esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Data Processing
#### 3.1.3 POI Data Processing  
POI is discrete point data, which cannot be directly used in the population spatialization model. It is necessary to transform discrete data into a continuous density image. Kernel density analysis is a density analysis method based on the first law of geography, which considers spatial variations and reflects the characteristic of feature center intensity decay with distance. In this method, points closer to the center are assigned higher weights, while those farther away receive lower weights. The estimated density for each point is the weighted average density of all points in that region. In kernel density calculations, the choice of kernel function and bandwidth significantly impacts the results of kernel density analysis. We utilize the kernel density analysis tool in ArcGIS 10.7 to analyze 11 categories of POI data. The selected kernel function is the quartic kernel function, as shown in Equation (4). This function is relatively smooth. The surface value is highest at the location of the point and diminishes with increasing distance from the point, reaching zero at the Search radius distance from the point. Compared to the Gaussian kernel function, it has a faster convergence rate. Bandwidth calculation is determined through Equation (5), which avoids the \"ring around the points\" phenomenon that often occurred with sparse datasets. Ultimately, density maps for the 11 categories of POI data are generated with a resolution of 1 km.  
\[\text{Density}=\frac{1}{(Radius)^{2}}\sum_{i=1}^{n}\left[\frac{3}{\pi}p_{i} \left(1-\left(\frac{dist_{i}}{Radius}\right)^{2}\right)^{2}\right] \tag{4}\]  
\[Radius=0.9*min\left(SD,\sqrt{\frac{1}{\ln 2}*D_{m}}\right)*n^{-0.2}, \tag{5}\]  
Here, \(i=1,\,\ldots,\,n\) is the number of input points. Only include points in the sum if they are within the radius distance of the (x, y) location. \(Radius\) is the search radius. \(p_{i}\) is the population field value of point \(i\), which is an optional parameter. \(dist_{i}\) is the distance between point \(i\) and the (x, y) position. \(SD\) is the standard distance. \(D_{m}\) is the median distance. \(n\) is the sum of the population field values.
# Cathode Shape Design for Steady-State Electrochemical Machining
## 3 Results and Discussion
### Ill-Posedness of the Cathode Shape Design Problem and Its Consequences  
In practice, workpiece geometries are often provided as interpolations by splines or freeform surfaces used in CAD models. These anode shapes are not analytic, thus violating the condition for the existence of an exact solution, as discussed in the previous section.  
In the following, we investigated the shape of the numerically computed cathode in cases where the nominally analytic shape of a desired anode is disturbed by a nonanalytic perturbation. As an example, the 2D Gaussian-shaped anode shown in Figure 3a was chosen as the nominal anode. The \"perturbed\" shape of the anode was generated by interpolating the nominal anode by a cubic spline through 13 control points as illustrated in Figure 9a.  
The shape of the cathode corresponding to the \"perturbed\", i.e., interpolated anode shape, was obtained by adjoint-based shape optimization and will be called a \"perturbed cathode shape\" in the following. The surface profiles of both electrodes are shown in Figure 9a. As illustrated by the upper solid line, the numerically computed cathode surface profile exhibits features with a high curvature, which are referred to as \"peaks\" in the following discussion.  
The deviations of the perturbed shapes of both electrodes from the respective nominal shapes (Equations (17)-(19)), which are measured in the direction of the surface normal, are shown in Figure 9b. While the maximum deviation of the nominal and the perturbed anode shape from each other is less than 6% of the gap width, the maximum deviation of the nominal and the perturbed cathode shape from each other is more than 23%. The latter deviation exceeds 25%, when the number of mesh cells along the gap is increased to \(N_{x}=400\). For \(N_{x}>1600\), the simulation fails before convergence, which is caused by mesh degeneration around the \"peak\" of the perturbed cathode shape at \(x=0\), similar to the failure shown in Figure 6a.  
The high sensitivity of the numerically computed cathode shape to nonanalytic perturbations of an analytic anode shape can be attributed to the ill-posed nature of the cathode shape design problem as a Cauchy problem of the Laplace equation, as mentioned by [PERSON] and [PERSON] [40]. As also observed, mesh refinement \"deteriorates\" the quality of the results in the sense that the deviation of the perturbed cathode shape from the nominal cathode shape is increased. This paradoxical behavior is often encountered when solving a discretized ill-posed problem (see [50], Section 4.1).
# Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design
## 3 Direct Migration of Reflections of 3D Waves from Passive Source Based on Geometry
### Testing the Model of Direct Migration  
The model of velocity (Figure 6a) and the random noise records (Figure 7) described in the previous section were used to conduct an experiment on the direct migration of random reflection-induced noise. The process of calculation is shown in Figure 18. A geometry with a wide azimuth was used, as shown in Figure 14. The maximum offset along the inline direction and the crossline direction was 1500 m, that is, the signal from each source was received by 51 \(\times\) 51 receivers at most, and the aspect ratio was one. One line was extracted from every four for the calculations. After reading the SPS.X file, the source points of the data on the offset noise were placed in the channels described in the file, and the corresponding seismic channels of the receiver point in the SPS.X file were extracted to calculate the one-way wave migration with a time window of 20 s. The final migration profile obtained by stacking all of the results of migration is shown in Figure 19b. The velocities required for the migration and nmo were taken from the model of velocity. The results obtained by the conventional method of passive seismic exploration were consistent with those based on calculating the direct migration of reflections. The direct migration method required less computational and storage-related resources, and was accurate.  
When the forward migration-based imaging is carried out by using a geometry with a wide azimuth, there is no need for all sources or receivers to participate in the calculation; this significantly reduces the time needed for computations and yields uniform folds. By applying this geometry, we used only 1/4 of the source lines, and each source used only 1/4 of the near-offset receivers (the computational time was only about 1/16 of that incurred when using the entire dataset). As shown in Figure 18, the 3D seismic records obtained by the wide-azimuth geometry were processed by the direct migration method and yielded results of imaging similar to those of the virtual shot records of reflections from a passive source by using the migration method. This indicates that the proposed method can obtain accurate migration profiles by omitting the correlation in the virtual shot records, and thus requires less time and computational resources.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 2 Problem Statement
### Preliminaries  
**Definition 1**.: _Consider there exists a fixed and known positive constant \(\varepsilon\). For any given positive number \(b\), a barrier function can be defined as a continuous function \(L_{b}(x)\) in the interval \(x\in(-\varepsilon,\varepsilon)\), which satisfies:_  
* \(\lim_{|x|\to x}L_{b}(x)=+\infty\)_;_
* \(\min(L_{b}(x))=L_{b}(0)=b\)_;_
* \(L_{b}(x)\)_is strictly decreasing and increasing in_ \(x\in(-\varepsilon,0]\) _and_ \(x\in[0,\varepsilon)\)_, respectively._
* _Figure 2 shows the barrier function of this paper, which is given by:_  
\[L_{b}(x)=\frac{\sqrt{\varepsilon}b}{\left(\varepsilon-|x|\right)^{1/2}},x\in( -\varepsilon,\varepsilon) \tag{7}\]  
**Notation 1**.: _For any given vector \(\mathbf{x}=\left[\begin{array}{cc}x_{1}&x_{2}&\cdots&x_{n}\end{array}\right]^{T}\), denote its absolute value as \(|\mathbf{x}|=\left[\begin{array}{cc}|x_{1}|&|x_{2}|&\cdots&|x_{n}|\end{array} \right]^{T}\), its time derivative as \(\dot{\mathbf{x}}=\left[\begin{array}{cc}\dot{x}_{1}&\dot{x}_{2}&\cdots&\dot{x}_ {n}\end{array}\right]^{T}\), its sign function as \(\text{sign}(\mathbf{x})=\left[\text{sign}(\begin{array}{cc}x_{1}\end{array} \right)&\text{sign}(x_{2})&\cdots&\text{sign}(x_{n})\end{array}\right]^{T}\), its second power as \(\mathbf{x}^{2}=\left[\begin{array}{cc}x_{1}^{2}&x_{2}^{2}&\cdots&x_{n}^{2} \end{array}\right]^{T}\), and its 2-norm as \(\|\mathbf{x}\|=\sqrt{\mathbf{x}^{T}\mathbf{x}}\). For any positive definite matrix \(\mathbf{P}\), denote \(\lambda_{\min}(\mathbf{P})\) and \(\lambda_{\max}(\mathbf{P})\) represent the minimum and maximum eigenvalues of \(\mathbf{P}\), respectively,and satisfying following inequation: \(\lambda_{\min}(\mathbf{P})\|\mathbf{x}\|^{2}\leq x^{\mathbf{T}}\mathbf{P}\mathbf{x}\leq\lambda_{\max} (\mathbf{P})\|\mathbf{x}\|^{2}\). In addition, denote operation symbol \"\(\circ\)\" as Hadamard product symbol._  
The total missile velocity \(V_{m}\) and target velocity \(V_{t}\) are constant, and \(V_{m}>V_{t}\).  
The missile flight control dynamics are fast enough such that their effects can be neglected.
# Evaluation of Table Grape Flavor Based on Deep Neural Networks
## 5 Discussion
The total phenol content varies widely among grape varieties [29; 30] and is affected by various factors, such as environmental conditions during fruit growth and ripeness at harvest [31]. A high total phenol content can make the grapes bitter and affect the taste and quality of the grapes, while a low total phenol content can cause the grapes to lack certain flavors and textures. The model predicts the best flavor at a total phenol content of 1.62 to 2.23 mg/g. The composition of volatile substances in grapes is complex and determines the typical characteristics of grape flavor [32]. The magnitude of the volatile concentration varies depending on the variety, origin, and cultivation conditions [30; 33]. The model predicts the optimal grape flavor to be in the concentration range between 6.84% and 13.40%. The proposed flavor evaluation model based on a deep neural network considers the interaction effects between different flavor indicators to assess the taste and quality of fresh grapes more accurately. The experimental results show that our model achieves an accurate prediction of flavor quality scores, completing a critical link in the fresh grape evaluation system. Users are able to predict grape flavor scores by feeding the test data of grape samples into the model. Our work can be applied to a variety of applications in four main domains. First, in the food and beverage industry, companies can use this model to evaluate the taste and quality of raw materials used in the production and sale of wines, juices, and other products and to improve and optimize products based on the evaluation results [34]. Second, in agricultural research, the model can be used to evaluate the flavor characteristics of different grape varieties and help select and breed varieties with better flavor characteristics [35]. Third, in market research and consumer studies, the model can be used to assess consumer preferences for the flavor of different grape varieties to better understand market demands and develop suitable products that meet such demands [36]. Finally, in environmental research, the model can be used to evaluate the flavor and texture of different grape varieties and their relationship with other environmental factors (region, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**TSS (\%)**} & \multicolumn{1}{c}{**TA (\%)**} &
\begin{tabular}{c} **Solid/Acid** \\ **Ratio** \\ \end{tabular} & \multicolumn{1}{c}{**TP (mg/g)**} & \multicolumn{1}{c}{**VS (\%)**} \\ \hline Range & 18.10\(\sim\)22.30 & 0.40\(\sim\)0.70 & 30.90\(\sim\)40.45 & 1.62\(\sim\)2.23 & 6.84\(\sim\)13.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Range of indicators for predicting the flavor of grapes belonging to the first class. climate, etc.). In addition, the methodology in our research can be extended to evaluate the flavor quality of other fruits and produce.
## 5 Performance Analysis and Numerical Results
### Results of the Sensitivity Analysis on Properties of Data Dissemination  
#### 5.2.1 V2X Standard  
This experiment employed four data transmission modes with favorable data dissemination effectiveness: BPSK 1/2, QPSK 1/2, QPSK 3/4, and 16-QAM 1/2. Ten different random number seeds were selected, completing 80 (10 groups) dynamic simulations. The macroscopic results of one simulation (No. 4 random number seed, QPSK 3/4) are shown in Figure 9, while the comprehensive statistical results are summarized in Figure 10.  
In the specific pairwise simulations conducted under different V2X standards and identical conditions, as shown in Figure 9, a distinct improvement in the data dissemination distances is evident along the roads to the west and south of the intersection, as indicated by the green line marks. In specific numerical terms, the data dissemination distance on the western road increased from 3.1 to 4.0 km, representing a 29% improvement. Similarly,  
Figure 8: Simulation result of transmission reliability comparison.  
on the southern road, the data dissemination distance increased from 3.1 to 3.8 km, marking a 23% enhancement. We attribute this improvement to the enhanced transmission reliability of 802.11 bd. However, on the northern road, the data dissemination distance increased only slightly from 3.6 to 3.8 km. Conversely, on the eastern road, the data dissemination distance even decreased from 3.9 to 3.7 km. We believe that this outcome is attributable to the excessive traffic volume on these roads.  
As illustrated in Figure 10, despite some variations in the experimental results across different groups, it can be inferred overall that 802.11 bd has enabled advancements in the data dissemination effectiveness compared to 802.11p. According to the statistical data, it can be observed that under the same data transmission mode and random number seed, 802.11 bd outperforms 802.11p in terms of the number of vehicles that receive data packets and the data dissemination distance, with an improvement of approximately 10-20%. Taking the experimental group illustrated in Figure 9 as an example, 802.11 bd exhibits a 12% increase in the average data dissemination distance (from 3.4 to 3.8 km) and a 19% increase in the number of vehicles that receive data packets (from 300 to 357 vehicles). However, the gains from the two metrics diminish with the progression of the data transmission modes (corresponding with an increased data transfer rate). Concurrently, multiple  
Figure 10: Summary of the impact of V2X standards on data dissemination effectiveness.  
Figure 9: Macroscopic results of one group (No. 4 random number seed, QPSK 3/4).  
transmissions exhibit an amplifying effect on the transmission reliability. In the specific simulation scenarios, especially with BPSK 1/2, the two metrics achieve an improvement of approximately 25-30%.
###### Abstract  
In this study, various factors affecting the efficiency of the MAPbI\({}_{3}\) perovskite solar cell (PSC) were analyzed using the SCAPS-1D simulation program. The basic device analyzed in this study had a structure of ITO/TiO\({}_{2}\)/MAPbI\({}_{3}\)/Cu\({}_{2}\)O/Au. The thickness of each layer (electron transport layer (ETL), perovskite absorption layer (PAL), and hole transport layer (HTL)), PAL defect density and interface defect density were investigated as parameters. The optimized parameters that yielded the highest light conversion efficiency were an ETL (TiO\({}_{2}\)) thickness of 100 nm, a PAL (MAPbI\({}_{3}\)) thickness of 1300 nm, an HTL (Cu\({}_{2}\)O) thickness of 400 nm, a PAL defect density of \(10^{14}\) cm\({}^{-3}\), and an interface defect density of \(10^{13}\) cm\({}^{-3}\) for both absorber/ETL and absorber/HTL interfaces. The optimized PSC exhibited a maximum efficiency of 19.30%. These results obtained in this study are expected to contribute considerably to the optimization and efficiency improvement of perovskite solar cells using inorganic charge-carrier transport layers.  
perovskite photovoltaic solar cell; MAPbI\({}_{3}\); TiO\({}_{2}\); Cu\({}_{2}\)O; power conversion efficiency +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Analytical Methods
#### 2.3.4 Diffuse Reflectance Spectra (DRS) Measurement  
The sieved samples were put into a plastic ring with a diameter of 4 cm (under a pressure of 15 MPa) to make the test loess piece. The reflectivities of 227 samples were measured by a Perkin Elmer Lambda 950 spectrophotometer. The measured wavelength range was 400-700 nm, and the measurement interval was 2 nm. The reflectivity percentage of the standard color bands was obtained by analyzing the measured reflectivity data [27]. The reflectance percentage of the color band referred to the ratio of the reflectance percentage in a wavelength band to the total visible wavelength reflectance in the sample [32]. We calculated hematite and goethite concentrations in loess samples according to the method of [PERSON] et al. [27]. The loess samples extracted by the CBD experimental method were used as matrices. Then, standard goethite (Gt) (Hover Color Corp: Arlington Heights, IL, USA, SY610), hematite (Hm) (Bayferrox Corp: Koln, Germany, R4399) and Al(NO\({}_{3}\))-9H\({}_{2}\)O powders with known contents were artificially added into the matrices, respectively. Finally, a series of aluminum substituted standard samples were synthesized (\(n\) = 20). After calculation, the linear regression models for the hematite (Hm) and goethite (Gt) contents and characteristic peak intensities were:  
\[\rm Hm\%=2.766\ \times\ (I_{575}+0.007\ \times\ Hm(Al\ mol\%)\ -\ 0.002)\ -\ 0.077 \tag{2}\]  
\[\rm Gt\%=13.404\ \times\ (I_{535}+0.002\ \times\ Gt(Al\ mol\%)+0.071\ \times\ Hm\%+0.002)\ -\ 1.096 \tag{3}\]where I\({}_{575}\) and I\({}_{535}\) were the peak intensities corresponding to the characteristic peak for Hm and the main peak for Gt in the first derivative diffuse reflectance spectra of natural samples, respectively. Hm (Al mol%) and Gt (Al mol%) were the aluminum substitution amounts of Hm and Gt in loess samples obtained by XRD tests and calculations. Finally, the ratio of Hm/(Hm + Gt) was calculated according to the values of Hm and Gt.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 2 Materials and Methods
### Methodology
#### 2.2.2 The Augmentation  
The experiments were performed using _ls_ and _chmod_ Linux commands. The idea behind this choice is that these commands are widely used by any user of Linux distributions, even if they use desktop systems or servers. Another factor in choosing them is that they contain file descriptors in their original implementation, which will help us alter them as little as possible, using the already existing file descriptor as a parameter for the augmentation with the _ioctl_ syscall.  
For the original commands, we used the built-in commands and for the augmented ones, we used the code sources of the original commands from GitHub repository [34], downloaded in February 2023.  
To augment the original commands, we used the _ioctl_ system call, which is one of the most commonly used in attacks involving system calls. The attacks performed using this syscall led to privilege escalations, system crashes, or gaining unauthorized accesses, as per CVE [4] reports in the last 8 years.  
The augmentation was performed by calling the _ioctl_ C function near the usage of a file descriptor in the original code. The augmentation was performed slightly different than in the original study by minimizing the number of calls of the syscall. Moreover, we tried to avoid using similar data in the code to bypass cache references, since we concluded after some experiments that the OS optimizations for desktops are harder than the ones in HPC systems. The augmentation rate in terms of lines of code (LOC) is 0.10% addition for _ls_ command, and 2% addition in code for _chmod_ command.
## 3 The Proposed Model
### Feature Selection Using HEGSO Algorithm
The value that can be chosen for the crossover operator were mutated and then integrated. In the proposed HEGSO approach, the fitness function is intended to have a balance between the classification accuracy (maximum) and the number of selected features in all the solutions (minimum) attained Equation (10) characterizes the fitness function to estimate the solution. \[Fitness=\alpha\gamma_{R}(D)+\beta\frac{|R|}{|C|} \tag{7}\]  
where \(\gamma_{R}(D)\) signifies the classifier error rate of the given classifier. \(|R|\) indicates the cardinality of the selective subset and \(|C|\) denotes the overall number of features in data, and \(\alpha\) and \(\beta\) denote the two parameters corresponding to the significance of classifier quality and subset length, respectively. \(\in[1,0]\) and \(\beta=1-\alpha\) as explained in Algorithm 1. ```
Process HEGSO Start: Input: \(X_{i}\), \(Q\), \(z\), \(N_{it}\), \(I_{o}\), \(r_{o}\). Define fitness function  While \(t<N_{iy}\) do  For every glowworm do \(l_{j}(n+1)=(1-\rho)l_{j}(n)+\beta l_{j}(n+1)\) \(x_{j}(n+1)=x_{j}(n)+Z\left(\frac{x(n)-x_{j}(n)}{\|X(n)-x_{j}(n)\|}\right)\) \(r_{d}(n+1)=\text{ min }(r_{s},\text{ max }(O,\ r_{d}(n)+B(n_{e}-N_{j}(n)\) End while Implement the crossover and mutation Return \(X_{best}\) End Process
```  
**Algorithm 1** HEGSO Algorithm
###### Abstract  
The Dry Andes region of Argentina and Chile is characterized by a highly developed periglacial environment. In these arid or semi-arid regions, rock glaciers represent one of the main pieces of evidence of mountain creeping permafrost and water reserves in a solid state. However, their distribution, degree of activity, and response to global warming are not yet well understood. In this context, this work aims to show the potential of the Sentinel-1-based interferometric technique (DInSAR) to map active rock glaciers at a regional level. In particular, the paper presents an active rock glacier inventory for the study area, which covers approximately 40,000 km\({}^{2}\), ranging from latitude 30\({}^{\circ}\)21\({}^{\prime}\)S to 33\({}^{\circ}\)21\({}^{\prime}\)S. A total of 2116 active rock glaciers have been detected, and their elevations show a high correlation with the west-east direction. This result was obtained by using only 16 interferometric pairs. Compared to other remote sensing classification techniques, the interferometric technique offers a means to measure surface displacement (active rock glacier). This results in a reliable classification of the degree of activity compared to other methods, based on geomorphological, geomorphometric, and/or ecological criteria. This work presents evidence of this aspect by comparing the obtained results with existing optical data-based inventories. We conclude that the combination of both types of sensors (radar and optical) is an appropriate procedure for active rock glacier inventories, as both mapping methodologies are complementary.  
DInSAR; Sentinel 1; rock glacier inventories; mountain permafrost; Dry Andes 2018 10.3390/rs10101588 10.3390/rs10101588