WgPg15iWgQ0iDUCg3CDKrPYTihQXFw4x0CymtjYWXOdDOoAFBaBliqDCY00O+kurylzQILQqjq/hA44ls00mD6EuCt24GRfqBS5XGjhnCQGvrN/rr08spmgJTQiAaD0jnsw1GTJNJCBLxDiNgOxVCvSqHBXA6UlhsmosEXiiAYjqr2CIF3ACZbI6xmU3rhr6oZCLqBwAhQZoyZGIE+JjQwckzyIHbjDaejRkJs1kNk6Br2YZFIFMh2e9Eol+Dn8PyPj8WE+tEtMhgpfL3YkIrayQSlfH8j0FBZJnvOuf0hzF6RPs3ig12DKcfmm788EZOLMNrh0mMn4ctuF55aRyeiJl/7sqbrTSlvkvmNBz7CJ3vj/am25avwv58eh8PG1RjeRDOXGEhoGABKLCivtGPYk2YwIQgNIz35aZeODPD5uOkiGgBeaMhHRIMwSBZm55UQhAb/MICJuWiR7gyOCEKDwiy+bwioqIsZ6Qz71JS4rDdc6oSl1IRylaV66istmEEOoCTiByYeS1+ccgrw1m3AvveBWV/Vr3HOAwAXBWonSS83ldCZa6cxhQZaXcAv78/AcVTAa6iFNxiBLxhBuUXF7ySkMxmAuOintupEPzD2cNjLLbG0H0kqhXtCr+GEhn53ABWWElSWGedWzTAGUnnpcp3vHz32mar15Nja7cKZ97wnuSybmT+xn8RNZ8/Ka4h6PtA7TeVgQ6rMqRzfPmYibjpHOu3SF4zgkBtWx54LKQaj9feo4o0Rk3lhQyd+9sQG2feJq70AwD0XzsM588bp3byMuPP8uTGhAcgs6uDJ7x8Te6+AkM7UvnKZpIkmExsAY1iIAtQMsqIBtbaymOu/JGVVgNkGuI0nNAgRDQ0KYfxj7Fb0ulTmaWdDLKJBo9BgMPM5e4UZpekqSAB0VriiXn1EA0C9LQwymwzQfVFXYVF9UayzWTDL1E6fjJlL/7fMBkosQOfnsu/LCOcB+t+eptZ19RhDCowA3fehCIdmOZEx4Aa4CEr4aiaDHrWGkLWGSZ0Y5s8p1TNF/D3BXmFW8O0R0umMd2z0uwMsmoGhO5s7napFhlc2dePlTT2K60mxu38EbctXpYgM05oqYx3zTBF36Bsqy/DtRW0Zb6sYYSJDZnznobVoW74KbctXpRUZdt52ZuwYbF+5TFZkAIBySwnaVy7DL06bnvB62/JV+ONrxqq0lg3nzBuHt361OOG1W86V328/e2JD7LdoW74K2/mSk4VCqXKOlu088r2jUrbV4fCmfMaka15GNMpl9DmjBeNMk3gHAFs9am38jLI3JN8Bq2oxZKdyYERdmGxTlRUObwiBcESVqUnGxCIaMkidMAiDnqC6VAGfA7BPQLWVnjLDSh4NgOGqcAx5Qqr9GQCgrLQEh5v3wV9SCWttG32x1AK0zAE6P0v7Xs24+PKE1WPl17E1AY52fT83T/S66LkvGxLLn1PmSl5oGAmqy0MurzWM2CWkP9hVlN9COAAEnICtEbUVFgXfHmNHuSmW3mUwNPCtB9fi3R3x+9IhY6rxys+Ol1y3x+mPlW4DaHUJNcjlf3//xMm45sxDNLY4leQBwrrrTs16m8WCVOUPJjKkR82AcdstS2BVGa0px5WnTMOVp0xL+Lx73tiJe97YedD8RpMabHjke0fhkn+uBQBc//zmhFl7fygSKwmZzBl3v5vwvBCz/Xr5KRw/rTFlW8f9v7ckP2PytQd3ZIOBIhoGaEQDP9uV3vzLmEJDvzsAQoA6hRm95moqRPS5VM5qauDt7X34yn3v47n1HfFBcsXojWgYGgmq68j7hoHyWpSWmFBVVhqbfU1LeV3MW8QIDHuD6o34eGab9uGAZUpiJYhx84Gu9UA0ol/jBO+FtEJDQ86EHY7jcN8bO3HOnz/Azl79VflevpKEbAk3PirBWk2FhiEthpAGOR+Fc0oqPzwF4bzixWfF+wFgSN+eIU9QufQug6GStuWrEkSGN355oqzIEI5EsfCOuInqFytOV+wohyNRtC1flSIy/PTkqWhfuUwXkWFJ0mBlNA3wmMignmuf2xSbKZdienNlQsRCtiKDmPaVy7DztjMTXmtbvgqPfNSu22cUM8dPa8TFR8ejS8UlV63mkoT9vvXmJbLbmXTNy7Hf0B/Ssb+oQPI59d/PMjcRb1+5DH+6+PCE19qWr8KupONj0jUvg+MOzsgG4wgN3gHA1hCbcU1v/tViyNmrQU8Q9nLlMH5hMNKntsydSpy+EH7yn/XY1OnEr57+AsMDXTQNxaLSXEkoJ2qQgQ1AO/KqBtd+J2ClZfdqKsxwqY1oMMhsMgC4/KGYB4UqOA4TuC60m8Ynvt4yGwh59Y0ucHVRIcucpipDZRO9TugpcPC8ua0Pf3htBzYeGMb3H/lM91C4Pj4VShARU+DPqUp7EwCorzxhoNQJ4Zqu6hgURVvZKyzphb/yWprOY8B7gsMbRJ1NwznJYMiQPCDbe8fStGXqxC7r//jWAsX6823LVyW8BwCOn9aA9pXL8MvT9akANuwNYpso/Ho0DcKTRYYjJtaOqu+nB75gJDYw/c8n+1OW//1bC2ID3DU/PzGnbTGXmFJ+n+tf2JJ1OUyjcPtXZyc8l/veQtqJ8PfbpdJi48zrV8d+W8Wqbjog/u1++fRGBMKZ9xvPmjM25ViY+ttXcNlxiZ5iYkHmYEIXoYEQ8iAhpI8QIlmsnFDuJYTsIoR8QQiZr/lD+HzcmBlf2rrpfESDwdSjYW9IleN6cxUvNOgc0fDE2v0YCYTxr+8eBY7j0NnZod6fAeBd7q2GEhoGPUHl0paRMHWt54UGe4VZZepEHRAcoWHeBsDp0yg0eIdQyY1gT6Q58fVG/kbSv02/xrm7gWoFUyFbIzWMzMHx9/ja/WisKsPvzp+DPQMefLBbX5NPIXVCNm1KEBrqaBrT4IgWjwaHIa6F8dQJFcKfKNqqlj8fZWcLCOHvCcaKaOA4Dg6PunsCI05e+iMGQyrfP110gnh9S6kJpx7anHZdufKLj3zv6AxaK49gwgeoT+MwAhyXKDIsmdWC//5wUQFbVFzMWfEq2pavSjBkFLjmzJmxQexpaY7TXNG+chl23Joa3RA5CPLyM/E8uPyEybHfK3m/Ccy9aU3suhIMy/jx6cAXK06PPZ5xnXS6hxaS98c/3t+bss7BIkSJ0Sui4WEA8vExwJkApvF/VwD4q6athwN0oGerj3VCFSMaQl5qoGYghjxBxbQJID7rqbch5OotPZg73o4Tpzfi2KkN8Di6tQkNQHxgYwA4juNnDBX2ecBF/wsRDeVmdYprBV+q0CBRDS5fGNXlGmxbhnYDALaHmxJfb+Rnr/q26tQy0NSJdGkTQNxLZKRPv88FMBII463t/fja/HE4e95Y1JSb8fz6Ll0/o9flR53NIu+5wh9DFdUNsJSa1KdOlNcC0TAQ9OjU0tzh9IVgKTHBalZxW4qlTjTAXm5BJMphJBCWX7+q2XARDd5gBMFIVNU9gZHAw8hlf8RgaDUVTF5fbjDwzo5+WYEhFzPx4s/60eIpoyrfWTzTObe1BvdfckQBW1M8CINNlz/12i4cZ98/cUoBWpaIpTQ1umHKtS/jyU9Toy5GG8nfe0//iOr3Cvst3TVj+nWvpE2RyYZqqznmuwboIwJIpdUkc7CJDboIDRzHvQsg3WjqHAD/5igfA7ATQsao/gAhTLaiIZa/m9b8K1bi0lgzWLQCgnKnsrbCAnMJQa9bv5nyYW8QGw8M48TpdLB28swm2EIO+C112jZUXmuYcnouXxiRKKecA+130v9WOwA645r2+BOooPn0RvBpCIaj8IUiiuGxCQxSoWGzryFxNtlaDVS36hvR4Oqi5SvTIQgNHn2Fhg37hxGJcjh2SgPKSkuwaEo9Pto9oGu+Xa8rgKZ0JrD8OUXK69Bgs2BgRKXQwB+ztORsceP0BVFdblY3gBBVxInfE9JFuTUbzrcnVu6TRTRoIuf9EQOhVWR4f2dipJbU+tEoh7blq/DtB9emrJurUP+HPkicGbx6ycycfE4hSP6NXvjJcQVqSXEQ4Y8vqcHY2mtPyelxli3tK5fh/CNaY89/899NB8WgUvx7JJe41LqddL+vcFz84709GX9GMl+sOCPh+WtfZj9ulEqrSeZgOC4E8uXRMA7AAdHzDv41dXiFfNwGVJWVghAVEQ0ADbc2EGrzcU0mgsbKMl0jGj7cPYgoB5w4nUYwzBtvRz1xoZ+r1rYhA0U0CCUCFfe5MEgTeTSoi2gwjtDg8msw4hMY2oMoTNgbaUydTW6cAfRv16dx4SAdWCqlTlTykRUefdMaPm0fgokAh0+wAwAWTW1Al9OP9kGvbp/R6/LLV5wA6DllqQRKLairtGBIdXlLe/z9RQ5N3VEZUeMZAEylgNUeS/dRvCcYTGgQDC5ZRIPuZNcfMQhaRQaO4/B///wk7fpty1elmBWu+fkJOR/43fTSl2nbZVRYCcs4gsAw5drUPHZh8ClrllxE/P7rcyWNIkc7T//gmNhjvSID2lcuw5c3n5Gy7NZVW2Oigx4TPuLz7vJ/r9NtEomJDZR8CQ1SU1SSvyQh5ApCyDpCyLr+fn7WShTRYDIR5dB1oZyZgXJyaRi/+nzcpmqrrh4NGw8Mw1JiwuxxdgDAoWOqUAcXDgRs2jZkoIgGYcZQfURDYuqE4sWonI8GMYAZn2BuqSmiYXgffOXNCKE0NZS/bjI1g9Tjgu1WUdoSyFnqxOf7HZjRUo0qft8cM5n+rp+26/e7UqEhXUSDI1bVpd5Wpi11AjDEOen0hVRFdAGg4p/VDhASK4epeE/wDwMhfdPNcgmLaMgZqvojkn0Rg5DJAFYcvv/OrxcnLHN4grJpEtObqzJrpErEn7v++tNy+ln5hIkMFI5TFhiMhtSM9mgfVB7Zlhj9fMv/vpRZUxsVllJRmszklOVC5Yovu1xZfY74mqenaWP7ymX4YPnJssufW595xQujoCEhOys6AIit6VsBSCY5cxz3NwB/A4AFCxbQm78wI8zPENvLzQphsvzMpoFSJ7zBCILhqOrygs3VZdg7oF/e9caOYRwypgqWUqo9lYVHABLBbo8VmiyJyu2GmD0F4s79iuUtk4QGe7kZoQgHbzACW1maU8hQEQ00IkGTR4OzAyHbWMBBB0UT60WiVN0k6m3hHQJs9Vk2jr9UVCtEN1vtdJZb5xKX23vcOIFPKQKAyQ2VsFlKsKXTCSwYn+ad6ghHohgYCShENAzFohPqbRbs6lOZBymkThjgnHT6QmiqUjljJaoCI4gTae8JVbxJ2EgvUDsxm2bmjVhEAxMa9EZVf0SyL2IAMhnAJr9HfC2XExjywYe7EqPTRovodmAoMRpObn+GI1G0D3rx+T4HXtncjbe2K9/bvjJ3LC5Y0IrZ42pQozYVrUDIDb6NKC5I0b5yWcJ3bFu+atR8NynE3/ef7+/F9Wcdquv2rznzkFiJ3ORjZ+m97wEAzCUEO2/TbhSb0H8F8OLGLpw9V2FySyXj7OUpx4LAz5/ciCWzxqDcol/51WIjX0LDiwB+Qgh5AsDRAJwcx6nPaxA6yby5Xk25guu/tQYoKdNfaHD3Ai/8CBgzF1h8LVCi3+7TGibbXG3Fx3v0mVGNRjls7nThq4eLokf5KJKdHo2hagZKnXB4VHbkk4UGISfcF1IQGoxjBqmptGDsTR2I1s8DOpA6w17Ll/Vx7M1eaHALQoNCdLPJRKMadPRocHpD6HMHMK0pXgbOZCKYNbYGmzqdunzGoCeIKIf0YaE+RyxCps5m0R7RYACPhmFvCNOaVM6OJggNwvmYZp8IUW4jffoKDbvfAt6+Azju58CM9AZQWhny0HOSpU7oTnb9kSImE5EhOe1NeI/LH8KcFWsSll15yjT84rTpWbZSPRf/I30qh9Hodflx9O1vpLyu52z3Sxu78NJG7WbFr//iRExtki93qiffenAt3t2RKpqMht84mYNNbNh685JYdZBcfldhu9/8x8f4YFd8Mi8U4WL7W+tni3+rKx9fr5vQILV9MYfcsHpUHxO6jJQJIY8DWAyggRDSAeBGAGYA4DjufgAvA1gKYBcAL4DvaPoAIew3liNvSR8mSwjvMq6z0PDcFcCet4FdrwP1U4F5F+u2aQffqbSrzJFvrrbC6QvBH4rAas5OCds35MVIIIzDxon8GHhfjL2+Crj8IfUh9VY7EPYBIR8td1nEDGoVGvgZ5VhOuDeEcfY037HEDJRVG0Jo0Jw6EY0Cri6UTP4KAAmhoY4XGob2Aq0LsmycIDSouOjbGoAR/SIadvXTyjXTmhM7YLPGVePxtfsRiXIoMWU3YyR4rbQoCQ1NdHagvrIMvlAE3mAYFRaFS3jMo2E4qzbmA03lVUVCgyqPhlxEubl7gccvote7Jy8Bfr457g+kAw5PECUmgiprvuYDRgc5748UKZmG4h9246sp7ylkFIOAuA0PGKgKw4Pv78XNOoWN55NT75I38Vt//Wm6RJNEopxsisRo5mASG5Jn5g8MeTG+riJnn/fYZQsB0Pvl4be8lrAsE8Fh9VXHY8nd78Xer/fvJCc2jOZjQpceDMdxFyks5wD8OOMP8A8Dlio6cAMNXd8/qJA2UKmz0NC/g4oMp94EbP4v8P7d+goNGsNkBYf6PlcAE+qzO4m399DB1IwWkdDAh58PcdXY0+/BvPF2dRsT54QXudAw5AmiwlKiLNT4hgFiomZ8AGr4EqtpZ1AFKuoMkTqhOaLB0wdEQyhrmABAKqKhjf53pNYR1oyrCzDbqGijhK1J19SJnb00RSF5pv2QMdXwh6LYP+TFpAaNPiZJ9PJeK2k9GrxDIo8GevwNjgRRUadwCbdUAqSk6KOMIlEObn9Ym9DAR7hYzSUoKzXBqVR1AtD3nrD+ESoyXPw08J8LgHUPAiddq9vmh7xB1FaYYcpSyDrYyHl/pAjJVGQQv2/W2GpwHJeSn5zvKAYpzpiln4CnF53DPhy78k1dtvX0D45JyXHPFU5vCHNvXqO8oojkARwAPHHFQiycrD5aUWpwtef2pQfN9e1gEhvE3/X4O9/Ky/estVlkhVLh+d47liqmEs1sSexnhiJRmEv0tTOUExuOueMNfHTNKbp+VjFgjKkSnyM+MwcVqRMA7Vjy5fd04Ysn6WBz7kV0AP3K1dTsThhQZYkgNKg1QxPyuXvd/qyFhl19/KytOGyOH6wNcNXY3TeSgdDgUM6pLzBDnqA6YUeYPeUvUELUSdqBjUBFvSGEBqHqRLXagZ6zEwBQVjcelhKCoeRyn+ZyWo5ySA+hoZNGM6jJNa1sAgZ2ZP+ZPHsGPLCUmlIiV6Y0UnFhT/+IDkIDjWiQ9WjgOHo+VcRTJwB6/CrOFBBCz8kiT51w+zUKXaKIBoCek2k9GmwNAIi+RqFfPAm0HQ9MPx2YdAKw9SVdhQaHJ6jas4dx8PLP9xOvsWo79aFINOH5RUdNSBEZCjUQEnfCd9+uPd9ab/yhCGZevzqj9774k2Mxp9UOIPF7FWrf1lSYFT/7ibX7sfzZTWnXufBvHyc8H1tjxYcSg6RolEupVLJwch2euOKYlHVHOweT2HDpojY8/GE7AODeN3biylOm5e2z5QQH4fqmJDiIf6dpv30lJ79R+8pluObZL/D42ngBpG6nH4FwBGWlo8uvwSBCw3Dc1Ay0U+nyhRCNcvJqaGUzsO9D/dqw6zVgwiKakjF5MX1t91vAAn2iLlX7BfA08bOfepS43NE7gnH28kS/Ad6jYZhUazOdFAsNRc6QJ6hsBAmkDGpUhWoLlNfFy7MWMS5fGJYSE8pKVSq3LuqUS2paUWfrx9CIRHRH7SSdIhq61aVNAHRA6emng3MdTLAODHnRWluecp2Z3EBFuT39HpxySHaf0efyw0TSmJIG3AAXiZ1bdZVxoUEVBjBoFUSCTIUGxUpEJWYq+ukV0eDspILWEZfS55NOAN68BfAMZu9JwjPEhAaGAoMjgQR3dy0d4mm/fSXh+XXPb054XiwDoGxT0zJhc6cTZ933vqb3KM3Oiwc96Vzoi4ELj5qAC4+akPL6/1u9DX99W3oCr8vpT/iOd543Bxs6hvGfT/YnrFcsx1WhOFjEhhVnz4oJDXe9tiOvQoOAkuCgdr8PjATQUKlQnS4D7vjaHJx+aAu+8/CnsddmXDf6/BqMITT4h1MiGqIc4A6kCbWtbKZO7eEgUJplZ83vBHo2ASdcTZ83TKezte3v6SY0DHlDIER9R7uZd2fXo8Tljl43pifloMMzAJTVoMFahc5hn/qNGUxoaKjULjSIzSAVqagHBrZn2sS84fSFUK3FodrdQ/9XjUGtzRmLyEmgbhKwK9X4SjOuLmDS8erWtTUBYT8dnFtVpFoocMDhxfja1KiBWpsFtRVm7BlQWf0hDb0uehMrlQvPE84l/txSVc5RjNVe9B4NmlJ3Qn76G4vPyXKLcipTZbN+EQ17+XzmSSfQ/23H0f/7PwQO+YouH+HwBrOOlmGMXjiOwxG3vh57LlVvXo7Vm3tkl229eUlBHdALMesvZXyZDq3tikYTi5ak9XYqYn6zZCZ+s2RmwmtyRpZX//eLhOdTGm1445eLc9U0Q5EsNnQO+wx7TKTjmR8cg/Pv/wgA8O+P2vGtY9oK0g6llAqp81n8Gy249fWcXYtOmtmEt361GCf9/u2Edo0mscEYQoPPATTE1TChM+pKZx4mlDPz9AE1rdl9/v6PAS4KtB1LnxMCjD2cig864fAEUVNuVq3e2yvMsJSY0OvOLqKB4zjsG/Ri0ZSGxAWefsDWgHGWcnQ6Rq/QoKoGuN+ZEFFTbi5BqYnEDBTTUlFvDDNIf0hbaUt3Dy0lWVGPepslZqyZQO0kYKQHCHoBS4bpPdEI4NYS0cCXofT06yM0DPlk04YmN1Zid3/2JWZ73X7l0pZAXGiIlXPUUHmiyKNqBKFBlRlugK+XLY5oqDCnlIxLobJJv4iGfR/QaKWmWfT52PmAyQx0fKqb0DDkCeGIiSyigSGNOM3h2qUzlY1hRfzg0c8kXx9NnVslLv/3Orz2pfL14L2rT8razE6cOjDa9nHy95ETHnb3ezKuBjAaEQ9kj1355qjcJwtEviM3vLClYEKDQDrB4Xfnz8HX05Qr73cH0Filf1QDAExqsGHddadigUg4fmFDJ86Zp1BpzSDo63CRK1JSJ1TUTdfT/KtjHfVnGCdyz28+DBjcRasr6IDDqy1MlhCCpuqyrCMaBkaC8IUiGF+XpKZ6+gFbI1pry9HhUOjAixEiT4o8JxwABj0B1NlUDGz8wwmDGkIIqsvNMV+DtFTUAsERIJx95Ekucfk0VBYB6HllawJMJtTaLLHUnwSEyhOO9swb5umnaQOahYbsB9YufwhOX0gyogEAJjfYsEcPocEVSG8EGYtooDftar4KgaqIGsAQqROaIhpi5WbtsZfsSqkTgL4RDV0bqNhs4m+hpRYa6da3VZfNcxyHYY33BMbBQ3JH+YoTpqh+7zl/kk4JKIaBzgUPfBR7nAtvhrblq2J/ciLDu78+Ce0rl8X+shUZAuFIVu83EuEk3w85hN/gufUdOW5RcfPfHy6KPdazxGkxccs5s2KP9Uj11gPh3Bbz62e+QNvyVQiGownrCRx52+vIJQ2VZVh//Wmx5z97YgM261RCvdAYQ2jwD8dnyhHvjKavm84LDW4dhIaeL2gnUjwr23IYjXLo06eMkYN3GNdCc7U16xP3AC8iTEi+mXoHaURDbTl6XP4U4yhZLFUACOB3ZdWuXOMNhuEPRVFnU6FQJqVOAHSw5/aHZd4gooLP1y7yqIa00UFSjPTGoobSRjQA2fk0uKjpJKpUCg0V+kXUCDPkch3NyY2VGBgJqBOc0tDn8qNJqbQlELsGlpaYUFVWqj51ory26FMnhjUJDRIRDaqEBj6igePSr6dEyEcFhbHzEl9vPlQ3ocEdCCMc5VR79jAOHu5/JzFHXqtAsLEjsfP66lUnFIXIAABr98bvk3p5M4jFBTnEwkK25trJzLgubiJZLPs5F/S7A5ia5Psh3q9rfn5Cynt+/uRGxd9mNHPExNqE53etKf40W61cIopiOPp2HVJpdaR95TK8d/VJCa9Nv+4V2eNR9TgoQ2ptloRy1mfd9756L64ipviFhpCP5uOKPBpirv9p66brGNHQswlomZP4WvNh9H/vluy3D8DhCWnuVDZXl2UvNMgNpvjUidbackQ5oMep8nNMJlqG0F/cSpxw8mZiBgkAVVaz+tQJoOgrT7j8YfUVJwAq4FXSkmO1FRa4/eHUi7CdN5MaPoCMcXXR/2ojGnRM3ekapsf8WJncycmxyhOZRzUEw1EMeoIxzxVJvImpEwBNFVBV9QSgM/9+JxDN7U0yG4RzSdUxKERLiVJj7BVmeIOR9LOHlc1AJJD9tal3C42yGTMv8fWmQwDnAV1EViFCiEU0MMREohxWvrIt9lzrwFWqDOaMFhXpg3nAE4gL93/8xtystqUkLjx5xcKEQXCu4LIVNQ3CvkFPwozv1UtmpOzX6c1Vsf391cNTQ8KF38udpXBvNMT76d43dxWwJQcn4+sq0L5yGazmxOFw2/JVePbzjoTfJ9lANxdsWpHotTP/ltcSoiyMSPELDcJMXFKYLKCQOiGEUGcrNHgG6azqmCShobaN5uTqVELT4Q2qLm0p0FRlRZ87u5D8Dt5/obVWNJiKRvmIhkaMs1ckrKcKa41hhAZFcSccBELehOMPAKrLS+FSE9HAh7sXu9Dg9IVQo8WjYaQnFtEgVEFISZ+wNQCl5XTwlSmubvq/WmWuWkxoyD6CpM8tlJ2UjnoRl7jMlP6RQNrPABC/BiZFdamPaLAD4IBA8Z6TTl8IVrMJVrMKE7pY6oTYo8ES244sMfE5y/SJHt7kLPme0HQo/d+/DdkiXJ9q1aR2MQ4apohy/T+77lTV7+M4TlJkKCZm3fhq7PFXD9fuq7Vv0JNWXPhw+cmxge7Rk/WpDKOE2Eej2Pa3XvQ4/Tjxd2/Hnq/5+Qn40eKpad/zx2/MkxV5Zq9Yg7blq7BvMPu0RKOw+ab44HI0RneIf+fFv3urgC2RZ9stZ6Ycj794amNBfo89SWlj06/LvcCRS4rfDFKYvRJ1sqvVuK6XWvQpZ9bHRyw0z0p83VQC1E4EhvZkt30ehzeoOaKhqboMbn8Y3mBYkxGUmP2DXjRUliW+3+egaSG2RrTU0AFQnxbTSWt13LCtSBmMdeQV9rkwqBFF1ABAtdWMfreKAaYQ0aDDwDdXcBynzaMhEqYeCHxEQx0/yBvyBhNTAAgB7OOB4X2ZN87VSQW9CpUdw7IaAESXiIY+VwCEQLas0YQ6G0pMRFv51ySEiKQmJY8GS2VC9Rx7hVmDR4MgvgwnXEeLCadXQ+qOlNAgMghukosOqRJFuTVOz7SpQP92wGwDapLKv9XxefJDe4HxR2W+fYjNMVlEA4OS3OGtV1luLRyJpoS0axEpip10A4HHLjsax05tkF3OyA5/KIKFd8TD4T+59pT0xsYSyBn0CeLF2t+eIn9NHyVUliX23zmOU18BzGC0D2rwfCsA7SuXoc/tx1G3Sad5LLn7Xay+KjUNSE+kyuQauRKFASIahPxke+wlq7kEVrMpP+Zf/XzOVOPM1GV1k7PLP+fxBSPwh6LqHNdFCOHWvVkYQh5weKWNIAGgoj42cNSUomGEiIYRlakTEoMaAKiylsLl0+LRULwRDb5QBOEopz51wtMPgAMqadSQIJAJ+zQB+4TsUyeqx8RN95QwmXQzP+xzB1BXYYFZpuykpdSE1tryrISGPpcQNaFQdSJJIKgpN6uvOiFE4xSxIeSwL5iV0KAqyk2vdLq+rUDjjNRj0j4BAMnO/JRHiJaqthb/XAAj93iDifcatR1OXzCSIjIA6kWKfCHOQ95525mK63sC4bTRC8JseSFFBnGE39ablxSsHblk5vVx/4k3f3miZpFBjPCbXXXqtITXj7rtDbQtX4VIdHSnoYjPaXEkzGhBrnpXMdJUZZW9xm7rceelDVKfb9RoFwMIDcP0f1JH215uUe5oVzbTMnzZ0L+deg5UjUldVjeZzl5lmYcXm70q1+rRQC/qfVn4NBxweFNd9QWhwdaIqrJSVFhKtIkZBhAaHPyxI4T9yyIjNFRbVVadEI7bIjaD1OT4D8RLJdqShAap87FmPDC8P/PGubvVp00IlNfpMqjud/sVyxnRqiyZV54Rzqv0QoMjJaKmptwCpxqhC4gfg0VcCcapxYzU76SlVc3x65Yg0qYXGprofz3EZynh2WylXiJ6CA2CZ4WWSjCMUcuhN8TTCvbeoa4aw8BIAIfcsDrl9YcuPVK3dunF/Fteiz2WE3YB4C9v70Lb8lUJaRYCu29fmnPPBS0cLvpO5RYVKWEGQzzoWfm12ZjcWKnLdq86dTraVy7DjxYnVlKZcu3Lhh1oZcJo8/d4/sfHxh6/uLGrgC1RT/vKZdhxa6rw+dm+/PTnqyQmGox4DhS/0BAz/rInvExn9PIR0bCNVpyQCmOqnURLFwoD8wwRzG+qteTII57X3ZuhT0M4EkXXsD+14oRIaCCEoLnaih7NEQ3FnzphLiGoKlPY57HjL0loKKfmc4rlnEotVKgqYqFBiMxQPagRojMq6GxRTGiQcse1T6Az8oEMfQxcndIiXzrKa3WLaEhbDQLA+NqKrISGPrcfpSYSSz+RxOeIe33w2CvMcPqC6jojgkhRxBENTl8YNWqFVsGcVXRNFkTatOkkVjtQYskuosHnoP4kjTOkl9e26RTRoMEckzGqOeT6RLFATUh1r8ufUJNdzEkzm3RpVz4RohfuXJ3qyi+IC3pVqWAokzzYufCoCTJrZs7VS2bKzuo+89noLIs52qMaBK58fH2hm6AaS6kp5Tg8768f5WXAn2wMKfDR7uKNkJai+IUGidQJgHddV0qdqGqmncJslMGBHek7lQDgyCIHHfFOZZXG2aumLCMaup1+RKJcauqEMJC00YFkc3WZts8wQtWJEeqJodhpi0U02BNeFpRGdSUu64o6dcKlVejy8BENfFqIMJssKzQAmRlCchyfOqGy4oRAea0uwk6vy48mFRENAyMB+EOZ1UrvdQXQWFUmmZMXw+eQTJ0IRTj41HxuLHViOKM25gNN5VUlqsDUqPHtISR78Vnw5GmYJr1cJ6HB7Q/DUmJCWWnx36IZuYPjEs9xNbP1XcO+hDJyf/nm/Jy0LRfcd9HhCc/l0iMevHRBUUUvpENs9DcaeOrTxHt5rn8Dqd/5V08XxqQv1yRPHGzqcGJThxMHhrwYGAmkr6rEyClyoleuI08ukhDxLvr7x4aKeCn+BFDfMADCm7zFsZebsX9IwVSkshmIBOmsdCYmaIEROvtVP0V6eQ3vjOzqAJB5OGJ8Rlnbz1FtLcVc8wEs2PgfoPY84LCvaXp/rLSlZOoEic2iNldbsX7/sPoNW2uoGWQ0qj63Ps8MeoLqSsfJRTTwopDLH1I2lKyoL2qhQSiTqD51gh/E80KUucSEamtpeqFh+AAt/6cFn4OWttWcOlFLBcIsiEQ5DIwEFYUGoSxsh8OLqU3ay8T1uvyKURPwpno0iD0JFI1gBZG2iFMnhr0aPRokPFMIAZyK6XRN2UU0DPGePLWTpJfbJwLuLiAcAEozz4N3+UL8d2KztAcz4lnNXSq8C4Y8QSxa+Wbs+X8uPxoX//2T2PNiHJh/ti8eafWVuVRUlhtEbr91CcpKiz8N4bZVX8YeJxv9GZ2r//tF7HE+j6f2lcvg9ocwe8Wa2Gtty1fh42tOQUtNcZpFvrqlB99/5LOM3/+VP72fdRv+c/nRWDSlOAxR773ocENFMyRz8dET8J9PElOBJ13zMh7+zpFYPCM3kWJ3fG02Hl+bmn486ZqXi/J6LkXxXwH9w7RTmTRgVZ06AdAZrEyEBsEtX4hcSKaGHwA5O7VvW0SmYbIkEsKfS/+I1oEe4Lk1NMWj5TDV7xeEmvFSqRMVdUAJPTyE1AnVTrjWGgAcEHSnDAiKhSFPAPVK/gxAWjNIQGVEQ3ld3NegCIkdf6pTJwZAhaj4OVVfWaYgNGQQ9ePi8/iqM0mdGNb+eSKGPEFEopyiuZVQFvaAw5eR0NDnCmBifYX8ChxHBZeKxNSJGpHQMNZeLvXOOOZyoNRatKkToUgUnmAkK6HBZCL0nqDGIDgbc1LB/Ld2ovRy4Z7g7pa/b6jA5Q+ztImDnF19ielmpWm8CwBaAUDsdfDo94pngJGO8/76YeyxnMCw946lhhLd/v5e9ibhxYj499lSgEiNKqsZ7SuXJbRDqHpRqEGXVFWXYkIsNCbz0k+Ow+zW/PXRz547NiY0RKNc+kjOIuT2r85OERoA4NKHPgWQu2PwkDHV2Nqdmo5ulEoUxS80SBihAUKOskqhwZ0mpzYdsdmrNunlVjstc+bKVmigg1Up44+0bH4GrVwP7qv5NX7qux/4+K/AuX9W/fYDDi9KTARjktVgz0As/x4AGivLEAxHMRIIq0vvsFbT/xIDgmLB4Q1hXHIkhxR+Jy2vaE4czFWLyukpUlEPDKTmlhYL2s0gB+lg3hSfWaqtMMcMNhOwNdG8+EwMIWNCg8aIhoo6IOCkZThLMrvECeVcFSMa+GOoQym6SoZetx9HTaqTXyHgBrhIaupEhYpUATE6iC+5whUr5ahBaJAQn+zlau4JTUDHOq1NjONop/cVi016uZDm4+zMTmjwhVjFiYOcU+96J/ZYqTPJcVxCBYDff30ujpvWkODU//dvLdC/kTnGaALDaGYkkDipYitgpEb7ymXY0uXEsnvjM/75GHRxHJe1d8K3jpmIm86elfa4Fgspar9TvzuAI2+T9mWRQypiYk5rDV748bE5P+/++s5u/PikqTn9jFyz4YbTMO/muLibq2PwlZ8dLyvCbjgwXPQVPYq/JyNT+91eYYEvFIE/FIHVLBNKJ45oyAQh11YuTJYQOoPlzM6YJmOH8e0vw1HaiGfDx+Kns7qBLc8DS38HWFQMoAEcGPJhrN2aOlPiGYhVFADiZn+DI0GVQgMvLhSxT8PgSEC5tCUgaTwHJKZOKFJRbwgzSNVCl2cgXraTp85Whg6HxGDbZKKVJzLxaHALQkMGHg0AjYayZTaj18cbrDZVpxcaGirLYCk1ZWQI6Q9FMOwNpRczfPxxI1F1BwCcPg0lLos0okGz0CUjYKqOcvMOANFIglCmmqF2+fsBAFQL6XTZuWq7/SHNnj2M0cP97+yOPZ41tlpxffHg57z5rTj/CHocTrk2/vpphzbr2EJ9cMvcP/fcvtRws52jncNElT6KYRZ11tialOiGXAz0tHpB/Oeyo7Eoy7Kqu29fmnDuqqGxqkzxu7cPeLD492+nXeeLDmeKmLLhhtNgV5NqrIHfvbrd8EKDvcIieQzmWiDde8fS2G907p8/KIrzMR3FLzT4h1OM+IDEGWVZoaFKEBoyLHHpaKfeEOnSLqp1EBr8IVhKTfLfQ4pwENj9NvbYT0ZvXwDcrPNA1j8K7PsAmHaaqk3sH5IobQnQ1InmWbGnQorBoCeItgaZmTwxMaGhOCtPhCJRuPxhlR4NTsmIGmFQ7lJlBllLq5NkmbedK1z+EGyWEsXQ3BjewZQBfJ3NjE2dMoNee4YlLl1dAEhcMFSLcL76HBkLDf182cmmqvSpEyYTQau9HAekRBalz3CrLG0J6BDRYC9a4W9YL6GhwqIidaIJ4KJULKvKYODlaAfajpNfLkRa6BDlVqx5x4zcs/KVbbHHq648Pu26yQOhP1wwNydt0hupAdwHy0/GOKVUMAOx8cbTC90EXRALQoqVuvKM3mKDllSIXA0oxRVU/vHeHlx2/GRdttvWYJPcN1u7XTjznvdk3yeetQeAbbcs0TZeGUUk+94AqcfgpGtexufXnxabpNUD8WdMuuZl/PPbC/C9f9HozGJPoSiuK4YUPkfcdFGEXeQyLmumVlZNc5MzNf9y7AXq2qRLWwrUtAJ9X8ovV4HbH9YeJtuzCQi6MdRyLLwdEYw0H4GqEgvQ/p5qoaHD4cWph0h0tj39CQO0ehsdHA+OqCyjWeQRDQ7eS6BOjUeDb1hyUKM5dQKgUQ1a/QbygFOL4z9AhYa6xBtfnY16NEj6eNgnANtT67kr4uqkIkOJxpldsdCQIULqRKNC6gQAtNZlVuIylp6RLmoiJjQklbcUeTSoorw2O2+CHCKIJao8CcJBIOxLMQcGeIPgQU/691eKxGetQkM4QI/JujQRDWVVtG1ZRjTQ1AkW0XAwcs2zm2KPlSpGJA/W5Tqb3z5GxlOkAMjNEBdzRzlTNN1XixixAeOmIqyika3YEI1ymKwQQWA1m7DtFmVDVr25ddVW3YQGOQ4ZU52yvx5fuz/hWiRGnKZ1ycKJuOVc9d5wRkfsezPsDcYiPZKPwfm3vJZTI85TksZuH+wawLFZRtLkiuIsCSDG75SMaBDyedPOYBFCZ7DcmQoN7cp5tlUtdGAezbzsTEadyu4NAABuDC0H1esrAcYtANrVudR6g2EMjARTjSAjIT7kPJ46IUQ0SJr9SVEm8mgoQoZ4LwFNqRNJVJVRl3t1EQ2C0FCclSdcvpA24znvoETqBC23mJzHCQComQB4+oCQxsF4JqUtAZ2EhgCqraWqVPvW2vJYBRct9Loyj2iosJSg1ESUZ/AFijh1QpNHQ4CPkpJJnVDt25NJOp1jHwBO+Z5QPVaHiAaN5yRj1CB2GF86W16YVhIZXtwYF7tuOqfwA4FgODoqSxIyioPk41/NsSaUT5UTGXbedmasvGYhRIZCctFRE2LfvX3lMjz9g2Mk13vk432x/Xiwnd/JkR7tK5fhzxfHxeGL//4Jbnhhs26f997VJ8Ue7+pzJxzz3/yHvOlnoTGI0CDdqQTipflkqWzJLKIhGqEdy3T5uADtuHLRrAaRLn8YVVo7ld0bgPJaVDVTpbPP5QcmHgN0bVA1oDswRNcRXPNjCN9DFNEQ82hQKzQIwlCgOFMnhkb4iIYshAaTiaDSUiqbY5qAMBtdrEKDlkENx8kIDXRWPm3lCa0pRq7u7ISGLHwx+lwB5bKTPONrK+DwhqRFljT0umhEQ1qhwSvt0UAIUWeIK1BeW7TlLTV5NMhUgQHiBsFRkQFeCpV8CapM7glKnj0C1WOyimgIhqPwh6JFF6LMyD3Pr48LVPf/3xGy623rSby3Ss3eFlMZubblqzD9usRw9L13LMWvTp9eoBYx1HLVE/HjqNijTtSIDRzHpR0U771jaWxwbVabTnoQcGRbXYLwIMfBKjoILJszBhtuiEeV//ujfZpNOuUQTwyfete7AIB7LpwXe61Y93lxn0UcB0SC8SoGIgQzNFU5uZnMXrm6gGhIefZKmPnP1HASNP9Nc+pE90ZgzFw083m8vW4/MPZw6lDfu0Xx7cIM7ITkiAbhe9jiNWGt5hJUlpVicESt0KBfRIM/FMHPnliP4+98E+v36zMjKwgm2UQ0ADTUWzBSTIswKPcVpyGk0xdWH1HjHwaiYUmPBkBOaBhP/2socfnixi64+/fhM0c5OC7NwFEKnVInlCpOCAhinaQZZhp6XQGYSwhq083kC5UiJHxiasrNykKrQLmd+oREVK6fR4T0D1XHoCCWyIjPUQ5wpxN8bNkIDXwVonSpE8JneDIvZ+vOsNwxw/hc9eSG2OMlh7VIrsNxHJbcHc+nLubBXyAcSen8Hje1Ae0rl4EQgrte21GgljHU8vyG7NLA8o2c2CAci1JVIz665uTYAJpVOVGHWHR48FLpijaC4PDG1gyjyg2KvcKCvXcsjT3vdwdyJgKcMy+xKls4Es3J52SDLkIDIWQJIWQ7IWQXIWS5xPLFhBAnIWQD/3eDqg1zfIcxTUTDsFRJPTFVLZmZQartVAozZJ7MhQbNqRPRKNC/HWiaFZt17XUFgDHz6PIu5ZkMwbwuJXVC+B6i1AmAzv4PelR6NJSYadlPHYSGRz/ehxc2dMEXjODKJ9brchIJg+Fa1UKDXXJRlbVUfdUJoHgjGnwhVJerFLqEGfZMIhpUegQMjARw838/QRW8eK2jBG9t13huWWsAkKyEhiFPEPWV6oQG4RzqGNKWGtLn8qOpypq+Y+NzAJZKoDT1WFWVKiAgHMNFWOLS6aNmpJZSFbejtBENdB+l9U2xVNDUroxSJ9rpdS3p2phCZSO9jmoVyHiEdCzV5yQjgZz1R3KM+Np5VJt8yVvxQOnZHy1S3O6KrxyaXcMypG35Ksy4LtGbp33lMjx62dGx5+mCjxiMTJESG5KPRWG99pXLMKam+AxIi8lXRYmTZzanTbH43r/WHXRRDoSQjNJ5lNh9e1zA2D9Ix3Hiz1FrZJpPshYaCCElAP4M4EwAhwK4iBAidWd7j+O4efzfzao2HuUHlRLGX1VWPkdeTU6uz0GNvLQQC5NtS79ebIYsC6HBH9bWqXR1AGE/0DAVlWWlqCwrpWHYNa00TJ/3b0jH/iEvys0lqbP6wkxcZVPCy/WVFvUeDQCNasgyVDsa5WjYUVstbj33MBwY8mHNl9kro4OeIAiBctWJkA+IBOQjGqxmlakT2Yfy5xKXFjNI4fioSIpoqEjj41E1BjCVqq488e8P21Eb7gcAuMua8dSnGlMuTCX0N8tCaHB4Q6hT4xmAeESD1soTfe6AYvlM+IZkq97UlJsxrLa8pbjkZ5Ex7NVw/KURGmrUGmRWNmUW0TC0l94PlGa8bI30+hxwa/8MZFHumJHb/kiOmX9LPN/3KZl86OSO6vwJ0tcGXzDuGXXpsQqTJTkguZ1nHtZS1JEXDGnE0YRG+/3StVcp/L8Y8IUy930rJOIUi3nj7YVuTk6442uzVa+rt9ggrkhywu/eklxne09mfY9coUdEw1EAdnEct4fjuCCAJwCco8N24waLEqkTJhPhO9oqOpWAdiFgaC8dHFWnVrxI3L5eqRMaOpWDu+j/+mkAqGt9nytAO8AthwF9WxU3cWDIhwl1FamzqbHUicSBZL3NggG1qRMAHQhkWd5yW48b+4e8uGDBeJx2aAuaqsqwalN3VtsEaNUJe7k54YSVJM2gBqAzjqpSJ0otdBa1CIWGSJSDO6AhdUKIyqhInHGrS2cYairhy8Cqi2hYvaUHJ7XQ7UyfNhNvbOvV7H+A8tqMhYZwJAqnL6S6bnS9zYJyc4nmyhO9Lj+aFcpnwueQLK8K0NB6txozUiC+jSI0hHT6QqhRW6NbwaMBgLL4UtmcYUTDXmXhGYiLz55+7Z8BxH7TKiY0ZELu+iMFZktXYoRguoHS3JvXyC7LJZs7nZImlX9N4zcxmvl4T3FGMarlsU8yKEtdBMjNnhtBYBB4ap3GCZYi5PkfH5t2nxs1yuHCI8drWj/5+x+78k09m5PyGWfc/a7u288GPYSGcQDEI4gO/rVkjiGEbCSEvEIImaVqy5wgNEgP9GrKzSpmr/g8R60dS0c7UDMeKFGINBBKaGaYOhEIR6jxlxaPhsHd9H/9VABAS7U1ZiyHxplA/w7FsN0Ohxfj6yTCxTz9QElZvHIET72tDENqUycAXmjILnXiI/4mfezUBpSYCBbPaMS7O/qzTp8Y8gTVG0ECaSMaVKVOAHRgXoSpEyOxQY3a1Ak+oiFJiLJZSmApMcUqeqRgn6AqomHfoAc7ekdwfDPdzswZhyAU4bT7c1TUZeyJIYiXamsgE0IyqjzR6/KjWTGiwZFS2lKg2mpWV14VEPlWDKtvYJ5w+oKoURvRlU5oyGVEA8fRe4JSKh0QF58zFBpcMY8GljqRAbnrj+QQccdTrlO+7N73FdcRCIbzn6fbtnwVzrovseqVUQZ1ueLCv31c6CZkxXXP6+eYnw+iUS7twDXCcnUKxmgSHDLx8RB/985hH1a8qOylJ8d/Lo+nn4mjjg4ZEx+3iasOFRo9hAapPZ58Nn8OYCLHcXMB3AfgedmNEXIFIWQdIWSdy8kPLspSIxoA2rFULmeWofmXY6+6TiUhdAZrJLvZK03GX4O7aN52FRVRmqut1AwSABqmA0E34Jaf+ec4DvuHvGitrUhd6Omnob9JJ1Idnzqh2pivrDp7oWH3ICbWV2CsnQoii2c0we0PY8OB4ay2O+gJaBMaZGaUq6ylGmaUi1NoEAY1qkPXYxENiUIDIQR1NkusokcK9gmqPBo+2k23P6vSDYDg0BnTYSLAp+0ahYYshC6HFg8Pntback0RDb5gBC5/WLmyhc8hmzohHH+qzknBo6EIUyecvlDM3FcRvxMgJYDFlrIoVolITTqdVuHZ3UPTIVRFNGQX5cZSJ7JCt/6IuC/S35/Z/V0tncPprx3iTri4fFqxkDxIeOfXiw96kYGRXxbd8UZKmcrkwe0UmTKWjOLAaIKDVsTH4sMftuPzDA3uF02J97/ve3NX7PErPzs+9riYqg7pITR0ABDHkbQCSJBSOI5zcRw3wj9+GYCZEJI4Uomv+zeO4xZwHLegupLvTMpFNFRYlFMnqoSIBo2GkI52dZ1KgM5gZZLziww7lUN7aYk1Xgxoqi5DrytABxyNM+g6/dvl3+4JwhuMpFacAKjQIMzIiai3WRCKcDGjMkWsNVmXt9zYMYwjJsYHWUfyBlmZnpwCQ54gGtQY/cVmT+2Si2noekjdQK+iviirTghCg+owbc8AUFpOTfWSqLVZ4EgX0eDuVvRKWbfPgTqbBbWhPqCyCVU2Gw4ZU4117Rr3XTZCAz8jXqc2nB/UEFKLR0MfLwwqVrbwyns0VJebEY5y6nI5izh1QrNHg7VG0iehWrXQ0ESvTUENEShqS1sCuqVOsKoTGaFbf0TcF2lsVDAAzQLxLKtUSO7W7sT76LI5Y3LWFq1IzSC3r1yGifWpQqAUvz5jRi6axTjIaFu+Cl1Of+z55EabrNCluYoVIyesuvI42SiH0Sw47LrtzNjjr/3lQ3i0pgUnkVy558cnTYk9fkWHVHM90CM281MA0wghkwB0ArgQwMXiFQghLQB6OY7jCCFHgQocytO7aTwaADqDtX/Qk34bmcwu+YZph1xNpxKgHUuV+efJuLWGrgP0s0QiSHOVFcEwn1fewN+4B3YAU06SfPsBfuY1peIEQPdTVWpHpp7PwR8cCagbFGSZOjEwEkC/O4BDRaFAjVVlGF9XnnVEw8BIEEdNUjGIFMLM06RORDnAE4ygUqnmfUU9MCAv/miC44AvnqRmc/O/LVmRQC2Cx4SmqhNJFScE6m2WWOnQFGrGA+AAZwdQP0V6HQCf73Ng/oRaEFcn9XUAMHtcDV7d0gOO49SHrGXhESL4TNhVmkECwPjaCrj9YTi9IdSoeF+fmwouzekiGjiOXocqpFMnhGuG2x9GhUXh9yvyqhOq93WacrNWcwnKzSXKlYgqm+n/kV51UWuA+ipEQDytKIvUCROh6UgMzeSuP5IjxLOsK8+bk7L8zHsyL2X5rRw617cPeLD4928nvqaxfd88egJ+96pO98Ui4sPlJ2ORTnnYXcM+3balhs03naHcnykipISuZNpXLoutN+malw0VbVOoqjG5QCzyzBobv48Lv0fybyk8N9LvpURpiQnvXX0Sjr+TGjnOuvFV7L1jqW5lVX99xkz8+S2aXv/Dxz4vin2X9dWE47gwIeQnAF4FUALgQY7jthBCfsAvvx/A+QB+SAgJA/ABuJBTIytyEYCYaJqABKpSJ0rMdGDk1hDRoLbihEBlE9D5mfrti3BprZnOcTQEvS0eItMsKnFpb26iHfE0EQ37h4TSllIeDQNAS2pnR1y+cLKayR1BaOA4ZZd2CQTXVHHOEQAcPr4Wa/dmHhkQjkTh8AZRb1MT0TBM/8sMbISBnssXUiE01OlnBrn+UeDFn9DH7h7glOsz3lTs+FMb0eB3yqaS1Nos6JCb1RdKXDoPyAoNTm8IewY8OH9BK7C5MxadM725Ck98egADI0E0KkUACGQV0UAHqmo9GoDEyhM1FdLHixjBUyWt0BBw02ugXEQD/5u5fKH02wGo14ylqugiGvyhCALhqPrrn98pKzwDVBxSvCfEoty0CA3t9F5Uo8IEqsRMf7MsUieqrGZWzz0DctofKQDijrcWp3OBG7+SG/uJP7+1K0EgmNJowxu/XKx5O2oNd42GkO4J0JD+D685Je36kShXNGH9h934quTr3U5fUZWBjEY5yVSJ0YDYVLsQVWNyhbg0rxTpBIc7z5+DCxZoM2EsVsbXVeB7x03CP9+nExiZiF8XHjkeT3wqPcF94vRGvLODTnRQLzCF/mGO0UW25MMPX0567X7R4z8B+JPmDUcjNNdfpsMldCqjUQ6mdBUEKlu0dfoEoUFtJ7SyiZrkRSPUYV8DsRll1QO9YerBYI+fcIKhXK/LjxktVUDDDBrRIINgWjc+2aOB49KmTgBQX3nCWg1Ew7REpESYvRJCuOjMlqqE1+e01uDFjV0YHAmgXk36QxIObwgcBzRU6mAGyQ+OXP4QxkLhBlxRBwRHaOpAqfZ2x4hGgDdvBcYvpMfAh/cCx14p20YlYmHaWoQGmc9KG9EgHK9pDCG/5H/zWWOqgQ86gSm0cza9mR4DO3rd6oWGsmpamjTkB8zaLrLCTV6x/KkIITqow+HFYeOUf4sep4rUCSHVJo1HAwD16UzltUXn0SCIAuojGlxpj3VVBsF8pAxcneo+E6DpatWt6qOHbE1ZRDRoLHfMSCBn/ZEc84MTEwXYaJJx3UVHTdC8TcXKShmQPAh4+crjcehYefFPLVu6nAmznKMFcUg/QGd1lQZcanj2R4tky5uqYVffCE696x3V6x9zR2JUhaXEhB2iMPB8IiXMKA3UNt5weqwai6boyAIgLnV7MNK+clnKeXL1M1/g6me+wO7bl+bkupZvrj/r0JjQANDrqhaxYeV5c2JCQzgSRWlJ3AnhX989KnadPvr2NwouwBV3b4aLpJ29qimnoevuQDh9OL9Wl3EhTNauMuzQ1gRwUTpjLTFIT4c7liOv8qcQDPVqxEIDHUj1xCpPTAd2yJe36nB4UW+zwJY8C+8fBqKheLqJiPp05QulEAYDfmdGQsPO3hHU2ywpYkJ80DmCYzIQGgb5yhl1qiIanNSPQEYYEAbnqgwhhXQD7xBQnUWO7d53qd/Imf8PqB4LbHoa2PEqMOeCjDYX8whR7fo/LHte1FZY4PaHEYpEYS5Jsn+pHkdnhNMYQsaEhjoAIQ9QQweE01toRNOOXjeOnSpp7ZKK+PjTKDQMe4MoN5egXEPouiDaHRhSZwjZNexHubkk/QBbiD6QqzohErpUUV5TdKkTgtCgyaOhYarsYlUlj6vH0v8uDa7Mjr1AXZv69SszFxo0lztmGJYfP/Z57PHyM2cmLBPP1ha6oyiQLDLo2elfdu/7RfM99eCUmU14Yxud4NKab37ruYfh/xbmLu0FAKY2Vabd30qCSDASTfheb/1qMSY1qPPmyAaOSxQZvn5EK3739bmK7xOnNP7k8fVFaarKiEMIQfvKZegc9iVU5RF++9FwrRCn9ADAb5/bhNu+qj1y7drnNuHO85XPgUKhhxlk7ohGFGevACiXeKtq0Sg0tNOBYRqRIwFBXMjAEFJz6oTgBSGKaGjiIxq6h4XKEzNouU2ZMOn9Q160Svoz8B1jwcxMhBBGPjiissSleKCXAe2DHsmb1oyW+Ox2JgzyERn1qiIahtMef+LUCUWEwWK2lSe+fIGGwE9fAoxbQP00tr6U8eYEkUR1TmaaiIY6fp86pMSoEjNQNTZ9REOXC41VZWiI8schP/PcWFmGmnIzdvWNqGsjIKqyoP34G/KENKVNALQTU2UtVW0I2TXsw7ja8vSzKjGhQTl1QhVWe9GlTgjRB5qqTqQ5J+0VKkp+llXTdDxNQkO7+lQ6gIq1GadOhLV59jAMyyoZs64BtfdZCZIjIfRCKhd+NMws5oKfPr4+JjIosfeOpTFTPOEv1yKDGoSBnoDQTjlO+v3bMRO/ZANTPRGLH5cualMlMiSz6oviMMmT4tGP98Ue7ypQxEguEB8Te+9Yqvp94+zlaF+5LMWzKNfHWTrCEf3KB4vPqcc+2Y9AWIW5dxJPretIu91CG2sWd28mGgHK0nUqaed02BvCeOlJP4oQ0aDWL0Co6qCWmMu49o6lyxfWZvzl5A8oUURDWWkJmqrK0DnMD3JilSd2ABOORjIHhnyYO96eum1hBk4iKqOstASVZaUxR35FhN8tw8oT+wa9krPXTVV00Jmp0CB04FSnTqQZ1GiaUY5FNGQpNLS/B7QdF5+lbzse2PtOxl4YLn8INktJQthVWtIJDfz5OOQNSpdttE9Ia5q6rcdFU2WcfEh7TSsA2uGZWF8R8xZRhTXz48/hDWoyghQYX1sRS0tSonPYl5DHK4mi0BA3g1RFeW3alKpCkFFEg0wVGGE7iqkThNCoBrWpEwE3vTZquic0ZmUGKVkRiHHQsODW12OPtc7chXMgNKgx3DvYUdOZN3KeuZBHnvzbS31vsYGpnkZ34s8qNRGsODs3HiSF5LrnN8ceq+6XGQDxMZHJ8bDl5iUAEo8BYZv5vh5d/8IWXbe387YzMe23rwAAZly3etRdX4v7KObURTQM+1S4jEeC6mfztM5exVzMtXcs3f4Qqss1GH+5uwGTGahIHISPqy2P1+Ku50OLh3anvD0ciaJr2IfxtVJGkLxQIpE6AQhhyRmkTmjEF4ygx+VHW31qZ5sQgunNldlHNKhNnUgnNGgZ6AlCQzYlLl3dwOAuKjQItC6gIpqWfHPxJn0h9dE00QgduMuYQQpRAENyPh72CbIRDRzHYe+AB1ObKgEXL6YJufQAJtRlKDRk4Ekw5AlqjmgAqLlqh0Nt6oQP45SEBm96jwbtqRP2IoxooMeKKqEhEqIpNWkjGizqrlHVY9VHNDj4GSZN94RGeq6E/MrrJqHpnGQYFrH/5GfXnRp7nO0sXUjH2TYg9yLDhhtO03V7+USYwVc7Y2hUkSEd4kgMKSZd8zLalq/Czgz7bAJHJPkW7Lpd/ay4URCX8b75nNEnouhB+8pleOknxyW81rZ8VVZRYFp5fK18ZG4mmEtMmCyK4NYrAuGJKxbGHjvVThLngOIWGqLpPRqEWUdFl3EtObmREI0aUGsECcQjADKJaPBrDJN1ddNUEFPiTzfOXo5OYZBjnwCQEmAwVWjodvoRjnLSpS09A/S/ROoEwJtvqj1YsxAahAHlRJl8v+nNVdje486oHvKgJ4ASE1E3sFEQGqq0hK5X6JA6sf8j+n/iovhr4xbQ/x3rMtqk2x/WZgQJyEc02OIRDZLYx9NzMJIqzPS5A/AGI/Ri6+ygx68g4AGYWF+BTodPfchaFsffsDeoyQhSoLW2Ah0On+Jx6QtGMOgJYpxdwTtC8FOQERrKSk0wlxBtEQ3F6tGgJoJEKFeqID77Q1H4Qwrhh9XjNAgNGkpbCsSi3DIRnzWckwzD8o/34kZgYi+ibMpZAvS6oBf5iGQQV5449IbVum9fb7b1uNKKC099/xjFwbeReOHHx8YeK6XlpPvep/3xXbQtX4U3t2lPMQaQYDQ9GvarFOff/1Hs8beOaStcQ3RGXMlES9qEHLNba1KOgQW3vl7wFIFsePNXixOeb+vJPi1k4eR4KXrBCLUQFLfQoBDRYBciGpQGv0KaQZqw7RjOA/RztcxelVUDptKMBpEun0bjL3d3vDybiHG15ega9tMbQYkZqJ0oGdEg5JCnVJwAaE4xMcUHxUnYK1QYrQlkMdBrH/QAACbKhA9Pb66Cyx9Gn1u7gjk4Qmer01YpEVAQGiylJljNJnWu/zGPhiwiGnq+oNEszYfFX2s5jB573Rsy2qTLH1IvdCkIDbU2ehzLGobaJ9BzSyL6Yk8//c0nNVTS1CX7eFqSkWdCXQXCUQ7dTpUzxFkcfxlHNNSWwxeKKFZm6XJSQXCcVFSRGJ+DegnIVDoghKDaqsKTQMBq5ytxqIu6yAdOXwiEAFVqPEIUys0CcfFZcZ9Uj6WlYSVErxSG+AGh1tQJQLP4HIlycAeYR8PBwG0vb015TRypN7c1swoMevkmFCJdwhvUnp+cLwRxYcnd76Use/oHcXHhqEmJ/aeNN5yesA2jIU6zTS4nmQ5hf1x2XOJ187sPr0Pb8lWxyktqEO83PQaqxYj4Oz506ZEFbIn+jATi91k9q320r1yWsq/alq/KaBKyGBBfY6WuM8l8+5jCe7moobiFhmiUDuJlEMJLFSMaYkJDqmFGCpl0KgmhA8kMBpGaZ6/cPdQAMIlWezmCkWg8fKh+Kg2zT6KDd8WXzAH29NEQf5kSnfZySyzUWREhEiWDgV73cPqBmFB5YnuP9lC8gZFgrFSnIr5h2TQBgSqrOVY5JC2lFnosZyU0bKb+G+KBZ2kZUDeF+nFkgMuvIUw7NtCzSy4WPBoG5QbaaQS/vQO80NBoozPISeffhDoa3bJvUGX6RIbHXzgShcsfzsyjgT+nlAwhhcijsUo1yX1DstEMAlXWUg0RDXZ+u8WTPuH0hVBTblYv/AEq0+lUCA1cRJ0Q4Ginx7zCtSCBSiGiYUD9ewCMCOVmWerEQcnpf3w39viFpPBgtejRkX/4g70Jz0frDLIS0SgnG71QVVYaG0wf2SZvEpYcrWXUQZCA1vZfd9ahaF+5DM+LIiMAYOEdb6gSXpKjGIu5LGWmJEeKnDRTOqrYiDwhSjN48SfHplkzM06a2ZRyfZp0zcvY06/BPDxDls5OnfTNlgcuOSL2WOn8WH7mIYrbE6fleQIq+4o6U9xCA7i0qRNWcwmsZpOy0GBrBEos6iIaHO30v5aIBoBGAWSQf69pRhmQFxqE8npC+kTdFGBwDzUJFNE+6EGpiWCMVNi2SzpaQqCmwqy8rwVKrXSfZyI0uPywlJhiA9dkpjfHyx1qZdATQIOaspgcpxjRAFCfBpdPQ+h6NqkTvZsToxkEGqcDA9sz2iQVuvSJaCgtMaHOZpHPlbPzdeAlfBr2DoygrNSEMdVWKvbVTU5Y3sqLTjHDUyXMFTTSQ+PxJwxQM/NoEEpcpm9jl4KQFsPnUBQaqsvNGjwa+G0VUfrEsDekzQgSUIhyixsEp0Xw/1CTPuHYqy1tAojva43CYqwKEYtoOOjIxG08Fwx5gljx0pex5/kQGcSz1Nc8uynnn6fESCCMtuWrJGfw99xOqy9suukM1dsT78N0JSOLFT3aP2+8He0rl+HO8+ckvN62fFVC+cJkpvImecntGE2Ij7PRFrGxXHQ+z2m15+xzko+Nk//wTs4jiO698HDdt3nGrMQxmGQVNx41JdjFaXmzRCks+aTIhQYoDvSoy7jCLLvJRB3sh1UKDSVlkoP5tJTXAV7tM4WajL+CHiDgBKpT2zYuNhATDCGnUOO0pJKbewc8mFBXAbOUm627m5YglMHOO7qrUrQJob9dBkJDr9OPpuoy2VnO+soy1Nks2J2BYjk4ElRX2jLoUUzdATQO9CrqMxcaPIP092mREBoaZtDBeVhltIkIly8U85pQRMVAr7GyTD6lha8iIXUe7h2g5UxNfgeNnEga2DVXW0EI0DWsMtwydvxpy3MTLuqZeDQIUUJKURedwz6YCP1OaVEhNGiKaBAiUYososGup9Cg1rdHuL6rMVF1tGuLcANEoo62fa253DHD8Ky/npohzrgu7k+g14CqQ2W5XQGO4zBfZLq37ZYlurRDCfEstd5Ga1oY9gbRtnxVQk65gBC9oCr6SoKZfGluwJgpFGIufWhtxu+9YMH4lOO7c9hn+H2SKeLv/Z1j20ZVxMZFf/s49vjBSxfk/PPaVy7DG788MeE1vY+rC0Q+GrmqCiI+Pw5PMkE1IsUvNKRJnQDoDJaqWfaaVnWpE4691N/ApHHXZBzRoCF1wt1D/1dKeDTwDvYxQ0hhRjgpfUIY0ElvP31Eg73CjHCUg0dtHmVZdUblBbudfoypST8Im9xgw24+r18LgyMB9RUnAEWhocpqVufRAFChIdOqE728KiwZ0TCDiiISnhzp4DiOHn/lKmdPY+aEdtlVGqvK0C8nNJTyAp5ERMMe4bgUjPeSBnaWUhMaKss05XVmInQJ/hKZRDRYzSUYZy+PpYHI0TnsQ0u1VVrsE6MmokGLR4Pwu2VQiSNXDGsRWrWkTiiJz2ojGiIhWnVCa0SD1U79bjSe70J0FPNoGN2IoxdqM7jWqOW4//eWpvXFs9V/+PpcWM0qy27rwOmHNiuvlCOC4Sjalq/CvJsTO/WVovSIbFl91QkJzz/anWWp6zwj3gdvb8+sdG/y9qRKZXqD8f7U61/2JqyvF5tWnK68Uh5IHgTf+JXRVWnioz3xY/zkmfk5v6c0VqoqwZopa9uzSH/WgDjy+t43dma1re+fOFl5pRxS/L0ZpYiGChV10wGaH75bPjwrxuCelLBtVZTXag6TjUQ5jGgx/hrh84mrUk9YW1kp7BXmeGi5UOJycHesHGI0yqF90IPjpjakvB+REHVIr04X0SCEJQdRqca8zVqTUZh2r8uP2QohVpMbbXhzm7abnS8YgScYURfRoFJoqLaWokNt2cWKuoxTHNDDCw0ts1OXiX/rJuWcLQFfKIJIlNOt6gQANFWVpR9o14wHhvclvBSORLF/0Isls1qAIb6GtMTAbmyNNWakqIoMhAYHfy3R7NEwvB/44F78X3ktVvefmHbVTocPY5VKWwL0eqKrR0PxpU64fCFpvxgphN8yjfhcozaioaKORq4pCQ2OfVTEE84xtZhMVGzIOHWCRTSMZp5YmxjVteLFeF32QoVOJ3fGzzuiNa+f/7dvLYi1oW35qryFyUsNQk6e2YQHc2DI175yWezzLvr7x9h7x1JDzWDvvO1MTONTGfT6jcT7BAAOveFV/O78Ofj6gvG47N+ZVdOSQvwZqqM4eTiOwwUPfIRP23MbDdi2fBW+WHH6qLj+i/f359fnv4Rt8nGVz2uKHqy77tRY++96bQeuPGVaxtu65sxD8MA7ewAAL2zoxDnzxim8Q18MIDSkj2ioKTcr5kTTFcfTiIBwUNbFHdEonRWecpL2dgoRDRxHw7ZVoNn4SyiVJjiaJ5FQ4rKmlXokiGa5e1x++ENRariXjBAtoeDRAND859b04x+KtUZzRAPH0coCpx2aPupgcmMlnlrXQc0MVV6UBz10pr1Bk9BgT7ua9tSJDNXQns00ksUmIRIJfiJJA3gl4rOnGoQGYqKVEGRorCpD/0gAHMdJd6DsE4COxLDLDocP4SiXFNHQlvLWMTXl2KUlXSYDoUGIDlDtGwDQc/6pbwFd6/FDAOthAccdK9uB7HL6MH+CwgnEcTSiQaYCjEC1VcPxV4SpE8PeIGrURtSoOP6qykpRYiLK4jMhVFRVSp0Qrp9ahQYgoyi3jI4/huG4USQsAMDDH7bHHhdi4HnXmkQB3Egd8kyREhhKTAS7b8+t0LP2t6fgqNveAEAjSIy0r80lJhwxsRaf7aP3ED3Fhje39eK7D1Nh4dfPfIHnN6hIa8sBgyMBHHHr6wX5bACYs0K6DOGOW8+ERcfStblkMMmnK5MIUT2QEhuyEffEaeOfXHtK1u1T4rWfn4DTeINgvc61nz2xgQkNKSgM9OzlZmxWmzoBjnYs5cJgXR1A2J9Zp7K8DogEaW5/mXxHOOHjtBp/qRAahNKQMJXQ8PPBuNDQLjj716cTGuQjGoS8ddWGkNYa9bXqeZy+EALhKFoUHPkn8+kfe/o9mCcqv5QOoRqCutSJYfpfMXWiVEPqRB0QHAHCAZpGoIXeLdL+DACdqbZU0dlXDcTzwdUO9Ibp/khzkW6sKkMwTCs3SA6WGqYBm/9LSyya6W8sREBMbrQB69uBymbAknqMjrFb8d7OfnkRI5myampwqoGMcuR3rgG61gPL7sLIG3fi/zwvYGDkSjRWpf7G4UgU3cN+jJ2jENEQcPMeIfa0q1VZzfAGIwhHosr5gmXVdKBeJKkT0SjHezSo7IQEXHwpYfnvSQhBTblK01r7eGXfHiH1LKN7Qq1mUUeIThkNM1oMdYg7r4XokEeiHO59M55iWciB7947lsbSN3I1A9nn9scG+mLy9b2bqqw4e+5YvLiR9o2MNtP63x8uyslM8ckzm7H79qWYwhsjfrArHnZ/30XZme6J27vj1jMTljk8wYzy4BdOrsN/Lluo2bMj0zD+6de9kvD8scuOxrFS0clFgFioKfSxnSw2TLrmZey+fWlGZYDFqWWKHls6MK25SnklA1D8QoOSR4Pa1Am7qMSlnNCQTadSmHn0DWkXGlRHNPCl0irqJRe31lbgvZ0D8YFY/ZQEoWGPuIRgMm5eEFDwaAAAh5YSlxpnlLv5HPwWhZN4ciPdx3v6R1QLDUI1BH1TJ8wIhqPwhyLK+azl/DHiHZI09JQlGgUGdwKTZULyCaG+IhojGoSynJoiGhQGvsLgut8dkBEapgPg6LnGp4HEjsuGSmBIPnVpTI0VnmBEXsRIJoOIGqcvBBMBKi0aLo1fvkg/a/630X/gAE744o9Yv3cbGufMTVk1Fr0hJfaJEUxDZc51AUEkcvvDyrneJhOfzlQcEQ0jwTCinIbZexVVYADeIFiV0DAR2KHgwjy4ix7zCpElkpTXUd8bDQj3hErm0XDQIO686hVi/NClR+I7D3+qat0pIsf7fJk/ypHraA6pQV4hBkL3XnR4TGgAjCc25CosvcREUrYNAF+ZKz8BphVLqQkf7hrAxf/4RHHd2eNq8NJPMyszK0Xy90q3z3zBCA65YbXs8m8mtX/7rUtQVpo/TxU5xN/xj99I7QMVgvaVyzD12pcR5suITrk2c7Eh32y7ZQlmXk+Pg+TzzCilcos/DkdF6oQvFFEuDVUjCA1pZrCEQXlDBrkw4kGkSjQbf3n66SxZiXTHfFKjDb5QBL0uPmypfgoNRY/SOsR7BzwoN5eguUpiEB+LaJAfANtjRmsaIho0Cg09Ll5oUDCDnFBXgRITUTTeEyPsF1VKpIbUCQDqwteFQaN3QHldMa5OPtJmivw69onaIxp8wuyphtB1hYFeY2VcaJCkYTr9P7Aj9tLegRHUlJtRW2HmzVilhUDhd5PddjIZpk5UWc3qZymiURrRMOUUoKQU1rnnAQDC26VnSPYOphH7xAjXEQWhQRCJNFWeKBKPBid/HUmuMy+LFqFBjRhaOxHw9AHBNKl3g7szE54BPnVCY9UJXxiVfPoHg5EpJ81sij2OROU7o8kDn3yaP8qx67b4jLNeJm47et0p2/rbJUcUdHCfS8O6fJDL9if7lGQzoEpuV9vyVbIiw+abzoiZVLavXKabyBAIRzSJDAAtXShuS/vKZdiSpqTqjOtWo235KrQtX6W+j6Qzyd/xq4fn1+clHbtuX4qFk+MTBlOufVnTcSVe978/XKRr29KR7pr8RYf6/q34nMq3QFHcQgMxKYaY16gN5xdcxtNVnhjYCc5Siae2BrWXThRHNKhEs/GXp182bQIQpRMM8G2vm0IHqHwecvuAB20NNulBlKsLMJnTDmyEQbWm1ImwT1PZRaGqgJLQYCk1YXxtOfZoqDzR4/KDEEiGtKcQExrSC13CIF3VQK+SN/EUTD3VoiZPvLaNRjRouIBojqhRIzQIEQ0jMje6+qkACDAQd9Hd008rTpCQl84Ay0QcCS68A3LbTsZqB0JeTcef0xfSlh/fv5UOVqfRmcimtlno4BpQ3fWu5OpC+lKbXhEN/PGn2qehvDbj1IlNHU48v75Tt5uUU6sfgUqhwV6hshKHvY3+TxcJNLgb3uo2PPnpfvX7WKC8LiMzSNXCH2NUMadV+djOBHHEgpjkyZlimVFPTgHL9nrTtnwVTv9j4vW4feUynD5LPnozX0gN1kORaIFaox2p9m/u1F7SPBlCCD665uTYc3HUjxZuemmL4jrigbwqk3ONtC1flVC6VvjMTLCJqqC0r1yGjTdIV8848rbXY6JDOE/H0xcdwwnPi+V6IuaJK47B8dPi6SZajivxukdMVGNSpx/iSLMzRNeyc/78geptiKPFXt+qcQySJUUuNCir68Isu1Nplt1sBWxNCp3KXdiHsbj62U04908fxAYFqsggokEYnKruaHsG0goNk0S+BQDiM+B8SggtbSnj8O7uoWkTafKfreYSlJtL1M0WAvFoAA3h6z1OKgY0qRADJjdWahKE+lx+1NvKlMsKAnRQY6mUjR4REEQiVQObSn6WSavQoCalp6aVDqo1zKAK3hKqI2p8w2lLWwJxoaHPJVOG0mylokjfl7GX9g54qEjWt42+IFM5Q5yWoQphUKrh+NNU7hOg3gwA0EodyktKTPjCMh+tzs9ikURi2gc8qCwrVTYkFQRLhZB9IaJBvdBgzyh14oNdA/jKn97HVU9uwP9bnWHllCQEocGut9CgNnVCMByViwQKegFXBx7cWorf/HcTfvjoZ4immR1OobwWCHmoJ4tK3H4N5T4NDiHESgg5nxByDyHkaULIvwkhVxNCRleNN5W8+BP9QrTVIB78FNugQNyeTAeZHMelzLC+8csTi/q7AsC0376Cv72rrVR1IUlu/1n3va9LdMOYJJ+uSddo2+ZFf/sYD33QLrlMPFjPFeFINGU/3HXBXF0/s6bCrPhdpv72FbQtX4VD06RhZEskyuHsP8UHvYWqnKOGR753NOpFaaZGiCQSRzVs73Vnvb3LdazmoobiFhpMykJDrG66mo5l3WRgaK/s4kDvdmz0NeDCI8cjEInin+/Lr5tCLKJBw0DPJ+TIa0idkKo6wNNSbUW5uSSeTlDHCw1DuxEIR7B/yIvJDTL+Ee7utGkTAqo9MYC4v4aG8PUepx8NlerEgMkNNrQPelR3/ntdfjRXqzRhFIwPFRDnyCsSi2joTb9eMoO7AXNF+t9H8NYQUmBUIBx/mspbKpWbLTfDUmKSj2gAqDdDDy1j6Q2G0e30U5Gsj599aDpU8m2KaRnJCG3VcPxpjmjoWk+NOOviaS0D9rmwRUeo30QSewY8aGuoUM5F1ujRIKTBKFJem1HqxD2v78SYGitOPaQZD36wV5fQzFhEg6bUCbviajR1Qo3QMJH+d8hc5/nfb0e4CV+bPw4f7BrEB7s1pD1VCOVEtdwTNJQ7NjCEkBUAPgBwDIBPADwA4CkA/5+9qwxz4zq7Z8TSSistk2HNjHGcOMxOvG3TpCmlTZuUvjKDwxxv25Sbtilj2pTStFmHmWMncWJmGZZJWjHO9+POjEbSwB3BriTrPM8+uyuNRlej0ei+5573nDiAboZhHmcYZvnUjbCy8cW/vjnVQ9AErRPjFw+MZBEU7u4uzGmi88+abLi7u/D996X62e/avKcsCiAe7u4uvHZ9ugt/58Ye7OzLX93Ag1bY8vLBUXRu7MHLh0bTbv/P504vOrnAo3NjD+Zen27e6O7uwuWri9tKoEQ6BKMJQeVQaOm8WDX1yrXnl3xk6+sZXjhqn7WzvvO08Hemmehk4V+fWSf8vdWdY3rdFKHsiQbeoFBV0QCQFWF+dTgTsTCM/l706Ttwy7uW4B3L2vDvN44jHFPxfuDBZ9Rr8Wjgjb9o5VoqrRM6HYNZjTU4MMSt8jvaSIE6egj7B/2IJ1ksapNpBfD1KxpB8qA2WgNEhZ6HbnuQ9oY2lbYJHrOb7AjHkujzhqi2H5yI0DvFhjxURIOmFWWzHTDW5KBoOEgKWaWLdy1nlqTBfG4iHIPJoKPvy6UgGhiGIRGXSoVo63LSDhLxwT1C+uNnN9mBwV3kfJXxaHBajTDoGA2tE9rPv4kQfVwqAKBvG9C+Mk0JlGxfDQCIHs02Y9s74MN8Gifh4ChRdFGYkQIpY09VWFyaFQ2Hhv14zT2Gj50+C9duWIhoPIl/vK6S1kABngygTp2g9WiwmTARjqkTkDVNhCQalV49TIyQ74ppc5bhrsuWodZiwD9fV2i9y0Quvj0a4nrLHFtYlj2JZdmvsSx7H8uyT7As+xDLst9nWfadAD4EYGoy0SoEYrlt5jxGbERYaiv8PMTjenwXPTnfubEnzSiv1mIo2dcoxuWrp2HvHelmnMUoCouFZocl6zh3/bgw6gYeSvviFSwf/NUrabf/5IOr4O7uojYNzwd8IS/Gp8+eMyXnH084vLTxvKz7Zl27GZ0bexT9W2ghfr03dC1SbXsuFWjxGDk6lvJxmqqI0ZNmptStV/zi5SkZQ64o7aUTqtYJMhehKn4b5gDb/gyEJ7J67xOjB6EHi5r2hbAY9XjHijb8+81evHF0HKfNoYiQ0RvJCr4WjwbO+Es1lg4AEnGybwWiAQAWtjpSq246HafiOIjd/UQ+vqhNpsjxDQBzsi9ImXDZjHSkDpDTivKAN4wZDTLtHRmY3ZRqFZlWp/6YwYkwVtB+2VAWNanWCcoVZXtzDoqGVEKDLARFAz3R4AvH6fvB4xHit0GxotyoSjRwr2VwJw57yKryrMYa4I0dRM0g076j0zFotKvsWwz+Mx6mb53QpGhIJkns6Jpr0m5unrMcgW1mhA68gsbVHxRuHw9EMeSLYGErJdFga1AmlyA6/2jNIK0ucm6zrOq+eTy3j8TqXrSkBTMbarC0oxZP7xnCZ8/hWnmCdG0emdDk0ZCIk2hYytYJliXnt6JagmGI8e/ofsm7j+/fhpkAVq06CRajHhuWteGht/uRSLJ0Zo05+vZQEVFlDpZls2Z1DMPoANhZlp1gWXYIwOQ2kk4hipHJLiaQF974iDCxFk+ot95wQcGft5DYddt6LL6JJMPQJBtkFgs7b12PmiL03RcLZoNeMo4PmBpCKBRN4KrfvIqtR/JLKuJfT7Few5o7HseIP7ul98LFLQVNrJCDXJFaCgRXu8sq+dkHUkqEXBMYxPtrdpjxiTOlE8NKFZmftU//6XX84qqT0rb5n4iU/fv/rUMV2lH2igahdYLGN4BPkxjLXsE6sud1AEDnwpUAgJM766HXMXj54GjWtrKw1mn0aIjRy2R5KbVC6wQALGqrxeBEBKP8qm/9bGD0AHb3+2Ax6jBTyoQu4id97BSKBpfVBE+I1qOBJxroC71hf4TOrBEi80sKn4ZoPInRQFRD6wQl0SC0TlCSL/YWbURDIgaMu5UTJwDArp1o0LR6Txn3CZAWB0UyoI2TiPa+jsOccWlnvYm0IbSvVN63w6zcliFGDkTXhJYe+YnjhHzhkzQ4LOmowy52JpL9b6fdvmeA9NYtaFU2GAWQIhpUwMcgUnmEAOQaxSaACH2f33P7RzCzwSZcO86Z34w3jnoIUXD4OeB7C4DvLwJ636DeJwB4QlGY9DpYjBRfQ7zPBmXqBL9/VTTOSzMmFSNw7G0cZZtxxuJOAMC6OQ3wR+ICaasKq/bWCV/4xGid4MEwzH0Mw9QyDFMDYBeAvQzDfGOqxzXZmIxMdiDbWJE32C1V2DJihpVWHaVc/cuJZBDD3d2F9Uta0m7r3NhDP8/QAF4JIPWz6KZH8iYZxMjc/94B5e8hqUI9M2msc2OPJMlw9Wmd+NVH1uQ3YAUkkqykggEgHgWlQDJkQq6tYs51mzUpT5LJbP+T164vbdJSDuLj8cjOAQQi6Ys2XxC1ma2dlUPMdQEhHqv4+L8ooVqRAo33XTFQ2kQDhaLBYTGAYSgn2ryZ3kh2+4Tn8JuIsXosWb6W268Ry6c5tRENtnrNq1eaEicAVUXD4nZSxOzu5y7gDXOAcTd29Y5hYWutNGvJpVIIyRwK0OTRYNHm0ZBIshgPRqknP00OM+xmA1XEJV+cUk/oKIkGq1EPg46hT+KwN2trnfAcJYWhWsSe0UKk2hNaWificGgx4gOoFA1NDrNye0NtGzHiO/ISDo0E0Oa0wDa+l6xYTz9Vcd+NdlPRPBoi8QTCsSS9ooGP6MwgGqbX2eBmZsA+cSCtsXTvAKcqolI0jFERDXodA7vZoC3eEqAuflmWxetHxrFudmosZ85rRCLJ4rVDI8D/vgwYrIDeDPzvS3Rj4DARisFpM9L1c/LtLyopMECqnY7qOtU4j1z/otnXkFrvPgxY5sBqIt9DJ3eSScZrhymv8RpbJ1iW1d66U/5YzLLsBIB3A9gMYAaAq6Z0RBWEJ792tvB3Ismm+RaUYiEkhcxxSi0saI0OLAfce9WarNex7JbH8m5F2N0/kVbs52q2WQis/+FzaWP54RP7VB9z7t3PAJA2++TxzNfPwS3vKo6nLD9WqTQXnmAodY8COcJBjjgRY9+gD7NFr73dmd0yU24Qm1cuuflR4e9fPZfy2brpHdLeYaWADpdVfSMAD37+9CKPRBoFoXsZhrkYwI8A6AH8mmXZ7oz7Ge7+DQCCAK5mWVZ9+UshASG1CUPvG1A/G9AZONO596bdZRjaiWP6aZjtSk1kV0xz4e9bj9FLZTXGmU2ENDjcB7jiVIVo4D0YdvR5cca8RlKgJuMYOb4fZ51yivSDPEfJb9cM1WE4beRYsyyrfjHVWOiNB6NgWag78nNgGAazm2pwiIJoGORSEFo1EQ0uqjE4LBoKPXsLWQWmBU3iBA9HmyYzSJ+WKD3eQFAldQIgRMNoIIpYIilv6jnzdGDvw3DbfaRt4ijXUztD5hwV7XsX7YqyxvOPb3+hPib8SnjTgrSbdToGQedc2CaeIAQhlzay7ZgHzQ4znWInOJpFYMih1mLQFm8JcIX7TNXNj4+H4A3FsEwUvbd8mgt6HYORXc8QddjlvyYE68PfJD4bLXRfyJ6ghjYVDYoawbeH5juBP8Yj+4D2VcLNoYAfbfHjONKaMo1qd1nR4bLijaPj+BikfUTSoLF1IhBNIMlCW+pJ+cPIMIwRhGj4KcuyMYZh8mocLtp8pAwhNj+Ui7ksBxzetEEoiM/73rNCYcOyrKTpYyXB3d2F3794GLf8L5XUpKUVQeoYKeHWdy3BR0/r1DxOGqy87TFFAviHT+zHD59IKczc3V145drzceqmJ9O2i8QTWZGRPA5v2lDwQl+t+C7Xc06upaJzYw9+e/UanLcwXVWz9OZH4Ret+N/Qtajs2iWkwDAMPrpuJv7wMkmg4tu07ty8W9jmY2dQfOdPAj52+iz89kUNQQUiZCa5TBbyntEwDKMHcA+ACwEcB7CFYZj/siy7S7TZJQDmcT+nAPg591tl53QmddQu4wYz0LRQcLznwbIsWkL7cdyZ3puzuL0WwWgCR0YDxKxODbZ6ybYMOfgiMTQ7KAvfAOe7oEI01NeYMLuxBlsOj+HTZ88R3PCnJfuwplMm+5WP/KQgGlxWE6LxJMKxpLDSJwuTHWB01IXeKCd/a6ihl/fMbqzBFrf66uyglxANzTStE8kkkWpTFDUAOf/oFQ0tpMiLR8j5qAaeaKhXaZ0ASOuLxtaJdtoLj4ZCr8NlAcsSv43p9TLeGZ1nANv+Akt4B2YtPwM48Dg5/5zTFfdN1BJRJJMsdGrkn8bzj38PqVsnRvaRwl1CeWCfvhTYCQR6d6BmAZG1bXGPY01nHd0kiLJ1AiDqK2pJLU8UUSoa+Ez0pe2p991q0mNhqwON7r8Q886FG0gU5CPXAjv/TU00aPLD0HD+ObX49rQsJb8HdqQRDYd2b8UShoVjxoq0zRe11QotMKow2ojSg5J81pwCUxm4F4AbwFsAnmMYZiYA+l67DBR1PlJBKLfCiGEYPPSFM/COn7wAgBQCYvKBR7m9LlpcffosXH36LMmCEMh+3ckkm7bqLIdXrzt/0tp2AGDbTRcBIGlTvPeGEuQKfDmSoVDvP41q5M0bL0RdDd2iWKlDinD42O+3yt4HkHSJcjF+pMGtly4ViAYg/fX+49Ol481w0zsX50w0TBUKsXSyFsABlmUPAQDDMH8DcClIvyWPSwH8kSUNgq8wDONiGKaNZVnlqojCowEg5l/UhV7LUuDQM2k3DfcfQwvGcKwl3XBvMacO2NU/QUc0WOuBoLYos7lNGhInAFWPBgA4ZXZ9yrSM6+3vZAZw0kw5ouEooDOm+vwVwBcG3lBMnWhgGFIYROjmjbyvRAOlogHJBFbZx/FfTwChaEJxPLyigepLNeoD2CQ10VBrNdKvKHOr2/APAS7lohoAccS3uOiM9mrbgKFd6ttx8IU1KGoE6br6MWnnZFy9npA80bDgErB6E86LPAuTcx2w/Sng1M+qGhQ22s1Ci02DWouNxvOPfw+piYaxw0QlJTHmGQtXAzuB3n1vYv6C8zDgDaPXE6JjxZNJ6tYJMl4DvRmp0Drhodp8R58XBh2DBRntHiumu7Bo22tgF54LxlQDmGqIv4b7RekdSWA8GKOW/GkjGvgkIgqPhrpZJHmi/y2IFfu+A8TVuW1JutRwUZsDT+8dQjiWUE9rYRiunY7uO4FXRTlOIKKBZdkfA/gx/z/DMEcBnJvHLos3HykSXi+yIaPYULGcsbQj/bN/opAMYiitQNPg+W+eK/+dPImwmVJJIFvdYwVz0ac9Dre+awmuOnUmdvVPCOQVDV7ceB79d1YZwt3dhWFfBCff+YRwm5wHRam3h+SCTHNIHnzbZBW5oRAeDR0AxFlnx7nbtG6TDUqioVZL5GLrUsA/APiHhZuGdj8PALDMTmet5rXYYdAx2NWnwfwr4iUO6RTQZDwXGCZtHxRy/lNmNcAXjpPVyJomBBkrVtWMyRfZnqOk6KVoVeELU+rC2lxLvaI8EiCFAVXrBMsC//siPrr1MvzVdAcODym/R4O+CIx6BvU2in1rKGoAUthQm/HZOSkahU9DLJFE/6EdGLPOAJWW2NFGjCaTdJGsE+EYfVGjgWjgv4h7xxViR6118HSciyv0z2H94W4gGQeWXq66b77tQMr8SRIazj9NKQgA4D0mq8BYOn8BJlgbxt3bAQDP7CXv9yk0ZkIRL/Hl0KJoiOTSOqGOvQN+dDbWZBXVa+qCmMYMwduyNnXjjHVA7+tErUOB8UAU9TW0518OZpA0KjedDmhbzhENKRgH3sQIXGhsT1cSLWh1IJFkUxHCarDWURMNKaKr8lsnGIb5MJcykQaWIM4wzByGYc7IYdfFm48UED/70Grhb1XCNE9kGiqWc0EuN/Zyfk25QK7HXgq8b4C7u6skSIZMrOmsF8Yn7pUvJm7+707Mvm4zFcnAj83d3VXRJAOPJodZ8dwqBw+KfLAlw9Ty4F2Tc07mit23Xay+0RSjEESD1BmXWRfRbEM2ZJhPMQyzlWGYrV6feu89ALhsJrrVKwDo4Nojjr8m3JR0v4wIa0T7onSiwWzQY2aDDYeG6cYhyJIpVlBZluXiBTUQDTVNVJF05y5ohsmgwwNv9mJgIoIDiVassCmYWnqOUrVNAOmKBipYnBpaJ0iBUk/TOuF+AXjzz4jaO3CKbg/ir/9BcfPBiTCaHRZ1uT2gmWiotWhpneAVDerJE995ZA8SowfxzEgt/v1Gr/q+Ha1EiREYVt2Ub3+h9iPQYAYpVjQo4bXZn4UZMbQcexhY/oE06bocmrhJuSZDSGqPBg3SdZYFvMcB5zTJu61mA4ats2AY3YtYIon/vd2HzgYblrTTJE7wcZGUigaLBkWDxtaJQyN+zJVQcy1PkN7F/ZblqRtnnAokIiQ9RAUsy2IsGKWXnmr4TJoMOtSY9PSfybYVwMB2IJ76Dmmd2A63dTGYDPKVjyalbp/Q4NtzgrVONAB4k2GY3zIM8zmGYd7HMMxHGIa5jWGYZwF8B4DGHGAABZyPiOciw8Pq11QtSLJU1HFREIrSEdFVlDZ4laYaptLsUStKcaybRH36VWQn11Qa7n50b9r/NGbzUwlF43UFJJKT9z4Wgmg4DkC8rDcNQF8O2wAAWJb9Jcuya1iWXeNsppCWQ2PrRMdJgMFCClX+8SNbsJuZg3pnthv8rEY7/YmmwdE9GE0gkWTpo8wCI4BNvW0CIIaNFy1uwT+2HsOn//w6jrCt6EgqFKpjhwGXujEckJoEe6mTJ7QQDVHoGPJ+qmLbXwCzE/HPvIodyU40H/iH4uaDE2E6fwZAO9FgNWBCixkkoEo0jPojuO/FfZjGjCDk6MR3Ht2DpNqFwcHlRU9IfrTS4NPaJhD2kn5zo3rricWoR6PdrKxoAPB2pBVXxG9D/LJfA+/8EdUwGjlFw7CfbpKVE9FAs6IcGAHiYUWCztq+GJ3sMXz0t6/hpYOjeNfKDkp/Bm1EgyaPBqMN0JuoWidiiSSOjgYxuyk7End6YDuCrBnboqKFYJ7EzVAHSCEYTSAaT9IpjADuPWRImwMFqA2CAWDmaSSm9PgWAEBi/Cg6kn3wNJ6UvWlDDfQ6Bm7a7wSrS3PrBNVnklK1VKpgWfZHAFYD+CuAJgDnc//3AriKZdn3sCwrnTuqjILNR8RzkaYmZW8krfj8fSkybtsxT0H3nQneZ4XHopuk+9vLAXLS+HyTGMoJ3mAMnRt7cMpdT2bd9xbngZAJcbJDKRWKmdGaxcbvrjkZh+7akKZSUFuxvve5QyV7/IoBtfdi1rWb8ebRwkWelhru33os7f8Lvv/sFI2EDmd+5+mcHkcVcFAgFEKjuQXAPIZhZoFMEj4A4MqMbf4L4PNcv+QpALyF7IfkzfioDOIMZmD62pTz/0Q/ZoT24JXaj2KlxOazm2rw3P5huuQJfrWQQpasuR88NA7YZDwWJPDlC+bj2X3D2HbMA9fsxTD2vwrEwtmFYnCMuKI3zqPaL69ooG6dsDgJkUGB0UAE9TVm9fcwHgV2/RdY9h7Yahx43ngGPuP7M+A5Jut70O8NY0ELXZEiFKYUCQsAeQ+piS7ezFOldeKht/sxjfuILFiyCoPPR7D1yLhyjq+D89igSJ6YEPrBNSgaKIkXAOios6oqGg4OBRCqXwTDinOo9yu0TvgoFUwWJzB2SH07pI4J1Yqyl0tqUTCvbJu7Esyhf2DvwUOYVt+KT51F6c4c5NRHNL4cSBFdVEkwDEMIUYri9+hYEPEkm+Zcz8M8sgtv6zqxd1hE+DjaSKvA4E7VfY9xbVKaFA2WWqr2LgBw2kz0MbydZxLT0ENPA52nw/tWD+oBRGZl984b9TpMq7Pi8KgGooGW6OKuqVSfScp9ljJYlk0AeJz7KRSmfD6iFe++58WiSv+l5OFUc6USw/ofpKc1ZfZT807xlQq59IgPrp2OTZenlGX8MfjPm7348v3bsrbP3Mfbt1w06SqqYpEKubz/eh2T9TilpI5yjIilwbGxoGTRyr/GM779FI5zi0eX/ewlOMwGbL91/aSOsdgQn5cfXDsdf32NkA5f+Oub+MkH1RW3VUgjb6KB66f8PIBHQeKkfsuy7E6GYT7N3f8LkHzsDQAOgMRJXZPv84rhshmRZAF/lLIVYd564LHrgaHdSB5+HjoAwx3STPCsxhpE40n0KRnb8eAVDRSTQJ+WogYghUEzfY7r3GY7HvjsaRjxR7E2EAH+9RtgZC+RCYshxCdqIxqK0Tox4o/S+TP0bwNiAWAuiZ471HguMPhnklyw5mNZmyeTLI6Ph3DBopas+yTBr/ZqaJ0grQgUBnEGE5FTqygaHt05gNPqPEAQWLJ0FcwvD2Hz9n5loqGWUzT41BUNmmXaIQ818QIA01xW1RjK/UM+zGumMFkVwWE2wGzQYZhWLmZxafJoMBt06u8hQNomANnWCQBgmhcCAH73DidmrD4DdjPl5VYj0eCwGJFIsgjFEln92JKwuqjIUL5lLEvRkEwCA9sxYDsH+8VeBQwDNC+hMiQd51rdNCkaNBBdLi2+KVYX0LEG2P0/4Nzrodv9II4km9Ewc6nk5p0NNfSKBouL2g+DHy8V0UCpkih1cITAFwB0QjQfYVn2XbnsrxTmI+WA2ddtLqsiaSwQxd7BVLsSP/YThWyQKsw/um4mbr1U+hoFAO9e1YF3ryKKM7kiEgCW3/KY5O0H79pQ0FVPpTFI4fUbLkCD3YwfPbEfP3hiX9p9cqZ9nRt78MBnT8OqGfSLclJgmHTyYcgXxto7sxUk/Bi23XQhXLTfZSUGOVIl0/DxhW+dB28whhW3kfPFF4lX1Oct83zadPlygWj431t9JUM0lKN6qyCuUyzLbgb58hbf9gvR3yyAzxXiuaSQchmP0RVOKz4APHEL8MwmJI+9jh3JWajvXCa56axGMsk+NBJQJxqE/meP6hA0TSoBMrG0art4zm12YG4zgBHutQ3uzCYaRjh1asNcqn3y4y0G0TAWiNIlTvBtLzNPAwBY2xZgdMCJ+qOvgJEgGkb8EUTjSUyrK7zDPZBSpUyEY3RFqr1FkWiIxpN44+g4PjbdAwQBa+t8rJ0VxyuHFHw2AM7DQ0elaNDscJ+DouHx3YOyK2fReBLu0SAuWdpGvU+ATAAa7WaMaPJooEyd0BK36OHkdUrJIRx5t9w6DGiZhAhEA61HQ4r8oyMa6qiuUUe4VfvOhgyiYfwwEPUh3LoEB9y+dCVFyxLS1pRMKqoPclM0aCAabEYcHKY0bASAk64GHvws8PiNcA2+jJ8nPohrGqVJsM4GG14/Mk6nILG4gFiQqLAMyq91IhyHxaiD2UBxDakQogHAfwD8BsD/ACQLscOpno+UEu579ajwd2ZxFojEUUNLfk4xVt+eEr1kFjaVTDbsHfBh/Q+fy7pd6+ubXm9Le8x3H92De55WjmKfQxGPWWhIva5MkkG8rVTRddnPXpLdV65odliE/Y0FomnnIwCsvI38/8P3rxTInXKA1PH7w8fW4uz50q1iTpuxIj9vme1r/Ot55drzceomQjBVwut8q8htenIohEfDlEOTyzhAIiJP/TSw60EYfMfRHf8g5rdKm7TNbCDkglq/OYDURLjQrRMsmxPRIKB+NvGlGNiRfd/oAZJmUUfn0WDQ62A3a4nTc5K4SIqe4lF/BA00RpBHXwEaFwhRn7Ob7NiSnI/kkVckNz82HgQATK+jdFzmiQYzhXEfRO0ktORLbZuij8L2Xi/CsSQWGodI5KjZgZNm1mHvoE+5ZUWnJz4eFGaQmh3uNRZ67U4LovEkRgPSLQ7u0QASSRbzWrQpGgCgvsYkrIirwlJLzj+KJBhvSEMKjPc48QtQMsd0TiO+FrxqiBbBUeKjYKI7Nvx76KP1CaFsnTg2FoTdbIDLlnFMuNYIY8dyBKIJ9HlF7RPNi4CoH5g4rrhvQdGgiWhw0W0LzqOB9vsAAJZdQcjWl36CCWMj/qlbjxYZT5fOxhr4I3G65BOhnY5G5UZJlAOVRDSEWZb9McuyT7Ms+yz/M9WDqhRc98D2tP/FE+UlN5dH5KW4qLn7vSskt8ksADo39iAYpbwelig6N/ZkkQxa0iaU8I31C9N8Cg5v2oCLFlMqPguEl689L20MUq9Lypdq7x0pl32lY1Gsld/6GpPseL98/zZ0buzBq2qLQlMIsS9GJtzdXbIkQ+Z2YpTjKrsY774nFcstTj5pdap7kk0VPntOKhGLTzVTw6Wi1zmZqAiigZcsUa+yA8AFtwHv/DH+uer3eCm5VLbgaXZYoNcx6FPpNwegKaOeL9SpXP+jfhL/lyvRoNOTAmBwe/Z9w3sIEaGn79FzavEk4ItTiiSOUT+loqH/LaAjFQ82q8mOrcn50HuPSHof8H1l0+s1KBrMtfTxqoLKg3JiU9uRkt5L4PUjxAywJd4LNJCLyZqZ9WBZ4M2jHuV91zQRo0IVaG6dCHs0FXodHKkj59Owf5CsNkv1/6uhrsaEMS1mpADV+TcR1qBo8B4jRILSirZOTz5bo8orR1kIjhA1A2WEFK9KoTaEtNZRkaHHxkm7WNaq/QhxZW6aRfqC94kkzfz5quaLMRYgYy1W64TTpsEMEiDePR9/HFh/F25u/TmaGhpk1QozOGXbcY7AVITQTudR3XQiFNemcKsM/IhhmJsZhlnHMMxq/meqBzXZeOBNZWKukJgralf7wePSq8Wlgid3pyv/rjhJvlUts/hZfNOjuOxnUzOxzgeJJJtVuD3z9XOKuprKMAx++ZE1WYU//7PvjkvwnSuWq+9IBeJ9tjnV52OzJVQVmYovd3cX3r5F3gSzmOBfy/2fOjXt9vf/8hV0buxBLFEQkVZB8MbRcXRu7JFsk8iFwKoUskE87qe/fk7W9/5bN6fOrY/9fsukjUsK4rF+8+KFwt9X/25qx6WGCiEaOEVDiHKVEyCy3pM+ipcjs9HsMMsWXHodg9ZaCx3RYLSSlUiKSaVPMP6iKGz4SWWuRANAogP7thFJsxj9bwOt2r5AHBYDPdHAqwJUVvTCsQR8kTga1FY4/cOAfwBoTbW6zG6swU62k/wzmK3a4ImGDpcGRYOGoqZWq0GmcxoQGALi0vL/XX0TaHNaYPQcIoUqgBXTyXi2H/co77umgUrR4Cu2GSQfcSmjBDow5AfD5EY01NuMGJdRSmRBUBmpryh7QzH6uE/PUeW2CR4Nc7QrGvxDqXQSCvBj1hRxSUGGHhsLYrpUu9HwXsA5HXOnkTHuFxMN9RzRoEKujAei0OsYbecfpcIIAFxWk+CbQg1bPbDuc3jLY8puFxGBnyD3i5UccuDPPxryOaxBUVM5RMMyAJ8E0A3ge9zP3VM6oinAV+5XT2rRCnEylHiV7omvni38/aMncwn2mDx8/A9bhb9pCiF3dxd+e/Ua4f83j3rKqgD6w0vurJYFd3cXOhvlr0eTAZNBh/etmS5LRPA/+++8RPLxhVJiLOuQnoPUWoyy+5+M9/+U2Q1wd3fhX585Le32edc/POXnH69euJxrKREj3/el3MkG8XjbnRahVV4M8eLTU3volANVpKMiiAbNrRMiHBkNqF7EO1xWHKchGgRHd4/qpppc/wtBNEw7mazqjohWMIJjROLcpo1ocFqN2lInANVCj+/ZbrCrtE7wqoyWlAlSh8uKQ7pO8o9Ee4h7JIAmhxlWE51CIReZNqCldYLr4ZNpn9jVP4GTm0EIg6YFAAghNb3eij0DPsnHCKBVNIRj0DFADU1PP8vm5NEAAL0e6VXf/UM+TK+z0b8nItTVmOiJBr44pVE0hOIaFA3HFRMnBDTMJakrFK0bAvyD2ogGzUkwLnI8FNqZWJYYqEr60gzvBZoWwGUzob7GhMMjovfY0QYYrOqKhmAUdTYjvfN9Dh4NgPbvhHgiiWNjQcXvBJ5EoyKftSQRhU7I1onLAMxmWfZslmXP5X7Om+pBVQJ40zYAWat0z3z9HOHvUi0OxON64VvnUj/uvIUtOJQRWdi5sQfxElpdlkLnxh7c/N9UYs/7ucK+XHDS7Y9j3vUPp91WKIKBx/++cIbi/XwbSCYmKz7zpJl1cHd3YcV0V9bzHxujUMAVCPc8fUD2NX/qrNkFfV8y9/O+e18uyH6Ljcxj89K158tu+4ePrRX+nsz3UYwNP3pe+HvP7RcrbFl6qCiiQVPrBAf3aBCzFFavAKDdRaloAKgd3Se0ONwXimgAhKx4AKm8e42Khlotju6URMMo1++sqmjgiQSRokGnY1DX2IoxfaNktN6hkQBma1kRCHu0KRosGokGJ0809GY/dSyBg8MBrHNyPX6NC4T7FrbWqhMNtkbq1gmHhbLQiwa41h2X+rYcnFYjai0GHBmVvigfGPJrTpzgUW8zwReJIxqnmDhqVTTQEA3RAImEVUicENAwF0jGUnGYNPAPAfZm6s15snKC1qOBv44oHJMRfxShWCJb0ZBMEgNZ7ryc2WDD0TFRAoNOB9TPolI01NG2TSTixGdDS+sETz5rUbmBqBRiCRazGuXVT7VWA2wmPfo8NIoGF/lNmUR0Aioa3gLgmupBnGjIJNKGac11JwmZkvNptP5KHHQSkYVzS2B1WQ6Z4zp01wZ8uwCtCpOFzo09aX5MV5w0reAkyT8+vY5qOz4x4oCEuoIvvl8/Utzr54OfOz3r9Z/5naeLev5t2rxbeH3ffXRv1v377rgE7u4uXLdhUcGfW/xaXzs8RtdWOIXIfB/UzlWxb4WW5JRCQpziRlU3lhAqgmiwGPWwmfTCqjgtiKFXBDMVJpUA0O6yYsAbRkLCmCZ7MC6qSeVELpPKfIiG+jkkVvGIqGfx+BYATHYShQqcORENyivKIwEy0VFVNIzuJ6v2GdF/s5tqsA8zJYmGg8N+zNFS1GpundCYxFHLFajebKLhwJAfiSSLJUYuOaJpvnDfolYHDg37leXgNU1AxCvblsHDF9bQD64xhYPH3GY7DojjDznEE0kcGg6k9QprAZ9U4KExhKQkGpJJFj5ajwbeX8M1Q31bPs2F1qchmcyhdUIj0SWk48hPtvq9hFhtc2UQDb4+IB4SvBhm1tvgHsmYVNTPpvBoiNIbQfJqFI3xlkC6fJwGh7nYypkK5DPDMGhzWoRjpAihdUJ9YjsRjtF/JoNjdNuVPloA7GEY5lGGYf7L/0z1oKYCk10EiyfXJ9/5xKQ+txrEK+P5Sru/ftH8tNs6N/aABI+UBqSKHmqlVwlAavxypp1acM3vXkv7/+ROurhnHga9DrtuWy9533t+/pKIdCjetdTd3YWeL6arMAr5OedfQ+fGHtz7XPZ37juWtwnqBZOhuOWeWElyxrenphingVaSoRTw3L5UO/QvPnxS3vv712foSLtCoSKIBgAk8s6vjZXns9CV+nEBQjTEkywd60/Z/5zTpDIfokGnA+acBxx4IuXTcPBpQjLYtF3AtZlB0nk0UCsaxg4LvgVizG60Y2e0BezogTQfirFAFJ5gTKOiQRvRYDboYTHq6FeUeUWD91jWXXyhMy1xlCSFiOT5C1prkWShHNvHJXEIEYkymNDicJ8H0SA1VvdoANFEEvNaHJr2x4MvUMeoiAb+/FMmuvzROJIspTmmr5/8dlBEcwpEA6VPQ2gMYBOaiAaLUQ+TQUffOsFfRxSuUwOc/0BrbYbr8rib/K6fBYAU5P3eECJxEfnlmknIGIXJ/HhQA9GQw/nn5FonxjUSDW4u0lOqV1OMdpe1oK0TLMtiIhQ/EVsnbgZpn7gLKY+G703piCoAYrd+pYn07e9OtSCWymr/4ASFUkgDPn/evKxjMOvazSXxesux6BGjWON/8+g4nt6bKq5y3S9N3PN7fv5yWsFeaBJqSbtT0stAKlFDCbxJqPhHCo32VCrGT6+cPF9dhmHw38+fLvxfCp+vTORzvoq3/eY/C++po4SP/DZFul28tFX4W+y3owbxHO2kmdpqvnxRMURDg92kmWjgZd18hKUcBGM7quQJZ/H6cfMhGgBg3kWk77/vDVJ4HX8NmEPf+8ij1mJEIJqgc9SlXFHmV6frVImGQ5JEw7wWOw4nW8DEQ6lCEKmivJiKBoAcE+oVZVMNUR54jmTdxZNfzoAbaJiXlnzRySlv5NoRAKSIBhVDyIlQXEO0pYf81uBbARCiYcQfzfJT2N5LzgU5Yyc18JJ7KgUT5fnHv3dUigY+2YSGDKhpJGOgJRr8nMu6htYJgBhCaoq3BBSLVX6y35YZ7zR2mPyu44kGG5JsynAVAGkpiQUU9z8WiKl/1nnkoGjgY3JHA1rJ5yCsRj2aHcrKqnanNT3WUw4GM/GsUDn/IvEkookk/WeyQogGcaTliRpvWYxYwRsflIiylsBVp6bHWj+4LVtlN9k45a4nhb8LWXi7u7swpymdQOzc2JO2WjiZqJIM0tjR68VlEsaFuSJzXGr+BDwJpVbQ5zuO2ddtRigqrU7d0evNGkOmSagYf/+/dcLr2nrDhQUZby5YPs2V9n8pkQ2FPF//vnXyUoJeEUWlbljWmnafFlXwghseKdiYtIJyVlP6aLSbNZt08KtXSjJZgKxeAcT866SZKsU+pRmk5n5cow0w5pnpOv8iwFgDbPk10LaS9N0vfIfm3Ti5ybAvHFdflaRMnRgPEhd6Rdf/WIj4GkgQDSumufB3lvsQjh0UVAO8pwG1H0AyQQobDX4EgEaVBwDUdaaKNhEOjwbQWmuBfnQfMG1N2n38ecqft5Ko4XrJ1IiGcEza6E8KeSgaAODAsB8n16QY1LePe2E16rMmfbQQFA00RAOlGaQQN0tT6Pm4thYaMoBhiKpBM9GgrfjQRHRRrLL3e8PQ65jsVqZxN8DoBX8K/pw8OhpMJYgIip3jkmoplmWJokFLtCWgjWjgYnJHfNra6dyjAcxskIj0zECby4JhXwSReCIrbi0LFqfqdwKvRjlRFA0Mw/gASC3pMQBYlmXpI0bKHL/8yBphEty5sacgBdtfXqX3hHF3dwnP/6W/bcOlKzvyfv5cIf4OpY6j1oAnv3YOgPSig18tPLxpg+rnvlCokgzS+PGT+/F9icjVW/+3Eze/c0nO+/35h1bjM395AwAp4pd2pKsMHnjzuGLyi1rBfP+nTsUpsxtUx+Hu7sLCGx9GOEYW6RbdlFvxd97CZvz26pNzemyxIb6eAERdNdWtQOX8efvAL18R/v7Zh/Jvm5gKVBDRYMKbR7VNvo6MkjQCu1n5MLS7SIFPLZUNe4l8XycvGJkIxwRnflWEPPmrGQCyj9UfAV79OfDWX4Hpp2YVszTgZcneUEydaNDpAZNDtdDzBGNwWY3KX/SCbDubaJjZYMOoiWszGD0IzDoLALDjuBd1NqOgSlFFDqunAGeQSStdB8iK8NFXsm52jwQwr0EP9B8FVn4o7T672YBGuxlHMnvixRCIBuXWCV9Yg0w7R6JhURupFXb0etP6K3f0erGkvRYGfW6CqroaThZPQzTw558K0cVPcKnIP/8gIf7MlK0fDXOBI5QrNIJaQpuiwWE1ajeDVChWBybCaHaYoc+cIIwfJrGeenKceDVYGvnFm2RO9Eom2kyE4kgkWXpFQw7nn1Gvg8tmzKmdbkGr+vvKk8+D3ghmqCjiaAyCeaKLup2uzIkGlmVz65uqEJgMOjoz20nCC986V+irLhTZkQtW3JpKynj+m8ULH3F3d2H/oA8X/uA54bZZ124W7ismbn9oV9ZYygnFKtoy9/vmjRdi1e2PAwB+96I7L6LhkmWpNsd3/OSFrDFftmoaLluVMnc+NOzHed+jF1a9/5fZc7lC4sCdl+Q8X5oKiMmG2ddtntJzvJDnq/h17eqbwOL24vLhn7/vDeHvT545q6jPVUxUENFgxlggikSSzZ4cy8A9EkSn2iQRJFrQYTHQEQ0WFwBWdVVccz9uIYgGADj3WlKw+/qAd/04p13w46b3aXBStE7EBAJDFryhngTRwDAMmqfNRvS4EaaxlPHe2xx7Tb1Swa88mrVdQGotBoz4Naye1nUCO/4JxKOAIVVwuUeDuHr2BAAWaJyX9bDOBpuyosHGseqqrRMxDTJtD/mtsXWizWlFS60Zbx3zCLeFYwm8fdyLD2dIdrUg1TpRuPNP04qyf5AQAbTnVMNc4O37gWgQMKlcb3JWNBg0GLS6yG+FVfbBiTBaM9smAHLtqOsU/m2oMaHGpE9v5+F9RbzS8kLeW6O+prhEl1bfnngiiWPjQaxf2qq6bbuTU7l5Q+pEA4VBsHD+0RBdySRVe14VpYvfX30yrvz1q8L/93/qVKFYGZoIoznTG6XIyEx1mEqyYbIwr8UBd3cXlt78KPyRFEnLFxLFeP2ReAK/eSGlZCy3Y/z8/vR5RbFIhqkurGc32SVf24EhHy74/nMSj8gft1+6BB8+deakqWomE/e9ehRXnkJhnl1gFFPJsOHHzxf98/vQ26k28Ou7Fhdkny9tnPz06IohGhpqTEiyRILfqJZcwME9GsBZotgSJXS4rOiliTMTy5KViIawlkKvgESDxQlc+be8dsH3sWtKnqBonVCNu+Od7Oulmb21sxvhPtqMzqH9MIEUtfsHfThvYTYxIYsc/TCcViMODisQAJmonwWwSWIIyTn4e0MxjAWiWCwkTizIetjMhhq8eEAhvtLiBHRGRaIhmWThj8bhKLKiASAtLdtERMObRz2IxJNYRyEzlINRr4PDYsA4jRkkQAwhKRUNdB4Ng4BdvRgVwL2/GDsEtC5V3tY3SNqbzNoSOWotRvoIXoOJPIdK68QCKbPOscPA4kuFfxmGwcyGGhwRk1+2RkBvljQ7BVItL9TxljkTDdp8e/o8JNqShnxu41Ru1MkTIt8YKfDXUiqiK+Il144qyhanZlz/xLLrtXc9OSUFaKbkebLJBvFzT+bz7rh1fdbzi/8v5FjEfdJil/5ywVW/SZnSFeK4PLitF1/627a028T7ffW68wXPDpZl8yrCxed3nyckqNK0YG6zo6Dnw6f/9Doe2Unmezc+uBNXress2L6nGuLjfd0D2yeVaNg36MNFP0gnhMqN1BNfjx798lkF21cu532+KB8tjgoaOfOuUcpV5WA0jiFfBDMp+9TbXVZKM0gX+a1Q2IRjCUTjSY2KBhfdtpMAvhgrtKKhTk3RMHaIRHTKkABnzGuCm21FeHA/AODVw2OIJ1ms0eKwyhMNGpM4NLdO8KqMkf3CTbwR5OzkEUBnSCUWiNDZYMPARFjWRAgMQwwIg/JkhC8SB8tC2Q9DjLAXMNkBvXZectWMOrhHgxjizAVfPjQKHQOsnZ2f6219jYk+zpZG0aCldcI3qK21QUvyhF/jvjnUWg30rRMAl46jYAbpDaMlc1U17CWpGCJFA0DaJ46I/XF0OuLTIKdoCPCKBi1EA6NZZUQUDfQqo8OjdClEgEjRQEs+qygQeCNPJw35XOZtE1UgrWd5L+cjVAy8ddNFmrbPnIxL9ctXKuRMAnkzPk0eTBIQT/avKsOVa/H4X742/1XRzo09iiQDgLTvIL61pRA4rfupgu0rH/ziqvSe+1IyTywEnvn6OcLfs6+dnNfWubEnjWRY3FZbUJLhrsuWFWxfckhkJJLQtHOWMiqGaOBdxmlXsHipbydl7GFLrQXDPg2KBgVZMj+ppC70CqloKABqNRMN6ivKnmAUTmtuiRM8lnU40a9vh9V/FEgm8fSeIZgNOqybo2H1PA9Fw0QoRh+N1LSQ/B7eLdzEt0S0BPYATYuIY30GZnLn61El49OaRiAgTzRoSlgAckrh4HHuQqIYenz3IFiWxSM7+rF6Rh09ySaDOptJg6KBjmhgGMCh4tcCgJABDg2KhnpO0UBNNGh3oddkBgkomtb6wjEEoonsxImMaEseMxtqcGwsmP7lWCtPNAxx19FmB6U8POwlJIOC540UGu1mjNBEEnPgVRk03wlWkx51NkoVicUFhOhaJ6hURlWioaKw/odkUiyeDBeq4FBtR5SAeKX9x0/uFyKXiwnxtePfnz2t6M+nBDnCYcWtj+WcQhCOpS8MiGNFywGZ85o2Z+6rolLHcPstF5XdinOhkPm6RzX6CpUyxN+lGtM8c3u+jPPqhW+di81fOrOgzyFWZvi0LC5qgDhhpByVT5moGKKhycG5jGskGtSiLXk0O8wYDUQRV4t0FBQNHtlNNPXjsmzJEQ1C6wTth4yqdYJG0XBYkWjQ6xjUTV8EIxvDUO9BPLJjAKfPbYTFqOIKL0aOREOtxYgki7ReT0VYXYCjDRjaI9x0eCQAhmFhG9slaaIHQJB1qyZPKLROeIJaiQaPZn8GHgtaHJjZYMNDb/Vje68X+wb9uGx1/q7mmhQN5lr11IlwHA6zQd0dORYmx0OL6sBsJ+/16EH1bf1DOSkaHBYDIvFkWlayIqx1steoAS62McujgScaJBQNsQSb3kbgnC5PNExESBiHXYOiwaLddKnRboIvEs+a5Mvh8EgANpN6tCWPVqdVOFaKsDjJ+ZeU/+4QUk+qREMVUwyGYbDrtvXC/+fe/Qz9dSVHiCfWq2eUxlxHKQaRL5Zpry0Lb0y1TJRjQS1WE+Q6fjmSxt3dpUiwFoOAKzWIX+NJdzwxhSMpPPjWJADofniPwpa5wx+JZ50bhzdtyPKeKTSW3fKY+kYaIX4dDrNBUfk0TLGQIo7vPXjX1JAWFUM0pBQNdMUHv3o1s55O0dDkMINlKfbPr/xSKRooJpWxIJCIlBTRYDboYNLrCtY6EY4lEIollF3o41HS861ANADAqlVEivaVn/8LAxNhfOIMjU6tuRINnORZk3y9aWG6omEkgOW1YTCBYaBVmmjgz9cjioaQyooG/n1zaemRz1HRwDAMPnzKTLx8aBTv+umLcFqNeMfy9pz2JUZ9jYkudQKgIrq8IQozUgAI8KkQGhQNAH3EZa6KBo408lEnT7hkC9YBrs0lq3WCj2OVIBoAEnEpwDmN+BIksq8RQ74I6m0mGGnNvnI8/3ivHlry2T0SwMyGGmpJc7PDjGGafVtdIAbB8ufgRDgGo56BxUhxTCjik6soT+y/8xLh71yLKmpVnQJsJgMe+0qqL3jBDY8UZL/lCJ5weFHCRG3hjY+oqhxeEnkqUadfVRCUCIZcSIv9g8VrNZpKXHvJQuHvRSJiqtwhTvX7xbMUiy0a0bmxB0tvfjTtNnd3V9m1JgHZ1+7tIpJGCv/3p62q++TjewFQByUUGhVDNDitRhh0DLXs6MhYEC6bkVpayK9yDam1T1Bk1PMSZ6oosxwL32KCYRjiSRCiLGr4FT2ZiQqVEZ+vHwCbis6TwfQ5RJI4Rz+Mq0/rxGlzG+nGyCM4RuIQ9dokp4JvRVCDlKp5MTC8D0iQ43hsPISzbVwx17Fa+nlsRtTZjHCPKrVONCkSDZ4QKdBdtLLasCdnogEArj69E+ctbEaTw4zvv28FvZJCAfU1JiG9QBUWJxCWP/8ALoWDhvjz5ZYKQUU0xCOcWiK31glAi0GrS7Zg5VfpJVsnrPVZ58JMztPAnUk0sElJE8RhXxhNlKoBAAUgGmjJZ7oUIh5NDjOGJii+byhSPnxhcv5RTY6qioaKwH2fOCXrNmryTQHhWGGMQue3OHD3e1cI/8+6dnPRyYYbuhYVdf/5oMNlpVI5dG7sSTtO4nQRKbKi1PGZP78u/E1LDPzquUOyBMPhTRs0Ewzi7S/MMPmrFPzf2XOEv0OUSplywbffk/I1KFSsL8uyWefXvVedVJaKIR5i5dC+Oy5R2JLgjaOeIo6mcKgYokGnY1BfQ+8yfnQ0KEyQacDHTalKVUx2gNErTio1tU4IREN+5nmFhtOqIU7PXEuKjqhf8m6+117RhX6ij/yubZPfBiASdZ0Bt55di1velUPucmgcsGkndYRCT0vPVsdqIB4ChnYCIE7Ia5g9gMEKtK2UfdiMehuOqXk0xAIkTlECmhIWANJfnocZqVGvw2+vPhlbrr8A5y/SXkRLoc5mQjiWlDfFFMNSC7AJICqvAvGGYvSJEwDgyIFoCI0RIktt3zm2TgAFUjR4ZRQN44clE1/aai0wGXQ4MiY6vjwhKNE+MeyLaIvvy5Vo4MgMGp+GeCKJo2NBas8egJDPI/4IkmrNp/zYFVQ1E6E4HfEMKJ9DVZQNxCS4OElo+y0pA8dcVA2FXMi74qRpuOb0TuH/YpAN4vjjT5ypISFqCsETDnvvuFjy/lnXbs4qtH965arJGl5B8fCOAartDg37hdd85+bdWffzxyzXlWax836ltlAUQtFUinj/ySlfg/k3PJz3/jo39mSZg7q7u7B+iUalaQlB/H7bTHqYDPmX5zf+Z4fw91S1TQAVRDQAQIPdTJ064R4NUCdOABBW4IbUJq0Mo+oyrql1ogQVDQAhSTS1TgCyE+3xANmPokfDRC/3xCr9/To9UNsORiZaTxU5+mHUao38BIDpa8nvY68hnkhicCKMBZEdwLQ1JIJQBu0uq7IJnY0zvwyOSt6t3aMh99aJYoE/V0YDNCvKFIVemFLR4OcmXbkoGgBlnwYvd46rqHakUKvVN8VaR0iuWPZ5NDARhstmzPY2GXdntU0AhOSdXmfFkZEMRQMgSTQM+SLUPggA8lA00Pv29HpCiCdZzNJAPjc5zIgnWXjUPvM0KrdwjI54Bsg1SmMCRxWljQ+JVr2pY4dlUGjRwc3vXIILFqXIz1nXbi7YqiQAXHrPiwXb12TDbNALBfQOFZnz5+97E50be3BgSHrBpRwhVnGc971ns+6/oWtRzi0Smch03q+kQpxHIRRNlQxPMJr1vs9ttk+6iqHQyqun9wyl/b/rNmkCUyv+9MoR4e+papsAKoxooM1Nj8aT6POEtMlkORkutVRWcfWqvFsnAC5lQYsZJCB7TLyclF+xjYWXYNdS9Pc7Z8ga0akiNJbTsdYc+QkQwzxHG3D0ZQz6IqhnvWgO7AU6z1B8GCEawvIrSypEgzcUg9mgozPJTCZJ20uJEQ28v4SHplWFH7uCISS9omEIYHSkPUULaCIu+XPWOV3bviFunaBUNPBEiX8o664hXwQtmYkQiRjgOQbUSXuedDbUpBuU8qkcvvTVsGSSJYoGTURDbuefFo8Gt0ZzYCCVmqHaTkfROkHdugOUXNxxFYXHgTxWNs0FWAnLxK8/ejLetyZFgM6/4WG6uO8TCHazQSiqlZziL/j+s2kFemarRSlBPC53d1fWuOXAH4dCK1QyC8pKJBu23nCB8HeawXKZQ6zWoDVRFaNzYw9W3vZ42m3u7i488dWz8x6bVnxcq/ebCq75/Rbh70KRJuLP7uoZroLsM1dUGNFAl5t+fDyIJAvM0LB6ZTLoUGczYthPGXGp0jqh1zGwmSgKvRImGgqmaAjyigaV1gmTnW4lzzmNFEW5IDSeU5tKqnVCgxkkwwCzzwX2P4H+EQ/W67eAAQssepfiwzpcVoRiCfkiW41oCMbo/RkiXgBszqkTxQKvaKAiGvhzRkW6zht6KsI3QMw2dRqSTACgbiZpqVIkGo6S307tqRw8aUlN/vHtGRLpJCP+CBodGZ9F73HSfiKhaACIT8PRsWDqy81cCxhtqXYQDuPBKOJJlt6jIQ+iy2LUw2E2UH0nuLkIv1kaWif416DaTicoGuTPP1+Y8vwDSi6FqIrc8euPrJG83ZCxsknjLs5DNTknR3znihW4VdSOeHr3U/jtC4eL8lzljsz2gC3XXyCzJYG41UL8o9qWVST0ekJZ8nSlon5hq0MgF4q9ulwIsmHT5cvUN5oi8AQ5AKzb9NQUjqSwEKs1Fmowu5Qitb733hVT6sVQSKNJ8Wt7XGTAqwU//mB2W5b4s/vvz56e034LBcqZTXmAVzSwLKt4Ihzh+tu1KBoAsoJFr2jwyN7tC8dRa1GOLRFQokRDrUUL0cAXetIrynQeDb1k9Z/mmLmmA74+sgqr0dQx10m8nS/0tCgaAGDZFcBb9wF7HsIH9E8h6poDU7OyLKudc67u9YSkkzoEokG6l9sTisJl1ZA4AZScooF/3eM0hpBC5Kx0oReNJxGKJegVDTmYNUJvJEW6mqLBWg+Y6ItdHqnUCY1EQwYRABCiIStibpwrKCQ8GgCgs9GGYDSR8l9gGHKcMswg+daz5kzFhBwiEyBEV27nXyNlMgQfbanFpFJop1P7ThCIVo/sJhPhGBxmLYqG0vo+qCI3XLA4dS3p3NiTNnnmV5AB4OQ7nygJk7OPntaJdXMacBFnyHfbQ7tw20O7CjK2t0XeFJWGJoc57RhF40mqXvXZ121W3QYA/u+s2fjsuXMVv8Mi8QT+vvV4Wt92Pji8acOUOfuLPxtA9mdHCs/sTan3Prh2hsKWVZQCfvncQdy1OTsOsxSug4VCJoEyr8Uhs6Uy3rFMxbtuipEX0cAwTD2A+wF0AnADeB/LslkOYwzDuAH4ACQAxFmWlabx80STw4xIPAlfJK4oQz3CrV7N0Eg0NDnM6h4NAJlY8pnzEpgIxej7MEPjgN4MGEsrFslpNWIiFFMldQCoFnreYAwmg0452m2ij65tAiDSczZJHlM3k+4xAFk9zXESr9cxcFgM2lonAGDW2UDDXKzZ+nVAB4TP/IUqmcJHZPV5QljaIVGAUbROaPJnAEqOaHAJigYaokGZ6NJkzhoYBuwa2yZ4NMxV8Wg4npM/AwDUmPTQMbm0TqQTDSzLYsQXTVtVAZC6nikoGgDSgiAYPTpaUykdHASioZayoM/z/Gu0m6jMII+Maou2BFJJRKpEBo1BMK2iBiDXqBxUL5WOUpuPFALvXtmO/2wjRsg0xVQmqL1nNGB+iwM7b12PJaJIuVzGBqTLews9zlKB1Oq5yaDLOl4sy+JDv34VLx2U/t5Wwr3PHcK9zx3KeYw0KLUCTyvZcPXvtsjeV2o4vGmDsCJNNccuE3S4rKotV28f9+BdP832bTl014aiqbWmApkkg9bP19o7nxD+zjwu4n2L2/CmCvm2TmwE8CTLsvMAPMn9L4dzWZZdWcwvdd4lfdCr3N5wZCxIVq8yJ9MqaHaY6SSMKmaQE7nIZEvsQuO0GpFkAX+EorBRbZ2Ios6mEu020a9uBMnDxfW4azWEjEwQgsKWW8JHrUWDbwUPvQF4/1/grlmOX+JyWFZ/QPUh7S5ynssaQlpdABhFM0jaWFehOCqxnnBekTGuxaNB5jOpKYUjMExaJ3IBH3GZlDFS8x7PyZ8BIFI+h5bzj/eYyPBoCEQTCMUS2Sv7Y4cBvQlwSJN9swSiQeTTYG9JmWdyGJog12Zqj4a8iQY6RYN7NIhZjdqI5xqzATaTXv07QTAIlr7+xRJEUUPv0ZCbj8wJgJKaj9BCqX/2hx9Il8Rq7eVffstjuQxJFTVmQ5YPQS4SdqrWtzLE60dSakLa1XOGYXDfJ09Na0EQez786eNrizVcAMA/P70u63lLHVJtFFvd5Z/KI54Lf/0fb0/hSAoLcbxr5rVs+3EvOjf2ZJEMr153PtzdXRVFMqy8Lf26nMtnjWrRG9lteFOBfFsnLgVwDvf3HwA8A+Bbee4zZ7Q5yUpvvzesKEE5MhrEjHqbZpawqZYQDaoMI59Rz7KSBIF246/Sm1TyRImXRp3B98hH5D0aFNsmkgkiwaZWNHBf7FoNIUPcF1SOx5tXeWhG80Lc3vR99JnC+BTFOVlfY4LZoEOfHKGm05PXoKBoWFrmigaTQQe72UDXOqHi0cC/Z1SfyeCodiNIHg1zSNKDry9bucCywPgRonDJEbVWA328pd5IlC8ZigZ+9V9S0eCaCeikv7TaXRYYdAyOZBpCHngybTvNrRN5nn8ttRY8t29Y8ZodTyRxbCyIS5Zqj8Zq1qJykyG6+PeMyhxYUF2VVtxxiaCk5iO0+PdnTxeKdKlV2T23Xyz0NM+6dnPJFIAMw0iuKr9100XURPb9W3P0UipxvOfnLxd0fwzD4Mx5TSXz3pcSMs/BK37xsnA7D/H95XYM//XGcXzvfSumehgFxxb3ONbOqsdvXziM2x7alXX/Lz58Ei7O4Tu51PHnV46kEayFPh/F5/obN15Y0H3ninypjhaWZfsBgPstFwDPAniMYZjXGYb5VJ7PKYtWTtEwMKGiaBgNaHIX59HssCCaSKrL460uYpwWlY4x8oW1ZKaXJtHgFOIcKQobgwkwWBVbJxRXk/1D5HjWUvYh8bJirYaQgh9GjooGq4Feup6BPm8Y7U664othGCJBG1eJuAxJM/veUAyuMicaAM6QlGZFzGgh7UcyqRP851m1dSIWIp/pmgatQyVonEd+S/k0+AeBWICQETmi1qKR6KppzlI08AkNfDSkgHG3rD8DQFjz6fU2uMURl45WIOoDoinyYdgXgcNsgJXGCBdInX85xjm2uywIRBPwKSiv+GjLTg3mwDyaHGYMq6VOACnyWQITtOcfQI4nmyzJ74QSQEnNRwqFzHSgUnPaz5wor7jtMeoxdj+c3YNdRRVa4e7uSktrAMjn5H9v9U3RiKpQw/vufRmdG3uySIa7OaPHSiQZ9g74cIPII+XgXfLpNLS496qTZO+rl/JwmwKoEg0MwzzBMMwOiZ9LNTzP6SzLrgZwCYDPMQwja63JMMynGIbZyjDM1uHhbEd0JfB9vwMKrROJJItjY6GcJ5UAhWRFxZNAU99kaDxnKX8xwY9fU/KEYuuESuIEQN86YbSSVWfexZ8WeRpvakriyECfJySYPNKgXa3XzdYgqWiIxBMIRhP0qRMlTDTU1RjpFA2A4vnHJ4U41dqZAiPkd86KBoWIS967QaGYV4PDYtDWumNXIhpEigaWJUSDjD8Dj5kNtozWieyIy2FfRJPhYr7nXyunclP6TjjMefZ0akic4NHssNApGhTa6QSPEJrvhBI1B54sTOZ8JJ+5iFZ8/aL5wt/xRHZrVWYxH4lrj4crJtzdXfjLJ05Ju63UCJEqKhuNdnPW5+QLf32zrNUMJxIe+OxpcHd34YqTcvOpKnWM+iNY/8PnhP/fuuki6HNsB7nlvzuFv9cvSREy4nP9tevOz2nfxYAq0cCy7AUsyy6V+HkQwCDDMG0AwP3ODmUn++jjfg8BeACAbLMZy7K/ZFl2Dcuya5qatE3oLUY96mtMioqGgYkwoomkZiNIQGT+RRtnprCCRbV6BZRsZjo/furCRpFoiKGuRuF4+HiigbJ1AiC97loVDcH8JvF1NhN94SuCPxKHNxTTSDRY5D0aAI5oyFY0aPIjAEhxxOgAU25uuMUEOd75n39e2tYJPgoyV48GRxuJfJQyhBzjjLzq81M0ULdOAJyHQnrrxDAXBZlGBoTGiRqkTpkE6WyowZFRUcSlgzOcFBENQ77wpBINbZxKqF+BaHALRIP274QmWt8ehfOPf8+ovhNOcKJhMucj+cxFtOLz580T/p57vXQagXj1a8ENyvFw752CyfrpcxslfRv2DfomfSylgsxV9nLD5i+eOdVD0Ax3d1fJSMbzgcNcOaGAv3vxsGRMJQDsuHU93N1dWJWZdFXCkCKDlTARjuGkO1LmjU989Sx6nzQJ/P4lt+o2gil3CSDf1on/Avgo9/dHATyYuQHDMDUMwzj4vwFcBKAw+ToSaK21KJpB8j3EuSgamgVFg4pUVlA0eLLuiieSCES1GH+VdutEvooGlmXhDUXhVIpb1KpoAIghpFYzSH4Sn6OCxMURDVoNu/o5woA3eaRBu8uKIV9EfmXLVi+paOBl2k4lBYkYYS+Rrcv05k8lXDYTXeoEQJIn5FInaKXr/PGsyZFoYBjSGjGyP/u+sYOAzpizGSRAxq+pdYJXNIjOV96jIU1yN8ZFW6ooGjobbPBH4hgNcO8Jr2gQGUL2e8NopWwRApB364RANCiQcodHAqjJwRwYIESDLxxHOKaywkzROkHVTneCEw0qKLn5SCGh1zFYvyQ9DlMOd16WnXQwGeB9G8S46AfPnbDqhiyvmzLD4vbUdVfrvGYqUV9jyiK9AMgWu6WID55S3hGciSQrHO9b/5ftwQAQUshehoTKl+7fRr2tJxhNM+X988dPwdzmwi/cic/rfXdMfdKEGPlWD90ALmQYZj+AC7n/wTBMO8MwfABwC4AXGIZ5C8BrAHpYllWm4/NAu8uiKCk/NEyIhlw8Gho5omHEp1Lc8KtvEhNLPqWBalIZCxHzuBKcVAqKBtrCxupKTZJFCEQTiCVY1CmxexO9xPHepqE33jmdmEFq+XLkPQ14okgj6muMiCVYBKLaZK28qaPW1gkAGPTKrKbyrRMZr583odEUb1mCbRMAUGczFkTRMBHi41VVfAN4RUOuRAMANC0CBndm3z6ynxTy+ty/dEnrhEZFQzwERFIrjiP+COpsRhjFTsXjHNGg0tYxk2s94BUCcPCtE0Q1EU8k0e8NY1qdhqjesJeoaXI8Li21FjCMsqJh/5Afc1scOUWINWlRuYU9ktcjTfGqwfwMayscJTcf0YJDIsWCXDF071XpIRlr7nhccjuTIfX5vfJXrxRgdNrg7u5CzxfPSLutc2MP+r3K0XZVlC74uMVygdJ4+QL40LC0j1opYM9AeSqB+GM757rs4/+tixdOwYgKj563+6m2OzIawMrbUtfon3xwFc6Yl8f8EalWTwDYfdvFALLT/8TX/1JAXlQSy7KjALIaQThp4gbu70MAJs0ydVqdDa8eGpN1Gd8/6EONSY8ODUUdD4fZALNBpx6Xxrc6SCgaeLNAOpks9/gSnFQ6zAYwjBaioR4YzjZ+GudWP1U9Ghxt2iI+XTOAeJj01dspZa+hcbJymmNR4+Jew3ggqoml7RMUDfTnJH/+9npC0m1AtgYgESXmheYUe8orUKjNIEOekmzdAcjxngjHkEiy6r1uFqdsCslEWMWMlAfv0ZBr6wQAtK8Ctv+dtBM4Ur11GNgOdKzOfb8grRP+SJzueABE0QAQAsVCVq5G/BIeCjzR4JqpuLvZHNFwcNiPNZ315LqlNwmKhoGJMBJJFtPqNJC8Ya8wtlxg1OvQZDcrthkdGPLjrPm5SePFvj3T6xVel8UJJONALAiY0tV0wneCFkVDCfr2TDVKcT6iBbTxbWKX/RF/FAMqKqGXDkqnDxUbS9qdWYkA6zY9BSDVK9+h5jVURRU5QHzOrV/SIhB0mQTeed97Vvi71PwbnttXXF+YQoFlWVUSSnxsv/3IiWEA+/D2fnzmL28I///umpNx7gI5f2J6nHv3M8LfvKn20psfFW4rtfMYyF/RUHKYVmeFj+t5l0I+q1cMw6DRbhbkxbJQMINMGX+Vt0xWp2NQa9FgfmitS3kgiCB4BigqGvq1tU0AqfhALYaQebap1PNEg0afhj5PCDoGaNHQu57qPZeZpPHqj4z2CV7RoMkMsoQVDSxL2b5jrlX0aKD6PAaGSXqFOQ/ZG08m9L2Zui3sBTxHgJalue8XKfLST6tq4IkGkU/DiD8qHW1pbwVMygTB9DobLEYd9g1yq0QMQx7HKRr4lBRtigZPzgojYVz1NhwbD0re5w3GMOSLYF6zPad98+0WI2rkM/8aJFRuvnAMOgaoMdF8J3jS91dFReHla1M581Irgjz23nGx8Pepm56U3a4UIGcUec/TB/CN9QumaFRV0OLnH8qPAJ9sZJIJYhWQu7sL7u4uXLi4JfNhwkr8O37yfNHHWO54Zu+QcLzkSIY9t18sHO8TDZf97MU0kuF/nz+jICSDFMTn+9wc5zHFRvk1x6iAXy07NhYSVpjF2Dfox7kLcjd2anSY1RUN5loAjOSkkicaHBXgMF5rNdATDbZ6Es2WiAH61Gvni3JlRUMv0CEf4SIJvtfdc4z+sYERbe0ZGeANLccC2oiGXk8ILbUWGPT0vF8b56YvKwkXEw2i3nqPZjNIL9A4l3pckwmeLPEEo+oxPoqtE3G64xEcJW0TOZCUAlqXAYweOL4VWMD10fGtFK359VXz7VgT4Rid0ZCdm2ylEQ0RrJjmSt9uzK3qzwAQ8nF+iyPd/M3RIigajnNEgyY1WciT9/VvRr0Nrx2Wjno9MEzGOq8lty/oBi4GVJVoEKvcnOmkqTcUg91soFvRDo0DJjuJDK6i4sBf1wHS4ywHs0GPX3z4JHz6z68DIJPNzAn9fz9/Ot710xeLM1CNOH1uY5a64buP7p3CEVVBi0uWpWLFpc6zUkImySA31l99hJAP0XgS829IN1/d0TtRTarIQCiawKKb1DvM9tx+sXoLagVhzczsuUnmOfj6DRegoUBeLb98LmUkLiabeTzx1bML8jyFRsUpGqbXky9qqRWs8UAUI/4I5rfkviLZZDdhxK9SSOp0XGHjybor1TpR3ooGgBSr1D3h/GvI8Gnge+xlPRpYlrRO1LZJ3y8HF0c0yMjlJREYSq3y5gCe2PLQ+gZw6PeENbVNAEQyVWczykvCBaIhvcDyhmJgGEqiCyjIinKxILSq0BxvSy1ppYlnF4Re2hSYwHB+/gwAkc23rwIOp2KOBHVD6/K8dq05clYgGlLm/MO+SLaiYewQdezm/BYH9op7S+0tQuoETzRoOtfDnrxbd2bU29DnDSEaz3aKPjBE1Bdzm3L7TmioIcdqVO07gVcFSarc4vQO1CVqDlxF4SB2+lcyrsvMmc/cdrmIMCwVAzx3dxe23VT+iQBVlB5oSQYxTAadsOp+l4yBKr9y37mxB79+/lBBxlrqeFqkWOjc2KNIMhzetEE4hicSyQAAf1aJ9D1w5yUFIxkA4K7NqbYTs0Gf9nx7bs8mHkoFFado4Ptkj41lEw38Sluuq1cA6cnddkx6ZTQNFqeiooEuM720jb80t04AZKIsKua9nKJBdqIdHAMSEe2tExYXMZHTkjwRGAHacm/fzbl1whtKmxTSos1pVVA0cD3cGa0T3mAUDrOBPr+3pFsneGKH4ngL7UwTWZ4dE+EYZjdRpNAERvLzZ+Ax+xzghR+kju2hZ4CGudrJtAzw5CV1xKW1nqgrOCIgGI0jGE2g0SFaLY/4SbxsA52qZUGLA/98/TjGA1HU1ZiID4X7BQDAkbEAWmrN2iYjIU/eRNeMehtYliiHZjWmv8/7B/2wGHXo0NLOIYLJoIPTasyrdcIbovQIAUo27riKwkHs9K+GTJVAqa84A4Qgdnd3Ye51mxEXqTb411Hq49eCI6MBzMwh4azUcHjTBkEiX4rnWC4kQyauPGUGruSSHp7YNYhP/HFr1jZ39OzGUiZPSwAAcG1JREFUHT270247vGlDTq3YtPj9NScXbd8AsNU9hit+8TL19g994Qws7SjNOeFk4K7NqfdfPJcpxDlIi1mNNVnPV8okT8URDbUWI+psRrhHJYgGbvVqXh6Khka7GWOBiLrhGu8yngHqKD2gLBQN+4coXXv515Cxws6vRrvk4i19fLRlu7bBMQxRNXgoPRqSSW7FOndFQ63VCIZJGVzSPS2Lfk8YFy/Rnnnb7rIIq8RZkPFoGAvGSAFIg3iUmNeVqKKBV8HQKRpEK8oZRAPxaKA0g2ycr3WY2ZhzHvD83cCBJ4CF7wTcLwIrP5j3bvnXwJOZqtDpyOeKi4/l03TSFA1jnFSPkmiY30qurfsGfThldgNRNIQ9QDyCQ8MBzGnSSPIWQtHAmaUeGQ1kEw1DfsxpstMTbxJotJu0tU5kgPr8Awj5XKLfB1UUDq9ce77gvaBW2CmRDaVstniAS9nInDB3buzBxUta8YurNLZLliDO/u4zJVeU54JiFtL5wBOMprn6A4Up8C5Y3CLsJ5lkMVvBL0XKo2BhqwOPfPmsnJ9f/Jk9p0C9/R/+9at44cCI5sc99bWzMVvr93YF45fPpatafOEYloniK4HikAzi6+RjXzkL865PtfyU+jWm4ogGgBhiHJQogA8M+mA3G9CuJcc9A412M5IsWbVWzEi2uCRlsoJ0nSaVIDQO6IykJ7cE4bQa6VMn+BX2rNYJktAgG8fCFUFwaCQaAJI84aFUNIQ9xBW+Jnf/Dr2OgcuqIXIRwGggimgiKZg7akGb04ot7myDTQCksGb02URDIIIGWqKBP39LdAXVpUXRYOZWCSPpn0mWZTFBu6IcHMm/dQIAZpwKOGcAr/+BtAbFAsC8i/LerUA00H4mAWKayrUX8d4zaakTowfIbw2KBkBMNJBJEusfwsFhPy5dqeFzXCCii48yPjQcwDkZ3nMHhvw4uTO/wp0YBKu1TrjIbxlFA7UZZWgcaF6saXxVlB+UUiSkIEc2vLjxPOH2Vbc9hjdvyv86Mxl4ZOcAOjf24HPnzsE31ldGJF65o9RUDVLtQMUYk07HpO1XjXgASDQlTbvS7645GefMb8oick7vfopqbIFIHNc9sB0Pbuuj2l4Na2fV4+//t64g+1LD74qs1JgMnHv3M2lxk8DkFP1ikkHKq6HUUKFEgwOP7MjOOd036MfcZnte7GyjyGVckWiwuoCh7BgXfvWK2vjLWpef+VwRUWvNpXUiwzMgqFLkTfRyT5Yj0XCEUhIW4KKE8vBoAIicf0xD6wSfGqHVowEA2lwWeEMxBKNx2DId6xmGqBoyiIZRf5Q+XpAnGkq0daLWQlpAqFpVZHrkA9EEkiyFZ0o0QIreQhANOj2w5hrgyVuBw88CjQuAufn3LWtunQAI0XDsNQApQ8Mm8XVtlFc0zKHaXUutGbUWA/byhpCcD4RnuBe+cFybooFf/c+T6Gqym1FnM2L/UHouuS8cQ68nhCtbZuS1/0aHGbv7JpQ3UvJo0Nw6UVU0nAg4eNcGIXmCprBTa6PQQoBPFd666SKsuC21OnjP0wdxz9MHccVJ03D3e0sylfSEQea8+SdP7scXzp83JWPJLOJ7vngGlrRPzjwlk3gACPmw5s4nNBuBX/O7LarbFMtfpZAmhbngnBwjpUsJYpLh3qtOwvolrQpb5w65c6BreRvMhtJtmeBRoUSDHePBGEb9EeGDxLIsdg9MYP3i/E6ERs5lfNgXwUKlXcmaQVbOpNJpNSISTyIcS6j3B1nlFQ18WoMkJvoARpcyrtM0wOlkBTvkUS9WeEO8PBQNAElCoFph58CbOeZCNLRzDuV9nrB0rI0E0TAWiGanCsihxIkGhtGgIJEp9Ly0KRwBTnJYCI8GAFj3eaDvDUJGvvNHpI0hT9jNqdQJajinATv/AyQTAtGQRqCO7CefIyPd+ckwDBa0igwhOeJusO8ogBptREOBohwZhsk2qQSwvZecC/n2mzbZzXhOrXVCp+ciVj1Zd1F7NLBsyX8nVFE4ZLbzvHF0HKtnKL/34lVngExQL1vVgQfe7C3KGAuBV687H6fcRdpEnDYj3N1dODoaxFnffVrY5p+vH8c/XyfKq6leSafBPz69Du/V0PdeLhCTWd97fN+kEw1zrtuclcZSCueDTsfgjRulFwui8SS+8vdt6Hk7e/GzmPjRB1biHcvb82oLLDSuf2C78HeptuMoQa7gP3DnJZpS4wqFe64sj+jZiiQaeBnqvkE/1nGTZvdoEJ5gDKtmuPLaNy8rpjL/KojxV+lOKnmfiYlwTJ1oMDsAnUHSo0E52rIfsLcC+hxOVRe3Uuk9pk408IqGPImG+hoTej0yBo0S6OO2za11wsLtI6RANKSON8uyGA9GUW+nbZ3gSKES9WgAyMTUS5s6ARAzSBEEzxS1HnmeaMjz/BBgMAHv/3Nh9sXvUq9DjUkvJNtQwTkNSMYA/5Ag/28Qnx+jB6jVDDwWt9XiH68fRzyRhIEjCEcHjgJYpC3xp0CKBgBY0OrAA2/0gmVZYYLzFmfquzxPoqGhxgRfOI5IPKG8uiBhEByOJRCJJ+k8e6J+0t5Vwt8JVRQW4sLu8p+9pFpUMQyTRTaISYZSkLxnoqU29d33+fvewE+vXI0ZDTa4u7twZDSAs7/7TNr25WAaeXJnvfD3ywdHsW5O7rHZpYbtt1wk9KRP1vkUiSew4Ibs5INSPgd4mAw63HPlatxzJd32lRyt+ZdXKT3TygjFfo/kyI1yOjcqLt4SSLk2b+/1CLe9eZQUTatUVgTU0MgTDWo9uVYXSUuIpRsxVRTRYOFWUGnaJxiGvJYMRYPq8Zjozd2NnycaaAwhC9Q64bKZNCka+r0hmA061NP6JojAqyD49oss2OrTFA0T4ThiCVa7R0OJKhoA0qqST+sEtaIhyBMNBVI0FAm1VqNGRUMqBnbEH4HLZoSRZ+ZZlrRONGhbtTqpsx7BaAJ7BnwCMeMd7kWzw6yt9zxUOKJrfosDvkg8zTz17eMezGyw0ZujyoD/TlCPuHRlnX+VZA5cRXHwnfekYm9pZNQ82VCOeChj1XdmQw3c3V3Yfku2twQfvReJJyZreDnhg796ZaqHUFBkRmMXOzq1c2NPFsnARypWUcVkIfM8f9+aaVN2DpaDL4MYFUk0NNrNmNlgw+tHUkXtG0fHUWPSS6/8aoCDMy6kjjOTKGzoiQZPSU8q+dfhpV1BtdZneTSMB6Mqioa+3PwZAMA1k/ymJRoYXd7Hu77GpKlPr88bRpvTkpOMrKXWAoZJqSKykNE6Mcqds9SkRlkQDZStEyY7eX8jMooG1dYJXvFS4kSDxQifFqKBJ+PG3dm+M4Fh0npEaQTJY81M8hna6h4DDGbAWoeotx8rprs07UdY/S+AooFXsvHfCckkiy3uMazSOiYJiH17FCGRRMSTQnRmpKUdd1xFcfC+k6en/S9LLIugRDZc8fOXCjKuyYTDQloqpF7TghseQefGHsy/4WGJR1ZRDGQWWMUgG3giSYzPnjMH7u6uspTd06CS1QxivHLt+VM9BGp86NevZJ2HhzdtwHeuKL5njNTn6tZ3LSkLXwYxKpJoAIDVM+rwxlEPWJYFy7J4Zu8w1s1pyLtfiWEYNNnNgkO7LPjJcYZU1huK061eASWvaOAnx9Qu9xmKhkSShTcUE2IKJTHRB9R25DZAWz1grKFLnvAPkf57XX4fYJeN+FaEonSrLP2eENqc2v0ZACLJa7KbFRQNXOtEMgkAAgFCbQBUBkQDtYKEYbge+RLxaCgSHBaDttaJuk4ADDB2EMO+iOBBA0Bz4gSPdpcVHS4rXjhASK6ErRnm8AhWTNN4HgmtE/lfAxe21sJhMeDVw6RY39U/gRF/FGfOy78Vhj9m6uRzdusE9fkHpK6dtnrl7aqoOIgL7HWb6BzpGSbbtA4Ath4Zl9h6anHwrtTr29GbbZjKg39NUq8rGk8KxSlVO12Rsf/OS4S/3zrmmbqBFAmZ78Gymx8tyH6lCAb++b55ceUmkLAsq75RGUP8nmpN1ZkqdG7swYsHRrNun0qi66OndU7Zc+eKiiUa1s6qx7Avgl39E9g/5Mfx8RDOW5iDoaAEGh1mDPsoJpVA2gqWpii9eJT05JYw0VArKBo0RFwGU5McXzgGlgWccoqG8AQQ9eWuaGAYwDUd8BxR3zYwUpD+e16dQSXnB9DvDaPNlftFt81lRb9XQdHAJoRIx1GeaKBVNIQ8gN5EbQQ4FSCKBkoFicWZLV3nEhrUPRqGAYMVMNXkMsxJg+bWCaOV+DSMHsSIP4Imh+hcHN5LfjdqIxoA4MLFLXh+/zACkThG4EIT48UZWot6wQwyf6JLr2Nwcmc9Xjo4ApZl8ew+olA5c37+xJGgaKBpp8tQNORENJTwd0IVxQHDMLj93UuF/7WsIEsV5X97rbR6pcULQO/4yQtUj+EJh6e+dnbWfStue0y2YJ0sGEXmcJfe8+KUjaOYEJ9bvkgcJ9/5RE77YVlWkWCo5NV9HmJflRPh9ZYylK4dv5+kWM7Jim+dDFQs0XDxklYYdAwe3NaH+7ccg17H4PxF+fXf82iymzCi2o/Lxzl6hJvCsSSiiaTGSaUrpzFOBpwiM0gqZCgaeMm7rKLBx/VrOnIkGgAiDadqnRgC7IUjGmjaJ+KJJAYnwujIIXGCR7vTIiRXZMHGGVBxkmt+TJpaJyzOko1XBYiiIRwjySeqsNRmmUF6QzEwDFECKCIwQtomSvhYAMQ3RVO8JUDMHkcPYMQfTVc0DO4gKhCn9vjHi5e2IhJPYvP2fhwK1aBN581N0WCyA3pKBZjamJa04shoEK8cGsNfXzuKtZ31aHbkv7LCEw2qKjcJg2CvYEZKYXZbJRpOaFx16sy0/7/xj7eoH5s5Qd347+0Vs4I6u8muWIzyRcNUkg6VDLHaZtgX0XSc7332IDo39qQV2TxOFIIBAL7/+L6pHsKk4dcfWTPVQ5CFPxJXPX/PWVCYOlIJO/uyVV3l/FmoWKKhrsaECxa14JfPHcJvXjiMd6/sSHM3zgeNdjNdPy6QtoJaaTJZfhWYWqZorUvzaOBXomU9GiY4t+xcFQ0APdHgHwJq8r+A8KSJh+KYDPkiSLLIuXUCII/t94alJ40C0UCkXzkTDSUMTQoSGTM+u9kAnVpLVXCk5P0ZANLLrEnRAAD1c8COHYQ/Ekv3aBjYAbQsySl6c21nPZZ1OPGNf76NHRMWNOu80EzRhDwFTTx554p2OCwGfOGvb+D4eAgfO6OzIPu1mvSoMekpvhPqgHgozSCYv3Zq+k4o4RSYKooL8WTzH68f10QWZE5UZ127GS8dHCnY2PKFuGBdddtjOe2DL07FbQtiiEmHySBaxMe8UokOKT8QpdcqVi9senhP1v0nEsHA48dP7hf+rsTXLj4fLlhcGGV5odG5sQdLM9p/Dt01NaajXT9OV3WV+zlRsUQDANz+7qVY2OrA2ln1+Mb6BQXbb6PdjLFANCvPNw2CGaRHuKnSZLImgw5Wo56+dcJaB8SCQIxI/YVJtpyiYaKP/M6XaAh7slays1Cg1gm+iB8NqBQdSJl65dM60e6yIBhNSPfl8yQVRzSM+qOoMenVo0h5hD0lX9S4uHNnPEATcSnROkHbyhQYLnl/BgCotRowEYppm0Q3zgMT9qIJ3pSiIZkEBncCLUuVHysDnY7BpsuXYVFbLWrq22FMhkkrmBaEPQVVdFlNevzoAyvBssDHTp+Fixa3FmzfjQ6zeuqEhG+P0LpD+51gtAHG8uhvraI42HbThcLfUivBSsicsF75q1dLpgAW9z1TGfwqwKjXCQXr1hsukNxm1rWbhYL35YPZfdhV0EPKDyTzvOKPtdQ5+9MrV52QBAOQfpzevPFChS2rKAak1E5fPG8u3N1d0OmYtPsmI80ncyyV8Jmg0GuWL5ocZjzy5bMKvt9GuwmJJIvxYDR9BVAMfiVYNKkUZLLWypHJOrX0hPOFb9gDGFspFA1860SO8ZaAKL7vGGBZIr1NNADEAgVpnWjiou5UPTyQSotoz1PRAAB93lA2YZOlaIig3q4hyi/sLRuiwROiUDSYa7NTJ8IxdX8GAAiMAs0y508JodZiRJIFAtEE7GbKy3vrMgDAEp0bjXZuUj5+mPijcPflgqUdTjz8pTOBt3qBB35NVENmB/0OCqxoAIDzFrZg6w0XFNzMiU7lxl3Lwx4hstcbiqHGpE/r55ZFiZsDVzE5cNlMWNJei5195FrWubFH02T0Tx9fi6t+81rabVr3USz86iNr8Mk/bgUAuEcC6GzM3xOn0W4WXtuIP4I1d2T7CGRGUB68a0PexuE83N1dQvFQKse5WBC/VkBdxVHJx4IG869PT0rJN2q5FLH4plQ06b47pJVGU4E/vezGjQ/uzLpd6ZwstgnklRnXoUN3FZ/YmAxUNNFQLPCGaVlxcGLoDaS/uIIVDQAhTTQpGgDiGeBoVfdomOglxXI+K3jiiMsWmUJRiC7Mn2hwWo0wGXSUREP+igb+sf3eEBa11abfmUE0jAaiaKihTJwACNHgmqm+3RSCJ6loWlWkFA1UcbMsS86RmoZchzlp4DPO+ZYQKnBkwmLGnbqeHeW+8KavzX9Qdq4lyT9I/CBoEfYA9bPzf/4MFGOy0Gg34fBIQHkj/von8qnxhmIaU4hKt5WuislDzxfPzCroaIs2uaSVzo09OHTXBvU2siLiQpGs+py7nyl4ISomHViWlVWEzLku/fY9t19MrwRUAcuyFRvPeN73nlHdZvstFwnfUycyvMEYoomk8H+lki5BUQKbyTD1Ivqjo0Gc9d2ns26XuvaJr7EHi1z0X/WbV/GSSFm1946Lp/RaXEhUiYYcIMSZ+aKAkvo2w/yrEokGp9VIH6fHT5I5nwZvMMoZ8Sm0TuTTNgGQ1glA2afBN0h+2/PvHePjT4coiIZ+bxh2s4FuRV0GvBqCV0ekwVQD6M1prRNtWmKFQp6SNiMFtHo0OImiIZkQYkwnQnF0NtqUHxf1A4lIQYioYoNXeHhDMbTTmoxanPDZZmCpz41GB080vEyuX40FaDnjP1f+QW2PK4KioVhotJuxxa0SG8i/lozvBKrvA4AjGly5DK+KCkTm6vEfXnJTR5/tuHV9Vj8yAMy+bjMuWtyCX06hYZvTahTmSt5gTL61Mk9kyv3fODqOy3/2kuS2C298JO3/dqcFL117PvVzid+rWddurpiiUmvbTaW87kJghciHZDIk+VOBd4vSVp74anY6zGRiIhzD8luyvV+e+OrZmNtsV318oRROUji9+yn0ikzd13bWw2woDLFZCqgSDTmAn4xTGULmYwbJ6Incu4ThtBqli1wp8GZ6nIJgPEgm2bIf4Ik+wNmR3wBrGkksoRLRIJhO5vlcHJprzRjyqR+TPk9IW+EvgSaHGQYdI/g9pIFhiKpBZAa5pJ3yfGLZsjCDdGkw3xReS2RCIPC8IYrWiQIqXooNF3dtoY785NBvX4Q1gVdRy0/qj7wETD8lJyPILAhEw5C2xxXYo6GYaLSbMR6MIp5IwiDXBiGhaJjQqmhonJ/nSKuoJBzetEFYlb/5vzvxrhXtVPLrTLXTvGY79g8RD5XHdg1OqcT/rZsvEgrYFbc9NmnjWD2jLu25/vbaUWz893bJbfu8Yckim3a1/s6eXbi+a3Hug50CXPWbV/H8fnrzUHd3F0LRBBaJpPOdG3vw4sbz8kraqgSkEYQfW1uxCpdtxzzC3zTFfDEgRzB85YL5+NIF82QfJ36PitnCIHUd+fun1xXt+aYCVaIhBwi56TRxZhKtE1SyMX71qsQvQLUWI3aHfHQb86kOfp5oiMr7MwCEAJieZ2Ytw6gnTxQi3UKEZocZh4ZVZNQgioa2PL9w9ToGLbUW9MuRPbYGIDgGlmUxFojSezREA0AyVvKKGotRD6tRj3GKONG0Qk9ENLjUVswC3OSqHIgG7vNEnQTDYaf1ZFzGPAqM7AAMZmDsIHDqZwozKGs9IU21KBriUWIcWzaKBhNYlpB5zXLpRkISkUe4yRuKYXq9iqKGR9WjoYoMMAyD12+4ACdxvgOrbn8chzdtoCpcxKvs+4f82HfHJZh/Q6pnvHNjD16+9ry8UpFyxbQ6K46PE/L89SNjOGnm5LcMfWDtDHxgbSradywQxerbH1d8zDKJggYgr0dMCv3q+cMlSTQkkyxmX6fNYJSH1HlnNemzlDendz8F4MRVN2QWlmfPL/15RS4Qv86XNp436c9/ZDSAs7/7TNbt62Y34K+fOlXxsZlx6cVqYZAiGSpR3VIlGnJArcVA14dvcQLjbuHfiVAMDouBToJTJpPKWq1mkIweCJCVTUXZcCxMWiwcBSj+XdOViQZvL/HTKNDqfbPDglcOjalu1+8NYWlH/oqVNqclTXaVBls9EByFPxJHNJFEA3W0pYf8LoNCr85mpHMpz1hRjsQTCMUSQnEuC0HRUPqpE3U1vKJBG9Hwqn41LgUD3Z6HuFsYYNG7CjMonY74NGghGvjzr4wUDQAw4lcgGsxOAEy2ooGGeGbZsvlOqGJy0WA34+73rsDX//EWAG3S/I+dPgu/ffEwAGD+DQ9nFYXrNk1NUfjCt84TxvGen79cEkVpfY0paxxDvjDW3vmk6mOPj4ey/CCkioxGuwmbv3Qmmh2FTZZJJln89sXDuKNnd0H296/PrNNE/ri7u7DtmCdNSt+5sQeXrmzHjz6wqiBjKgdUYqIADajbOAuAP7zkxs3/zTZ5XNpRi4e+cCbVPsStUsV6j6Q+/w994YyKVLfkRTQwDPNeALcAWARgLcuyW2W2uxjAjwDoAfyaZdnufJ53qsH34Q/TtE70e4R/tffjlv6k0mk1wheOI5Fk1QkUnZ4Ua1zBMR6MoknOTNNXgGhLHq4ZQO/r8vdP9JLnKdAHvMlhhjcUQySekO2zisQTGPFHC7Ja1Oay4i2RRC0NtgZg4G0heo/aDLJMPEIAwGkzwUPTKpBBNKRSYCqpdYIzx6RJ4RDBHbZhq/lUrH3uu+SGBV2Ao4B51/Zmba0TZXT+AZTtdDodITNz8WiIBoBEtGyOx1TgRJ2PAMAVJ03Dvc8eFNofaFsfbnrnYoFoAIDxQBTu7i48tnMAn/pT6juzc2MP3rr5Ivr5SwHwg/evwFfuf0t4/lIsypodFslxHRsL4szvZBvOqWHEH6UiLiYTB+68RL4dTANWTndlEVkPbuvDg9v6TgiDyBOJZBC/1gN3Tk7ShJxfyBUnTcPd711BvR9ecQMAM2jVhhohN9alHaXdqpwr8lU07ABwOYB75TZgGEYP4B4AFwI4DmALwzD/ZVl2V57PPaVotJswopabLmEGSU80jBXEnLDY4Is0XzimvjIMkPYJvnUiEMP8Zpm4u4kCEg31s0nhEhxLRWymPVdvwfwZANI6AZCIy2l10heqAS9pdcjXowEgxlSP7ggjmWSzJV6cR8PgBHm+FrnV1kzw520ZrCjX2Yzw0CSfCESDB0CqvcBFSzTYSl/RYDXpYTbo6DwrRBjxR/G/1s9hrW8QYHTAJd8u7MDsLdoUDZyviOTntQRB3U5nrRNIlHgiiUA0oc0cuEyOxxThhJ2PAMDjXz07pyQKsaR/1e2Pw93dhYuWtGYVhStuJW0Bk1UgXbZqmkA0lBum19tkj5NS2sVU4A8fW4uz5jVO2kqqlHcD33JC2/ZTbjiRSIZ/v3E87f9CkFRKkCvaf/Hhk3DxUiW3fmmI1cHPffPcnMclB7nxVvI5kRfRwLLsbkA1LmwtgAMsyx7itv0bgEsBlPUXe6PdjH6viuGf1QXEAkAiBuiN2hUNTYvyHmexwb+eiVCcjmiwN6W3TshGW/JEQwEIgIa55PfYIRmioQ+YU7hj3VxLio4hBaKBN9AshKSszWlBNJHEaCCKJkeGYsHWAIQ8GPISz4iW2spTNNTZTNg9MKG+YYaiwUNrzhoYIaas+cSsTiJcNiOdZ4UIo/4I2NmdwDVvFmdQ9mZgQNpYTRIC0VD65A4gSiKiIRq4tpCJMEnrqbVSfA2X0edxqnAiz0d4ZJIDNGQDwzD40vnz8KMn92c9xt3dhT++7MZNorz5zo09+PPHT8EZ84r/2dx7x8VYcMMjWeMqZzAMk0buAJVdZEiB9264a/Nu/PK5Q8Lt/DGppONxIpEMAPDVv6fIwWK91u89thc/eeqA5H35qGPE79W2my7MaR9yODDkwwXff07yvko/JyYj1LQDwDHR/8e528oajTStE3x/O5c8oY1o8JTFpLLWQibJXpoVZUBQNETjSfgjcXkzSIFoaMt/kDzRMCpxYYpHAN8A8XEoEPj+yqEJ+fPj2FgQADBdhojQAt5QUjJ5wtYAgIV3jJA7sv3jmSgjjwaXzUi3gs9/noJ8vGpMeLwiAsNl4c/Ao85molN4cIglkhgPxoRV+aLA3kJaJ5JJ9W2BlAGnraF4Yyog7GYDzAadusrN6hJIAz4ZpJ7GN6VKNBQKFTkfESNz0koTQfiVC9PTTC77WaqX/iPrOrP2+eHfvKo52jAXZLYe3vO0dHFRbmAYBveLDOkm41iWIq7bsEiyyOrc2IPOjT2IJyi/L0oQoWjihCMZxK/3+QKrAViWFc4LKZLB3d0Fd3dXziTDZ/6c3l5NtXBKic6NPbIkw+7bLi7Y85QqVIkGhmGeYBhmh8TPpZTPIbW8wCo836cYhtnKMMzW4eFhyqeYfDQ5zBgLRJFMyr6UlLkgL9WmJRoSsbQIvlKGoGigNYTkTOE8QVKE1ykpGkwOwCzTWqEFrplEDj56MPu+8SMAWKBuVv7PwyHVOiGveDkyFoBBx6Ddlf8qOU9WHBuTIhqIgsM/PgSLUScQQ6ooo8KmjvNoUPwsAoDeQJQJmhUNw2Xhz8CDEC/0ioYxTv1QdKKBTZCWMBoIiobyIBoYhkGj3YwRNYNgUesErzpRTN7hUUafx2JiMucj5TIXkUIuZIP4MW8e9WQ5r7u7u9DzxTOy9lvsIlk8ru8+ureozzWZOGV2+rXtRCUbgFSRmIm51z9M0k8Ojk7BqHLH1//xVlprCFD5JMOq29ITV6jTlFTAX2Ok2o1u6Foke+5oxcM7BoS/C/leKX2uf3v1GlhN0j5ulQRVooFl2QtYll0q8fMg5XMcByBeLp4GoE/h+X7JsuwalmXXNDWV7uS+0W5CIskq59WL4sxYloUnGKXKuOYVEOUwqeRbH6gVDfZmIBGBzzPGPV7meHiPFU5lYDARsmF0f/Z9Y5xsr75wREOD3QwdQ1on5HBkNIiOOmtB+tdmNpALuntUIlKTK9TC3mG01Fro+x9DHkBnAEw1eY+v2HDZjEiygI+ToitCtKLMn7O8gaIsAiPlRTRYTZo8Gvj0nOISDXy0LaVPQ3CUJMGUSbsKQL4TqFRuHPE8ViUaNGMy5yPlMheRQy5kw57bU6trYud1HkvanbIr0I/v0uDBohGvXHt+2nNVCnJ5jyoZckXjB3/1yqSQWoVA58Ye/PP1lE/Bzz+0uuJJhkAknpZ0le/rvVLl/T68aQPc3V34xJmz83oeHuLneeumiwqyT16BIQebSY/zFpa+D18hMBmtE1sAzGMYZhbDMCYAHwDw30l43qIi5TKuQDQIrRMe+CNxxBIs6itsUslHs2lqnQAQGOsHAPnj4TkGOAvXzoCmBcDQnuzbxznH7QIqGvQ6BvU1ZsX406NjwYI52taYDWhymHFEgWiI+4fRoiUyKzROzt8yMGbiCzVF0o+HaEXZG4yCYQCHmsojMFJerRM1lHGfHHhfgSZH4aSCWeCNbbUQDWVmfNhoNwvpLrLgPRqSSYEM4iNJFcErQcrgO6HEUZHzETloLWQtRj0+f+5c1e3d3V04dFd63vsn/7gVnRt7EI0XXu7emmGaXA4FJy2qZEM2eMLhyxfMy7qPL0APcAkrpYTM9+7gXRtwybICtP+WOJbc/Kjw98GM6wItrv7da8J7+5KEguXZb5wjnBeFNAzNfM9kfeM0YNPm3aqGr7tOgJYJHnkRDQzDXMYwzHEA6wD0MAzzKHd7O8MwmwGAZdk4gM8DeBTAbgB/Z1k2O+S0zMCv/ikVk4KiIeTBeICfVFYW0ZAyg6RVNJCVodA4kSnJ9sd7jxbUNwHNi4miIZ5RCIwdJiunBS4kmx1mVUUDr0QoBDobbHCPBrPv4Io1NjgmmFRSIewpi/MPSJ1DmomGUAy1FmN2UocYySQQLC9Fg9NqgjcUBcuqtJJw0Bx9mgsEooEy4jIwUjZGkDwa7WYKM0gXwCaBqA9jWjwagmOAwQoYJy+PvNxwIs9HlCBVyIaiCZmtga+vX5C1vRR0Ogbu7i48+LnT026ff8PDRSmWM1/Hrj4KA+AyQZVskMaXL5gPd3eXZB/7Bd9/VihMab/rioWP/vY1ST8G1cj3CoD4df/w/Ss1vWb+/evc2INn9ma3p33mnDkCuTCzofDq2u6H0xcfC6E86dzYg3tFBqeL2mqztql0hUsm8iIaWJZ9gGXZaSzLmlmWbWFZdj13ex/LshtE221mWXY+y7JzWJa9M99BlwKo4sxEiobUpJKCLQuWz+qVzaSHQcdoVjTEJjhFg9QkO+wlP4VUNLQsAZLx7PaJsUNEzVDglfvmWjOGZDwavMEYvKEYZtYX7sI5s6FGWtFgJUQDExzTlnARGi+LaEsgZdpDHXHJrQ57aDxTQuOkMCwjoqHOZkQswSKgUEyIwV/DGjMTSwqJXFonysSfgUejw4RRNd8eUfLJeCAKk0EHq5GiRzM0XnYKj8nGiTwfUYO7uyutNWrRTY/ghf0jituLoVT4rpjuyto//5hCF8yHN6VWSzf8+PmC7nuqUSUb5MGnVMgVaLOu3TxlpEPnxh48uy9VJDc5zCdMIZl5jr57lbKv7rAvkkYuSOGs+U3Ce/2tixcWbKyZiMQT+MWzKd+2fN8zfySe9Zp23bYeu/vTCdET5dwQI694yxMZTQ4aoiFlBjkW4M0PaRQNHNFQBhNLhmFQazVq8GggK5tJ3xCAdunj4eFMwV0zCjNIAGjm4isHdxHSgcfQLmDm6dKPyQNtTgu2H/dK3ndkjBACMwqsaPjn6xGEool0cxmTDazBitr4BCx1WogGT6o4LHHwhqJUBojW+jRFA1XiBFBWrRMu0fGwm9Uv8SP+CCxGHWqKaUpksgNGG72iITia+syWCRrtZiSSLDyhmLxKQSAaPBgLMKi3mehkoMFRgTSsoopcsPWGC/D7Fw/jlv+RJM8P/+ZV2Ex6WQlvZlTmzQ/uwK2XLlXcP5BdfPD/F2KCzTAMHv7SmbjkR88L+66kiXsu8aQnGvjjkUyymH1dtjxdLFl/5uvnoLOxOD5TH/v9Fjy1J/377MCdlxTEd6scQJuoQUOYdV++DB9YW8D5PgX42Fwg/yhLqdeY+VnmbzsRUSUackStxQCTXqds/mW0AAYLUTRwrRNU8mTBcb08JpZOq1HIhFeFrR5gdNAFSAqCpOOqtwhEQ8M8QG8G+rcBy99LbguOARO9QKv85ClXTKuzYTQQRSASR01GsXeEa3EoZOsELys7OhbEgtb0pI6ouR71ER/qtCoaGuerb1cCEDwaArSKhnGhR54qcQIoK0WDoPAIxjCNQhQ14o+i0W4uaN9jFhhGSJyhQjkqGkQqN1migVe5hcYxHnTQtdIB5FpVJt8HVZQurj59Fi5c0orTu58CAAS5CD65CfDhTRuEwu0PLx9B1/J2rJ2lfB7y+yoW4ZApRa60YrxKNtCBb90BSETzvOsfztrmnLufSfu/EMeRZVnJ/vsT6T2SK6Dljo0Udt928ZQlLojH/431C3KOskwkWczJILs2XrIQnz57TpVkEKFKNOQIEmdmwohPzfyrHgiOY9zCOYzTtk7ouCi+MoAmRYNOD9gaoQ+NKhtBAoVtnTCYgPaVwPEtqdsGudZcscKhQJjGqQeOj4eyCv+jY4RoKJQZJAB0ckSDezSQ9XwhoxN18GFanYbnKyOPhlqrEQxDq2ioE3rkJ0Ix4X2SRRkSDZrMMUEK46ImTvCwt9ARDdEgEAuWHdHQYCfHfcQXwfwWmVhe/jMV9mA8aJaP981EaAxwLivAKKs40dHhsuLgXRvSJshyxSzDMNhx63os5cze3nfvy3jl2vOzzBmlUEzCodKL8Up/fYWGUa+jKnYzz8XDmzZoItilVq733XEJTIYTQ8UAABf/8Lms22hUC1++YB6+fMHUL15ljvVzIvPbfPYDpM6nKsmQjhPn01EENDrM6nFmtnogNIaxYBRGPUMlZUZojExIy8DxHyDqDmqiAQDszbBGRuRX87xHifqg0MXdtJOBvm0pQ8iB7eR3S+EVDXyG8LGxbING90gATQ4zbKbC8Xx8G4aUT8MEU4t6xocO2taJZIJ4ZJSJR4Nex8BppUxaEPXIU3k0BLg+5jIiGlKtE3SfSaJoKGLiBA97M13rhKDoKi+ioYlXNAQoIo85jwZNioZq60QVBYJetBrMo3NjDwKRbGWi3WzAM18/R/j/1E1PYiJM/30v11vP92mHY3ReMlL7zdxfJUHq9Y0pXVuqAEDIMf6cc3d34RsZ5qZiiL0dOjf2ZJkD8pDyFHBYDHB3d50wJMOfXjmCzo092DPgo9p+Rr0t7X0oRZIhFwJgPBDN2s+Z8xqFNIwqyZCNqqIhDzTZzej3Shv+CbDWAcExjJuiqKPuxx0rq0m202pE73iI/gH2ZtSMHkddg1K05TRAV+AL+PS1wMs/BXpfB2auA9zPA66ZgKO1sM8DYDqnHjg2nk00HBkLYmYB1QwAeQ/qa0ySyRNjrAMNusN0JBdASAagbBQNAFnFp06dAMAGx+k9GhhdWR0LlxbPChBFw4ppzmIOicDeAhymMHAL8uRO+fhiAKLWCcUkohTRNRaM0sUdJ5NEYVRtnaiiwMhcOV9y86OYXm/F8988L227zsYa/OFja/HR374GAFh+y2PYfstFcFjoo+DkFA4LbyS90nddtgxXnqKtXbLSV/4zX9/q2x9Hm9OCl689fwpHVV743Llz01at33/vy3j18Jjktr949mCaQaAcKukcy8TLB0fxwV+9ovlxv/rIGly4uKUIIyoMCkEAKKkYCvUclYgq0ZAHGu1mbO+VNvwTYKsHhnZjzBilizEDym71SlPrBADUdqAu8aaCouFYYaMtecw6G9AZgb09wLQ1pOhZ9p7CPw+ARrsJVqMex8ayCZiDQ36cv6jwRosz6m1wj2QrGgbiNZjLaMic5uNV+X7yMoDTaqRbwecKvdDEMBJJls6jwdZAWn7KBC5ryqNBDckki7FAdHJaJ2rbScEcDQImBaKtTBUNTqsRBh2jbBBstAJ6M5JBD7yhGJ2iIewh7T5l9J1QRfnA3d2Ff2w9hm/8820AwLGxkGTBfvb8Jvzw/Svx5fu3AQCW3fIY3rjxQvp5jej5gOxJ+XUPbMd1D2xP24Z2f+J9XXrPi1mRm+UMd3cXtrjH8N5fvAwA6PeGK45QmUzc/3/r0v5/+7gH7/rpi5r2IT7f3rzxQnplWgkgkWSx8rbH4KP1VZPA5as68L33rSiur1MBkS8B8Ik/bMETu9PVmLdfugRXresEIN2uU/18plAlGvKAOM5MJ5cda60nigZDlC5xAiCtE/WzCzfQIoOYQcbAsizdhcc5HY3sOBotMjFEnqPAfGkn7LxgdQGzzwZ2PUiSJqI+YM55qg/LBQzDYGaDDYdH0gv8YV8Eo4EoFrQW3n9jXrMdT+/NlqYfCVlwMRsAEjFAT7ECFfaQ32W0il9nM6q3MQEposE7AsApFOWyCAwDtvJaWTcZSIIETSvJeDCKRJKdnNaJWi76ytcPNMyR3y7AEw3lddx1OgYNdpMy0QAA1jpE/aNgWdB5NPDEX1XRUEWR8N4103Hpyg7MvyFlqNe5sSer//zdqzoQSyQFUmL17Y/jia+ehbnNMp4kCuAn4uIkDPFzA8CW6y8QEr6UIDatfOuYB5f/7EX8+7OVQzac3FmPQ3dtSEtZkHp/qtCO5dNcwrn42xcO47aHdqk8Ih2rbn+cetuffWg1LlnaWtACPZlk8fCOAXzuvjcKtk8lHLprg3y9U4LIh2QIxxKC4kpuH72ekGCum8tznAioEg15gI8zGw9G0SC3ImgjcXqjuggWtVHKk4NjQMdJhRtokeG0GhFLsAjFElS+AwlHO/QAphs82XfGQqS4c80s+DgBACddA9z/IeCvHyCpFsUgNDjMb3Hg9SPjabftGyT9bQtbtU/M1LCorRb/eP04hnxhNDuIWZcvHMOxsBUwgpxXDgppG1/YlIlHA0BaJ/YNUqg2uGIt4hsF4EQtjUdDmUn4AZI8QdM6MeIn28hevwqJ2nbye6JXmWgos9QdMRrtZuGYysJah3iAfMaoVoODnMy3qmiooogwGXRZ6gCeeBBPnN+7Zjpaai34CNdGccH3n8MP378S717VkdPzXn36LFx9+izJSf3Jdz4h/K00eWcYJq0Qf+OoBwtvfBh7br8kpzGVIviUBbX3pwrtkPP3uO8Tp+C0uanv/7FAFKs1EAuZ+OxfJocM0Ir/O2s2Nl6yMIsAydc8c6qRD8kgdU7suHV9Wgvy5/7yBnq29wv/nzanAfd98tQcRlrZqBINeSAVZ6ZANFjrATaBWMCDehpDOZYlioYymmTXcn2a3lCMimjwW1rhBNDOjGbf6TlKfhcy2lKMhV3A6o8CR18GLu4GDMUrsOY12/Hft/rSIi55Ix1ZV/o8sLCN7HNPv08gGg4NBzDGcs8VHKUkGjzkdxkpGmgLa74dJO4fBTCbzqOhbUXe45tsNNpNyqaEHIZ8xGOmpVbdRT5v8IqGiT7l7YIjAKMvq9YdHg12M4WiwYUkRx5QqdxCHNFQRt8JVZQv3N1duPvRvfjp0weE2zKl+mfNb0LPF89A149fAAB8+f5t+OPL7rxUBBajXratQnzbDV2L8IkzsxWfOh2DA3degrlczGE4lqzIFgN3dxf+9MoR3PifHcJtlfg6JwNyBMO/PnMaTpqZPf+przEpHudYIom7H92Le587VLAx5otPnjkLXzx/niY/FaD8/QakSJJcHgcAXcvbcM+VqxW3+/dnT8PqGeUzZ55MVImGPMBL+kb8ESyATOHITQ6Z8DhdH1fUDySiZdWfzPe5T4TioBFteIzNcAJoTg5n3znKTW4ac4ucUQXDAO/6cXH2nYF5HJmwf8iPldNdAICdvV402s1UclCtWMS1Y+zun8BZ8wmpdWDIj3GIiAYalKFHQ53NiEA0gWg8qSwlNZgAkx2JACneqFInyihxgkcjjVEtgMEJUhS31E6CosHRRn5P9CpvFxwl181Cm8FOAhrtJhwcUlHWWOsAL5mIUhENgqKhOompYnLw9fUL8NUL52dJ9a85vRM3v5PEQS9pd+KNGy8UVnjfOOopWMHL72M8EM2Spt/Rsxt39OwGAGy94YI0fxmDXpfWRsGPu9yKJDVcdepMfPiUGVmvEyi/gnCysXfAh/USEY1A/n4LRr0O125YhGs3LMp5H1ONeCIpkHU8yu2cyoUkmX/Dw4jGk1m3yyXmiLHn9othMZaPj9dko/xmciWElKJByWWcEA0u+FFP049bhjJZvlijNYQcYkjhVh8fzL6TJxrqFaTVZYIFrbzCYEK47Y2j41g9w1WU56urMWF6vRVvHvUIt7113IOQgXs+aqKBe3wZtU64angDRApVg60BDJdsoEg0xCNAxFuWREMTTfQugMGJSVQ0mGykWFZTNARGys6fgUeTnRx3lpXxnwEAiwv6iAcAUFdD49FQVTRUMfnQSURg/u5Fd9oku77GhL13pLcfFjJmso5bQXZ3d+ECCQPlNXc8IUQPhqIkJpNhmKzVy0qLvgRSMY41pvQCp3Njj2TBdKKDP0+kSIbDmzbA3d1VVqaOxcDdj+494UiGr//jLcnPDH9OiBGIxCX3XyUZlFFVNOQBPjd9WCnOjJsc1jF+uotYGU4qa63kNKIlGsaiOgyyLjjDEgXH6AFSZJRRkSuHzgYb6mtM2OIexwfWzsCIPwL3aFBzhJcWrO1swNN7hwRjzjeOjmNGewcwAHqiITgCmBxFbSspNHhTvfFgDM1qRXNNE3Qc0aDYI88frzL0aGi0mzEWIEaPegXjpgFvGE6rcfK+KGs71IkG/yBgL3wqy2SgudaCaDzJRafKnFvWOhhjhHyk9mhgdIB5EiJIq6giA+7uLvjCMSy75THhts6NPVg7qx5//791MBv0k6Ii+PVHTwYg7fAOAItuSvk7vLTxvIqPvuSx87aLs45J1buBYP0PnsNezhcrE7Mba/DU18+Z3AGVMDIL6JvfuRjXnD5rikaTG7SQDJse3o17n81ucZGL7Z19bQ+SGesHJ/rnixZVoiEP1FoNMOl1yiuHgqLBV7HGX1oVDeOBKA6zbVjld2ffOXoQaChS28Qkg2EYnNxZhy1u8p6+eoj8lur9KxROmVWPf71xHPuH/JhWZ8Xufh/OO3MmRzRIZ0dnITAC1JRP6w6QinQcp1E02JthHtsHm0mvXGAHuNaeMlU08Ea1StGVgxNhtE6GmoGHo029dcI3CMxcp7xNiYI/lv3esALR4IIpEUSNIQkrDcETGiNKkDJsJamiMuCwGLMK99cOj6FzY49gECdV2BfDoZ5/LoA47ovbO3icluECX+wxTTX4Y7LxX2/jb1uOCbd3buxBa60Fr1x3/hSObnJx/QPb8ZdXj8rev/eOi2E2VFegxcgs0MvtGGmJl/zS397Eg9uyFzte3HgeOlxWycdkHp/vvGc53nfy9BxHe+KhSjTkAYZhiOmaT6G4ESsatPTjlpFHA6/UGKcwnwPIqnMy2YpTPNvT72BZYHgvsKB4SRCTjbWzGvDozkEcGQ1g8/Z+NNrNWDm9eETD6fPI6vsjOwYwv8WORJLFyXNbgG212hQNZSZd500dPRSRjqhphDX6ijrx5y9fokHc1qVGNDRPhj8Dj9p2oH+b/P0sC/gHADuFaWkJotVJiIaBiTAWtclE2HJeCzOsUToH7+BYWRHPVVQu3N1dWekQ/ASfb3EQT8pnX7cZt797Ka46tTgpUuL2DjmlQyZmX7cZP/vQamxY1laUMU0lut+zHN3vWZ72HgxMhNG5sUfW4LASoNYa850rluN9a6qFYSau/fd2/PW1dFKm3Fbp3z7uwbt++mLabTS+Cjwe+fKZWCgTN9/vDWHdpnTS8uBdGxRVolVko0o05IlGh4rLuMUJFgzqGD8aaLLqy7B1wmE2wKhnMEpNNEQxwbSDCT5NjAd5kzP/EClyW5YWcbSTi65lbdi0eTc2bd6DZ/YN4f1rphf1ItXhsuK0OQ34+9ZjaHda0VJrxmlzGsn5REs0BEYBZ25RZVOFOi0eDTXNsCc8aLCpXP58XGyRozXP0U0+eLPRYV8ECxWGPzgRKUoCiixqO4hSJB6Rbs0JjRMzXEd5FgFtPNGgZMTJXe9m2dQ9NACUXQpRFZUNPh0ic+LeubEHP/7gKri7u/CJP2zFE7uJB9ON/9mBG/+zo+gFjFjpwI9HDuKYwXIrrGjg7u5CLJHEPFG//Xt+/hIAYOet64UUrHKFnJJFjFUzXHggjxSUSofU56PcPguZr0EqXlLuOvDU187G7CY79b6B8js+pYLyvtqUABrtZsFQTRI6PSKGWrjiPm2KhjJy/GcYBvU1JowF6CbOY4EoGPN0IA5g9BAw7SRyxyCncKggoqHVacG7VrTj32/2wmzQ4epJ6Hn75Jmzcc3vt+D4eAjfWL+AEBu2Bg2KhtGyi3QUezSooqYJOiQxw6qSyuAbIL/LkGho5EhNJf+YRJLFsD8yOUaQPGrbyW9fP1DXmX2/cMzLU9HQ5DCDYaCc+MF5fsywBul2GhwHnNMKMLoqqigc3N1dWSqCL/71TXzxr2/i8KYN8IZiWHlbKjFisj0SxM+1s88rRHFmotxj/ORg1Ovg7u7CK4dG8YFfviLcvuTmRwEAB+68BAZ9+bRj0Rh6fuz0WbjpnYsnYTTli4fe7sPn73sz7bZ9d1yinNZVgsg8H579xjmY2VAjez+P12+4AA0KKk8pEuu3V6/BeQvLc05SCqgSDXmi0W7Cjl6v4jZ+fS2aDUE6w7XQGGBxAvryemvqa4j5HA08wShY2yxgAsDQThHRsJP8bllSnEFOEe68bBlWzazDghYHZjXWqD8gT5y7sBm/+egajPgjKbmgrYEoRtTAskRVUmYeDVajHiaDjk7RYCetENNMAeXtfP3kuJWRKSYPcfSuHEb9ESSSLFqcU0A0TPRJEw1+jmiwlx+5A5DJfZPdjEElooFrS5pmVDn/eITGgLblBRhdFVUUFryKYN+gDxf9IOXmL26nyPRt+M1H1+D8RZM7aV/S7qRWO2Tep9S7XQ44dXYD3N1d+L8/bcWjO1NJX3y6QKkWmbRJIeX+/kwmKmGVXqpFivdd2d0/gUt+9Lzk42h8J6SOD+9BU0XuKK9qtgTRWmvBiD+CWCIJoww7PAEHmvWUk8rgaFn5M/BoqDFRt04M+6Nw1s4EIk6g701g9UfIHf1vEWl1hcmErSZ90XpU5ZA1kbM1AEN71B8Y8RHpepl5NDAMgzqbkc4MsoYkGnQYpN2oBfj6y1bCbzcbYDboFBUNA3y0pWMyPRq4lhy55AkfNxEuQxUJj1anBf0KKreEtQF6AK1Gv/rOWJbzaKjM3uoqKgPzWxyy7RSZ+PgftgKY2gJHaqxyOF3CWPLNGy8suyjEe69aAwCYdW0PxOm7fELFVBbsT+4eFM4LGpRbcTzV+OQft+LxXelx8q9dd756QleJ4eYHd+APLx9Ju03ts0xzriSSLOZkqBjWzKzDPz9zWm4DrSINVaIhT7S7rEiypCd3er1Ncpsx1o4GHaXjf5kaf9XXmHB0jE4KPOKLYHZjPWBcAfRyvZIsC7hfBDqrPXVFAW3rBBf7WI6RjnU2E8YC6q0TYVM9LACa9TREQ3kWvAzDoMlhxohfnnjhfQRap0LR4D0mfX8Z+2LwaK214Mio/LVwHA40AmhkVM4/AIj6gXioLA1JqzjxwE/qpSb+nQ02uEWfi86NPVMq33d3d0mugB7etAE3/GeHYnLBqtsfz7qtXNIdDm+Sfo94QuXjZ8zCje8oXvvB5/7yBnq292t6THVVOTfIeVmUI1GTeb6aDDpE40nJa82itlo8/KUzc9ovUD3fCo0q0ZAnOuoIA9znCckSDcMJG2YzRyTvy0JwtCxXUYlHg/pqMsuynBO+CahfDbx8DxALkRVO/wAws0o0FAW2eiAWIMfaqLBqEeDIiDJTNADEL2WUwidkXOdCG4BGKLc8wTdQ1m08jXazoqJhkLtvUj0aLLWESB2XuR56j5H7TcVvMSoW2pwWvHJIntQbDiZhYGtQx6qcf0AqYtXeXKDRVVFF8eHu7kI8kRTk+QDSSAYe/P1TVfgsaqvNWhEVt33wyHwtUuDTHaTwu6tPxrkLS+szzL++j/9+C57ck2qr/M0Lh/GbFw6nbaMV0XhSUEpoxY8+sBKXriwvM+pShNS5uPu2i2E1lU9sJUA83VZLEHvReDLrNjX/BTG2H/finT9N921Z1uHE/75wRm4DrUIWVaIhT/BSs15PSPJ+lmUxELOhRk2mzSMwXJb9uA01JvgjcUTiCcU+KLJNkkTuTTsbePGHwIEngYleskEnHQtZhUbw7TjBMeVECUHRUH7tO412E9xH1FuURuI2NLE6uFiP/EbJBOAfLEvSj0eTw4xjCiqjQW8YOgaK8ZdFQd1MwCNHNBwve+PDFqcFE+E4gtE4bKbsr9hhXwQmthZ1SQqioYwjVqs4sWHgzAhH/BGsueMJxW07N/ZM6Sqiu7sLV/7qFbx0MEUQisfEvxYxWJbFdQ9sx19fk1FniXDN77eobjNV7Ri/ufpkAMAW9xje+4uX0+7ji9VXrzs/jZDeMzCBi38o3QuvBV88fx6+euH8vPdTRQpSBMOMehue++a5UzCa/EDb3qSVEKsEr4pyQpVoyBPtrpSiQQq+SBwjCTvMuiAQjwIGhS+SZJIQDTWlxXzToJ5zuR8LRNHmlF8x56XcjXYz0HkWWb3c/ndg5ABJOmicNynjPeEgEA0q0ZWBkfTtywhNXNQsy7KKE9axUByjqEVtYlx+Z4FhgE2WNdHQaDfjjSPyr3FwIowmh3nyM6HrOokfixQ8x4CGOZM6nEJDHHEpFZ815IvACgfaYx71nQWqREMV5Y1Guxnu7i7JvHsxpJQEk4n7PnlqltGc0pgYhsGmy5dj0+XpC0Msy2LTw3vwy+cOaXp+qXYMWkyvt+Ijp3bizPmN6GyokTUejyWSODYWxBb3GO577RjeOuah2v8pdz2Z89gAQMcA++/cMPnfNScQ/v3GcXz179nfq+VYQEfiCSy44RHFbXKJaZUiGP7wsbU4e371+7WYyItoYBjmvQBuAbAIwFqWZSXdXBiGcQPwAUgAiLMsuyaf5y0lWIx6NNpNsoqGoYkIxsDl1AdHUj3KUgh7gGS8LGWyDRwTP+pXIxqIXLvRYSbJGquvAl78Ebnz0nuAal9UcSAmGpTAKxrKtHUiHEvCH4nDYTHKbjcWiGCEdaIzpuCbIngFlC/R0OQwYywYRTyRlOyDHpgIo3UqzKDqOoHd/yOqEZ1oQsyypHVi9tmTP6YCgl/5kyMaBrwhONhamKMUvj0BTtJcJRpUUZ2PlDaWT3PB3d2FN46O4/KfvSS7XefGHrx180VwWuWv4cUCn6JxznefzvKTeGnjecLCkto+rtuwCNdtWCR5/9HRIM767tMFGzMAHBsL4c7Nu4HsdvxJw+fOnYOvXbgAuiqZMKk4NOzHed97Nuv2Uk0TUcIze4dw9e/k1T93XbYMV54yQ/N+73n6AL776N6s28uRhClH5Kto2AHgcgD3Umx7LsuyI3k+X0mi3WXF8XFpomHAG8YI6yT/+IeUiQZ/+U4q62uI/FrNp2GE6wtv5BQQOO9GIB4hxceKK4s5xBMbtERDYAQwWMqyRz4V6RhVIRpiGGVrsSCiUOhNlL8pYZPdBJYln0kpd+leTwgLWx2TP7C6TkKoTvQCLtGkIewh5ofO6ZM/pgKCJ1oHZJIn+rxhtBtc0AXlzeYE8AqjMvxOmAJU5yNlgNUz6uDu7sKOXi/e8ZMXJLdZcetjAKauEHjmG+dmqRtO48wS8x3TjAYb1T4mwjH87bWjuGszRVpUEfGD96/ABYtasOyWx2S3+ddn1uGkmeVnYl7OGPKFsfbObKXJ4185C/NapuB7PUfc2bMLv3r+sOI2uX7mxgNRSaUQH4dZxeQgL6KBZdndAE54d84OlxX7BqU9GPq8oXSiQQn86lUZKhrqa1KtE0rgFQ1NfF+43ghc8u2ijq0KpAoVtXMwOErUDGX4mea9BoZ9EcxqlCdKxgIRjMIFfdAtv7OKUDQQcmFwIpJFNLAsi97xEC6Y5Dx7AEDdLPJ79GA60eDhep3L3KOBV4n0e6WJhgFvGFEzlwKTTAI6hVWnwDBgcSq33FUBoDofKTcs7XDC3d2FAW8Yp26SluZ3buzB0o5aPPSFyfdu4tUND7x5HF+5PyVJ5+XXxSZBai1GfOqsOfjUWaXRSsa/3rsf3YufPn0g7b73/Dzl7fCbj67JjteuomA4NhbEmd/JVsTcdukSfGRd5+QPKAfQeC/k8/nKJAl5PP/Nc2VN+6soHibLo4EF8BjDMCyAe1mW/eUkPe+koMNlxdN7hyR7w/s8IQzDRf4JqBR5gqKh/IgGoXVChWgY9kfBMClioopJgrWOKBV8fcrbBUbK0ggSECsalJMnxgIx+A11YAJbiVxfqjDxDQCMrqxXksVGtcumOdPuG/ZHEIknMa1uCnLTmxaQ3yP7gDkig6pRbvJa5h4NVpMe9TXy7XR9nhBYWwMQThAVh01hJdA/VJbfByWOip6PlBtanRa4u7tkkwp29E6gc2MP3rjxwimZN1y2ahouWzUtqzjq3NiDW965GFefPmvSxzSV+Pr6Bfj6enINlyoYP/6HVMdSo92MrTdcMGljq2Q8vmsQn/xjdjfY+9ZMw3euWDEFI6LHa4fH8L57X1bfEMCvPrIGFy7OnaiSOic/dMoM3HnZspz3WUV+UCUaGIZ5AoCUfvh6lmUfpHye01mW7WMYphnA4wzD7GFZ9jmZ5/sUgE8BwIwZ2ntxpgLtLivCsSTGg7GsL8I+D5eBHgdxsVdCGUeZOa1G6HUMxlTiBUf8EdTZTFOWnX3CgmFIG4BvQHm74EhZ+jMAKUWDOtEQQdhcD4RDRKpvlpAZ+vpJgacvX79cPnpXquDlW72mhGiwtwBmJzCc0TPJEw315U00AMD0epts4sfARBj66Y3AGAixp0Q0BEbKmuwqNCZzPlKOc5FyhslAkh3kViP5iLupaqdwd3chmWQx+7rU2G753y7c8r9deO3689HsmAK/mymG+L2QKvBG/JG028vRN2Cqcca3n5Jszb7m9E7c/M7SjN/ONd40n8+2nEqi6sMw9VCdRbMsmzcdybJsH/d7iGGYBwCsBSBJNHCrC78EgDVr1rD5PvdkQJjQj4eyiIZ+bxgNdS5gojYVVSYH/xCgMwAWV3EGWkTodAzqbEYqjwbBn6GKyYWjPeU9IIfAKNBYnnFT9TUm6BjSOqGE8UAMEUsTEAYhXiSJhgGgtnzbJgCgzmaE1ahHr8QkJUU0TIGMkGGApvlE0SDGyH7iz2Aqf2njjHqbpKN7KJqAJxiDuZZbsQmOAFD4vAWGgGZpU7kTEZM5HynHuUglgG9ZAIDTNj2JvowWJL6g2H7LRYpePMWATkfGlmnoyPfKH7jzkhN2EUVc0H3yj1vx+K7shbXM4rNaBMpDrnD+yQdX4Z0rFLzepgA0KRFKyIeAqhIMpY+iL9cxDFMDQMeyrI/7+yIAtxX7eScTShJlwXAt0UShaBgiq1dKPbsljPoaE0b96h4N/MpzFZMMRyvQv015G96joQyh1zGorzGrKhpGAxHEatsBDwDvcelI1Yk+oG5mUcY5WWAYBh11VvR6slfW+dX2DgoX9aKgaQGwL8NcbHQ/0DB3asZTYMyot2Lz9v6sxA9eXWKv5xblAyp+hIFhoKa8UzhKCSfCfKSS8NK15wMgUbyZEYtic8LJLip4Q8fvPbYXP3kq5Vcw93pSSB/etOGE9gr51UdSQS5S7x2PzCLxRD9u/d4Q1m16SvK+UvIX6Hm7H5+77w2qbe/75Ck4bU6jJCGw6fJl+ODa3NRiVYKhfJBvvOVlAH4CoAlAD8Mw21iWXc8wTDuAX7MsuwFAC4AHuIuHAcB9LMvmTn2VIPjJ+vHx9Al9MsmizxPCuQuagUhLqjVCDv7hspbJ1teYKMwgo1g53TU5A6oiHbXtwL5H5H0JIn4gFgBqypNoAIhPg5qiYSwQRXJaO9ALknyQCZYFPEeBWWcVZ5CTiA6XVbJ14vBIAE0Os+Yc6oKhZSnw5p+Jwqa2jZgijuwHVlZG8syMehsSSRb93nDa5PDoWAAA0NTKGV4qfSckYkBovKy/EyYT1flI5aKl1qLYVsEXHZ89Zw6+efHCSRvX1y5agK9dtCCr6OHHWHW3T713PD7++y14co+0X1nme/ud9yzH+04u7xQiGiiZI071OdTrCeH0bmnyQwo3dC3CJ86cLfxfSEJASTlRJRhKF/mmTjwA4AGJ2/sAbOD+PgSgtJ1K8oTLZoTLZsShkUDa7YO+MMKxJDoba4BgEzC0W3lHgaGy9Gfg0VBjxu7+CcVtqoqGKYSjDYgFgbAXsLqy7+cVN0oRrCWORrsJwwqqmmiceKmY6zrJDV4JoiE0DkR96YkIZYppdVa8ddyTdfuhYT9mKyRzFB0d3IpX7+tA7TuImiHqB9pWTt2YCgieXHCPBtKIBvcIpyRp7yA3KMXN8moHe5VooEF1PlL54Nsq4omkoB4Q42fPHMTPnjkIAHjiq2djbrN9UsbFFzmZRRXv57D7tothNeknZSyljt9cfXLa/ytufQzeUExy22/+6218819vp932jfUL8Llzy1/59vz+YVz1m9dk75+Kwvk7j+wRPj+0+Oen12FNZ7bPUCEJhqf2DOJjv882wsx1f1VMLsrX6ayEwDAM5jTZcXDIn3b7YY54mN1YA4w2A/5nlXfkHwaayrcflxR58qvJwWgcwWgCjY6qR8OUwMHJtX390kQDH+loL99oqiaHGYeGA7L38+dng8tBzB4njmdv5DlCflcA0TCrsQaeYAxjgWiaf8yhkQAuWTqFHhStywCdEejdCix6B3B8C7l92hrlx5UJ5jaRAufgkB9nzksRBUdGA3CYDaivtRNDTKXWCT6lqKpoqKKKNBj0xDjSG4xhxW2PSW5zwfdT863Xb7gADZOwwCFHOCy6iazC/vuzp2H1jLqij6Oc8NbNF6X9//LBUXzwV6/Ibv/dR/fiu4/uzbqdl+iXMuQIMh6T5Tvy5b+9if9sU0kgk0Cj3Ywt15+v2N4iRzDk0hajpPSoEgzlgyrRUCDMbbLjyT3pHgw80TCrsQbobSFRZvEIYJD4wmNZTtFQvpPK5loLfOE4QtGEJHs/4iMrzVVFwxSBVyr4+qUN5vhECkf5miA22c0Y9kcko2YBYGiCGIs1O8yAs0Na0eA5Sn5XANEwh1vROzTsR30NWXUYC0ThCcYwp2kKFQ1GCyEbjrxE/j++lRTeDRJ+GWWIJocZTqsR+zLIZ/doEDMbbeTcrGlUbp3g76vGW1ZRhSScNiPc3V0Y8Uew5o4nZLc7SXTf018/h8zJigg5wuHyn5HrXX2NCW/ceGFRx1CuWDenIauI/P7j+/DjJ/crPu7KX70qe9+3Ll6Iz5wzNWlGcu0+PGY31eCpr51T0OdUIuBocea8RvzhmrXUbRtypMDBuzZAr6H1Q+l4fe3C+fjC+ZUxRziRUCUaCoQ5zTW4f2sUnmAULhtZOTw8HIDFqENrrSVFIASGAee07B2EvUAiWtaTymYHIRCGfGHMbMj+IudXk5uqRMPUgCcQ5JInBKJBKj2uPNDkMCMaT2IiHIfTmr0yMMT5NzQ7LEBtB/EFyMR45Sga5jRyK+vDfkHeeHCYFL+zp5JoAID5FwPPbCJkz/7HgJnrytYINxMMw2B+ix37B31pt7tHA1jazhkGO1qVDYL5lKIy9kypoorJQKPdTEU4AMC5dz8j/P2Zc+bgW0X0dOAL5tsf2oXfvHBYuH0sEBUKs+rKrDq+euF8fPXC9HSeY2NBnPmdp2UekY5vP7IH335kj+p2j33lLMxvkUih0gg1cgHIbYV/1B/BBd9/FuNB6VaTXLHz1vU5+zXJEQxaE1juefqApFIFAF697ny01J548bGVgirRUCDMayYXp70DPpwyuwEAkSd3NtQQRpCXo/sHpYkGfvWqjD0a+AvB4EREkmgY5FaTqxeMKYLQOiEjmfP1AwYrYHFK318G4NUyI/6IMtFQawbqOoEDTxAjQnGBO+4mx0CqvaTM0FFnhcmgw0FRO8nOXi8AYFFb7VQNi2DJu4Fn7gLuez8x5Vx/59SOp8CY2+zA5u39gromEInj6FgQl6/irv+OVqDvTfkdVFsnqqhCE3jCIRxLYOGN6h6fP3/mIH4u6kkvVtF/4zsW48Z3LJYsjvlC7f5PnSrMHatQx/R6m+T7lUyy+MY/38a/3pBoi1TBRT/ISrktGtSIiELjL584BafPLQxpnUyygv9IJrQQDGqEzImeQlIpqBINBcLSDlKcbe/14pTZDWBZFtt7vTiT/2DzSgW/jFTWX/6TSp5AGPKFJe/v5/Kw25xVomFKYLQC1rqUciETvgHA0SKdSFEm4FU1g94w5jRlG4ENT4TBMEBDjQlomAPEw6TIdYmcrUf2AY3zsx5bjtDrGMxurMHegdTK+vbeCTTaTURpNZVoWgAsuQzY+QAhfRZsmNrxFBiL2hz462tH0ecNo8Nlxe7+CbAssKSdI3gcbYDvYfkUGN8AYKwBzPmvsFVRxYkEi1GvmFIhB/Hq7PvXTMe3r1he0HHxxbHUuN7/y5QvQVXlkDt0Ogbfe98KfO990p6v8UQSf3j5CG5/aNckj6x4WDXDhd989OQ0H6Zi4Om9Q7jmd1sk79NCCsy+tgdJVvq+zNSKKsofVaKhQGhymNHhsmLbMQ8AYGAijGFfBMuncavDfOuEnFTWzxV/ZWzE11LLFXkT0oaQA94QzAYdXLbim91UIQNHu3LrRBn7MwBAGxc12+eVJruGfBE01JgJ417P9WyOHcwgGvYDc88v9lAnDSunu/DwjgEkkyx0OgY7er1Y2uEsjZWCS38GzD4XWPwuae+aMgZv+vbGkXF0uKzY2UcSeZZ08ERDK0mBiUxIq4gmeomvSim8T1VUUYbgUyoAZWM5Kdy/9Rju33pM+P/315yMcxYURnEqHteHf/0qXjiQbgrLj/Vz587BN9ZPXlzniQCDXoePnzELHz9jFtX28UQS+4f8eHBbH37xrLZEhlzwvjXT8JF1nVjQ6oBRQ+tBMVEIU8bHdg7gU396Xfb+qY7xrKJ4qBINBcTK6S6BaHjrGJEnr5juInfyioaAdH6wYErn7CjeAIsMp9UIk0EnGO5lot8bRrvLWhoFzokKR6t864T3KDD9lMkdT4HBq2X6PSHJ+wcnwgIhhnqONR89CMw+h/wdniCkX2PlGA6tnlmHv205hkMjATQ5zDgw7Mf6JSVCaJpswEkfnepRFAULWx2wGvV4/cg43rmiHdt7vaivESlJeFLPNyBDNPSXddRsFVWUEviC6P4tR/Gtf22X3OZfnzkN7/n5S5L3XZ2xknvdhoX41Fn5Gwz++RPkOzcaT2L+DemJBPc8fRD3PE2K2y3XX4AmR2WRsaWO4+NBnPFtdR+I2Y01eOrr5xR/QJOIiXAMy2+RNpR8z+ppsooRMdR8NH53zck4t0DkXRWliyrRUECcMrsePdv7cWDIh5cOjsBs0KX6oI0WMpn0ySgaJvoAkx0wT3HfdB5gGAbNDjMGZIiGAW946uXaJzpq24DBHdm3J+LkHCxzA0SLUY+GGpOsoqHPE8aMBhv5p7YDMFiAsUOpDUY5c8gKaZ0AUivrW9xjqDEbkEiyOHtB+bZolQsMeh1WzXDhpYMjSCRZPLN3COvmNKSIVnHcbNOC7B1M9AGzzpy8AVdRxQmA9588A+8/eYZkYS8mGdTiM+/avAd3bU43GMxnVdZk0AlkyPce24ufPHUg7f6T70yZXO674xKYDKWx2l1JuPvRvfjp0wfUNwSw49b1sOdooFjKUFIv0MRvqpELDAMc3lRtDTqRUHmfkinExUtacfN/d+I/b/ah5+1+XLCoBRajKOaxtoNMKqUwcZzcX+ar/e0uK/o98oqGU2bVT/KIqkiDq5O070SDZDWZh68fSMYB53TZh5YL2lwW9HulFQ19nhDWzeEMt3Q6olwY3JnagP+7qXLkqnOaajCrsQb/fuM4mh0W1NeYsHJ6Nct9MnDJsjbc+J8d+NPLboz4o7hosUhJYueIBqlWpmSCfCarioYqqigKxIX93Os2I57RNM4XXI12E7beQKIoxwNRrLr9cdl9Shnk5WJo97WLFuBrFy1IG4cYYoJkz+0Xp88zq6DCoWE/zvves9Tb/+dzp2Mlr1CuMDyxaxCf+ONW2fvV2iOe3jOEa34v7d3Ao2rseOKiSjQUEM21Fpw5r0lgRC9bldEG4ZwGeI5KP9jbW9ZtEzymuax45dBo1u2JJEtk61UjyKlFPdeX6DkCNC9K3e7lelFdFUA0OK04OhrMun0iHIMvEke7S3QOtq0E9jyUMuTrfZ0oj+ro+jfLAQzD4INrpwurb/931mxNudZV5I53rWjHHQ/twi3/24VaiwHnLhTJRHkSYaI3+4H+IYBNlL1nShVVlAMO3EWMaKVUDiP+VBTld65YnlZ0Kbnv85Ayo/z3Z08TlGZq4J8vnkhi7vUPZ90vTteo5GI4H9z77EFselg93lKMF751LqbV2dQ3LFOoJbO8fO15aHNaZe9feOPDCMeSis9R9V2oAqgSDQXH3Vcsx2f+8gbOnNeI8xdl9B45pwHHZVjDiT6gZXHxB1hkdNRZMTARRiyRTDOyGZgII55kMb2CL9xlAb6AHjucTjR4eKJh5uSPqcBod1rwysFssotX2qR9ebavAt78EyEA62YSoqF9dXrcZQXgqlM7cXgkiAFvCF+5sHLaQkodTqsRP//watz96D5cu2EhasWyU5MNsDWkSD4xeB+VqqKhiiomDWKVw79eP46v/eOttPu/+c+38c1/vg0AuO8Tp+C0uY1Zq72DE2GccteTis9z+c+kfSA2XrIQnz5b2vfBoNepkg7vvufFtP9PtPSKHz2xHz94Yp/mx505rxF//Njail9xV0thMRl02HfHJZL3+SNxLL35UdXnqCoXqshElWgoMJprLfjXZ06TvtM5DQiNAdEAYKpJ3Z6IETl77bTJGWQRMa3OiiRL/Bim16dIBX6FeWZDlWiYUvCKhvHD6bd7OaWNs/zPwen1NvgicXiCUbhsqbinPs4gst0lJhpWkt+9rwM1jcDgLuCMr0ziaCcHVpMemy5fNtXDOCFx3sIWnLdQxnzTNUNa5cbfVuaeKVVUUa54z0nT8J6TyPehVPvClb9+Vfj7e+9dIWzbUmvJKvB94RiWyRjridH98B50K6y8v33LRai1GNNIB6XiUTzuFzeehw6X/Ap1OUCOYNGC8xc249cfXXPCFMOJJIs5KqobOXKAJqnltDkNuO+Tp+Y8vioqH1WiYTLB9797e4Em0aqi9xgAtiJk6x0uQiT0ekJpRMOxMUI0zKivEg1TCmsdMRwdd6ffPnKAeIQYy3siAgAzGwiJ5x4NYqWIaDguEA2i1onW5eSY7HsEAEvk6nwCRRVVFBvO6cDQ7uzb+c9nBSiMqqii3CEmDqSKr6/94y1B/TCj3obnvnlu2v0Oi1FSXaBmnJcJuRQAGpze/VTa//vvvKRk4hNZlsWfXz2KG/8jYVSdB1677nw0n4AG5DRpGVLeHrQRsJXeVlJFYVElGiYT/Gqx91g60cC73ldAX3hHHSlUj4+nm/EdHQtCr2OE+MEqpggMAzTMAUYy5IUjeysmaYFXzRwZDaT1qx4dDcBs0KHFIToH9UZg4TuAnf8hMYP2VmCmjCKpiioKDdcMYP9jKY8QHuNu0lZhKd8UoiqqqESoKQmOjgXTCjYlKfn0epskAcGyLB54sxdf/ftbEo8qDOblqQwoFfz8Q6tx8dLWE0ahIIdL73kRbx3zKG7DK2IA4i9CSyycs6AJv79mbb5DrOIERZVomEzwMtjM1eQxTsZeXwFEg8uK/2/vzqPkKss8jn+f7k4nIZ2FJN1Zyb6RNBhCThJ2CEEChkUHBpRVHBEddDg6SJRRccQF8SAqehTBI7KIeJRhlV3AGQiBhARIQjbIZgKdpAnZl+5+5o9bTW9VXdWpW7fqVv8+59TpqlvvrXre971dde9T731vaYmxZsuuFsvX1u5mSJ/ulBVIBr1Tq5oUHNw0coctK2HyRfmLKUSNo2bWbGk5IeQ7m3cxsn+PtpMTHX05LL4f3n0BZn4LSjSDt0Sk92FQtxd2bYaKZnP6fLAGDh2Rr6hEJA0za5EkmPOLf/DWP7e3Kdc8GTF70kB+fcnRGb32p6YM5VNTUp/KuG7rbi6+8xXW1bad+LiYPHL18VQP6dXpEwmt7dlfz+HfTj2ZY6NV3z+DstISfvHsyg6NiNFcCxIWJRqi1HMwlHWHratbLv9gDZR1a7rcWYyVl5UwvO8hrN68s8XyNVt2aX6GQjFgIiy6B3ZuhorKYCLS/TtbjrKJsW5dShnUuxtra1smu97dsovxA3u2XWHoVLj4L8EQ9ulfiChKEZqSCbXvtko0rA0mKhWRWHj0yyd8dH/hug+STvj4xJL3WvyK/NtLp3LaxBTzt6QxrF/bUzRScXfe2bKLvy7cwC//vjr9CjlyRvVAvnTyGCYO7qUrHx2ETEcgzD1jwkdzfWQyp8Wg3t14ae5MJRYkJ5RoiFJJSTBsfeuqlstr3w12OItkpvvRVRWsqmlKNNQ3OCve38ElM3S+cUEYMCn4W7MEKk5uOke8//i8hRS2kf17sLrZNnigvoF1tbuZXZ0imTfqpOAmEqX+Y4O/W1fCsOnB/foDwel1kz6Zv7hE5KBNGXboR6Md2rsE5uf/0PIqZDecNZHLjwt/ZKuZMbqygmtPn8C1p09IWa5m+16mpbliRjKNv5pLeNJdIaI97U0oCh27vKpItpRoiFq/0fBeqwlvaldD31H5iScHRldW8PzyGurqGygrLWHt1l3sq2tgXLJfkyV6A6qDvxsXBRMfbpgPVtJ0BYYiMGFgL+59Ze1H2+D62t3UNTijKivyHZpIkz7DobQcNi9vWrZ1NTTUQWXqAwIRiYeSkpanWGzduY+jb3wmadkbHlnKDY8sbbEsyktUViW5YsbdL6/hWw8taXe9VL+aP/PVkxhTpe/cTKyq2cGsW14M/XUvP3YE3zlrokYrSN4o0RC1fmNh2aPBr1alXeDA3uD8+Alz8h1ZaEZX9uBAvbO2djejKytY/t4OACYo0VAYevQPRi+8+yIcfw2sezlIPnQtnv6ZOLgX++oaWLN1F2OqerJ0U3Du7PgBxVNHKQKlZdBvTPAd0KgmcaBRdXh+YhKRnOlX0bXFwfx7H+5lxg9TjyJoPVz+8mNHcMPZk3IWX2uXHDOCS44Z0WLZc2+/zxW/fy35Cs3MuuWFlM8dNawPf/3isZ3yADiMy3Qm8+iXj6d6SO/QX1ckG0o0RK1yfHAJvS0rgiHsm5cFjwdW5zuy0Bw5tA8Ar6/bxujKCpZt2o4ZjK3SQV7BGHUyLPwD7N0OGxbAUcUxEWSjiYOC2fqXbtrBmKqeLFq3jfKyEiYM0jYoBab/OHjvjabHNcvASovmKjAiktrA3i1HEeyrq2f8f6We5O/3L63h9y+tabM8ysn7Zk4Y0Gbkg7tz3/x1XP9gZpeofH3dtoxODbh4xjBuOGtSLE/NWPn+Dk77afijFO6/cgYzRvUL/XVFckGJhqgNnhL8/eeCINHQeBrFgCPyF1PIxlZV0KtbGQvW1nLe0UOZ904t1YN7071cs/kXjLGnwfzfwF1z4MAuOPysfEcUqjFVFXQtK2Hh2g84+2ODWbxhG9WDexXMdcNFPjKgGpY+BHu2Qfc+wYiGfqOhiy4FLNLZdC0rbXMQ/+gbG7n6vtfbXS/ZQXt5WQkrbjwj1PhSMTMumj6ci6a3nYtrX109Nz66jLvnre3w694zbx33zFsXRoht9Cgvpd6dvQcacvL62fj+J6v5zLRhnXLEhxQXJRqi1ncUdOsdJBqmXAqbFkOXQ4ri0paNSkqMKcMP5dU1H7B7fx2vr/+AK44vnvoVhdGnBkmvjQth4JEw4oT068RIeVkJx4zux/PLa9i1bzxvbPgw6Q6QSN4dNg1w2PAqjJkF6+cHI45ERIA5Rw5mzpGDWyx74q1NXHXPwnbX21/XkPJKBZfMGM73zo1mJG3XslK+d251yvdzd15cuYXLfjc/knga7dpfH+n7NTpxXCW3XjCZvj3K8/L+IlHKKtFgZjcDZwH7gdXAZ919W5Jys4GfAaXAHe7+o2zeN9ZKSoIDvPWvBo9XPwfDj4OS4vq1/6RxlXz3kaXc8tQKDtQ7x4/pn++QpLmSEviXO2DVMzDxHCjCrPkp46v4zvIl/OzZleyra+DMI+J/+VgpQkOnBqdKrH8Feg2BXTW6AspB0P6IdCazqwe1GflwoL6B6T94ltpd+9Ouf/e8tWlHGLz8jZkM6t09qzgzYWacNK4y44kvGxqc1Zt38sgbm/j5syvTr5AH/33OJC6ZMVwjEqTTM3c/+JXNPg485+51ZnYTgLtf16pMKbACOA3YALwKfNrdl7Z+vdamTp3qr72WfsKZ2HnpNnjqevj0n+CPF8AZN8P0K/MdVai27z3AMT94ll3765kwsCePfeUEXTdZIlWzYy8zf/ICO/fVMbJ/D5772kn60pfC9NtToW4vfOxCeOq/4Jo3oc+wdlcxswXuPjWiCAteLvdHinZfRDoFd+ehRRu55k+Lcvo+d142lZkTqgrie3Z/XQO3PrOCXz2/Oqfv8+K1pzCs3yE5fQ+RQtfe/khWIxrc/almD+cB5yUpNg1Y5e7vJIK5HzgHSJtoKFpHnA9PfytIMlgJjDs93xGFrle3Ltz2mSn8ecF6/u2EUUoySOSqenbj1gsmc9/8dXx99viC2PkRSeroy+DhLwdJhqHT0iYZpC3tj4gkZ2ace9QQzj1qSNLna7bvZdoPUl/5IlOfu6v4knGfP2Ek182eEMvJKEUKQZhzNFwB/CnJ8iHA+maPNwDTQ3zf+Ok5AE68Fl74MZw0Fw4tznPHT5lQxSkTqvIdhnRisyYOYNbEAfkOQ6R9R5wPC+6CTYvg4zfmO5pioP0RkQxV9eqW9rQFd2fppu184e4FbPhgT0SR5c5Rw/pwy79OZmT/HvkORaSopU00mNkzQLKTm69394cSZa4H6oB7k71EkmUpz9cwsyuBKwGGDSviX3VO+SZMvwq6H5rvSEREJJ+6dIfPPwt1+6Csa76jKVhR7o90mn0RkQyYGZMG9+Z/r5t5UOu7O3sO1LN5xz7e376PlTU7WLZpO2/+czvvbN7Jjr117a4/pE93Jh/Wh+ohvTl8UE8OH9SLfj3KNdJApMClTTS4+6z2njezy4A5wKmefMKHDcBhzR4PBTa28363A7dDcF5kuvhi7ZC++Y5AREQKhZIM7Ypyf6RT7YuI5JiZcUh5GcP7lTG8Xw+mjdT+r0hnkFUqMDF783XA2e6+O0WxV4GxZjbSzMqBC4GHs3lfERERkUbaHxERESks2Y45ug3oCTxtZovM7NcAZjbYzB4HcPc64GrgSWAZ8IC7L8nyfUVEREQaaX9ERESkgGR71YkxKZZvBM5s9vhx4PFs3ktEREQkGe2PiIiIFBbNoiIiIiIiIiIioVGiQURERERERERCo0SDiIiIiIiIiIRGiQYRERERERERCY0SDSIiIiIiIiISGiUaRERERERERCQ0SjSIiIiIiIiISGjM3fMdQ0pmtgNYnu84Oon+wJZ8B9FJqK2jpfaOjto6OuPdvWe+g+gMzGwzsDaElyq2/49iqw8UX51Un8JWbPWB4quT6pPecHevTPZEWchvFLbl7j4130F0Bmb2mto6GmrraKm9o6O2jo6ZvZbvGDqLVDtQHVVs/x/FVh8ovjqpPoWt2OoDxVcn1Sc7OnVCREREREREREKjRIOIiIiIiIiIhKbQEw235zuATkRtHR21dbTU3tFRW0dHbR0/xdZnxVYfKL46qT6FrdjqA8VXJ9UnCwU9GaSIiIiIiIiIxEuhj2gQERERERERkRjJS6LBzGab2XIzW2Vmc5M8b2b288Tzb5jZlEzXlZaybOvfmVmNmb0VbdTxdbDtbWaHmdnfzWyZmS0xs/+IPvp4yaKtu5nZfDNbnGjr70Yffbxk8zmSeL7UzF43s0ejizq+svzcXmNmb5rZIl2ZIr/M7GYzezvRRw+aWZ8U5WKxX2Nm5yc+MxvMLOWs5XHaBjtQp7j0UV8ze9rMVib+HpqiXEH3UbbfOYUmg/qcbGYfJvpjkZl9Ox9xZird8UEM+yddfeLWP2mPKSLrI3eP9AaUAquBUUA5sBiY2KrMmcDfAANmAK9kuq5u4bR14rkTgSnAW/muSxxuWW7bg4Apifs9gRXatnPW1gZUJO53AV4BZuS7ToV6y/ZzJPH8V4H7gEfzXZ9Cv4Xwub0G6J/veujmAB8HyhL3bwJuOpj+LpQbcDgwHngemNpOudhsg5nUKWZ99GNgbuL+3GTbXKH3URjfOYV0y7A+J8fp+5E0xwdx6p8M6xO3/kl7TBFVH+VjRMM0YJW7v+Pu+4H7gXNalTkH+IMH5gF9zGxQhutKk2zaGnd/EaiNNOJ4O+j2dvdN7r4QwN13AMuAIVEGHzPZtLW7+85EmS6JmyarSS2rzxEzGwp8ArgjyqBjLKv2lsLh7k+5e13i4TxgaJJisdmvcfdl7r4833GEKcM6xaaPCOK6K3H/LuDc/IVy0IrtMzBO209GMjg+iFP/FN3xTobHFJH0UT4SDUOA9c0eb6Bt5VOVyWRdaZJNW0vHhdLeZjYCOIrgl3ZJLqu2TgzlXwTUAE+7u9o6tWy361uBrwMNOYqv2GTb3g48ZWYLzOzKnEUpHXUFwa9HrRXjd3CxbYNx6qMB7r4JgoMNoCpFuULuo2Lbd8001mMsOKXzb2Y2KZrQciZO/ZOpWPZPO8cUkfRRWdgvmAFLsqz1r4mpymSyrjTJpq2l47JubzOrAP4CXOPu20OMrdhk1dbuXg9MTpwz/aCZVbu75iJJ7qDb2szmADXuvsDMTg47sCKV7efIce6+0cyqgKfN7O3ErzWSA2b2DDAwyVPXu/tDiTLXA3XAvcleIsmyvH0HZ1KfDBTUNhhCnWLTRx14mYLqo1aKbd81k1gXAsPdfaeZnQn8DzA214HlUJz6JxOx7J80xxSR9FE+Eg0bgMOaPR4KbMywTHkG60qTbNpaOi6r9jazLgQfCPe6+19zGGcxCGXbdvdtZvY8MBtQoiG5bNr6PODsxBdzN6CXmd3j7hfnMN64y2rbdvfGvzVm9iDBsN1COYAoOu4+q73nzewyYA5wqrsn24krqO/gdPXJ8DUKahsMoU6x6SMze7/xdMzEMOiaFK9RUH3USrHtu2ayL7K92f3HzexXZtbf3bdEFGPY4tQ/acWxfzI4poikj/Jx6sSrwFgzG2lm5cCFwMOtyjwMXJqYEXMG8GFiCFgm60qTbNpaOu6g29vMDLgTWObut0Qbdixl09aViZEMmFl3YBbwdoSxx81Bt7W7f8Pdh7r7iMR6zynJkFY223YPM+sJYGY9CCYjVAItT8xsNnAdcLa7705RrKj2a4p0G4xTHz0MXJa4fxnQZsRGDPqo2PZd09bHzAYm9gMxs2kEx2dbI480PHHqn7Ti1j8ZHlNE0keRj2hw9zozuxp4kmAm1t+5+xIzuyrx/K+Bxwlmw1wF7AY+2966UdchLrJpawAz+yPBTKv9zWwD8B13vzPaWsRHlu19HHAJ8KYFcwcAfNPdH4+wCrGRZVsPAu4ys1KCL4sH3F2XXUwh288R6Zgs23sAwalAEHy/3+fuT0RcBWlyG9CVYGg6wDx3v8rMBgN3uPuZcdqvMbNPAr8AKoHHzGyRu5/evD7EbBvMpE5x6iPgR8ADZvY5YB1wPkCc+qjYvnMyrM95wBfNrA7YA1yYYgRUQUh2fEAwsXbs+gcyqk+s+ocUxxTAMIi2j6yw20lERERERERE4iQfp06IiIiIiIiISJFSokFEREREREREQqNEg4iIiIiIiIiERokGEREREREREQmNEg0iIiIiIiIiEholGkREREREREQkNEo0iHRyZtbHzL6U4rkRZran2XV4U73GvWZWa2bn5SRIERERKUpmVm9mi8zsLTN7xMz6ZLDONWZ2aTvPzzGz74YaqIh0iBINItIHSJpoSFjt7pPbewF3vwh4OMSYREREpHPY4+6T3b0aqAX+vb3CZlYGXAHc106xx4CzzeyQ8MIUkY5QokFEfgSMTvyacHN7Bc2sh5k9ZmaLE788XBBRjCIiIlL8XgaGAJjZaDN7wswWmNk/zGxCosxMYKG71yXKfcXMlprZG2Z2P4C7O/A8MCf6KogIQFm+AxCRvJsLVKcbtZAwG9jo7p8AMLPeuQxMREREOgczKwVOBe5MLLoduMrdV5rZdOBXBEmG44AFzVadC4x0932tTrt4DTgBeCDXsYtIWxrRICId8SYwy8xuMrMT3P3DfAckIiIisdY9MRfUVqAv8LSZVQDHAn9OPPcbYFCi/CBgc7P13wDuNbOLgbpmy2uAwbkNXURSUaJBRDLm7iuAowkSDj80s2/nOSQRERGJtz2JUZXDgXKCORpKgG2JuRsab4c3lge6NVv/E8AvCfZPFiTmcCBRZk8UFRCRtpRoEJEdQM9MCprZYGC3u98D/ASYksvAREREpHNIjJL8CvCfBAmCd83sfAALfCxRdBkwJrG8BDjM3f8OfJ1gguuKRLlxwFuRVUBEWlCiQaSTc/etwP8lJndsdzJI4AhgfmIY4/XAjbmOT0RERDoHd38dWAxcCFwEfM7MFgNLgHMSxf4GnJi4XwrcY2ZvAq8DP3X3bYnnTiG4+oSI5IEmgxQR3P0zGZZ7Engyx+GIiIhIJ+HuFa0en9Xs4ewk5dea2VYzG+vuK4HjW5cxswFAd3d/M/SARSQjGtEgIu2pB3onRjCkZGb3AicBe6MISkRERDq1uTRNDpnMMOBrEcUiIklYcJlZEREREREREZHsaUSDiIiIiIiIiIRGiQYRERERERERCY0SDSIiIiIiIiISGiUaRERERERERCQ0SjSIiIiIiIiISGj+HxMRZlugc8n7AAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,5))\n", - "\n", - "# the real and imaginary components of the signal in the plot on the left\n", - "ax1.plot(t,s.real,label='real')\n", - "ax1.set_title('received signal time series')\n", - "ax1.set_xlabel('t [s]')\n", - "ax1.set_xlim(0, 0.05)\n", - "ax1.plot(t,s.imag,label='imaginary')\n", - "ax1.legend()\n", - "\n", - "# The Argand diagram on the right\n", - "ax2.plot(s.real,s.imag)\n", - "ax2.set_title('received signal complex plane')\n", - "ax2.set_ylabel('Im(s)');\n", - "ax2.set_xlabel('Re(s)');" - ] - }, - { - "cell_type": "markdown", - "id": "1ebabdd3-1284-4086-9885-08aa320732e9", - "metadata": {}, - "source": [ - "## 5. Compute frequencies with a fourier transform\n", - "The signal recieved by ApRES is a combination of signals from every reflector. The frequencies of these signals is directly proportional \n", - "to the range to the reflectors. The frequencies that make up a signal can be extracted using a fourier transform. [This video](https://www.youtube.com/watch?v=spUNpyF58B) is an incredibly clear explanation of how fourier transforms achieve this. " - ] - }, - { - "cell_type": "markdown", - "id": "dbc96d8d-dbe3-47d3-9d9b-7fd36f24d41a", - "metadata": {}, - "source": [ - "The cell below computes the discrete fourier transform of `s` and the frequency bins. By default `np.fft.fft` produces as many freqency bins as there are time-domain samples (`=len(s)`), evenly distributed between 0 Hz and the sampling frequency (12x10$^3$). Therefore the value of the frequencies is computed by multiplying the indexes by `sampling_frequency/no_of_samples`, which is equal to $1/T = 1$s. Note also that the frequency spectrum is normalized by the number of samples. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c0458e90-d02a-4226-bb9e-1ee33fdb6d08", - "metadata": {}, - "outputs": [], - "source": [ - "no_of_samples = len(s)\n", - "S = np.fft.fft(s)/no_of_samples \n", - "indexes = np.arange(no_of_samples) \n", - "frequencies = indexes * sampling_frequency/no_of_samples" - ] - }, - { - "cell_type": "markdown", - "id": "d5d8c9e4-e391-4a0c-9651-1aa89af17c81", - "metadata": {}, - "source": [ - "Next we compute the absolute value of the complex numbers in `S` and plot them against the frequencies yielding the usual depiction of frequency domain." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c26caebd-e11c-4ff5-a25b-7dd1b9850e0d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(18,3))\n", - "ax.set_title('frequency domain')\n", - "ax.plot(frequencies, np.abs(S))\n", - "ax.set_xlabel('frequency [Hz]')\n", - "ax.set_ylabel('amplitude')\n", - "ax.set_xlim(0, 800);" - ] - }, - { - "cell_type": "markdown", - "id": "7bb6d103-bdf3-4ad8-a175-53eb3affae40", - "metadata": {}, - "source": [ - "The plot displays two peaks in spectral energy at " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b71fbb6e-0f2b-4c1c-8015-8d67bea296a7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " first peak detected at 117.000 Hz.\n", - "second peak detected at 282.000 Hz.\n" - ] - } - ], - "source": [ - "peaks = argrelextrema(np.abs(S), np.greater) # this function finds local maxima \n", - "print(f' first peak detected at {frequencies[peaks[0][0]]:.3f} Hz.')\n", - "print(f'second peak detected at {frequencies[peaks[0][1]]:.3f} Hz.')" - ] - }, - { - "cell_type": "markdown", - "id": "db5dd9df-03a9-48e9-8581-9895f8ed8eb7", - "metadata": {}, - "source": [ - "Notice how the frequencies are close to the frequencies we computed above (`f1` and `f2`) but because `frequencies` is quantized and (in our case) increases in 1 Hz increments, the retrieved spectral peaks are restricted to be an integer number of Hz. This is why Brennan et al. call this the coarse range measurment. " - ] - }, - { - "cell_type": "markdown", - "id": "f99826e6-89e5-4fc0-ab6d-587b13b507fe", - "metadata": {}, - "source": [ - "## 6. Convert frequency to range" - ] - }, - { - "cell_type": "markdown", - "id": "dd06f597-962e-4a08-a4aa-c2f592ee0f45", - "metadata": {}, - "source": [ - "Next we convert `frequencies` to depths using a combination of {eq}`eq:tau1` and {eq}`eq:f_d1`,\n", - "\n", - "$\n", - "f_d = 2KR\\frac{\\sqrt\\epsilon}{c},\n", - "$\n", - "\n", - "Rearranging this expression for $R$ gives" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d290f163-3675-47ec-ac50-059fb860ec55", - "metadata": {}, - "outputs": [], - "source": [ - "range = frequencies * c /(2*K*np.sqrt(ep))" - ] - }, - { - "cell_type": "markdown", - "id": "d7266a63-b918-40c7-b70a-3ba446b59313", - "metadata": {}, - "source": [ - "which allows us to produce the amplitude-range plot:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4cdc92e8-d972-4c87-9ccb-696c07cb26fc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(18,3))\n", - "ax.set_title('amplitude-range plot showing our two reflectors')\n", - "ax.plot(range, np.abs(S))\n", - "ax.set_xlabel('range [m]')\n", - "ax.set_ylabel('amplitude')\n", - "ax.set_xlim(0, R2*1.2);" - ] - }, - { - "cell_type": "markdown", - "id": "1c5b4399-f460-485d-ae1c-b96d83c4b99f", - "metadata": {}, - "source": [ - "...and compute the range of the reflectors:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3fc18c3a-019f-4f0b-8d22-49d815d4810f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " first reflector detected at 49.804 m.\n", - "second reflector detected at 120.041 m.\n" - ] - } - ], - "source": [ - "print(f' first reflector detected at {range[peaks[0][0]]:.3f} m.')\n", - "print(f'second reflector detected at {range[peaks[0][1]]:.3f} m.')" - ] - }, - { - "cell_type": "markdown", - "id": "a43ce3e0-c734-418f-866f-7e0cf960f2a0", - "metadata": {}, - "source": [ - "The retrieved ranges to the two reflectors are close to the prescribed values:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ca1534ea-3592-402d-85c7-f2d4117ba459", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "50.0\n", - "120.0\n" - ] - } - ], - "source": [ - "print(R1) \n", - "print(R2)" - ] - }, - { - "cell_type": "markdown", - "id": "b19e7ab1-a3d0-409d-87dc-40f9048c2ee7", - "metadata": {}, - "source": [ - "but, again, they are a coarse measurement due to the resolution of the frequency spectrum, which in turn derives from the bandwidth, $B$ and the rate of increase in frequencies $K$. " - ] - }, - { - "cell_type": "markdown", - "id": "a7618192-3812-4abb-908e-ba3a84fe97aa", - "metadata": {}, - "source": [ - "## below this is scrap" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fa894e8a-92a6-4ee3-8511-6eb6c9c1faa2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " first reflector detected at 1.446 rad.\n", - "second reflector detected at -0.303 rad.\n" - ] - } - ], - "source": [ - "phase = np.angle(S)\n", - "print(f' first reflector detected at {phase[peaks[0][0]]:.3f} rad.')\n", - "print(f'second reflector detected at {phase[peaks[0][1]]:.3f} rad.')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d095c0fc-dd7f-426b-8498-8d8abccd5a20", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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H3H/wuL718FHNxOFHJmU6b6hb1140pDe/4LIVKw9wprWyxcMGSR+Mx3lISfqEu3+uheUBAAAA0OGCcuNdLU5XfzGrp523Rk87b011XakcaveRcT342Ak9ePC4vvrgIf3df+3WG55zsQpZWj+gPbVyVou7JV3dqusDAAAAwHyVWS1OtavF6cqmU7poXa8uWterF125UWt68vqjz92v6VJI8IC2xdwuAAAAABCrtHjInIEWD40oxmHDZKnc4pIAp47gAQAAAABi1TEeUsn4qFTIRuWYInhAG0vGuwkAAAAAEiCoDOy4goNLno5K94qpgOAB7YvgAQAAAABipbjFQzYxwUOlxUPY4pIAp47gAQAAAABi5XhwyXRiulrEYzzM0OIB7SsZ7yYAAAAASIBSwgaXpKsFzgYEDwAAAAAQq8xq0arpNOcrZKLgYZrBJdHGkvFuAgAAAIAEmO1qkYwWD8Vc3OKBMR7QxggeAAAAACBWKidzcMlJWjygjRE8AAAAAEAsCCvTaSbjo1Klq8UUwQPaWDLeTQAAAACQAEGY0MEl6WqBNkbwAAAAAACxIGGzWuQz0Uc2WjygnRE8AAAAAEAsKCdrcMlUypTPpAge0NYIHgAAAAAgFoSuTMpklozgQYq6WxA8oJ0RPAAAAABALAhdmYTMaFFRyKYY4wFtjeABAAAAAGKlcqhsKlkfk4rZtKYCWjygfSXrHQUAAAAALRSUk9jiIa3JGYIHtK+WBQ9mtsXM/sPMHjCz+8zsN1tVFgAAAACQoq4W6YS1eMhn05oK6GqB9pVp4bUDSW9w9zvMrFfS7WZ2q7vf38IyAQAAAOhgQTlUNmktHpjVAm2uZVGeux909zvi5yckPSBpU6vKAwAAAABJHFyymEtrmuABbSwRbYjMbJukqyV9p862G8xsh5ntOHz48BkvGwAAAIDOEU2nmYiPSVWFTFqTBA9oYy1/R5lZj6R/lvR6dz8+f7u73+Tu2919+9DQ0JkvIAAAAICOEZRDZVLJavHAdJpody0NHswsqyh0+LC7f6qVZQEAAACAUtmVSbf8+9k5Ctk0YzygrbVyVguT9H5JD7j7X7aqHAAAAABQUQ6T2OKB4AHtrZVR3jMkvULSs8zsrvjn+S0sDwAAAIAOl8TBJaPgga4WaF8tm07T3b8uKVnvaAAAAAAdrVQOlU3a4JLZlGbKocqhK52w1hhAI5L1jgIAAACAFkrih/tCNi1Jmg7oboH2RPAAAAAAALFocMlkBQ/FOHiYnCF4QHsieAAAAACAWBCGyiZuVouoPFMB4zygPSXrHQUAAAAALRSUk9vVgpkt0K4IHgAAAAAgFoSubMK6WuQzBA9oby2b1eJUjE0H+vpDR2QWT4dhksmqy2a1zytHzdseb6scp5p9q+dacN6F54kvX/dc869TXb9YmeftK1NDZZ5/7jn7zt4AAAAAAA0KyqEyCZvVopgjeEB7a6vgYfeRcb38/d9pdTHaylJhiUwLQo3aAKNywMJQZzbgUG0Yssi5quVYIizRvOVU5XqV5VR0bCq+SCrev7pfTWhUWZeKr127PP+1zNm3Zp+U1e4XX6/yPBUVOFV7vZrzVs+Rmn1tqcVeV/WYxa9X2b/6mlOLXM9m/03nvOZFrjf7GmrusZlS8bXSZtXrplKz+6bjfSrnT6Xix8q+tdtTlf1nX0N139S8fSvXTRnBGQAAaJkgdGWS1tUiE4/xUGKMB7Sntgoezhvq1gd/4+lyd7kkd8nliv+rLrtH+0fron1Vs8193rb4WFW2zztXZd/Z/ZY417zjVOdcqi1/zfPaMtc7bv7rU+W6i5RZC869cF/Vlr9mW+3+1essUqbq3r54mSvXWXiuxf+Nwup+Pmc59NryusJw9rgwlMoKq/uE815fWPNYucez5412rl2uvqbquSrHRwWec52aexjOK3O9bVja/BAjnVoYUqTicCS9SIiRStmCfWvDjVTNvtVQJTV33wWhSk1Ak66uqxwblaXyPGVWLXft+tl9a55XXuOcdTZnXeV1Vs45u6/q7Dt7/fnHpVPznlfOEa/LplOJ69sKAMCZEiRwVgvGeEC7a6vgoTuX0VPPXd3qYgArYmGgMje8qYYZkjycu642BJlzjrB+oFIbcs0JRepcL4zPH4Zzrxe6q+weLYeq7ufx+jnHV7fPHlvdHrrK868Vl6Uc+tx955Rl7r6he7z/wnNVyl67byPnKoeuUnluGco1r2f+uarlDaP9ymFNucLKfYmOL8fr28FgT15ff9OPV//IAQCgkwRhqEziZrWoBA+0eEB7aqvgATibVLtKKFmJOpprNqTwmpBCC9bN2R6HGnO21wQs9dbPOa5mXbkmKJldN1uuu/Yd07/f95hGJma0ob/Y6tsFAMAZl8SuFsU4eJikxQPaFMEDAJxBqZQpJVNSGxN85s5H9e/3PabJGf6wAQB0pqDsiRtcspCtjPHA72e0p2S9owAALTX7hw1NOQEAnalUDpM3nSZjPKDNETwAAKqqf9gE/GEDAOhM5dATN8hypavFdMAXA2hPBA8AgKrKHzZTdLUAAHQgd4/GeEjY4JLZdDTDFl0h0a6S9Y4CALRUgRYPAIAOFsQzUGUT1uLBzFTIpulqgbZF8AAAqGKMBwBAJwvKUfCQTtgYD1L05QBfDKBdETwAAKqq03XRlBMA0IGCMAreswmb1UKKfkfzxQDaVUvfUWb2ATM7ZGb3trIcAIAIXS0AAJ2s0uIhk8AWD/lsSpN0tUCbanWUd7Ok57a4DACAWCFTma6Lb1QAAJ2nFLd4SNrgklL0O3qa4AFtqqXvKHf/mqQnWlkGAMCsQq4yxgN/2AAAOk85Hlwyk7DBJaVoHCa+GEC7Sl6UN4+Z3WBmO8xsx+HDh1tdHAA4q+XSKZkRPAAAOlO1q0UCg4dijlkt0L4SHzy4+03uvt3dtw8NDbW6OABwVjMzFTL8YQMA6Eylcjy4ZEK7WjDGA9pV8t5RAICWir5RoSknAKDzVLpapBPY4qGQ5YsBtC+CBwDAHIUMo2YDADpTKe5qkU3orBZ8MYB21erpND8q6VuSLjazYTP71VaWBwDANyoAgM4VVGa1SCXv+9liNq1pprtGm8q08uLu/rJWXh8AsFAUPPCNCgCg8wSVrhYJbPFQyKY1OUPwgPaUvCgPANBS0XRd/GEDAOg8lVktsgls8VDIpjQV8MUA2lPy3lEAgJaiqwUAoFMF8awWmSS2eMikVQ69OvMG0E4IHgAAcxSzaU3RhxQA0IEqXS0yCZzVophLSxIDQKMtETwAAOagDykAoFNVB5dMJ+9jUj4bBQ+0SkQ7St47CgDQUkzXBQDoVJXpNJPY4qGQiT66TfM7Gm2I4AEAMAfTdQEAOlW50tUiiWM80OIBbYzgAQAwB10tAACdqjJwYyaBs1oUs4zxgPaVvHcUAKClmK4LANCpqtNpJrrFA7+j0X4aCh4s8nIz+/14eauZPbW5RQMAtEIxy3RdAIDOVOlqkU7iGA/Z6KMbXS3Qjhpt8fA3kp4u6WXx8glJ72lKiQAALVWgKScAoEOV4lktsgmc1YIxHtDOGn1H/bC7v0bSlCS5+4ikXNNKBQBoGabrAgB0qiDJs1rwxQDaWKPBQ8nM0pJcksxsSBJtcAHgLMR0XQCAThVUZrVI4OCSla4W/H5GO2r0HfUuSZ+WtNbM3i7p65Le0bRSAQBappjjGxUAQGcKKrNaJHlwSaa8RhvKNLKTu3/YzG6XdJ0kk/TT7v5AU0sGAGiJQoauFgCAzlRt8ZDk4IHfz2hDjc5qcb6k3e7+Hkn3SvoJMxtoZsEAAK3BdF0AgE5VmdEpkV0t4q6QkzP8fkb7afQd9c+SymZ2gaS/l3SupI80rVQAgJYp5uI/bPhGBQDQYcqhyyyZ02lm0ill00ZXC7SlRoOH0N0DSS+W9Nfu/luSNjSvWACAVsnT1QIA0KFKZVc2ga0dKgqZNL+f0ZaWM6vFyyS9UtLn4nXZ0724mT3XzL5vZrvM7HdO93wAgNNHH1IAQKcKymEix3eoyGfTdIVEW2o0ePgVSU+X9HZ3321m50r60OlcOJ6e8z2SnifpMkkvM7PLTuecAIDTV5nVguABANBpgtAT2c2iophL8fsZbanRWS3ul/S6muXdkv7kNK/9VEm73P0RSTKzj0m6XtL9p3leAMBpqAxexTcqAIBOE4Shsmm6WgArrdFZLS40s1vM7H4ze6Tyc5rX3iRpf83ycLxu/rVvMLMdZrbj8OHDp3lJAMDJ0NUCANCpgrIrk+AWD4UswQPaU6Nx3j9Ieq+kQNKPS/pHSf90mteu9472BSvcb3L37e6+fWho6DQvCQA4mUrwwKwWAIBOE4TJDh6KjPGANtVo8FB0969IMnff6+5vk/Ss07z2sKQtNcubJR04zXMCAE5TOmXKpVP8YQMA6DjR4JLJ7WqRz6b4YgBtqaExHiRNmVlK0kNm9lpJj0pae5rX/p6kC+OBKh+V9FJJv3ia5wQArIB8lsGrAACdpxR6ome1KGTTOnxiutXFAJat0Tjv9ZK6FA0weY2kV0h61elc2N0DSa+V9EVJD0j6hLvfdzrnBACsjCJ9SAEAHajcBmM8TAe0SET7aXRWi+/FT8cUTa25Itz93yT920qdDwCwMhi8CgDQiYIwVCaV3K4WRVokok01FDyY2UWS/n9J59Qe4+6nO84DACCBClnGeAAAdJ5S2ZVNeFcLxnhAO2p0jIdPSvpbSX8niZoOAGe5In/YAAA6UDl0pRPe1WJ8OtDN39itC9b26oK1PVrXl5dZcssMSI0HD4G7v7epJQEAJEaerhYAgA5USvisFtvPWaWPfXef3vav91fX9eYzOn9tjy5Y26MLq4+92rSqmOgQBZ1lyeDBzFbHT//VzF4j6VOSqsOouvsTTSwbAKBFCtm0RidLrS4GAABnVBC6CtnkBg/PuXy9dl62TofHprXr8THtOjymXYfG9NDjY7rtB4d1y+3D1X3zmZTOG5oNIy5Y26Pzh3p0zpouFbLpFr4KdKKTtXi4XZJLqkRlb5i3/bwVLxEAoOWK2ZQOHafFAwCgswShK53gwSUlycy0tregtb0F/cgFg3O2jU6UtOvwiWoYsevwmG7fO6LP7jxQc7y0oa+gbYPd0c+aLm1b061zB7u1ZTWhBJpjyeDB3c+VJDMrSvqfkp6pKIj4L0VjPgAAzkIMXgUA6ERBOVS2jbsn9Hdldc05q3XNOavnrJ+YCfTwoXE9cmRMe45MaM/Rce0+Mq4v3HNQIxOzLRzNpI39RZ072K1z1nTp3MFubVsTBRRbVheVzxBK4NQ0OsbDByUdl/SuePll8bqfb0ahAACtVcgwxgMAoPMEZVcmwbNanKquXEZP3tyvJ2/uX7Dt2MSM9hyd0J4jURix9+i4dh+d0OfuPrig2+W6vry2rOrS5lVFbVkdP67q0uZVXdowUFA2weNjoLUaDR4udvcra5b/w8x2NqNAAIDWK+bSTKcJAOg4QRgqk/CuFittoCunq7pyumrLwIJtI+Mz2nN0XHuOjmvf0UkNj0xo/8iEduwd0b/efVDl0Kv7pkza0F/U5lVFbV7VpS2r48dVRW0cKGpdX0G5TGfdW8xqNHi408ye5u7fliQz+2FJ32hesQAArZTPpuhqAQDoOEF4drZ4OFWrunNa1Z3T1VtXLdgWlEMdHJ3S/pEJDY9MaviJ6HH/yIS++fARPXbnlHw2l5CZNNiT14b+QvxT1Ib+gtb3F7RxoKj1fQXCibNYo8HDD0t6pZnti5e3SnrAzO6R5O5+RVNKBwBoiUImrZkgVBi6Um3c1xUAgOUIyt5xLR5OVSad0pbVXdqyuqvu9umgrIPHomDi4LEpHRid1GOjUzowOqXdR8b1zV1HdWI6mHNMJZzYGAcSG/qLWtuX19regtbFj2t78xroysqMv0/aSaPBw3ObWgoAQKIUc9HgUdNBWH0OAMDZLupqwQfalZDPpKszZyzmxFRJj41O6eDolA6OTurAsak4nJjUI4fH9Y1dRzU2L5yQpFw6paHevIZ681rbm9e6viiQqIQUQ/Hz1V05ZRh3IhEaCh7cfW+zCwIASI5C3MxxslQmeAAAdIyzdXDJpOotZNVbyOrCdb2L7jMxE+jQ8WkdOjGtQyem9Pjx6PFwvG7P0XF9d88TOjZRWnCsmTRQzGpNT15runMa7MlrTU9Oq7tzWtOT12D8uLo7p8GenPqLtKRolkZbPAAAOkhlDm9mtgAAdJJSOWRmhoTpymW0bTCzZMsJKeracfhEHFAcn9LhE9M6MjajJ8ZndHQ8ev7gY8d1dHymbkghSZmUVUOJNd25akixqiunVV1Z9cePq7qikGJVd07duTRhRQMIHgAAC1RaORA8AAA6SRC60nS1aEv5TFqb46k9T6ZUDjUyMaOjY/HP+PS8xxkdHZvW/v0TemJsZsFYFLWyadNAV04DxSiQGOjKaqCr8jwXP8+qv5hTXzGjvkJWfcWsevOZjhpHi+ABALBAPhMFD8xsAQDoJMxq0Rmy6VQ8UGWhof1L5VDHJkoanZzRyERJI3GriWPx8rGJGY2MR8v7npjQzuFo/Uyw+NTkZlJPfjaI6Ctk1FfMqr+Yjdct3FZdX8yqJ9dewQXBAwBggUI2amY6VVr8FyYAAGeboBwqy6wWmCdbM5hlo9xdU6WoZcXIxIxGJ0o6PhXo+FRJxyfj55OleDlav/+JCd0fr1+qlYUUBxe5jLrzGfUUosfefEbd+bR68ln15NPV9T3xz+w+0TGVdV3ZdNNDDIIHAMACxXiMh2laPAAAOkQYukIXLR6wIsxMxVxaxVxRGweKyz6+HLrG4qBidF5AcXyyVA0nxqcDjU0HGpsua3w60OET0/FytC0IvYGySt25ShCRVk8hCi66chl159Iqxo9dubS68nPXFXPpKLw4yWDkLQkezOznJL1N0qWSnuruO1pRDgBAfZXBJelqAQDoFJUPaEyniSRIp0z9XVn1d2W15RTP4e6aDsJqCHFiqjaomA0nxqbLGquz7YnxSU3MBJqYKWtiOtBEqSw/eY5RV6taPNwr6cWS3tei6wMAljA7qwVdLQAAnSEIo995GWa1wFnCzFTIplXIpjXY03g3kcVUuo+MzwSamC5rohRofLqsyZmyxmcCPfdPFz+2JcGDuz8giWlHACChikynCQDoMKUyLR6Apcx2H0lLPcs7NvFxnpndYGY7zGzH4cOHW10cAOgIlcEl6WoBAOgUZbpaAE3TtBYPZvZlSevrbHqzu/9Lo+dx95sk3SRJ27dvP8UeJQCA5cjT4gEA0GGCMl0tgGZpWvDg7s9u1rkBAM1VndViifmnAQA4m5TiFg9ZZrUAVhxxHgBggWzalDJpcoYWDwCAzlCOx3hIp/iIBKy0lryrzOxnzGxY0tMlfd7MvtiKcgAA6quMgkxXCwBApyjFs1rQ4gFYea2a1eLTkj7dimsDABpTyKY1FRA8AAA6Q1Cd1YIWD8BK410FAKirmE1rcoYxHgAAnSGIWzykmdUCWHEEDwCAuvLZFC0eAAAdo9Liga4WwMojeAAA1FXIpDXNGA8AgA5RafHAdJrAyuNdBQCoq5hLa5LgAQDQIWbHeKDFA7DSCB4AAHUVsilNlRjjAQDQGYKQ4AFoFoIHAEBdhQzTaQIAOkepTFcLoFl4VwEA6irQ1QIA0EHKtHgAmobgAQBQVzS4JF0tAACdoVQZ44FZLYAVR/AAAKgrGuOBFg8AgM5QmdUiS1cLYMXxrgIA1FXM0tUCANA5Kl0t0nS1AFYcwQMAoK5CNhpc0t1bXRQAAJqu0tUim+IjErDSMq0uAAAgmQrZlEKX/uEbe5TNpJRJmdIpq3lMzS6nF1lf75g5+y48JsU3TQCAFgiqs1rwewhYaQQPAIC6zh3skST94efuP6PXNdOigcSigUV6iVCkuj2ltEnpVErZdLQuEz/PplPKpFPKpix6jMORbCalbCoV7TtvezYdhTGz+6eUy0SPmTrbK8tm/EELAElUYlYLoGkIHgAAdb3gig269uKfVCkIFYSucugKwjB+jJfLi6wPXeUwVFB2hV5v/3j7nP0r2+usj8+/4Brl+ueaLJXnlKH22KAc7RuErlI52l6K150JmTgIydYEFFHwEYcd6Zr18/bJZ6IQI5dJKZeJ1uUyKeXS8c/8dfP2y6ZN+UxKuXRa2YwpV3Pe2v0ISAB0onK1xQNdLYCVRvAAAFhUTz4j5VtdijPDvRJMuErhbCBRCSeCMFSpHG2fKYfVAKN2+0w5DjbmnSMIXaUgVKkm+KgNPUrx8XP2j689E4Qanw40U3bNBGWVyq6ZYHbbdPy4ksyiUd3z6ZSymdpQw5TLpJWrCT9y6ZQK2bTymZTymbQK2ZTy8fLs+tl11X0yaeWzKRXix7n7p5VNE34AOLMqATRdLYCVR/AAAIAkM4u7REhFpVtdnGWphCaVMGImCDUTP68EFTPlsmYCr1k/d7/adaXybKBRmnee6Zp1U6VQxycDTQdlTZVCTQdlTQehpkuhpoKyTmdc0pSpGk7MDyWqwUW8vpBNq5hLqZhNq5jLRI/ZlLpyGRVy6Xg5rWKu/iMhBwCJwSWBZiJ4AACgzc2GJil15Vpdmoi7q1T2BaHEVGk2nFgYWMSPc/abd1z8fLJU1rHJGU2VQk3OlKvrJkvLDzzSKVNXNt1QSFH72J3PqLv6mFF3Pn5esz5Lk22gbZTDqPUY02kCK68lwYOZ/Zmkn5I0I+lhSb/i7sdaURYAALDyzEy5TNQlo7dw5q7r7poOojBiolSuhhITM3EwMVPWZCnQ5EyoyVJl2+zy5EwQBxihJmcCHTpRio6ZmQ02pkqNd23JpVPqzqfVlcuoJ59RVz4dPc4JLGoCjJrwoq+QUW8hq95CdGx3LsOsL0ATVVs80NUCWHGtavFwq6Qb3T0wsz+VdKOkN7WoLAAA4CxhZtXuF6uadI0wHsB0fCbQxHRZY9OBJmbKGp8OND4TRI/TleW56yv7HTo+PbvvTLmhcTrMonFX+mrCiN6acGL2Mf7Jz13fV8yqN094ASwmCEOlGVwXaIqWBA/u/qWaxW9LekkrygEAALBcqZRVWyWod2XOWSqHUYgRhxFj04FOTAU6MVWa9zh3/eGxaT1yZLy6rvKN7aJlN6mvmNVAMav+rpz6K8+LWQ10RY/91eXcnPWFbHuNfQIsVxA63SyAJknCGA//XdLHF9toZjdIukGStm7deqbKBAAAcMZk0yn1d6XU35U95XNUupksDCqi58enSjo2UdLoZEnHJqPH0YkZ7T06Hj2fLC05PkY+k6qGEAPFnPq7slrdldOq7pzWdOe0us5PVy7Nt8doG0HZlSV4AJqiacGDmX1Z0vo6m97s7v8S7/NmSYGkDy92Hne/SdJNkrR9+/YzM8k6AABAm6ntZjLUu/x5cMPQdWI60Gg1nJiJHuPlKKiYXb//iQnt3H9MIxMzi7a0yGdSdQOJNd21gUVeQ73RTzdBBVooKIfKMCAs0BRNCx7c/dlLbTezV0l6oaTr3E9nwi0AAACcrlTKqt0slsM9CixGxmd0dHxGT4zN6ImJGT0xPjO7Lv7Ze3RCI+MzOjEd1D1XMQ5NhnrzGuqZDSTmLw/25JXL8AERKysIXRlaPABN0apZLZ6raDDJa919ohVlAAAAwOkzM/UVsuorZHXOmu6GjpkOyhoZL+mJ8RkdHZ/WkbFpHT4R/RyKHx8+PKZv7z6qYxOluucY6MrOCSPW9ua1vr+oDf0FresraEN/QWt783yDjYYFZVeGGS2ApmjVGA/vlpSXdGvcnO7b7v7qFpUFAAAAZ1A+k9b6/rTW9598rtXpoKyjYzPVYOJwTUhRWb5z3zE9fnxK0/NmB0mZNNSb1/q+gtb3F7Shv1gNJaLlKKRg4ExIUikMlUkRVAHN0KpZLS5oxXUBAADQXvKZtDYOFLVxoLjkfu6u0cmSDo5O6bHRKT12fCp+PqmDo1PafWRc33z4qE5MLezmsbo7p82rito0UKx57NKmVdFyb+HUB/1E+yiHtHgAmiUJs1oAAAAAp8XMNNCV00BXTpdu6Ft0v/HpQI8dj8KJSjBxYHRKwyOT+sHjJ/TVBw8taDnRV8jMCSIqwcTmVUVtWd217HExkExBmTEegGYheAAAAEDH6M5ndP5Qj84f6qm73d11dHxGwyOTenRkUsMjE3r02KSGRya17+iEvrnriMZnynOOWdUVjW+xbU2XtsaPleXV3Tlm6mgTpXKoLGOCAE1B8AAAAADEzEyDPdHMGVdtGViwvdKlYzgOJfY9MaG9R6OfHXtH9NmdBxTWzNfWk8/onDVd2ramW1vXdNWEEt1a15cnlEiQcuhK0+IBaAqCBwAAAKBBtV06nrSpf8H26aBcbR2x5+i49saP9x88ri/e95iCmlSiK5eOW1906/yhHl2wtkfnr+3ROWu6lM8w4OWZVgqdWVCAJiF4AAAAAFZIPpNetCtHUA51cHRKe46Oa8+RcT18eFwPHx7T9/aM6DN3Hajul06Ztq7uqgYS56+NznfBUI/6uxhPolmCcqgsLR6ApiB4AAAAAM6ATDqlLau7tGV1l370wqE528anA+0+EgURuw6N6eHDY3r40Li+9oMjminPDnY52JPX+UPdunh9ry5e36tL1vfqonW9zLyxAgK6WgBNQ/AAAAAAtFh3PqMnbepf0H0jKIcaHpmMgog4lHjo0Jg+dcejGpuenRp000BxThhx8fpenTfYo1yGrgONCsqhunJ8PAKagXcWAAAAkFCZdErbBru1bbBb1126rrre3atTgD742Al9P/752g8OV8eRyKZN5w32zAkkLtnQp439BQa1rCMIXZk09wVoBoIHAAAAoM2YWbXbRm0gMROEeuTImL7/2GwgcXs820bFQFdWl2/s0+Ub+6uP5w52d3w3g6DsynT4PQCaheABAAAAOEvkMildsr5Pl6zv0/U1649PlfTQ4yf0wMETuu/Acd13YFQ3f3OPZoJo/IhiNq1LN/TqSZtmw4gL1/V01OwaQRgqk6JrCtAMBA8AAADAWa6vkNU156zWNeesrq4rlUPtOjSm+w4c172Pjur+A8f1qTse1T9+a6+kqKvGhWt7dfnGvnj8iT5dtqFfxdzZGUYEZbpaAM1C8AAAAAB0oGw6pUs39OnSDX16yTWbJUlh6Nr7xITuOzCqex+NWkZ85cFD+uTtw5KiqT4vWterKzf368otA7pic78uWterbLr9WwqUwpCuFkCTEDwAAAAAkCSlUqZzB7t17mC3XnjFRknRQJaPHZ/SPcOjunt4VDuHj+kL9z6mj31vvyQpn0npSZv6dcXmfl25eUBXbhnQtjVdbTeAZbnsypwFAQqQRAQPAAAAABZlZtrQX9SG/qKec/l6SVEYsffohHYOH9PO/aO6e/iYPvrdffqHb+yRJPUVMrpic9Qi4sotA7py84DW9xda+CpOrhS6snS1AJqC4AEAAADAsphZdZrP66/aJEkKyqEeOjSmnfuPaedwFEa872uPqBxP77m2N68rNg/o6q0DuiruptFbyLbyZcwRlMOOn9kDaBaCBwAAAACnLVMzZsRLnxqtmyqVdd+B47p7+Jh27j+mu4dH9eUHHpckmUkXre2tBhFXb12lC9b2tOzDfxA6s1oATULwAAAAAKApCtm0rjlnla45Z1V13ehESXcNH9Od+0Z01/6540X05DO6ckt/FERsWaWrtg5osCd/RsoalOlqATRLS4IHM/sjSddLCiUdkvTL7n6gFWUBAAAAcOb0d2V17UVDuvaiIUnReBG7j4zrzn3HdNf+Y7pz/4j+9rbZLhpbV3fFLSKiVhGXbehTLrPyLROCMGRwSaBJWtXi4c/c/fckycxeJ+n3Jb26RWUBAAAA0CJmpvOGenTeUI9+Np7Wc3KmrHseHdVd+0d0575j+u7uJ/TZndH3lLlMSpdv7NPVW1bFYcSANg0UT3sWjairBS0egGZoSfDg7sdrFrsleSvKAQAAACB5irm0nnruaj313NXVdQdHJ3XXvmO6c3/UTeMj392rD3xjtyRpsCdfDSGu3rJKV2zuV3e+8Y865dDlLsZ4AJqkZWM8mNnbJb1S0qikH19ivxsk3SBJW7duPTOFAwAAAJAoG/qL2vDkop735A2SpFI51PcfO6E7941Uu2ncen80cGXKpIvW9erqrXGriC0DOn+oR6lFWjSUyqEkKcMYD0BTmHtzGhuY2Zclra+z6c3u/i81+90oqeDubz3ZObdv3+47duxYwVICAAAAOFuMjM/EA1dGQcRd+0Z0fCqQJPUWMvGglQO6auuArtqySqu7c5Kk8elAl7/1i7rxeZfoN649v5UvAWhbZna7u2+vt61pLR7c/dkN7voRSZ+XdNLgAQAAAAAWs6o7px+/eK1+/OK1kqQwdD1yZDwatDJuGfHu/9ileNxKbVsTDVx58fo+SWJwSaBJWjWrxYXu/lC8+CJJD7aiHAAAAADOXqmU6YK1PbpgbY9eEg9cOTET6J7h0epYEd98+Kg+c1c0cGVPPt3K4gJnraZ1tVjyomb/LOliRdNp7pX0and/tIHjTkj6fpOLh7PDoKQjrS4E2gb1BY2irmA5qC9oFHUFy0F9QaPOdF05x92H6m1oSfBwqsxsx2J9RoBa1BUsB/UFjaKuYDmoL2gUdQXLQX1Bo5JUV+jEBAAAAAAAmobgAQAAAAAANE27BQ83tboAaBvUFSwH9QWNoq5gOagvaBR1BctBfUGjElNX2mqMBwAAAAAA0F7arcUDAAAAAABoI20RPJjZc83s+2a2y8x+p9XlQbKY2RYz+w8ze8DM7jOz34zXrzazW83sofhxVavLimQws7SZ3Wlmn4uXqSuoy8wGzOwWM3sw/n/M06kvqMfMfiv+HXSvmX3UzArUFVSY2QfM7JCZ3VuzbtH6YWY3xn/3ft/MfrI1pUarLFJf/iz+XXS3mX3azAZqtlFfOlS9ulKz7Y1m5mY2WLOuZXUl8cGDmaUlvUfS8yRdJullZnZZa0uFhAkkvcHdL5X0NEmvievI70j6irtfKOkr8TIgSb8p6YGaZeoKFvPXkv7d3S+RdKWiekN9wRxmtknS6yRtd/cnSUpLeqmoK5h1s6TnzltXt37Ef8O8VNLl8TF/E/89jM5xsxbWl1slPcndr5D0A0k3StQX1K0rMrMtkn5C0r6adS2tK4kPHiQ9VdIud3/E3WckfUzS9S0uExLE3Q+6+x3x8xOKPhhsUlRPPhjv9kFJP92SAiJRzGyzpBdI+vua1dQVLGBmfZL+m6T3S5K7z7j7MVFfUF9GUtHMMpK6JB0QdQUxd/+apCfmrV6sflwv6WPuPu3uuyXtUvT3MDpEvfri7l9y9yBe/LakzfFz6ksHW+T/LZL0V5J+W1LtgI4trSvtEDxskrS/Znk4XgcsYGbbJF0t6TuS1rn7QSkKJyStbWHRkBzvVPQ/4rBmHXUF9Zwn6bCkf4i75vy9mXWL+oJ53P1RSX+u6Julg5JG3f1Loq5gaYvVD/72xcn8d0lfiJ9TXzCHmb1I0qPuvnPeppbWlXYIHqzOOqbiwAJm1iPpnyW93t2Pt7o8SB4ze6GkQ+5+e6vLgraQkfQUSe9196sljYum8qgj7pt/vaRzJW2U1G1mL29tqdDG+NsXizKzNyvqZvzhyqo6u1FfOpSZdUl6s6Tfr7e5zrozVlfaIXgYlrSlZnmzouaLQJWZZRWFDh9290/Fqx83sw3x9g2SDrWqfEiMZ0h6kZntUdRt61lm9iFRV1DfsKRhd/9OvHyLoiCC+oL5ni1pt7sfdveSpE9J+hFRV7C0xeoHf/uiLjN7laQXSvold698YKS+oNb5ikLwnfHfu5sl3WFm69XiutIOwcP3JF1oZueaWU7RgBifbXGZkCBmZor6YD/g7n9Zs+mzkl4VP3+VpH8502VDsrj7je6+2d23Kfp/yVfd/eWirqAOd39M0n4zuzhedZ2k+0V9wUL7JD3NzLri30nXKRpviLqCpSxWPz4r6aVmljezcyVdKOm7LSgfEsTMnivpTZJe5O4TNZuoL6hy93vcfa27b4v/3h2W9JT4b5qW1pXMmbrQqXL3wMxeK+mLikaJ/oC739fiYiFZniHpFZLuMbO74nW/K+lPJH3CzH5V0R+FP9ea4qENUFewmP8l6cNx8P2IpF9RFNpTX1Dl7t8xs1sk3aGoCfSdkm6S1CPqCiSZ2Ucl/ZikQTMblvRWLfK7x93vM7NPKAo6A0mvcfdySwqOllikvtwoKS/p1ijf1Lfd/dXUl85Wr664+/vr7dvqumKzrXQAAAAAAABWVjt0tQAAAAAAAG2K4AEAAAAAADQNwQMAAAAAAGgaggcAAAAAANA0BA8AAAAAAKBpCB4AAEAimdkeM7vHzLYv45jzzewuMxtrZtkAAEDjmE4TAAAsyaJJ483dwzN83T2Strv7kVM4dszde1a+VAAAYLlo8QAAABYws21m9oCZ/Y2kOyRtMbP3mtkOM7vPzP6gZt89ZvYHZnZH3ELhknj9kJndGq9/n5ntNbPBeNvLzey7ceuE95lZuoEy7TGzd5jZt+JyPMXMvmhmD5vZq5t1LwAAwOkheAAAAIu5WNI/uvvV7r5X0pvdfbukKyRda2ZX1Ox7xN2fIum9kt4Yr3urpK/G6z8taaskmdmlkn5B0jPc/SpJZUm/1GCZ9rv70yX9l6SbJb1E0tMk/eEpv0oAANBUmVYXAAAAJNZed/92zfLPm9kNiv5+2CDpMkl3x9s+FT/eLunF8fNnSvoZSXL3fzezkXj9dZKukfS9qBeHipIONVimz8aP90jqcfcTkk6Y2ZSZDbj7sWW8PgAAcAYQPAAAgMWMV56Y2bmKWjL8kLuPmNnNkgo1+07Hj2XN/n1hi5zXJH3Q3W88hTJVrhPWPK8s83cNAAAJRFcLAADQiD5FQcSoma2T9LwGjvm6pJ+XJDN7jqRV8fqvSHqJma2Nt602s3NWvsgAACAJ+GYAAACclLvvNLM7Jd0n6RFJ32jgsD+Q9FEz+wVJt0k6KOmEux8xs7dI+pKZpSSVJL1G0t7mlB4AALQS02kCAICmMLO8pLK7B2b2dEnvjQeTbPT4PWI6TQAA2h5dLQAAQLNsVTSA5E5J75L068s8/rCkr5jZ9kYPMLPzzewuSY8v81oAAKBJaPEAAAAAAACahhYPAAAAAACgaQgeAAAAAABA0xA8AAAAAACApiF4AAAAAAAATUPwAAAAAAAAmobgAQAAAAAANM3/A8z+z3RyOoklAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(18,3))\n", - "ax.set_title('amplitude-range plot showing our two reflectors')\n", - "ax.plot(range, np.angle(S))\n", - "ax.set_xlabel('range [m]')\n", - "ax.set_ylabel('phase')\n", - "ax.set_xlim(0, R2*1.2);\n", - "ax.set_ylim(-np.pi, np.pi);" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "66f6fefc-f363-4937-a869-36d0c145048a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.06530727388987519\n", - "-0.013693240278268263\n" - ] - } - ], - "source": [ - "f_c = 300e6 # [Hz]\n", - "lam = c/np.sqrt(ep) / f_c\n", - "Rfine1 = lam * phase[peaks[0][0]]/(4*np.pi)\n", - "Rfine2 = lam * phase[peaks[0][1]]/(4*np.pi)\n", - "print(Rfine1)\n", - "print(Rfine2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3460a8ea-77e1-48f2-bca6-fdfa713d7e05", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# this does not recreate the phase analysis here: https://www.gaussianwaves.com/2015/11/interpreting-fft-results-obtaining-magnitude-and-phase-information/\n", - "\n", - "\n", - "#X2=X;%store the FFT results in another array\n", - "#%detect noise (very small numbers (eps)) and ignore them\n", - "#threshold = max(abs(X))/10000; %tolerance threshold\n", - "#X2(abs(X) Date: Thu, 14 Nov 2024 15:19:17 +0000 Subject: [PATCH 2/3] add google id to config --- src/_config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/_config.yml b/src/_config.yml index 98be21a..1808ce8 100644 --- a/src/_config.yml +++ b/src/_config.yml @@ -28,7 +28,7 @@ html: use_multitoc_numbering : true # Continuous numbering across parts/chapters extra_navbar : Powered by Jupyter Book # Will be displayed underneath the left navbar. extra_footer : "" # Will be displayed underneath the footer. - google_analytics_id : "" # A GA id that can be used to track book views. + google_analytics_id : "G-4GXWLSDF9Y" # A GA id that can be used to track book views. home_page_in_navbar : true # Whether to include your home page in the left Navigation Bar baseurl : "" # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/ comments: From e9eaa6bbc365d5949b5268fbdf3c133946bca00b Mon Sep 17 00:00:00 2001 From: jkingslake <48723181+jkingslake@users.noreply.github.com> Date: Thu, 14 Nov 2024 15:20:18 +0000 Subject: [PATCH 3/3] edit apres theory pages --- src/_toc.yml | 1 - src/references.bib | 55 ++++ src/sections/radar/apres/apres_data_1.ipynb | 4 +- src/sections/radar/apres/beat-frequency.ipynb | 7 +- .../radar/apres/figures/apres_at_JIRP.png | Bin 0 -> 2597383 bytes src/sections/radar/apres/theory_1.ipynb | 307 +++++++++--------- 6 files changed, 219 insertions(+), 155 deletions(-) create mode 100644 src/sections/radar/apres/figures/apres_at_JIRP.png diff --git a/src/_toc.yml b/src/_toc.yml index 0c1ecb9..4a7287c 100644 --- a/src/_toc.yml +++ b/src/_toc.yml @@ -34,7 +34,6 @@ parts: - file: sections/radar/apres/apres-intro sections: - file: sections/radar/apres/theory_1.ipynb - - file: sections/radar/apres/beat-frequency - file: sections/radar/apres/apres_data_1.ipynb - caption: Appendix diff --git a/src/references.bib b/src/references.bib index e1e9316..47cfdcd 100644 --- a/src/references.bib +++ b/src/references.bib @@ -16,3 +16,58 @@ @article{brennan_phase-sensitive_2014 pages = {776--786}, file = {Full Text PDF:/Users/jkingslake/Zotero/storage/7YHSEPTX/Brennan et al. - 2014 - Phase-sensitive FMCW radar system for high-precisi.pdf:application/pdf;Snapshot:/Users/jkingslake/Zotero/storage/NDCHEJ9K/iet-rsn.2013.html:text/html}, } + +@article{Nicholls_Corr_Stewart_Lok_Brennan_Vaughan_2015, + title={A ground-based radar for measuring vertical strain rates and time-varying basal melt rates in ice sheets and shelves}, + volume={61}, + DOI={10.3189/2015JoG15J073}, + number={230}, + journal={Journal of Glaciology}, + author={Nicholls, Keith W. and Corr, Hugh F.J. and Stewart, Craig L. and Lok, Lai Bun and Brennan, Paul V. and Vaughan, David G.}, + year={2015}, + pages={1079–-1087}}
+ +@article{kingslake2014full, + title={Full-depth englacial vertical ice sheet velocities measured using phase-sensitive radar}, + author={Kingslake, Jonathan and Hindmarsh, Richard CA and A{\dh}algeirsd{\'o}ttir, Gu{\dh}finna and Conway, Howard and Corr, Hugh FJ and Gillet-Chaulet, Fabien and Mart{\'\i}n, Carlos and King, Edward C and Mulvaney, Robert and Pritchard, Hamish D}, + journal={Journal of Geophysical Research: Earth Surface}, + volume={119}, + number={12}, + pages={2604--2618}, + year={2014}, + publisher={Wiley Online Library} +} + +@article{brisbourne2019constraining, + title={Constraining recent ice flow history at Korff Ice Rise, West Antarctica, using radar and seismic measurements of ice fabric}, + author={Baird, AF and Kendall, JM and Kingslake, J}, + journal={Journal of Geophysical Research: Earth Surface}, + volume={124}, + number={1}, + pages={175--194}, + year={2019}, + publisher={Wiley Online Library} +} + +@article{young2021rapid, + title={Rapid and accurate polarimetric radar measurements of ice crystal fabric orientation at the Western Antarctic Ice Sheet (WAIS) Divide ice core site}, + author={Young, Tun Jan and Mart{\'\i}n, Carlos and Christoffersen, Poul and Schroeder, Dustin M and Tulaczyk, Slawek M and Dawson, Eliza J}, + journal={The Cryosphere}, + volume={15}, + number={8}, + pages={4117--4133}, + year={2021}, + publisher={Copernicus Publications G{\"o}ttingen, Germany} +} + +@article{vavnkova2022ocean, + title={Ocean variability beneath the Filchner-Ronne ice shelf inferred from basal melt rate time series}, + author={Va{\v{n}}kov{\'a}, Irena and Nicholls, Keith W}, + journal={Journal of Geophysical Research: Oceans}, + volume={127}, + number={10}, + pages={e2022JC018879}, + year={2022}, + publisher={Wiley Online Library} +} + diff --git a/src/sections/radar/apres/apres_data_1.ipynb b/src/sections/radar/apres/apres_data_1.ipynb index 91c0fa3..3183c28 100644 --- a/src/sections/radar/apres/apres_data_1.ipynb +++ b/src/sections/radar/apres/apres_data_1.ipynb @@ -2071,7 +2071,7 @@ ], "metadata": { "kernelspec": { - "display_name": "full_py_env", + "display_name": "xapres", "language": "python", "name": "python3" }, @@ -2085,7 +2085,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/src/sections/radar/apres/beat-frequency.ipynb b/src/sections/radar/apres/beat-frequency.ipynb index cbae2ea..9fbc8a2 100644 --- a/src/sections/radar/apres/beat-frequency.ipynb +++ b/src/sections/radar/apres/beat-frequency.ipynb @@ -420,7 +420,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "full_py_env", "language": "python", "name": "python3" }, @@ -435,11 +435,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" - }, - "vscode": { - "interpreter": { - "hash": "d30c63c32c419ea9f0bba7809c4e773ec43b1ff3bce90afeee7dfc4932bbd9b5" - } } }, 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