esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Encoding  
The mapping from the problem solution to the genes is called encoding, and the form of encoding directly affects the performance of the algorithm. When solving problem \(\mathbf{P}2\) using a genetic algorithm, the optimization variables need to be encoded at each latitude, where the pitch angle is a continuous variable and the number of off beams is a discrete variable. A proper encoding method has been employed to represent progressive pitch scheme, and the difference of the optimization variables are encoded.  
The difference of pitch angle between adjacent latitudes is coded, which can be expressed as \(\chi[p]-\chi[p+1]\), and the adjustment of pitch angle is divided into fast adjustment and slow adjustment. The change of pitch angle for fast adjustment is \(T\Delta\chi\) and for slow adjustment is \(\Delta\chi\), where \(T\) is an integer. The change of the number of off beams \(K[p]-K[p+1]\) between adjacent latitudes is assumed to be at most \(1\). The whole action space at each latitude is denoted as \(A\), where \(A=\{a_{1},a_{2},a_{3},a_{4}\}\), and they are no adjustment, slow adjustment, fast adjustment and closing beam, respectively. The length of chromosome is \(\left\lfloor\frac{90}{\Delta\Psi}\right\rfloor\), and each gene on it has four expressions. We denote the chromosome as \(S\), where \(S=\{s_{1},s_{2},\cdots,s_{l},\cdots,s_{\lfloor\frac{90}{\Delta\Psi}\rfloor}\}\) and \(s_{l}\in A\), and the gene \(s_{l}\) denotes the action of the latitude of \(90-l\Delta\psi\). Therefore the pitch angle and the number of off beams for the latitude \(90-l\Delta\psi\) could be represented as the following,  
\[\psi_{90-l\Delta\psi}=\sum_{k\in\{1,2,\cdots,l\},s_{k}\in\{a_{1},a_{2},a_{3}\} }s_{k}, \tag{21}\]\[K_{90-I\Delta\psi}=\sum_{k\in\{1,2,\cdots I\},s_{k}\in\{a_{4}\}}s_{k}. \tag{22}\]  
In order to obtain more precise adjustment of the progressive pitch scheme, the action space should be extended. The chromosome could be divided into two distinct parts: the mapping of the difference of pitch angle and the mapping of the difference of the number of off beams. However, this extension could impose burden on the searching of optimum progressive pitch scheme.
## 3 Results
### Decoder Analysis
Figure 3: (**A**) Correlation coefficients for decoders trained and tested on Dataset 1; (**B**) correlation coefficients for decoders trained and tested on Dataset 2; (**C**) coefficient of determination for decoders trained and tested on Dataset 1; (**D**) coefficient of determination for decoders trained and tested on Dataset 2. Asterisks indicate that each category on the higher side significantly outperformed each category on the lower side. Regarding the coefficient of determination for Dataset 2, we found that swSNN-SLSTM statistically outperforms (\(p<0.05\)) all other decoders. The memSNN-SLSTM and LSTM model performed statistically similarly (\(p=0.84\)) and performed similarly to swSNN (\(p=0.178\)). They outperformed memSNN and UKF5 (\(p<0.05\)). swSNN, memSNN, and UKF5 all performed similarly (\(p>0.113\)).
## 3 Results
### Cracks Distribution  
#### 3.3.1 Surface Cracks  
The distribution of surface cracks generated by the laser beam was studied after the rock specimens were naturally cooled. The images of the irradiated specimens are presented in Figures 8 and 9, from which obvious cracks can be observed on the surface of each irradiated rock specimen. In addition, the detailed number and angle of cracks caused by laser irradiation were also investigated, as shown in Figure 10.  
Figure 8: Cracks of irradiated granite on the lateral surface.  
Figure 9: Cracks of irradiated granite on the bottom surface.  
One can observe that the crack numbers and angles both increase from 3 to 7 and 17\({}^{\circ}\) to 48\({}^{\circ}\), respectively, when the laser power increases in the range of 400-1000 W, as shown in Figure 10a. The crack numbers and angles increase by 133% and 182%, respectively. The ratio of the cracked area at both ends also increases from 14% to 50% when the laser power increases in the range of 400-1000 W, which illustrates that a higher-power laser beam results in a larger cracked body. This may be attributed to the high-temperature field with a super-large temperature gradient achieved by the laser irradiation when the laser power increases, thereby causing more thermal damage [34]. However, Figure 10b shows that an increase in the laser-beam diameter can lead to a decrease in the crack angle. For instance, the crack angle decreased from 48\({}^{\circ}\) to 36\({}^{\circ}\) when the beam diameter increased in the range of 6-12 mm. Interestingly, it was also found that the ratio of the cracked area at both ends decreased from 50% to 29% under the same range of beam diameter, which was attributed to the decreased power density on the irradiation surface. It led to less thermal damage when a larger beam diameter was used [33]. Finally, Figure 10c shows that the crack angle reduces when the beam's moving speed increases, given the same laser power and beam diameter. For instance, an increase in the beam's moving speed from 0.5-4 mm/s led to a decrease in the crack angle from 48\({}^{\circ}\) to 29\({}^{\circ}\). In addition, the ratio of cracked area at both ends was also reduced from 50% to about 10% due to the increased moving speed in the range of 0.5-4.0 mm/s. This is understandable considering that less time was required to achieve the same irradiation length when the beam moving speed was increased. Therefore, less thermal damage to the granite specimen can be expected because less thermal energy was irradiated to the specimen.  
Figure 10: The influence of (**a**) laser power, (**b**) beam diameter, (**c**) moving speed of laser beam on crack angles and ratio of cracked area.
## 3 Results
### EBSD Measurement of As-Built Samples  
Inverse pole figure (IPF) EBSD maps obtained from the as-built samples are shown in Figure 8 (ys = 80 \(\upmu\)m, Vs = 1000 mm/s and ys = 140 \(\upmu\)m, Vs = 850 mm/s). From Figure 8, both as-built samples show columnar orientation along the building direction. The effect of constitutional supercooling is dependent on the quotient (G/R), where (G) is the temperature gradient and (R) is the rate of solidification. (G/R) determines the solidification morphology. For rapid solidification processes, like LPBF (10\({}^{4}\)-10\({}^{8}\) K/s), heat is conducted away from the building plate in a directional manner. This results in the formation of typically columnar oriented structures due to the direction of flow of heat flux as shown by the EBSD measurements [26; 35; 36; 37]. A melt pool exposed to the atmosphere transfers heat flux by radiation while the underlying solidified substrate does so by conduction; grain growth is, therefore, favored in the direction perpendicular to the solid-liquid front [38].  
Figure 4: LOM micrographs of 140 \(\upmu\)m hatch distance print set: (**a**) unetched, dense as-built cube (scanning speed 550 mm/s); (**b,c**) etched with CuCl\({}_{2}\) showing epitaxial growth and columnar grain formation; (**d**) unetched, dense as-built cube (scanning speed 850 mm/s); (**e,f**) etched with CuCl\({}_{2}\) also showing epitaxial growth and columnar grain formation.  
Figure 5: SEM micrographs of as-built laser powder bed fusion (LPBF) samples at 140 \(\mu\)m (Y8): at 550 mm/s (**a**) showing discontinuity in epitaxial growth; (**b**) combination of both epitaxial and cellular dendritic structures; at 850 mm/s (**c**), (**d**) different subgrain orientations at higher magnification.  
The 80 \(\upmu\)m hatch distance sample recorded an average equivalent grain diameter (ECD) of 8.45 \(\upmu\)m with a mean aspect ratio of 2.13 compared to an average ECD of 11.93 \(\upmu\)m with a mean aspect ratio of 2.52 for the 140 \(\upmu\)m hatch distance as-built sample. The smaller ECD of the 80 \(\upmu\)m hatch distance as-built samples compared to the 140 \(\upmu\)m hatch distance samples is a result of the different local thermal and solidification behavior of the different hatch distances. The hatch distance affects the nature of local heat transfer such that wider hatch distances not only reduce the rate of local heat accumulation but also the local cooling rates and lead to coarsened microstructures [38].  
Figure 6: LOM: (**a,b**) LOM of unetched as-cast samples showing randomly dispersed NbC particles; SEM/energy-dispersive spectroscopy (EDS): (**c**) EDS quantitative measurements of NbC particles in as-cast sample.
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 2 Methods
### Problem Definition  
Let \(D=\left\{x^{i}_{s},y^{i}_{s},e(y^{i})\right\}_{i=1}^{N}\) where \(D\) denotes the dataset, \(x\) is the features of remote sensing images, \(y\) is the class label of remote sensing images, \(e(y)\) denotes the classes semantic feature. The dataset \(D\) is split into the seen and unseen datasets. Let \(D_{s}=\left\{x^{i}_{s},y^{i}_{s},e(y^{i}_{s})\right\}_{i=1}^{N_{s}}\) represent the seen datasets and \(D_{u}=\left\{x^{i}_{u},y^{i}_{u},e(y^{i}_{u})\right\}_{i=1}^{N_{u}}\) represent the unseen datasets. Class label set \(y\) includes the seen class label set \(y_{s}\) and unseen class label set \(y_{u}\). The seen class label set \(y_{s}\) is disjointed \(y_{u}\). The relationship can be represented by \(y=y_{s}\cup y_{u}\) and \(y_{s}\cap y_{u}=\phi\). The purpose of the ZSL task is to predict the class label \(y_{u}\), and the purpose of the GZSL task is to predict the class label \(y\).
## 3 Experimental Results and Discussion  
Figures 6-17 report the comparison between measured and simulated scattering parameters at the four studied values of the ambient temperature. The simulations are achieved by using the three models: equivalent circuit (see Figures 6-9), ANNs (see Figures 10-13), and GRUs (see Figures 14-17). As can be clearly noticed, the measured output reflection coefficient (\(S_{22}\)) is affected by the kink effect, which consists of an abrupt change in the behavior of this parameter at a certain frequency [63; 64; 65; 66; 67]. The GaN HEMT technology is prone to the kink effect in \(S_{22}\) due to the relatively high transconductance (\(g_{m}\)) [66]. All three investigated models allow for accurately reproducing the measurements over the full studied frequency range and under the four different ambient temperature conditions. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **GRU Structure** \\ \hline Re (\(S_{\bar{y}}\)) & 5-1 \\ \hline Im (\(S_{\bar{y}}\)) & 5-1 \\ \hline \end{tabular}
\end{table} 
Table 2: Structure of the trained GRU: size of the GRU layer and the dense layer. Figure 4: Illustration of the model based on using GRU for the tested GaN HEMT. Figure 5: Illustration of the internal architecture of a GRU unit. The symbols “\(\bigoplus\)” and “\(\bigotimes\)” represent the operation of addition and element-wise multiplication, respectively.
## 5 Ablation Studies
### Progressive Multi-Scale Dynamic Aggregation  
As shown in Table 7, we validated the effectiveness of the dynamic decoupled filter in both the channel and spatial domains, and the results indicate that the PSNR improved by 0.4 dB, proving the module's effectiveness in enhancing image quality.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hash Bucket Size** & **PSNR\(\uparrow\)/SSIM\(\uparrow\)** \\ \hline
112 & 27.73/0.795 \\
128 & 27.81/0.802 \\
144 & 27.88/0.810 \\
160 & 27.86/0.803 \\
176 & 27.79/0.808 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative Evaluation of Hash bucket size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Resblock** & **CSA** & **PSNR\(\uparrow\)/SSIM\(\uparrow\)** \\ \hline Baseline + HAM & & 27.88/0.810 \\ Baseline + HAM + Agg (w/o CSA) & \(\surd\) & 28.22/0.836 \\ Baseline + HAM + Agg (w/ CSA) & \(\surd\) & \(\surd\) & 28.62/0.853 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative Evaluation of CSA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hash Rounds** & **PSNR\(\uparrow\)/SSIM\(\uparrow\)** \\ \hline
1 & 27.79/0.801 \\
2 & 27.84/0.806 \\
3 & 27.85/0.809 \\
4 & 27.88/0.810 \\
5 & 27.87/0.810 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative Evaluation of Hash rounds.  
Figure 7 presents the ablation comparison of the CSA module and the corresponding feature error maps. It can be observed that when the target image exhibits significant differences in color, lighting, style, etc., the CSA module can more accurately extract image features and adaptively aggregate relevant texture information, resulting in clearer and more realistic reconstructed images. Compared to the case without the CSA module, the reconstructed images with the CSA module show richer details, significantly reduced errors, and a substantial improvement in overall quality. This demonstrates the exceptional feature extraction and aggregation capability of the CSA module in handling complex image scenes. We have marked the areas of interest (i.e., enlarged details) with red boxes in the figure, and presented corresponding enlarged images for comparative analysis below.  
Figure 7: Comparison of using and not using CSA. (**a**) HR, (**b**) w/o CSA, (**c**) w/ CSA.
# Introducing Cement-Enhanced Clay-Sand Columns under Footings Placed on Expansive Soils
## 4 Results and Discussion
### The Unconfined Compressive Strength
#### 4.5.2 Effect of the Curing Period for Different Clay Contents  
The curing time also plays a crucial role in the strength of mixtures. Cement undergoes a hydration reaction with water, forming a strong binding matrix. Longer curing times allow for a more complete hydration process, resulting in a stronger and more resistant structure with a higher compressive strength.  
Figure 13: The SEM images of samples (**a1,a2**) for Al-Qatif clay, (**b1,b2**) for Al-Qatif clay with 2% cement.  
Figure 14a-d present the effect of the curing period for different clay contents considering each cement additive. The strength is always improving with more time allowed for curing.  
Ideally, figures depicting this relationship would show a progressive increase in compressive strength with increasing cement contents for all curing times. The rate of this increase might be steeper at early curing times due to the rapid initial hydration process. There could also be a peak strength observed at an optimal clay content, with a decline in strength at both lower and higher clay percentages. Finally, the strength gain would likely be more pronounced for longer curing times across all cement contents. It is important to remember that the specific type of clay and sand used can influence the observed behavior.
## 3 Conditions for an Acoustic Wave in a Tire Acoustic Cavity to Act as a Plane Wave
When tire acoustic cavity resonance occurs, the existing distance \(L\) of the evanescent wave can be calculated by setting \(\eta=5\%\) according to  
\[ \begin{array}{ll}\eta=&\sum\limits_{n_{x},n_{y}}e^{-\sqrt{\left[\left(\frac{ n_{x}}{l_{x}}\right)^{2}+\left(\frac{n_{y}}{l_{y}}\right)^{2}\right]\pi^{2}- \frac{4\pi^{2}}{{L_{c}}^{2}}}L}\\ &\hskip 14.226378 ptexcept\hskip 14.226378 ptn_{x}=n_{y}=0\end{array}  \tag{15}\]  
Obviously, similar to \(\alpha_{n_{x},n_{y}}\), \(L\) does not depend on the type and temperature of the medium, but on the tire size parameters. For the tire described in this paper, according to Equation (15), the relationship between \(\eta\) and \(L\) is calculated and shown in Figure 8 when tire acoustic cavity resonance occurs. Since the evanescent wave is recognized to disappear completely when \(\eta<5\%\) based on the assumption in Section 2, it can be observed that the evanescent wave only exists within a range of 160 mm away from the sound source no matter what kind of gas is inflated and no matter what the temperature of the tire is. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Gas**} & **Adiabatic** & **Gas** & **Molecular** & **Sound** & **Tire Cavity** & & \\  & **Index \(\gamma\)** & **Constant** & **Weight** & **Velocity m/s** & **Resonance** & **\(f_{cutoff}\)** = min\((f_{01}f_{10})\)** & **Frequency** \\  & **J/(Kg-K)** & **Weight** & **(\(-\)20–50 \({}^{\circ}\)C)** & **Frequency/Hz** & & & **Range/Hz** \\ \hline Air & 1.4 & 287 & 29 & 318.59–359.97 & 212.39–239.98 & 1027.7–1161.2 & 235–264 \\ Helium & 1.66 & 2077.5 & 4 & 934.8–1055.42 & 623.2–703.6 & 3015.5–3404.6 & 688–776 \\ CO\({}_{2}\) & 1.31 & 188.9 & 44 & 252.09–284.84 & 168.06–189.89 & 813.2–918.8 & 186–210 \\ Nitrogen & 1.4 & 296.7 & 28 & 324.23–366.34 & 216.15–244.23 & 1045.9–1181.7 & 239–270 \\ \hline \end{tabular}
\end{table} 
Table 3: Basic parameters and related frequencies of four gas media. It can also be seen from Equation (15) that the larger the tire cross-section size is, the greater the existing range of the evanescent wave is, and the larger the circumference \(L_{c}\) of the tire is, the smaller the existing range of the evanescent wave is. The conclusion of this research is universal and applicable to other types of tires. When the tire size and cross-section change, the relevant frequency range and cut-off frequency also change.
## 2 Numerical Method and Validation
### Validation
#### 2.2.2 Delta Wing  
Extensive and systematic studies on the transonic vortex and fracture characteristics of the delta wing models provided by VFE-2 have been conducted by National Transonic Facility (NTF) [14; 25], European Aeronautic Defense and Space (EADS) Company, NLR, and The University of Glasgow (Glasgow) [26]. As depicted in Figure 4, the model used to verify the transonic calculation method has a sharp leading edge with a sweep angle of 65\({}^{\circ}\). The computation states from reference [26] are listed in Table 3.  
A multi-block structured H-H type grid consisting of 161 \(\times\) 129 \(\times\) 81 grid nodes is generated, as shown in Figure 5. Figure 6 shows a comparison of the upper surface pressure distribution between the NTF wind tunnel experiment [25] and the numerical calculation performed herein at typical locations. At \(\alpha\) = 18.5\({}^{\circ}\), the calculated suction peak value and location of the main vortex are in good agreement with the wind tunnel results. In addition, the predicted location of the second vortex is reasonably accurate, but its predicted suction peak value is slightly higher. At \(\alpha\) = 23\({}^{\circ}\) and x/C\({}_{\rm r}\) = 0.2 and 0.4, where x is the x coordinate of the section location and the C\({}_{\rm r}\) is the chord length of the root of the model, the C\({}_{\rm p}\) distributions of the calculation are in good agreement with the wind tunnel results. However, at other sections, there are considerable differences between the wind tunnel results and the numerical calculation owing to the breakdown of the main vortex, which cannot be accurately simulated by the numerical method. The upper surface pressure contours of the calculation are shown in Figure 7. The calculated pressure contour obtained herein are almost the same to those of the other numerical calculations [26].  
Figure 4: Delta wing model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ma** & **Re** & \(\alpha\) & **Description** \\ \hline
0.85 & \(6\times 10^{6}\) & 18.5\({}^{\circ}\) & Pre-breakdown case \\
0.85 & \(6\times 10^{6}\) & 23\({}^{\circ}\) & Post-breakdown case \\ \hline \end{tabular}
\end{table}
Table 3: Computing states of delta wing.  
Figure 5: The grid of delta wing. (**a**) Volume grid; (**b**) Surface grid.  
Figure 6: Pressure comparisons of the experiment [25] and CFD calculation on the upper surface of the delta wing at typical sections for Ma = 0.85, Re = 6 \(\times\) 106. (**a**) \(\alpha~{}=~{}18.5^{\circ}\); (**b**) \(\alpha~{}=~{}23^{\circ}\).  
Figure 7: Pressure coefficients distribution contours by CFD herein on the upper surface of delta wing, Ma = 0.85, Re = 6 \(\times\) 106.
# DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
## 5 Experiments
### Malware Category Detection Based on Dynamic Features Selection  
The result of the malware category classification based on dynamic feature selection is displayed in Table 11. By utilizing DroidDetectMW, we can improve accuracy to 89.2%. Accuracy levels of 79.6%, 80.8%, 93.5%, 84.6%, 81.2%, 81.2%, 83.5%, and 80.8% are attained using the alternative traditional methods of KNN, SMO, SVM, RF, DT, NB, and MLP, respectively. When compared to conventional approaches, DroidDetectMW demonstrates a noticeable performance gain. Regarding f-score, accuracy, precision, and recall, DroidDetectMW displays a competent growth of 88.2%, 89.2%, 87.5%, and 89%, respectively. This work finds that the ROC AUC curve is 88.5%.  
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline
**Algorithm** & **K-NN** & **SMO** & **SVM** & **RF** & **DT** & **NB** & **MLP** & **Proposed** \\ \hline Accuracy (\%) & 0.927 & 0.938 & 0.935 & 0.942 & 0.935 & 0.935 & 0.931 & 0.973 \\ FPR (\%) & 0.094 & 0.069 & 0.082 & 0.050 & 0.064 & 0.051 & 0.076 & 0.028 \\ TPR (\%) & 0.955 & 0.948 & 0.956 & 0.934 & 0.933 & 0.919 & 0.940 & 0.975 \\ Precision (\%) & 0.883 & 0.917 & 0.900 & 0.942 & 0.925 & 0.942 & 0.908 & 0.967 \\ F-measure (\%) & 0.918 & 0.932 & 0.927 & 0.938 & 0.929 & 0.930 & 0.924 & 0.971 \\ MCC (\%) & 0.854 & 0.876 & 0.869 & 0.884 & 0.868 & 0.869 & 0.861 & 0.946 \\ AUC (\%) & 0.895 & 0.924 & 0.909 & 0.946 & 0.931 & 0.945 & 0.916 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The effectiveness of dynamic feature selection for binary classification.  
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline
**Algorithm** & **K-NN** & **SMO** & **SVM** & **RF** & **DT** & **NB** & **MLP** & **Proposed** \\ \hline Accuracy (\%) & 0.858 & 0.854 & 0.850 & 0.869 & 0.862 & 0.835 & 0.846 & 0.915 \\ FPR (\%) & 0.129 & 0.155 & 0.142 & 0.132 & 0.134 & 0.170 & 0.158 & 0.090 \\ TPR (\%) & 0.843 & 0.866 & 0.840 & 0.871 & 0.856 & 0.841 & 0.851 & 0.922 \\ Precision (\%) & 0.850 & 0.808 & 0.833 & 0.842 & 0.842 & 0.792 & 0.808 & 0.892 \\ F-measure (\%) & 0.846 & 0.836 & 0.837 & 0.856 & 0.849 & 0.815 & 0.829 & 0.907 \\ MCC (\%) & - & - & - & - & - & - & - & - \\ AUC (\%) & 0.860 & 0.826 & 0.846 & 0.855 & 0.854 & 0.811 & 0.825 & 0.901 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The effectiveness of static feature selection for malware family classification.