esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Functionality of Emergency Response
### Post-Earthquake Network Functionality  
In seismic analysis of transportation network, it is a common model to map the degraded serviceability of a link according to its damage condition. In this paper, based on the 'bottleneck' assumption, the damage state of a network link is considered to be equal to that of the most damaged bridge on the link, and the adopted mapping relationship is given in Table 1. However, in the topology space, it is difficult to consider the impact of multiple damage states on network performance. In this study, a penalty-based approach is used to address the issue. The idea is that it takes longer than normal to get through a degraded link due to bad road conditions and traffic congestions caused by reduced serviceability, which is equivalent to a longer journey for travelers. The length measurement can be incorporated into network connectivity analysis. Specifically, the serviceability of a network link is considered to be equal to that of the most damaged bridge on the link according to the 'bottleneck' assumption, and the degraded serviceability is transformed into a penalty for the length of the link in network topology. In this paper, the penalty coefficient of a linkis assumed to be inversely proportional to the remaining rates of its traffic capacity and travel speed, and the post-earthquake penalized length of a link, \(l_{p}\), can be computed as:  
\[l_{p}=\frac{l_{0}}{r_{DS_{j}}^{c}-r_{DS_{j}}^{c}} \tag{14}\]  
where \(l_{0}\) is the physical length of the link; \(r_{DS_{j}}^{c}\) and \(r_{DS_{j}}^{c}\) are respectively the residual rate of traffic capacity and travel speed, which is referred to Table 1 according to the damage level of links. Then the post-earthquake network performance is calculated based on the converted network topology composed of penalized links. The normalized network functionality measurement is written as:  
\[Q=\frac{G_{post}}{G_{pre}} \tag{15}\]  
where \(G_{post}\) and \(G_{pre}\) are the post-earthquake and initial network performance value, respectively. A more detailed formulation and algorithm of the network emergency response functionality can be found in literature [75].  
\[G=\frac{1}{n_{O}}\sum_{O=1}^{n_{O}}IPE_{O,D} \tag{13}\]  
where \(n_{O}\) is the number of normal nodes in the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Damage Level**} & \multicolumn{2}{c}{**Residual Rate**} \\ \cline{2-3}  & **Traffic Capacity** & **Travel Speed** \\ \hline None & 100\% & 100\% \\ Slight & 70\% & 75\% \\ Moderate & 30\% & 50\% \\ Over extensive & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between damage level and residual serviceability of link.  
Figure 1: Flowchart of emergency path search algorithm.
###### Abstract  
With the rapid development of distribution networks and increasing demand for electricity, the pressure of power supply for medium- and low-voltage distribution networks (M&LVDNs) is increasingly significant, especially considering the large scale of customers at the low-voltage (LV) level. In this paper, an outage sequence optimization method for low-voltage distribution networks (LVDNs) that considers the importance of users is proposed. The method aims to develop an optimal outage sequence strategy for LV customers in case of medium-voltage (MV) failure events. First, a multi-dimensional importance indicator system for LV users is constructed, and the customers are ranked using a modified Analytic Hierarchy Process-Entropy Weight (AHP-EW) method to determine their priorities during outages. Then, an elastic net regression-based method is used to identify the topology of the LV network. Finally, an outage sequence optimization model based on the user importance is proposed to reduce the load-shedding level. Extensive case studies are conducted in the modified LV distribution network. The results show that the proposed method results in fewer outage losses throughout the restoration periods than traditional methods and effectively improves the reliability of the power supply to LV users.  
distribution network; reliability; outage sequence optimization; user importance; load shedding +
Footnote †: journal: Applied Sciences
# Visualization of Caregiving Posture and Risk Evaluation of Discomfort and Injury
## 2 Materials and Methods
### Caregiving-REBA (C-REBA)  
The REBA method showed limitations in assessing the risk of caregiving postures, as it failed to differentiate experienced and inexperienced caregivers, and it could not provide meaningful postural guidance for inexperienced caregivers. We firstly found that the original rules for the Extra A and Extra C scores are limited in their applicability to caregiving work. The initial rule for assessing Extra A primarily focuses on load considerations. In the realm of caregiving tasks, where the load consistently exceeds 10 kg, this corresponded to an additional 2 points within the original Extra A scoring rule. Consequently, this inconsistency invalidated the applicability of the scoring rule to caregiving tasks. Asymmetric load was a notable contributor to muscle injuries [29], and this evaluative parameter is frequently disregarded during caregiving procedures. Thus, we adjusted Extra A by emphasizing the aspect of asymmetric loads.  
The initial iteration of Extra C predominantly emphasized the rapid and large range changes in postures, the duration of posture maintenance, and the action frequency as evaluative metrics. These aspects are more tailored towards evaluating postures among factory laborers. Conversely, for caregiving work, factors such as the load-bearing time, the COG trajectory of the power position, and the pose stability related to COG changes seem to be more pertinent as assessment criteria. Consequently, we adjusted the Extra C rules accordingly. The specific parameter adjustments for adjusting Extra A and Extra C were determined based on statistical variations observed between the inexperienced and experienced groups. Elaborate details regarding these improvement methodologies are expounded upon in Section 3.3. Our C-REBA method optimized the Extra A and Extra C parameters of the original REBA, amalgamating critical factors that are pertinent to caregiving tasks, notably including asymmetric loads, load-bearing time, and COG changes. These adjustments rendered C-REBA more precise and applicable in the assessment of caregiving.
## 4 Conclusions  
As a vital auxiliary technology for trenchless construction in urban underground spaces, it is imperative to effectively mitigate the surface settlement induced by the pipe-roof method. However, many factors affect the ground response during pipe-roof construction, including the lubricant Young's modulus, tail void, burial depth, and pipe-roof arrangement. Using the calibrated FLAC3D model, the effects of the lubricant Young's modulus and its interactions with tail void, burial depth, and pipe-roof arrangement on the surface displacement were investigated. The principal conclusions are as follows:  
1. In general, increased surface settlement is observed with greater burial depths and larger tail voids. Moreover, a V-shaped transverse settlement trough, characterized by higher surface displacement, is caused by the gate-shaped system, while a U-shaped transverse settlement trough, associated with lower surface displacement, is induced by the horseshoe-shaped system;
2. Typically, increasing the tail void and burial depth diminishes the lubricant's ability to control surface settlement, whereas the horseshoe-shaped system is more conducive to effective control (compared with the gate-shaped system). Under these three cases, augmenting the lubricant Young's modulus can reduce surface settlement to a greater extent;
3. Lubricants manifest their control effect by influencing the interaction among adjacent pipelines. Broadly speaking, increasing the lubricant Young's modulus more efficiently suppresses the repeated disturbance effect or enhances the shielding effect between each pair of adjacent pipelines, thereby providing better control over the surface settlement induced by pipe-roof construction. This effect is especially pronounced in soft soil layers.  
Methodology, software, validation, and writing--original draft preparation, S.Z.; Conceptualization and supervision, T.S.; data curation, A.H.; project administration, X.H.; writing--review and editing, H.S.; software and validation, P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: Vertical displacement of the ground surface: (**a**) changes in ground settlement of 40 mm tail void considering different lubricants (Line 3): (**b**) comparison of surface settlement between the two tail voids when \(h=4\) m, \(E=400\) kPa in gate-shaped system.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author ([EMAIL_ADDRESS]) upon reasonable request.  
**Acknowledgments:** The authors would like to thank the editor for handling this submission and the anonymous referees for reading the manuscript. Additionally, this work was also generously supported by the China Scholarship Council, for which we are deeply grateful.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Mmpal _sciences_
## 4 Experiments and Results
### Experiments on CelebA  
CelebA includes faces without occlusion in front and occluded faces. It has a total of 202,599 face images of 10,177 individuals who are well marked with 40 attribute images, such as wearing sunglasses and smiling expressions. Due to the diversity of occlusion, positive samples are defined as faces without occlusion in front, including faces wearing ordinary glasses, and are manually labeled as 1 in the sample annotation; negative samples are other face images, including wearing glasses, side faces, overlooking, and other face images, and are manually labeled as -1 in the sample annotation, as shown in Figure 5. A total of 100 people were selected in the CelebA dataset as the test object, 50 positive samples and 50 negative samples for each person were selected as training samples, and 1000 (500 positive samples, 500 negative samples) were selected as testing samples. Color images of \(218\times 178\) in the CelebA dataset are preprocessed to 200 \(\times\) 150 gray images.  
Table 3 shows the comparison of experimental results on 1000 testing samples. As it is shown, our algorithm obtains a higher average recognition rate than the original algorithm, with an average increase of 3.87% in positive samples and 3.56% in negative samples. It indicates that MMPCANet is more suitable for processing complex images,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Subject** & **Images** & **Description** \\ \hline CelebA [35] & 10,177 & 202,599 & \begin{tabular}{c} Includes faces without occlusion in front and occluded faces, such as wearing sunglasses and smiling expressions. \\ \end{tabular} \\ \hline AR [36] & 126 & 3276 & \
\begin{tabular}{c} Includes different expressions, illumination, and occlusion changes. \\ \end{tabular} \\ \hline FERET [29] & 1199 & 14,126 &
\begin{tabular}{c} The images of the same person have different expressions, illumination, posture, and age changes. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed comparison of datasets.  
Figure 5: Some examples of CelebA: the first line represents positive samples, and the second represents negative samples.  
which proves the advantages of multi-layer fusion features compared with traditional single-layer features. Compared with other similar algorithms, our algorithm also has significant advantages, which are 12.09%, 2.91%, 2.16%, and 0.99% higher than those of ESRC, NNAODL, 2 DPCANet, and L1-2D\({}^{2}\)PCANet, respectively.  
In order to verify the performance of the proposed algorithm in all aspects, we conducted time-cost experiments. Table 4 shows the average training time and test time of different methods on the CelebA dataset. Compared with PCANet, 2 DPCANet extracts image features based on a two-dimensional image matrix, which saves computational time. Therefore, the training time of 2 DPCANet is shorter than that of PCANet. MMPCANet takes a long training time, but in turn extracts more useful image features. The short test time of MMPCANet benefits from the random forest classifier.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
### Gauss-Poisson Process  
The GPP can be defined as a homogeneous Poisson cluster process in which clusters exhibit independence. Based on the parent-process intensity, denoted as \(\lambda\), the clusters in GPP can be characterized as single- or two-point clusters with probabilities \(1-a\) and \(a\), respectively. In case of a single-point GPP, the cluster is located at the position of the parent. However, when GPP is composed of two points, one is designated as a parent point and the second point is randomly distributed around the parent point [29].  
The proposed system model is described in Section 2. The closed-form expression of the outage probability at the final receiver under a multi-cell interference is presented in Section 3. The performance of the proposed system and the impact of different parameters on outage probability is presented in Section 4. Finally, the paper is concluded in Section 5.
## 4 Discussion
### Moment-Rotation Envelope Curves
#### 4.2.2 Effect of the End-Plate Thickness on Joint Performance  
Figure 14a illustrates the initial stiffness of specimens SJ-2 and SJ-4 respectively increased by 37.7% and 20.7%, compared to those of specimens SJ-1 and SJ-3, indicating that increasing the end-plate thickness could increase the initial stiffness of the joint; this effect was more pronounced for joints with thin steel tubes. The flexural resistances of specimens SJ-2 and SJ-4 were 10.5% and 26% higher than those of specimens SJ-1 and SJ-3. When the connections exhibited failure mode I, an increase in the end-plate thickness had a limited influence on the flexural resistance, which was controlled by the CFST columns. However, when the connections exhibited failure mode II, a thicker end-plate resulted in a greater increase in the flexural resistance. This phenomenon occurred because the flexural resistance was controlled by the bolts, and the end-plate thickness directly affected the deformation of the bolts.
## 2 Measure and Use Differentiation in Multi-Armed Bandit
### Algorithms
#### 2.2.1 muUCB1  
muUCB1 derives from the well known and widely used UCB1 algorithm [16]; the choice of proposing an algorithm derived by UCB1 is justified by the fact that UCB1 is often used as a benchmark in the evaluation of MAB selection algorithms because it reaches the best achievable performance, i.e., a regret growing logarithmically over time, but at the same time presents a low complexity.  
In the following, the UCB1 algorithm is briefly described, focusing on aspects that are relevant to the proposed muUCB1 algorithm; a more detailed description of UCB1 can be found in [16].  
UCB1 operates by associating an index to each arm, and selecting the arm characterized by the highest index. The value of the index is determined by the sum of two elements:  
1. the reward mean value estimate of the arm;
2. a bias, that eventually allows the index of an arm with a low reward mean value to increase enough for the arm to be selected.  
The index of arm \(k\) is therefore:  
\[I_{k}=\hat{\mu}_{k}+\sqrt{\frac{2\ln\left(N\right)}{N_{k}}}, \tag{8}\]  
where \(N_{k}\) is the number of times arm \(k\) has been selected so far and \(N\) is the overall number of selections done so far (equivalent to the number of steps \(n\), if considering the classical MAB model).  
The arm with the highest index at time step \(n\) is selected and the index of every arm is updated. Normally, the arm with the highest reward mean estimate also has the highest index, and is therefore selected. This corresponds to an _exploitation_ in MAB terms, that is to say that the available information is exploited for the arm choice. Eventually, however, the bias significantly affects the index value, leading to the selection of an arm that does not present the highest reward mean estimate. This corresponds to an _exploration_ in MAB terms; in other terms, previously acquired information is not exploited for the choice, but another arm is \"explored\", and its estimate is updated.  
muUCB1 inherits from UCB1 the rule for the arm selection: when the arm \(k_{IND}\) with the highest index corresponds to the one with the highest reward estimate, \(k_{EST}\), the selected action is _use_. When the arm with the highest index is not the one with the highest reward estimate, the selected action is _measure_. This choice creates an ideal correspondence between _measure_ and exploration on one hand, and _use_ and exploitation on the other.  
The pseudo code of the algorithm is reported in Algorithm 1.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 4 Experiments and Models
### [PERSON]'s Test Results  
This test determines whether a learning model is better than another at a specific task. The McNemar test performs analysis by taking the number of couples that are positive but then negative and the number of couples that are negative at first but later become positive. The test utilizes a z-score for measuring the differences, as shown in Equation (4).  
\[Z=\frac{(|A_{1,0}-A_{0,1}|-1)}{\sqrt{A_{1,0}+A_{0,1}}} \tag{4}\]  
Suppose that there are two algorithms X and Y. \(A_{1,0}\) represents X algorithm succeeding and Y algorithm failing and \(A_{0,1}\) shows X algorithm failing and Y algorithm succeeding. After the calculation, when the z-score = 0 we can say that the two algorithms have shown similar performance. If the value of the z-score increases past 0 in the positive direction, this points out that their performance differs significantly; confidence levels corresponding to the z-score are presented in Table 2[42]. One-tailed prediction shows the effect of a change in one direction and not the other. A two-tailed prediction does not have a direction and does not have to be specified prior to testing. A two-tailed prediction has the possibility of both a positive and a negative effect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Z-Score** & **One-Tailed Prediction** & **Two-Tailed Prediction** \\ \hline
1.645 & 95\% & 90\% \\ \hline
1.960 & 97.5\% & 95\% \\ \hline
2.326 & 99\% & 98\% \\ \hline
2.576 & 99.5\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confidence levels corresponding to z scores for one-tailed and two-tailed predictions.  
For our study, first we calculated the PA value of each image used for validation for each segmentation model. After that, we compared the PA values for each image with each other (using HRNet-PSPNet, HRNet-UNet and PSPNet-UNet). The algorithm which had a higher PA value was set to 1, and the smaller one was set to 0. Then, the z-scores were calculated and models were compared with each other.
## 4 Experiments
### Summary  
In the evaluations, we compared our proposed approaches, FANA-IBCF and FANA-UBCF, which use two groups of users' ratings (friends and natives) to generate recommen  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment \#** & **IBCF** & **UBCF** & **PersMean** & **FANA-IBCF** & **FANA-UBCF** \\ \hline
1 & 99.404 & 99.373 & 99.401 & 99.407 & 99.374 \\
2 & 99.410 & 99.343 & 99.408 & 99.413 & 99.343 \\
3 & 99.589 & 99.537 & 99.492 & 99.589 & 99.537 \\
4 & 99.431 & 99.404 & 99.041 & 99.431 & 99.408 \\
5 & 99.490 & 99.348 & 99.417 & 99.490 & 99.348 \\ Average & 99.465 \(\pm\) 0.069 & 99.401 \(\pm\) 0.071 & 99.352 \(\pm\) 0.159 & **99.466 \(\pm\) 0.068** & 99.402 \(\pm\) 0.071 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of normalized discounted cumulative gain (nDCG) performance among IBCF, UBCF, PersMean, and our proposed approach, FANA-CF, using 5-fold cross validation. We have calculated the average of nDCG values and the standard deviation for each approach.  
Figure 6: Box and whisker plot of nDCG with Friedman test results. Note that a \(p\) value \(<\) 0.05 means the experimental result is statistically significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **UBCF** & **PersMean** & **FANA-IBCF** & **FANA-UBCF** \\ \hline IBCF & **0.062** & **0.062** & 0.346 & **0.062** \\ UBCF & - & 1.000 & **0.062** & 0.371 \\ PersMean & - & - & **0.062** & 1.000 \\ FANA-IBCF & - & - & - & **0.062** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Pairwise Wilcoxon signed-rank test of nDCG performance among IBCF, UBCF, PersMean, and our proposed approach, FANA-CF. The results in bold are statistically significant with a 90% confidence level.  
dations, with existing IBCF, UBCF, and PersMean approaches, which use every user's rating to generate recommendations. As shown in Figure 7, our proposed approach managed to outperform or be on par with IBCF, UBCF, and PersMean approaches in MAE, RMSE, and nDCG performance evaluations. It can be seen that our approaches achieved relatively lower MAE/RMSE and higher nDCG than other approaches. This shows that our proposed approach produces a lower error rate and generates higher quality recommendations.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
###### Abstract
At the moment of executing a join, leave or refresh operation, the KDS selects a KDC instance according to a certain policy and triggers the execution of the operation to that KDC instance, which coordinates the actual execution of the operation itself. We experimentally show that our solution scales up to clusters of tens of inverters and displays a limited memory footprint so that it can be easily accommodated in memory-constrained devices. As a case study, we have built a prototype for a cluster of Power-One Italy S.p.A., a member of the FIMER Group, leader in the market of smart string inverters, under to following constraints: (i) the cluster must accommodate up to 50 inverters (notice that currently real cluster setups come with 40 inverters at most); (ii) the maximum rekeying time must be smaller than 60 s (s) and, finally, (iii) the available memory is 512 MBytes (MB). The proposed KMS turns out able to rekey 50 inverters in about 5.7 s, i.e., 9.5% of the maximum rekeying time, using an RSA-3072 cryptosystem. We also show that the public key cryptosystem has a relevant impact on the rekeying time and thus the overall scalability. In particular we show that, with an equal security level, 256-bit Elliptic Curves make it possible to halve the rekeying time. Furthermore, we show that the KMS displays a virtual memory footprint of about 5 MB, i.e., 1% of the available memory. The rest of this paper is structured as follows. In Section 2 we discuss related work. In Section 3 we define the system model. In Section 4 we describe the group key management scheme. In Section 5 we evaluate the performance of the scheme. In Section 6 we comprehensively discuss the scheme and provide a security analysis. Finally, in Section 7, we draw our concluding remarks.