20200508
12102300
1.0
FALSE
Copy C:\Users\cecilia.CODEXREMOTE0\AppData\Roaming\ESRI\Desktop10.7\ArcCatalog\V03.sde\sisdia.v03.d2_1_qualidade_agua_superficial C:\Users\cecilia.CODEXREMOTE0\AppData\Roaming\ESRI\Desktop10.7\ArcCatalog\SISDIA.sde\AMB21_Mon_Hidrico\sgbde.sisdia.qualidade_agua_superficial FeatureClass #
002
sgbde.sisdia.qualidade_agua_superficial
148009.087800
253017.279300
8222762.545900
8284751.253900
1
Server=10.233.47.40; Service=sde:postgresql:10.233.47.40; Database=sgbde; User=sdb_sisdia; Version=sde.DEFAULT
ArcSDE Connection
Projected
GCS_SIRGAS_2000
Linear Unit: Meter (1.000000)
SIRGAS_2000_UTM_Zone_23S
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.6'><WKT>PROJCS["SIRGAS_2000_UTM_Zone_23S",GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-45.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",31983]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>1900</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>31983</WKID><LatestWKID>31983</LatestWKID></ProjectedCoordinateSystem>
150000000
5000
20200529
04364800
20200529
04364800
4M0AEIIN-8CYJ-78YZ-BMTO-4MXD3BPWPY98
dataset
SISDIA
SEMA/SUGAT/COINFA
IDE Ambiental do Distrito Federal
BR
SISDIA
SISDIA
20200529
SDE Feature Class
0.000
Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1.9270
Qualidade Agua Superficial
2020-05-11T00:00:00
SISDIA
SEMA/SUGAT/COINFA
IDE Ambiental do Distrito Federal
BR
SISDIA
Qualidade Água Superficial
Qualidade Água Superficial
N.I
Controle e Monitoramento Hídrico
Hidrografia e Hidrologia
Controle e Monitoramento Hídrico , Hidrografia e Hidrologia
SISDIA
SEMA/SUGAT/COINFA
IDE Ambiental do Distrito Federal
BR
SISDIA
SEMA/SUGAT/COINFA
IDE Ambiental do Distrito Federal
BR
SISDIA
False
SISDIA
1
-48.289168
-47.302287
-15.490292
-16.062603
SISDIA
SEMA/SUGAT/COINFA
IDE Ambiental do Distrito Federal
BR
SISDIA
SISDIA
348
EPSG
7.8.3(8.1.2)
sgbde.sisdia.qualidade_agua_superficial
Feature Class
348
objectid
objectid
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
gid
gid
Integer
4
10
0
nome_rio
nome_rio
String
255
0
0
afluente_direita
afluente_direita
String
255
0
0
extensao
extensao
Double
8
38
8
ra_codigo
ra_codigo
Integer
4
10
0
afluente_esquerda
afluente_esquerda
String
255
0
0
qualidade
qualidade
Integer
4
10
0
geom
geom
Geometry
4
0
0
st_length(geom)
st_length(geom)
Double
0
0
0
Simple
FALSE
348
TRUE
FALSE
/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a
HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy
MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA
AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA
AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3
ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm
p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA
AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx
BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK
U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3
uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii
gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA
CiiigAooooAKKKKACiiigAooooAKKKKACo2nhWTy2lQP/dLDP5UkyTOy+XKI0/iwuSfpUfk21rES
4zu4JbLlj/U1SSIlJ9B6XcEk3lI4Z8E8e1SRuskauvQjIqpFsurkOA6JB9xShTkjrgjOKkXNvcbA
D5Mn3fRW5JH41UorZbkxm3q9izUYmUzmIAlgNxOOBUlVbRi0k5kXbLv+Yeg7fpUpaNlydmkWqKKZ
LKsMZdycD0GSfwqdym7asjF0nmlCGX5tm4jgnj/Gp6qQAXMU4dSu6Q/KTyMYx0+marQz3VrMLaRT
M23dnJJxnnB7/l+Na+zvtujD2trOWzNSimo6yIHQgqRkGnVkbkIuUa8e2XlkQOx7DJOB+hqVm249
CcVmWs8cmv3qjcjiNE2spG/bklgcYI+YDj/CtGTlQvduKhSurl1o8jt5L8hWcqfukj1FMNwibvMI
QKMncR0qWo5IyWDKATnkHvTd1qZSv0I4LtbiV1VGUKAfn4J/DrirFQyR+bjzIYnx03HP9KjCQwsX
VWiC8Nt6H6j+tXeL2JUpLctU3c4P3Mj2NN8+H/nqn/fQpGnjXbg7yxwApBJo5WU5LuO3PnJTj2PN
AlUnGSP94Efzp9HWpsx2Y3eC+0c8ZPtTqghw080i428IMd8Zz/P9KnptWFF3VxkUqyqSuQQcEEYI
NJBIZY8sMMDhh70y4jQAzF2jZV+8pxn/ABpbWNorZFc5cjcxx1J5P61bStclOXNZkiuGLAfwnBp1
QMHhlMiKzo33lGMg+op0VwsrFcFHHOxuDj1pOPVFKWtmS0UUVJQUUUUAFFFFABRRRQAUUUUAFQRD
zZ3lK8L8iE+3U/n/ACqeimnYTVyiJC2ph1OY8eUcdz1z+HI/Gp7nAWInp5i5/OoY41SVULhJInOz
P8SHnH9Pwq26LIhRwCpGCDWkmk0ZQTcWOqucxXikfdlGD/vDp+mfyqZF2IF3Fsdz1qK7B8kOvWNl
f8Aef0zUR3sXPa/YV5/nMcSF3HXsB9TSJAd4kmcu46AcKv0H9TUwIIyOh5paL20Q+W+rK10gQCdP
lkUjJA6jPINLKwS8hZxhdrANjgE471JOrPCyKoJYY5PFQygGSOJ5DsVCz84zjGM/rVR13ImrXt5C
mxjFwZ43kjc/eCN8rfUdKlnnjtoWllbCL1wCT+AHU0scscozG6sPY1VvFaa6s0UblSbfIoPba2Cf
+BYP4VnUm0tTalCF9Nhum221WvZoyLu5wz7hygxwnsB/PNWZuGQhtp5ANTUxxudB6HJ/z+NQ42jY
KsnN3YquCcdD6GnVE0XAAOQOx7fSpFBAAJyfWmm+pCv1FpMDOe9RNcfOUjRpCv3tpGB+Z6037ZEA
d29COzoR+vStOV9g549WTbE/uL+VNEEazeaq7XIwccZ+tOV1aMOD8pGc+1U0luWiklSE7n5XcRgD
tgfr2pxTZMnFW0LoIJIBBI6j0pJEEiFGzg9cHFQ28kIAjjYljknIOc9yfSrFS1ZlJqSEChVCqAAO
gFMkmSJgHyoP8RHH4nt+NSUUeo35EBMdy6hWVlQhjg5Ge3+NT1GCGkG3sOSP5UsqNImFdkOcgikn
cldWRedi5kV5EVVxhTwTx1pkqrfKEEf7rOTIw/8AQe/406EgzyJKEM4UZI/iXnHHbvVmtG+V6Epc
y12Kag2UuxVY27n5QiElD6cdu9XKKiNuPM3iSUHPTecflUt33KScdFsS0VGRIuSrAj/a7Usb70z/
ACqL62KvrYfRRRTGFFFFABRRTGJZtinH940m7CbsPoqKSJiAYpCrjpuywP1FMiLXCbnO0ZKlFPQg
4PNDuldIE9bMkPlzZQgNj9DUlIAFAAGAO1DLuRlPcYoV7ajduhFbu8oaViQrEhF9gev49alIBBBG
QabGVWJBwAABT6pu+qJjsRiHbF5au4HY5yR7c0yL97AyS/PglT74PWp6qxuLYskoIUszCTscnPPp
VK7XmS7JrsPs232yEsWJz1OSPY+4pDahp2kdywbHy/T+n+eac9tbzHe0UbMf48DP50W7EF4mYlkP
BPUr2P8An0ovu0JLaMhZLaGVtzxgtjGaZ5S25DRDAJAYeue/15qxUczokZ8wZB429c+1ZzTnGy+R
ouWL5iSkK5YHJBHp3qO3R0t41k++BzznHtT5HWKNpHOFUEknsKbQL3kOqvParMc5cHIz87AY78Z9
Khgnv5IUla3hIcBgu8qVB7Hg81Mkl2ZAJLeJUPUrKSR+G2lGdtUXOje6dvvRJuht1CZSNR0GQKVX
imUhWVx0I60LCiMzBfmY5JPJNK65GR94dKpvsZ6jFiZIxGrAoBgBlzxRb5ECg9uB9BTmkATKjLdA
vvTEg2oFMjkegOP5Vi5Pn93UqMUlckZ0T7zAfU1E11HwEO9iQAq1KkapnAPPqc0rMFHc+gFaRb3k
KXkwZgvU1EWBJ80FF7Z6H61IqncWYAE/pRJGJY2jbOGGDii19xO7Q7pRUEcjo4hl5JztcDhvr6Gp
JJEiGWPXoAMk/gKq3QFJWuMuVTyi7fKycqw6g0qGURqXALEAkDsaYqvO6ySLtQcrGeufU/4VYJAG
ScCiW1hJXfMM8xcdT9MHNOVlboaNy7d2Rj1qJzkeZwgX+JuOKi7G3YdMfk2jqx21J0qt5pkRJAjA
BgQTxkdOnWrNC+JhF3dwoooqigooooAKYn+sk+o/lT6ZF90nuWOfzpPdCe6H1XMMyu5hkRVc7juX
JB9uasUVSdgcUyKBZlDec4Y54wMDFSMQoyaRm29sk9AKQKScsfoB0FJyuxLRWQ0wiSAo/c5+hzn9
KaBcqMZik9zlf8anrPmu5AfL6jO1jCNxHtz3/P6VcItqyIm4w1HmSbzNv2lS+cYWElc+hP8A9ero
6c1UjkbzIoEgkhUDdltvIH0J7kVPJMkWNzcnoo5J+gpyXRBBpJtv+vmMNuY8m3cR5OShXKn8O1Qy
tIGWR4xE+dvmBsj8fY/1qbz5sBjbPtPYMNw+o/8Ar0qzRTZjYEEjlHGD/wDXoTa1YmovRO39f1sO
hk82IPjB5BHoRwaPKXzfMJJI6ZPA+lRRIbONYyS6ZPz45H1/xqwrBhlSCPUVkqibaRqouy5hajuI
VuLeSF87XGDipKKZSbTuipazzCQWt0F84JkOp4kA4Jx2Pt71bqvd232mL5W2TJ80Ug/hbHX6e1Jb
XfnM0MqeVcJ96MnqPUHuKlaaMuS5lzR+ZOHUuUByw6j0p1QNG8crSxDdv++h7nGMg/hUm9h1jJ+h
FVKyMU+4kqZwyqCynIpFkMo+Tj1J7USTbImYKdwHCnuaEt1VerbjySGIyawbfPaHz/QtJOI7yx3Z
ifXcaPKXduyd3rmgRkf8tHI9M0uwerf99GtEr9CGl2DZ/tN+dOpuz+6zD8c/zpRnHOM+1UhoQoDI
rnqoIH40kjMoBWPf7AgH9aFlDSsignb1PYH0p9VtuGj2IBdwbclwp6FW+8D6YqRVJ+ZuvUD0pXA2
scduaVc7Rnk4pOzYknfUYEUyMxHeo3Alu1jYZVF34Pck4H5YNTMmVYKxUnuMZH51D5MykGO4ycYP
moG/linBJEyXSxJOR5XJxkjH1zUlV2lK/JcLgHo652n/AAp1u6tvCMGQHgg5qWmn6jUlzE1FFFMs
KKKKACmCPByGYDOcdqfRSauKwUjMF6/gBS0h2g7jwemaGDIGlkWTd9mkYY+XaRx9eaURSyHdLIUH
ZEOMfU9anoqk7LQnk7sruRaDdmR1YgBfvEH15pIo8O07ps4JCdx6k47mp5HSNCzuqL6scVHbSmWL
JIYqSpYHIOO9Vd2uKy5rDbfdLK87I6H7iq3XHr+NJaANH5rcytw5PYgnj6CrNRQja8y4wN+R+IH9
c0nIFGzRLTZI0lXa65AOR7H1pGc7tiDLdTnoBSFZW4LhR/sjn9ay9pr7qua8t9yOOXyyYpC5YE7S
VJyv1FSGNWyyZRjzkd/qKckaoSVHJ6nuadQ05/Gv69RL3dENQuR84APsetOooqkrKwBUF1aR3Sru
JSROY5F+8h9RU9FNq41Jxd0UDc3tt8klnJdHtJAVGfqGYYP0zSHULof8wi8Pfh4un/ffWrkM0dxC
ssTbkboakqXFrS5p7SPWK/H/ADM973dtint5IHZl2q2GDDI7jIz7VfZlRSzEBR1JqKYecfJGccFm
BxgUC2jBBO9sHI3OSM1NNJzbb8vzIqPblVh8cnmKTsZRnjcMZp9FQ3Tlbdgoy7fKo9Sa1Su9DNuy
ux8UglTeAQueCe49abKsr/LHIqKep25P4VIq7UCjsMUtF7PQLXVmVVR7TOMyQliT3ZSTz9RU8c0c
pIRgSOo6EfhTsjIHc02RAcOAN69Dj9KHK+rEly7bCv8Acb6UZxtB70vDL7EUzou188dGqH3GxUb5
VB680jy7JY0KnDnAYdj1waRQyuqnnqc+tRzRTykANGgVgynBJz+mP/r1VPXclt20LNRw4ww7hjmo
2nmi/wBZblh/ejYYH1Bx/WpIlIBZhhm5NKStJDvd6ElFFFMsKKKKACkyN23POM0xpDkqilmHXsBS
xoVyXILMcnFTe7sib62Q+gjIwaRiFUsegGabvARSwOWxwKHJLRlWECOBgSHA9RTJVuShEciLx128
/hzU9FO1thcqIIoYW2y/fbHDOckf4VMu3lVxx2FNMSkk4IJ7qcUEeUvyqSM885NTzT+0NRithGkI
bCxO+OpGP6mmxOGkkGCDkHBHt/8AWqUEMoIOQeRVeCQtfXSEjK7MDvtI6/nn8qlp8yd9P+Ay0rp+
RMqlZHPGGwRT6aWw4XB56GnVcbLREsKKKKoQUUUUAFISAMk4FLTBGM5JLeme1J36CZS0fY1o8qnP
mzSSHHbLHH6YrQphjRjkqM0yT9xG0gZiFGSp54oqTd3J7BdixndNKcYAIX68f/XqWoLZ0aN5Q6nc
xLc/d9jTfMEs8TRFiATuIztIwfwPOKVKMuXXzCU1fQnWRHLBWBKnBHpTZozLHgHawIZT6EUSQJKQ
xyHHAZTgimRvJG4hmy2c7JP73sfetF3RLfSQB2lBiYtDL1+XByPUEjpTJk+zIJUL/Kw3ZYnKk4PX
06/hU8iBsNgbl5BpswL2sgA5ZDgfhQpa2E1o+49kyQwODSYc8bgPcCljYNGrDoQDTqhouyepGFaI
fKxYeh/pT+GHqDQRkEetMVtr+WzA8Ag9zS20FtoJtMcoOSVbjB7VLTJOij1YUrusaF2OAKFZXBWV
xsnzuIwR6tUlRwrhSxHzPyf8Kkoj3CPcKKKKooKbISF46k4FOqOY7Y9+PukGlLRClsPVQgwBQCCS
PSm7nb7q4Hq3+FHEaE8k/qTSv2FfsOVgy5x9RUMEfyq5Ynuq9lzSmBXC7y3GeAxAye9U4pZ57iVL
EIlunymWQFgzAnIUZ7dznrUTiuaLeppTUpJ9DSoqkZry3cefEs0R6tAp3KfdcnI+n5UyXWLdRsi3
PckhUgZSjEn1yOB3J7Dmr5l1LVGb+HX+vw+ZoUyOWOZd0UiuuSMqcjI6iqptJrkr9slVoxyYY1wp
PuTyf0q4iLGgRFCqBgADAFNNkyUUt7saYY85C4J7rx/Ko3hSJhMijevDN1JXuM/rU9BAIwelRKlF
rRaiUmRyg4DryV5x6inghgCDkHkVQIubAt5URuLYndgyfOnqBnqPxqZY/M2tCrQg9TjBI9MVEpuM
tFe/Qtw030LVFRrEqsGyzEdNxzimPOSWSFC7g4J/hU+5/wAK2hzPdGMmkT0VDK7RMjk/u+j+3vU1
VYE7uxG0qq20HL9lB5NV/MkSUgK7gKSA2Mg++O1IY2e9LqSGU+gxj1NTiFlZn84gnk/KMf4/rXnt
1KrvZqz6W2+fmbaRHxY8sYbdnv602b5tid2YY9sc1DFMEkdmOEPVtpAB9/8APapQ6tdDBBBTg+vP
atVVjOCj8v69RcrTuOa3hd97xIzepUGmS3CRMqD5mzyqjJA9cCi5fNsXVjs43MvUL3P5U1nS3Bjt
4d8hG7auB+JJrtSvuc0nbYlkMox5So3ruYj+hpgheR1aZwQvIRRgZ9SepplsEjiedpidxLOWOApH
B47VJ9pTGdsm3+9sOKGnsgunrIeY8jAdwPQGk8snhmyo7Dv9adHIkqB43V1PdTmnVm4ouyepH5WM
AOwX+6DSoSvyNnPOD6in0yYZjP4UmraoGraoc2eAvU9/Sk8tcYI3eue9KrK2QrA49DTdrHjecfTm
jcBjqqMjBc8/U0EiSdRjhRnBFShFXkDn1qNCWnk9FAAqWunclolooorQ0CiiigApGAZSDyDS0UAM
hOYl5zx19aH+8g9TVOe6e2to0hVWmeUQx5zgnqTx6AE/hTTpEc0hlu55ppWAHyyNGoHoApHH1zWa
baSRUIJwUpOwt9ILmRbCJmLOR55TPyR8k5I6E4x681eRFjRURQqqMAAYAFRJFDaRhIIUQE4CoAMm
nLGXJaUcnoueAKXM72WrLk00ktiWio/Ijz0OPTPH5UO+P3aY3/yHrVczSvJGduw8kKCSQAOpNNVx
Ip2H8ccUgiQEFiXb1Y5qShcz30DQi3tGf3mCv94cY+tOeQqQqjc56DOPzp5GRg9KYkax52559Tmp
cZrRPT8UO6GiHcQ0rFj6dFH4UssqxYyGZj0VRkmnsWCkoAWxwCcA1VDsszHb5s5HIU/Kg9Mn/wDX
WtOmlsZzmxJ/tH2cl5BGWwAqDJ5OMZNWkRY1CooVR2FUMXEtot08w3KfMRAo2ge/fp71dRZAcvKG
GOgXFaTVlYzg7u9h0gUxOHGVIOfpUNlKZbONmzv2gNnrmrBAIwRkGmhVTcQMZOTipvpY0s+a4oUK
MKAPoKbLt8tt7bV7nOOKb9oi2ofMHz/dHc/hULwfa7kNMpMEY+VG6M3qR7dqylZe4aQs9W9BG1Gy
jYxJKJHAz5cILkD3C5x+NQbrppY5DpoQBtwKyKWAx3HGD9M1opGka7Y0VV9FGKdUzpKatItTjH4V
9/8AwLfqU4ZENnMGJVV3Ek9gcn+RqK1EDxRqsxS62gtzhs47g1LcQRxsswX5AwMijoffHfnFTtcw
qpbzV6djk1pSrOV4SXvf1qc06aVnfQgRZfOdSkR5BbOefceh4q5VOBpUj4t385+WMjADP1Gfyq1H
v2/vAob/AGTkVpMKexFJGyS+dCMk8Omcbvf6ipPMA6hh+Bp9FQ22Vy22GeauMjJA7gcUnnRcjcD6
45qSip1HqRJsM2U24C44qWmRfx/7xp9EdhR2CoosKXTGMNn86lqGQhZoyMbjxjHNEtNQlpqTUUUV
RQUUUUAFFFFAGVrP+haa98rnNmDMo25BwCCMe4J5q+pklUPu8sHkDHP41S1pfOgt7RiRDdTCKYjP
3MEkceuMfjWgHyOEYiseVczV9DWTjGlFddfu/wCHuCRhTkksx6setKXUKzE8LnJpP3h7hfbGagI+
0Hyl/wBSpw7f3j6f4/lWsIrZbGEpMmhZmQFyNx52jsD2piOI/M3n59x47n0x+FNby59x/wBVNGcb
uMj/ABFED7ykjAZkQcjsR1FTWi01Jf1cdOXRkqR5G6TBc9fb2FLtZfuHPsxp9FNQSVkN66hRRRVA
VJpYpHKmWUgcbIgeT9Rz+tRyGdYwkcaQxswUAHLnJ59hxnnJq78qADhR27VGEd5/McYVCQi/1rRS
SMZQbJVRVQIAAoGAPaqVurh5IUncbCQqsoK7f58Z9avVC0qrOoaNgSdquRxnrj9KUW9SppaMkjLl
B5igN3AORSsdqknPHoM02SNnxiV0H+zjn8xTLeQsDG5zInDZ7+h/GlbqVezsQRO0cpdosRtxv2BT
knuPSrtIQGGCAR703YR9xiPY8ilKV+goxcRTvHIIPsaVTuGRSK27qMEdRSfdkwOjc1HmPzH1Rjjf
eyKkStGcgsmd2Scc9qvVQjE32id4HDEvhlc5A9CP8KlytVj53/z/AEKkrxZZkleO2Lui+Z0CK2QT
2GcCpEDKgDtubucYqAxXMm3fJEu1sjapOf14p6eaku1zvUjIYDGD6Vu0raGabvqBuoNoYSA54AXk
k/Qc0onJIxDLj1IA/rUgVQxYKNx6nFLU6dCrS6sRXVhwRS0hVT1UH8KaYkPbH04qdR6kcqEA7SST
k7e1SoylRtORQqKvQUjRIxJK8nuOtSotO6JSad0PqJcG5Zh2UA0vlttCl8r3yOT+NJCApkUDGHND
bbV0Du2rktFFFWWFFFFABRRRQBVLxXF95YLFrb5mxnAYjgH1ODnHuParLMFxnqegrM0tmNzqZVB5
RuyFORkkIoP65rRBG9yeMcf1qFK6LrLllyry/K4FmIwFIJ7nHFOVQihVGAKTev8AeH50BtzEDkDv
VGegjRRuQXjViO5Gajn2xbZemGGRnr2qeoYl8xvNfnn5B6Cs6t2uRb/l5lRS3Fe4jQhRl2PRUGf/
ANX41IpyoJBGex7VGsOx8o7Bc52ADFPdgiFj0FaXtG8idbjVlGxnbhdxA9+1LGGyzsMFugz0FNih
2YZiS3Jx2GeuKduZpSBwqjn3NYx5rJzLdtbDmVXUqyhgexFQQ7opjAeUC7kJPIHpVioJWEc8Uhzt
5UnHTOMV0x7GUtPeJ6iuVLW7gDLAZA9xzT3dY0LOcAVVlkkO2ZInXaD1Gcj3HWiKd7hOSSsWkdZE
DoQVYZBFNdQp8wcMBycdRVa38i4YTRsElydwR8jP06EfhVrYSfnbIHQdKU1Z2QotyQvmJgHevJx1
p1IUU9VHTHSmK2wbWDdeCeai9tyr23HMob2PY0wiQFWOCB2FSgg9KKGkxtXE3DbuzxjNQWikRuSD
hnLDPoalG5OApZe2O1Mtc+R/wJv5msnrVjfs/wBCov3WTUUUVuIKikmEZ+aOQr/eVd36DmpaKa8x
O/QhS5R3CYdSem5CM1NTJYxKgGcEMGBHqDmjMn91P++j/hSbXQSbW4+imZk7qv4N/wDWo3P/AHBj
60rjuPqN1YOHTGcYIPelMm37ysPfGaf1o0YaMjEnzhHG1j05zmpKayBxgj8fSo0kIk8tzuznDUr2
dmK9tGTUUUVRQUUUUAVtPtjaWMULEF1HzsP4mPJP4mphGN7MQDnpT6KXKrWHN875mJgegpaKKYhs
il12hiuepHpSgBVCgYA4FLRS5Ve4X6DWDFcK20+uKZ5Tsw3yBlByBtwT9aloqZQUndjTaCmou0HJ
ySck06iqsr3EFMkjEqFCcDIP60+imDV9AooooAjlgjlA3DBByGHBB9qeoKqAWLEdz3paKd3sKyvc
KOtFFIY0IFYkcZ7U6iiiwBTURY0CqMAU6ilZXuAUUUUwCiiigAooooAKKKKACkAAGAMUtFABTQij
GFAx04p1FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR
RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/Z