Simulation results. 4.1. Vehicle parameters and the values used in the simulation that are not taken from the actual test vehicle (implicit):
Simulation results. Esitmated value g(1000,0.01,w ) Simulated value L 400 350 The number of bits leaked to Eve 300 250 200 150 100 50 0 50 100 150 200 250 300 350 400 450 500 Block length w in pass 1
Simulation results. This section presents some numerical examples illustrating the performances of our proposed schemes and finally compared together. For simplicity, the scenario is as- sumed with a single secondary BS serving two secondary users and a single primary cell-edge user within the cognitive cell. It is also assumed that there is one primary user per primary cell which is located in the outer part of the cognitive cell, but within the close vicinity. Note that each user is equipped with a single antenna. As shown in Fig. 3.1, secondary and primary cell-edge users within the cognitive cell are located in sector 3, i.e. q=3. The experiment is done with a single scatterer, i.e, Q = 1. The angular spread of local scatters surrounding the users is to be assumed 2 degrees. The spacing distance between the array elements is λ/2. The carrier frequency is 2 GHz. The noise variance plus the intercell interference is set to 1. In this simulation, SeDuMi solver under optimisation solver CVX [6], [89] is used to attain the optimal solution for the problems stated in (3.16) and (3.21). The azimuth directions (angle of propagation with respect to the antenna array broadside) of the users as well as the angular spread due to the local scatters cor- responding to the sector of the secondary BS can be estimated using the algorithm
Simulation results. Box and whisker plot (median, first and third quartiles, range) of estimates of the survival function minus the true quantity. Comparison of estimates when using inverse Gaussian (IG), Xxxxxxx (W), Xxxxxx Xxxxx (KM) and growth curve (GC) with a threshold models at the median failure time for each experimental factor detailed below plot.
Simulation results. Initialization: To initialize, Ƈ chooses a random bit from the given set {0, 1} and constructs a secure hash-digest function to respond to the queries from Ɲ, where h0=hqi be the pseudorandom function, while h1 is a random function. and ∏ Training: The Ɲ chooses the nonce Rg and NIDi in the protocol, F1: Anonymity, F2: Mutual Authentication, F3: Resist Man in the middle Attack, F4: Resist unjustified failures of login attempts, F5: Supports forward secrecy, F6: Resist impersonation attack, F7: Supports Session key security, F8: Resist Denial of service attack, F9: Biometric security (3-factor authentication) F1 × × ✓ ✓ × ✓ ✓ ✓ F2 × × ✓ × ✓ ✓ ✓ ✓ F3 × × × ✓ ✓ ✓ ✓ ✓ F4 ✓ ✓ ✓ ✓ ✓ × ✓ ✓ F5 × × × ✓ × ✓ ✓ ✓ F6 × × ✓ × × ✓ ✓ ✓ F7 × × ✓ ✓ × × ✓ ✓ F8 × × × × × ✓ × ✓ F9 × × × × × ✓ × ✓ Similarly, the event beginESP (bitstring) and event endESP(bitstring) are employed by ESP to authenticate Ui. We compute the results of queries and the order of the two Ui,S ,∏ and models ∏𝑠 𝑡 S,Ui by responding the queries, pair of events remained stable. The results in Fig. 3 depict that GN,S Execute(∏𝑠 𝑡 S,GN ) and Send(∏𝑠 ,𝑚), respectively. our scheme achieves mutual authentication and session key Ui,S Ui,S • Test (∏𝑠 ): Using this query, if qh is constructed, Ɲ selects at secrecy since the session key is robust against attackers. random v ∈{0, 1}, then it responds by returning legal session key qh in case v = 0, or any random string if v =1. Or else, Ɲ returns φ, indicating null string or emptiness. R,T • Test(∏𝑡 ): Its modeling is also similar to above query. Challenge: The Ȃ submits the Test query toward Ɲ after VI. PERFORMANCE ANALYSIS In this section, we examine the performance of proposed scheme with other contemporary authentication schemes for smart grids. Table I depicts the comparative analysis of GN,S having queried the oracle Execute (∏𝑠 𝑡 ,∏ ). S,GN features and performance efficiency between our scheme and other protocols, which manifests that the schemes [5-8, 10-12] Guess: Upon having queried 𝑇𝑒𝑠𝑡 (∏𝑠 ) or 𝑇𝑒𝑠𝑡 (∏𝑡 ), the Ȃ outputs a bit Ui,S S,Ui are unable to ensure the requisite security properties of an b as 0, if it takes the responded message as valid session key, or else it outputs b as 1. Finally, Ɲ produces the b' as 0 if b'=b, or else it will return the output as b'=1. The probability analysis for b'=b is alike the analysis performed in Lemma 1. The Ȃ could win this game if it guesses the equality for b'=b having the real experiment-ba...
Simulation results. The proposed activity-based approach provides a dual benefit: it fa- cilitates the derivation of aggregate metrics for demand and enables the simulation of demand on a network, specifically the rail network in EGTRAIN or other simulator. Here, we provide the comparison between the simulated output for a synthetic population used in estimation and the original TU data set, together with aggregated statistics of a simulation run. Figure 5 shows the distribution of simulated tour purposes. The re- sults are consistent with the TU overall. Note that the work purpose is slightly overestimated and that the remaining purposes are, on the other hand, slightly underestimated. 35,00% Percentage of Tours 30,00% 25,00% 31,94% 30,95% 25,56% 25,93% 20,00% 15,00% 16,38% 16,62% 17,48% 17,73% 10,00% 5,00% 4,00% 4,06% 4,65%4,72% 0,00% Work Education Personal Shopping Leisure Escort Purpose Simulation TU survey
Simulation results. 4.2.1 Overall evaluations across all settings r Figure 3 shows the biases and coverage probabilities of different IRA measures compared to the “true” chance-corrected IRA K among all the 9 × 5 × 5 × 5 × 5 × 5 × 5 × 4 = 562, 500 parameter constellations in our simulation study. In the boxplots, each IRA measure’s mean bias or coverage rate over 1, 000 iterations under a single simulation setting was regarded as a data point. From left to right, the 10 IRA measures were ranked based on the magni- tudes of median bias values, and we kept this order through all the result presentations for consistency. These figures give an overall impression of how different IRA measures perform when no information about the raters or the rating task is acquired. Over the vast range of settings, Xxxx’s AC1 (median bias= −0.009), Xxxx’s Y (−0.023), and Xxxxxxx et al.’s S (−0.038) show smaller median bias across scenarios compared to other IRA methods. The overall bias performances of Xxxxxxx and Xxxxxxxx’s r11, Xxx’s ρ˜, Xxxxx’x κ, Xxx Xxxx’x I2, and Xxxxx’x π are close to each other (medians vary from −0.074 to −0.084), all of which overall underestimate the “true” XXX X. Finally, the observed proportion of agreement pˆa, without any correction of chance agreement, will always overestimate K. Regarding the overall performance of each IRA measure’s interval estimate, the coverage probabilities for AC1, Y , and S will be larger than 75% under most settings. While the interval estimate for Y seems to be too conservative in some scenarios (median coverage probability=96.9%), AC1 (88.8%) and S (88.3%) show similar coverage performances across various simulation settings. Figure 4 gives an overall assessment about the closeness among the 10 interrater agree- ment methods as well as the benchmark measure K based on their estimates/values across all simulation settings. IRA methods with similar estimates across different scenarios are agglomerated into clusters. By gauging the change of within-cluster and between-cluster variabilities along the increase of cluster numbers, 3 or 4 clusters should be an optimal cutting number. The hierarchical clustering indicates that the percent agreement pˆa itself forms a cluster, which highlights its different nature compared to other chance-corrected XXX Xxxx to K 0.4 0.2 0.0 −0.2 1.00 0.75 Coverage Probability of 95% CI IRA measures Percent agreement Gwet's AC1 Xxxx's Y Xxxxxxx et al.'x X Xxxxxxx and Xxxxxxxx's r_{11} Mak's trho Xxxxx'x kappa Xx...
Simulation results. A hierarchical in-network caching system consisting of 4 levels is considered. Without loss of generality, the maximum possible storage capacities of hierar- chical caching levels 1, 2, 3 and 4 are set to 200, 400, 500 and 600 gigabytes, respectively. In order to analyze the effects of maximum possible storage ca- pacity on the performance of the proposed approach, the cache size is extended in increments of 20% until the maximum storage capacity of the first, second, third and forth level caches reach 600, 1200, 1500 and 1800 gigabytes (typical storage capacities available today). In defining the cost and return functions, it is assumed that caching in the lower levels of the in-network caching system is more costly and results in more transmission bandwidth saving benefit. The total number of popular videos is considered to be 4000 with 3 popular quality layers. As in [116, 117], it is assumed that the video popularity is Xxxx-like with a parameter of 0.6 and the video file sizes follow a Pareto (0.25) distribution with a minimum size of 60 megabytes. The KKT conditions are solved for each dual variable associated with the dual problem deploying IWO and the allocation vector xk is computed using (5.13). A pseudo code for the cache provisioning algorithm is given as Algorithm
Simulation results. All the measurements and simulations were performed on a 2 GHz Pentium dual core processor with 2 Gb RAM. We consider two cases of simulations, depending on the timeout value T for the calls to the garages (see site Timer(T ) in Table I ) : 1) No timeout (equally, T is infinite) 2) T is a finite value, which is lesser than the maximum response time of a garage. Case 1: No timeouts Based on the way delays of site calls are generated, we performed two types of simulations: those in which delays generation is done by 1) bootstrapping measured values, 2) sampling a T location-scale distribution, previously fit to measured data.
Simulation results. As discussed above, it is challenging to design child support policies that ensure that the basic needs of all family members—father, mother, and children—are met, especially in the context of complex families and given that many parents (both custodial and noncustodial) have limited economic resources. With this challenge in mind, we evaluate the consequences of alternative approaches to an SSR that reserves an initial set of resources for the noncustodial parents, before a child support order is determined. We first focus on the consequences of different SSR scenarios for child support order amounts and father economic well-being (income 4We do not generally have information for the children of noncustodial parents or custodial parents in new still-married or still-partnered families. poverty based on amounts owed under different scenarios). We then compare economic well- being for each parent, given variation in the amounts of child support ordered under different SSR scenarios. By illustrating the consequences of each scenario for both fathers and mothers, we provide a more complete view than is available when considering only one perspective. As noted above, we also look separately at fathers with limited economic resources (incomes less than 200 percent of the federal poverty line), and those with only nonmarital children.
