esa-sceva/satcom-chunk-collection huggingface dataset:
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
## 2 Literature Review
[12] established the tunnel structure resilience analysis model under multiple disturbances, proposed the tunnel structure resilience evaluation index under different typical working conditions, combined with the structural safety control indexvalue given in relevant specifications, and constructed the tunnel structure resilience classification standard. [PERSON] et al. [13] developed a computational fluid dynamics model to analyze user safety and resilience simultaneously. The relationship between the resilience index and the expected value of risk under different scenarios was studied to serve as a reference in the choice of the most appropriate strategy for the recovery of the system function. [PERSON] et al. [14] assessed the resilience of a twin-tube motorway tunnel in the event of a traffic accident or a fire occurring within a tube using simulation. They conducted a simulation analysis of various traffic disruption scenarios and proposed several measures to improve the recovery efficiency of tunnel functions. To sum up, numerous existing research studies on tunnel resilience focus on operating road tunnels. The main application scenarios mainly include fire and traffic accidents. There are also some studies about tunnel design, construction, and structure in China. The concept of resilience describes the ability of the system to respond to external disturbances. The external disturbances faced by the tunnel include, but are not limited to, fire and traffic accidents. The existing research cannot reflect the randomness of the influence of various external disturbances. So, it is necessary to build a theoretical evaluation framework including various external disturbances that road tunnels are subjected to.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion
### Field Tests
#### 3.3.3 Wave Data
The linear regression equations for \(H_{S}\), \(T_{Z}\), and \(DWD\) are given as follows:  
\[\mathrm{H}_{S(GNSS)}=0.975H_{S(ATC)}+3.89(\mathrm{cm}), \tag{2}\]  
\[\mathrm{T}_{Z(GNSS)}=0.989T_{Z(ATC)}(\mathrm{seconds}), \tag{3}\]  
and:  
\[DWD_{(GNSS)}=0.99 DWD_{(ATC)}(\mathrm{degrees}). \tag{4}\]  
According to these equations, the \(H_{S}\), \(T_{Z}\), and \(DWD\) measured by the GNSS receiver are almost the same as those measured by the ATC sensor. The scatter plot and regression line in Figure 10 show that the significant wave heights obtained from the GNSS buoy were consistent with those obtained from the ATC sensor. The outlier in Figure 11 corresponds to a zero-crossing wave period of 7.5 s from the GNSS buoy and 8.0 s from the ATC sensor. Obviously, the difference is small. Compared with the scatter plots in Figures 10 and 11, the peak wave directions in Figure 12 are scattered around the regression line. This scattering was caused by the rough resolution of \(11.25^{\circ}\) (\(360^{\circ}/32=11.25^{\circ}\)) in the \(\theta\) direction. Based on this resolution, a small deviation in both sensors may have caused a direction difference of \(22.5^{\circ}\) in extreme cases. The outlier in Figure 12 has a maximum direction difference of \(33.75^{\circ}\). This outlier occurred at 23:00 on 21 August 2013 when Typhoon Trami  
Figure 11: Scatter plot and regression line for the zero-crossing periods. Figure 10: Scatter plot and regression line for the significant wave heights. passed the waters off northern Taiwan. The significant wave heights at that time were approximately the same; at 155 cm from the ATC sensor and 157 cm from the GNSS buoy. A detailed analysis shows that during the period, from 18:00 on 21 August, to 02:00 on 22 August, the peak wave direction experienced large changes. However, the directional wave spectra obtained from the ATC sensor and GNSS buoy at 23:00 on 21 August 2013 were similar, and as shown in Figure 13a,b, the corresponding peak wave directions were 22.50\({}^{\circ}\) and 56.25\({}^{\circ}\), respectively. Under such circumstances, the peak wave direction was presumably sensitive to the sensors. However, further study is required to clarify this ambiguity. In the future, wave data obtained under severe sea conditions (e.g., typhoons) will also be collected using both the GNSS receiver and ATC sensor. The differences in the significant wave heights, wave periods, and peak wave directions will be analyzed and compared.
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
## 4 Results and Discussion
### Availability of an Additional Point of Measurement at Lower Elevation  
Another possible strategy to improve the accuracy of the results is that of adding a further point of measurement at an elevation lower than the lowest point provided by the Lidar. This is accomplished starting from measurement Setup #0 in Table 2 by adding a point of measure at an elevation \(\zeta\) equal to 0.1, 0.2, 0.4 or 0.7 times the gap between the zero speed elevation (\(z_{0}+z_{d}\)) and the elevation of the lowest measurement point of Lidar (30 m).  
The results are presented in terms of bias and CoV within an ensemble of \(M\) = 1000 cases of the mean values \(\mu_{z0}(N)\) and \(\mu_{zd}(N)\) for all the cases reported in Table 1 (Figures 7-10).  
The results highlight that the addition of a lower point of measure allows to eliminate any possible bias existing on the estimate of \(z_{d}\); however, it reduces but not always eliminates the bias on \(z_{0}\). In particular, it is noticed that the lower measurement point is unable to eliminate the bias on \(z_{0}\) for cases (a) and (c) in Table 1, corresponding to a roughness length of 0.05 m. In any case, the bias seems not to be affected by the number of available profiles. On the other hand, in all cases and for both \(z_{0}\) and \(z_{d}\), the uncertainty on the estimated parameter reduces when a lower point of measure is added; the CoV of \(z_{d}\) can be reduced to values lower than 2% when \(N\) = 100 profiles are available, and the CoV of \(z_{0}\) can be reduced to values lower than 7%.  
Figure 7: Bias and Coefficient of Variation of \(\mu_{z0}\) and \(\mu_{zd}\) as a function of the sample size \(N\) of available measured profiles: Case (a) (\(z_{0}\) = 0.05 m, \(z_{d}\) = 5 m).  
Figure 8: Bias and Coefficient of Variation of \(\mu_{z0}\) and \(\mu_{ad}\) as a function of the sample size \(N\) of available measured profiles: case (b) (\(z_{0}\) = 1.5 m, \(z_{d}\) = 5 m).  
Figure 9: Bias and Coefficient of Variation of \(\mu_{z0}\) and \(\mu_{ad}\) as a function of the sample size \(N\) of available measured profiles Case (c) (\(z_{0}\) = 0.05 m, \(z_{d}\) = 20 m).
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 3 Method
### Pruning Module  
In order to generate meshes with the correct topology, we prune the mesh under 2D hole constraints between the two deformation blocks. As shown in Figure 4a, the proposed  
Figure 3: The overall framework of the proposed method.  
estimator under the contour constraint takes \(G\) and the \(\alpha\) channel of image \(I\) as input and returns the index of the pruning surface. Specifically, the estimator performs a boundary tracking algorithm [38] on the \(\alpha\) channel of the input image \(I\) to detect the location of holes. Further, we use 0 to represent the non-hole area on the 2D plane and assign different values to the pixels at different hole positions to make the hole feature map. The estimator collects 2D hole features \(f_{hole}\) corresponding to \(V\) through a camera matrix and bilinear interpolation. Since the 2D hole constraint only works in the visible case, an error estimate module of TMNET [18] is introduced as a complement in the case where the holes are not visible. In order to save training time, we use the whole feature map of the corresponding view prepared in advance in actual training.  
The entire pruning procedure is depicted in Figure 4b. We noticed that the coarse meshes generated by the deformed blocks tended to be sparse in the hole region. Therefore, two loop subdivisions [39] are performed on the pruned surface to distinguish between pruned and unpruned regions. In order to locate the subdivision area, the first estimator uses the sampling point \(V_{s}\) as the input point and determines the subdivision face according to condition a (see Figure 4). To prevent erroneous pruning, the second estimator input point is the mesh vertex \(V\), and the face to be pruned is determined according to condition b (see Figure 4). Our method improves robustness by introducing the hole contour constraint, which effectively handles holes with uncommon locations and small areas and requires only one pruning operation to achieve the desired effect.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 3 Ground Testing  
The purpose of the ground tests was to quantify the changes in shape, the actuation power, and energy requirements along with the combined wing mass and actuation system mass.  
Using the quantified camber and twist differences between different actuation configurations, estimates of variations in lift distribution, rolling moment, and RBM could be calculated, resorting to the lifting line theory (LLT) coupled with viscous airfoil data estimates used in [26].
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 7 Conclusions  
This paper discusses the current state of path planning for multiple UAVs and reports research addressing the issues described in the literature. In this paper, we compared the traditional MTSP as the baseline algorithm with our proposed two multi-UAV collaborative search algorithms using five search paths each. In order to overcome the energy limitation of a single UAV, we developed the multi-UAV path collaborative search algorithms MUCS-GD and MUCS-BSAE based on the five search paths, which divide the path planning for single aircraft coverage into multiple segments, each covered by one UAV, to improve the coverage speed and efficiency. After comparative evaluation, the performance of the MUCS-BSAE algorithm was found to be more than 14% higher than that of the MUCS-GD algorithm at any speed, and even up to 25.8% at 10 m/s speed. The multi-UAV path cooperative search algorithm was able to find the path assignment with the minimum number of UAVs to complete the entire work interval and the minimum total time consumption to reduce the system cost. The simulation results obtained show that the \"square wave signal\" search path performs best for UAV search work with the least total work time for a single UAV energy constraint. In addition, we also improved the MUCS-BSAE algorithm with the \"square wave signal\" search path as the working path to the IMUCS-BSAE algorithm to reduce the invalid path of the UAV, and further improved the performance of the algorithm by 2%, so as to find the path assignment result with the least total working time. The IMUCS-BSAE algorithm performance was compared with existing related research findings. Our algorithm supports multi-UAV cooperative search under the constraint of UAV energy consumption to ensure higher efficiency and stronger scalability of the algorithm.  
Conceptualization, Y.Y.; Methodology, Y.Y.; Software, Y.Y.; Validation, Y.Y.; Investigation, S.L.; Resources, S.L.; Writing--original draft, Y.Y.; Writing--review & editing, Y.Y. and S.L.; Visualization, Y.Y.; Supervision, S.L.; Project administration, S.L.; Funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Global Scholarship Program for Foreign Graduate Students at Kookmin University in Korea and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1F1A1074672).  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ MTSP & Multiple Traveling Salesmen Problem \\ TSP & Traveling Salesman Problem \\ CPP & Coverage Path Planning \\ MUCS-BS & Multi-UAV Cooperative Search Algorithm \\  & Based on Binary Search Algorithm \\ \end{tabular}
## 4 The Early Warning Model
### Introduction to the LSSVM  
The LSSVM is a derivative method of the SVM and is characterized by the successful introduction of least-square estimation into the SVM. In this study, the regression form of the LSSVM is adopted. After a series of strict derivation and simplification, the problem is finally transformed into the solution of \(a\) and \(b\) in linear equations by the least-square method, and the regression function of the LSSVM is as follows [12]:  
\[f(x)=\sum_{i=1}^{N}aK(x,x_{i})+b, \tag{16}\]  
where \(a\) is the weight variable of the regression function, \(K(x,x_{i})\) is the kernel function, and \(b\) is the deviation coefficient.  
The key to solving the LSSVM is to solve the prediction function. By combining the prediction function in Equation (16) with the structural risk function [14], the solution of the LSSVM can be equivalent to the following nonlinear optimization problem [31]:  
\[\begin{cases}\min J(w,\delta)=\frac{1}{2}\parallel w\parallel^{2}+\frac{C}{2} \sum_{i=1}^{l}\delta_{i}^{\,2}\\ s.t.\;y_{i}=w^{T}\varphi(x)+b+\delta_{i}^{\,2},\;i=1,2,\cdots l,\end{cases} \tag{17}\]where \(\delta_{i}\) represents a relaxation variable, and C represents a regularization parameter, C \(>0\). The regularization parameter is an important parameter in the LSSVM, and a reasonable value guarantees the efficient and accurate prediction of the LSSVM. If the value is too small, the prediction model will punish the prediction deviation too little, thus increasing the possibility of the under-fitting of the prediction model and reducing the prediction performance. If the value is too large, the prediction model will punish the prediction deviation too much, thus increasing the possibility of the over-fitting of the prediction model and reducing the prediction performance.  
Compared with the polynomial kernel function, the radial basis function (RBF) has the advantages of a lesser number of iterations, higher running efficiency, and only one kernel parameter. Therefore, the RBF was chosen for use in this study:  
\[K(x,x_{i})=exp(-\frac{\parallel x-x_{i}\parallel}{2\sigma^{2}}), \tag{18}\]  
where \(\sigma\) represents the width parameter of the RBF, which is the key calculation parameter. If the value of \(\sigma\) is small, the range of learning variables is small. Although the LSSVM model has a high calculation accuracy, the calculation and prediction results cannot be effectively promoted. If the value of \(\sigma\) is too large, the range of learning variables will be larger. Although the generalization of the prediction results is improved, the calculation accuracy of the LSSVM model will be poor.  
Therefore, when using the LSSVM for data regression analysis, it is necessary to optimize the regularization parameter and the width parameter of the RBF.
## 4 Experiment and Results
### Data Augmentation and Data Equalization
#### 4.2.2 Data Equalization  
Generally, the number of samples in the data set is assumed balance. The balanced samples contribute to training the model with better classification performance. Unbalanced samples can make the network model more inclined to a larger number of categories, which will lead to poor generalization of the model. Data can usually be processed to eliminate the impact of data imbalance. For example, the distribution of equilibrium data categories or the sensitivity of the modified algorithm to different samples can be used to obtain a better classification model.  
Based on the statistics of the pixel number for each object in the data set, the proportional distribution of different objects is shown in Figure 8. It can be seen that in all samples that the proportion of water bodies and roads is significantly lower than that of vegetation and buildings, which will make the network more inclined to vegetation and buildings. Thus, the data set is processed by the sample equalization algorithm, which is used to reduce the imbalance of the samples of the training set. For the images of \(256\times 256\) pixels obtained by data preprocessing, if the proportion of water or road is greater than the average ratio in the entire data, the image is up-sampled. Data enhancement methods, e.g., contrast transformation, adding noise, rotation and flipping, are randomly used to generate new training images, thereby increasing the proportion of fewer samples.  
The flowchart of data preprocessing adopted in this paper is shown in Figure 9. First, the large-size remote sensing image is cut into small-sized training images by sliding window cutting and random cutting. Then data augmentation is performed on the data set. Data augmentation can increase the number of samples and increase the diversity of samples. Finally, sample equalization processing is performed for a small number of categories. Increasing the number of samples of objects, such as  
Figure 8: Different categories of sample statistics in the data set.  
Figure 7: Data augmentation examples.  
water bodies and roads, a multi-object classification training set of 40,000 images of \(256\times 256\) pixels can be obtained.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 2 Preliminaries on Routing Protocols for Ad-hoc Networks
### Dynamic Source Routing (DSR) Protocol  
The Dynamic Source Routing (DSR) protocol is a reactive routing protocol, similar to AODV, specifically designed for use in multi-hop wireless ad hoc networks composed of mobile nodes. Since DSR adheres to the _source routing_ philosophy, it does not use any periodic routing advertisement. This feature leads to a reduction of the control messages in the network--ideally to zero: when all nodes are stationary with respect to each other and all routes for current communication have already been discovered, there is no need for any route discovery process. A DSR-based network is completely self-organizing and self-configuring, thus requiring no existing network infrastructure, since nodes cooperate to forward packets over multiple hops between nodes not placed in visibility. The DSR protocol is based on: (i) route discovery--when a source node \(S\) wants to send a packet to a destination node \(D\) and does not know a route to \(D\), it sends a route request that will be filled by each intermediate node with the hops the packets have to follow to go from \(S\) to \(D\); and (ii) route maintenance--used when the data are actually transmitted and to detect network topology changes (e.g., a link along a route no longer works) using an acknowledgement-based system. In particular, when a source route is indicated as broken, the node \(S\) can use any other known route (which are cached in the node) to \(D\), or it can start a route discovery to find a new route to \(D\). Focusing on the route maintenance, thanks to the caching mechanism of the routes, DSR-based nodes have a rapid reaction to topology changes. In fact, a node with multiple routes to a destination can try another cached route if the used one fails. In this way, there is no need for a new route discovery procedure, leading to a significant reduction of control messages across the network.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**AV/Test** & **A** & **B** & **C** & **D** & **E** & **H** \\ \hline PandaDome 20.01.00 & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ \\ BitDefender 1.0.17.221 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ TrendMicro 16.0 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ Sophos 10.8.10.810 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ Karspersky & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ Avast 21.2.16.590 & & & & & & & \\ AVG 20.10 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ Avix 15.0.2101.2070 & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ \\ Best 14.0.22.0 & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ BullGuard 21.0.387 & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ EBDS & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Execution of malicious code modified by applying string substitution. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**AV/Test** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** \\ \hline PandaDome 20.01.00 & ✘ & ✘ & ✘ & ✘ & ✔ & ✘ & ✘ \\ BitDefender 1.0.17.221 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ TrendMicro 16.0 & ✘ & ✘ & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ Sophos 10.8.10.810 & ✘ & ✘ & ✘ & ✘ & ✔ & ✘ & ✔ \\ Karspersky & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\
21.2.16.590 & & & & & & & \\ Avast 21.1.2443 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ AVG 20.10 & ✘ & ✘ & ✘ & ✔ & ✘ & ✘ \\ Avix 15.0.2101.2070 & ✘ & ✘ & ✔ & ✘ & ✘ & ✘ \\ Eset 14.0.22.0 & ✘ & ✘ & ✔ & ✔ & ✘ & ✔ \\ BullGuard 21.0.387 & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ EBDS & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution of malicious code without any encryption method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**AV/Test** & **A** & **B** & **C** & **D** & **E** & **F** & **H** \\ \hline PandaDome 20.01.00 & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ \\ BitDefender 1.0.17.221 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ TrendMicro 16.0 & ✘ & ✘ & ✘ & ✘ & ✔ & ✔ & ✔ \\ Sophos 10.8.10.810 & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ \\ Karspersky & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\
21.2.16.590 & & & & & & & \\ Avast 21.1.2443 & ✘ & ✘ & ✘ & ✔ & ✔ & ✘ \\ AVG 20.10 & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ \\ Avix 15.0.2101.2070 & ✘ & ✘ & ✔ & ✘ & ✘ & ✘ \\ Eset 14.0.22.0 & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ BullGuard 21.0.387 & ✘ & ✘ & ✔ & ✔ & ✔ & ✔ \\ EBDS & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Execution of malicious code modified using AES encryption.