esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Image Preprocessing  
The OD-LSF method proposed in this paper needs to use the depth information of the pixels, which can be obtained by stereo matching. As stereo matching is not the focus of this paper, we chose the relatively mature algorithm PSMNet [29]. In this paper, a three-dimensional coordinate system is established with the focus of the left camera of the binocular camera as the origin. The specific coordinate (\(X\), \(Y\), \(Z\)) of each point in the point cloud in the coordinate system can be obtained by the following calculation formulas:  
\[Z=\frac{B_{line}*f}{d} \tag{1}\]  
\[X=\frac{Z*\left(x_{p}-x_{ol}\right)}{f} \tag{2}\]  
\[Y=\frac{Z*\left(y_{p}-y_{ol}\right)}{f} \tag{3}\]  
Among them, \(B_{line}\) represents the baseline length of the binocular camera, \(f\) represents the focal length of the camera, and \(d\) is the disparity. \(x_{p}\) and \(y_{p}\) represent the column coordinate and row coordinate of the pixels in the left image, \(x_{ol}\) and \(y_{ol}\) represent the column coordinate and row coordinate of the center point of the left image.  
The size of the point cloud obtained by stereo matching is usually very large, and it inevitably contains some outliers and invalid value points due to mismatching or distance. Therefore, point cloud filtering [30] is used to preprocess it and improve the quality of the point cloud while reducing the data size. In order to calculate the local spatial feature of the point cloud, this paper adopts the SLIC algorithm [31] to perform superpixel segmentation on the image, and takes the superpixels as the smallest detection unit. The segmentation result of the SLIC algorithm is shown in Figure 2. This algorithm is different from the simple rectangular superpixel segmentation. It can better retain the contour information of the image and make the pixels in the superpixel come from the same object as much as possible. In addition, the SLIC algorithm can reduce the difference in the depth of the point cloud from the same superpixel, which is beneficial to the subsequent plane fitting.  
By preprocessing the stereo image, the segmented superpixels and the corresponding point clouds can be obtained. In order to classify the superpixels, this paper proposes two independent methods based on the local spatial features of the point cloud; namely, OD-PNV and OD-SPH. The final probability that each superpixel belongs to an obstacle is obtained by comprehensively considering the outputs of the two methods.
## 4 NIST Guidelines for the Secure Deployment of IPv6  
The National Institute of Standards and Technology (NIST) was founded in 1901 and is now part of the U.S. Department of Commerce. Its mission is to promote U.S. innovation and industrial competitiveness by advancing measurement methods, standards, and technology in ways that enhance economic security and improve quality of life. NIST has laboratories for many different areas of science but mostly physical science as chemistry, physics, biology, and others. Among them is the Information Technology Laboratory and one of its divisions, the Computer Security Division [63]. It provides standards and technology to protect information systems against threats to confidentiality, integrity, and availability of information and services. Thus, the division publishes their own standards and works together with other standardization organizations such as the IETF. Beside standards, NIST and the Computer Security Division also publish security guidelines for different areas, e.g., cryptographic algorithms, and also provide evaluations and best practices for the use of standards [64].  
In 2010 NIST published the \"Guidelines for the Secure Deployment of IPv6\" [3]. The document is meant to give a comprehensive overview about IPv6 and point out possible threats regarding the different  
Figure 7: Extended methodology.  
areas and technologies that are influenced by IPv6. Special focus is given to the security risks that might appear during deployment and transition to IPv6, since this is a critical evolution step for any network. It also provides a suggestion for a deployment strategy with a rough outline of recommended steps for the transition. The full document is 212 pages long and available for free download [3]. It does not require the reader to know IPv6, but knowledge about IPv4 and other networking protocols is necessary to a certain extent.  
The NIST guide is structured in the following way. After a short introduction, the second chapter introduces IPv6, its history, major features, and a comprehensive comparison of threats for IPv4 and IPv6. The third chapter is focused on general IPv6 concepts. These include IPv6 addresses, addressing and allocation as well as the IPv6 header, extension header, ICMPv6, and DNS. The fourth chapter covers advanced IPv6 features in more detail. These include multicast, QoS, Multihoming, DHCPv6, and Mobile IPv6. The fifth chapter continues with security mechanisms such as privacy addresses, cryptographically generated addresses, IPsec in IPv6, and secure stateless address autoconfiguration. The final chapter focuses on the deployment of IPv6 and describes transition mechanisms, secure addressing schemes, and recommends steps and actions for the preparation of a deployment.
###### Abstract  
Given the challenges of rotating equipment fault diagnosis under variable working conditions, including the unbalanced transmission of information during feature extraction, difficulty in capturing both global and local features, and limited generalization across different working conditions, a Chebyshev graph convolutional neural network (ChebyNet) method is proposed to address these issues. First, a symmetry processing mechanism is incorporated into the framework of the ChebyNet to balance the transfer of information between nodes in the graph to ensure the fair and efficient integration of information. Secondly, the wide-area feature extraction capabilities of the ChebyNet and the adaptive nodes of the graph attention network (GAT) are integrated to achieve the comprehensive mining of fault characteristics and accurate characterization of complex interactive relationships. Finally, the node reconstruction task of self-supervised learning and collaborative node classification tasks are used to enhance the model's ability to capture complex changes in variable working conditions data, significantly improving the generalizability of working conditions. In comparative and cross-validation experiments, the proposed method achieved an average diagnostic accuracy of 99.72%, representing an improvement of up to 17.96% compared to other graph neural network (GNN) models. It significantly enhances the accuracy, stability, and generalization of fault diagnosis. Ablation experiments further validate the effectiveness of the proposed method in improving fault diagnosis performance under variable working conditions.  
fault diagnosis; variable working conditions; Chebyshev graph convolutional neural network; multi-task collaboration +
Footnote †: journal: Applied sciences
# Coordinated Control Method of Bus Signal Priority and Speed Adjustment Based on Stop-Skipping
## 5 Case Analysis
### Analysis of Results
#### 5.2.1 Stage 1--Stop-Skipping Decision  
The decision of the stop-skipping was made for each station combined with the historical passenger flow of each station. Among them, the passenger capacity of 6 vehicles was taken as the average daily passenger flow during this period, as shown in Table 4. It can be seen from Table 1 that \"Y\" represents the decision variable of whether the vehicle stops at the intersection. The intersection was considered as a \"virtual station\" (no pick-up and drop-off), and all vehicles were set to pass through the intersections with priority, which was represented as \(Y_{i,x}=0\):  
MATLAB was used to run the genetic algorithm to obtain the parking situation of each vehicle at each station, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline PassangersStations & \(\mathbf{S_{1}}\) & \(\mathbf{S_{2}}\) & \(\mathbf{S_{3}}\) & \(\mathbf{S_{4}}\) & \(\mathbf{S_{5}}\) & \(\mathbf{S_{6}}\) & \(\mathbf{S_{7}}\) & \(\mathbf{S_{8}}\) & \(\mathbf{S_{9}}\) & \(\mathbf{S_{10}}\) \\ \hline waiting (V\({}_{1}\)) & 7 & 1 & 10 & 3 & 8 & 2 & 8 & 8 & 1 & 0 \\ dismounting (V\({}_{1}\)) & 0 & 0 & 9 & 0 & 10 & 0 & 7 & 5 & 0 & - \\ waiting (V\({}_{2}\)) & 8 & 7 & 2 & 12 & 1 & 5 & 12 & 3 & 8 & 0 \\ dismounting (V\({}_{2}\)) & 0 & 5 & 0 & 5 & 0 & 10 & 2 & 0 & 3 & - \\ waiting (V\({}_{3}\)) & 11 & 1 & 12 & 1 & 20 & 5 & 1 & 12 & 2 & 0 \\ dismounting (V\({}_{3}\)) & 0 & 0 & 5 & 0 & 7 & 0 & 0 & 6 & 0 & - \\ waiting (V\({}_{4}\)) & 9 & 7 & 1 & 8 & 1 & 15 & 8 & 2 & 10 & 0 \\ dismounting (V\({}_{4}\)) & 0 & 5 & 0 & 6 & 0 & 10 & 7 & 0 & 9 & - \\ waiting (V\({}_{5}\)) & 5 & 3 & 7 & 1 & 13 & 3 & 10 & 9 & 1 & 0 \\ dismounting (V\({}_{5}\)) & 0 & 0 & 3 & 0 & 5 & 0 & 3 & 6 & 0 & - \\ waiting (V\({}_{6}\)) & 12 & 8 & 15 & 10 & 12 & 7 & 12 & 11 & 9 & 0 \\ dismounting (V\({}_{6}\)) & 0 & 5 & 6 & 3 & 9 & 5 & 6 & 7 & 5 & - \\ \hline \end{tabular}
\end{table}
Table 4: Passengers waiting and dismounting at each station.  
Figure 6: Flowchart of combined scheduling strategy.  
The schematic diagram of the stop-skipping decision is shown in Figure 7a below, where \(\bigcirc\) means stop-skipping and \(\bigcirc\) means stop. Figure 7b shows the passenger flow trend of the number of passengers dismounting at terminal S10 on the basis of Figure 7a (blue dotted line).  
During the actual operation of the bus, passengers can decide whether to take the bus after obtaining the stop-skipping status and timetable information through the APP program in advance [42]. We use a Sankey diagram to show the visual dynamic change in the passenger flow of each vehicle at each station, as shown in Figure 8.
# Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
## 2 Literature Review
The idea is first to construct the one-barrier coverage with the minimum number of sensor nodes. Then, based on this construction and the final position of sensor nodes, a 3D maximum-level k-barrier coverage solution with all sensor nodes is proposed. However, the proposed solution does not take into consideration the effect of the water currents in the final deployment and the sensor nodes' displacements. In [12], an adaptation of the distributed virtual force algorithm to the underwater environment is proposed. The main objective of this research is to achieve full coverage of the network area while minimizing the number of nodes used. The idea of the algorithm is to maintain a predetermined distance threshold between neighboring nodes. Hence, if the distance between two neighboring sensors is smaller than the threshold, a repulsive force will be exerted to avoid node stacking. On the other hand, if the distance between two neighbor nodes is higher than the threshold, an attractive force will be exerted to avoid the coverage hole. To determine the distance threshold, a regular dodecahedron is used to fill the network area where the sensor node will be located at the center of the dodecahedron, and it will have 12 neighbors with the same distance. The water currents are added as a force in the virtual force equation. Simulation results show that the proposed solution ensures a very high coverage rate. However, the impact of the water currents on the deployment protocol is not shown. In [28], [PERSON] et al. propose a hybrid multi-objective node deployment for the energy-coverage problem in mobile underwater sensor networks. The primary objective of this research is to propose a novel approach for deploying sensor nodes in mobile underwater environments that can simultaneously optimize energy consumption and coverage performance. The research generates a set of Pareto front solutions, representing a range of trade-offs between energy efficiency and coverage. The Pareto front solutions empower decision makers to effectively monitor the region of interest. Simulation results show that the proposed solution is more effective than the current multi-objective algorithms. Table 1 summarizes and compares the solutions presented in free movement deployment solutions for UWSNs in terms of coverage, connectivity, energy efficiency, and water currents consideration. We notice that the first objective of all this research is area coverage, which is very important to achieve the application target. However, the water currents are almost never taken into account in the network deployment and nodes' displacements. In this work, we first study the impact of the water currents on the deployment protocol. Then, we propose a solution to achieve the deployment, considering the water currents. Even though our solution can be applied to any proposed deployment protocol, in this paper, we chose the distributed virtual forces algorithm (DVFA) for our discussion. In the next section, we briefly explain the assumptions of our solution.
# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
## 2 Main Procedures of the Proposed Simplification Method  
The main procedures of the proposed simplification method for the point cloud are shown in Figure 2. In order to divide the point cloud into regions, octree coding is used firstly in this paper. In this step, the bounding box of the original points is constructed, and this box is divided into multiple sub-cubes with the same side lengths. Thus, the topological root model of the data point cloud is established. Secondly, the \(k\)-neighborhood search is performed for the points in each sub-cube, and the curvatures as the features of the point cloud are calculated [23]. Then, after an adjustable curvature threshold is set, the regions with larger or smaller curvatures can be identified based on the curvature threshold and the mean curvatures of all sub-cubes. In this study, the regions with smaller curvatures are the flat regions of the T-profile steel plate, and the regions with larger curvatures are the welding regions of the T-profile steel plate.  
Next, two different simplification algorithms are used for the regions with smaller or larger curvatures. For the flat regions of the T-profile steel plate, the random sampling method is used, which is effective for the reduction of data points. For the welding regions of the T-profile steel plate, the regional center of gravity simplification algorithm is selected. The processing speed of the latter method is relatively low. However, most useful information is retained after data reduction by this method.  
The proposed simplification method combines the advantages of the two simplification algorithms with the consideration of the points curvature distribution of the T-profile steel plate. For the T-profile steel plate, the number of the regions with larger curvatures is obviously far less than those with smaller curvatures. Therefore, the simplification speed  
Figure 2: The main procedures of the proposed simplification method.  
and the retention of most feature information is ensured in the proposed simplification method of the point cloud for the T-profile steel plate.
## 3 Results
### Characterization of the Spatial Distribution of Fishing Vessel Operations  
The monthly distribution of fishing effort by mainland China's light purse seine vessels in the Arabian Sea in 2021 and 2022 is illustrated in Figure 8. The statistics revealed that fishing effort was primarily concentrated from January to May and from October to December. Among these, January and March had the highest levels, while September had the lowest, with almost no fishing vessel operations observed from June to August. The following analysis focuses on January to May and October to December.  
Due to the optimal spatial alignment between fishing effort and production data, fishing intensity was utilized to analyze the spatial distribution characteristics of fishing vessel operations. The monthly distribution of fishing intensity for 2021-2022 is visually represented in Figures 9 and 10. The spatial intensity maps for each month reveal the patchy nature of the spatial distribution of fishing vessel operations. The distribution was characterized by discrete patterns, with no contiguous areas of concentrated operations. However, small yet highly concentrated areas were consistently present each month, with cumulative time exceeding 100 h in a 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) area. Notably, certain months exhibited maximum cumulative times ranging from 200 to 350 h, indicating highly aggregated fishing activities in those periods. The spatial distribution of fishing effort displayed significant monthly variations, highlighting distinct seasonal changes.  
In Figures 9 and 10, the areas with the duration of fishing effort per month in 2021 and 2022 were assessed at a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). In October 2021, a peak concentration was observed at 63\({}^{\circ}\) E, 15\({}^{\circ}\)54\({}^{\prime}\) N, lasting for up to 322 h. Subsequently, in February 2022, another concentration was recorded at 62\({}^{\circ}\)30\({}^{\prime}\) E, 16\({}^{\circ}\)42\({}^{\prime}\) N, lasting for up to 350 h. These findings suggest that fishing vessels had identified a substantial abundance of fishery resources in these areas. These monthly observations provide us with valuable insights into the dynamic utilization of fishing resources.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Oct.** & **Nov.** & **Dec.** \\ \hline FE & 0.71 & 0.73 & 0.75 & 0.73 & 0.69 & 0.78 & 0.79 & 0.77 \\ KDE & 0.69 & 0.71 & 0.74 & 0.69 & 0.68 & 0.69 & 0.68 & 0.72 \\ KDHSA & 0.62 & 0.64 & 0.71 & 0.67 & 0.68 & 0.63 & 0.6 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indicators of spatial similarity between the extraction results and the actual data by the three methods in 2022.  
Figure 8: (**a**) Monthly distribution of fishing effort in 2021; (**b**) monthly distribution of fishing effort in 2022.
## 4 Results
### Insights from Case Studies
#### 4.1.3 Metalworking (Molds and Plastics) Industry  
Concerning the metalworking industry, in first company studied, Company 5, a sensors system that collects data from machines in real time was implemented, allowing the control of production times, waste, and velocities. This information allowed a major degree of accuracy in OEE calculation, allowing a better understanding of company performance. This topic was also implemented in the second company investigated in this industry, Company 6, where the manufacturing data were collected in an MES system and, after that, integrated with the ERP and OEE calculation systems. This information allows a real understanding of the products (in various stages). Factory sensorization was a point considered in the third company studied in this industry, Company 7, where the development of an integrated platform, including tools such as ERP, MES, or a document manager, as well as a web platform that digitalizes the operations through the value chain, could be seen. The best practice that was common and may be highlighted is the creation of dashboards to facilitate data analysis. In Company 7, prediction technologies such as AI were also used to improve processes and predictions. The last company studied in this section, Company 8, needed precise statistical process control (SPC). In this sense, as best practices, the quality management processes of digitalization can be highlighted, creating a data network that allows real-time data collection, without manual work as occurred previously (avoiding manual collection errors). This system ensures the visibility of these data to lab teams. As in Company 7, Company 8 also created a bundle of dashboards to analyze data. Company 8 also introduced optical scanners, increasing sample traceability. Associated with DT but out of high-tech solutions additions, these two companies had some best practices to emphasize: Company 8 trained its teams in software utilization but also in the SPC domain; Company 7 started with the digitalization process with maturity level analysis and benchmarking actions. In the metalworking domain, as best practices, it is possible to state the creation of data networks, combined with integrated solutions that aggregate the different companies' tools and the whole value chain. In this sector, the role that lean principles may have in a preliminary version of DT is also evident, since there is a wide diversity of pieces and processes, and a technological addition without the real perception of needs may result in adverse effects.
## 4 Discussion
### The Effect of Constraint Terms on Inversion  
To highlight the contribution of constraint terms during the inversion process, we used the Bouguer gravity anomaly data of the Gazelle Fault as observational data and conducted 100 inversions under both unconstrained and constrained conditions. A comparison of the inversion results with and without constraint terms is shown in Figure 9.  
From Figure 9, it is evident that the inversion results with constraint terms are significantly more stable than those without. The inversion results in constraint terms oscillate within a small range. The amplitude factor \(M\), fault dip angle \(\theta\), and fault origin \(x_{0}\) each exhibit one \"abrupt change\" phenomenon, but the magnitude is small and does not affect the overall stability of the inversion. Observing the inversion results without constraint terms, we observed that the oscillations of the amplitude factor \(M\), fault dip angle \(\theta\), and fault origin \(x_{0}\) are more pronounced, indicating that these three parameters are less stable during the inversion process due to the non-uniqueness of gravity inversion. The top center depth \(z\) and bottom center depth \(w\) of the fault are relatively stable and show a certain correlation. When \(z\) undergoes an abrupt change, \(w\) also changes abruptly. An interesting observation is that, when \(z\) and \(w\) change abruptly, the RMS error increases, suggesting that \(z\) and \(w\) have a greater impact on accuracy, which aligns with the results of the parameter sensitivity test in Section 4.2.  
Figure 8: Process of penalty coefficient selection.  
In this section, we first compared the effects of different inertia weight reduction methods in the C-AWPSO algorithm and chose the linearly decreasing method, which strikes a good balance between convergence efficiency and accuracy. Next, we analyzed the sensitivity of five fault parameters. The results showed that parameters \(z\) and \(w\) had the highest sensitivity, and their inversion results would be relatively stable. Given the large number of parameters involved in the constraint terms, we also conducted tests to select appropriate regularization factors and penalty coefficients. Finally, we verified the impact of the constraint terms added to the objective function on inversion. The results indicated that inversion results with constraint terms were significantly more stable than those without. These experiments fully demonstrate that the C-AWPSO algorithm can yield accurate fault depth information when  
Figure 9: Comparison of inversion results under conditions with and without constraints. (**a**) The comparison result of \(M\); (**b**) The comparison result of \(z\); (**c**) The comparison result of \(\theta\); (**d**) The comparison result of \(w\); (**e**) The comparison result of \(x_{0}\); (**f**) The comparison result of RMS.  
applied to practical fault inversion. After introducing constraint terms, the instability in the fault dip inversion results is notably improved. Therefore, the C-AWPSO algorithm is feasible and robust for fault parameter inversion.