esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[17] analyzed the constraint relationship between green wave bands for passenger cars and buses on arterials and proposed a green wave bandwidth optimization model with the minimization of the weighted sum of bus travel time as the optimization objective. [PERSON] et al. [18] proposed a multi-mode (passenger cars and transit vehicles) arterial green wave control model with the function of an arterial system partition. Aiming at the green wave control problem of passenger cars and buses, [PERSON] [19] proposed the BUS-MULTIBAND and the BUS-AM-BAND based on the MULTIBAND and the AM-BAND [4]. [PERSON] et al. [20] proposed an arterial bus signal priority method considering multi-path coordination of social vehicles, addressing the issue where implementing bus priority can reduce the green wave control optimization space for social vehicles. In summary, many scholars have studied the green wave control of passenger cars and buses at the arterial level. However, there is little research on the green wave control of the two at the level of closed road networks. The existing arterial green wave control methods for passenger cars and buses cannot address the green wave control problem in closed road networks. The reason is that the green wave control for passenger cars and buses in closed road networks is different from that on arterials. A closed road network consists of multiple intersecting arterials, and these arterials are not independent of each other within the network. Moreover, the arterial green wave control can only improve the efficiency of one single road, while the green wave control for closed road networks can simultaneously coordinate the green wave bands of multiple intersecting arterials within the network, which is more conducive to achieving efficient regional control effects. Therefore, this paper takes passenger cars and buses as the research objects, analyses the spatio-temporal relationships between their driving characteristics and green wave control using time-space diagrams, and then presents a green wave control model for cars and buses in closed road networks. The highlights of the presented model are summarized as follows. (1) Phase sequences under the symmetric phase scheme are employed as a decision variable, which breaks the traditional practice that phase sequence is fixed under the symmetric phase scheme and improves the applicability of the model. (2) A sufficiently large positive number \(M\) and \(0/1\) variables are introduced to relax certain constraints, which can prevent infeasible solution scenarios when solving the model.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 5 Experiment and Results
### Data Information  
DTU fault data from Mar 2020 to Jun 2020 in one district of Nanjing, China was collected. After data preprocessing, a DTU fault database containing 12,320 item sets was obtained. By scanning the database, \(FA\) was constructed. Fault types \(T\) had five elements, which were the remote control fault, remote signaling fault, DTU offline, frequent online-offline, and other faults. Factors \(F\) have 10 kinds, including mis-operation, humidity, wireless signal quality, power failure, effects of primary-side equipment, hardware operating state, software operating state, DTU upgradation, family defects, and long-time service. Fault modules \(M\) had five elements, which were the CPU module, communication module, sampling module, power supply module, and control circuit module. Brands \(B\) had 15 elements.
## 3 Results and Discussion
### Atmospheric Concentrations of PAHs and OH-PAHs
The sampling point was located next to a main road in a residential area of Kanazawa city and there are low PAH and OH-PAHs emissions from residential areas. [PERSON] et al. [2] reported that the long-range transport of PM and PAHs from China has relatively high contributions to atmospheric PM and PAH contamination in Kanazawa. Therefore, the current results, which are consistent with the previous study, and seasonal variations in atmospheric PAH and OH-PAH concentrations were caused by local emissions and long-range transportation. In this study, the detected atmospheric PAHs and OH-PAHs were bound to airborne particles, such as PM\({}_{2.5}\). Atmospheric concentrations of PM\({}_{2.5}\) were highest in spring 2017 (21.7 \(\pm\) 12.1 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), spring 2018 (17.5 \(\pm\) 9.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), winter 2018 (14.2 \(\pm\) 2.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), autumn 2017 (12.3 \(\pm\) 5.1 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), and summer 2017 (10.6 \(\pm\) 4.3 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) [2]. There were no significant seasonal variations (Tukey's test: \(p\) = 0.07-0.99). However, the total atmospheric PAHs and OH-PAHs concentrations were significantly correlated with atmospheric PM\({}_{2.5}\) (\(p\) < 0.01, \(r\) = 0.496 for PAHs; and \(p\) = 0.02, \(r\) = 0.394 for OH-PAHs; Figure 4a,b). The observed significant correlation between atmospheric PAHs and PM\({}_{2.5}\) concentrations is consistent with a previous study conducted in Japan [46]. The correlation between OH-PAHs and PM\({}_{2.5}\) was weaker than that between PAHs and PM\({}_{2.5}\). In this study, PM\({}_{2.5}\) was thought to be released mainly from vehicle exhaust with PAHs and OH-PAH adsorbed on the surface. The correlation weakness observed in the current study suggests that OH-PAHs are formed by secondary mechanisms after release into the atmosphere [27]. Additionally, the weakness of the correlation between the PAHs/OH-PAHs and PM\({}_{2.5}\) also suggests the degradation and/or chemical change in target chemicals in the atmosphere and on the filter [47]. Figure 3: Correlations between the atmospheric concentrations of total PAHs and OH-PAHs.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Influence of Parameter \(\delta\)  
Weakly trusting user trust relationships are filtered out and a small number of strongly trusting user trust relationships are fused in the GraphSAGE-CF. Conversely, a smaller value of \(\delta\) means that a large number of user trust relationships with relatively low trustworthiness are fused in GraphSAGE-CF. In this section, a sensitivity analysis of the trust threshold \(\delta\) is performed through a set of experiments. In Epinions and NC, \(\delta\) is set to 0.8, 0.85, 0.9, and 0.95 and in LastFM and WI, \(\delta\) is set to 0.4, 0.5, 0.6, 0.7, and 0.8. In addition, the number of neighbors N in both datasets is set to 5. The experimental results are shown in Figures 7-10.  
Figure 5: The Impact of parameter \(\alpha\) on NC.  
Figure 6: The Impact of parameter \(\alpha\) on WI.  
Figure 8: The Impact of parameter \(\delta\) on LastFM.  
Figure 7: The Impact of parameter \(\delta\) on Epinions.  
Figure 9: The Impact of parameter \(\delta\) on NC.  
From Figures 7-10, it can be observed that in the four datasets, the RMSE values have a rough trend of decreasing with increasing \(\delta\) and improving recommendation accuracy. The results show that it is more beneficial for GraphSAGE-CF to integrate fine-grained and strong trust relationships with users to improve recommendation performance. Finally, GraphSAGE-CF did not obtain the lowest RMSE with the same \(\delta\) value in both datasets, possibly because the network embedding technique learns different scales of user trust strength in different social networks. For example, in Epinions and NC, the maximum user trust strength is 0.9553, while in LastFM and WI, the maximum trust strength is 0.8769.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method  
The edge regions of an image typically exhibit higher local entropy and more complex texture features, which not only provide a greater capacity for information embedding but also contribute to stronger pixel disturbance masking effects. Building on this idea, we design an edge-aware dual-stream U-Net architecture in the encoder to optimize the steganography performance through edge enhancement. The method first applies the Sobel operator to the cover image for edge detection and enhancement, creating an edge-enhanced image E that strengthens structural information and provides additional texture details for subsequent feature extraction. The original cover image and the enhanced image are fed into the network as dual inputs, where each undergoes independent feature extraction via separate contracting paths. These features are then fused in the shared expanding path, integrating both edge and global information. To further enhance feature extraction, a multi-scale median attention (MSMA) mechanism is applied after each layer in the contracting path, improving the representation of steganography-related features while reducing redundant information. Additionally, the enhanced channel includes the InceptionDMK module, which utilizes multi-scale convolutions to capture multi-dimensional features. The combined features, along with the secret information \(M\), are iteratively optimized using dense convolutional blocks and Gated Recurrent Unit (GRU) units to generate a high-quality stego image \(S\). The decoder, a relatively simple feedforward convolutional neural network, is responsible for accurately extracting the hidden information \(M^{\prime}\) prime from the stego image \(S\). The discriminator, similar in structure to the decoder, is trained as a neural classifier to distinguish between the cover and stego images. It is adversarially trained with the encoder and decoder, thereby improving the quality of the generated stego images.  
The overall framework of the proposed method depicts an overview of the proposed framework (Figure 1).
## 5 SARA Data Model  
One of the objectives of the SARA architecture is to be platform and framework independent. With this goal in mind, we have established a minimum data model that can be easily adapted to the most common concepts used by the main platforms and development frameworks. Most of them (e.g., Unity, ARKit and ARCore), work with the concept of _Scene_. This _Scene_ groups in a hierarchical way a series of elements such as lights, cameras, and 3D models. In this case we have sought that the translation between these elements and those of our architecture to be as direct as possible. Figure 7 gathers in a class diagram the main entities of the model. In all the class diagrams presented, the prefix \"SARA\" has been added to those classes that could be confused with those of the development frameworks. Next, each entity will be presented in detail.  
Figure 7: Class diagram of the most basic data model handled in the architecture.
# Cube of Space Sampling for 3D Model Retrieval
## 3 3D Model Feature Representation
### Experimental Results  
In Table 1, we observed that among all of the features, the CSS based feature, \"Hollow + TFIDF\", had the best classification accuracy. In comparison with the baseline, using \"Hollow\" and \"Hollow + TFIDF\" features performed better, with a significant level of performance difference based on the Wilcoxon rank-sum test (\(p<0.05\)). Although using the \"Full\" feature provides similar accuracy to the baseline, it still performed slightly better.  
We used two sets of different values (5, 20, 60, 140, 220, 300, 380, 460, 540, 620, 700, and 859) and (1, 3, 5, 7, 9, 11, 13, and 15) to calculate the precision and recall rates of each feature, respectively. In this experiment, for simplicity, the Euclidean distance was used as a similarity measure to distinguish between 3D models. The results of precision and recall are shown in Figures 8 and 9, respectively.  
These results show that the baseline performed slightly better than CSS. However, since the proposed method was only based on simple statistics, the CSS features still  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **2-Fold** & **5-Fold** & **10-Fold** & **15-Fold** & **20-Fold** \\ \hline Baseline & 33.53\% & 42.61\% & 44.82\% & 46.33\% & 47.03\% \\ \hline Hollow & _37.60\%_ & _47.38\%_ & _50.76\%_ & _52.15\%_ & _52.27\%_ \\ \hline Hollow + TFIDF & _37.49\%_ & _49.48\%_ & _52.85\%_ & _54.25\%_ & _54.60\%_ \\ \hline Hollow + 2 & 25.73\% & 34.46\% & 36.32\% & 37.72\% & 37.72\% \\ \hline Full & 33.53\% & _43.77\%_ & _46.92\%_ & _48.54\%_ & _48.31\%_ \\ \hline MMF & 28.17\% & 37.95\% & 40.16\% & 41.68\% & 41.33\% \\ \hline MMF + \textasci{2} & 24.91\% & 33.41\% & 35.27\% & 36.44\% & 36.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of each feature representation method.  
Figure 8: The precision rate of each feature representation method.  
Figure 9: The recall rate of each feature representation method.  
contained a lot of noise and non-critical data, which was likely to be affected by the Euclidean distance leading to poorer precision and recall.  
Nevertheless, considering the classification accuracy, using the SVM classifier can effectively highlight the discriminative power of CSS features; thus, better results can be obtained.
# Analysis of Lateral Displacement of Pile Foundation Caused by Large-Diameter Shield Tunneling
## 2 Lateral Displacement of Front Pile Caused by Using a Shield Machine
### Pile Foundation Displacement Caused by Soil Displacement  
The additional load \(p\) acting on the pile foundation can be approximately expressed by the displacement field of the soil.  
\[p=\eta\left[\frac{mk}{m+1}S_{x}(z)-\frac{m^{2}G}{(m+1)^{2}}S_{x}^{\prime\prime }(z)\right] \tag{10}\]  
In the formula, \(\eta\) represents the load correction coefficient, and it ranges from 0.4 to 1.0; \(\mathcal{S}_{x}(z)\) represents the lateral displacement of the soil free field caused by the tunnel's construction.  
The equilibrium differential equation of a single pile on the Kerr foundation is:  
\[-\frac{EIG}{Dc}\frac{d^{6}w_{l}}{dz^{6}}+\frac{EI(c+k)}{Dc}\frac{d^{4}w_{l}}{ Dc}-G\frac{d^{4}w_{l}}{dz^{2}}+kw_{l}=p \tag{11}\]  
In the formula, \(w_{l}\) represents the lateral displacement of the pile foundations; \(z\) represents the buried depth of the pile foundations; and \(c\) represents the spring stiffness on the right side of the shear layer. Assuming \(c=mk\), \(m\) represents the second layer spring parameter of the foundations; [PERSON] et al. [17] used the finite difference method to solve Equation (11), which is a sixth-order differential equation, combined with the boundary conditions of the pile top and the pile end to obtain the horizontal (\(x\)-direction) displacement equation of the pile foundations:  
\[\{W_{l}\}=\left[K\right]^{-1}\cdot\{P\} \tag{12}\]  
\[\{W\}=\{w_{0},w_{1},...,w_{n-1},w_{n}\} \tag{13}\]  
\[\{P\}=\{p_{0},p_{1},...,p_{n-1},p_{n}\} \tag{14}\]  
The stiffness matrix [K] of the soil in the horizontal (lateral) direction when the top and end of the pile are free:  
\[\left[K\right]=\begin{bmatrix}\delta+2\gamma+4\beta-8\alpha&-4\beta-10\alpha &2\beta+2\alpha&2\alpha&&\\ \gamma+2\beta+6\alpha&\delta-\beta-2\alpha&\gamma-\alpha&\beta&\alpha&&\\ \beta-2\alpha&\gamma-\alpha&\delta&\gamma&\beta&\alpha&\\ \alpha&\beta&\gamma&\delta&\gamma&\beta&\alpha&\\ \ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\\  
\[\left\{\begin{array}{l}\alpha=-\frac{EIG}{Dch^{6}}\\ \beta=\frac{6 EIG}{Dch^{6}}+\frac{EI(c+k)}{Dch^{4}}\\ \gamma=-\frac{15 EIG}{Dch^{6}}-\frac{4E(c+k)}{Dch^{4}}-\frac{G}{h^{2}}\\ \delta=\frac{20 EIG}{Dch^{4}}+6\frac{E(c+k)}{Dch^{4}}+2\frac{G}{h^{2}}+k\end{array}\right. \tag{16}\]  
The expressions of each item in the formula are substituted into the equation, and the matrix and the expressions are expressed using MATLAB software ([[https://www.mathworks.com/products/matlab.html](https://www.mathworks.com/products/matlab.html)]([https://www.mathworks.com/products/matlab.html](https://www.mathworks.com/products/matlab.html))). The lateral displacement of the pile foundation caused by the soil displacement can be solved.
## 2 Materials and Methods
### Image Analysis and Statistics
This was achieved by searching for the largest overlap with respect to Intersection over Union (IoU) of segmentation masks, where everything with IoU smaller than 0.7 was discarded as segmentation errors or cell divisions and thus these cells were not used for calculation of dynamical features. As dynamical features describing cell motility, we have extracted cell speed (Euclidian distance between cell centroid in two consecutive frames) and Cell Dynamic Score (CDS) defined in [23] (Euclidean distance of cell pixel values in two consecutive frames). Cell dry mass and cell density are calculated as a sum and a mean of cell pixel values, respectively. Statistics was performed in R version 3.6.1 (R Core Team, 2020) [38] using ggpubr 0.2.4 (CRAN, 2020) package [39] with Wilcoxon test. Unless noted otherwise, \(p\) level \(<\) 0.05 was considered significant.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions  
Consequently, from the publications analyzed, the machine learning algorithms are divided into six taxonomies:  
1. Regression (R)-based methods,
2. Monte Carlo (MC)-based methods,
3. Numerical (N)-based methods,
4. Neural network (NN)-based methods,
5. Structural health monitoring (SHM)-based methods,
6. Random/hybrid (RH)-based methods.
## 2 Optimization Strategies
### Optimization Algorithms
#### 2.2.2 Derivative-Free Algorithms  
Unlike the previous one, this method only requires the value of the objective function, not the information of the derivative. If some discontinuity exists in cost functions, derivative - free algorithms may act in a more efficient manner. The Hooke-Jeeves pattern search method is one such method. It incorporates the past history of iterations in producing a new search direction [23]. Some other examples of this type of algorithms are the trust-region method and the Nelder-Mead downhill simplex method [24].
## 4 Discussion of the Findings
### Comparison with State-of-the-Art DSL Tools  
While Table 1 provides a functional overview of the most widely used DSLs for RESTful API specification--such as RAML, Swagger/OpenAPI, WADL, and API Blueprint--it lacks a benchmark-oriented discussion grounded in practical development scenarios. To address this, we propose a comparative evaluation based on three real-world use cases frequently encountered in web API engineering: (i) rapid deployment of CRUD operations, (ii) dynamic result transformation through integrated template engines, and (iii) extensibility and maintainability over time. In these scenarios, RestRho, through its JSON-based DSLs (DBRestRho and SQLRho), provides higher abstraction, seamless modularization, and native support for Handlebars templates, enabling faster and more customizable API generation. In contrast, existing tools often rely on rigid specifications and require additional frameworks to achieve equivalent flexibility, increasing both complexity and coupling.  
From a software engineering standpoint, the proposed solution outperforms the state of the art in both structural and empirical dimensions. RestRho exhibits low cyclomatic complexity (CNN = 1.4) and a high maintainability index (MI = 121.1), reflecting simplified logic and strong sustainability over time. Furthermore, empirical data collected from 39 final-year computer engineering students shows a 100% task completion rate in foundational API setup activities (T1, T8, T9), validating the tool's effectiveness and ease of use. These findings suggest that RestRho not only is viable as a domain-specific solution but also is competitive against mature alternatives when developer productivity, extensibility, and runtime customization are critical requirements.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 5 Results and Discussion
### Spectrogram Analysis of the SEAs  
Figures 11 and 12 compare the spectrograms of enhanced speech produced by each SEA for noisy speech data set (Section 4.2). Typically, the noise reduction is visibly improved when going from the KF-non-oracle method to the KF-oracle method. Specifically, the biased gain of the KF-non-oracle method passes a significant residual noise in the enhanced speech (Figures 11c and 12c). Additionally, the poor estimates of the a priori SNR introduces a high degree of residual noise in the enhanced speech produced by the MMSE-STSA method [9] (Figures 11d and 12d). The degree of residual noise decreases in the enhanced speech produced by the AKF-IT method [13] (Figures 11e and 12e). However, the residual noise appears as musical noise. The enhanced speech also gets distorted due to processing the noisy speech iteratively by AKF. The AKF-RMBT method [22] exhibits less residual noise in the enhanced speech; however, it suffers from distortion due to the underestimated Kalman gain (Figures 11f and 12f). The MDKF-MMSE method [17] produces less distorted speech (Figures 11g and 12g) as compared to AKF-RMBT method (Figures 11f and 12f). It can be seen that the proposed method produces enhanced speech with significantly less residual background noise and speech distortion (Figures 11h and 12h) than MDKF-MMSE [17] (Figures 11g and 12g). In addition, the enhanced speech produced by the proposed method is closely similar to the KF-oracle method (Figures 11i and 12i). It is due  
Figure 10: Average STOI score comparison between the proposed and benchmark SEAs on NOIZEUS corpus corrupted with: (**a**) _babble_, (**b**) _street_, (**c**) _factory2_, and (**d**) _f16_ noises for a wide range of SNR levels (from \(-5\) dB to \(15\) dB).  
to the reduced-biased Kalman gain of the proposed method, which is very similar to that of the KF-oracle method.