esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## SEIRS-D Agent-Based Model
### Coefficients
#### 3.2.3 Detection Coefficient  
This coefficient refers to the probability that malware in the infected sensor will be detected. The detection time of the malware may be higher or less according to the effects of the phenomenon of interest on the duty cycle and useful battery life. Besides, an infected sensor may receive maintenance that could increase the probability of malware will be detected.  
The detection coefficient of the \(i\)-th sensor agent at the step of time \(t\) is represented mathematically by \(d[i,t]\) where \(1\leq i\leq n\) and \(n\) is the total number of sensors. As a result, the probability that the malware will be detected in an infected \(i-th\)_sensor agent_ at time \(t+1\) is given by the following coefficient:  
\[d[i,t]=\prod_{1\leq j\leq 5,j\
eq 2,3}X_{j}\left(\widetilde{k}_{j}\right), \quad 0\leq d[i,t]\leq 1,\]  
where \(\widetilde{k}_{j}\) is a vector describing the characteristics on which depends the variable \(0\leq X_{j}\leq 1\). The factors \(X_{2}\), \(X_{3}\) and \(X_{6}\) have not been considered in this coefficient due to malware, topology and devices do not directly influence the detection of malware in an infected sensor, because discovery is performed through alerts generated by security mechanisms or by network administrators at the time of maintenance on the sensors. The risk of detection can be evaluated using the Equation (2).  
For instance, a network with sensors of high computational capacity, a high level of security and where the phenomenon of interest allows the system to be frequently monitored by network administrators, are more likely to detect a malware that has infected a sensor if following conditions are successful:  
* The variable \(X_{1}\left(\widetilde{k}_{1}\right)\) depends on \(\widetilde{k}_{1}=(\alpha_{1}=2,\alpha_{2}=3,\alpha_{3}=5)\) such that \[X_{1}\left(\widetilde{k}_{1}\right)=P_{2,2}\cdot P_{3,5}\cdot P_{5,3}\] (11) since values \(q=2,5\) and \(3\) are considered for \(k=2,3,5\) respectively.
* The variable \(X_{4}\left(\widetilde{k}_{4}\right)\) depends on \(\widetilde{k}_{4}=(\alpha_{1}=13)\) such that \[X_{4}\left(\widetilde{k}_{4}\right)=P_{13,1}\] (12) since the value \(q=1\) is considered for \(k=13\).
* The variable \(X_{5}\left(\widetilde{k}_{5}\right)\) depends on \(\widetilde{k}_{5}=(\alpha_{1}=14)\) such that \[X_{5}\left(\widetilde{k}_{5}\right)=P_{14,3}\] (13) since the value \(q=3\) is considered for \(k=14\).
## 1 Introduction  
Excessive population growth in urban areas is one of the biggest challenges for every government around the world. The congestion generated by public and private transport is the most important cause of air pollution, noise levels, and economic losses caused by the time used in transfers, among others. For example, the inhabitants of Mexico City lose an estimated 23 h per month in transfers, which translates into losses of more than 1.5 billion dollars per year, which means 1% of the Mexico City's contribution to the country's GDP, a very significant figure for a developing country [1].  
The implementation of vehicular network standards and advances in wireless communication technologies has enabled the implementation of intelligent transport systems (ITS). One of the objectives of ITS is the real time traffic management using vehicle data collected from the road infrastructure. With this data, it is sought to characterize the traffic and thus be able to detect, control, and minimize traffic congestion. The main challenge of this approach is to forecast the congestion and re-route the vehicles without causing new congestion in other places [2].  
Commercial solutions such as Waze [3] and Google Maps [4] are capable of providing alternative routes from origin to destination based on the traffic information published by users. Although these systems are capable of predicting long-term traffic congestion as well as its duration, they are often used with reactive solutions that still cannot prevent congestion. In addition, the routes suggested to users are based exclusively on the algorithm of the shortest route to their destination, without taking into account the impact that the re-routing has on future traffic conditions [5].  
In this work, we present a predictive congestion avoidance by re-routing system that uses a mechanism based on deep learning with the goal of characterizing the future traffic conditions to make an early detection of congestion. Based on these predictions and a short route generation algorithm, alternatives for the vehicles that are about to cross the possible congested areas are provided. In addition, the system uses vehicle to infrastructure communications (V2I) for the early detection of non-recurrent congestion. The results obtained from simulations in synthetic scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles. The rest of this document is organized as follows. In Section 2, previous research in this field is summarized. Section 3 presents the details of the proposal. Section 4 presents the tools used in the development of the proposal and the scenarios used for the evaluation. Section 5 shows the obtained results. Finally, Section 6 presents conclusions and future work.
## 4 Discussion
### Longitudinal Deformation Calculation of Xigeda Stratum  
To quantify the influence of water content on the longitudinal deformation law of the tunnel in Xigeda stratum, the Levenberg-Marquardt method and universal global optimization method were used to perform nonlinear fast three-dimensional fitting on the numerical simulation results. Considering the 'S-type' characteristics of the LDP, the coordinate of the excavation face (i.e., \(x=0\)) is used as the demarcation points to fit the formulas ahead of and behind the excavation face to improve the fitting accuracy. The fitting formula is as follows:  
\[\frac{u_{r}}{u_{\max}}=\begin{cases}\frac{p_{1}+p_{2}(x/R)+p_{1}(x/R)^{2}+p_{4 }(x/R)^{3}+p_{5}\omega}{1+p_{6}(x/R)+p_{7}\omega+p_{8}\omega^{2}}&,x\geq 0\\ \frac{p_{1}+p_{2}(x/R)+p_{3}\omega}{1+p_{4}(x/R)+p_{5}(x/R)^{2}+p_{6}\omega}&, x<0\end{cases} \tag{9}\]  
where \(\omega\) is the water content and the unit is %.  
Figure 7 shows the three-dimensional response surface of the fitting formula. The fitting parameters and error analysis results are shown in Tables 2 and 3, respectively. From the error analysis results, the maximum value of RMSE is 0.020753, the maximum value of SSE is 0.067620, the minimum value of \(R\) is 0.990577, and the minimum value of DC is 0.981242, each analysis index shows that the fitting results are effective.  
Figure 7: Fitting formula three-dimensional response surface.  
The three-dimensional response surface intuitively reflects the changing trend of \(\mathrm{u}_{r}/\mathrm{u}_{\mathrm{max}}\) under the coupling influence of water content \(\omega\) and distance \(x\) from the excavation face. At the same \(x\) level, with the increase of \(\omega\), when \(x=0\), \(u_{r}/u_{\mathrm{max}}\) tends to increase; when \(x=0.3R\), the above trend decreases, consistent with the previous analysis. At the same \(\omega\) level, the three-dimensional response surface shows the same law as the LDP curves.
## 3 CNN Architectures
### Xception Network Architecture  
The Xception (which stands for extreme inception) network was introduced by [PERSON] [46], and it was inspired by the InceptionV3 architecture. The main idea that is exploited by the Xception architecture is to replace the inception module with depthwise separable convolution, followed by a pointwise separable convolution. This network is 71 layers deep, and it has 22.9 million parameters. The Xception network achieved a 94.50% top 5 accuracy rate on the ILSVCR challenge. The main advantage of this network is that it has a deep architecture but with a small number of parameters, thus making it computationally efficient compared to other deep networks. The main drawback is that this network requires very large datasets to be able to train all its parameters.  
Table 1 shows a summary of the proposed networks with their number of parameters and their accuracy over the ImageNet dataset [37]. The accuracy is calculated by dividing the correctly classified observations over the total number of observations. The \(top-k\) accuracy is the accuracy of the architecture over predicted labels \(\hat{y}\), where the top 5 accuracy represents the accuracy over 5 classes accuracy and the top 1 accuracy represents the accuracy for a single-class classification. When \(k=5\), the accuracy is measured by taking into account if the label \(y\) is present in the top 5 predicted labels \(\hat{y}\), while if \(k=1\), \(top-k\) is the de-facto accuracy measure. The \(top-k\) accuracy measure was used here because the ILSVRC challenge had 1000 classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Number of Parameters** & **Top 5 Accuracy** & **Top 1 Accuracy** \\ \hline AlexNet & 62,378,344 & 84.60\% & 63.30\% \\ \hline VGG16 & 138,357,544 & 91.90\% & 74.40\% \\ \hline GoogLeNet & 23,000,000 & 92.2\% & 74.80\% \\ \hline ResNet-152 & 25,000,000 & 94.29\% & 78.57\% \\ \hline DenseNet & 8,062,504 & 93.34\% & 76.39\% \\ \hline Xception & 22,910,480 & 94.50\% & 79.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top 5 accuracy, top 1 accuracy, and the number of parameters of AlexNet, VGG, Inception, and ResNet in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) challenge.
## 3 Application Protocol for Wireless Intelligent Sensor Networks  
The main objective of our research is to design and implement a distributed system, based on a WSN, that allows the execution of a set of applications in the sensors (measures, actuations, FRBS, collaborative FRBS, _etc._), updating of the sensor KBs, obtaining sensor data and inferences generated in the sensors, and managing the sensors.  
The proposed system is composed of a computer connected to the Internet, where the host application runs, an access point or base station that allows the host application to transmit data to the sensors, a WSN, a FRBS adapted for execution in the sensors, and the communication protocol. The main functions of the components are as follows:  
1. Computer: edit the KBs (variables, fuzzy sets, and rules), access the sensor network, communicate with sensors, and monitor sensor state.
2. Sensor network: allow sensors and computer to communicate. The network consists of an access point and a set of sensor nodes with sensing, data processing, and communicating capabilities.
3. Application protocol: allow the elements of the system to communicate data and knowledge.  
The proposed system was designed with the aim of minimizing sensor communication to prolong battery life. Therefore, the sensors operate in a work cycle in which they first execute the application (measure, actuate, infer its output, _etc._), decide if it is necessary to connect with the computer or other sensors, and then are configured in a sleep mode. On the other hand, the protocol is independent of the platform, so it can be used in different sensors.  
This work is focused on an application protocol that allows communication between the computer and sensors, as well as among sensors. The following sections show the multi-agent structure of the sensor software and a detailed description of the communication protocol.
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 4 Full-Coverage Path Planning
### Full-Coverage Path-Planning Algorithm  
Based on the proposed priority-setting rule, the glass-cleaning work can be performed on most walls according to the following steps:  
Step 1. Set the robot's initial position; mark U-shaped obstacles whose opening direction is consistent with the initial coverage direction using the edge learning strategy.  
Step 2. The robot determines the next-move direction according to the improved priority heuristic full-coverage path-planning algorithm, and the robot covers the moving direction following this plan.  
Step 3. If the robot moves to a U-shaped obstacle whose opening direction is consistent with the initial coverage direction, execute the switching command operation to cover its area, and then proceed to Step 5 after the coverage is completed.  
Step 4. If robot enters a dead zone, execute the escape from the dead zone strategy, search for the nearest uncovered grid to the dead zone position, and set it as the initial target point. Use the direction function to plan the path to move to this grid and set the first uncovered grid in this path as the intended target point, so the robot can move to the target point for escaping the dead zone. Proceed to Step 5.  
Step 5. Judge whether all the grids in the grid map have been covered. If not, return to Step 2; otherwise, end the algorithm.  
The flow of the above improved priority heuristic algorithm is shown in Figure 11.  
Figure 10: Dead zone escape strategy.  
To fully demonstrate the improved priority heuristic full-coverage path-planning algorithm, a complex wall with multiple obstacles was simulated. On this wall, L = H = 20, so the overall robot motion scheme was longitudinal reciprocating motion, and the starting point was the lower-left corner of the grid map. The improved priority heuristic full-coverage path-planning algorithm was used to plan the coverage path, as shown in Figure 11(a).  
When the robot moved to the position shown in Figure 11(b), the robot obtained the obstacle information in the working environment through edge learning and determined the A positions based on the information. If the robot moved to point A, the robot would be judged as entering the obstacle.  
As the robot moved to the position shown in Figure 11(c), obstacles or covered grids were all around the robot, so the robot fell into a dead zone. At this time, the robot attempted to escape the dead zone, and the wildfire algorithm was used to find the initial target point.  
Finally, the robot completed the global cleaning work according to the planned path, as shown in Figure 11(d).  
Therefore, full coverage of the working area on a complex wall was achieved by switching the rules of motion direction priority while avoiding special calculation, which verified the feasibility of the proposed full-coverage path-planning method, and effectively  
Figure 11: Improved priority heuristic algorithm.  
solves the problem of efficient global cleaning of a wall with densely distributed obstacles by a robot.
## 2 Materials and Methods
### Design Overview  
SLIM, shown in Figure 2, consists of four main components: a robotic arm, the actuation box, the robot base, and a dedicated device acting as the end-effector.  
Figure 1: Possible inspection scenarios: (**a**) moving around obstacles (**b**) entering through a small entrance (**c**) under-ground inspection (**d**) wall side inspection.  
The robot can deliver the end-effector in a workspace up to 4.8 m from the base. The robotic arm is composed of 12 moving modules and is mounted on the actuation box. The actuators are located within the actuation box, which can rotate around the axis \(J_{1}\). The robot base supports the whole robot and is fixed to the ground. Then, a dedicated device acting as the end-effector is integral to the tip of the last module. Figure 3 shows the whole robotic system in its parking position.  
Each module of the robotic arm is composed of a rigid link and a circular pulley acting as a revolute joint driven by a cable. The cables are routed along with the structure of the robot through rollers that constrain them to pass along the neutral axis of each link. The 12 parallel revolute joints allow moving the end-effector in a specific Cartesian position on a plane. The first joint is connected to the actuation box, which is able to rotate around an axis parallel to the axes of the other joints. The actuation box allows the arm to wrap on itself in a compact configuration and unroll as needed. In this application, the end-effector is a pan and tilt mechanism with 2 DOFs that allows a camera, acting as Tool Centre Point (TCP), to reach a desired attitude in the Cartesian space. Therefore, for the task of positioning the camera in a bi-dimensional Cartesian space with a specific attitude, SLIM has 10 degrees of redundancy. Assuming \(q_{i}\) as the rotation angle of the \(i\)th joint, Table 1 lists the modified DH parameters, according to [31], describing the kinematics of the robot.  
Figure 3: The whole robotic system in its parking position that is enclosed within a volume whose dimensions are 152 cm \(\times\) 88 cm \(\times\) 158 cm \((L\times W\times H)\).
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 3 Results
(**b**) Energy consumption of EAFFB algorithms and the proposed algorithm for the probability of earthquake occurrence > 30% (8% reduction). Figure 8: (**a**) The critical regions of Greece for the probability of earthquake occurrence > 40%. (**b**) Energy consumption of EAFFB algorithms and the proposed algorithm for the probability of earthquake occurrence > 40% (9% reduction). Figure 9: (**a**) The critical regions of Greece for the probability of earthquake occurrence > 50%. (**b**) Energy consumption of EAFFB algorithms and the proposed algorithm for the probability of earthquake occurrence > 50% (4% reduction). In the initial two cases of >20% and >30%, there was evident potential for a higher reduction in energy consumption, given that the total energy consumption of all backup paths was calculated to be 66% and 20%, respectively. However, due to the constraint of maintaining network connectivity, not all critical links could be removed, resulting in a lower total reduction of energy compared to the total energy consumption of all backup paths. In the >40% and >50% cases, the energy reduction calculated by the algorithm was maximized, as the total energy consumption of all backup paths was calculated to be 9% and 4%, respectively, aligning with the energy consumption reduction achieved by the proposed algorithm. As the earthquake occurrence threshold increased, the reduction in energy consumption diminished, attributable to fewer areas being considered critical, resulting in a lower count of critical fibers and alternative paths. Consequently, fewer fibers can be removed from the network. The significant deviation in the algorithm's results in the >50% case, presenting a 4% reduction in energy consumption compared to other cases, may be influenced by actual data retrieved from the National Observatory of Athens. In this specific case, only one area exceeded the >50% threshold, whereas in other cases, multiple regions surpassed the specified threshold. Consequently, alternative paths were calculated only once for the single link (link 341) passing through the affected region (region 34). Upon a comprehensive review of the results, the algorithm emerges as effective in achieving a substantial reduction in energy consumption. It accomplishes this by identifying links passing through critical regions, subsequently removing or deactivating them from the network. This strategic relocation reroutes all traffic passing through those links, eliminating the need to create additional backup paths that would contribute to increased total energy consumption. In summary, the outcomes of the proposed algorithm simulations underscore that improvements made to the backbone network can yield a significant 7.5% reduction in energy consumption, on average.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 3 Methodology: Mapping Study
### Planning
#### 3.1.2 Paper Selection Strategy  
The search for existing documents related to this topic was conducted through the Scopus database that indexes important digital libraries such as IEEE, Xplore, Springer, Link, Science Direct, or ACM. This database is complete and contains much information from the existing literature in a domain [24]. In this case, it corresponds to 3D path planning methods for UAVs. The search is based on the research questions and the keywords that generally cover the entire field of study. Figure 6 shows the first search string performed in Scopus for this study.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & **Research Questions (RQs)** & **Basis** \\ \hline \multirow{2}{*}{RQ1} & \multirow{2}{*}{Is there UAV path planning in 3D?} & To know about path planning for UAVs in complex 3D environments that are collision-free from the start position to the target position. \\ \hline \multirow{2}{*}{RQ2} & \multirow{2}{*}{Are there optimal methods for path planning in 3D UAVs?} & Know if the optimal methods used by researchers provide exact results for path planning in UAVs in 3D, with a high computational cost. \\ \hline \multirow{2}{*}{RQ3} & \multirow{2}{*}{Are there approximate methods for path planning in 3D UAVs?} & To know if there are approximate methods with reduced complexity that are executed with less computational cost and running time. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Research questions related to the systematic study of literature.  
Figure 5: Process flow and tasks of the systematic mapping study, based on [24].  
From this first search string, a total of 1233 documents were obtained. As the number of articles is quite large, a second search is carried out with the inclusion and exclusion procedure indicated below.