esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Electromagnetic Stress
#### 2.1.1 Electric Field  
Dielectric stress due to the electric field in a material plays a key role in the performance and reliability of electric drive components, including motors, converters, cables, and insulation materials [17]. As a result, its impact on the lifespan and degradation of these components can be analyzed through the following aspects:  
* Dielectric breakdown;
* Partial discharges (PDs);
* Electromagnetic interference (EMI);
* Thermal degradation.  
Insulation materials, such as those used in cables and motor windings, are the most sensitive and failure-prone components when exposed to prolonged electric field stress. These materials are particularly vulnerable to mechanisms such as dielectric breakdown, PDs [18], and thermal degradation, all of which accelerate aging and reduce their reliability over time. In [19], the authors propose a method for the accelerated aging of the electric  
Figure 2: Classification of electromagnetic stress.  
machine insulation. The presence of electric field stress in the method is introduced through pulse-width modulation (PWM) voltage frequency. A short voltage rise time induces a high-magnitude electric field, which breaks chemical bonds in insulation materials and accelerates thermal degradation, thereby reducing reliability over time. To evaluate the level of electric stress, the inverse power law model is commonly used (1) [20]:  
\[S_{use}=kE^{-n} \tag{1}\]  
where \(k\) and \(n\) are positive parameters specific to the material and the test method, \(E\) is the magnitude of the applied electric field vector, and \(S_{use}\) represents the lifespan under the given stress conditions. Additionally, electric fields significantly impact electromagnetic compatibility (EMC) in electric drives by generating EMI through radiated and conducted emissions. High-intensity fields resulting from voltage transients and high-frequency signals can degrade insulation, induce parasitic currents, and disrupt grounding systems, leading to performance issues [21]. As noted in [22], mitigation strategies such as shielding, EMC filters, and effective grounding are crucial for maintaining reliable system operation.
## 5 Algorithm Descriptions
### Feature Extraction
#### 5.1.2 STFT (Short-Time Fourier Transform)  
The time-frequency matrix str is acquired after short-time Fourier transform (STFT) of the sample signal. The signal time-frequency matrix describes the characteristic of amplitude distribution in a quite wide range from low frequency to high frequency comprehensively. Each element represents the amplitude on corresponding moment and corresponding frequency. It aims to get the number of singular value from large to small by singular value decomposition (SVD) [28] of the matrix str. A larger singular value contains the larger information of the matrix. SVD algorithm is described as follows:  
\(A_{m\times n}\) is the sample matrix and there are orthogonal matrix \(U_{m\times n}\) and \(V_{m\times n}\).  
\[A=U\Sigma V^{T} \tag{2}\]  
Among \(S=diag(\sigma_{1},\ldots,\sigma_{r})\), \(\sigma_{1}\geq\ldots\geq\sigma_{r}\) is the singular value of \(A\).
## 3 Methods
### Discriminator of the Proposed Model  
The discriminator adopted is PatchGAN [40], which determines whether patches of \(N\times N\) are real or generated. The main benefit of this model is the low number of parameters, execution time, and the possibility of using images of any size. To perform the training, the value of \(N\) is the same as that used by the HDR-GAN model [16], consisting of  
Figure 6: Proposed model architecture.  
using patches of \(512\times 512\) from the training set, randomly cropping, rotating, and flipping the images.  
To improve the geometric representation of the learned features, the adversarial loss function used is based on the Sphere GAN [41], where the discriminator is projected onto a hypersphere \(\mathbb{S}^{n}\) through inverse stereographic projection, ensuring that distances are measured on the surface of the hypersphere.  
Given the discriminator \(D(\cdot)\), its output is mapped as a vector \(\mathbf{q}=D(\cdot)\in\mathbb{R}^{n}\), which is projected to a point \(\mathbf{p}\) on the hypersphere via inverse stereographic projection, according to Equation (8).  
\[\mathbf{p}=\left(\frac{2\mathbf{q}}{||\mathbf{q}||^{2}+1},\frac{||\mathbf{q}|| ^{2}-1}{||\mathbf{q}||^{2}+1}\right) \tag{8}\]  
The distance between two points projected onto the hypersphere can be measured using \(d_{s}(\mathbf{p},\mathbf{p}^{\prime})\), defined by Equation (9).  
\[d_{s}(\mathbf{p},\mathbf{p}^{\prime})=\arccos\frac{||\mathbf{q}||^{2}|| \mathbf{q}^{\prime}||^{2}-||\mathbf{q}||^{2}-||\mathbf{q}^{\prime}||^{2}+4 \mathbf{q}\mathbf{q}^{\prime}+1}{(||\mathbf{q}||^{2}+1)(||\mathbf{q}^{\prime }||^{2}+1)} \tag{9}\]  
In the context of GAN training, the adversarial loss is formulated as the distance between the features of the real and generated examples, mapped relative to a fixed reference point, the north pole of the hypersphere (\(\mathbf{N}=[0,\ldots,0,1]^{T}\in\mathbb{R}^{n}\)), and given by Equation (10).  
\[\mathcal{L}_{a}=\min_{G}\max_{D}\sum_{r}\mathbb{E}_{\mathbf{z}}[d_{s}^{r}( \mathbf{N},D(\mathbf{z}))]-\sum_{r}\mathbb{E}_{\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3}}[d_{s}^{r}(\mathbf{N},D(G(\mathbf{x}_{1},\mathbf{x}_{2}, \mathbf{x}_{3})))] \tag{10}\]  
where \(d_{s}^{r}(\cdot,\cdot)\) is the distance of the \(r\)-th moments on the hypersphere (\(r=\{1,2,3\}\)). Here, \(\mathbf{z}\) represents the real data (ground truth) and \(\mathbf{x}_{i}\) the Low-Dynamic-Range (LDR) input images.  
Finally, the complete model is trained with a hybrid loss function calculated according to Equation (11).  
\[\mathcal{L}=\mathcal{L}_{1}+\mathcal{L}_{a} \tag{11}\]  
where \(\mathcal{L}_{1}\) is the reconstruction loss and \(\mathcal{L}_{a}\) is the adversarial loss.
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
## 3 Active Beamforming and Phase Shift Design
### ZFBF and Co-Phasing Technique Using Partial CSI
In summary, the proposed beamforming scheme comprises two stages. First, to perfectly eliminate the IBI, the ZFBF vector is designed as follows: \(\mathbf{w}_{k}=\bar{\mathbf{t}}_{k}/(K\|\bar{\mathbf{t}}_{k}\|)\). Next, to maximize the designed signal power based on the partial CSI, the phase shifting of the RIS is designed as follows: \(\theta_{k,n}=-\angle\bar{f}_{k,k,n}^{*},\forall k,n.\)
## 3 Results
### Model Comparison  
The outcomes of the two methodologies were evaluated by calculating the coefficient of determination (\(r^{2}\)), the root mean square error (_RMSE_), and the mean absolute percentage error (_MAPE_) (Table 2). This analysis aimed to identify the method that delivered the most accurate predictions and, consequently, determine the optimal conditions for maximizing TRS yield.  
Both methods achieved satisfactory data fits; however, the artificial neural network (ANN) demonstrated superior predictive accuracy, with an \(r^{2}\) exceeding 0.99 for all three variables studied: particle size, acid concentration, and time. In contrast, the \(r^{2}\) values obtained through the RSM were lower. Similarly, the _RMSE_ and _MAPE_ followed a consistent trend, as the ANN model produced lower errors compared to the RSM model.  
This work provides a precedent for the implementation of ANNs in the prediction of TRS amounts in the chemical hydrolysis of mesquite (_Prosopis laevigata_) and its use in the production of bioethanol.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 5 Limitations of Current AGB Maps and Future Improvements
### Novel Approaches to Forest AGB Mapping  
Current AGB maps retrieved from remotely sensed datasets mainly use GIS-based modelling methods based on some assumptions or empirical algorithms. Along with the development of machine learning algorithms, in particular deep learning algorithms, more advanced data-driven algorithms or a hybrid modelling that couples physical process models with data-driven machine learning could be considered in large-scale forest biomass mapping [123].
## 2 Trajectory Initialization Based on MDF-Clustering
### Basic Concepts of Fuzzy Clustering  
In the application of the fuzzy clustering algorithm [17; 18], consider a set containing \(n\) data points, let the dataset \(\mathbf{X}=\{\mathbf{x}_{1}\ldots,\mathbf{x}_{n}\}\), and its fuzzy set \(\mathbf{K}\) is represented by the matrix \(\mathbf{U}=\left[\mathbf{u}_{ij}\right]\), where the element \(\mathbf{u}_{ij}\) denotes the degree of affiliation of the data point \(\mathbf{x}_{j}\) to the ith category, and \(\mathbf{u}_{ij}\) satisfies the following conditions.  
\[\forall j,\sum_{i=1}^{n}\mathbf{u}_{ij}=\mathbf{1} \tag{1}\]  
\[\forall i,j,u_{ij}\in[\mathbf{0},\mathbf{1}] \tag{2}\]  
\[\forall i,\sum_{j=1}^{n}\mathbf{u}_{ij}>\mathbf{0} \tag{3}\]  
The current widely used clustering criterion is to minimize the sum of squared weighted errors within classes, i.e.,  
\[(\mathbf{min})\mathbf{J}_{m}(\mathbf{U},\mathbf{V})=\sum_{j=1}^{n}\sum_{i=1}^{c}\mathbf{u}_{ij}^{m }\mathbf{d}_{ij}^{2}\big{(}\mathbf{x}_{j},\mathbf{v}_{i}\big{)} \tag{4}\]  
This is usually carried out by determining the clustering center \(V\). In this method, \(m\) is used as a weighted index and \(d_{ij}\big{(}\mathbf{x}_{j},\mathbf{v}_{i}\big{)}=\big{\|}\mathbf{v}_{i}-\mathbf{x}_{j}\big{\|}\) as a measure of the distance between \(\mathbf{x}_{j}\) and \(\mathbf{v}_{i}\).  
The Lagrange multiplier method is employed to solve this objective function, providing and optimal solution for the affiliation matrix \(U\) and the clustering center \(V\):  
\[\mathbf{u}_{ij}=\left[\sum_{k=1}^{K}\left(\frac{\mathbf{d}_{ij}}{\mathbf{d}_{kj}}\right)^{ \frac{2}{m-1}}\right]^{-1} \tag{5}\]  
\[\mathbf{v}_{i}=\frac{\sum_{j=1}^{n}\mathbf{x}_{j}\mathbf{u}_{ij}^{m}}{\sum_{j=1}^{n}\mathbf{u }_{ij}^{m}} \tag{6}\]
## 7 Tests and Results
### Measurements  
As explained, the control system manages the isle providing the switch-on times (in minutes) and the absorbed currents of the lamps (in Amperes). Every control card of the lamp posts collects these data sending them to the coordinator when it requests them. After that, the coordinator control card provides to post the data on the website. To avoid data collisions, the coordinator calls the lamp posts one by one exploiting a specific identification number (ID) associated to each of them. Data collected during some autumn and winter months of 2013/14, showed the lamps absorb about 1.5 A during the activity for a period between 22.5 and 24 h per month.
###### Abstract  
The global positioning system (GPS) is widely known for its applications in navigation, timing, and positioning. However, its accuracy can be greatly impacted by the performance of its receiver clocks, especially for a low-cost receiver equipped with lower-grade clocks like crystal oscillators. The objective of this study is to develop a model to improve the stability of a low-cost receiver. To achieve this, a machine-learning-based linear regression algorithm is proposed to predict the differences of the low-cost GPS receiver compared to the precision timing source. Experiments were conducted using low-cost receivers like Ublox and expensive receivers like Septentrio. The model was implemented and the clocks of low-cost receivers were steered. The outcomes demonstrate a notable enhancement in the stability of low-cost receivers after the corrections were applied. This improvement underscores the efficacy of the proposed model in enhancing the performance of low-cost GPS receivers. Consequently, these low-cost receivers can be cost-effectively utilized for various purposes, particularly in applications requiring the deployment of numerous GPS receivers to achieve extensive spatial coverage.  
GPS; time and frequency; stability of clocks; clock bias errors; precision timing applications; atomic clocks; time transfer +
Footnote †: journal: Electronics  
Article
## 3 Simulation Experiments  
### Analysis of Simulated Signals  
To validate the effectiveness of the PSO-optimized VMD method in handling strong cultural noise in geomagnetic studies, simulation experiments were conducted using the four types of strong cultural noise signals shown in Figure 1. Given that strong cultural noise typically has amplitudes several times or even orders of magnitude greater than the original signal, impulse noise, square wave noise, triangular wave noise, and periodic noise with a maximum amplitude of 6 are each added to the original clean signal. The experiments employed EMD, ITD, VMD, and the proposed method to denoise the signals and compare the denoising performance of each method.  
The parameters [K, \(\alpha\)] for the VMD were optimized using the PSO algorithm, with the parameter settings listed in Table 3. The population size M was set to 10, the maximum number of iterations Gmax was 10, the iteration range for K was [3; 10], and the iteration range for \(\alpha\) was [100, 2500]. Taking the simulated impulse noise signal as an example, the final parameter iteration convergence result is shown in Figure 2. When the iterative process stabilizes and converges, it indicates that the optimal solution has been found. During the eighth generation, the local minimum entropy reaches its lowest value of minEp = 6.113, corresponding to the optimal parameter combination of [K, \(\alpha\)] = [8; 230]. The parameter optimization process for the other three types of simulated strong cultural noise signals followed the same methodology.  
Based on the optimal parameter combination, VMD is performed, resulting in eight IMFs ranging from low to high frequency, which form a three-dimensional representation of the simulated signal decomposition in Figure 3. The PSO-optimized VMD was applied to four types of typical simulated strong cultural noise signals, ultimately producing a comparative visualization of the simulated signal before and after denoising, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gmax** & **M** & **C1** & **C2** & \(\omega\) \\ \hline
10 & 10 & 1.5 & 1.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameterization of PSO algorithm.  
Figure 2: Convergence result of parameter iteration.  
Figure 4: Comparison of simulated signal before and after denoising. Horizontal coordinates indicate sampling points and vertical coordinates indicate amplitude. The first line in blue indicates the initial signal, the second line in red indicates the extracted noise signal, and the third line in blue indicates the reconstructed denoised signal. (**a**) Comparison of simulated impulse noise signal; (**b**) comparison of simulated square noise signal; (**c**) comparison of simulated triangle noise signal; (**d**) comparison simulated periodic noise signal.  
Figure 3: Three-dimensional representation of simulated signal decomposition. Different colors represent different IMFs, blue means initial signal IMF0, orange means IMF1, green means IMF2, and so on.
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 2 Charge Transport in Metal-Organic Frameworks (MOFs)  
Due to the electrically insulating or low conducting feature of most MOFs, rendering a facile charge-transport rate within the framework becomes one of the main challenges for achieving promising performance with MOF-based electrocatalysts. Based on the charge-transport mechanism, approaches to rendering charge transport in MOFs can be briefly classified into two categories: redox hopping and band transport [31].
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Charge Rates on Battery Performance
#### 3.4.1 Effects of Charge Rates on Battery Capacity  
Figure 15 demonstrates how the charge retention rate and capacity recovery rate vary with different charge rates. As shown in this figure, the 2-C charge rate resulted in the highest charge retention rate. On the other hand, these three charge rates did not make much difference in the battery capacity recovery rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Manufacturer** & **Method Number** & **Rest Time (min)** & \
\begin{tabular}{c} **Change Rate of** \\ **Charge Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Change Rate of** \\ **Discharge Efficiency** \\ \end{tabular} \\ \hline A & 2 & 5 & \(-2.9\%\) & \(-1.5\%\) \\  & 11 & 15 & \(-3.5\%\) & \(-2.7\%\) \\ B & 2 & 5 & \(-4.1\%\) & \(-7.1\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effects of different rest times on efficiency change rate of Ni-MH battery modules (\(DDC\) mode).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SOC Value** & **Manufacturer** & \begin{tabular}{c} **Method** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Rest Time** \\ **(min)** \\ \end{tabular} & \
\begin{tabular}{c} **Change Rate of** \\ **Charge Power** \\ \end{tabular} &
\begin{tabular}{c} **Change Rate of** \\ **Discharge Power** \\ \end{tabular} \\ \hline \multirow{3}{*}{80\% SOC} & A & 6 & 30 & \(-21.7\%\) & \(-22.2\%\) \\  & A & 4 & 5 & \(-20.8\%\) & \(-21.7\%\) \\  & B & 6 & 30 & \(-23.2\%\) & \(-20.1\%\) \\  & A & 4 & 5 & \(-22.9\%\) & \(-20.1\%\) \\ \hline \multirow{3}{*}{50\% SOC} & A & 6 & 30 & \(-23.3\%\) & \(-23.6\%\) \\  & A & 4 & 5 & \(-23.2\%\) & \(-23.4\%\) \\ \cline{1-1}  & B & 6 & 30 & \(-21.8\%\) & \(-21.1\%\) \\ \cline{1-1}  & A & 4 & 5 & \(-21.6\%\) & \(-20.8\%\) \\ \hline \multirow{3}{*}{20\% SOC} & A & 6 & 30 & \(-22.1\%\) & \(-33.5\%\) \\ \cline{1-1}  & A & 4 & 5 & \(-20.4\%\) & \(-32.9\%\) \\ \cline{1-1}  & B & 6 & 30 & \(-21.0\%\) & \(-21.4\%\) \\ \cline{1-1} \cline{2-5}  & B & 4 & 5 & \(-20.2\%\) & \(-21.4\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of different rest times on power change rate of Ni-MH battery modules with different SOC (\(FDC+10\%\) mode).
## 4 Discussion and Conclusions  
To summarize, we modeled ultrafast melting in a single silicon crystal triggered by an intense hard X-ray pulse under different pulse fluence conditions. We identified the specific mechanisms contributing to the Si melting (including their temporal characteristics) as a function of the pulse fluence under uniform illumination. The reason for the observed strong fluence dependence is that the fluence determines the strength of the initial electronic excitation in the system, necessary to trigger a nonthermal or thermal structural transition. At the conditions of the experiment by [PERSON] et al. [12], the pulse of 100% nominal fluence (peak absorbed dose \(\sim\)2.70 eV/atom) triggered an ultrafast nonthermal transition, whereas the same pulse with a 50% fluence fraction yielded a thermal transition only. At 10% of the nominal fluence, no structural damage occurred in the sample.  
We showed that, due to the non-uniform spatial profile of the X-ray beam used in the experiment, the recorded diffractive signal was a volume-integrated signal, i.e., its amplitude is a coherent sum of the contributions from various parts of the crystal, experiencing different local doses. This enabled a correct interpretation of the experimental data with the volume-integrated signal calculated from the simulations. This observation implies that possible effects of volume integration, due to the beam non-uniformity and the relation between the beam size and the imaged object size have to be considered in planning and interpreting future experiments investigating structural changes in solid materials with X-ray diffraction imaging. This should also support the development of techniques for'shaping' the spatial profile of X-ray beams, which could reduce or even eliminate the effect of volume integration.  
Conceptualization, V.T., M.M.A., Z.J. and B.Z.; methodology, all authors; software, V.T., M.M.A., Z.J., N.M. and V.L.; validation, V.T., M.M. and B.Z.; investigation, all authors; writing, V.T. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Czech Ministry of Education, Youth and Sports, Czech Republic, grant numbers LTT17015, LM2015083 (acknowledged by N.M.). The authors [PERSON], [PERSON], V.L. and [PERSON] acknowledge the networking support by the COST Action CA17126.  
Not applicable.  
The authors thank [PERSON], [PERSON], and [PERSON] for valuable discussions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FEL Free Electron Laser LCLS Linac Coherent Light Source XTANT X-ray Thermal and Nonthermal Transitions XSINC X-ray Scattering in Nanocrystals FWHM Full Width at Half Maximum
###### Abstract  
This study proposes optimizing the performance of free space optic signal transmission using spatial division multiplexing. The research uses different modified duobinary modulation schemes to model and optimize three hybrid mode division multiplexing-free-space optical (MDM-FSO) channels, each operating at 40 Gb/s-40 GHz. The study also includes the parametric optimization of various components to enhance system performance. The findings are significant for achieving high data rate links for backhaul solutions and improving bandwidth for future MDM-based wireless distributed networks. The research shows that employing three linearly polarized modes as data transmission channels with direct detection can be effective. Additionally, it is discovered that adjusting the bias voltages of the two LiNbO\({}_{3}\) modulators can improve power sharing between the modes, thereby mitigating the power penalty.  
free-space optical communication; mode division multiplexing (MDM); duobinary modulation schemes; parametric optimization Article
## 3 CFD Applied to Compost-Bedded Pack Barn Systems
### Mesh Generation and Independence Testing  
In order to obtain accurate and reliable results in the simulations, it is essential that adequate meshes are generated [31]. For dairy cattle facilities, hybrid meshes (structured and unstructured) with interfaces are usually used, which fulfill the role of uniting the two types of meshes and ensuring flow continuity. Typically, unstructured meshes are applied to represent complex regions, such as around animals and/or places with curvature, and structured meshes are applied to more regular regions, such as headspace [19; 29].  
The element size that makes up the mesh also influences the results achieved: the smaller these elements are, the greater the precision of numerical values and the better the modeling quality. However, meshes with too small elements demand more computational time, making the simulation process time consuming and costly [19; 28; 77; 81]. Failure to properly refine the mesh can lead to increased discretization errors and hinder the convergence of the iterative process with second-order discretization schemes [74].  
Seeking to minimize uncertainty in relation to mesh refinements, it is recommended to conduct mesh independence tests. To carry out such tests, meshes with distinct refinement levels in the interest regions must be generated, and adequate shapes must be used to evaluate the refinement effects in relation to temporal and spatial concentration gradients [41]. In CFD, several evaluation forms and/or metrics are used to analyze the mesh refinement effect on the results achieved, such as equiangular asymmetry [39; 44; 82], coefficient of determination (R\({}^{2}\)) [81], relative difference [15], relative error [77], root mean square deviation (RMSD) [14; 31; 81], unidirectional nonparametric variance analysis (Kruskal-Wallis test) [41], among others. Failure to carry out mesh independence tests may compromise the results obtained, as there will be no guarantee that the refinements performed were sufficient to reduce the discretization errors to acceptable levels and that the results are mesh independent.