Estrogen treatment is likely to have some beneficial effects on lipid metabolism and vascular function in MTF transsexuals; however, progestin administered with estrogen may have adverse effects on arterial stiffness.
We have developed the Venus AFES (atmospheric GCM (general circulation model) for the Earth Simulator) LETKF (local ensemble transform Kalman filter) data assimilation system (VALEDAS) to make full use of observations. In this study, radio occultation measurements among small satellites are evaluated by the observing system simulation experiment (OSSE) of VALEDAS. Idealized observations are prepared by a French Venus Atmospheric GCM in which the cold collar is realistically reproduced. Reproducibility of the cold collar in VALEDAS is tested by several types of observations. The results show that the cold collar is successfully reproduced by assimilating at least 2 or 3 vertical temperature profiles in the polar region every 4 or 6 hours. Therefore, the radio occultation measurements among three satellites in polar orbits would be promising to improve the polar atmospheric structures at about 40-90 km altitudes.
<p>Abstract</p> <p>Feasibility of radio occultation measurement (RO) among small satellites is investigated by observing system simulation experiment (OSSE) of the Venus data assimilation system in two test cases introducing real orbits. One observes cold collar in polar region. The other observes thermal tides in equatorial region. The reproducibility of the cold collar or thermal tides is investigated by several types of orbits. Results suggests that the cold collar is successfully reproduced with two or three satellites. In addition, zonal wind can be improved by temperature assimilation of the thermal tides with two satellites. Therefore, RO among small satellites would be promising to reproduce global phenomena in the Venus atmosphere.</p> <p>1. Introduction</p> <p>Venus atmosphere has a global, thick cloud cover at about 48&#8211;70 km altitude. Therefore, it is very difficult to observe under the cloud by the images. One of the most useful methods to obtain vertical temperature profiles is RO. For Earth, daily observations have been performed by GNSS (Global Navigation Satellite System). However, Venus has been observed only between one satellite, e.g., Venus Express or Akatsuki [1], and Earth. For multiple satellites, by transmitting and receiving radio waves between them, more observations, multiple points and better time resolution, could be obtained. In the previous study, we have shown that RO among small satellites can reproduce the cold collar with fixed observational points [2]. In order to check feasibility, however, we have to consider realistic orbits of flying multiple satellites around Venus.</p> <p>So far, we have developed Venus atmospheric general circulation model (AGCM) named AFES-Venus (AGCM for the Earth Simulator for Venus) [3]. Recently, using the local ensemble transform Kalman filter (LETKF) [4], we have succeeded in developing the AFES LETKF data assimilation system for Venus (ALEDAS-V) [5]. In this study, we performed OSSE assuming RO among small satellites in two test cases with real orbits targeting the cold collar and thermal tides.</p> <p>2. Experimental setting</p> <p>AFES-Venus solves dry 3-D Primitive equation on sphere. The physical parameters are based on Venus. The latitude-longitude grids (128 times 64) with 60 vertical layers are used. The simulation starts from idealized super rotation and spin up for 4 Earth years. Reproduced zonal wind is in good agreement with observations [6]. ALEDAS-V uses the LETKF [4] to produce an improved estimate (called analysis) by combining observations and short time ensemble forecasts of AFES-Venus. The number of ensemble members is 31. Assimilation cycle is 6 hourly interval. Observational errors and inflation are set to 3.0 K and 10%, respectively. Details are described in [2, 5].</p> <p>Two experiments are conducted; one is evaluated by the cold collar (experiment 1), and the other is evaluated by the thermal tides (experiment 2). Idealized temperature observations at 40-90 km are prepared by the GCMs assuming RO among small satellites. Experiment 1 uses data from IPSL VGCM because the cold collar is more realistically reproduced [7]. Note that temperature bias between IPSL VGCM and AFES-Venus is corrected. Experiment 2 uses data from AFES-Venus itself but experimental settings are different. Observation points are based on real orbits; Experiment 1 covers polar region and experiment 2 does equatorial region. They are assimilated to AFES-Venus by ALEDAS-V and results are compared with frf (free run forecast). The reproducibility of the cold collar or thermal tides is investigated by several orbits.</p> <p>3. Results</p> <p>Figure 1 shows the temperature distributions at 30-90&#176;N at ~66 km in experiment 1. Four situations are considered: (a) frf and (b,c,d) has different orbits. p1, p2 and p3 indicate each pair of satellites. Cold collar is most clearly reproduced for (d) in which all 3 satellites proceed RO.</p> <p><img src="data:image/png;base64, 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sKI6o2V/6Id+KD/zPIIUVeDXNct691lv7Y9tuPNeGyVb1hvOWrXStZF4pVbv2MAwMHcskdFYq/akflA+8N+E0hKfOo/mh74DebGVEiuNMhsmLatkw2B8lvrWTyQLqluiPd61bLW5j7TGyc+yUvMVVqosiF+Z6/n8IkgcmB/5kR85Nl1l3YRlSfIj1UpVRmrjxuegHllx8Y0qJhC+6lWvys8895GNhpbsPmbDjXeK+N9u3Y37LetfdoVTrolYq1I+Faw67edWVqBkiDFuc7A7dhhKUkoLsuy2tX+96s2fMVJSM6SZlGBJjJFFxpiTLO5YWnupzS2+xBpLf9XqCx9rleYdys/zY+Ixa8i9+tWvPrbiGvzCeyx53f2qt9AG5RdLiLzq/2Oluelq2yAqLib8Pl+QpR0b7j4gJfUe62+9xbrbD0tZbUi/iF9qiZSFaAHbpSKvqJwLSTcedZ6teK3IjXtfI8ys0z9lzfqG7B/dp4v88hx7+9rhWTxaiixNxK+jgSVJU6/tWTdZtJYU2ah6m80uvlT8+slSZh9l1ZmXPm+MTpaz+uEf/mEfGXocdL/h92z0wIr24FeZD5/2Umt+56f4bzeCF15sJktF7DvWufDbtvbOr7fVt36FXb37p6x75c8sSTesMptYdWFk5ZaIv5a6tzVWWtQ5zFHTtqGqm1Gay9NsMZWssdD27f7zk0lc5ltdp2dU5mWVaL/SHOodZWvUu24F2uCCdVf/0K4+8OO29r5/ZBv3faOOf19FkXfmYcQXHtL3XbXhb95nw98IKfnjR+WFvTDr4vkLFEViae+i7V76dVu9+6tt7QNfY2uP/Gfrbb7VUtu16vzI6ot98a14tZnI4BS/5koLHZPKE0/Er0PxU78hg7JZSBwrDcTL5flNGzaz8bl91ykN63qODNkR9iL2VU182hKfil+rC30rzZi1ZmSoojiTy9Zd+wtbe+iHbe3ur7L1e7/euiu/a6PB2guXX992UYbmo+M0ugclduOQ1HwBwD2rx6178f/Z5vtfZRdf/49t433fY1srD+jHttVnd6w8l2orZeOOjBiGmhGhmgjWRLg2oxQVTktJxO3Xcl2e3BjTdiRt00kXLRURk0Ykzh+Q3GHQdpxgCp7dUqL/c06MMduzUkOnG23J5oFtrrzfVu97lV1+9z+wzYf+nZTY68TcT+hhL6CBM9Sbgx0l1/BhNRTC2szdYQVxJrvSh8biyAct8DrFsw/ZqGfD9n3WufJbtn7ft9n5d32NbT78/dbeFo1b32ozMuhmpTvmuoFfclpw/tMxiioqnIF4aSglk4hXned0TaI01H5PW1K3nNlmf9m6YkCO+/qN32NK2Op+lBdKrSfe5Lkpz+T9+h1FVmopK1Jk5TkZvTOZVRsDS7OB7W68x1bu+167/J4vs/UHvkdK7P9Z0nlI5XyhDJwRnD8LSf9Zxu6hhx5y3mSOHfvwKN0HLGQMDx8Gqvx5CwhjsPYu23jXN9raW7/Mrr73+23nwh9aafColRuZzcxLkMlSKuFBeU2oNlFUUUmxRWlxDiWmU1E5YX3BEEWrLDLKoKlLZcH5vlKw6g5OxfvcosvfEVNkiqA8xafzQ2vNb3toxAZP2MalPxEzfJddee9X2cb932qDnfdalu19HuJ5Cw/fUEGkgNEos7e//e2+DNf73/9+X3Pyt3/7t31FblZG4RPyR4yMT/GhgPi1d/WPbf39X2Pr93yVXbn/J6299harZResLN5oysB0vhSveOQjb3t4ZsyL+o19zvEzV+B/oZC2dW5dioa0oX2OPUmoDmdXbacy8uMtJX4fJ65Xamu/q4QywyBFkQ2UehPvjB4Zxmd5PrXawlDGp/i1dMnWr75F/Ppv7cr7vlb8+o3W33q7aPmF6YWxbitrtNINxQRzVuVfW1vztShZDCLOwTsIVPXzC9nI0vZ56zzy67b+pq+2K2/9WmtffbP1+vKoyltWrfdlEUl4SbmMrTWYAMVAyI8tSkr/iYG7BYe1lVtxWFtsh/Vg2WGNkVIlCHrgqWQ7g2Ub6hkxhfPXJmcCtnpPX8+M74iJY95DPlyRkcQUWJyEKZqzW1apDqw/HFhn7U9l1f0zW//AP5e1+huW9i/pouenoC7P7Fp5dt3Ki0oLSs0tK6uO+OggS3m99KUvtXPnztlHfdRHuQeGRQdTTBXXswwS2klbguvR/2Yrb/8HtnLvd1h35x7bFTM0q2tWr8m7kvfiCgsDEvoXgrLa86zgEecPATUw0D6KBsWDokIZobyISaDI3OtS4rqBPK6d3hnbKZX9no5SX+dj4hpMwV1td5SiMuNavDP4t+jlIRucX3U+Gp+leiaPrGRzJ1asNJfovpF1t99iV+/+Z7Z2z1eKX/+3+PWCbghhzucbypJT5YU1Ky8pzSm1dn2hBxZxgF+ZIM6qKBcuXPAFzelLu16ERFX6/EGWyi2/9+dt7Y1fYRc/8ItigPeLcEpWLUtp1doh/EaKpUZBcYxnBVPkhJZgveEN5QnLKtU5CBQijtabW2Yi0KIVF1JmncaObWr/KGl8vxLHHT0/ki/EP85PZAwde4gChdsoW9aqWHNhS4xR8Rh/e+Nuu/rAT8hq/UrbefJnbZQwSOR5BjF+qTK0UlWpJrFSSVwpveUtb3FFRdjhD/7gD3z1fFbghzH49tJRV16Y4pkHo3h3HvxxW3v7P7Erj/yKDZOH1Y4Vq5QTa9UktETrGf3Ionc3JJXwdOAFj1DAB/qZFsUcgS+jUoE3UTQop6ioUEhbOZ85r3FdnrrNbetVMveodvPrYoo8zvU7+q2rZxHk4/m8h99QYrwDGZFKrkTZQT5dfugc+fdRxXpPa2ZDCrlklfnMdnsP2dojP2Jrd/8Ta5//eV30PAz5SwaXKjGFfkiG1v/SL/2Se1cszfXGN77RVyJ65StfaadPn77u1CtV83Mfo8GW9S78qa2//itt9cFftMFo05qVdcn1xGrl7p5ykufkQGHFUCAEpV0UAW5/UAzhmHg41htWVwwzoFjY91i4EiTGNb7lek8l6w4XnMBhBBL3wVQQOgwUz/Mct+jyxDNhjjW9q6jI/F1KY+sOxigqMXl5JVl6lbmhVecHVmt1rN3p2tYTP2fr932L9Tff+DxUYKoQ4GYtoz9L/pmLz/u8z3NPiy/ncvzyl7/cP9zHdqq4PvQYDda9v3n1LV9mq4/+H0uqbWu2JMjVNEwPcT6NxqT+R4UVw+vuzXBel8Ez8CP8wpZj50dtnW91PiodlEvkNfh1oCf089QdztqWDJ8tqbl24TzXcG3kTXjXn6k8usLjefodJbau33gXPO7ntXV+1bWDPJpCiv1tJgMXAV5r9sSzfWunQ9u8+PO2evfXWn/jDc8zflWFRDi/yi5pNu2bvumb7HM/93PtL/2lv+Sr/TPvkH14l3UtDwPV95zG4Oo7bPNN32Cr7/5u2+0+bNVGXzJ8KKtNNpGIxEfsSdA7IkOgyERg1B9KCmZAETBiiHMQN8ojekNRUcEQKDN+dwJWitabp/GxrKiZTdvRPs/x+5UIK6CsYCDOk6JFR+JdbDnHPW29Bwbg+vgu8sR95MGtu0L+6Swm/+XKyMrN1Ec2VmYqtrN1v63f+822cd83WH/rbV4Vz3W41Vbvy9saaKskzwswOZJvqzG7nxU54vfKOD9VWh9ijBLrr7ze1t/xz23lff/BdvtXrDHXDm0pBeHSCJ5l8JP3YQUB731J8Kf2OQsfwhvwG/wAj6DE8KzwllBSrkCU+A0lxm/dUmZrpdRWla7k6SpJv64318VjiW2VRrahcyt54vfitdy7rWt6eiMyAR6HL9nC23hifl77IfpSkB9KGMlxkAdlGveNCU3JjPJMZtu9h239kW8Vv36dDM436abnfvjQebXRDakpZ6IaBqbAn3w1AN6M/FncPwx5lT3HoIZMth+xzbf+a1t749dYe/c+q1S35V11VCC5olWRsVtsupYSkmAIwg54W9q4O4/1kxMOpEFXYJEZovXGvocEJAOx7MYhAyUUSF8MQeopdUTQHd2z1VsUk4w8QeyX5P2RYIB4fl2pqzeHe4J1N/BnhWfDeGPLLn8XifyQR/KCpeeemPajxxjj/T4suJZZc3FH5R/Y5saT3v+19fCrLfFRiM9dhoD4ywsbSmJ2trOyTlWfUzz7kGWJ+PU+W3/Pt9vGO7/BRwfWZtrWaG5LQI2C0kJRwZ8FLwuhjnCHtgGti8JCKaGgokKCT11ZiVc4zzHn/Tc9u629yygdcTN8htJJlFJP8H1qvY74VYp1LRvYqtJKnjayoe5h4H1MUmza8jwUmfOweBY+REGRB/KCYUs+SCgt/02J/HOO8iF34NVBQ+ckqzI8MD2LMGJSK9tG97JtPPrNtvnIv7Gk8+hzml/Lau/y/HZIc1tBed0EVHXPLWTDtnUe+BXbeP3X2MbFv7BRM7NqBe0tStD/YLEp5cTu8fHIEAJhBrd2IBSdgvjH3owSRAVimIHzbPGYsOkJH+yIWLG6ipYbCQttNT+/1VpzhmmLyHe03RbzknZ0HM6F86si1HDPaGzd8ZwNHe/qXbwzEj558fxo30OH+g1vzC0+bVFqkeEpI5ZdsOZ0Uudai1s2EqPsXHmtrd/zz6xz5X95P8NzE3lhgW/z/SmeVcjSnu0+8l9s/Z1fa+3Lb5QSqlh9pmuZO8JqM5pNtOqGptNqMCpRWNEAw5vCWMOoRDFxzG3QO7wJ38IPiPVwbWabOR/BT/ASSgrsigc3pYwujLr26KhjD4x27L60bfc3H7fHsrY9rnPF9Mho1+5NuSakB0dtnevYed1/FcUmLoyeGd4cHltb73ePK/KrEjyMrHGPkN/ysnCOwjCimDK7oi6XPWI0Iy8wqciobf+OrT78jda5+psqIFz/HIQ3ZJ6eBl5V1T1HIGsj7V6xLXlZq+/9z9bO+tZsbVlZXkx3tOxLL1lL1+FlxbpBgZFUSurLrZtcmEPG9B1BQO7G6xjCR0HFMEN07yGVTf0KE0Cg7G8rYblh0eEpYY1hqZ3Pus4UTwxG9gER+r1iCgj/fhE8CeLnnDOLmOZC1lHq2iUllBkWndjald663nVF5YMxUHIoMRRr9MY2xPxu2ZFvnYchSG7RaeuxdVdgKptc787wpIcQK3Mj2+qPZMn9kHtf6WDF6/e5hSCIHCrvFM8yiJ6G2/dKYX29rd3/S9btDq1Wl5c8I++r1LI0Y86IrsOoRHFBs0rQKkZlDJ8h2KFpFIGH2vJz0H9UWJzz8KF45LL4Bc+Kfir4EoXVRrmMBq50HnDFs2tXJD9QYB0pgqHus6TByk7uifV015YruMT5uq/fSQMlrt8Sn3N/VGoPawvvw6P0kSEjriof5Afl6oYn/KqEoQl/uhJTQv5QNo4pf+jLg69nrD+atYqMcqYC7A46tnnpP9jGQ99pSRfvixI/17DHsyrqTUFV9uwHowU7D/0PW//jf2xbl99q1dmONWo7+a8lWSZrlrWqllXcjAteFoMv2ArRgsMTceZQIvQHETnR6zh6LngzEBEET1gheFXylGRJoaQg8i0RPCGFJ6WgINpgjbXtMVliV0Y9u5z1bL286wxAcsIXoZG6zhADTzznkq4nXVR6AGXmSm7Hn4UCxKqDgaISc8WpvHSUUJyebxUbRoaJSTA65Rt7YOWqdWtNq82vBoGgNLdwRUJD5V7/A1v/wFdZZ+W1zylmIGZemdsOiaG2M20jzDLFhx5ZsmO7j/2KbbzrG2xr5UGrlXtWbzKqV4QnA6pWkXlWrVk2I8IV3QL6Z93LEq8C96Z0OYI9RkHYxj4l6BygaPByomcV/RH47qqUy0PizwfFV0/IQNwRH0EhPfEjSgv+fUh89jCelbytD0ipYWzel+76ffD2/dr/gO738zqO1z+ue1FWRFHWtX3Mr91xTwylh+wgjEi+2KLUPKSo8qBwSRigyBoM6MizlHE0Ut0wT3NOKlf1QTB1prGmuqpZe/fPbePRr7XdK79qI73vuYLy7I5V5jdDWtiwUosOlRuHqurZjVFv3Tbf9K229u6fsmHvstVZRYK1yNScToXyokqNTB5F2Q+9D4twoUqG0MbDihYcv2PdQCQwBsBjwcNiiwLDe1nLCQ7XH+WAOIfQsbBQUGwJI6yIMXZ0HibBGtuR4EfREBtf7ddl3QXCvw+G0P49vpUHlqf79awH9DtbFBX3YuG1c2aA8XjPfVJkXHPJrUTCjfLGlIrMCiNHK5SyRQUWrNKSrMmyhIWs0FyBswAbfYFVeV+dZN3WHvqPtvngv7JRskm1POvhw+CbeWcvSYpsig89Rv1V23jPt9n6/T9jve6utSqbElLiPOZNiuwc4k88f7Gsn/Mw2TisvcejeCrwbPSwUFjxHPEBvBt4gOhHMRQIz8AvKBF4iV+G8gAxFlFI9462xcMonqErGdIw97jcOM0y6+qJ8OJgnHRO/B2vx3BFed2v58HDl0fiTd2NJ/aQeBtj9lH9Bh+TP4xNjE6Pnmgf/ozhRLZFnt2WfEt1DXUTw/50cZRkuFarMnwlrLau/qTtXP5RObY3pwA+WChVGUTVD6nGyFEk7Y1D1fTsxXD9btt483fYzoW3BsE0p5aMVrV2jcWXW3hRJWvIikuqs/K8VCT9BhOE+RN+tRMFREKs3AlZW4R69LogWJQALj7hOpRVX8QOIRIHR1ltjULIYDRsuaW1mZRFnPKKujN2D4wyKNvFJLF2Z8GerK5ao3PKhj7sb05WVNXqw3lpR3Fw2pC1VPPfOspoW4wCE8FwwboLYcXH9OwrInwU4o7U03kYUnkh5IFS6ykvKDE6imFiWJRyUU4Ynf2+FNZmVrfdhjxA1RPKPA6n91GIqqxao2flOXl262+2jfu/yYa791FlU0xxDMgoWn+7rb/3X1p75b1ypPpWrYhnUVqiN4doz41K8SUruA9GLetW6x4eBB6SFz/CowC+xKAMxlc4B40TgkMB0M+MAhuIT1Ek8AWJfQzJflr2EOFqtyVF0rErg5rtjDJb7J6TkgqL5O7oAe1+0+6vrNjlfs0eH4qfBlVtU9vszdia0sVBxS4prQzFS8OqXdQzNyUL4EsUHLz5RNZzo5ZQ5GXxJkZtUGIYu9tumIZwojywAs+6QlQZ4VnCif1kxnryuLZrQ/fOKB9h/9hnzf5s84r4OLPt1T+wjce/zpLeg1xF9TyLkTdgxE1mV9XwLIQIsX/pDbb+xm+x/tW3WaWZWKUhC9ubkd+VsOBgAqDjTA3LGmblsohZpfJOThECwBXHDY9hBAgleiY8CgaAEWAIjvnk3CUR4kNp1y6mem/npJWleDalzSDEu0vr2nbsEdlYWFQXa1f17JFtVbel/Hr2WOOirYuw39143O4vbdh7y5ft3mxL2yv2AZHtPbZh94mEP5BtS9mZPZYMrdtZtN2+GFnMtqvyb0mVrkuZES7Ec4Mpnhz13ArEqsSaxKpD2W3rOqw4rE8sO2dmlW2zktlOlVGWPR2XPPRCPfC715EYAYZgciCLCVN/nf69tnr/t1pv7Q+8Jp61qKqtGrKzPWm/plLl5DDFBx+9y39oK+95tfXX32VV8QAjWkszYpg8XO+gv1k0B7KyuFn0Zkxn0LF7GzlPckz0IxqWOddb2w3LEGXA0MSIZIDF/Rl8sGsd8U4q12TUO2EPD4dSYh17UsrjQm1Nz5XCqGzZxaxr76g97r99QHz8aNa2h6sr7o1dqmlbbttKdd0G5b6t676rtVVb030b5R27orQxSl0xJt1FuyAj9VK/bA+LLx9XuoySHA097M8ADqIt5I9ICYqM0CSeIDzLSMUdKZ/Is24oq8xbzY71ZKRzzAhEeJZ96oQ+6+B9hW+EVSvi/a2HbOXRb7N++6264tmLTB72SPTgaVYlEs/eDFQtzy4warD72O/Yxhu/2Xpd+RMzNV/1eSyU2DICKTIEGxhCDYorvTM8Zf1a1T0LqoaGh/hBDDngmiOSCQOisBhsQaiBUMG6XnV/Ik9Hltdo2LRE7sn5xiUpmi17sLTmntagJMLVPRAfntKjWcctOmLjeGB4Spd0jvAiYQWGvDPukWAWKewTLpDCrG0qjz17sHHeHqpetXZ3Xgqz51biE2KArszRjt7GuwglosAeFJOu6dk8AybCO0OhEsMnLIE1x3YoZdvtnbKNWuohlsgchEbHIRftM4SefsByRVdUyzp3xdYf/0HrXP4N8xXon4UYtcQAS8M8yb6elyiL3vgUHzRkiRTG479ua+/917L8V3WiLD4U5+WDohxs41QUAZrDsGT4d7d3crwCBfxKC0YPhIhB4NNcYWkLDdPPfFWG5YOJBLeYOpEVlorWURrw4Xuqj4vHu/JYeh7KP58Rju/YveIRIhgM1pBqFb/XLMvm9JKTVk1vE4O8XJn4ME/Z6OU2Gr3ME/tJ9hK946Xi1TM69xK70EAOnJBBe9b6o6alw1l5WgP31p4gbCjDFSMTPkWJ3S2+RYlh6OKJ3SsvjAhKR0Zq4FlCkGUb6jlt1cs4YqJEXUR+9e4P1R3zT9mvlkX7wwu28th3W2fjN1WemwvBPVPIZuQrw6MxSZHdDCJpPSsAE2y/89/Z5tu/13pZ3RqzbauwnE8RhAeLoQdCg7kVh/Cl8w93mmpBadHgoOhlwSDruoKBFygPLun15+yhpOehvr5YoyOL657qJXufvCQ6blEKUo3uDV0QYeKNPSwFxT79TthIEqX+LtzBRnKLlUZihtGtSncpQy9TeukBSedHL1bi2tvtfLMvxVa3K1JoeFw+SjERcUuhXZJF186kaPU+7/sSM1yQciN8gteF8noit+a2JcRXmFDZlLWpHMVRTd6Xp2OPryt57aoCfGa/GKIi76wiBZao/FuXvs+2Hv1+Ka82Vz374JxcSFN8UJHJwNt+3w/a1gd+0PrpnNWZR1kSRREJEa2NwXEesveBUuJX/wSdjvszG7Y9ElOLafGyomEJJw1Fw/Qzr4hHmYYCVlxhdW1rZ9lGktyYkAxjvyfbtEdLm66wNkTVT+Y8+pj4A0VBP7RnYnRS6c7Ae1JKzpvivfLotNigpXw0nyJxzazuOSeFdlY8NSOFdpeUzVkd3yH50rB+0pBHtmRPJIT/e87HyAeMTCI1RFDO6zy8TTcEPNuV8t1ReVbq21LkzOcM8gq5RV0gy9z70nnA/FMUmHusun4kxbx98Qds+9KP2Ch5Fn8F4WniVVXFswMMdd94wzfY5uOvd6Juzmz7RxzHYFd0E5WUH2PV5QyBFRcGHYysPZyXUFbD6pA6ghEiM8AAK/RjiVEgfCYePiiv4kGpMrfQatv2pHwkBkQQw8YTIyyxIWvuQVdWxKoZkBGsv4wMQMjZKb3gRSrIKyToXyZCPiHrZ1nnxSjZvK4k84zXPyDlVp9xvRjCZNllqaw+MddueVkWYsOeqG9aR97XWr9hj0m5Xh3hVQ3tohj5nnTHmZO4PnF0Qojn04GU8YyIneH0YYg9HifeZ1TgHLOPxUvdED7sq34TnavV2pZWGraz/fu28cC32Igh889GKO/jNMUHDcn2o7b2p19mWxf/n1RHTfbjWvgBozLnyUlPi9B99BTgSwTxQLQ5Spvip4pHAqJhyRzGq+Iw6JYAfnuUif86trlz0vpZZleal8WzW/Z+8S39vXhRV8WXeFZBWQyc11MylJ3QQ1FWH659GYjZgt6ABUxG4d+GdUstKR74D8V2u66VMbkv6Zz/Bp/C7yjb/c+wbEn5vUX8dKs92UitNbhFnticrQ0rLjswPH3QhwzPC8ozfdmEERmFfJ+8x4tDRgrTB7bXfwe/YnS67FLdxJGHIHaJjGqZ1SpdGyZV2137Vds8/yopr7w9niVwXUW+80R3zM1ARf/QI20/aZt/8fW2c/keX1uQuR77lBYoKi3KzH6BIbDiYAg8rF5r3Qb9E5ZmVbfgsFoAocG1UdWSRMpCiVDc/bXztiMFMKh2fJZ8HCGEwqLTN3pXxMrxZEKF66HZYk7g0WOSZyXCje5ghRmWWGgwA0Sf4nUdkka3KJ0JyZUYlh3KDssOZgoW4q6suk1CeaOaraZi6t2THpbAp6L/C0vO4+/K4la/ZvfWz3tfHaFKLFcSzAzho7AYWozyhTkiQ+CteihCdVsuD+TxZra5+4BtPPqtlg6uetmeDcgIFZ6Qqs7DhdlCIZw8xTOKtHPZNt/2XbbVXbdadSg2JACuyofP8K5iO+ARINcFV1o5j8JBKC0EMRf3W1u2k87K0JPRpF+hU6Z/EPodplW76P3AA+uMZJjNPGkPy2S7z0JEoicldj7n0YvaEkZ0HsxOj/nG+dSVFS/Ui+Ex58s7lF7iaS86QpKicz4sJpRf/rvzbYyYcD/8Lx7mOt6RMYz5jK1U56TEzsqbu12ySOXrLdmjKf1hYcg8hib9XhiiiSroYv2qD+LoyWAmfEhEiNIg0+BXeDUOYImRJIAxMMyjJXyjaXP9fimvfyOD4NkzQhj+HJ1Q68Cz2mZzlObGAal9SDHqrdrm2/+tDTbutVKlbKVZNVVlQhtj2BQ7eWEIkoDVERkCRoAhWIHZ6rtSWqlbcGBHZ9cZ8qp91NID1UsS+LJ/3BtJ3ZMiFk2/EcxzVdbRgyIqvCvOMfrOJz45gRMHF1OMFRWKDGZB0Zy2kgi5lBAaRKmJ0FFybqUdklw55YrLmYJ7c6ZwCy+37LIZWYW3WVvM0C/N2oq8w4EYO9k95Zbb1ijx/jUUGENzGRV5WQrtESnjjo+AUrnEDDAF9eBLWSFQtPXQRIEhCEUkdbVDWdZcvWv9wd2yrv/9sycMIRrJ6mFQhm9Z5ivP+xTPHNKdx21DRuZg+/3W4IsLoqkx4MnYBhiWOc+mMkKLSovoB7zKPkaTr4bREC3r3JrzozCqWLc3a/fbhgT4rvUqHbtS6tgHRjtuYPpgKAl/BllsiLYT50H4E2UEf6JIMP6kRAjvjc4qiWcT/eaKJve8nLcq1krqVsJqc5GItoWvi0nP98LFlF+LkvLnn9Jzxbs83xUl0ZSKylhX+RqWjO6yteqijM5Fm+/eahfSgfgydDO0pcLeX1rxwR2UjZGRdAnArygvIj7R2HTjUk9GzhEpAdQrURJ4VrVszdqm7W69Vcrru8WvG+GiDzEy+JUBVeJTtsiVmwG1/yFDIibYfMO/sM6V96q6q2GS4mSOGCqbKykHNJUf02CRISB6GALQuFv1ng2H897IO71F29J2JAvuannH7i+vOGGgoNxTIQQhAkI9rY2Gbr1dwnPxuyFOQgRYbyJ4QoK+Xo1+iwyR5pade0/ndH7e6mIIiMjhSg0Cx5OSktqXpPz8Nwqam6d+X3y+GNCtOp6NR8YzOH+7dUq3SKku2ZXGqu0MK3ZlWPUQYtaft3vkSTKIIywzBYO3faIzZSIMgQJjkqTXlYievi+sO+oQhiAHYRka1UCtL8ZrWXfnDbb55HdaOrxCJqd4gSHdvWhbb/vX1tt81NJmyyruaeWAfJHvAIVV5FHIP6cp6AvjCFojBAbdgU45sY3ukg2lPGq7Z21VlP1AeVUiPbWePP8nZJARIXEaFe8yCIpzjDXGWBxHPvCMnGdRYuJFD/NhAGIY4nXlL/RrMBzhqbN6Ln1W8BgJfssNz5g8qkLS7xk8TkIW5Man8y4l1BaPzQ1QKVFXnig9wpZNle82eVZSlOkpqzKgI8ms05u3zVJYhIABHNtePqInfdVCWBLO55OqqPBq8FSDwo+DWkBYhV4/MrJaMqK/82bvpx6lz7/PGsVW/KADT2vnnd9r7bVHROc9K7UkcIteFSB3RaXFcT5PC0bwoaE6jOEuGhDSoUFTXTSS67yq483mpiWlxM6X1901H2Qs6zLysCDxcR8hKGJBYRF7xsMKLxGhe6gBrwkCJAMQphSOKxMYQkwzJtzweyYl1LMTYgTu5RpGK5FgADy2YhJxuwen331UE9frvgyLEY8uV2jOiHhkvPdF4VjKMctOixnuEHPXbL0q9SwlfV95zRjVhHJm9j/hB8KchA1ZnYOBJswfIRwT5n/lTCCmoOTsI2CoS+/3auhPs2NJOmNbmw/azqUfEDNgCnwIAVkoW265xe0UzxhQWptv/CbbXbnfP0lfLffyXwTkcuRTNuznh3HuEYBHo0cPjSGAOcSjYGTvsCGaSmt2eeYJezJrWyKFxW8oKSIfGJaEA4kqtDOEs/jOjUm+25R7RX4OpYGSQbHg+ZABfodn8LzgIXgOXuP4jPgIXsuVUIxwFBPP8d/mdD1hSBIhfp7B8+Bt+JJwIRVCXpAfMVwJP/Pcst61YO3SGdupzNidnRfbQ43zKmPPRyBiQMOzeJPIKoxNeJXwKSF/+DMqfCieOoRXo/IKK5CotPKGB8mM7a7/2bMjbIiXVVOOmbaiLR7YzYAW/aCDPi0mFncuv1MkwVwO1T6WQhG0PYMv4mlySkdvfhwZguLTcAhf31eFDOQRDfqLtlpRoycNeRUDt2SYGIgXBFGgtAgR0mt1ScwA4UAYHmaEiN26EiF655rgiiP2aaHIRMQOZciJGkKGYV5mJSmo2eRWKzmhcx2FiQUhl5MJ8DsMkt/jFiJhj1yZYd15XsgfDMG7CEkEDyyTR5jpXLdcl0clhu8suzfJcFuYnvKyogcKjJn9WHQM141z2HzIvrJAOIJBGx5Xp17zbHsYosVSW9u2s/lGMcN3fUg9L+aCpMtDS08qaTtanPZxPVNIdy/Y5lu/S0LwMZ9UzJzJMdjd168lotF/AM3EuZSEpePEYoQta4ICFBPB52wwr2sHvnYno+zgCla9wNBiy3V4XCuuwKSEXGmIB1BGvBzD0vusCOFjZAJ4pdAX7YYmnhI8iULaA1HxWARJVSV4MU/OvyBeUeTbCK6lH0xyA0XlHlnMm4xP9wpRcCi3Bd1N3k7bE6qvanraZodLPrGZ/nUGaDBCmD48Bm5EXiWMuqktSgojwI11PZ76hFeJnoA4aKNab1ulklh78222femHJR8+dEtEjebFryfEqycS59eMLqGbgIr4wUU23LXtd/+ADa68XUJx1splqY4iIwAODxpSm+cWpRUZgkl6CFzIiMEGPVkZQzXwamNT50cy4Np2tV/1kBmgL4i+LMifsBkduzADCiyE4CBuXHyImwfLfHEFgiIjrFcgYhSIX49FBaFibUHoZZUNJRivIxTIc1BIMNJBSc/iGr+WZ0So0K40CRnyHvKGAlM+nCnFvPIASzqujU7YdnnBkuxFdrm5btX+CQ8fro9CPx0WHAp8V54Yc8xYw+2Kyk84wplCibr0eSM5IxCKiN2orLhBvxeTH/s7b7D21Z+RwiyEiz6YUN58FRW2eFtsp3j6MUpsB369+nbVsYiBAECxrjmOLMFgKY4FIiL0u4A4mhVAS/ApvIHo2hi2RHMSxhKyTKq/Utqx2e4ZN7Dg056u8shB2nVvRFaubkT4Y8jxIPEMysAVlnjIhQS8LOXlxif8QvgQniLjvJkUQSYr1kjrMjjFvynKZyJxzhMGrZ7rxiVKKdy7VyHxufA6hizvV3K5wG/kNyjYkozMuWRWhmZFhvZZeVFzdmJwi/WHDV+FA0UFr16RMoNXH5bhSaSEleeZz0bdMejM+/SpayWUVwwjjkccaluVAd/ZeK3tiF+lvcIFz3GoWB888F2e3Qd+2XYu363KFrGUNqUrVNO0fQQVjyIrnvPj0CK4wsRyARacu8xqzLYIujdqSK8M5UVIq4sZUEw0/oY8DUYDMmScUAMqJcbJIYpg/eDhQGSEFgRXNPKs3EqTgnBAeIQKpKQ89CelERVchFtrdatISZVcIS2H+13p6V73kA5KKM38Ok/5fR4SjIqMfMJMKDA6l2EgGPlWqyQfZif6L1FZUH4zlirf65WGBIa8rMGMhEJYBzF0/u66l0lNsDI9c8IY4cTyUXHlDe/rUkJUhGHLngGv+1FdSn/Uss7mH1pn/X/qfR9iZijKoSmeNrC49c7dP2G7F94ofmpZXUpl3yApeDRGStjkBiiCFKE5jojomK3v6x74L0sbtiWl1auFCe705zAiln7oC9V1D9vDvygtIgfQZ/BcUFq5J+Mhe3gWhQV4EcaklJjzZn7dGPwuAobfUFIFpbRTnreRXw+vFe8BHHO+eK+S3wufwpPwKvxYvJ8K4FqMS7ywGDUhH3PiWZ1Lz0kuMhF6ydYqS8pHy2Y6Z73MeF7haxMossRlGV4Yq+QQOoTr4Ev4M86LYz+GY2M7MFUolTzqbrzGOlv/V7/cnLfzbICK+cHD4OLrbeueX7TqaFMvlkis6vX5kPYxClabw4+V9J+G8LlaApYFVlxJJ3eksDoV+UzloYR131dOB3gYKKaOvK52+6QLaRxt3HHmehBGc2IklECs2zkRKhCBuYclohyfixYUDKF9mMjBM1Ae0ZsKRDywlohR18QA/5h1j5IorO5zRuW5KDIYA6bgd37jPeSHcGbdbpV3tVLF+oyMTA2fkIK5w7arYV7LJblQhAtR48z/QoHRt4cyw6JDUPiH8rCA9QQUV1vFhznwvKLy8jZo9CxJRrZ+8Rds0Hln+OGDiKyW2WhGqZVvm6oXPLApnjYMV95pmw/9nqgxtap4x+oiAv0fI+dLB6QJqwgjGZlY+gDDMoaw2B/IO876S/IO+tau7xqTZ+FReBXj0ifpVlZssXdGhqh5eNCVlhuNeE9RadE/fIv2eXH+u/NxCMXtZQzoxfARisZ5Ca+J8H5UMiVrSQv4YKribRxfk0Dc5hfnxqoIMTzf3yOF5RVC3nQd+c6jJrPJ7XZbb842WFwBZeYG84yeytzSs3a1XrckbdpFVdy2uBKDGyMbQxMjMwzasDBlQFtkoXte2ichF6Mp6TJTWSvXutbtN2zz0i9Z0n88//WDB3g0nc/TXM6vNwEV8YODZOshXxUjicRCW0ZrLYJDyd4xyF0ePx9bcdpGoTrSs5i42MM7UiNulsPnBGhOGhhPC6Knf+fx+iUJ8ZIrrCtilBAahJhQRFIKgGNCDnhUzhBQA5YV19CvhTdGJvWriLUs62smWbZasmRzqTwsEe9iWvbI1ZJ8d8h2gWP9q8PJsqzm0pquq+kaKbt9KX9f/vz9oAJgjMh0XBvPL1o5fbmtVpalKPG29Cxi/R4ioTKbKjdhlJOhPyHNfMUPLFmUFkPniaPHkYeEVJn8yaciANYbggcy2+95qS2qTBLo2+aF77d0cCH88MGCqiA6qr5V9Uzx9CHZftg23/ztavzNQJGQb5FfIdUCGbpSE6CTGCKEblgnNO7vllAuFevKwNwtI1rDkmX04cCPGFd4Ghht5+sXxavMy4JP8Vpeoi1KS+/x/i0pB3+xkoff8GjiOcB5wn9LEg3L4j/xpziyJT4ke3NpyYtwUnxak1BpwM+jqt0i4V4eNa0pxVGV8pkbLnjkpJJilOLNQWxFXo3vi+BYb3BZggLD4MTLCvXDdmDLdrFGGU7qGELG4MQrO6PjimTkgvUlb9qlkl2UccigDfiSBXzhU5T8FfFsHC4fPa/gzYZUDPG78qIvrS7pOHzC1h7/lg9+/zRVlidElDfCTSDW5jOKUW/Ftt7+PdbtdiRGt/RSvC3VJgUpguNijpo6yI8ZUuuyX/sI0ESWWjdtaV81UN+WZUK4MPQr0bkLQwQF1vV4+Ual7Z8a2XEvS/DRRSgkmEF5ifMwPPTHU0RMCH+uYbSRn0MRVe30YMlu6Z20ZjJvlZEUkc7HQTI9LEo+I6LCZJKqPZu3LBXzwEBSOtV03reLw3kpvQU9S0znDCECJ4Ye4+gjJHJs3fzhvMlDFVwTPDCsxKWkLgHBM7DcCEeogtzywyODOWqWjW6Vijknppx3TzMshRPWXCQUgcCgExwhwvJRbT0Xz4s3xyG41BxhH7YwQ9qUxUjnb3vTNnzOyAd5tj6Zi1UzxdMGJhhvvfk7rD/oi4oZ0CQ6QGnlvOjAUNApB7yck6oHCrRPsxDKZ0sYi0FT/JiIcDbFr/yCugqelpSaKBFehYOZf/ioPLxKiuFFCB0vCuEvuNIiEsJd0DlhexlmbvFyLpwvpwvizUU7PWy4MTkjosWgRG5gbg1LhOfqkh8tVxT1hMjGgq2V53R+1icMj/TORAXFyKwqzaR1OzGcEQ/r2Sgy51X4Tve6NI4VAsgHFYAcQbEGL4yZVlXVwcjli7xGuiKYFuNlYdQwBqc8L0nKkco1zJbsUjq0y/Q1ij/p96OWGDrPwr14XjFCgpHJiEPeHPu8ovKiDYbNkc43bNC9ZNuXf1Ay4bnxSZSDUCTFZwR8urv9/p+0/up7JT7xh0LjujNQBKeLFh37uWJjIAZrnAHcYMINAxFQp7kjhiH+i0Clb4Z5WWEFDBoSpsBS4eiJtG8rtXW3pMbDWZ3bIBh5U+5lUR1K8mJKsuBqIvyaiGhehF/VdiaZk0BftLVKzZ5sZB768FGMUlRbUqCZCL0v6y/TfTuleRHerKwrKTAVjm9isd0kjKLthrYQGoMgTieZLYwyt/ywA8tYiihQV1C5ItvHGNrmHlhDynNzbABoxxkbBQyjx+Pbw74s167dKkvynDXSObsqi/bKCO/TfKkoJmETTEV5sTgpyiv2eXn4Qa8ljcMS2h/K5W82Nm13+15rr/xX1QErIz7zQEDS78kKK544nuLmIQHZfs+PW3/zYedXqNXJLuc/B3Ud+7rY5H1bgJGE0EYc7Qbca+8vqI0GonsMSvEwPCl6wzQiRI3hBJ2xz2o1XNWudmxx8DK9AtrXS51PEfIC/ABdE7b3DMnOlWXblDF7TkZlTfxRVyauVkfiOdGxaCSVshiKP0dSOH0pq0xKZyjDNZMB+Hi9KqG+t0wcHqIrAz2a/FIWBkPsqB4YEDejZzdFdFV5aXXxWhkF5soJXkW4hTztQccyZJf7y1LoyB0qh5QrMA/5a9+9x5eFrXg2VfkqMqrbSdkup4l/E4zh8ij68KHZvkdIWGWeLgDyH5eIirxa9Lys1bFydWg76++wzsZv60T89ZmF8yi0Ad2QqIKbwGTtPu3oX/hTX+19VGqqmSAJgUyrIPuAooqFIVc5M6C0GEUIfEitCNZXUG4wJc/EWrIscs+AEXIIYI7oyMTF5jwDMnxIhu6tYuEYLrkeBvH7GmSEGAAtfoctDO+SoqrKUqsE4pWVkkiZdUotHWMNCRlP5hmEBIqWlxSKTM6mfh+XdwyO9xId0QyCWJFOicoALEmR1XX/jJRZ2c1XiFuE7F5Wbnk6SiJqWaTx/V5xeoqHVvC2YGrBvS8sOzxHWZvyyjqlU3amd5dtpWGeTCIrF4sOq5dao1MYhY8Xi00ME8SRYTBxnOcFMwzqUrY67my8xgbtt4WLnmHgAaSzEqszIfkgHwmeKW4OvYf+n/Uee632qMu8Pmn3yJsA3oxVDR/n+6yQEUf7xknsmei3M5AhWG+7gIVrg4HZcyEMrzIvi7A1fMUo37YLUxTTXRLGuHZ6kntW8Cn0jVEHPUPzmS0nFSkqeUTDE6LZeVsVPwVFQw4w3gj3i3fYwp+RT8bJ7NbhyCrO0+HYC0VIEBnhKRigKFYiEDtl8ZGuQkGfEL/KjtUzdMINyuL7dG8BfQ+X8pvyzvMj6K4Ye1/IJaI9MjqV/4GdlgyatZnuOZ+TiTGOkcnoYJQXxjq8Sp8XvFvkTxRwMWyYkm9ltlzWcy79mPV33qSzsczPHEYMEhGPklK2eWj5RnFzdz8FCDlsv+8nbSipR29UxIF9W8X2jYyh5H0p2npjiHvwVrrVgRpE1oUuoS8Ggg+fC8BqgynCoAyYgj4t5m3BeVl2q+2Wq7YsRgqWDp4J+/J0xASn+n/J5pITNpfK0tG9q6KxVOeHKIIikVFtEKV7Q3nc3TO8B5/RP05wsxJKh+RKDsKNHTQhsVhppuvWq1Xri8Cw7FjJaFnKBV+shBJDOfq9NTspKwzm9PXtPU6u/Li1xzthOpgdS459MZQPGYb5uX7OrtQIiyxYX5YqazE6U6quCCGyECjKH4KHIbDmvA2UBZ6OFR37u/g+UNbqW7fXso0nX2VJ/7HwwzMN3p+nZ571nv9INx623ff+J9utnBRFI4UFKhZ+zdvaSb3ICvBt/hvTJQDCElrJ5I04fUpppeLX2G9K/+qWaIxHM8EWXmWf+YbQn7clnpUUFNNKTvYYwYtA50U1eTd3SMk07PSw5l5WT7ycShmsV8tOr4TJAo/ivYi/D/SAuIhMwhs1u1Jt2sgNPu4Rj7gxSELJ5MnP6Xfn15rKFOTRFXkQ8AOGHWMOUGTes+0GZ+D1st5zSvza8T4F3ouC43mSAZ4XoAp07yvvwiDM76FD8nbWnmhK7SezdilJXXk9koXPpKyKdx+XoRmntVDLeIrFsGGcsAzocsEzbveWbfvyj1o6fPasQXpUTLbm04bRYNu23vqvLNm9aOWcYB20UTHsAJDrMScQf/471htDOanwjgg/GUjIVhLrVwNxI1DZI9Ybln8JfVoMGeWNhBxo4EAQEMCClOBIjCXvL0GIi7D0XJZfOdV/sYRyRQ0+sot13e+EA9FjoRXgCgDFAfGS6UgOETBCQ/fPeigiEHyeXNmhBPUMJ1yUUH7OU85oPF9ppTyrMtVcQczJ+zozzN8lpqGfbGAzKg+VF0GF8c7gWTlQzDFUSCWnhFvEHNpHHVakzKo611GFoeSpOYQJ3ip9XhdVn7yVj/c5Q+ixCCXAHDo8MZDI2q7MbFua7DgzZKMPwne8yFhMU9wc0oG13/kT1htuWdMKSwTBf0V+LXhYoZ86HHj/s3ZpitC3xXey6hKYOlMOE91RKlzNYCBAPzRzKDnH2qBETPwKvCump+gpiS2LL1sS/FxVEa3eZrNpQ8Zl2daqfO5kVrJh1hXIGBiEzqMTvMs1rkzgNfgMxQG/LFhTCqHkPB2VXOTtIoFRyHh/4NEQCWGB4IYUhQxOvaKjOuI7ibFfjcFYJV2/LbkDD+2B/OR50TVjECb0AVWcj/ssMnDakux260kJ8jVmlDzeKvJufTS0VdUhMhC5CJAbcaI3bRIjJmBUT605d9X63YvWXv0lPT/6ZM8NFIrydELW+fk/su7K3WrIRb0E5ZED623yrZG+9JOPTqI9leLQd/qQeoMlK7U2rF3Bp5I1IRLAZkO44i7jITFslEbkdz66SF8XXbGBEUQAQqZtR0yxkLCeYNlu7b5IxH/WVmvhw44JkyzdSkP4FzOKUoCIeE5RWQAyLKLPlVQmwu7LCiR27j3VXrBQlusjXqf3+r0Q64L3q7VLDbtaK9stUl5zov4F0dmuzmWuEGG2+HxtYVwYy98tUHaPm8eBKGJYBm6o4juVoYh90c72XuHhGkI1kDBTBqg9hEtcIioOfvH+Lm09hh5fK6Q1vOCW9dtv9qQXhh+eAYxEGx5yyBOT2F22TXF8sATaQ79r3cf/TAZIQ9Sde1uCK60iGxTka3GABt4W9U/ojG8aD9q32qC+6+FweJMBBQClFVfCYC4hFAXdhSWdBPqsfICRHoShKWENX54ezlpD3sedvVMuA7ar8uT4Hb6LgGege4+CFHmUZymz0XhE+ThvULBQgJ1K3gVwLMCnGMXwvp4rRei8X8LTDCOfT6eZzcroPCURCG8Eo7JYoYI/A5mT86cDw5Y+PPGqK0v263p2TcrrFmsOFu18yiob9BUyKngomRfCr5iaTGuJvBo80KC8otEJWEwAg6Oz+mvWa79RZ55BfsWWmBPP5slF1k1gogafHox6a7Z5z39VPSTWygojzfS2a1bJwBHAigPQUk5v3smr08moJsFZtTIxch1DXMTK+Yw3TQMjcA5mwGoDWHErskDCC2W5+TpkahRZN2X6tHS+qee2krN2qTav+/UubnQvSIQ/1qRAL3ULDsIqngdkCM8MZhFBwjjhrC2O6A07DLwtpiNAhJ1JYaLELlebXmXE1HlPyB9Wm5RTVFSAvKKwxudgDohf1wLvm5MXqvOMtLrUSNzr7EryECqkVvnYHeKE/i48W6xmwhEoLF/XUNswzDl/JNvZtg1GVdu8+NPP7MrUvJ/miilU/RQ3gHTrUdt520/ZsNGSubWlFs4blE0xrA9BR6KGrHK+9cFT+X5HPICnVZm7KOGdn4NmtMUkwiCC6hnJygAN6ArBy+jWQMex/1k8JV6tiH7lZ8lrucVmkiV7uNVzg5ABUPu9FJQH9F44x3NcYqKwDjI493BumKhoBX50LazCxnRUoGTcEwtKdaVa9uWkWKaP2nCvzpXnZF54HxGYIs9yDqNb9/h9jK7kCxctuyyabw1OjnmUOozrHCILGVRFTVMi+s+jUi5GSWjnUWNou4PT1r7ys/ZMrj9KcGoy3Qxu8vZrkUlhbL/7By3bPU+15GcDvG8rrzQH+8WO3jgXRMdpnrOOVDMVnZRT61NgnUOAsmUSLaFAGIABBpzdEgvAHH6FMwITi/UAKR9i47f2F+XC1229ckKK6xZdlb/ICUMElyufABEQhOQWXMxkDojLrxdj7LtHh0o75QIbsENL+RR2MdZAz4uJY5f4R4Az7Jy1+FBmperrVt42yMXM2GorMjPnsNjiOSw3hszTX0D9ROVF/1/dNiswW83WZADgZeFt4bliEePVUowYMvS4eZ5t4udYchwyFWDUTCxNJAyv/LROFLztKZ51YHWM3vt/RbR41ao1hjrljQpgjZw9HJB5PC6EDDEyfatNV0YQS4KFvlenMg/zcwXhLLwv+DX0O9M3Hbx5fzhCmitFg6X0NlEl865q1kzutJVKy5hqQtgthNAKPOe8C48WMsvvzp+cn1QSBfBqKaar8jRHifgDnuyJ50ldvaenZ5C6bHVuyLuu8zxHKENQRAvugV1UHZ1MstD/5b9RBj1rEu5dFXmWaxnujyGKx0aXB6MNl21D3jCG5voo9W94sYg2xsHF3NCM/f+0y26eZWQpyiuCojRbGzbsPWDdzdfpDBXy7EeRLJ8GZNa/8CfWPv8mWeN1PXwibrpfvgd6ijQA8ee/+8ikSmbdwZIN6h0rlYfet0KVop5oFFQTXyVGQWF1wAwIWL4yGpQWBHar7qCValYRI5yR0rrQaEupzMhKWbCr9aHNp7Lm3GsSUUZOZOuWGoQfCShCVTYOxU0WKIDVPMoUYigi7El5OuHDCBC/joe6PyaOOd8XkfeVD5gnEZEeoNA4WkhH8rrqNhTh08d1uVa1Mz4KMb+CfHk4It6r/Hpe9cwIt+Ly+V6ERH3hT/rSUptPXmb1dMbXdCScQ78D4Qc60FFmnCNMCwiHxHYJc+v8tHQ6Rsasdbd+3wa7z9CqGrwLi17vHW+nODaSS++wnYf/yAatlqp0P796mDBvU98Wva+Jvi2ooN0/oXYYyehMxn0rhKxQTCQMIS5lC+fCqfCwqzXmURJKk0AojW734ezLwxkJ2ZPy6GckcPkET8lO98VHRXPdw3RFehdc+HPdAfzpVrF4awDv6RpXTIvW6sxYCR6EZ1lXLionrie50anzrti4R/fyDJ51HY/s1FDKJWM5qZqtqs5QNowaZgZmUGwHeV96nvMs9UG5CClgcFImyoaxyQCyZZsb3mpbajZ4E2OAkCujDVn3EY+LyBT8WeybZlsMGaa1kQ3Thu1c/WkpsIfys08zVG6nJ5qEbYyy3SAOr/EbQJZKyD3yG76kU3EUoYM35aGDMaIs5XTOFNAIFhxf+eXjciNVvIcZdD8NwNwiRthtivgJL4QRSsF6I0TI5EWvHVdaamRdc6r3YquLkFfqutsHK+wNTkjwyrw/K0JE5ESDpTZRPR4G4P4DfgMQt4g5E1H7vJXoUXF+n29MSWJio99QVigtlBcMFBUejBQkg4PQXIAIQYTNqMe1MpZb5had16Urbd3vB0DP36e8KCPzuxhuK1D+vF+B1eEHpQWb6y95n6H3HbqZkPmSWeSYOXMxDMGSUIQMfX8vmzLj+HxM33bXf1P53OszebrgfVpzSvNqR7aFLwdMcTQQHem8++fkAaxbpeKfb9wDdVkM6xeNA4SO2h24fJcnNBjOWcpnSUQJ8CpcSCI6Aq7kxiV0tKr3wsPrIyk4v4qH5FNSshN2R++czUhJrFZEx3wLzyG1KsFNpGEMBL/zaQFj2o+ZzREVVl+/kfCcooIS8XbkFUHDTw1dxYXcyzN68CpKTPko8CmgjBvedwaTBEXErZsIbu28iGiJK6Iib0ZQicpn9Mr43UOFXKt3EkmSMtyosLrHGdsc1LwuCcFiwMe+aT5bxBQE3kt/V2iNEC3hHKAKSo2+9XsD2139Zf0w4XA8DchYAWk28Cn8uq835gYw0bo3B1aQbl/9gBTKjFJTQq+Qu8kwIW+OWtf3wy4TjVEovcGcLPpQgVj2VDhicyDLC+Jfcc+Kzt4gVPmLEnO49TYrp61kC8M7JYwbYqZEhA+R5KFDIZMC6ssbY/mXcEIvSg+y1CA8EdGhDKHrYQY8J3lRJVHCgtz3iSuPDyw5GCJ/7vJQZZfVGQnOoTyjfIci5mGp7KOZGEIfCF0EPq50bd0KFfNGMDTeR2/xGx/LPON1n0mJ9eUx76peMAqwihnoEueN8PgwWz+Eg2LIMFpyGB2krjzb3Z032rDzvnDB0wnemb+3uJ8kia2srKgMsjbbbet2g0De3Ny0NH36GfK5jMHjf2aDK++xXnPJebVnS2rRQLXeFx3rFxTIxvfzOqdvK4MGRZepFBi0EadJwJPQx0DGJIOmOI1HAOe2JSGYc+mgX0t0yRJqJ/p32JMyWDeloDK+SefQnaJdwtiw6kyKVxaFev4y4PwNzU8g8ifGIPv7GCjgnIxuvld8fOj9yA0MTI+cBGMVpXXrcOj1MgZRHHmI1AnRiUsy0AkdVrgf3jxIvmAkj+URcohwKsbwklKImqxVG7ZV6dt2wjA4+v1DPyKDNeBReJXz1B1GBcAAZtAb8qvXX7ZBRaUv1/yrD8P+w+GiZwKF6gDr6+t26dIl51e2nU7Her2eXbx48br8OlFLN45Rd8V23vujVhm13duqKLFcTKd0WnWj1xQJHxQVWR4jRwcMxQid7lm174aYSR6RzqG4aAisN66MaxDG8BXgWzYEHYL3cFICvCqCmBXTLOkZErBupbB8kwgMYKkp4S0wZJdOYI87Rw0a4QoAKye/bwzepYwN9FzCF3ESi84j/FmX7UA2UF7G6ajQeyqDpm0Nlmw4JN/XNhve17YtuhChBD5d5BpG1n37+sFUeBcaeUiVCcuENbTfL52wM71b3OuihrHkIH4sOeZ44UOFryZJHuhWX0pHCnuzd9pDu2UZCrVG2ylh+/JPPLMDNXJA/Pfcc4+97W1vs/e///32zne+0/7v//2/9oEPfMD33/3ud/s1U6ipe5vWfd8vid/qVi9vq5UGvhzbbkm8J++jxGiCCJguyk72c37FrmIuU697UmwSlnGCn2LIuA9NaBu/e0eEhJUeAIaniyWPeCzbUjJrteQ2efBcqQcwBzGKJ/esAv8h9IcyjOeIUBQBP1+jtPQcwnkoLRTWIcAUvKp6c
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