esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Statistical Characterization of Seismic Activity
### Progression of Earthquake Activity
#### 5.3.4 Local Moran's I (LMI)  
Local Moran's Index (LMI) was utilized to uncover and interpret local autocorrelation patterns in seismic activity [61]. This analysis is crucial for identifying spatial patterns not evident in global measures, revealing clusters, outliers, and local spatial dependencies and heterogeneities [43]. The comprehensive LMI results presented in Table 4 provide a thorough analysis of spatial clustering and autocorrelation in seismic events, highlighting regions that may be influenced by seismotectonic activities. Incorporating Z-scores and \(p\)-values into LMI calculations enhances the precision in identifying seismic activities deviating from random patterns.  
In Table 6, the LMI analysis, categorizing seismic events in the Red Sea area by magnitude range, reveals significant spatial autocorrelation. The LMI Index values, across magnitudes ranging from 1.00 to 3.20, consistently indicate clustering, evidenced by positive values. For the 1.00-1.55 magnitude range, an LMI Index of 0.123003, a low but significant \(Z\)-score of 0.224410, and a \(p\)-value of 0.005188 suggest modest clustering, reflective of minor fault activities or stress adjustments in the crust. As magnitudes increase, the LMI Index slightly decreases, with the 1.55-2.10 range showing 0.090980, indicating a dispersed clustering pattern, potentially due to varying geological processes. The 2.10-2.65 range's further reduced LMI Index of 0.029216, alongside a \(Z\)-score of 0.063909 and a significant \(p\)-value of 0.001094, still indicates notable clustering but less intense than lower magnitudes, suggesting concentrated seismic activity aligning with major fault lines or tectonic stress zones. In the highest range, 2.65-3.20, the LMI Index increases to 0.062832, with a \(Z\)-score of 0.083825 and a \(p\)-value of 0.006475, denoting more pronounced clustering for higher magnitudes, possibly influenced by major tectonic features. Overall, the LMI analysis reveals a complex relationship between the magnitude and spatial clustering of seismic events in the Red Sea area. It highlights seismicity patterns that are intricately linked to the region's seismotectonic framework. The observed clustering suggests concentrated distributions of stress and strain, indicating that seismic activity is not random but rather concentrated in specific areas. These areas likely correspond to key geological structures like faults and rafts, underscoring the importance of this analysis in understanding and predicting seismic behavior in tectonically active regions [5; 8; 58].
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis  
In this section, the proposed method is validated and analyzed by conducting simulation data and real data experiments. The following experiments involved in the calculation of the formula on SCNR refer to Equation (11). In the simulation experiment, the K clutter distribution at different clutter center frequencies is simulated, and the simulation target is added to compare and analyze the enhancement ability of the SCNR before and after clutter suppression. In the measured data, the sea clutter is the real echo data, and there are more obvious strong sea clutter regions, clutter-noise mixing regions, and long-distance noise regions, and simulation targets are added in different clutter regions to test the per formance of the filter designed by the proposed method in different clutter environments. The simulation parameters of the DQN are shown in Table 1. The estimated value network and target value network have the same structure and both use fully connected neural networks with three layers: the input layer, hidden layer, and output layer. The number of neurons in the hidden layer is 64, and ReLU is used as the activation function.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
###### Abstract  
The design of the high-speed on/off valve is challenging due to the interrelated structural parameters of its driving actuator. Hence, this study proposes a multi-objective optimization approach that integrates a backpropagation neural network and artificial fish swarm algorithm optimization techniques to accurately model the electromagnetic solenoid structure. The backpropagation neural network is fitted and trained using simulation data to obtain a reduced-order model of the system, enabling the precise prediction of the system's output based on the input structural parameters. By employing the artificial fish swarm algorithms, with optimization objectives focusing on the valve's opening and closing times, a Pareto optimal solution set comprising 30 solutions is generated. Utilizing the optimized structural parameters, a prototype is manufactured and an experimental setup is constructed to verify the dynamic characteristics and flow pressure drop. The high-speed on/off valve achieves an approximate opening and closing time of 3 ms. Notably, the system output predicted using the backpropagation neural network (BPNN) exhibits consistency with the experimental findings, providing a reliable alternative to mathematical modeling.  
high-speed on/off valves; BPNN; artificial fish swarm algorithms; optimization design Article
# Experimental & FEM Analysis of Orthodontic Mini-Inplant Design on Primary Stability
## 2 Materials and Methods
### Mini-Implant Design  
The geometric analysis of orthodontic mini-implants considers measurements of thread parameters, such as length, pitch, bearing depth, diameter at multiple points, etc., as shown on Figure 2. Mini-implants are scanned with the two-coordinate measuring microscope Zeiss-ZKM 01-250 C (ZKM 01-250 C, Carl Zeiss Industrielle Messtechnik GmbH, Oberkochen, Germany), which is used for different measuring operations that involve lengths and angles. The measuring process is performed with magnification of \(50\times\), so that all important geometric parameters would be taken into account. Values of geometric parameters are shown in Table 1.  
Figure 1: Mini-implants (right-to-left: Perfect Anchor and Tomas®-pin, respectively).  
Figure 2: Detailed geometry. (**a**): Tomas®-pin, (**b**): Perfect Anchor.  
The acquired values are used for calculation of the thread-shape factor (TSF), which represents the ratio of the thread-bearing depth and pitch. This factor is characterized as the bearing evaluation factor for the implant-bone system, and it is particularly important for primary stability [20].
## 1 Introduction
The aim of this paper is to present, for the first time, an analysis of the field scattered from a zero-thickness perfectly electrically conducting (PEC) disk buried in a lossy half-space, surrounded by air, and illuminated by the field generated by a Hertzian dipole located in the air. It is conducted by means of a suitable generalization of the analytically regularizing Helmholtz-Galerkin technique, previously introduced and successfully applied by the authors to the analysis of the plane-wave scattering from a disk [23] and a holed plane [24] in a homogeneous medium. The problem is formulated as an electric field integral equation (EFIE) in the Fourier transform domain for the unknown spectral counterpart of the surface current density on the disk, obtained by imposing vanishing of the tangential to the disk component of the total electric field, i.e., the superposition of the electric field radiated by the dipole and the one scattered from the disk in a planar layered medium. Due to the revolution symmetry of the problem, all the involved functions can be conveniently expanded in a Fourier series and the EFIE recast as an integral equation in the vector Hankel transform (VHT) domain [25]. As a result, the scattered field can be rewritten as the superposition of a free-space contribution, frequently called a \"primary\" contribution because it contains the most singular part of the integral operator, and a scattered or \"secondary\" contribution related to the layering of the medium. Since the VHT transforms of the surface curl-free contribution and the surface divergence-free contribution of the general azimuthal harmonic of the surface current density are scalar functions, such contributions are conveniently selected as new unknowns. MAP is used to discretize the integral equation, thus leading to a Fredholm second-kind matrix equation. It is worth noting that the selected basis functions have some additional properties that deserve to be mentioned. First, the guaranteed convergence is fast because the considered basis functionsreconstruct the physical behavior of the surface current density at the edge and around the center of the disk. Second, the convolution integrals resulting from the Galerkin projection reduce to algebraic products since the basis functions admit closed-form spectral domain counterparts, i.e., the matrix coefficients are 1-D improper integrals that, in the worst cases, can be efficiently evaluated by means of an analytical technique developed by the authors in a previous paper [26]. This paper is organized as follows: After this Introduction Section, the formulation of the problem and the proposed solution are presented in Section 2. Section 3 is devoted to showing the fast convergence of the proposed method, to the validation of the developed in-house C++ software code by means of comparisons with CST Microwave Studio 2023 (CST-MWS), and to the reconstruction of the scattered field behavior. The conclusions are summarized in Section 4.
## 2 Related Work
### Current Challenges and Our Approach  
Existing research in SER and SA still grapples with several challenges, including the need for large, annotated datasets, difficulties in cross-linguistic/gender recognition, and the need for more sophisticated fusion techniques. Additionally, model interpretability remains a concern. To address these limitations, our work introduces a novel approach to merging SER and SA through multimodal fusion, enabling a deeper understanding of affective communication. By combining speech signals with sentiment analysis from text, we aim to develop a framework that captures the emotional nuances of human communication, even across languages.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 4 Emerging Biodegradation Technologies
### Bioremediation  
The degradation of hydrocarbon compounds into smaller organic and inorganic compounds via the action of biological agents (microorganisms, plants, or plant residues) and the restoration of soil or water to its original state is generally known as Bioremediationtion [60; 64]. This degradation technique utilized the intrinsic ability of the native microorganism to act upon the pollutants while creating conditions that enhance the increased biodegradation rate. Biological processes are often used as a substitute for chemical or physical clean-up of oil spills since most bioremediation techniques require less equipment or labor than other processes [62; 64]. These are classified based on the location. The classification is in situ (on-site where the pollution took place) and ex-situ (outside the location of the pollution) bioremediation [60]. The in-situ bioremediation technique allows microorganisms to work efficiently as the local environment benefits their growth with no phase of adaptation required. Ex situ bioremediation techniques, alternatively, are primarily based on the physical exploitation of the pollutants without microorganisms' direct involvement in the remediation measures [60; 61]. Conversely, the In situ bioremediation technique is preferable as it is cost-effective compared to the cost of transporting contaminated soil out of the site, likewise, it is more effective in the remediation of a large area. This section gives an overview of recent bioremediation technologies and the operating factors aiding the success of each technology.
## 2 Theoretical Formulation
### Geometrical Parameters  
The position vector of a typical point in the element \(\mathbf{\Phi}\) can be approximated as  
\[\mathbf{X}=\sum_{k=1}^{q}\ \psi_{k}(\xi_{1},\ \xi_{2})\ \left(\mathbf{X}^{k}+\xi^{3} \frac{h}{2}\mathbf{n}^{k}\right), \tag{1}\]  
where \(\psi_{k}\big{(}\xi^{1},\ \xi^{2}\big{)}\) is the \(k\)-th two-dimensional (2D) Lagrange interpolation function of order \(p,\mathbf{X}^{k}\) is the position vector, \(\mathbf{n}^{k}\) is the unit normal vector of the \(k\)-th spectral node, and \(h\) denotes the thickness in the \(k\)-th spectral node position, so the thickness variation can be easily considered. The total number of nodes per element is \(q=(p+1)(p+1)\).  
Additionally, a set of covariant basis vectors for each point in the element must be defined:  
\[\mathbf{g}_{i}=\frac{\partial\mathbf{X}}{\partial\xi^{i}}\equiv\mathbf{X}_{i} \qquad\qquad i=1,\,2,\,3. \tag{2}\]  
A differential line element in the typical shell element, in terms of the curvilinear coordinates, can be expressed as  
\[\left\{\begin{array}{c}dX_{1}\\ dX_{2}\\ dX_{3}\end{array}\right\}^{T}=\left\{\begin{array}{c}d\xi^{1}\\ d\xi^{2}\\ d\xi^{3}\end{array}\right\}^{T}=\left\{\begin{array}{c}d\xi^{1}\\ d\xi^{2}\\ d\xi^{3}\end{array}\right\}^{T}\left[\begin{array}{c}\frac{\partial X_{1}}{ \partial\xi^{1}}\quad\frac{\partial X_{2}}{\partial\xi^{1}}\quad\frac{\partial X _{3}}{\partial\xi^{1}}\\ \frac{\partial X_{1}}{\partial\xi^{2}}\quad\frac{\partial X_{2}}{\partial\xi^{ 2}}\quad\frac{\partial X_{3}}{\partial\xi^{2}}\\ \frac{\partial X_{1}}{\partial\xi^{3}}\quad\frac{\partial X_{2}}{\partial\xi^{ 3}}\quad\frac{\partial X_{3}}{\partial\xi^{3}}\end{array}\right]=\{d\xi\}^{T}[ \mathbf{J}], \tag{3}\]  
where \([\mathbf{J}]\) is the Jacobian matrix with determinant \(J\). In conjunction with the covariant basis vectors, a contravariant set of basis vectors \(\mathbf{g}^{i}\) is defined, as follows:  
\[\mathbf{g}^{1}=\frac{\mathbf{g}_{2}\times\mathbf{g}_{3}}{J},\quad\mathbf{g}^{ 2}=\frac{\mathbf{g}_{3}\times\mathbf{g}_{1}}{J},\quad\mathbf{g}^{3}=\frac{ \mathbf{g}_{1}\times\mathbf{g}_{2}}{J}.\]  
Figure 1: Mid-surface and spectral nodes of a shell element.
## 4 Experimental Evaluation
### 10-Fold Cross-Validation Approach  
The results of the 10-fold cross-validation approach are presented in Table 6. Each model was trained using the best parameters identified by Optuna optimization, and the performance metrics were averaged across 10 folds. Random Forest, XGBoost, DNN, and Extra Trees achieved consistent and perfect performance across all metrics (100% precision,  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Models** & \multicolumn{2}{c}{**Train/Test Split (s) 1**} & \multicolumn{2}{c}{**10-Fold CV (s) 1**} \\ \cline{2-5}  & **Optuna** & **Training** & **Optuna** & **Training** \\ \hline Random Forest & 1830 & 120 & 1971 & 238 \\ AdaBoost & 5620 & 191 & 5935 & 496 \\ XGBoost & 1820 & 106 & 3512 & 1532 \\ Logistic Regression & 1745 & 1116 & 3997 & 3647 \\ Extra Trees & 1819 & 47 & 3271 & 221 \\ Deep Neural Networks & 11,010 & 1022 & 40,387 & 5104 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 4: Comparison of execution time.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**[PERSON] et al. [8]**} & \multicolumn{3}{c}{**[PERSON] and [PERSON] [17]**} & \multicolumn{3}{c}{**Our Study**} \\ \cline{2-13}  & **Acc.** & **Rec.** & **Prec.** & **F1** & **Acc.** & **Rec.** & **Prec.** & **F1** & **Acc.** & **Rec.** & **Prec.** & **F1** \\ \hline LR & 0.89 & 0.50 & 0.48 & 0.49 & 0.90 & 0.60 & 0.72 & 0.62 & 0.97 & 0.86 & 0.98 & 0.88 \\ AdaBoost & 0.92 & 0.66 & 0.48 & 0.51 & 0.97 & 0.73 & 0.75 & 0.72 & 0.98 & 0.82 & 0.80 & 0.81 \\ RF & 0.96 & 0.76 & 0.76 & **0.76** & 1.00 & 0.97 & 0.99 & **0.97** & 1.00 & 1.00 & 1.00 & **1.00** \\ DNN & 0.96 & 0.76 & 0.83 & **0.78** & 0.99 & 0.97 & 0.97 & **0.97** & 1.00 & 1.00 & 1.00 & **1.00** \\ XGBoost & – & – & – & – & – & – & – & – & 1.00 & 1.00 & 1.00 & **1.00** \\ ET & – & – & – & – & – & – & – & 1.00 & 1.00 & 1.00 & **1.00** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model performance across different studies using split approach on CI-CloV2024 dataset.  
recall, accuracy, and F1-score). Logistic Regression achieved an accuracy of 97% and and 88% F1-score, indicating its sensitivity to imbalanced datasets despite its competitive performance. AdaBoost struggled with imbalanced class distributions, achieving an F1-score of 32% but maintaining a high accuracy of 92%. The results confirm the observations obtained from the train-test split approach and underscore the reliability of ensemble methods and deep learning models in detecting different types of attacks with high precision and recall.
## 7 Numerical Results
### Topology and Services  
We developed a Python tool to generate Walker constellations (that is, constellations to optimize Earth coverage) [41] and obtain the position of satellites and GSs over time, which are then translated into a graph, as is described in Section 4. In this paper, we suppose to work with a constellation of 24 satellites divided into six orbital planes. Values of orbital height and inclination are chosen to be consistent with EO applications, and are, respectively, equal to \(712.84\) km and \(98.24\) deg, leading to a cyclostationary period (i.e., the repeat cycle) equal to 2 sidereal days. We assume three ground stations, placed in Matera (Italy), Kiruna (Sweden), and Kourou (French Guyana). All this translates into having \(N_{S}=25\) actual nodes (24 satellites and 1 virtual ground station), and \(T=287\) time cycles. However, although this complete graph is considered as a real orbital scenario evaluated by means of the GSESH algorithm, due to the NP completeness of the proposed optimization problem, its solution is restricted to the case that provides for \(T=4\) time cycles. We consider services to be generated while a satellite flies over a user-defined region. We developed a Python tool to determine these events and obtain the following service generation. In particular, each satellite generates a task for each second of flying over the region of interest. Again, while comparing the optimal solution to the GSESH algorithm one, we will consider that services are generated when satellites flies over Italy during four time cycles, since this leads to a total number of 724 services, thus to a simplified, yet not trivial, scenario. Instead, in the real orbital scenario, we consider satellites to generate services while flying over Australia and Mexico during an entire repeat cycle, leading to a total of 111,483 services.
## 4 Results and Discussion
### Lining Behavior and Earth Pressure Acting on Lining
#### 4.1.2 Normal Effective Earth Pressure  
The normal effective earth pressure, \(\sigma^{\prime}_{\rm n}\) can be obtained from the gap from the original excavated surface before excavation to the outer surface of the lining, \(u_{\rm n}\). Therefore, the \(\sigma^{\prime}_{\rm n}\) tendency can be explained by \(u_{\rm n}\). Usually, \(u_{\rm n}\) is equal to the displacement of the excavated surface in the normal direction except the existing gap between the excavated surface after excavation and the outer surface of the lining because of ground self-stabilization as shown in Figure 4. In this study, the self-stabilization of the ground occurs only at \(k_{\rm n}=1000\) MN/m\({}^{3}\) and in the deep tunnel.  
From the normal effective earth pressure along the lining, \(\sigma^{\prime}_{\rm n}\) in Figures 7b and 8b, the following was found:  
1. The distribution shape of \(\sigma^{\prime}_{\rm n}\) is more circular than that of the initial normal effective earth pressure, \(\sigma^{\prime}_{\rm n0}\). This comes from the redistribution of \(\sigma^{\prime}_{\rm n}\) due to the lining stiffness. Furthermore, \(\sigma^{\prime}_{\rm n}\) is close to 0 around the invert at \(k_{\rm n}=1000\) MN/m\({}^{3}\) and in the deep tunnel. This is because (1) the high water pressure causes the shrinkage of the segmental ring, (2) the large \(k_{\rm n}\) reduces the normal displacement of the excavated surface due to ground self-stabilization, and (3) the buoyancy, which is larger than the self-weight of the lining, lifts the lining.
2. As \(k_{\rm t}\) decreases and the load model changes from type 0 to type 1, the distribution shape of \(\sigma^{\prime}_{\rm n}\) becomes more flat in the horizontal direction. These trends reflect the lining displacement.
3. As \(k_{\rm n}\) increases, \(\sigma^{\prime}_{\rm n}\) decreases and the distribution shape of \(\sigma^{\prime}_{\rm n}\) becomes more flat in the horizontal direction. This is because (1) the high water pressure in the deep tunnel reduces the lining diameter, (2) a larger \(k_{\rm n}\) decreases \(\sigma^{\prime}_{\rm n}\) under the same displacement in the active side, and (3) as \(k_{\rm n}\) increases, the \(\sigma^{\prime}_{\rm n}\) around the invert and crown decreases more than the \(\sigma^{\prime}_{\rm n}\) at the spring line under the displacement in the active side because \(K_{\rm h0}=0.5\).
4. For \(k_{\rm n}\leq 100\) MN/m\({}^{3}\), the \(\sigma^{\prime}_{\rm n}\) at the deep tunnel is larger than that at the shallow tunnel, and for \(k_{\rm n}=1000\) MN/m\({}^{3}\), the tendency of \(\sigma^{\prime}_{\rm n}\) is the reverse. This is because (1) the deeper tunnel has the larger initial normal effective earth pressure, \(\sigma^{\prime}_{\rm n0}\) and the larger water pressure, \(\sigma_{\rm w}\), and (2) a higher \(\sigma_{\rm w}\) reduces the lining diameter and \(\sigma^{\prime}_{\rm n}\) more, especially for \(k_{\rm n}=1000\) MN/m\({}^{3}\).
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 2 Model for Inter-System Bias Estimation and Descriptions of the Stochastic Models
### Model for Inter-System Bias Estimation and Processing Strategies
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Models/Strategies** \\ \hline Data & GPS + BDS, 8 stations \\ Processing-arc-window & 3 h, 8 stations in one-week period have totally 448 tests \\ Signal selection & GPS:L1 and L2; BDS:B1 and B2 \\ Estimator & Kalman filter \\ Elevation cut off & \(7^{\circ}\) \\ Interval rate & 30 s \\ Satellite orbit and clock & Fixed to MGEX (GBM or WUM) products \\ Tropospheric delay & Saastamoinen model corrected for the dry component; estimate the residual \\  & wet component with random-walk processing \\ Mapping function & Global Mapping Function (GMF) \\ Ionospheric delay & First-order effect eliminated by ionospheric-free linear combination \\ Receiver phase center & PCO and PCV for GPS from jgs14.atx are used; Corrections of BDS is applied \\  & the same with GPS \\ Satellite phase center & PCO and PCV are used with jgs14.atx \\ ISB & Schemes ISB-OFF, ISB-PW, ISB-RW, ISB-AD, ISB-WN \\ Phase ambiguities & Estimated as a constant for each arc \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Models and strategies for data processing. The estimation of the ISB parameter can be performed with different stochastic models, such as white noise, random walk, piece-wise constant, or processing-arc-dependent constant, which will be discussed and studied in detail in the following.