esa-sceva/satcom-chunk-collection huggingface dataset:
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 3 Methodology
### Field Data Collection  
A field campaign was conducted within three days from the first image acquisition date (3 January 2015) to collect reference data on croplands which in our study area are solely mono- and mixed maize cropping systems. Furthermore, field data were collected on non-cropland, composed of water bodies, artificial surfaces and natural vegetation. A stratified random sampling was followed to collect the reference data. A handheld Global Positioning System (GPS) device with an error of \(\pm\)3 m was used to locate the reference control points. Once a field was identified, we delineated the field boundaries (polygon) within a minimum area of 30 by 30 m. To avoid the edge effect, we collected the polygon data five meters away from the edge of each field. Geo tagged photographs of each cropping system in the sample fields were taken from the main four cardinal directions and from the center of the fields for further inspections of the cropping systems and crop age. To mitigate the effect of soil background on the crops' spectral features, we only sampled maize fields (mono- and mixed cropping systems) that were about three weeks old at the first image acquisition date. The reference data were randomly divided into 70% training and 30% validation set. The training set was used to train the RF classifier while the validation dataset was used to evaluate the accuracy.
## 5 Vertex Cover
### Algorithm for Unit Line Graphs  
We present a local algorithm with locality one for vertex cover on unit line graphs (ULGs). It achieves an approximation ratio of 2. The algorithm works as follows: Let \(G=(V,E)\) be a unit line graph. Let \(v_{L}\) be the leftmost vertex in \(V\). Define \(VC:=V\setminus\{v_{L}\}\). We output \(VC\). We refer to this as Algorithm 8.  
```
1// Algorithm is executed independently by each node \(v\);
2// let \(v_{L}\) be the leftmost vertex of \(G\);
3 Explore the vertices in \(N(v)\);
4 if\(v=v_{L}\)then become part of the vertex cover \(VC\)else do not become part of \(VC\)
```  
**Algorithm 8** Local algorithm for computing a vertex cover in a unit line graph  
We prove the correctness of Algorithm 8 in Theorem 14.  
**Theorem 14**.: _Let \(G\) be a unit line graph. Algorithm 8 has the following properties:_  
1. _The computed set_ \(VC\) _is a vertex cover for_ \(G\)_._
2. _Let_ \(VC_{OPT}\) _be an optimal dominating set. It holds that_ \(|VC|\leq 2\cdot|VC_{OPT}|\)_._
3. _Whether or not a vertex_ \(v\) _is in_ \(VC\) _depends only on the vertices which are at most one hop away from_ \(v\)_, i.e. Algorithm_ 8 _is local._
4. _The processing time for a vertex_ \(v\) _is linear in the number of vertices adjacent to_ \(v\)_._  
Proof.: Since \(\{v_{L}\}\) is an independent set for \(G\) it holds that \(VC=V\setminus\{v_{L}\}\) is a valid vertex cover for \(G\). For proving the approximation ratio of Algorithm 8 we distinguish between \(|V|\) being even or odd. If \(|V|\) is even then there is a maximal matching for \(G\) such that every vertex is adjacent to a matching edge: Denote by \(v_{1},v_{2},...,v_{n}\) the vertices \(V\) ordered by their \(x\)-coordinate. The matching \(M:=\{(v_{2k-1},v_{2k})|1\leq k\leq|V|/2\}\) is such a maximal matching. So it holds that \(|V|\leq 2\cdot|VC_{OPT}|\) and therefore \(|VC|\leq 2\cdot|VC_{OPT}|\). If \(|V|\) is odd then there is a maximal matching such that every vertex but \(v_{L}\) is adjacent to a matching edge: the matching \(M^{\prime}:=\left\{(v_{2k},v_{2k+1})|1\leq k\leq\frac{|V|-1}{2}\right\}\) is such a maximal matching. So it follows that \(|VC|\leq 2\cdot|VC_{OPT}|\). If a vertex \(v\) is in \(VC\) depends only on the vertices which are one hop away from \(v\) (the set \(N(v)\)) since it is sufficient for \(v\) to explore \(N(v)\) in order to determine whether \(v=v_{L}\). During the computation for one vertex \(v\) we only need to check for every vertex \(v^{\prime}\in N(v)\) whether \(v_{x}\leq v^{\prime}_{x}\). So the processing time for a vertex \(v\) is linear in the number of vertices in \(N(v)\).
## 1 Introduction
It is important to note that traditional fixed timings which are often used to maintain highly coordinated systems are often well-suited to support these systems, particularly in dense, urban environments. Adaptive traffic systems can be more useful in situations where traffic flows are well below capacity. The advantages of traffic signal control methods and experiments based on connected vehicles and floating car data (FCD) are mainly in the reduced infrastructure costs. With connected vehicles, it is possible to regulate traffic signals without changing the fixed infrastructures. All that is needed is to connect the intersection controllers to the internet. Many cities are already adopting traffic systems that can allow remote management via web interfaces. The literature review presented here shows that the field of actuated coordination with connected vehicles has yet to be addressed by researchers. Researchers still have to prove that the new systems can be competitive with traditional fixed- or semifixed-timing coordinated systems, especially with higher traffic flows (close to capacity). This work applies scientometric methods based on science mapping to review and frame the evolution of traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The motivation of the authors is to provide a contribution to the analysis of the state of the art of innovative approaches for traffic light control only recently explored by the scientific literature, highlighting the most relevant contributions. Other works have been focused on presenting a review and general observations, such as [50; 51; 52]. Nonetheless, these works are neither focused on the specific task of real-time signal control nor based on bibliometric analysis. This study, in detail, follows a seven-step procedure and applies four analytical scientometric tools: (a) topic visualization, (b) cocitation analysis to establish influential journals and references, (c) country analysis based on coauthorship and (d) trending-topics analysis carried out by overlay visualization. All bibliometric analyses were carried out on data coming from the Scopus database. Influential papers are identified and discussed in the last part of this work.
# Geo-Proxy-Based Site Classification for Regional Zonation of Seismic Site Effects in South Korea
###### Abstract  
Seismic site effects and topographic effects related to ground motion occur during an earthquake due to site-specific geotechnical or geological characteristics, including the geological or geographical structure and the characteristics of near-surface sub-soil layers. Site-specific site effects due to geological conditions have been confirmed in recent earthquake events. Earthquake-induced damage has mainly occurred at accumulated soft soil layers under basins or along coasts and rivers. An alternative method has recently been proposed for evaluating regional seismic site effects and amplification factors using digital elevation models (DEM). High-quality DEMs at high resolutions may be employed to resolve finer-scale variations in topographic gradients and consequently, correlated site response parameters. Because there are many regions in South Korea lacking borehole datasets, which are insufficient for site classification only using borehole datasets, a DEM-based proxy for seismic zonation can be effective. Thus, in this study, geo-proxy-based site classification was proposed based on empirical correlations with site response parameters and conducted for regional zonation of seismic site effects to identify the amplification of characteristics in the western metropolitan areas of South Korea, depending on the site-specific geo-spatial conditions.  
site classification; site response; geo-proxy; seismic zonation; geo-spatial grid +
Footnote †: journal: Applied Sciences
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
## 2 Preliminaries
### Heterogeneous Multi-Agent System  
By establishing fault models for UAVs and UGVs using Equations (4) and (9), the expression of the state variable for a heterogeneous multi-agent system can be constructed by merging state variables. The state variable model for heterogeneous systems is defined as follows:  
\[\begin{array}{c}\left[\begin{array}{c}\dot{X}_{\text{G}}\\ \dot{X}_{\text{A}}\end{array}\right]=\left[\begin{array}{cc}A_{\text{G}}&0 \\ 0&A_{A}\end{array}\right]\left[\begin{array}{c}X_{\text{G}}\\ X_{\text{A}}\end{array}\right]+\left[\begin{array}{cc}B_{\text{G}}&0\\ 0&B_{A}\end{array}\right]\left[\begin{array}{c}U_{\text{G}}\\ U_{\text{A}}\end{array}\right]\\ \dot{X}=AX+BU\\ \text{where }X=\left(X_{G}^{T},X_{A}^{T}\right)^{T},A=\left(\begin{array}{ cc}A_{\text{G}}&0\\ 0&A_{A}\end{array}\right),B=\left(\begin{array}{cc}B_{\text{G}}&0\\ 0&B_{A}\end{array}\right),X_{\text{G}}=\left(x_{\text{G1}},x_{\text{G2}}\ldots x _{\text{Gm}}\right)^{T}.\end{array}\]  
\[\begin{array}{c}X_{A}=\left(x_{A(m+1)},x_{A(m+2)}\ldots x_{An}\right)^{T}, and\ U=\left(U_{G}^{T},U_{A}^{T}\right)^{T},\\ U_{G}=\left(u_{G1}^{f},u_{G2}^{f}\ldots u_{Gm}^{f}\right)^{T},U_{A}=\left(u_{A (m+1)}^{f},u_{A(m+2)}^{f}\ldots u_{An}^{f}\right)^{T}.\end{array}\]  
To better describe the communication relationship between heterogeneous systems, a simple linear transformation needs to be applied to the Laplacian matrix of the system. The system consists of \(m\) UGVs and \(n-m\) UAVs, with a total of \(n\) agents. \(L\) is the corresponding Laplacian matrix, is shown as follows:  
\[L=\begin{bmatrix}L_{AA}&L_{AG}\\ L_{GA}&L_{GG}\end{bmatrix}.\]  
where \(L_{AG},L_{GA}\) represents information between heterogeneous agent systems. Regarding the actuator fault model of \(n\) heterogeneous agent systems described by Equation (11), when the heterogeneous multi-agent system reaches consensus, it implies that all agent states satisfy the following equation:  
\[\begin{array}{c}\underset{\text{t}\rightarrow\infty}{\lim}\|\text{P}_{\text{ i}}(\text{t})-\text{P}_{\text{0}}(\text{t})\|=0,\ \underset{\text{t}\rightarrow\infty}{\lim}\|\text{v}_{\text{i}}(\text{t})- \text{v}_{\text{0}}(\text{t})\|=0\\ \underset{\text{t}\rightarrow\infty}{\lim}\|\Omega_{\text{i}}(\text{t})-\Omega _{\text{0}}(\text{t})\|=0,\text{i}=\text{m}+1,m+2,\ldots n\\ \underset{\text{t}\rightarrow\infty}{\lim}\ \|\ \dot{\Omega}_{i}-\dot{\Omega}_{ \text{0}}\ \|=0,\text{i}=\text{m}+1,m+2,\ldots n\end{array} \tag{12}\]  
**Assumption 1**.: _The communication graph between agent systems is firmly connected, and at least one follower is linked to a leader._  
**Assumption 2**.: _During the operation of the leader-follower multi-agent system, the leader agent will not experience failures._
## 1 Introduction
One of the key methodological challenges of traffic data fusion application is to integrate multi-source (i.e., metro and bus passenger flow) observations, while the existing studies only perform single-source data forecasting. Next, because of the variable factors influencing transfer demand, current traffic prediction methods are not capable of completing the forecasting tasks for multi-variate transfer passenger flow, especially for incomplete traffic datasets caused by various hardware/software failures (e.g., equipment errors, communication troubles or bad weather). Like other time series, transfer passenger flow usually contains both linear and non-linear spatiotemporal modes, and the expected transfer model must be able to solve the problem of extreme flow, as passengers always arrive at short notice. To fill the gaps and tackle such concerns in existing studies, a joint DL-based model with MF imputation is created and tested for multivariate transfer passenger flow forecasting in this study. Correspondingly, the main contributions of this paper are as follows:  
* We provide a reliable multivariate prediction model of Joint-IF by taking into account a variety of spatiotemporal factors (e.g., weather and location) for the accurate and efficient calculation of transfer passenger flow between metro and bus stations;
* Before performing the multi-interval forecasting task, we adopt an efficient temporal regularized MF for recovering transfer passenger flow under both missing situations to further enhance the robustness of the network-wide forecasting model;
* We conduct a large number of experiments on real-world transfer passenger flow. Compared with the baseline models, the results demonstrate that the proposed Joint-IF predicts the transfer passenger flow with lower error and better robustness. The rest of this paper is organized as follows. Section 2 briefly reviews relevant work on traffic data modeling. In Section 3, the methodology of transfer passenger flow modeling is described in detail. Section 4 reports extensive numerical experiments and analyzes the performance of Joint-IF. Finally, the concluding remarks are given in Section 5.
## 3 Results
### Damage Area Fraction  
DAF in all image types increased with inflation radius (Figure 4). Uninflated (radius = 0 \(\mu\)m), DAF in the baseline \(\mu\)CT scans is significantly higher than the enhanced and BSE images (\(p\) = 0.026, \(p\) < 0.001, respectively). However, for every non-zero inflation radius, the BSE and deblurred \(\mu\)CT images have significantly higher DAFs than the baseline and enhanced images.  
Convolving (or inflating) the \(BaSO_{4}\)-labeled pixels blur the damage, which increases the area identified as \(BaSO_{4}\). Higher stain areas are measured on the BSE images when there is inflation as smaller areas of \(BaSO_{4}\)-labeled pixels are connected, forming larger regions. In \(\mu\)CT images, more of the \(BaSO_{4}\)-labeled pixels are clumped in regions representing larger areas of damage; pixels inflated at the center of a clump do not contribute to additional stain.  
While DAF is equal across the enhanced, deblurred, and BSE images without inflation, the deblurred and BSE images likely identify smaller areas of stain and estimate smaller areas for concentrated \(BaSO_{4}\) clumps than the enhanced images.  
Figure 3: BaSO\({}_{4}\) visualization of microdamage in trabecular bone of a healthy rat L1 vertebra with four imaging modalities. (**a**) baseline \(\mu\)CT; (**b**) enhanced \(\mu\)CT; (**c**) enhanced \(\mu\)CT with a deblurring algorithm applied post-acquisition; (**d**) BSE imaging. The red arrow identified matching \(BaSO_{4}\) stained structures in all images.
# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
## 3 Materials and Methods
### Workflow of Plastic Dynamic Detection
#### 3.4.2 Calculation of Plastic Angle  
The plastic is at a random angle on the conveyor belt. If the angle of the end effector is constant, it may grasp other adjacent plastics. In addition, adjusting the angle can maximize its fitting area with the plastic and improve the accuracy of grasping. Therefore, it is necessary to measure the angle of each plastic, and the robotic arm makes adjustments based on the angle information.  
The key to calculating the angle is solving for the OBB (Oriented Bounding Box), also known as the minimum bounding rectangle. To reduce irrelevant information, the image is first cropped and gray scale processing is performed. Then, the Canny [44] edge extraction operator combined with Gaussian smoothing noise reduction is applied to realize the edge detection.  
The minimum bounding rectangle is solved in three steps. First, the contour points are extracted. Then, [PERSON]'s scan method [45] is used to solve the convex hull. Finally, the minimum bounding rectangle is solved according to the idea of rotating calipers. The process of solving the angle is shown in Figure 13.  
The end effector posture adjustment takes a long time when the angle change is too large. To avoid this problem, we constrain the angle output range to be [0\({}^{\circ}\),90\({}^{\circ}\)]. The corresponding output rule is shown in Figure 14.  
Figure 12: Flow chart of plastic sorting.  
The output result is specified as \([(m,n),(a,b),\beta]\), where \((m,n)\) is the center point of the minimum bounding rectangle, \(a\) and \(b\) are the length and width of the rectangle, \(\beta\) is the angle between the side of length \(a\) and the \(x\)-axis, and \(\theta\) is the angle between the side of length \(b\) and the \(x\)-axis, which is the angle of the plastic. Taking counterclockwise as the positive angle, the angle is calculated as follows:  
\[\theta=\left\{\begin{array}{l}90-\beta,a\leq b\\ -\beta,a>b\end{array}\right., \tag{14}\]
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 2 Aluminum Electrolysis Process and Anode Effect Analysis  
### Aluminum Electrolysis Production Process  
The purpose of aluminum electrolysis is to obtain high purity aluminum. In the modern aluminum electrolysis industry, the Hall-Eruit method is still the main method due to its wide applicability and ease of operation. Pre-baked aluminum reduction cells are widely used in modern aluminum electrolysis. The structure diagram is shown in Figure 1.  
The entire production process of aluminum electrolysis is carried out in the aluminum electrolytic cell. The raw material for production is aluminum oxide powder, the electrolyte is molten cryolite, and the anode material is carbon. Under the action of high temperature and direct current, reaction occurs in the cell, and aluminum liquid is produced at the cathode. After processing, aluminum liquid is made into various aluminum products.  
Figure 1: Structure diagram of modern aluminum reduction cell.
## 7 Simulation Assessment
### Performance of the Separated Phase-Current Controls Using IBDGs
#### 7.2.3 Permanent SLG Fault  
The SLG fault was created by short-circuiting phase \(b\) to ground with \(R_{f}\) of 0.01 \(\Omega\). The operation of the recloser-fuse and SDG breaker is shown in Figure 16. The fuse melted prior to the third tripping operation of the recloser. Thus, the separated phase-current control could prevent the miscoordination of the recloser-fuse even during a permanent fault.  
These simulation results demonstrated the effectiveness of separated phase-current controls using IBDGs and current estimations. The separated phase-current controls could regulate \(V_{dc}\) within the withstanding voltage of \(C_{dc}\). During unsymmetrical faults, there were some ripples that occurred in \(V_{dc}\) because of an oscillating power component. This may have led to a distorted current waveform. However, these ripples could be mitigated by introducing the notch filter into the \(V_{dc}\) control loop [40]. Due to no disconnection of IBDGs caused by excessive \(V_{dc}\), the IBDGs could reduce the total fault  
Figure 16: Operating time of recloser–fuse and SDG breaker under separated phase–current controls during permanent SLG fault.  
Figure 15: Separated phase–current control during temporary LL fault: (**a**) total fault current and grid current; (**b**) DC-link voltage; (**c**) operating time of recloser–fuse and SDG breaker.  
current amplitude to the grid current amplitude without any problem. The recloser-fuse coordination was preserved during temporary and permanent unsymmetrical faults. When the recloser was operated, the current was not supplied from the IBDGs to the isolated phase to avoid the damage caused by out-of-phase reclosing. Not only could this prevent recloser-fuse miscoordination and damage from out-of-phase reclosing, but it could also regulate the grid voltage for a nonfaulty phase. During the SLG fault, the grid overvoltage of the nonfaulty phase was compensated for within the upper permissible voltage limit (1.1 pu) by the voltage regulation feature of the separated phase-current control. Furthermore, the grid and SDG current estimations integrated into the IBDGs could avoid the delay of using PMUs. The estimated grid and SDG currents were equivalent to real-time grid and SDG currents; therefore, there was no error in the current reference calculated by using the estimated currents. The performance of the separated phase-current controls was not degraded.
# Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN
## 2 Literature Review
Firstly, the construction of its network structure is highly dependent on domain knowledge, which may lead to subjective deviations. Secondly, the method faces challenges in directly addressing systems with numerous variables and intricate relationships, especially when the hierarchical structure among factors is ambiguous, thereby constraining its modeling efficiency and accuracy. ISM is a widely used structural modeling analysis method in systems engineering, particularly suitable for systematic analysis with numerous variables, complex relationships, and unclear hierarchy [PERSON] et al. (2018). As a primarily qualitative analysis method, ISM systematically categorizes key influencing factors into distinct hierarchical levels, revealing their interrelationships and constructing a multi-level directed graph structure. This methodology effectively clarifies the interaction pathways among factors and simplifies complex relationships, thereby enhancing the overall structural clarity. This characteristic of ISM provides a clear structural framework for BN to make up for the limitations of BN modeling. [PERSON] et al. [PERSON] et al. (2018) developed a prefabricated building quality evaluation model by introducing the ISM-BN method, demonstrating the applicability of it. [PERSON] et al. [PERSON] et al. (2018) combined ISM with BN to realize early warning of schedule risk in international railway projects and demonstrated the potential of combining these two methods. Mountainous highway service slope systems are characterized by large scale, complex environments, and dynamic nature. Slope instability disasters typically result from the combined effects of multiple risk factors with intricate interrelationships. Traditional single methods often fail to comprehensively characterize the risk evolution process. The ISM-BN integrated analytical method addresses these limitations through ISM's hierarchical analysis capability, which effectively clarifies complex interrelationships between influencing factors and establishes a clear structural framework. Meanwhile, BN's probabilistic reasoning capability further enhances dynamic analysis and certainty assessment of the risk evolution process.