esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing
## 2 Simulation Model and Experimental Methods
### Initial and Boundary Conditions  
Equations (1) and (2) must be completed by an initial boundary condition for the specific enthalpy \(u\). An ambient initial condition \(u=u_{0}\) at time \(t=0\) will be imposed. Furthermore, the system of Equations (1) and (2) requires boundary conditions for the temperature \(T\) on the six faces of the parallelepipedon \(\Omega\) (see Figure 1). However, only the boundary conditions on the surface scanned by the laser (\(z=H\)) are relevant if the domain is large enough as compared to the treated region. On the five other faces, adiabatic or  
Figure 2: Temperature \(T\) (continuous. line) and thermal conductivity \(k\) (dashed line) as a function of the specific enthalpy for the X38 CrMVo5-1 steel. This figure has been constructed based on information found in the literature, in particular Lucefin [17]).  
thermalization conditions are imposed. These have only little influence on the final results since they are far away from the laser activity. The boundary surface reads:  
\[k(u)\overset{\rightarrow}{grad}T.\overset{\rightarrow}{n}=\kappa(T_{0}-T)+a(u) \ I_{0} \tag{3}\]  
In this equation, \(k=k(u)\) is the thermal conductivity (see Figure 2), \(T_{0}\) denotes the ambient temperature, and \(\kappa=50\ \times 10^{-6}\ \mathrm{W/(mm^{2}K)}\) is a heat transfer coefficient which is typical for natural convection. Note that this parameter has little influence on the final solution of the simulations. Finally, \(a=a(u)\) is the absorption coefficient of the material for the laser wavelength. In principle, this quantity depends on the specific enthalpy \(u\). In this work, we will essentially consider that the absorption coefficient of the material takes a constant value \(a_{s}\) for the solid material and a constant value \(a_{l}\) when the material is liquid. Spectrophotometry measurements have shown that the value \(a_{s}\) for the solid is influenced by the surface roughness. For a typical state corresponding to CH30 (equal to a \(Ra\) of 3.15 \(\upmu\)m), which is representative of a treated workpiece, a value of \(a_{s}=35\%\) has been measured. Experimental investigations were not possible for molten material and the value of \(a_{l}\) is uncertain. Due to higher reflectivity of the liquid, \(a_{l}\) is less than \(a_{s}\). We performed a sensitivity analysis for the parameter in a range between 20 and 30%.  
The last quantity \(I_{0}\) on the right-hand side of the boundary conditions (Equation (3)) is the laser intensity distribution (unit \(\mathrm{W/mm^{2}}\)). This depends on (\(x\),\(y\),\(t\)) and is connected to the laser position (\(x(t)\),\(y(t)\)) at time \(t\), to the laser power \(P_{0}\) and to the laser beam shape (mode and radius). As an example, one uses:  
\[I_{0}(x,y,t)=\left\{\begin{array}{c}P_{0}/\pi\omega^{2}\ if\ (x-x(t))^{2}+(y-y(t))^{2}\leq \omega^{2},\\ 0\ otherwise\end{array}\right. \tag{4}\]  
for a beam with a flat top shape with radius \(\omega\).
To achieve this, it is essential to establish a precise system for evaluating the blasting effect and construct an optimization model based on this system that accurately reflects real-world conditions. However, due to numerous imperfect evaluation systems for the blasting effects and various types of optimization models available, most personnel involved in blasting still heavily rely on empirical knowledge to make multiple adjustments in order to attain optimal outcomes. This dependence on experiential methods has long impeded changes in the design of blasting parameters and techniques within engineering projects, thereby limiting advancements in mining technology and cost optimization. The development of a method that can directly correlate blasting parameters with production costs, while being easily implementable and capable of accurately determining optimal blasting parameters, is, therefore, imperative. In recent years, with the rapid advancement of computer technology and artificial intelligence, various optimization algorithms have been extensively utilized in transportation [11], logistics [12], energy scheduling [13], as well as diverse forecasting applications [14]. Moreover, their application in mining has also yielded significant outcomes. In light of this, the present study employs the enhanced Gray Wolf optimization algorithm [15; 16] to determine the blasting parameters for open-pit drilling. Compared to conventional algorithms, this improved approach enables faster and more accurate identification of optimal values. Additionally, by establishing a production cost model for blasting shipping and solving an objective function related to optimal cost determination, it is possible to directly link blasting parameters with production costs. Furthermore, using a limestone mine located in southwest China as a case study example, the mining and production model is established to calculate production costs. The improved GrayWolf algorithm is then employed to solve this model and identify drilling and blasting parameters that minimize consumption costs. The specific analysis process is illustrated in Figure 2.
## 3 Results and Discussion
### Chemical Reactivity Parameters and Reorganization Energy  
The Ionization Potential (IP) and the Electron Affinity (EA) were studied to explore the ability of organic solar cells to transport the holes and electrons [34]. Calculated values of IP and EA were listed in Table 1, which show that the XY1 has a smaller value of IP than that of D35; EA for D35 and XY1 are 3.042 and 3.068 eV, respectively, and estimation of EA and IP shows that there is improvement of charge transport for the XY1 structure in comparison with D35 structure. The chemical hardness (_lh_), electrophilicity (\(\omega\)), and electroaccepting power (\(\omega^{+}\)) are important parameters that have influence on the efficiency of photoelectric conversion (see Table 1). It is obvious that the \(h\) of D35 and XY1 are 0.804 and 0.774 eV, respectively. The \(\omega^{+}\) of D35 and XY1 are 7.380 eV and 7.715 eV, respectively. The D-A-\(\pi\)-A model XY1 possesses lower \(h\) and higher \(\omega^{+}\) compared with D35, meaning a lower resistance to ICT and higher ability to receive charge. The \(\omega\) reflects the stability of the dye system, and the calculated \(\omega\) of D35 and XY1 are 9.203 and 9.539 eV, respectively, and that exhibits the same trend as the value of \(\omega^{+}\).
## 2 Literature Review Methodology
### Development of the Review Protocol
#### 2.2.2 Literature Search Process  
This SLR emphasizes high-quality sources to examine ML/DL in UWA communication. IEEE, Elsevier, and Springer were chosen for their broad coverage and peer-reviewed rigor. Journal articles were prioritized over conferences for their depth and validation. To extend scope, Google Scholar was searched (first 10 pages per term) to capture impactful studies not indexed in standard databases. Table 2 summarizes the results for search terms like channel estimation, adaptive modulation, and modulation recognition, using a 2020-2025 filter across the three major databases. Figure 5 outlines the selection process: 41,261 initial entries reduced to 43 through title, abstract, and general screening based on strict inclusion criteria. This ensures coverage of core advances and challenges in the field.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Search Term** & **IEEE** & **Elsevier** & **Springer** \\ \hline Underwater Acoustic & 5189 & 8422 & 6187 \\ \hline Underwater Acoustic Machine Learning & 346 & 1885 & 1264 \\ \hline Underwater Acoustic Deep Learning & 601 & 2001 & 1350 \\ \hline Underwater Acoustic Channel Estimation Machine Learning & 24 & 586 & 296 \\ \hline Underwater Acoustic Channel Estimation Deep Learning & 60 & 649 & 342 \\ \hline Underwater Acoustic Channel Prediction Machine Learning & 23 & 672 & 326 \\ \hline Underwater Acoustic Channel Prediction Deep Learning & 19 & 744 & 363 \\ \hline Underwater Acoustic Receiver Machine Learning & 36 & 526 & 359 \\ \hline Underwater Acoustic Receiver Deep Learning & 55 & 561 & 373 \\ \hline Underwater Acoustic Modulation Classification & 52 & 419 & 299 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Search results for ML and DL techniques in UWA communication (2020–2025).
## 2 Mixer Design
### Filters, RF Transition Model and LO-IF Diplexer Model  
In order to reduce the overall size of mixing circuit, a hammer-head filter and a CSMRs filter which have characteristics of compact structure and good stop-band rejection were developed as shown in Figure 5a,b. The CSMRs filter was used as IF LPF (low pass filter) and the length was 0.86 mm. The S21 of the CSMRs filter was less than \(-\)40 dB with a range of 162-226 GHz, which can pass IF signal and isolate other signals. The hammer-head filter was used as LO LPF and the length was just 0.38 mm. The S21 of this filter was less than \(-\)40 dB with a range of 300 GHz to 420 GHz, which can effectively prevent the leakage of the RF signal.  
To obtain initial optimization parameters of the 0.34 THz sub-harmonic mixer, a RF transition model and a LO-IF diplexer model were established, which are shown in Figure 5c,d. The RF signal was put by WR 2.8 waveguide (0.712 mm \(\times\) 0.356 mm) and the LO signal was put by WR 5.1 waveguide (1.295 mm \(\times\) 0.648 mm). The thickness of the quartz substrate and the gold strip on its surface was 50 um and 1 um, respectively. The waveguide and suspended microstrip structure only allow single-mode propagation. As the frequency ranged from 324 GHz to 373 GHz, the return loss and the insert loss of RF transition model was more than 15 dB and less than 0.28 dB, respectively. The return loss and insert loss of the LO-IF diplexer model were more than 20 dB and less than 0.35 dB, respectively, with a range of 163-185 GHz LO frequency. After getting the desired results, the RF transition model and LO-IF diplexer model were broken into basic circuit units to form an improved active circuit model, as shown in Figure 4, and their calculation results were used as the initial optimization parameters.
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 1 Introduction
In Section 6, we implement _pShare_ under zone-based estimation data and non-discretized ground-truth map data to evaluate the real accuracy of the scheme. The ride-sharing service is an optional ride-hailing service since it can be replaced by ride-matching if there are no suitable sharing riders. Therefore, we apply the ride-sharing from the perspective of drivers and match several riders for each driver. For an available driver, the ORH server first searches his surrounding requested riders, then computes for the best matching riders with minimum detouring time. More specifically, our main contributions can be summarized as follows:  
1. We propose a flexible, privacy-preserving online ride-sharing matching scheme _pShare_, which performs matching between a shared driver and group riders with minimum detouring distance, while protecting their location privacy against the ORH service provider. 2. We propose a zone-based riders-filtering approach, which can divide surrounding requested riders on the similarity of their traveling routes. Meanwhile, we have designed a secure comparing protocol, which computes users' encrypted data and outputs the ride-sharing results along with the minimum-detouring route. 3. We evaluate the scheme using generated user datasets which obey real-world distribution. The experimental results demonstrate that _pShare_ can achieve the functionality with high accuracy and practical efficiency. The rest of the paper is organized as follows: In Section 2, we formalize the problem and introduce the system model and the threat model. In Section 3, we present some preliminaries of the paper. Then we present the specific construction of _pShare_ in Section 4 and the privacy analysis in Section 5, followed by performance evaluation in Section 6. Finally, we conclude our work in Section 7.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment
### Comparative Experiments with the VGG19_Simple Model
#### 3.1.1 Single-Layer Pruning Analysis  
The reduction in Top1 accuracy for various layers was individually analyzed under three distinct methods: MCP-L1, Lasso regression, and MCP regression. This analysis was conducted based on different pruning ratios.  
Figure 10 illustrates the impact of individual pruning on the 3 rd to 11 th convolutional layers in the VGG19_Simple model. The vertical axis represents the decrease in Top1 accuracy, where smaller values indicate superior performance. The horizontal axis denotes the specified pruning ratio, with 0.1 indicating a 10% reduction in the current channel. Observing the figure, minimal differences in Top1 accuracy reduction are noted among the three methods when the pruning ratio is small (i.e., 0.1). However, as the pruning ratio increases, all three curves exhibit an upward trend. Notably, the MCP-L1 curve exhibits the steepest ascent, followed by the Lasso regression curve, while the MCP regression curve demonstrates a more gradual increase. In instances of single-layer pruning, the MCP regression method excels in the selection of representative channels during pruning and in effectively reconstructing the output.
# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 3 Signal Analysis  
### Signal Preprocessing  
Following the signal acquisition, signal preprocessing is crucial in order to reduce different types of measurement artifacts. Diverse techniques are used to extract only the desired components present in the VAG signal.  
Artifacts in VAG signals can be a baseline wander, a random noise, or a muscle contraction interference. In the VAG signal, a baseline wander is usually observed when people with knee disorders tremble their legs when undergoing a range of motion due to pain [20]. Furthermore, random noise is usually generated by the thermal effect of circuit constituents. As discussed earlier, firmly attaching the sensors to the skin can minimize the generation of noise during the experimental protocol.  
Additionally, to suppress these artifacts, several methods are currently cited in the literature. The most common ones are fixed and adaptive filtering. Fixed filtering uses a band-pass or high-pass filter with defined parameters. Adaptive filtering is more complex and requires a change in filter parameters over the signal's time adapting to its changing characteristics [2].  
An example of fixed filtering is provided by [PERSON] et al. [15] with the use of a Butterworth 4 th order filter with cutoff frequencies of 10 Hz and 720 Hz and a wavelet denoising technique to smooth the VAG signal. This type of filter minimizes low-frequency movement artifacts and prevents aliasing effects [16].  
Concerning adaptive filtering, [PERSON] et al. have studied the use of a so-called cascade moving average filter to remove the baseline wander [20]. This filter combines two successive moving average operators. It shows very good results in removing baseline wander from the VAG signal of both normal and abnormal subjects [20]. Another method introduced by [PERSON] et al. [21] utilizes Ensemble Empirical Mode Decomposition (EEMD) and Detrended Fluctuation Analysis (DFA) algorithms to eliminate artifacts such as baseline wander and random noise in VAG signals. This method can be explained as follows. Each raw signal is processed into several Intrinsic Mode Functions (IMFs) that represent the fast and slow oscillations in the signal. The DFA algorithm is then used to study the correlations of each of the IMFs, in order to identify which IMF contains the dominant artifacts of baseline wander and random noise. These IMFs are then removed from the signal [21].  
Muscle contraction interference (MCI) is another form of important artifact in viborthrographic signals. It involves the acoustic manifestation of the mechanical activity of muscle fibers [4]. Yet, the results of different research concluded that the MCI filtering did not increase the classification accuracy significantly (compared with the classification accuracy obtained using the features of the original VAG signals) [16; 26].
## 5 Experimental Results
### Evaluation Metrics  
The effectiveness of the proposed framework is evaluated using traditional classification metrics obtained from the confusion matrix. The confusion matrix presents a comprehensive summary of the model's predictions. In a binary classification problem, the confusion matrix yields four key outputs: True Positive (_TP_), False Positive (_FP_), True Negative (_TN_), and False Negative (_FN_) (as shown in Table 2). These values can be calculated from the matrix to evaluate the model's performance. The following Equations (13)-(16) are used to calculate the classification metrics, respectively [51]:  
_Accuracy_ is a widely used metric, and it is calculated from the _TP_, _FP_, _FN_, and _TN_. The accuracy is defined by the following formula:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{13}\]  
_Precision_: The proportion of correctly identified anomalies among all identified instances. The precision is formulated as:  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
_Recall_: The proportion of correctly identified anomalies among all actual anomalies. The recall is formulated as:  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Predicted DDoS** & **Predicted BENIGN** \\ \hline Actual DDoS & _TP_ & _FP_ \\ Actual BENIGN & _FN_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix for the binary classification task.  
\[Recall=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{15}\]  
The \(F1\) is the harmonic mean of precision and recall. The \(F1\) score ranges from a maximum value of 1 to a minimum value of 0. The \(F1\) is defined by the following formula:  
\[F1=2\cdot\frac{\mathit{precision}\cdot\mathit{recall}}{\mathit{precision}+ \mathit{recall}} \tag{16}\]  
The area under the receiver operating characteristic (ROC) curve, known as the AUC, is a commonly utilized performance metric for evaluating machine learning algorithms. The ROC curve measures the ability of the model to differentiate between classes across different thresholds. A high AUC value indicates that the model has a satisfactory measure of separability, meaning it can effectively identify positive instances from negative ones. Conversely, a low AUC indicates that the model can not perform well in classifying the data.
## 3 Challenges and Limitations
### Particle Fusion  
The metal sulfides that form during desulfurization has a larger molar volume than the original metal oxides which means that the metal oxide particles grow in size during sulfidation [60]. In a packed bed where the neighboring metal oxide particles are in contact with each other, there is not enough free space in the proximity of the particles, such that the particles tend to fuse together as they go through the sulfidation process (see Figure 2a) [61].  
A possible solution to avoid such a problem is to make the bed porous enough that there is sufficient free space for the particles to grow. From Equation (3), it is clear that the minimum value of bed porosity is equal to \(B\). Let us consider a packed bed of metal oxides with a porosity of \(\varepsilon_{b}\) filled with pellets that have a porosity of \(\varepsilon_{p}\). Assuming that the fusion of particles during the sulfidation process reduces the pellet porosity to zero and the bed porosity to \(B\), we can derive a condition for the minimum initial bed porosity under which fusion of particles is less likely:  
\[\varepsilon_{b}\geq\frac{(B-\varepsilon_{p})+\rho_{p}G}{(1-\varepsilon_{p})+ \rho_{p}G}\quad;\quad G=\sum\frac{x_{i}}{\rho_{i,o}}\left(\frac{\rho_{i,o}M_{i,s}}{\rho_{i,s}M_{i,o}}-1\right) \tag{5}\]  
where \(\rho_{p}\) is the pellet density defined in Equation (A1) and \(G\) is the total volume expansion per unit mass of the sorbent. The subscriptions \(o\) and \(s\) refer to oxide and sulfide form of metal \(i\) in the sorbent, respectively.  
Figure 2: Schematic of pellet fusion. (**a**) The bed is fully packed with metal oxides with a low bed porosity which results in particle fusion during sulfidation. (**b**) Inert particles are added to the packing to prevent fusion during sulfidation.  
However, for a packed bed of pure metal oxides, Equation (5) predicts very large porosity values that are not feasible in a packed bed configuration. Another possible solution for preventing the fusion of pellets in such cases is to add inert materials in the packing [62]. This could be either inside the pellets or simply by adding particles that are made of inert material in the packing matrix as shown in Figure 2b. \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) is an example of a suitable material to be used as inert [61], especially with recent developments to synthesize it from waste materials [63]. In this case, the main question to answer is how much inert is required to avoid the fusion. Similar to the previous case, it is assumed that during sulfidation, pellet porosity goes to zero and the bed porosity decreases to a value that is equal to \(B\). This leads to an estimation for the minimum mass fraction of inert to suppress fusion of particles inside the packed bed:  
\[x_{\text{inert}}\geq \rho_{\text{inert}}\Bigg{[}\frac{(1-\varepsilon_{p})(1- \varepsilon_{b})G}{(\varepsilon_{b}-B)+\varepsilon_{p}(1-\varepsilon_{b})}- \sum\frac{x_{i}}{\rho_{i,o}}\Bigg{]} \tag{6}\]  
where \(x_{\text{inert}}\) is the mass fraction of the inert material and \(\rho_{\text{inert}}\) is the density of it.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 3 Problem Description
### Meshed FWA Deployment  
Given this context, the work presented herein aims to assess the optimal degree of meshing for a FWA 5G NPN as exemplified in the Liverpool 5G deployment. We will consider a trade-off between the number of additional nodes/links introduced to a deployment and the improvements in terms of efficiency and routing that can be achieved as a result. Thus, we cannot realistically consider a fully meshed configuration due to cost and physical deployment (e.g., LOS) issues. In this section, we first introduce the physical deployment and then describe the constraints (in the context of the previous literature) that we considered.  
Figure 1: Overview of Liverpool 5G backhaul deployment.
## 4 Dimensional Deviations Study
### Identified Patterns and Trends in Dimensional Deviations  
The artificial neural network (ANN) analysis revealed key patterns and trends regarding the influence of layer thickness, infill density, and nominal diameter on dimensional deviations in FDM-printed spline shafts and hubs. The genetic algorithm further optimized these parameters to minimize deviations.  
_Layer thickness_: The ANN identified that smaller layer thickness values generally lead to lower dimensional deviations due to improved print resolution. However, excessively small layer thicknesses increase the printing time and may lead to material inconsistencies. The genetic algorithm optimized this trade-off, identifying an optimal range where accuracy is maximized without significantly increasing production time.  
_Infill density_: The results indicated that higher infill densities contribute to better structural integrity, reducing shrinkage-related deviations. However, after a certain threshold, increasing infill density had a diminishing effect on accuracy improvements while increasing material consumption. The genetic algorithm determined an optimal infill percentage that balances accuracy and material efficiency.  
_Nominal diameter_: The ANN detected those deviations varied across different spline nominal diameters. Larger nominal diameters tended to exhibit lower percentage deviations due to improved stability during printing. However, deviations were more unpredictable for smaller nominal diameters, requiring tighter process control. The optimization algorithm provided recommendations for suitable diameter ranges that minimize errors.