esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Characterization of Rock Damage Distribution
#### 4.2.2 Width of the Fault Damage Zones  
Combined with the previous studies, we found a power-law relation can depict the relationship between them, even when the slip displacement reaches 100 km and the gap in the damage zone widths can be up to \(10^{2}\)-\(10^{4}\) m or the slip displacement is less than \(10^{-1}\) m (Figure 5c). The power indices of 0.52 and 0.73 for normal faults and strike-slip faults, respectively, are both less than 1, indicating that the development width of fault damage zones slows down with the continuous increase in fault displacement. Previous studies have shown that the threshold for this low displacement is approximately \(2\times 10^{3}\) m [50]. When the cumulative displacement is below this threshold, the rate of increase in the width of fault damage zones becomes more severe with the increase in displacement. That is, low-displacement faults have a larger proportion of damage zones compared to their displacement, possibly because the damage zone in low-displacement faults increases linearly with the displacement at certain locations [54] or only significantly increases during the early sliding period of the fault [66]. By contrast, large-displacement faults with cumulative displacements exceeding \(2\times 10^{3}\) m need to dissipate more energy to become wider as fault slip increases, generating wider zones of damage in the surrounding rock [67]. The significant difference in the width of the damage zones between normal faults and strike-slip faults may be due to the different fault movement types. The damage caused by the compressive stress state in the strike-slip fault is generally stronger than that in the normal fault in the tensile stress state.
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 4 Numerical Simulation Results
### Calculation Results  
The stress distribution pattern in igneous boreholes influenced by a weak interlayer is depicted in Figure 7. Furthermore, Figure 8 illustrates the plastic strain clouds of the wellbore under various strength parameters of the weak interlayer.  
Figure 6: The result of the overall partition of model grid.  
Figure 8: Plastic strain under different strength parameters of weak interlayer. (a) \(C=3\) MPa, \(\varphi=20^{\circ}\); (b) \(C=5\) MPa, \(\varphi=25^{\circ}\); (c) \(C=7\) MPa, \(\varphi=30^{\circ}\); (d) \(C=9\) MPa, \(\varphi=35^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Parameters** & **Normal Formation** & **Weak Interlayer** \\ \hline Thickness (m) & 0.03 & 0.001 \\ Internal friction angle (\({}^{\circ}\)) & 45 & 30 \\ Cohesion (MPa) & 15 & 7 \\ Modulus of elasticity (GPa) & 22 & 10 \\ Poisson’s ratio & 0.2 & 0.3 \\ Permeability (mD) & 0.005 & 50 \\ Porosity ratio & 0.1 & 0.2 \\ Formation pore pressure equivalent density (g/cm\({}^{3}\)) & 1.0 \\ Overburden pressure equivalent density (g/cm\({}^{3}\)) & 2.2 & \\ Minimum horizontal in situ stress equivalent density (g/cm\({}^{3}\)) & 1.9 & \\ Maximum horizontal in situ stress equivalent density (g/cm\({}^{3}\)) & 2.1 & \\ Cement slurry density (g/cm\({}^{3}\)) & 1.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model parameters.  
Figure 7: Stress distribution of igneous rock wellbore simulated.
## 2 Materials and Methods
### Contingent Valuation Method (CVM)  
CVM is an evaluation method traditionally applied to quantify the willingness to pay for ES through the administration of a questionnaire [8]. CVM relies on the social acceptance values of distinct urban arrangement scenarios and the consequent effects on population wellbeing [9]. The amount that participants declare to be eventually willing to pay or contribute to avoid a change or to maintain a better situation is determined through an analysis of their preferences; thus, CVM is considered mostly an economic assessment technique. Indeed, CVM is regarded as capable of estimating in monetary terms the value of non-marketable assets, such as ES provided by green belts along roadsides, parks, plants, trees, and green spaces [10].  
In the study area, the method assesses whether and how the residents currently use the landscape of urban and peri-urban green areas and the related ES, without the possibility that they can do so in the future. The study aimed to assess the quality and quantity of these services that affect the welfare of urban and peri-urban people in the local area by directly asking the local population about their willingness to pay to maintain green areas and their contributions to well-being. The accessibility, use, and level of visitors to green spaces in the study were measured in terms of the ANGST (Accessible Natural Green Spaces Standard Model). This model measured the population size, distance of green areas from settlement areas, and size of green areas/park in hectares. Unfortunately, our study found the current situation of the study is not according to the rules and standards of this model. A few parks are available for a large population, which creates different issues in terms of their catchment area and unavailability of space.  
Therefore, the method relies on querying individuals about their willingness to pay for particular peri-urban landscape services, or ES [33].  
The answers were aggregated and visualized through descriptive statistical analyses, and answers related to worries were weighted based on the score reached on the Likert scale.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Improved Performance Optimization Model Based on HMM Algorithm
#### 4.3.1 Channel Model  
The channel behavior follows a two-state Markov chain with idle (\(c_{0}\)) and busy (\(c_{1}\)) components, whose one-step transition probability is depicted in Figure 6.  
\[\left\{\begin{array}{c}c_{0}c_{01}=c_{1}c_{10}\\ c_{0}+c_{1}=1\end{array}\right. \tag{53}\]  
We obtain the solutions of Equation (53) as:  
\[c_{0}=\frac{c_{10}}{c_{10}+c_{01}},c_{1}=\frac{c_{01}}{c_{10}+c_{01}} \tag{54}\]  
Figure 6: Markov channel model.  
The steady-state probabilities \(\pi_{0}\) and \(\pi_{1}\) can be calculated from the Markov convergence theorem. Probabilities \(\pi_{0}\) and \(\pi_{1}\) are used in the dynamic game of the hidden Markov prediction model.
## 2 Materials and Methodology
### Collection Data in Study Area  
In the first step, field data from the study area were collected, and data mining was performed based on the information characteristic (e.g., revenue water ratio, leakage management activity, variation of the revenue water ratio according to the leakage management activity, leakage status, and the spatiotemporal characteristics of leakage). This study considered the variation of the revenue water ratio according to the leakage management  
Figure 1: Procedure of this study.  
activity as a data-mining standard and then, among the various leakage management activities, the factors that can improve the revenue water ratio were selected.  
Leakage can be categorized into burst or background leakage, according to characteristics such as actual quantity of leakage and type of leakage generation. Background leakage is leakage that occurs at the pipe connection or joint, and the amount of leakage is too small to be recognized. Burst leakage is divided into reported and unreported burst leakage. Generally, the reported burst leakage is a case in which leakage can be confirmed by exposure to the surface or by a significant amount greater than normal conditions in terms of supply, whereas unreported burst leakage is leakage that is difficult to detect [10; 11].  
The International Water Association (IWA) Water Losses Task Force is divided into seven categories, using the concepts of unavoidable annual real loss, current annual real losses, and potentially recoverable real losses, to analyze the change in the amount of water leakage according to the leakage management methods (i.e., pipe replacement, meter replacement, pressure reduction valve installation, sensor installation for leakage monitoring, leakage repair, leakage detection, and block system establishment). Among these, pressure reduction valve installation, block system establishment, and sensor installation for leakage monitoring were excluded in this study because it is difficult to quantify their effects, and these operations are irregular.  
Therefore, considering the leakage management factors suggested by the IWA, pipe replacement (PR), meter replacement (MR), leakage repair (LR), and leakage detection (LD) were selected as the water loss management factors to estimate revenue water ratio increase (or reduction of leakage ratio) following those activities. This study applied six raw data elements: four leakage management factors (PR, MR, LR, LD), the revenue water ratio, and the leakage ratio reduction. The variance of the revenue water ratio and the leakage ratio reduction according to the four leakage management factors were then calculated according to those six raw data elements. The data were collected every year for 10 years, comparing the maintenance effect with the leakage management activity. Therefore, based on the characteristics of the applied data, this study performed two types of data mining based on the revenue water ratio or the leakage ratio and time-series characteristics.
## 5 Results and Discussion  
### Experimental Verification of Reliability Allocation  
Based on the ship electronic information system, a reliability allocation model is established to verify the reliability allocation proposed in this study. The feasibility, importance, and cost coefficient of each unit of the ship electronic information system are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Name** & **Feasibility** & **Importance** & **Cost Coefficient** \\ \hline
1 & Optical frequency equipment & 0.9 & 0.9 & 0.06 \\
2 & RF integrated equipment & 0.8 & 0.8 & 0.03 \\
3 & Computing service equipment 1 & 0.9 & 0.9 & 0.04 \\
4 & Computing service equipment 5 & 0.9 & 0.9 & 0.04 \\
5 & Computing service equipment 2 & 0.9 & 0.9 & 0.04 \\
6 & Computing service equipment 6 & 0.9 & 0.9 & 0.04 \\
7 & Computing service equipment 3 & 0.9 & 0.9 & 0.04 \\
8 & Computing service equipment 7 & 0.9 & 0.9 & 0.04 \\
9 & Computing service equipment 4 & 0.9 & 0.9 & 0.04 \\
10 & Computing service equipment 8 & 0.9 & 0.9 & 0.04 \\
11 & Display device & 0.6 & 0.5 & 0.01 \\
12 & Remote air defense equipment & 0.7 & 0.7 & 0.10 \\
13 & Short range air defense equipment & 0.6 & 0.6 & 0.05 \\
14 & Electronic interference & 0.5 & 0.5 & 0.02 \\
15 & Sonar & 0.8 & 0.9 & 0.05 \\
16 & Torpedo & 0.5 & 0.7 & 0.06 \\
17 & Against underwater defense & 0.6 & 0.7 & 0.04 \\
18 & Short range weapons to land & 0.7 & 0.6 & 0.03 \\
19 & Remote weapons to land & 0.6 & 0.8 & 0.07 \\
20 & Network equipment 1 & 0.6 & 0.9 & 0.08 \\
21 & Network equipment 2 & 0.6 & 0.9 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 7: Parameters of the ship electronic information system unit.  
The unit reliability constraints are set to be reliable between \([0.95-0.999]\), air-to-air mission reliability \(\geq\) 0.90, sea-to-underwater mission reliability \(\geq\) 0.88, and land mission reliability \(\geq\) 0.92. The reliability model can be obtained by bringing the formula of sea-to-air mission, sea-to-underwater mission and sea-to-land mission into the formula as shown below.  
\[\begin{split}\text{min}C=\sum\limits_{i=1}^{21}C(R_{i})=\sum \limits_{i=1}^{21}k_{i}(1-f_{i})\ln\biggl{(}\frac{1}{R_{i,\text{max}}-R_{i}} \biggr{)}e^{cr_{i}}\\ s.t.\left\{\begin{array}{l}0.95\leq R_{i}\leq 0.999\\ R_{m1}\geq 0.90\\ R_{m2}\geq 0.88\\ R_{m3}\geq 0.92\end{array}\right.\end{split} \tag{29}\]  
1.1 Solve Using DE DE is encoded in real values and the relevant parameters are presented in Table 8.  
According to the reliability allocation model, the differential algorithm performs operations on the relevant parameters, and the result of calculating the cost using DE is shown in Figure 9 with an objective function value of 2.0433.  
Using the difference algorithm to solve the mission reliability model of the ship electronic information system, the reliability allocation of each unit is presented in Table 9.
## 2 Overview of Machine Learning Techniques
### Different Machine Learning Techniques and Their Applications  
Here are some specific techniques that are commonly used in machine learning:  
1. Deep Learning: A subfield of machine learning that is inspired by the structure of the human brain, deep learning algorithms attempt to mimic the learning pattern of the human brain to interpret data such as images, sound, and text. This technique is often used in autonomous vehicles, voice-controlled assistants, and image recognition [10].
2. Random Forests: Random Forests are an ensemble learning method that operate by constructing multiple decision trees at training time and outputting the class that is the mode of the classes (for classification problems) or the mean prediction (for  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ML Technique** & **Description** & **Common Applications** & **Strengths** & **Weaknesses** \\ \hline Supervised & Algorithms learn from labeled & Vessel traffic prediction; & Easy to interpret. & Requires large amount of labeled data. \\ Learning [31] & datasets to predict outcomes & anomaly detection. & Can handle unstructured and unlabeled data. & Results often harder to interpret. \\ \hline Unsupervised & Algorithms find patterns and structures in unlabeled data. & Anomaly detection; data exploration. & Can handle unstructured and unlabeled data. & Results often harder to interpret. \\ \hline Reinforcement Learning [33] & Algorithms learn optimal actions through trial and error to maximize some notion of cumulative reward. & Optimal route planning; resource allocation. & Can handle dynamic environments and adapt to new situations. & Requires a lot of data and computational resources. \\ \hline Deep Learning [34] & Complex algorithms inspired by the structure of the human brain (neural networks). & Image recognition tasks. & Can handle large volumes of unstructured data; can model complex relationships. & Requires large amount of data and computational resources; results often not easily interpretable. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative overview of different machine learning techniques: descriptions, common applications, strengths, and weaknesses.  
regression problems) of the individual trees. They are often used in banking, stock markets, medicine, and e-commerce [23].
* Support Vector Machines (SVMs): An SVM is a supervised learning model used for classification and regression analysis. It is commonly used in face detection, text and hypertext categorization, the classification of images, and hand-writing recognition [34].
* Neural Networks: Inspired by the human brain, a neural network algorithm is a set of algorithms that are designed to recognize patterns. They are commonly used in speech recognition, image recognition, and natural language processing (NLP) [9].  
Each of these techniques has its strengths and is suitable for different kinds of tasks. The choice of technique usually depends on the nature of the problem and the type of data available.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Monostatic Deployment
#### 2.1.2 Single Beam Versus Multibeam  
MIMO systems hold significant promise for achieving exceptionally high-capacity communication links by leveraging spatial multiplexing while simultaneously enabling real-time tracking of multiple objects, such as cars, cyclists, and pedestrians, on the sensing side. To utilize multiple antennas, the simplest approach involves phased arrays that perform sensing and communication in a time-division manner. Alternatively, substantial research has focused on developing beamforming strategies that facilitate the coexistence of communication and sensing beams [21]. This addresses the conflicting requirements of maintaining a stable communication link with the user equipment (UE) while allowing dynamic beam scanning on the sensing side to detect and track non-cooperative objects. Moreover, the alignment of transmitting and receiving antennas enables straightforward computation of range-angle maps through beam scanning.
# Electrophysiological Hyperscanning of Negotiation During Group-Oriented Decision-Making
###### Abstract  
Background: This study investigates the electrophysiological (EEG) correlates underlying negotiation dynamics in dyads engaged in a shared decision-making process. Methods: Using EEG hyperscanning, we examined single-brain and inter-brain neural activity in 26 participants (13 dyads) during a structured negotiation task. The participants, selected for their group-oriented decision-making preference, discussed a realistic group decisional scenario while their EEG activity was recorded. EEG frequency bands (delta, theta, alpha, beta, and gamma) were analyzed and Euclidean Distances were computed for measuring dissimilarity at the inter-brain neural level. Results: At the single-brain level, the results show increased delta and theta power in frontal regions, reflecting emotional engagement and goal-directed control, alongside heightened beta and gamma activity in parieto-occipital areas, linked to cognitive integration and decision-monitoring during the negotiation process. At the inter-brain neural level, we observed significant dissimilarity in frontal delta activity compared to temporo-central and parieto-occipital one, suggesting that negotiation involves independent cognitive regulation within the members of the dyads rather than complete neural synchrony. Conclusions: These findings highlight the dual role of negotiation as both a cooperative and cognitively demanding process, requiring emotional alignment and strategic adaptation. This study advances our understanding of the neurophysiological bases of negotiation and provides insights into how inter-brain dynamics shape collaborative decision-making.  
decision-making; group orientation; EEG hyperscanning; single-brain; inter-brain
## 3 Research Design
### Explanation Methods
#### 3.5.8. BLRP  
BLRP, or the Bayesian interpretation of Layer-wise Relevance Propagation [36], is a method that offers visual explanations for various levels of certainty. In this research, radiologists were shown alpha values of 5, 25, 50, 75, and 95. A lower \(\alpha\) value, such as 5, indicates the most dependable relevant pixels, while a higher alpha value, like 95, signifies less reliable relevant pixels. Radiologists were provided with an explanation of the meaning behind these alpha values during an initial learning phase in this study.
## 4 Analysis of Old Hanger Test Detection
### Steel Wire Detection
#### 4.4.3 Uniformity of Galvanized Layer  
The integrity of the galvanized layer of steel wire directly affects the chance of occurrence and the rate of the development of rust on the wire. For this reason, the resistance of the steel wire's galvanized layer to copper sulfate must be tested [21]. The test photo is shown in Figure 16. Within the specified time, the galvanized steel wire sample was immersed in copper sulfate solution, either once or several times continuously, and the replacement reaction was carried out to gradually dissolve the zinc layer. Moreover, defects were exposed on the surface to determine the uniformity of the zinc coating. Three wires were randomly selected, and the test results are shown in Table 2. As can be seen from the table, the maximum number of times the specimen wire resisted copper sulfate was two, indicating that the galvanized layer of the wire was significantly weakened and the protective effect on the inner layer of the wire was attenuated, which could not meet the specification requirements [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Testing Items**} & **Standard** & **Test Results (Times)** & **Single-Item Judgment** \\  & **Requirements**[18] & **Test Results (Times)** & **Single-Item Judgment** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Torsional \\ Performance \\ \end{tabular} } & \multirow{2}{*}{Number of times \(\geq\) 8} & 1 & 4 \\  & & 2 & 5 \\  & & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of the torsional performance of steel wire.  
Figure 16: Copper sulfate resistance test of steel-wire-galvanized layer: (**a**) experiment samples, (**b**) copper–sulfuric acid solution, and (**c**) copper exposed after sample test.
## 4 Discussion
### Currently Available Commercial Products and Computational Methods Used in BCI
EEG signal processing for BCIs demands rapid execution, typically under 40 ms, to ensure real-time feedback. Raw EEG signals undergo preprocessing, including filtering and artifact removal. Artifacts, which are extraneous signals from sources like muscle activity or electrical interference, can be addressed through various methods, including manual identification, linear trend removal, and source separation techniques. Spatial filtering is crucial for enhancing the signal-to-noise ratio and isolating relevant brain activity. The Laplacian filter, a commonly used spatial filter, highlights localized activity by referencing neighboring electrodes. Another approach, Common Average Referencing, subtracts the average signal across all electrodes from each individual electrode's signal, aiming to reduce common noise. The Common Spatial Patterns method is particularly effective for motor imagery BCIs, as it identifies spatial filters that maximize the variance between different mental states, such as imagining left-hand versus right-hand movement. Following preprocessing and spatial filtering, EEG signals are fed into classifiers for decoding user intent or recognizing brain states. Various machine learning algorithms are employed for this purpose, including linear discriminant analysis, support vector machines, and artificial neural networks. The selection and optimization of these computational methods are crucial for developing accurate, reliable, and robust BCI systems.
## 4 Experimental Results
### Experimental Setting
#### 4.1.1 Number of Progressive Stages in the Rain Streak Removal Stage  
The charts in Figure 5 show that the effect of the number of PRN stages (\(T\)) on the PSNR and SSIM evaluation metrics on the Rain100H dataset. It can be noted that the more stages in the network, the higher the values of both PSNR and SSIM. However, when \(T>8\), both PSNR and SSIM stop improving. Additionally, Figure 6, lists the average running time when tested on the Rain100H dataset on models with stages \(T=2\), \(3\), \(4\), \(5\), \(6\), \(7\), \(8\), \(9\), and \(10\) on a computer equipped with NVIDIA GTX 1050 Ti GPU. Considering the trade-off between efficiency and deraining performance, we set \(T\) to 8 in the following experiments.
## 3 Modeling of Heterogeneous Cluster Deployment Tasks  
This section establishes a theoretical framework for heterogeneous cluster mission planning, starting from the orbital coordinate system and equations of relative motion. Firstly, from the perspective of coordinate systems, the geocentric inertial coordinate system and the orbital coordinate system are defined. The geocentric inertial coordinate system is used to describe the motion of satellites within the Earth's gravitational field, while the orbital coordinate system is employed to depict the relative motion of satellites with respect to a primary satellite. Secondly, considering the orbital deployment requirements of satellites in actual missions, a satellite state transition model based on the equations of relative motion is proposed.