esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Statistical Analysis and Regression Model  
Analyzing the experimental data (Appendix A; Table A1), collected for this research with analysis of variance (ANOVA), was accomplished with the assistance of the Design Expert computer program (version 13.0.5). The goal of this analysis was to establish which input variables had the largest effect on the response variables, specifically the RD, hardness, and compression properties. The results of the ANOVA are presented in Table 2, which include the F-value, the \(p\)-value, the lack of fit, the sufficient precision, the adjusted R\({}^{2}\), and the predicted R\({}^{2}\) at a confidence level of 95%. All of the results had \(p\)-values that were lower than 0.05, which suggests that the independent parameters, individual model coefficients, and interaction terms all had a significant influence on the responses. These data suggest that the models that were anticipated were adequate. It was revealed that the weight percentage of the Al\({}_{2}\)O\({}_{3}\) was the most significant factor in all of the responses. This was followed by the relative H/D and the pressure value. The signal-to-noise ratio (S/N ratio) was computed with \"adequate precision\" in order to determine whether or not the model was viable. The obtained ratio was greater than four, which indicates that the model was usable in navigating the design space.  
In order to model the output responses, the interactions among the independent variables and a wide variety of regression transformation forms were examined. The degree of correlation that existed between the input parameters and the output responses was analyzed using a second-order polynomial regression model. Models created in an experimental study are statistically significant and can be used to predict the outcome responses when their adjusted coefficient of determination (adjusted R\({}^{2}\)) and predicted coefficient of determination (predicted R\({}^{2}\)) are close to 1 and the adjusted R\({}^{2}\) values are within or close to 0.2 of the predicted R\({}^{2}\)[58], as was the case in this study.  
Process optimization was established to identify the ideal parameter combination for AMCs for the intended responses based on a thorough analysis of each independent variable. The values of the AMC parameters and responses are shown for all RSM results as red dots and blue ones, respectively, as depicted in Figures 6, 11, and 18. A genetic algorithm (GA) was used to identify the best feasible combination of the AMC's independent variables that resulted in the material's maximum RD, hardness, and mechanical properties. To improve the results of the GA, a hybrid of the design of experiments and the GA (DOE-GA) was applied.
# Dynamic Coal Flow-Based Energy Consumption Optimization of Scraper Conveyor
## 2 Development of the Dynamic Coal Flow Model and EDEM Simulation
### Dynamic Coal Flow Simulation Model Based on Discrete Element Method  
#### 2.2.1 Discrete Element Contact Model for Coal Particles  
(1) Contact model theory  
This study employs EDEM(2022) software for simulation modeling. EDEM is a widely used discrete element simulation platform for modeling the behavior of particulate and bulk materials. Due to its accuracy and flexibility in modeling particle contact behavior, it has been extensively applied in studies of coal mining and transportation processes, such as coal cutting, conveying, and unloading ([PERSON] et al., 2024; [PERSON] et al., 2018) [31; 32]. Previous research has demonstrated that EDEM offers good applicability and reliability for simulating the dynamic behavior of coal flow in equipment such as scraper conveyors.  
In EDEM, particle interactions are governed by multiple contact models, including the contact stiffness model, slip model, and bonding model. The contact stiffness model defines the relationship between force and displacement during particle collisions; the slip model describes tangential relative motion between particles; and the bonding model simulates particle adhesion or cohesion. These models together form the simulation foundation for accurately reproducing the behavior of coal particles in dynamic conveying processes, enabling realistic representation of coal flow within the scraper conveyor system.  
Given that coal contains moisture, adhesion between particles is influenced by liquid bridge and van der Waals forces. The \"Hertz-Mindlin with bonding\" model is employed to simulate these interactions during the coal-cutting process. Coal and gangue particles interact via bonding forces that break under stress, after which the particles revert to elastic contact. For two colliding particles with velocities \(v_{i}\),\(v_{j}\) and angular velocities \(x_{i}x_{j}\), the contact mechanics model is shown in Figure 4.  
_Appl. Sci._**2025**, _15_, 7366 The coal particle model in this study is built using spherical particles. While real coal particles are often hammer-shaped or blocky, spherical particles are used to simplify calculations and improve simulation efficiency. In EDEM software, a model of typical coal particle shapes is created, as shown in Figure 5.
# Mechanical Integrity Analysis of a Printed Circuit Heat exchanger with Channel Misalignment
## 3 Results and Discussion  
### Stress Distribution at the Surface  
2D stress distribution resulted by FEA simulation in the misalignment case of 0.85 mm is shown in Figure 4. The simulation result indicates that the highest stress intensity occurs at the tip area of the water channel. In the ten cases, the highest stress was in the tip area of the water channel. This was due to the higher pressure load in the water channel area. The maximum equivalent surface von Mises stress is shown in Figure 5. The highest maximum stress intensity was observed under the misalignment condition of 0.85 mm, while the lowest intensity was observed under the misalignment condition of 0 mm. This result indicates that the maximum stress distribution increases with the  
Figure 3: Mesh sensitivity test.  
Figure 2: Applied boundary conditions for numerical simulation.  
misalignment. The maximum stress intensity in the case of 0.85 mm misalignment is 10% higher compared to the 0 mm misalignment condition.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 2 System Model and Problem Formulation
### Network Model  
As illustrated in Figure 1, we consider a wireless IIoT with multiple BSs and devices with different network requirements. We denote the set of BSs and devices by \(\mathcal{M}=\{1,2,\ldots,M\}\) and \(\mathcal{N}=\{1,2,\ldots,N\}\). Moreover, the devices are categorized into three typical scenarios, i.e., eMBB, mMTC, and URLLC, which are denoted by \(\mathcal{N}_{\text{eM}}\), \(\mathcal{N}_{\text{uR}}\) and \(\mathcal{N}_{\text{mM}}\), respectively. Hence, \(\mathcal{N}_{\text{eM}}\cup\mathcal{N}_{\text{uR}}\cup\mathcal{N}_{\text{mM} }=\mathcal{N}\).  
We further denote the bandwidth available in the \(m\)-th BS by \(B_{m}\), \(m\in\mathcal{M}\), and its transmission power by \(P_{m}\). Additionally, a binary variable \(x_{nm}\in\{0,1\}\) is used to denote the association between BS-\(m\) and device-\(n\), and accordingly, \(b_{mn}\), the bandwidth allocated by the BS-\(m\) to the device-\(n\) if \(x_{nm}=1\); otherwise, \(x_{nm}=0\).
## 3 Results and Discussion
### Vibration Acceleration Analysis  
The effect of the change in cutting parameters and toolholder types on vibration acceleration is shown in Figures 7 and 8. A change in cutting speed caused a roughly linear rise in peak-to-peak and RMS values, irrespective of the toolholder type in which the tool was mounted. However, several times lower values were obtained with the shrinkfit toolholder, which was also sensitive to changes in cutting speed.  
A similar relationship was observed when milling with variable feed per tooth. Peak-to-peak and RMS values progressively increased throughout the range of feed change. Once again, the smallest values were observed using the shrinkfit toolholder, whose change due to feed variation was small. For the other toolholders, several times higher parameter values were obtained, and their increase due to increase in feed was much greater.  
The outcome of the investigations demonstrates that the implementation of trochoidal milling made it possible to obtain comparable or even lower values of vibration acceleration than in the case of magnesium alloy milling along straight paths, previously described in other papers.
## 3 Methodology
### Theoretical Background
\[v_{t}=\rho\ v_{t-1}+(1-\rho)\cdot{g_{t}}^{2} \tag{4}\]  
\[\Delta w_{t}=\frac{\eta}{\sqrt{v_{t}+\varepsilon}}*\ g_{t}^{2},w_{t}+1=w_{t}+ \Delta w_{t} \tag{5}\]  
where \(p\) represents the input learning parameter, \(\varepsilon\) is set to a small value to prevent the gradients from blowing up, \(\eta\) is the learning rate, and \(v_{t}\) and \(g_{t}\) denote the gradient squares with exponential average and the gradient, respectively, at time \(t\). Adam optimizer combines together the heuristics of the RMSProp and momentum. The update in the gradients of the Adam optimizer are given below in Equations (6)-(9). \[v_{t}=\beta_{1}*\ v_{t}-1-(1-\beta_{1})*\ g_{t} \tag{6}\]  
\[s_{t}=\beta_{2}*\ s_{t}-1-(1-\beta_{2})*\ g_{t}^{2} \tag{7}\]  
\[\Delta w_{t}=-\eta\frac{v_{t}}{\sqrt{s_{t}}+\varepsilon}*g_{t} \tag{8}\]  
\[w_{t+1}=w_{t}+\Delta w_{t} \tag{9}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are hyperparameters, \(s_{t}\) is the exponential average of squares of gradients, and \(v_{t}\) is the exponential average of gradients. The comparison between the RMSProp and Adam optimizer is analyzed for hurricane intensity estimation. The dataset description and the proposed methodology are described in detail in the subsequent subsections.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 6 Multi-Vehicle System Model  
### State Equations  
In order to formulate the CL algorithm, let us first consider a three-vehicle system shown in Figure 2. In the illustrated scenario, a vehicle designated as \(b\) or \"base vehicle\" obtains a pose measurement of another vehicle designated as \(t\) or \"target vehicle\" relative to its own body-fixed reference frame. Referring to (6)-(8), the system may be represented by the 14 states representing absolute poses of the two vehicles. The state equations are written as:  
\[\dot{x} =g(x)u \tag{12}\] \[\times =\left[r_{0}^{b^{\top}}\quad\dot{q}^{b^{\top}}\quad r_{0}^{\top} \quad\dot{q}^{t^{\top}}\right]^{\top}\] \[u =\left[r^{b^{\top}}\quad\omega^{b^{\top}}\quad v^{t^{\top}}\quad \omega^{t^{\top}}\right]^{\top}\] \[g =\text{diag}\Big{[}R_{0}^{b}\quad\frac{1}{2}G^{b}\quad R_{0}^{t} \quad\frac{1}{2}G^{t}\Big{]}\]  
The \(14\times 14\) Jacobian matrix, \(J\), for the two-vehicle network is simply defined as:  
\[J=\text{diag}\Big{[}J^{b}\quad J^{t}\Big{]} \tag{13}\]  
where \(J^{b}\) and \(J^{t}\) are defined for vehicle \(b\) and vehicle \(t\) according to (9)-(11).
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 4 Case Study
### Comparative Study of Different Signal Processing Methods  
Currently, there are many methods for processing faulty traveling wave signals in the time-frequency domain. For example, EMD is used in this paper, as well as CEEMD, CEEMDAN, VMD, etc. EMD algorithms may suffer from end-point effects as well as modal aliasing when decomposing time-domain signals. Many scholars have further improved on the basis of EMD to solve the possible problems of EMD. To address this, we conducted a comparative study of the above algorithms for fault localization.  
As shown in Figure 16, CEEMD and CEEMDAN add Gaussian white noise to the original signal to resolve the modal aliasing phenomenon of EMD; however, the high-frequency Gaussian white noise still remains in the intrinsic mode function of the signal, resulting in a large fluctuation of the \(inf_{1}(t)\) component and making it difficult to locate the second peak point. Unlike EMD, the VMD method constrains the bandwidth of the eigenmode functions so that the frequency of each eigenmode function is around a center frequency, similar to a band-pass filter; however, this method also affects the identification of the wave head. In addition, the signal is decomposed by different methods, and the EMD decomposition has a much better advantage in terms of wave amplitude than the other methods.  
Figure 14: Scatterplot of locating error.  
Figure 15: Schematic of SVD detail components with different transition resistances.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 3 Challenges of Parallelizing Stream Joins on TEEs
### Partitioning Schemes  
Parallel join algorithms are based on the theoretical foundation that the sets of records manipulated by a database query processing system can be partitioned into disjoint subsets, such that join results are computed independently across records. For lazy approaches, we may _physically_ or _logically_ partition input tuples into individual threads. The goal of the physical relation partitioning is to break at least the smaller input (i.e., tuples from \(R\)) into pieces that fit into the caches. Thus, it avoids the hash table being shared among  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Design Aspects** & **Design Decisions** & **Description** \\ \hline \multirow{2}{*}{Execution Approaches} & Lazy & joins a complete set of tuples \\ \cline{2-3}  & Eager & joins a subset of tuples \\ \hline \multirow{2}{*}{Join Methods} & Hash-Based & Builds hash tables before probing \\ \cline{2-3}  & Sort-Merge & Sorts both relations before merging \\ \hline \multirow{4}{*}{Partitioning Schemes} & Logical Partitioning & Passes pointers instead of values \\ \cline{2-3}  & Physical Partitioning & Passes values instead of pointers \\ \cline{2-3}  & Join-Matrix Partitioning & Content-insensitive matrix \\ \cline{2-3}  & Join -Biclique Partitioning & Content-sensitive bipartite graph \\ \hline \multirow{2}{*}{Distributed Scheduling Strategies} & Static Initialization & Assumes equal partition sizes \\ \cline{2-3}  & Dynamic Adaptation & Adjusts partition sizes dynamically \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Design Aspects.  
threads. However, it brings the additional cost of replicating tuples. Alternatively, we may only logically partition input tuples among threads by passing pointers. For eager approaches [5; 6; 10; 26], there are two stream partitioning schemes that have been proposed. We revisit the impact of both of them on TEEs: (1) _join-matrix_[26] and (2) _join-biclique_[9], where the former is content-insensitive, and the latter is content-sensitive. Intuitively, the join matrix model designs a join between two datasets \(R\) and \(S\) as a matrix, where each dimension corresponds to one relation. Alternatively, the join-biclique model organizes the processing units as a complete bipartite graph, where each side corresponds to a relation. It is superior in memory efficiency, but is sensitive to the consumption of network bandwidth for tuple routing. Figure 4 illustrates both partitioning schemes. Note that in the JM case, groupings are optional. Each join partition can be independently processed by a separate process.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 4 Experiment
### Baselines  
We selected a basic neural network model and several state-of-the-art models of LJP as a baseline. The effectiveness of our model is proved by comparative analysis. The descriptions of the baseline models are as follows:  
CNN [42]: A text classification model based on CNN. Text features are extracted using CNNs with different width filters, combined with fully connected neural networks to predict the charges.  
HAN [31]: A hierarchical attention network model for document classification. The model uses word-level GRU to extract features, an attention mechanism to aggregate words into sentences and further extract features from sentence-level GRU, and an attention mechanism to classify documents.  
Few-attribute [9]: A model for few-shot charges prediction. The model introduces legal attributes as external knowledge, and multi-task joint modeling is used to predict both charges and attributes.  
MPBFN [20]: A multi-perspective bi-feedback network for legal judgment prediction. The network effectively utilizes the dependencies among subtasks of LJP, and a word collection attention mechanism is proposed to increase the accuracy of penalty prediction.  
LADAN [8]: A model for confusing charges prediction. The model uses a graph neural network to extract the differences between legal articles and re-encode the case descriptions to predict the charges.  
In the experimental process, this paper treated the charge prediction as a classification problem, selected a set of optimal experimental parameters by reviewing the range of experimental parameters given in the original paper, conducted three iterations of the experiment, and took the best single result as the final result.
## 2 Materials and Methods
### Statistical Analysis  
Measurement-testing and content analysis were used for obtaining data, and a questionnaire, mathematical, statistical, and logical methods were used. Data are presented as means, percentages, and standard deviations (SD). The Shapiro-Wilk test for normality was performed on all variables. Data showing a normal distribution paired sample t-test was used to find significant differences in pre- to post-tests from selected fitness tests in the aerobic and resistance training group and volleyball group. For non-normal distribution data, the Wilcoxon signed-rank test was used. Two-Sample \(t\)-test and Mann-Whitney test were used for comparing the results of both groups with each other. The level of significance was set at \(p\)\(\leq\) 0.05, and statistically significant differences were marked with a symbol (*). In order to interpret the practical significance of the research results, the effect size (ES) was reported. [PERSON] defined effect sizes as small, d = 0.2; medium, d = 0.5; and large, d = 0.8 while [PERSON] defined correlation r = 0.1 to 0.3 as small, r = 0.3 to 0.5 as medium, and r = 0.5 to 1.0 as large [24]. Statistical and data analyses were performed using the statistical program IBM SPSS Statistics 21 (IBM Corporation, Armonk, NY, USA).
## 3 Improved Adaptive Ant Colony Algorithm
### Improved Heuristic Function  
To reduce the blindness of the ants in the early stage and to find a short and smooth trajectory in an uneven environment, the heuristic function is redesigned. The smoothness and height factors are added to the heuristic function, as shown in Equation (6). The comprehensive heuristic function includes length \(d\), a smoothing function s, and a height function \(h\).  
\[\eta_{i,j}(t)=\lambda d(i,j,g)+\gamma h(i,j)+\psi s^{k}_{i,j}(t) \tag{6}\]  
In the heuristic function, the distance function \(d(i,j,g)\), height function \(h(i,j)\), and smoothing function \(s^{k}_{i,j}(t)\) are defined as follows. \(\lambda,\gamma,\psi\) are weight parameters and they depend on the application.  
\[d(i,j,g)=\frac{d_{\max(i,g)}-d_{j,g}}{d_{\max(j,g)}-d_{\min(j,g)}+0.01}\times\omega \tag{7}\]  
where \(d(i,j,g)\) is the corrected distance from a certain adjacent grid cell \(j\) of the current grid cell \(i\) to the target grid cell. \(d(i,j,g)\) is designed to enlarge the distance gap between adjacent grid cells. \(d_{\max}\) and \(d_{\min}\) are the maximum and minimum distances between adjacent grid cells and the end point. \(w\) is a correction parameter, and 0.01 avoids the situation where the denominator is 0.  
\[h(i,j)=\left\{\begin{array}{ll}\frac{h_{\max}-|h(i)-h(j)|}{h_{\max}-h_{\min}+ 0.01}\times\omega,&|h(i)-h(j)|\leq h_{c}\\ \infty,&|h(i)-h(j)|>h_{c}\end{array}\right. \tag{8}\]  
where \(h(i,j)\) is the corrected height from the current grid cell \(i\) to a certain neighboring grid cell \(j\). \(h(i,j)\) is designed to guide ants to visit flatter grid cells. \(h_{c}\) is the height constraint value of the robot, which should meet the limit of the height threshold. \(h_{\max}\) and \(h_{\min}\) are the maximum and minimum height differences between adjacent grid cells and the current grid cell. \(w\) is a correction parameter, and 0.01 avoids the situation where the denominator is 0.  
\[s^{k}_{i,j}(t)=\left\{\begin{array}{ll}u,&dir^{k}_{a,j}(t)=dir^{k}_{i,j}(t) \\ 0.1*u,&otherwise\end{array}\right. \tag{9}\]  
where \(s^{k}_{i,j}(t)\) is the smoothness of the trajectory passed by the \(k\)-th ant in the \(t\)-th iteration. Generally, ants can move in eight directions adjacent to the current grid. Suppose an ant reaches the current point \(i\) from \(a\), and then goes to the next point \(j\). If the moving direction of the previous step \(dir^{k}_{a,j}\) is consistent with the moving direction of \(dir^{k}_{i,j}\), the reward \(u\) will be given, and the value of \(u\) is set to 5 in this paper.
# Passive Sampling and Analysis of Naphthalene
## Experiments
### MS Detector Calibration with Naphthalene Standard Solution  
Naphthalene purity was confirmed by its direct injection to GC-MS. A methanolic solution of 5.5 mg of crystal naphthalene was completely dissolved in 5.0 mL of solvent. The concentration of the analyte in the prepared solution made up 1.10 mg\(\cdot\)mL\({}^{-1}\). On the second dilution, a 10 \(\upmu\)L of the first solution was diluted with 990 \(\upmu\)L of MeOH to the concentration of 10.0 ng\(\cdot\)mL\({}^{-1}\) and diluted again to concentrations of 10, 30, 50, 100, 300, 500, 1000 and 3000 pg\(\cdot\)mL\({}^{-1}\), respectively. All prepared standards were analyzed in triplicates. Resulting chromatographic peaks were integrated and identified using previously established retention times of single analytes. Detector response was plotted using MSD ChemStation software and MS Excel software. Obtained detector calibrations were linear in the whole studied concentration range with coefficients of determination R\({}^{2}\) > 0.999 (Figure S1), and relative standard deviations (RSDs) ranged from 3 to 6%.  
Detector response factors (RFs) (i.e., slopes of calibrations) for target analytes were used to estimate experimental mass extracted onto SPME using experimental RFs for naphthalene peak area (PA) counts.