esa-sceva/satcom-chunk-collection huggingface dataset:
# Control for Ship Course-Keeping Using Optimized Support Vector Machines
## 2 Problem Formulation
### Support Vector Regression  
SVM is a kind of machine learning algorithm for classification and regression. Since it was invented, it has been widely applied in scientific and engineering areas due to its powerful learning abilities. For a MISO system, support vector machines for regression (SVR) have a general form as  
\[f(\mathbf{x})=\mathbf{w}^{T}\cdot\Phi(\mathbf{x})+b,\mathbf{w}\in\mathbb{R}^{n},\,b\in\mathbb{R} \tag{6}\]  
where \(\Phi(\cdot)\) is a nonlinear function vector. The above calculation is performed in a so-called high-dimensional feature space to approximate the hidden mapping relationship contained in the original training samples:  
\[(\mathbf{x}\times y\in\mathbb{R}^{n}\times\mathbb{R}_{r})\ :\ (\mathbf{x}_{1},y_{1}), \cdots,(\mathbf{x}_{i},y_{i}),\cdots,(\mathbf{x}_{l},y_{l}),i=1,2,\cdots l\]  
where \(l\) is the number of samples.  
To improve feasibility, SVM has been developed during the last decade and some effective modified SVM versions have been proposed, one of which is the Least Squares SVM (LS-SVM). Rather than solve the problem of convex quadratic programming that is required in standard SVM, LS-SVM provides a simpler solution by solving a linear matrix equation [30; 31]  
\[\left[\begin{array}{cc}0&1^{T}\\ \mathbf{1}&\mathbf{Q}+\mathbf{C}^{-1}\mathbf{I}\end{array}\right]\left[ \begin{array}{c}b\\ \alpha\end{array}\right]=\left[\begin{array}{c}0\\ \mathbf{y}\end{array}\right] \tag{7}\]  
where  
\(1=\left[\begin{array}{cc}1,&\cdots,&1\end{array}\right]_{1\times i}^{T}\);  
\(I\) is a \(l\)-dimensional unit matrix;  
\(Q=\left(q_{ij}\right)_{l\times l}\) is the kernel matrix with the element \(q_{ij}=\Phi^{T}(\mathbf{x}_{i})\Phi(\mathbf{x}_{j})\);  
\(C\) is the so-called regularization factor, an important hyperparameter that controls the tradeoff between empirical risk and generalization ability of SVM;  
\(\alpha=\left(\alpha_{i}\right)_{l\times 1};\mathbf{y}=\left(y_{i}\right)_{l\times 1}\).  
After the constant \(b\) and Lagrangian factors \(\alpha_{i}\) are calculated through (7), the estimated function in (6) can be determined by  
\[f(\mathbf{x})=\sum_{i=1}^{N_{s}}\alpha_{i}K(\mathbf{x}_{i},\mathbf{x})+b \tag{8}\]  
where \(N_{s}\) is the number of the support vectors associated with nonzero \(\alpha_{i}\). \(K(\mathbf{x}_{i},\mathbf{x}_{j})\) is the kernel function defined by \(K(\mathbf{x}_{i},\mathbf{x}_{j})=\left(\Phi(\mathbf{x}_{i})\cdot\Phi(\mathbf{x}_{j})\right)\).
## 2 Materials and Methods  
To form stable, energy-efficient atomic supercells of 2D pillared graphene without and with silicon clusters in its pores, the SCC-DFTB (the self-consistent charge density functional tight-binding) method was applied [32]. This method allows calculating the energy and electronic characteristics of structures with supercells consisting of several hundred and even thousands of atoms. This method has been successfully implemented in the software packages dftb+ [33] and Kvazar-Mizar [34].  
For quantum-chemical study of the lithiation process, molecular dynamics (MD) with the reactive force-field ReaxFF [12] was implemented in the LAMMPS program [35]. Previously, this force field was successfully applied to establish the relationship between the LIB durability and the rate of lithiation/delithiation in the LIB with anode material on the base of silicon, silicon dioxide, and alumina [36; 37; 38]. The modified ReaxFF force field [39] allowedto describe the mechanism of Li ions interaction in ideal and defective carbon materials. Also, simulation of the lithiation process of Si and Si-C materials by the modified ReaxFF force field made possible the design of the new anode material with the capacity of 1345 mAh-g\({}^{-1}\)[40]. The application of this force field was caused by the large number of atoms in the supercell and by the necessity in molecular dynamics. The limitation of the SCC DFTB method and the ReaxFF force field is absent of parameterizations for wide group of atoms, but at the present time this problem is being actively solved [41; 42; 43]. In this work, the time step was 0.5 fs, the temperature of 300 K was maintained by a Nose-Hoover thermostat with a damping parameter of 0.01 fs\({}^{-1}\).  
For simulation of delithiation process and for evaluation of its efficiency, the profile of the potential barrier of lithium atom elimination from the carbon frame and silicon clusters was calculated. It also allowed to find the binding energy. It was realized by the high-precision DFT method in the Quantum Espresso 6.5 software package [44]. Since the DFT method is the most resource-consuming and calculation by this method is limited by a hundred of atoms, we used only parts of the considered supercells. In the frame of DFT, the BFGS method (Broyden-Fletcher-Goldfarb-Shanno) and the \(25\times 25\times 1\) Monkhorst-Pack sampling was applied.
# Groundwater Prediction Using Machine-Learning Tools
## 3 Techniques Used
### Performance Metrics  
The accuracies of the above machine-learning models are evaluated using the root mean square error (RMSE) and the mean absolute error (MAE). Both metrics are commonly used to measure the forecasting accuracy [59]. RMSE is more sensitive to outliers and is more appropriate for Gaussian-distributed errors, while MAE weights all errors equally [60]. The RMSE and MAE are computed as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}^{obs}-y_{i}^{pre})^{2}} \tag{1}\]\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}^{obs}-y_{i}^{pre}| \tag{2}\]  
where \(y_{i}^{obs}\) and \(y_{i}^{pre}\) refer to the observed and predicted value of the \(ith\) output, respectively.
# Injury Patterns and Frequency in Swimming: A Systematic Review  
[PERSON] 1,2,3, [PERSON] 1, [PERSON] 1, [PERSON] 4,5, [PERSON] 4 and [PERSON] 5,6,6,6  
1 Faculty of Physical Activity and Sport Science, European University, 28670 Madrid, Spain;  
[EMAIL_ADDRESS] (L.M.F.-G.); [EMAIL_ADDRESS] (J.A.S.);  
carcinique.lopez@universitadipale.deuteppeas (C.L.-N.)  
2 Faculty of Sport Science, Jaime I University, 12006 Castellon de la Plana, Spain  
3 Health and Sport Sciences University School (EUSES), Rovira i Virgili University, 43002 Amposta, Spain  
4 Research Group on Exercise Therapy and Functional Rehabilitation, Faculty of Sports Sciences, Universidad Europace de Madrid, 28670 Madrid, Spain; [EMAIL_ADDRESS] (A.S.-S.);  
[EMAIL_ADDRESS] (A.L.-M.)  
5 Physotherapy Research Group of Toledo (GIFTO), Faculty of Physiotherapy and Nursing, Universidad de Castilla-La Mancha, 45071 Toledo, Spain  
6 Faculty of Health Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain  
5 Correspondence: [PERSON]### Abstract  
Swimming is a widely practiced sport with significant physical demands, placing athletes at a considerable risk of injury, particularly in the shoulder, due to repetitive high-intensity movements. The aim of this systematic review was to analyze the patterns and frequency of injuries associated with swimming. Methods: A systematic search of the Web of Science, PubMed, Scopus, and SportDiscus databases identified nine relevant studies. Results: The findings revealed a higher incidence of injuries in female swimmers compared to males, with the shoulder being the most frequently affected joint. This disparity is partially attributed to the Female Athletic Triad, a syndrome characterized by low energy availability, menstrual dysfunction, and poor bone health, which increases injury susceptibility. Other commonly affected regions include the knee, often associated with the biomechanical demands of breaststroke, and the lumbar spine, which is impacted by degenerative changes resulting from high training volumes. However, variability in the injury measurement protocols across studies limits their comparability and highlights the need for standardized methods. Conclusions: Based on these findings, professionals in the field can identify injury patterns to enhance diagnosis and treatment, design personalized prevention programs, implement early interventions, and innovate equipment and training methods to improve swimmer safety and performance.  
Keywords:injury prevention; rehabilitation; epidemiological study; risk factors +
Footnote †: journal: Applied sciences
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 4 Experimental Results and Discussions
### Experimental Results
Figure 12 illustrates the moving average PER curve for a sliding window of 1000 of sensor nodes 5 and 6. The PER of the experimental group with the algorithm is approximately 1%, whereas the PER value of the control group is less than 1% most of the time. The control group is located outside the office on the second floor; therefore, there are more people walking around to cause interference. From the statistical results, the overall PER of the control group is low, but there are several PER values that are high owing to external interference. Compared to the PER curve of the experimental group at the same timeline, the PER is lower than the control group. Although the power consumption of the two are similar, the PER performance of the experimental group is relatively stable. All sensor nodes are powered by two AAA batteries in series, and the power that can be supplied is approximately 1200 mAh. Herein, a simple straightforward formula is adopted to evaluate the battery life in years as  
\[Battery\ Life(Years)=\frac{Battery\ Capacity(mAh)}{365\times 24\times I_{avg}} \tag{8}\]  
where the battery capacity in this formula is 1200 mAh and \(I_{avg}\) denotes the overall average current of the sensor node in mA. Table 9 presents the battery life table of each sensor node from the experimental group calculated by the above method. The table also includes the estimated results of the most power consumed parameters. The calculation results ignore the leakage of the battery itself. The battery life with the transmission parameters of the most power consumed is approximately 2.04 years. However, the best battery life is approximately 7.26 years, and the shortest battery life is approximately 6.82 years for the sensor nodes with the proposed  
Figure 11: Experimental results for (**a**) Node 1 and (**b**) Node 2. Figure 12: PER in 1000-window of moving average for (**a**) Node 5 and (**b**) Node 6.  
fuzzy control algorithm. It can be observed from the calculation results that the sensor node can operate for more than two years with TDMA architecture. Furthermore, with the control algorithm of ultra-low-power consumption, it can extend the battery life to more than 6.8 years. The above proves that the control algorithm proposed in this study has an excellent effect on power saving.
## 3 Results
### Decelerations Results  
Regarding deceleration, the analysis of 53 million data points shows that soft decelerations (0 to \(-\)0.269 m/s\({}^{2}\)) represent 49.55% of all actions (\(\approx\)26.4 million), with a mean of \(-\)0.116 m/s\({}^{2}\). Low decelerations (\(-\)0.270 to \(-\)0.635 m/s\({}^{2}\)) account for 30.12% (\(\approx\)16 million), and moderate decelerations (\(-\)0.636 to \(-\)1.222 m/s\({}^{2}\)) for 16.97% (\(\approx\)9 million), with a mean of \(-\)0.848 m/s\({}^{2}\). High decelerations (\(-\)1.223 to \(-\)2.545 m/s\({}^{2}\)) represent 3.03% (\(\approx\)1.6 million), and maximum decelerations (\(>\)\(-\)2.546 m/s\({}^{2}\)) only 0.32% (\(\approx\)170,000), with a mean of \(-\)3.548 m/s\({}^{2}\). These results indicate that the vast majority of decelerative actions also occur at low to moderate intensities (Table 3).  
In relation to deceleration (Figure 3), U14 players perform the majority of their actions at low intensity, with 49.5% of all game actions occurring within the range of 0 to \(-\)0.270 m/s\({}^{2}\). When extending the range to include moderate decelerations (up to \(-\)1.222 m/s\({}^{2}\)), this proportion increases to 96.64% of the total actions, indicating a clear predominance of low-to-moderate deceleration demands during match play.  
Only 3.02% of actions fall within the high-intensity deceleration range (\(-\)1.223 to \(-\)2.545 m/s\({}^{2}\)), with a mean value of \(-\)1.595 m/s\({}^{2}\) across approximately 1.61 million instances. Maximum-intensity decelerations (\(>\)\(-\)2.545 m/s\({}^{2}\)) are rare, representing just 0.32% of all actions (\(\approx\)170,000), with a mean of \(-\)3.548 m/s\({}^{2}\). These findings confirm that only 3.34% of total game actions are performed at high or very high deceleration intensities (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Decelerations (m/s\({}^{2}\))** & **Soft** & **Low** & **Moderate** & **High** & **Maximum** \\ \hline Cluster centers & \(-\)0.116 & \(-\)0.423 & \(-\)0.848 & \(-\)1.595 & \(-\)3.548 \\ Ranges & \(<\)\(-\)0.269 & \(-\)0.270 to \(-\)0.635 & \(-\)0.636 to \(-\)1.222 & \(-\)1.223 to \(-\)2.545 & \(>\)\(-\)2.546 \\ n & 26,384,648 & 16,038,458 & 9,036,276 & 1,613,430 & 170,395 \\ \% & 49.55 & 30.12 & 16.97 & 3.03 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 3: Results of the five-k means cluster of the variable deceleration.  
Figure 2: Graphical interpretation of all accelerations carried out in the national championship of men’s under-14.
## 5 Simulation and Discussion
### Scenario 2
Figures 5 and 6 respectively show the trajectories of AEK-VB-TPHD filter with the AUK-VB-TPHD filter and the average value after the Monte Carlo runs of standard deviation of measurement noise. It can be seen that even under the condition of nonlinear and time-varying covariance, the VB-TPHD filter can still perform well in the estimation of trajectory and noise standard deviation. In Figure 5, the tag-VB-PHD filter delivers poor performance. When AUK-tag-VB-PHD is used to track target 2, there is a series of wrong targets deviated from the true trajectory, and tremendous trajectory switching errors are produced after targets cross. Table 3 shows the average time required by AEK-VB-TPHD and AUK-VB-TPHD under different window lengths to achieve similar covariance estimation effect. With the increase of \(L\), the time of the two filters both increase. The time increases dramatically especially after \(L=10\). On the other hand, the time of AUK-VB-TPHD is basically almost 3 times that of AEK-VB-TPHD. Table 4 combined with Figure 10 shows the RMS TM errors of AUK-VB-TPHD and AEK-VB-TPHD under different filter windows \(L\). Increasing \(L\) can reduce the RMS TM error and improve the estimation performance of trajectory. When \(L\) is less than 5, increasing \(L\) can improve the trajectory performance significantly. In addition, the filtering error of AUK-VB-TPHD is slightly smaller than that of AEK-VB-TPHD, which has a slight advantage in filtering performance. Figure 5: (**a**) The trajectory estimates of AEK-VB-TPHD until time 85. (**b**) The trajectory estimates of AUK-VB-TPHD until time 85. (**c**) The trajectory estimates of AEK-tag-VB-PHD until time 85. (**d**) The trajectory estimates of AUK-tag-VB-PHD until time 85. The trajectory of the true target is marked with blue line segments in the period, the circle represents the starting point of the track, the triangle represents the end point of the trajectory, different trajectories are marked with different color styles (\(L=5\)) and the black asterisk represents the measurement at the current time. Figure 6: (**a**) Standard deviation estimation of theta of AEK-VB-TPHD and AUK-VB-TPHD. (**b**) Standard deviation estimation of distance of AEK-VB-TPHD and AUK-VB-TPHD (\(L=5\)). Figure 8: Comparison of VB-TPHD and tag-VB-PHD using EK and UK nonlinear realization forms under RMS TM metric under time-varying noise measurement (\(L=5\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**L** & **1** & **2** & **5** & **10** & **30** \\ \hline AEK-VB-TPHD & 1.6515 & 1.8393 & 2.0058 & 2.4287 & 5.8131 \\ \hline AUK-VB-TPHD & 3.0019 & 3.3813 & 4.7062 & 6.9537 & 21.7574 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average run time of the AEK-VB-TPHD and AUK-VB-TPHD filters. Figure 7: The black line represents the cardinality of true targets, the blue line represents the cardinality estimation of AEK-VB-TPHD, the red line represents the cardinality estimation of AUK-VB-TPHD and the green line represents the cardinality estimation of AEK-tag-VB-PHD. The yellow line represents the cardinality estimation of AUK-tag-VB-PHD (\(L=5\)).
# Intelligent Simulation Technology Based on RCS Imaging
## 2 Target Drone Simulation Principle
### Variable Principle of Corner Reflector
The transfer function \(G(k)\) of multiple scattering centers is defined as:  
\[E^{s}=E^{i}\frac{\exp(j2\pi ft-jkr)}{r}G(k) \tag{9}\]  
where \(E^{s}\) is the scattered electric field, \(E^{i}\) is the scattered electric field, \(f\) is the frequency, \(t\) is the world, and \(k\) is the wave number, \(k=2\pi/\lambda\). The above transfer function references the spherical wave transfer factor, and according to this definition, the RCS of the target scattering center is:  
\[RCS=4\pi|\mathrm{G(k)}|^{2} \tag{10}\]  
After the equivalent aperture is projected onto the equivalent plane, it can be regarded as an irregular plane, and for specular reflection on a flat plate, the transfer function is:  
\[G(k)=-jkA_{eq}/2\pi \tag{11}\]Therefore, the RCS value of the equivalent aperture \(A_{eq}\) is:  
\[\sigma=4\pi(-j\frac{2\pi}{\lambda}A_{eq}/2\pi)^{2}=\frac{4\pi A_{eq} ^{2}}{\lambda^{2}} \tag{12}\]  
A spatial coordinate system is established where the virtual and real apertures are located. The real and virtual aperture triangles are denoted by \(A^{\prime}B^{\prime}C^{\prime}\) and \(A^{\prime\prime}B^{\prime\prime}C^{\prime\prime}\), respectively. The origin of the spatial coordinate system where the real aperture is located is still at point O. The \(O^{\prime}_{x}\), \(O^{\prime}_{y}\) and \(O^{\prime}_{z}\) axes extend in the \(OA^{\prime}\), \(OB^{\prime}\) and \(OC^{\prime}\) directions, respectively. The area covered by triple reflection in a corner reflector is usually a parallelogram or hexagonal, as shown in Figure 2.  
In order to simplify the RCS calculation of complex structures, the RCS of the variable corner reflector can be calculated only by obtaining the shape of the \"equivalent aperture\". Subsequently, the shape can be integrated to obtain its area \(A_{eq}\), which is determined by regional projection. In this way, the RCS can be used to estimate the angular reflector when the plane wave containing the wavelength is vertically incident on the plate:  
\[\sigma=\frac{4\pi(\mathtt{a}*\mathtt{b})^{2}}{\lambda^{2}}=\frac{4 \pi A_{eq}^{2}}{\lambda^{2}} \tag{13}\]  
Figure 3 shows the variation of \(A_{eq}\) with \(\theta\) when \(\varphi=45^{0}\).
## 1 Introduction  
In today's industry, the demands for manufactured products continue to increase. Primarily, flawless quality is demanded at low prices and high quantities. Therefore, to meet these requirements, a one hundred percent inline inspection of the manufactured parts is desirable in most cases. In many applications, a system from the field of automatic optical inspection is used [1, 2]. Since inline inspection systems are often limited to a two-dimensional inspection, in this work, a system was developed which optically extracts three-dimensional information of a cylindrical surface. This additional information can then be used to realize tolerance limits in production. This is mainly applicable to metallic or reflective surfaces [3]. The rolling elements of a cylindrical roller bearing are an example of a cylindrical reflective surface. These bearings are widely used in both the automotive and engineering industries. Although the manufacturing process of the rolling elements in the bearing is highly optimized, a defect in the production of these rolling elements can lead to undesirable quality losses due to the constantly increasing requirements. These defects are often described by the term rolling contact fatigue, and play a major role in material research for bearings [4, 5]. Parallel to defects that only occur in the later stages of a bearing's life, undesirable quality degradation, including bearing failure, also occurs in new bearings. Defects in bearing have a wide range of effects. Most importantly, they lead to undesirable vibrations of the bearing and, because of increasing forces, faster material fatigue [6, 7]. These undesired vibrations are often analyzed during the life of a bearing to provide information about its condition [8, 9, 10]. To avoid the aforementioned problems at an early stage, the idea of this work is to develop a low-cost inline inspection method with a short inspection time for the surface of rolling elements, which detects defect spots and deformations caused by production before the bearing is finally assembled. The system is expected to distinguish from existing inspection systems by extracting three-dimensional surface information with a very short acquisition time. For this purpose, the work of [PERSON] et al. [11] is extended. The recording mechanism and the illumination device presented here are used to extract and subsequently reconstruct the surface information with the use of additional techniques presented in the literature.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 3 Experimental Verification and Discussion
### Measurement Results over an Optical Channel with 600 Gb/s/\(\lambda\)
#### 3.2.1 General and Memory Polynomial (MP-) Volterra nonlinear Equalizer (VNLE)  
Figure 7 shows the performance improvement by applying general VNLE full kernel architectures of different orders. The gray dashed line depicts the theoretical upper limit for DP-16 QAM at 88 Gbaud over an additive white Gaussian noise (AWGN) channel. The computational effort of a full kernel architecture gets extremely high with rising orders. Thus, only up to 6 th order kernels are considered and evaluated. On the left hand side, Figure 7a plots the preFEC BER related to the OSNR. On the right hand side, Figure 7b shows the improvements of these base-line curves. The gains in OSNR at their crossing with the FEC threshold are plotted against the order. The general VNLE of 5 th order improves the non-equalized baseline curve by 2.3 dB at a target FEC BER of \(1\times 10^{-2}\). Increasing the VNLE order further to 6 th order does not yield significant additional gain.  
Figure 8a shows the performance of the reduced size alternative, the MP-VNLE, which operates only on subset of kernels. As before, the performance was evaluated for different maximum polynomial orders. In comparison to the general VNLE, the architecture of the MP-VNLE is less complex and hence the computational effort to evaluate the kernels is lower. Therefore, higher orders are applicable for real systems. The MP-VNLE of 5 th order improves the baseline curve by 2.0 dB at the FEC threshold of \(1\times 10^{-2}\) shown in Figure 8b. It can be observed, that the reduction costs performance of 0.3 dB and that higher orders do not improve the performance. On the contrary, the performance is rather inferior. This indicates that the higher order computations lead to numerical instability when identifying and extracting the VNLE kernels with the least squares (LS) solver approach. However, besides the numerical instability such high orders are out of scope due too high implementation complexity.  
Figure 6: Overview training and evaluation process.  
Figure 7: optical back-to-back 600 Gb/s/\(\lambda\) general VNLE preFEC measurement results.