How • Base infection probabilities. Given the fact ever, in general WiFi systems, an individual may that we cannot test every individual, each untested have multiple devices, and removing the duplication individual has a base probability of being infected. is significant in this case. (...) This tion probability can be deduced from the positive chance may also be timevariant. (...) For in probability that got infection from a contact with stance, a university randomly tested 1,000 students .
Language:English
Score: 775805.57

https://www.itu.int/en/publica.../files/basichtml/page113.html
Data Source: un
Total collision rates is obtained by summing up all the shell contributions • DebrisDebris, DebrisIntact, and IntactIntact collisions
• Probability of “k” collisions in a given time period (in 2,000 km orbital region)
Probability of k collisions in LEO (DebrisDebris, DebrisIntact, and IntactIntact collisions)
All collision probabilities are
calculated beginning mid2013.
Probability of exactly 4 collision by
2030 = 18.7%
Probability of 1 to 4 collision by
2030 = 50%
8
ISSUE 2
HOW “PROBABLE” IS
IT FOR TWO THINGS
TO GO BOOM IN
SPACE?
(...) ISSUE 1
WHAT HAPPENS
WHEN TWO THINGS
GO BOOM IN SPACE?
Are the “probabilities” useful?
• The probabilities determined in the previous slides are a “bird’s eye” view.
Language:English
Score: 773168.7

https://www.unidir.org/sites/d...rinpresentationeng0803.pdf
Data Source: un
Figure 1
Geometry of the link
2.1 Expression of the probability of rain attenuation from the spatial correlation of rain rate fields
The joint probability can be expressed from the correlation between the random variables R(x1) , R(x2) ,…, R(xN) . (...) So the product U0U1 is not equal to zero only if U0 and U1 are simultaneously equal to 1, which occurs with a probability of:
(14)
The probability can be obtained thanks to the transition matrix of the Markov chain:
(15)
and so: (16)
as: (17)
then: (18)
and, (19)
Finally, the equality of the covariances of the processes Ui in (18) and G(xi)>α in (11) leads to:
(20)
and so: (21)
Eventually from (11), the probability to have rain attenuation on the link becomes:
(22)
3 Conclusion
Figure 3 illustrates the probability to have rain attenuation on the link in function of the probability of rain and the ground projection of the link.
Figure 3
Probability to have rain attenuation on the link in function of the probability of rain and the ground projection of the link
This contribution has presented a model of the probability to have rain attenuation of an Earthspace link.
Language:English
Score: 766907.8

https://www.itu.int/dms_pub/it...0a/04/R0A0400007F0001MSWE.docx
Data Source: un
The implementation always uses a 0 to represent the mostprobable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model.
Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 764714.5

https://www.itu.int/wftp3/ava...e/2002_05_Fairfax/JVTC029.doc
Data Source: un
The implementation always uses a 0 to represent the mostprobable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model.
Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 764714.5

https://www.itu.int/wftp3/ava...deosite/0201_Gen/JVTB033.doc
Data Source: un
The implementation always uses a 0 to represent the mostprobable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model.
Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 764714.5

https://www.itu.int/wftp3/ava...te/2002_01_Geneva/JVTB033.doc
Data Source: un
PowerPoint Presentation
SEA Pilot Project to Support Spatial Planning in Gurjaani
Practical Aspects of Effective SEA Implementation
Doug Hickman Nova Scotia, Canada
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Transport
Residential
Industrial
Etc
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Transport New road – capacity: 500 cars/hour
Residential New apartments for 2000 people Single family houses for 1000 people
Industrial Light industrial buildings – 10 ha Warehousing – 4 ha
Etc
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Transport New road – capacity: 500 cars/hour
Land use change Noise
Residential New apartments for 2000 people Single family houses for 1000 people
Land use change Demand for environ mental services
Industrial Light industrial buildings – 10 ha Warehousing – 4 ha
Spills of hazardous substances
Etc
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Transport New road – capacity: 500 cars/hour
Land use change Noise
Loss of habitat
Loss of habitat
Loss of high quality agricultural soil
Ambiance of sites impacted by vehicle noise
Removal of houses
Residential New apartments for 2000 people Single family houses for 1000 people
Land use change Demand for environ mental services
Loss of habitat
Loss of habitat
Soil pollution from Increased solid waste
Increase in GHG’s from organic waste
Odour from organic waste
Increase in water demand exceeds rate of recharge
Industrial Light industrial buildings – 10 ha Warehousing – 4 ha
Spills of hazardous substances
Air pollution from spilled items
Water pollution from spilled items
Toxic impact of spilled items
Water pollution from spilled materials
Etc
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
Example of Scoping Matrix Planning Item
Proposed Action
Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas
Cultural Heritage
Health Socio Economic
Trans boundary Effects
Flora Fauna
Transport New road – capacity: 500 cars/hour
Land use change Noise
Loss of habitat
Loss of habitat
Loss of high quality agricultural soil
Ambiance of sites impacted by vehicle noise
Removal of houses
Residential New apartments for 2000 people Single family houses for 1000 people
Land use change Demand for environ mental services
Loss of habitat
Loss of habitat
Soil pollution from Increased solid waste
Increase in GHG’s from organic waste
Odour from organic waste
Increase in water demand exceeds rate of recharge
Industrial Light industrial buildings – 10 ha Warehousing – 4 ha
Spills of hazardous substances
Air pollution from spilled items
Water pollution from spilled items
Toxic impact of spilled items
Water pollution from spilled materals
Etc
Probable significant negative effect – to be addressed in SEA Report
Probable insignificant negative effect – will be briefly addressed in SEA Report
No significant effect or positive effect – will not be addressed in SEA Report
Legend
SEA Pilot Project to Support Spatial Planning in Gurjaani
Example of Scoping Matrix
Example of Scoping Matrix
Example of Scoping Matrix
Example of Scoping Matrix
Example of Scoping Matrix
Example of Scoping Matrix
Language:English
Score: 759159.96

https://unece.org/sites/defaul...EA_16%20March%202022_ENG_0.pdf
Data Source: un
Single Year Mortality Under Age 5
Latin American Pattern M a l e s
Interpolation parameters T(f)
Life exp at birth
E(O)
35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 43.00 44.00
Probability of dying between ages X and Y Q(XY)
Survivors at age X IOU
Life exp at birth
E(O)
Single Year Mortality Under Age 5
Latin American Pattern Both sexes combined
Probability of dying between ages X and Y Q(XY)
Single Year Mortality Under Age 5
Chilean Pattern  Males life exp at birth
Probability of dyins between ages X and Y Q(XY)
Survivors at age X I00
Interpolation parameters T(1)
T(1) T(2) T(3)
Single Year Mortality Under Age 5
Chilean Pattern  Females
Life exp at birth
Probability of dying between ages X and Y Q(XY)
Survivors at age X I(X)
Interpolation parameters T(I)
T(1) T(2 ) T(3)
.06980 .I7984 .32596
.06630 .I7446 .32 181
.06295 .I6930 .3 1779
.05973 .I 6412 .31373
.05665 .I5917 .30978
.05368 .I5428 .30585 ,05085 .1496 1 .30202 .04812 .I4495 .298 17 .04550 .I4038 .29434 .04299 .I3596 .29059
Single Year Mortality Under Age 5
Chilean Pattern  Both sexes combined Life exp at birth
Probability of dying between ages X and Y Q(XY)
Survivors at age X I00
Single Year Mortality Under Age 5
South Asian Pattern  Females
Life exp Pmbability of dying between ages X and Y Survivors at age X at birth Q(XY) !(XI
E(O) Q(O1) ~ ( 1  2 ) ~ ( 2  3 ) ~(311 ~ ( 4  5 ) l(1) l(2) 1(3) 1(4) 1(5)
Interpolation parameters T(I)
T(1) 70 ) T(3)
.I9922 .97869 ,50836
.I8883 .94452 .49894
.I7884 .91124 .48968
.I6932 .87950 .48074
.I601 1 .84808 .47186
.15 132 .8 1795 .46322
.I4291 .78877 .45481 13486 .76053 .44659 .I271 2 .73285 .43843 .I1970 .70597 .43044
Single Year Mortality Under Age 5
South Asian Pattern  Both sexes combined
Life exp Probability of dying between ages X and Y Survivors at age X at birth QOCY) Io()
E(O) Q(01) Q(12) Q(23) Q(34) Q(49 l(1) l(2) l(3) l(4) l(5)
Single Year Mortality Under Age 5
Far Eastern Pattern  Males Life exp Probability of dying between ages X and Y at birth Q(XY) 
~ ( 0 ) ~ ( 0  1 ) ~ ( 1  2 ) ~ ( 2  3 ) ~ ( 3  4 ) ~(45) 1(1)
Su~ivors at age X I(X)
Interpolotian parameters T(I)
T(1) T(2) T(3)
Single Year Mortality Under Age 5
Far Eastern Pattern  Females
Life exp Probability of dying between ages X and Y Survivors at age X at birth Q(XY) I(X)
E(O) a(o1) Q(I2) a(23) ~ ( 3  4 ) a(4a I(I) 1(2) 1(3) ~ ( s ) r(5)
Interpolation parameters T(I)
T(1) T(2) T(3)
Single Year Mortality Under Age 5
Life .xp at birth
E(0)
Far Eastern Pattern  Both sexes combined
Probability of dying between ages X and Y Q(XY)
Survivors at age X 100
1(2) 1(3) ~ 4 )
80106 77706 76128 80810 78527 77024 81498 79326 77895 82170 80106 78746 82825 80866 79574 8 6 8160 80383 84095 182335i 8 1 172 84708 83043 81942 85309 83735 82695 85895 84410 83427
Single Year Mortality Under Age 5
General Pattern  Males
Life exp Probability of dying between ages X and Y at birth Q(XY)
E(O) a(o1) a(r2) Q(23) ~ ( 3  4 )
Survivors at age X I(X)
Interpolation parameters T(I)
Single Year Mortality Under Age 5
General Pattern  Females
Life exp Probability of dying between ages X and Y Survivors at age X at birth Q(XY) I()o
E(0) Q(01) Q(12) Q ( 2 4 0 0  4 ) Q(49 l(1) l(2) l(3) 1(4) J(5)
Interpolation parameters T(I)
Single Year Mortality Under Age 5
General Pattern  Both sexes combined Life exp at birth
Probability of dying between ages X and Y Q(XY )
Survivors at age X I(X)
Language:English
Score: 755856.25

https://www.un.org/en/developm...ls/model/lifetables/annex2.pdf
Data Source: un
DERIVATION OF A SMOOTH LIFE TABLE FROM A SET OF SURVIVORSHIP PROBABILITIES
A. BACKGROUND OF METHODS
I. Necessity of smoothing and completing sets of sumMwrship probabilities
There are several situations in which one can obtain estimates of lifetable probabilities of survivorship, I(%), but in which one would still want to smooth' these esti mates with reference to a model life table. (...) In yet other situations, one may wish to derive com plete sets of lifetable probabilities of survivorship, 1 (x ), from childhood mortality estimates and conditional sur vivorship probabilities for adults (such as the probability of surviving from age A to age B, I (B)/l (A )). (...) Both require as input a set of child survivorship probabilities, l(z), and a set of conditional survivorship probabilities of the form 1 (x )11 (y ).
Language:English
Score: 754660.1

https://www.un.org/en/developm...estimate/manual10/chapter6.pdf
Data Source: un
TUNISIE Aucun développement significatif n'est probable.
LIBYE Aucun développement significatif n'est probable.
(...) YÉMEN Des ailés en petits nombres peuvent probablement être présents dans le Tihama et une reproduction à petite échelle pourrait probablement voir lieu.
OMAN Aucun développement significatif n'est probable.
ÉMIRATS ARABES UNIS Aucun développement significatif n'est probable.
Language:English
Score: 754143.7

https://www.fao.org/ag/locusts/common/ecg/1394/fr/DL159f.pdf
Data Source: un