esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Modeling in IPT Systems
### Determination of the IPT Electric Circuit Parameters  
Concerning the IPT circuit parameters, in general we can practice 3D electromagnetic field computations for the ICT, with for example the finite elements method (FEM) simulations, counting for the whole structure of the IPT (see Figure 2), to obtain its mutual coupling and inductances (see Figure 1). This considers the source voltage and frequency as well as the geometric positioning of the coils (parameters d and sh, Figure 2). Moreover, the influence of shielding by means of coil-closed ferrites to decrease the leakage fields and to limit the infiltration of the field within the vehicle is counted in such simulations. Furthermore, the EV chassis is also modeled in the configuration of the IPT system [52]. Actually, the existence of the EV chassis alters the field values, and hence the matching inductances of the power system and the electromagnetic compatibility (EMC) emission level.  
This can be achieved by solving the system of Maxwell equations. This equation system can be formulated mathematically under different forms function of the considered problem. One of the most common is the basic full-wave electromagnetic formulation given by:  
\[\
abla\times\mathbf{H}=\mathbf{J} \tag{1}\]  
\[\mathbf{J}=\sigma\mathbf{E}+\mathbf{i}\omega\mathbf{D}+\mathbf{J_{e}} \tag{2}\]\[\mathbf{E}\ =\ -\
abla\ \mathbf{V}-\mathbf{j}\ \omega\ \mathbf{A} \tag{3}\]  
\[\mathbf{B}\ =\ \
abla\ \times\mathbf{A} \tag{4}\]  
where (**H**) and (**E**) are the magnetic and electric fields, (**B**) and (**D**) are the magnetic and electric inductions, (**A**)and (V) are the magnetic vector and electric scalar potentials. (**J**) and (**J\({}_{\mathbf{e}}\)**) are the total and source current densities, (\(\sigma\)) is the electric conductivity and (\(\omega\)) is the frequency pulsation. The symbol (\(\
abla\)) is a vector of partial derivative operators, and its three possible implications are gradient (product with a scalar field), divergence and curl (dot and cross products respectively with a vector field).  
The magnetic and electric behavior laws respectively between **B/H** and **D/E** are characterized by the permeability (\(\mu\)) and the permittivity (\(\varepsilon\)).  
The solution of the Equations (1)-(4) permits to determine in a system with a given 3D geometry the concerns of electromagnetic fields for a frequency pulsation accounting for the magnetic materials behaviors through the permeability, for eddy currents in electric conductors through the electric conductivity and for displacement currents in dielectrics through the permittivity.
## 2 State of the Art
### Technology Readiness Level  
The maturity of technologies is a key factor used to predict the implementation risk and the risk of adoption of a specific technology to name two cases. The technology Readiness Level (TRL) is an established method (first developed at NASA in 1974 and formally defined in 1989) [32] for estimating the technical maturity of technology across different domains. The TRL is generally determined during a technology readiness assessment (TRA) that investigates the application, its technological requirements, and the demonstrated technology capabilities. TRLs are based on a scale from 1 to 9 with increasing values for the most mature technologies. In 2010, the European Commission started using the TRL scale for research and innovation projects and then from 2014 in the EU Horizon 2020 program. In 2013, with the publication of ISO 16290:2013 [33], the scale reached high visibility. The European Union refers to the TRL scale (slightly different from NASA's but clearly inspired by it) as presented in Table 3.  
The introduction of digital technologies necessitates a proactive evaluation, considering both technological compatibility and the readiness of the company itself. To gauge this readiness, predictive measures, including indicators, are considered. This leads us to focus on the Acatech Industry 4.0 Maturity Index in Section 2.3, which serves as a key tool for evaluating the company's maturity in navigating digital transformation.
## 5 Numerical Results  
In this section, the performance of the proposed MASCSA is investigated by comparing the results of the proposed method to those from other implemented methods, such as CSA and SDCSA. Two test systems are employed as follows:  
1. Test System 1: Four hydropower plants and four thermal power plants with valve effects are optimally scheduled over one day with twenty-four one-hour subintervals. The data of the system are modified from Test System 1 in [7] and also reported in Tables A1-A3 in the Appendix A.
2. Test System 2: Four hydropower plants, four thermal power plants, and two wind farms with the rated power of 120 MW and 80 MW are optimally scheduled over one day with twenty-four  
Figure 3: The flowchart for implementing MASCSA for OSWHT problem.  
one-hour subintervals. The data of the hydrothermal system are taken from Test System 1 while wind data are taken from [45] and also reported in Table 10 in the Appendix A.  
The implemented methods are coded on MATLAB and a personal computer with the CPU of Intel Core i7-2.4 GHz, RAM 4 GB for obtaining 50 successful runs. The optimal generations of two systems are reported in Tables 11 and 12 in the Appendix A.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 2 System Model and Basic Assumptions
After that, reference signals are inserted, and therefore, these symbols of each antenna port, including data and reference signals, are modulated into complex-valued time-domain CP-OFDM signals, which be up-converted to an mmWave FR2 band by means of \(\mathbf{M}_{\text{ST}}\). It should be noted that the generation and configuration of the signals and channels in the NR-UE fulfill the uplink 5G NR standardization of the 3 GPP, which is one of the main novelties of our proposal.
## 5 Simulation and Analysis
### Experiment with Real 2-D Data
According to the simulation results on both synthetic data and real data, the estimation quality of the proposed method are lowered with the increase of the value _M/S_, where \(M\) denotes missing rate, and \(S\) denotes the number of nonzero coefficients. Fixing \(M\) and total number of data _N = 64 \(\times\) 64_, a smaller \(S\) will produce better performance, or lower RMSE. The three synthetic data sets in Figures 5, 8, 12 all have _N = 64 \(\times\) 64_ and _S = 64_, and the real data set in Figure 16(a) has _N = 64 \(\times\) 64_ and _S = 240_, which means it has more nonzero coefficients. That is why the advantages are not so evident on the real data as on the synthetic data. However, one can still observe the proposed method can overcome the obvious blocky artifact of KNN in Figure 18. What is more, if sensor data contain fruitful high-frequency components, e.g., rapid changes in localized regions, the advantage of the proposed method will become more obvious, as demonstrated in the results of synthetic high frequency data.
# Passive Control of Base Pressure: A Review
## 2 Various Types of Passive Controls
### Passive Control with Dimple  
The research on passive control in the form of a dimple is done by [PERSON] et al. [19]. They investigated both experimentally and numerically the passive control effect to control the base pressure in a Backward Facing Step at various NPRs. Two dimples of diameter 3 mm are located at 180\({}^{\circ}\) intervals at the base region along the Pitch Circle Diameter of 23 mm. This study was done for NPR 1.27, 1.38, 1.52, and 1.69. Figure 10 shows the design and dimensions of the nozzle and the duct.  
The model is designed to deliver four BFS with an incidence angle of 15\({}^{\circ}\) from which the flow expands suddenly to a 25 mm square duct for different duct length 4D \(\leq\) L \(\leq\) 10D to see the effect of the base pressure's geometric parameter. The presented investigation revealed that the passive control dimples are highly effective at higher NPR, and the wall pressure distribution at higher NPR too was stable. The spatial parameter also affected the base pressure for a particular NPR.  
CFD analysis was also done on the dimple and dimple with pressure and velocity distribution profile using the Navier stokes equation, Turbulence model SST, and Reynold's number (Re) = 122.56 \(\times\) 10\({}^{3}\). From this analysis, it is clear that an optimal L/D ratio can be found for a given nozzle pressure ratio, which results in a maximum increase/decrease in base pressure, and the passive controller can be efficient in controlling the base pressure.  
Findings state that the level of expansion (NPR) strongly affects the base pressure and influences dimple as a passive control. At higher NPR, it is found that the base pressure is low, and hence dimples as passive control are very effective in controlling the base pressure if achieving the low base pressure is the mission requirement. For example, in the combustion chamber, we want to achieve a minimum base pressure for better fuel-air mixing to enhance its efficiency.
## 1 Introduction
Therefore, in this work, an extensive and comprehensive study of the acoustic performance of differently shaped single and double reflective structures (cap elements) attached to the top of conventional noise barriers was carried out by calculating their sound pressure _insertion losses (IL)_. Acoustic _IL_ is a parameter commonly used to evaluate and compare the performance of different noise barriers, so this parameter will be considered. To calculate the _IL_ in this study, an innovative, fast uniform theory of diffraction (UTD)-based method developed by the authors was applied in all simulations. The cap shapes chosen for analysis covered a wide range of structures including cylinders, rectangles, trapezoids, and Y/T-shaped structures. All of these shapes were properly standardized with equivalent dimensions and/or envelopes to ensure that reliable comparisons could be made. Moreover, four different configurations (with two transmitter and receiver positions) were considered. It should be noted that the use of absorptive materials on barrier caps would further enhance the barriers' _IL_ performance; however, real-life testing of certain barrier caps (e.g., T-profile caps) has not revealed a significant difference between the _IL_ performance of absorptive and reflective configurations [33]. Therefore, absorptive cap materials were not considered in the present work. Finally, the influence on _IL_ of the ground reflection of the acoustic wave on the barrier side closer to the receiver and transmitter is also discussed (unless otherwise specified, ground reflections are always considered in the results presented in this study). The novelty and main contribution of this study lie in the fact that it comprehensively addresses and compares different types, sizes, combinations, and numbers of noise barrier caps for different scenarios (including sloping and absorbent grounds) and sources (\"car\" and \"ambulance\") for an extended frequency band up to 10 kHz. To the best of the authors' knowledge, a study like this has not been previously published. Moreover, all cases have been studied with a frequency resolution higher than 3000 frequency bins. In this regard, it should be mentioned that this task has been carried out with quite low computational effort thanks to the speed of the UTD-based approach developed by the authors; the same extensive and painstaking analysis would have been extremely time-consuming with other approaches such as the boundary element method (BEM).
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 4 Three-dimensional Imaging of Moving Targets with InISAR Technology
### Interferometric Imaging
In different imaging windows, the positions of the baseline will vary due to the adjustment of antenna pointing direction during tracking. Generally, a rotation angle of \(\varphi_{b}\) and \(\theta_{b}\) around the azimuth axis and vertical axis will occur, as shown in Figure 3. A coordinate transform operation is necessary to get the imaging results in the global coordinate system. The rotation matrices in the azimuth and vertical planes are defined as:  
\[rot_{az}=\left( \begin{array}{ccc}\cos\varphi_{b}&\sin\varphi_{b}&0\\ -\sin\varphi_{b}&\cos\varphi_{b}&0\\ 0&0&1\end{array} \right) \tag{33}\]  
\[rot_{el}=\left( \begin{array}{ccc}1&0&0\\ 0&\cos\theta_{b}&-\sin\theta_{b}\\ 0&\sin\theta_{b}&\cos\theta_{b}\end{array} \right) \tag{34}\]  
A coordinate rotation is performed on the initial 3D coordinates to get the target coordinates in the global coordinate system:  
\[(x,y,z)=(u,v,w)rot_{az}rot_{el} \tag{35}\]  
With the coordinate transform operation, the target images in different windows will be displayed in the same coordinate, which can be used to analyze the target status variation during tracking.
## 4 Experiments and Discussion
### Model Parameter Setting  
The parameter settings have a significant impact on the prediction effect of the model. For the KELM model, GA is used to optimize the regularization parameter \(c\) and the kernel parameter \(\sigma\) in it, and the Gaussian kernel function with better generalization ability is selected for the kernel function. For the LightGBM model, the Bayesian Optimization method is used to optimize the maximum depth of the tree in the model (\(max\_depth\)), the leaf tree of each tree (\(num\_leaves\)), and the learning rate (\(learning\_rate\)). The parameters of LSTM that affect its prediction effect are mainly the number of hidden layer neurons \(n\). The parameters are determined by experimental comparison.  
The component RBIMF1 is used as an example for parameter setting, where GA-KELM uses the error as the fitness function to obtain the optimal \([c,\sigma]\) combination, and the fitness curve is shown in Figure 9, which reaches the optimum at the 9 th iteration when \(c=74.238\) and \(\sigma=1.961\). LightGBM uses \(R^{2}\) fitting goodness as the search criterion to obtain the optimal combination of parameters, \(max\_depth=2\), \(num\_leaves=165\), and \(learning\_rate=0.136\); LA's parameters \(n\) are taken as 16, 32, 64, and 128, a total of four combinations, and the learning rate \(lr=0.01\). The prediction error of different parameters of LA is shown in Figure 10, and the prediction error is minimized when \(n=32\).  
Figure 9: GA optimizes KELM’s fitness curve.
# Mechanics of Screw Joints Solved as Beams Placed in a Tangential Elastic Foundation
## 5 Determination of the Screw Elongation  
It follows from Equations (2) and (9):  
\[\frac{du}{dx}=\frac{\text{F}_{\text{E}}}{EA2^{m}}\bigg{[}1-\cos\bigg{(}\frac {\pi\,u}{u_{\text{E}}}\bigg{)}\bigg{]}^{m}, \tag{12}\]  
Figure 9: The course of the calculated continuous tangential reaction \(q_{N}\) at the base at point B of the screw (measurement No. 1), see Figure 5.  
i.e., with the solution \(\int\frac{du}{\left[1-\cos\left(\frac{\pi\,u}{u_{\rm E}}\right)\right]^{\pi}}=\frac {\rm F_{E}}{EA2^{m}}\,\int dx\,+C\), where \(C\) [m] is the integration constant, which is determined from the boundary condition \(x=0\), \(u=u_{\rm B}\in\langle 0;u_{\rm E}\rangle\) (see Figure 4).  
The result obtained is valid for the selected value of the position of the threaded portion of the screw from the interval \(x\in\langle 0;\rm L_{2}\rangle\) (see Figure 4) and, at the same time, according to the selected known value of the displacement measurement from the course of the pull-out test (i.e., according to the selected value \(u_{\rm B}\)). Then, the displacement \(u\) in the general position can be determined and, therefore, \(u=f(u_{\rm B},\ x)\). A positive displacement direction (elongation), \(u\) and \(u_{\rm B}\), is shown in Figure 4.  
The solution of Equation (12) can also be done using a definite integral with a variable limit:  
\[\int_{u_{\rm B}}^{u}\frac{du}{\left[1-\cos\left(\frac{\pi\,u}{u_{\rm E}} \right)\right]^{m}}=\frac{\rm F_{E}}{EA2^{m}}\int_{0}^{x}dx\;,\;\text{where}\; x\in\langle 0;\rm L_{2}\rangle\;\text{and}\;u_{\rm B}\in\langle 0;u_{\rm E}\rangle. \tag{13}\]  
By determining the primitive function \(G_{(u)}\) [m]:  
\[G_{(u)}=\int\frac{du}{\left[1-\cos\left(\frac{\pi\,u}{u_{\rm E}}\right) \right]^{m}}\;, \tag{14}\]  
which is the higher transcendent function of elongation \(u\) (i.e., the solution of the integral exists, but can only be determined by numerical methods or by the development of infinite series), and relationship (13) can be rewritten as:  
\[G_{(u_{\rm B})}-G_{(u)}=\frac{\rm F_{E}\;x}{EA2^{m}}. \tag{15}\]  
The searched displacement \(u=f(u_{\rm B},\ x)\) is \"hidden\" in the implicit function \(G_{(u)}\) and its determination is slightly more difficult but solvable. An example of the course of the dependence of \(u=f(u_{\rm B},\ x)\) for measurement number 1 is shown in Figure 10.  
From the course in Figure 10, it is clear that the dependencies \(u=f(u_{\rm B},\ x)\) are approximately linear (practically constant) for the selected values of \(u_{\rm B}\) and decrease  
Figure 10: The course of the screw elongation dependence \(u=f(u_{\rm B},\ x)\) for measurement No. 1 (i.e., the solution of Equation (15)).  
slowly with the distance \(x\), which is not in conflict with numerical and other solutions in reference [25].
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods
### Machine Learning-Based Filter Bank (MLFB) Algorithm
#### 2.3.2 Generation of Refined Pulse Signal  
As described in the previous section, the SVM was trained using a 10-fold cross-validation approach with data from 10 subjects [27]. The SVM classified the decomposed pulse signal into binary values of 0 or 1 dependent on informativeness. The accuracy of the binary series, which are output by the SVM, was evaluated by comparing them with the referenced binary series. These binary series were calculated by assigning binary values to the sub-bands, where each binary value is set to 1 if the cPPG heart rate frequency is within the sub-band and 0 otherwise. To evaluate the performance of the SVM, these binary values of 1 and 0 were assigned to positive and negative predictions, respectively. The true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values were calculated  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature Types** & **Feature** & **Description** \\ \hline Spatial redundancy & Phase coherence [25] & Coherence in the phase of a narrow-banded pulse signal across 9 ROIs \\ \cline{2-3}  & Frequency variability & Variability in the number of peaks within a specific frequency band across 9 ROIs \\ \hline  & R, G, B standard deviation & The standard deviation of amplitude in each red (R), green (G), and blue (B) signal across 9 ROIs \\ \cline{2-3} Relative pulsatile amplitude & POS standard deviation & The standard deviation of amplitude of extracted pulse signals across 9 ROIs \\ \cline{2-3}  & R, G, B amplitude & Averaged amplitude in each red (R), green (G), and blue (B) signal \\ \cline{2-3}  & POS amplitude & Averaged amplitude of extracted pulse signals across 9 ROIs \\ \cline{2-3}  & POS [13] & The amplitude of the pulse signal extracted from 9 ROIs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of rPPG signal features used in machine learning.  
between the predicted and reference binary series. Subsequently, the performance of the SVM model was quantified using the following metrics:  
\[\begin{array}{l}\text{Accuracy}\;=\;\left(\text{TP}\;+\;\text{TN}\right)/\left( \text{TP}\;+\;\text{TN}\;+\;\text{FP}\;+\;\text{FN}\right)\\ \text{F1 score}\;=\;2\;\times\;\text{TP}\;/\left(2\;\times\;\text{TP}\;+\;\text{FP} \;+\;\text{FN}\right)\\ \text{Sensitivity}\;=\;\text{TP}/\left(\text{TP}\;+\;\text{FN}\right)\\ \text{Specificity}\;=\;\text{TN}\;/\left(\text{TN}\;+\;\text{FP}\right)\end{array} \tag{2}\]  
Finally, the resulting binary series, \(\text{DSTM}(t)\), was multiplied with the decomposed pulse signals to produce the refined pulse signal, rPPGMLFB(\(t\)), which retains only the frequency bands.  
\[\text{rPPG}_{\text{MLFB}}(t)=Y(t)\cdot DSTM(t) \tag{3}\]
## 4 Experimental Results and Discussion
### Comparison with State-of-the-Arts Methods  
The performances of the proposed scheme are compared with the state-of-the-art methods (HiDDeN [10], ReDMark [11], and [15]). For a fair comparison, we adjusted \(s\) to fit the PSNR similar to the other methods. Because ReDMark [11] showed precise numerical results, we first compare it with ours separately for various attacks. We adjusted the PSNR to 40.58 dB by adjusting \(s=1\). The results are shown in Table 6. The kinds and the strengths of attacks are from [11]. From the values in this table, it is clear that the proposed method performs better for all attacks except the Gaussian noise addition attacks.  
The other state-of-the-art methods did not show the clear numerical data. Therefore, we use the data presented in [15], which is the result of comparing [15] with [10] and [11], for a specific set of attacks. The comparison results with used training and test dataset are shown in Table 7. For this comparison, \(s\) was set to 2.75 for our scheme, to adjust the PSNR to 33.5 dB. As a result, the proposed method showed excellent results, except for the crop (0.035) attack, compared with [10] and [11]. However, when compared with [15], our method only showed better results for the JPEG compression attack.  
Figure 7: Invisibility and robustness according to a change in the strength factor: (**a**) Invisibility, (**b**) No attack, (**c**) Gaussian Filtering, (**d**) average Filtering, (**e**) median Filtering, (**f**) salt and pepper noise addition, (**g**) Gaussian noise addition, (**h**) sharpening, (**i**) JPEG, (**j**) rotation, (**k**) crop, (**l**) dropout, (**m**) dropout.  
The crop (0.035) attack uses only 3.5% of the watermarked image to extract the WM information, which is unrealistic because 3.5% of the image is not useful. Also, the method of [15] used the same kinds and the same strengths of attacks in training. That means the only trained attacks were evaluated. Therefore, it cannot guarantee the result for other kind or other strength of attack that it cannot be said to have good robustness results in a real application. Our scheme shows high utility from the comparisons because it demonstrates exemplary performance in all attacks, except the Gaussian noise addition attack and high-strength crop attack, which also result in valueless images.