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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 time-variant. (...) For in- probability that got infection from a contact with stance, a university randomly tested 1,000 students .
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Score: 776933.74 - https://www.itu.int/en/publica.../files/basic-html/page113.html
Data Source: un
Total collision rates is obtained by summing up all the shell contributions • Debris-Debris, Debris-Intact, and Intact-Intact collisions • Probability of “k” collisions in a given time period (in 2,000 km orbital region) Probability of k collisions in LEO (Debris-Debris, Debris-Intact, and Intact-Intact collisions) All collision probabilities are calculated beginning mid-2013. 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.
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Score: 774290.4 - https://www.unidir.org/sites/d...rin-presentation-eng-0-803.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 Earth-space link.
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Score: 767746.53 - https://www.itu.int/dms_pub/it...0a/04/R0A0400007F0001MSWE.docx
Data Source: un
The implementation always uses a 0 to represent the most-probable 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.
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Score: 765356 - https://www.itu.int/wftp3/av-a...e/2002_05_Fairfax/JVT-C029.doc
Data Source: un
The implementation always uses a 0 to represent the most-probable 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.
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Score: 765356 - https://www.itu.int/wftp3/av-a...deo-site/0201_Gen/JVT-B033.doc
Data Source: un
The implementation always uses a 0 to represent the most-probable 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.
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Score: 765356 - https://www.itu.int/wftp3/av-a...te/2002_01_Geneva/JVT-B033.doc
Data Source: un
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Score: 759997.17 - 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(X-Y) 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(X-Y) Single Year Mortality Under Age 5 Chilean Pattern - Males life exp at birth Probability of dyins between ages X and Y Q(X-Y) 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(X-Y) 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(X-Y) 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(X-Y) !(XI E(O) Q(O-1) ~ ( 1 - 2 ) ~ ( 2 - 3 ) ~(3-11 ~ ( 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 QOC-Y) Io() E(O) Q(0-1) Q(1-2) Q(2-3) Q(3-4) Q(4-9 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(X-Y) - ~ ( 0 ) ~ ( 0 - 1 ) ~ ( 1 - 2 ) ~ ( 2 - 3 ) ~ ( 3 - 4 ) ~(4-5) 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(X-Y) I(X) E(O) a(o-1) Q(I-2) a(2-3) ~ ( 3 - 4 ) a(4-a 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(X-Y) 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(X-Y) E(O) a(o-1) a(r-2) Q(2-3) ~ ( 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(X-Y) I()o E(0) Q(0-1) Q(1-2) Q ( 2 4 0 0 - 4 ) Q(4-9 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(X-Y ) Survivors at age X I(X)
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Score: 756604.93 - 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 life-table 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 life-table 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 ).
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Score: 755014.16 - https://www.un.org/en/developm...estimate/manual10/chapter6.pdf
Data Source: un
Also, lower distances provide Outage Probability higher performance. 0.4 0.2 5.2 Outage Probability 0 The outage probability is a crucial metric for vehicular 0 100 200 300 400 500 Distance [m] communication [24, 25], and can be defined as the instan- taneous mutual information rate that falls below a certain Fig. 9 – Outage Probability vs. the distance, in case of (i) a meta-surface ∘ ∘ threshold , i.e.: with beam width = 20 and = 90 , and (ii) without a meta- surface. [ < ] = 1 − , (16) meta-atoms needed in the metastructure for a high tun- ing of the phase. (...) Based on the outage probability, given a specific target 6. DISCUSSION rate as a Quality of Service (QoS) parameter, we can de- In order to assess the performance of the system and to rive the throughput of success delivery with constrained evaluate the potential impact of the RIM in a vehicular outage probability , as : context, we have considered some assumptions to sim- = (1 − ) (1 + ). (18) plify the analysis and give some useful insights for fu- 2 ture work. Firstly, a perfect alignment between the relay In order to evaluate the impact of the presence of the coatedwith the RIM and the receiverhasbeen considered. meta-surface, in Fig. 9, we consider a target rate of 1 Gbps This assumption cannot be ensured above all in a con- and derive the corresponding outage probability as com- text characterised with high speed.
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Score: 754823.4 - https://www.itu.int/en/publica.../files/basic-html/page105.html
Data Source: un