esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 4 Methodology
### Overall Algorithm and Complexity Analysis  
In Section 4, we propose a priority-aware decomposition and dynamic resource mapping approach for optimizing multi-satellite cooperative computing. The method integrates three primary components: task decomposition based on graph theory and ILP, resource allocation modeled as a game-theoretic problem solved by DRL, and dynamic scheduling formulated with LTL and MINLP.  
The task decomposition module utilizes a DAG representation. It transforms the problem into an ILP formulation, where a Lagrangian relaxation-based heuristic efficiently explores feasible task allocation solutions while satisfying dependencies and resource constraints. The resource mapping and allocation module employs a game-theoretic model, where satellites compete for resources, and a Nash equilibrium-based strategy is determined through a DRL approach. This enables adaptive real-time optimization in response to system dynamics. The dynamic scheduling module leverages LTL to model task dependencies explicitly and incorporates an MINLP solver to iteratively adjust task execution in real time, ensuring the temporal constraints are met.  
At each scheduling step, the proposed method executes three sub-algorithms sequentially. The complexity analysis shows that the dominant computational cost arises from the ILP-based task decomposition, which has a complexity of \(O(N^{3})\), given that the graph-based task dependency representation typically requires cubic complexity for solving the associated ILP problems. Meanwhile, the reinforcement learning-based resource mapping algorithm contributes an additional complexity of \(O(K\cdot N^{2})\) per iteration, where \(K\) denotes the number of training iterations, and \(N\) is the number of satellites. The dynamic scheduling algorithm based on temporal logic and MINLP further contributes a complexity of approximately \(O(N^{2}\log N)\) due to the nonlinear constraints and temporal logic encoding.  
Therefore, combining these analyses, the overall time complexity per iteration of the proposed method is \(O(N^{3})\), dominated by the ILP component. Thus, the overall computational complexity of the algorithm across all iterations is \(O(T_{\max}\cdot N^{3})\), where \(T_{max}\) denotes the maximum number of iterations until convergence.
###### Abstract  
The application of a recent field theory of deformation and fracture to nondestructive testing (NDT) is discussed. Based on the principle known as the symmetry of physical laws, the present field theory formulates all stages of deformation including the fracturing stage on the same theoretical basis. The formalism derives wave equations that govern the spatiotemporal characteristics of the differential displacement field of solids under deformation. The evolution from the elastic to the plastic stage of deformation is characterized by a transition from longitudinal (compression) wave to decaying longitudinal/transverse wave characteristics. The evolution from the plastic to the fracturing stage is characterized by transition from continuous wave to solitary wave characteristics. Further, the evolution from the pre-fracturing to the final fracturing stage is characterized by transition from the traveling solitary wave to stationary solitary wave characteristics. In accordance with these transitions, the criterion for deformation stage is defined as specific spatiotemporal characteristics of the differential displacement field. The optical interferometric technique, known as Electronic Speckle-Pattern Interferometry (ESPI), is discussed as an experimental tool to visualize those wave characteristics and the associated deformation-stage criteria. The wave equations are numerically solved for the elastoplastic stages, and the resultant spatiotemporal behavior of the differential displacement field is compared with the experimental results obtained by ESPI. Agreement between the experimental and numerical results validates the present methodology at least for the elastoplastic stages. The solitary wave characteristics in the fracturing stages is discussed based on the experimental results and dislocation theory.  
nondestructive evaluation; comprehensive description of deformation and fracture; electronic speckle-pattern interferometry; field theory of deformation and fracture
## 2 Materials and Methods
### Methods  
#### 2.2.1 Feature Computation  
The primary task of feature computation is to perform a series of operations on the EMG signal to represent the original signal. To fully reflect the information in the signal, we calculated 27 characteristics from four categories. Feature vectors with a total of 252-dimensions were obtained. The gesture classification procedure based on sEMG signals is shown in Figure 3. Four feature extraction methods were used to treat the feature vector, and six classification methods were used for the feature extraction of the data.  
* Time-features  
The time-domain feature had the advantage of simple calculation and effective representation of signal characteristics. Therefore, ten time-domain features were selected for feature calculation. They were the mean absolute value (MAV) [15], root mean square (RMS) [16], Wilson amplitude (WAMP) [15], zero-crossing (ZC) [17], waveform length (WL) [18], the mean absolute value slope (MAVS) [30], slope sign change (SSC) [31], variance (VAR) [32], autoregressive (AR) coefficient of sEMG signal [27], and the AR coefficients from the first difference (FDAR) of the sEMG signal.  
* Frequency-features  
The power spectrum waveform of the sEMG signal varies greatly, so the frequency characteristics obtained by the power spectrum calculation were relatively stable. However, its calculation is difficult to achieve, so the median frequency (MDF) [18] and the mean frequency (MNF) [33] were used to characterize the power spectrum estimate.  
* Time-frequency features  
The time-frequency analysis method is a novel signal processing and analysis method in sEMG signal analysis. Wavelet transform (WT) represents this analysis method and plays a vital role in the analysis and processing of non-stationary signals. Wavelet Packet Decomposition (WPD) is a more sophisticated analysis method that can more accurately reflect signal characteristics. The twelve time-frequency features we use include the maximum values of WT and WPD, singular values, average energy, variance, standard deviation, and WL [34; 35; 36; 37; 38; 39; 40; 41]. In this paper, the wavelet-based sym3 is used to perform level = 3 wavelet decomposition on the EMG signal, and the third-order Symlet wavelet packet base is used to perform WPD on the EMG signal.  
* Nonlinear dynamics features  
Figure 3: Gesture classification process based on sEMG signal.  
Information entropy is a measure of the degree of disorder of the transmitted information, and the maximum value of the Lyapunov exponent can quantitatively describe the motion characteristics of the sEMG signal. Therefore, three nonlinear dynamic characteristics (including the entropy of WT [19], the entropy of WPD [21], and the maximum value of Lyapunov exponent [22]) were selected.
## 4 Experiment Results and Analysis
### The Performance of Classification on the Salinas and Pavia University Datasets

Table 4: Classification results of the Salinas dataset, including classification accuracies for every class, AA, OA, Kappa, and Time obtained by methods based on spectral, spectral + PCA and spectral–spatial + PCA. The best results are highlighted in bold. Figure 9: Classification maps for the Pavia University dataset. (**a**) Label map; (**b**) method based on spectral; (**c**) method based on spectral + PCA; (**d**) method based on spectral–spatial + PCA.
## 4 Conclusions  
SiNW array structures were fabricated on p-type c-Si wafer substrates, using electroless Ag plating from solutions with various AgNO\({}_{3}\) concentrations and MAE, over different etching times. With increasing AgNO\({}_{3}\) concentration, the resulting Si nano-structures changed from the nano-walls to SiNWs, with decreasing average diameters. The length of the SiNWs depended on the diffusion of the etching solution, such that the length of the SiNWs could be reasonably controlled by adjusting the etching time. The reflectance of the SiNW array structures decreased with increasing SiNW length, indicating that the SiNW array structures with SiNW lengths >7.5 \(\upmu\)m exhibited an anti-reflection effect and could be expected to realise solar cells, with a high \(J_{\rm{sc}}\), while maintaining a high \(V_{\rm{oc}}\). Using the optimum etching process, we fabricated a solar cell, using an absorber layer composed of a SiNW array structure with a SiNW length of 9 \(\upmu\)m etched within a 10 \(\upmu\)m-thick p-type c-Si layer on an insulator substrate. A hetero-junction solar cell structure was fabricated by depositing an n-type a-Si:H layer onto the SiNW array surface. The photovoltaic effect of the solar cell was confirmed. However, a low efficiency of 0.0017% was obtained because of the low \(J_{\rm{sc}}\) and \(V_{\rm{oc}}\) values. The low \(J_{\rm{sc}}\) was caused by the high series resistance and by the absorption loss from the n-type a-Si:H layer. Additionally, the low \(V_{\rm{oc}}\) was caused by a high surface recombination velocity of the uncoated SiNW surfaces. These results demonstrate that [PERSON] the surface recombination is vital for improving the efficiency of solar cells with SiNW array structures as the absorber. Thus, the inclusion of a passivation film, without the SOI substrate from the structure, is necessary to improve the efficiency of the device[PERSON]  
Conceptualisation: S.K. and Y.K.; methodology: S.K.; validation S.K.; formal analysis: S.K and Y.K.; investigation: S.K.; data curation: S.K.; writing-original draft preparation: S.K.; writing-review and editing Y.K, K.G, and T.S.  
This research received no external funding.  
The authors declare no conflict of interest.
## 6 Grid Integration and Interface of Mobility Technologies  
Electrical vehicles (EV) are expected to become the standard in the coming years. They may become an important asset to reduce fossil fuel dependency and provide a cleaner and less noisy environment in urban areas and city centres. One of the challenges concerning the growth of EVs depends on the availability of charging stations for long-range trips. Without an infrastructure that can recharge the vehicle in any region at any time most consumers will not consider buying an EV. In addition to this, the increasing number of EVs will also represent a substantial increase in the consumed electrical energy. The global electricity consumption from EVs is projected to reach 550 terawatt-hours by the year 2030 [125]. To meet this demand the number of EV charging stations and their installed power will require a massive increase.  
Nowadays, AC charging stations from public facilities allow a maximum charging rate within the range of 22 kW to 43 kW [126], making use of the AC/DC EV onboard charger to convert AC power supplied by the grid into DC power that feeds and recharges the EV batteries.  
DC charging stations are a good option to reduce the charging time and increase the charging power, as they convert AC to DC power and supply directly the car's battery bypassing the EV's onboard charger. These stations are designed to supply power ranging from 50 kW up to 200 kW [126]. However, they are more expensive and technologically more complex than AC charging stations and that is why recent research has been focused on DC charging topologies and control methods for vehicle-to-grid (V2G) and grid-to-vehicle (G2V) applications.  
Figure 17: Novel variation of IMC-based topology proposed in [124]. (**a**) respective topology. (**b**) output currents. (**c**) grid voltages and currents.
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 5 _Citrus_ Flavonoid Extraction Techniques
### Conventional Extraction Techniques  
Various traditional extraction procedures can be used to extract bioactive chemicals from plant sources. The recovery of bioactive chemicals from plant matrices, using common solvents, is referred to as conventional extraction (with or without heat treatment) [49]. The majority of these approaches rely on the extraction power of the various solvents in use, as well as the use of heat and/or mixing. The known conventional procedures for extracting bioactive chemicals from plants are (1) maceration, (2) infusion, (3) decoction, (4) hot continuous extraction (Soxhlet extraction), (5) hydrodistillation, and (6) percolation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Flavanones**} & \multicolumn{4}{c}{**Flavones**} & \multicolumn{4}{c}{**Total**} \\ \hline  & **Hesperidin** & **Nariutin** & **Naringin** & **Didymin** & **Eriocitrin** & **Diosmin** & **6,8-di-C-** & **6,8-di-C-** & **Stinestin** & **Flavonoids** \\ \hline Orange & 28.6 & 5.2 & - & 1.89 & 0.31 & 0.09 & 0.35 & 5.72 & 0.37 & 18.34 \\ \hline
\begin{tabular}{c} Sour \\ orange \\ \end{tabular} & - & - & 1.97 & - & - & 0.15 & - & - & - & - \\ \hline Mandarin & & & & & & & & \\ orange & 24.3 & 3.92 & - & 1.44 & 0.31 & - & - & - & 1.05 & - \\ \hline Clementine & 39.9 & 4.64 & 0.08 & - & - & 1.25 & 0.2 & 0.5 & - & 19.23 \\ \hline Lemon & 20.5 & - & - & - & 16.17 & 3.12 & 4.95 & 1.17 & - & - \\ \hline Grapefruit & 0.93 & 7.60 & 23.0 & 0.3 & 0.41 & - & - & - & - \\ \hline \multicolumn{8}{c}{Mean values are expressed in mg/100 mL of juice.} \\ \end{tabular}
\end{table}
Table 3: Flavonoid composition of some _Citrus_ juices [4; 43; 44].
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 5 Simulation  
In this section, the purpose is to analyze the performance of the various estimation methods presented in sections above. Given a proposed model, a simulation study is used to analyse the behaviour of the offered methodologies and assess the statistical performances of the estimators. Calculations have been performed using the R-statistical programminglanguage. Calculating MLEs and HPD intervals in R-language is done through utilizing the \"maxLike\" and \"HDInterval\" packages. Based on the following data produced by the TLMW distribution, where X is distributed as the TLMW distribution for various actual values of the parameters, Monte Carlo experiments were conducted:  
In Table 4, the actual values of the parameters are selected as first combination: \(\alpha=0.5\), \(\beta=0.6\), \(\theta=2\) and \(\lambda\) is 0.6 and 3. In Table 5, the actual values of the parameters are selected as second combination: \(\alpha=2.5\), \(\beta=2.5\), \(\theta=2\) and \(\lambda\) is 0.6 and 3. In Table 6, the actual values of the parameters are selected as third combination: \(\alpha=0.5\), \(\theta=0.75\), \(\lambda=2\) and \(\beta\) is 0.6 and 3. The sample sizes used in this simulation were 30, 80, and 150. The optimum method is one that minimises the relative bias (RB), mean squared error (MSEer), and length of confidence interval (LCI) of the estimator. In contrast to the LCI of MLE and MPS, which is the length of asymptotic confidence intervals (LACI), and Bayes calculates the length of confidence credible intervals (LCCI). Tables 4-6 provide the results of the simulation, and the following conclusion remakes are noted as a result. * For techniques of estimation methods, the RBias, MSE, and the LCI related to the parameter estimations drop as \(n\) rises. * It is noticed that the measures of the Bayesian estimation method are better than MLE method, as it is recognized. * It is noticed that MPSE measures are, on average, more accurate than MLE and Bayesian estimate measures. * Table 4, shows that the actual value of parameter \(\lambda\) increases then the measures of \(\beta\), \(\theta\), \(\lambda\) decreases. * Table 6, show that the actual value of parameter \(\beta\) increases then the measures of parameters increases. A heatmap is a graphical representation of data that uses a system of color-coding to represent different values. Figures 3 and 4 show heatmaps summarizing the results of the RB and MSE of simulation results. The dark red shading indicates RB and MSE with large value. The light yellow shading indicates RB and MSE with small value of parameters for TLMW under different estimation methods. Figure 3: Heatmaps of RB for parameters under different estimation methods.
## 6 Seismic Sedimentology Technology
### 90\({}^{\circ}\) Phase Adjustment  
When seismic reflections originate from thin-bedded sandbodies, direct correlations between sandstone layers and seismic events are inherently ambiguous. This challenge is addressed through 90\({}^{\circ}\) phase rotation or shift, a technique that aligns the central lobe of the seismic wavelet with the geometric centers of thin sandbodies. This allows for the establishment of a robust correlation between seismic events and subsurface lithology. Comparative analysis of 0\({}^{\circ}\)and 90\({}^{\circ}\) phase seismic models demonstrates that phase-adjusted data significantly enhances the interpretability of thinly layered geological features. Furthermore, 90\({}^{\circ}\) phase models exhibit improved agreement with acoustic impedance profiles and well logging data. Direct waveform comparison with impedance data enables systematic analysis of the relationship between seismic events and stratigraphic architecture [15; 16]. This phase adjustment methodology substantially reduces the uncertainties associated with thin-bedded sandbody identifications and streamlines workflow efficiency.  
Post-rotation seismic profiles of the Dainan Formation reveal enhanced phase-to-lithology correlation. Notably, nearly all of the penetrated sandstone units correlate with  
Figure 4: Core photos of lake sediments of the Dainan Formation of the Gaoyou Sag (**a**) Fine sandstone with burrow pore in littoral-shallow lake, well Y9, 2686.2 m (**b**) Gray mudstone of semi-deep lake with horizontal bedding, well H20, 3198.1 m.  
seismic troughs (denoted in red, see Figure 2), rendering the seismic sections more geologically intuitive. This transformation clarifies the litho-seismic relationship. Interpreted seismic events directly represent sandbody geometries, thereby assigning seismic reflections explicit stratigraphic significance. The optimized phase alignment enables direct mapping of sandstone distributions through continuous event tracking, effectively bridging geophysical data with sedimentological interpretations.
## 2 Related Works  
Blind and non-blind watermarking methods fall into two main groups. While blind approaches, such as the ones used in one study [7], need the original image in order to extract watermark data, non-blind methods, in fact, need these. Work [8] used mammographic pictures to apply the Fast Gradient Sign Attack (FGSM) assault, by using the \"Digital Database for Screening Mammography\" (DDSM), which includes both malignant and healthy pictures. While the SSIM index dropped below 0.2, the accuracy declined by more than 30%. Authors in one study [9] used FGSM attack on CT scans and X-rays to find COVID-19. They demonstrated the vulnerability of these models by using VGG16 and InceptionV3, whose accuracies have dropped by up to 90% and 63%, respectively.  
Some authors [10] used white-box FGSM and black-box one-pixel techniques to target the NLST dataset. While the one-pixel technique merely decreased the model's accuracy by 2-3%, FGSM decreased it by 36%. The FGSM and PGD attacks were used by some [11] to identify skin cancer on Dermoscopic pictures. The performance of the model dropped by as much as 75%. ResNet50 was used in one study [12] to evaluate several of the most well-known white-box assaults, including FGSM, PGD, C&W, and BIM, on three datasets, with the model's performance dropping by 100% in some circumstances. Others [13] suggested the Adaptive Segmentation Mask (ASMA) technique, which is a focused attack for medical image segmentation. This attack achieves high Intersection-over-Union (IoU) deterioration while producing undetectable samples. By creating adversarial instances, and by using geometrical deformations to represent anatomical as well as intensity fluctuations, researchers [14] presented an attack for medical image segmentation. A wavelet tree quantization was used in one study [15] to offer a blind picture watermarking approach that improved the resistance against geometric attacks, such as rotation, scaling, and cropping. A robust multi-scale full-band picture watermarking based on the Distributed Discrete Wavelet Transform (DDWT) together with the Singular Value Decomposition (SVD) was introduced in one paper [16]. (DDWT). This approach is reasonably resistant to both cropping and rotational attacks. A strong picture watermarking system based on a computer-generated hologram was presented in one study [17], to lend off geometric attacks such as translation, rotation, cropping, flipping, and scaling attacks. By using histogram modification [18], researchers developed an image watermarking method that is resistant to geometrical attacks, such as rotation, cropping, scaling, and translation attacks.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.2 Analyzing Round-Trip Time in mmWave Networks  
Round-trip times usually increase as the number of users increases because of increased network traffic. This can be reduced by improving beam management and cutting down on transmission delays with efficient control of the transfer approach.  
The comparative analysis of round-trip time (ms) is represented in Figure 4 and Table 6. From the below figure, it is clear that the proposed design has the lowest RTT of 65 (ms) starting with 20 users, which is faster than SIM-ADMT at 95 (ms), FB-TCP at 85 (ms), and UL-SCH + PUSCH at 100 (ms). As we move toward the middle of 60 users, the recommended plan performs even better, with an RTT of just 58 (ms). In contrast, the other two plans have higher latency, with UL-SCH + PUSCH reaching 135 (ms), SIM-ADMT achieving 120 (ms), and FB-TCP rising to 110 (ms). After 100 users have completed the test, the suggested technique maintains a fast RTT of 120 (ms), which is much faster than the 180 (ms) of SIM-ADMT, 150 (ms) of FB-TCP, and 175 (ms) of UL-SCH + PUSCH. Effectiveness in lowering round-trip time (RTT) is demonstrated by the proposed scheme's ability to sustain steady state performance in high-density 5G mmWave network conditions, which makes it the ideal choice for accommodating effective communication.  
Figure 3: Number of users vs. throughput (Mbps).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No of User** **(r-Axis)**} & \multicolumn{4}{c}{**Throughput (MBPS)-(y-Axis)**} \\ \cline{2-5}  & **SIM-ADMT** & **FB-TCP** & **UL-SCH + PUSCH** & **Proposed** \\ \hline
20 & 17 & 23 & 35 & 53 \\ \hline
40 & 30 & 35 & 55 & 85 \\ \hline
60 & 47 & 56 & 43 & 70 \\ \hline
80 & 56 & 62 & 58 & 83 \\ \hline
100 & 32 & 40 & 60 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical outcomes of throughput (Mbps).