esa-sceva/satcom-chunk-collection huggingface dataset:
# DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
## 3 Preliminary
### Dataset and Malware Categories  
We determined that the Canadian Institute for Cybersecurity (CIC) [44] offers a competent real-world dataset named CICAndMal2017 after examining the most comprehensive and coherent set of related papers. First, the CIC amassed around 4000 malware apps from various sources as Contagiodumpst [45] and VirusTotal [46]. In addition, nearly 6000 benign apps from 2015 to 2017 that were uploaded to the Google Play market were collected. CIC has only been able to install 5000 (benign 5065 and malware 429) on actual An-droid smartphones to undertake real-world scenario testing. Several articles make use of the Drebin dataset. A total of 5560 apps from 179 distinct malware families are included in this data collection. The MobileSandbox project generously provided us with samples gathered between August 2010 and October 2012. A total of 4890 recent Android apps were downloaded from virusshare and apkmirror and selected from DREBIN, CICInvesAndMal2017, datasets of them there are 1910 samples of malware and 2980 samples of benign. The used dataset consisted of static and dynamic features to evaluate the proposed model. The realm of malicious software encompasses a plethora of classifications, ranging from worms and viruses to adhere, spyware, Trojans, SMSware, Ransomware, and various other insidious variants.  
In this data set, labels can be found at various depths. Beginning with a binary classification system, files are either malware or benign. The CICAndMal2017 dataset comprises both benign and malware applications. It includes four distinct categories of malware, namely Adware, SMS Malware, Scareware, and Ransomware. There are four categories of malware in the second level:  
* Adware: To generate as much revenue as possible from unsolicited banner ads, the ad-ware will display these ads automatically [47].
* Ransomware: One goal of malicious software is to prevent apps from accessing system resources. To extort money from users, it can encrypt their files and demand payment before allowing them to access their files or recover their devices [48].
* Scareware: This malware software uses scare tactics to convince users to buy bogus security updates [49].
* SMS malware: A malicious malware that makes sms calls and sends text messages with-out the user's permission. The malware operator can use the compromised handsets as a high-end SMS distribution channel [50].
## 2 Methods  
### The CWI Analysis  
Coda waves, characterized by their multiple scattering, spend significantly more time in the material compared to direct waves. This extended propagation time allows them to accumulate signatures of very small changes encountered along their path, providing dense sampling of the medium. Consequently, a notable advantage of coda waves is their high sensitivity to weak perturbations in the medium.  
This paper leverages the advantages of coda waves to analyze both low-frequency and ultrasonic wave propagation. The CWI method, which has been applied to seismic [28,29] and ultrasonic [30,31] waveforms, is therefore proposed.  
In the CWI method, two coda wave fields are correlated. The cross-correlation (\(CC\)) value is used to indicate discrepancies in wave propagation in the local area under different structural conditions. If damage, prestress loss, or other influence factors alter the local structural properties, the wave propagation will change. Therefore, the cross-correlation of the original wave signal \(u_{o}(t)\) and the altered wave signal \(u_{a}(t)\) is used to quantify their consistency, as shown in the following equation:  
\[CC=\frac{\int_{t_{1}}^{t_{2}}u_{o}(t)u_{a}(t)dt}{\sqrt{\int_{t_{1}}^{t_{2}}u_{ o}^{2}(t)dt\int_{t_{1}}^{t_{2}}u_{a}^{2}(t)dt}}. \tag{1}\]  
The \(CC\) value ranges from 0 to 1. A \(CC\) value of 1 indicates that the original wave signal remains unchanged, while a \(CC\) value of less than 1 signifies that the altered wave signal differs from the original wave signal.  
The stretching method [32] has been proposed to explain the alternation in the original wave signal, which is stretched or compressed by a \"velocity perturbation\" in the time domain. This velocity perturbation can be formulated as follows:  
\[\frac{dv}{v}=-\frac{\delta t}{t}=\alpha, \tag{2}\]  
where \(v\) is velocity, \(t\) is time, and \(\alpha\) is defined as the stretching factor. The altered wave signal can be represented by the following:  
\[u_{a}(t)=u_{o}(t(1+\alpha)). \tag{3}\]  
To estimate the value of velocity perturbation \(\alpha\) in a range of \([-\frac{dv}{v},\frac{dv}{v}]\), the reverse of the altered wave signal presented as \(u_{a}(t-\alpha t)\) is used to correlate the original wave signal \(u_{o}(t)\). By reaching the maximum cross-correlation (max. \(CC\)) value of the original signal and the reverse of the altered signal, the velocity perturbation \(\alpha\) can be retrieved:  
\[max(CC)=\frac{\int_{t_{1}}^{t_{2}}u_{o}(t)u_{a}(t-\alpha t)dt}{\sqrt{\int_{t_{ 1}}^{t_{2}}u_{o}^{2}(t)dt\int_{t_{1}}^{t_{2}}u_{a}^{2}(t-\alpha t)dt}}. \tag{4}\]  
In this paper, the original wave signals were selected from the reference data obtained during our monitoring, while the altered wave signals were derived from other data sources.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 2 BE-RC Combined Test Device
### Verification Test of the BE-RC Device  
In order to test the reliability of the BE-RC test device, silty-fine sand from Hangzhou Bay in China was used to carry out a series of tests according to the standard ASTM method [29; 30; 31; 32] and the basic physical parameters of the sand are shown in Table 1.  
The head saturation method was used to saturate the prepared specimens. The back pressure was uniformly set to 50 kPa, the confining pressure was increased to 150 kPa, 250 kPa, 350 kPa and 450 kPa, respectively, and the consolidation was carried out for 12 h. Then, the BE and RC tests were carried out independently at different stress levels to complete tests on the saturated sand specimens. For each specimen, BE test was first  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specific Gravity**} & **Minimum Dry** & **Maximum Dry** & **D\({}_{\textbf{50}}\)** & **D\({}_{\textbf{10}}\)** \\  & **Density** & **Density** & **(mm)** & **(mm)** \\  & **(g/cm\({}^{3}\))** & **(g/cm\({}^{3}\))** & **(mm)** \\ \hline
2.68 & 1.197 & 1.541 & 0.243 & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main physical parameters of silty-fine sand.  
Figure 5: Calibration result of the unsaturated BE-RC test system.  
carried out, so the maximum shear modulus \(G_{max}\) could be measured. This was followed by an RC test. Due to the limitation of the test strain range, the RC test data were fitted using the Hardin-Drnevich equation and the back-calculation method to obtain the \(G_{max}\) of at the strain of \(10^{-6}\). The results of the BE and RC test measurements are compared in Figure 6.  
Figure 6 shows that the BE test results agree well with the RC test results under different net stresses, with errors within 5%, indicating that the measurement performance of the improved BE-RC test system is reliable.
## 3 Results and Discussion
### Chromium  
The average Cr contents were measured at 0.25 mg/kg for red deer and 0.16 mg/kg for wild boar. It is worth mentioning that limited data regarding Cr content in red deer meat were available, with only [PERSON] et al. [25] reporting a lower concentration of 0.005 mg/kg, and [PERSON] et al. [29] found that Cr levels in fallow deer ranged between 0.058 and 0.071 mg/kg. On the other hand, Cr content found in wild boar meat was similar to those reported in previous studies (0.12-0.14 mg/kg) [30; 31; 32; 33]. Notably, the Cr concentrations in game meat were significantly higher than those observed in other meats sourced from slaughtered animals, such as beef (0.004 mg/kg) and pork (0.004-0.017 mg/kg) [34].  
Hence, the results obtained from the probabilistic model simulations with red deer and wild boar indicated values for Cr of 0.0129 and 0.0068 mg per week, respectively, for the 50 th percentile. This shows that the intake of Cr of at least half of the population consuming this game meat will not be lower than these values. In the case of the red deer, that intake complies with 37% of men and 51% of women of the DRI for Cr. This DRI would be fulfilled from the 85 th percentile for men and 75 th percentile for women (data not shown).  
In addition, considering the 95 th percentile Cr intake as the worst-case scenario, the Cr exposure levels amounted to 0.11 and 0.06 \(\mu\)g/kg body weight (bw) per day for the consumption of red deer and wild boar meat, respectively. Remarkably, these exposure levels are 11 to 20 times lower than those outlined by the EFSA under the same scenario, which is in comparison to the lower 95 th percentile dietary exposure range (1.2 \(\mu\)g/kg bw per day) observed in European adults [35]. These Cr intakes from the consumption of game meat represent only 0.04% and 0.02%, respectively, of the Tolerable Daily Intake (TDI) established by the EFSA for this element (0.3 mg/kg bw) [35]. It is noteworthy that the primary toxicological form of Cr is hexavalent, which is virtually absent in food [36; 37]. The outcomes of this analysis indicate that the Cr (III) content in the examined game meat falls below accepted safety limits, posing no discernible risk to human health.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 3 Developments in Seismic Imaging
### Full-Waveform Inversion
[49] observed velocity anomalies along major faults and structural discontinuities at depth, providing evidence for the existence of fluids and weakened materials and suggesting a possible coseismic slip partitioning structure. While the majority of FWI applications in the oil industry traditionally focus on analyzing geological structures at intermediate scales, in particular for the subsalt imaging (e.g., [PERSON] et al. [50]), there is an emerging use of FWI applications in the near-surface, as evidenced by the recent scholarly literature. For instance, [PERSON] et al. [51] employed frequency-domain acoustic FWI to analyze seismic data collected from a quick-clay landslide scar in southwest Sweden, using sledgehammer data. Their work stands out as one of the earliest examples of acoustic FWI in a near-surface investigation. By utilizing FWI, the authors were able to generate P-wave velocity models that exhibit greater resolution than those obtained using seismic ray tomography and a smooth parametrization. As a  
Figure 3: Interpretation of structures along the eastern Nankai trough superimposed to the final P-wave velocity model from FWI (from [PERSON] et al. [49]). The thick black dashed line represents the upper part of the coseismic slip zone likely to be activated during the next Tokai earthquake. The thick gray dashed line marks a splay fault branching on the plate contact and continuing upward, along the backstop thrust T1 and eventually, the Tokai thrust. T1, T2 and T3 denote the thrusts slicing the backstop. result, the FWI velocity models were found to be not only easier to interpret but also more suitable for pre-stack depth migration.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
## 5 Results
Figure 6 compares the effective capacity of selected user achieved with NOMA and TDMA schemes under the proposed strategy and random selection strategy. It can be seen from Figure 6 that curves with the proposed NOMA scheme are superior to those with the TDMA scheme for all cases, demonstrating the advantages of employing the NOMA scheme in delay QoS-constrained satellite communication networks. Moreover, since the proposed DQN-based user selection scheme can find the optimal action for each state, and, thus, it can provide superior performance as well as a much bigger performance difference between NOMA and TDMA schemes than those achieved with a random selection strategy in each time slot. Figure 5: Convergences of the proposed DQN user selection algorithm with different learning rates. Figure 6: Effective capacity of selected user achieved with two access schemes under the proposed strategy and random selection strategy.
## 1 Introduction  
An optical vortex is the optical wavefront dislocation with the indeterminate wave phase and zero amplitude at a specific point. Its spiral phase form, \(\exp(il\phi)\), can carry an orbital angular momentum (OAM) of \(l\)h per photon [1, 2]. Optical vortex beams are attractive for numerous active fields and applications, such as optical tweezers [3, 4], phase contrast imaging in microscopy [5], trapping and accelerating cooled atoms [6], quantum computation [7], and controlling the chirality of twisted nanostructures [8]. Despite various works devoted to the investigation of isolated optical vortex beams, the generation of optical vortex arrays or vortex lattices has attracted considerable attention for decades for photonic crystal fabrication, optical metrology, and optical manipulation [9, 10, 11]. Optical vortex arrays can, not only be directly generated in diode-pumped solid-state lasers with spherical cavity [12, 13, 14], but also be obtained by exploiting external optical devices, such as holographic grating [15, 16], astigmatic mode converters [17, 18, 19], and spatial light modulators [20, 21, 22, 23]. Since [PERSON] et al. demonstrated a regular optical vortex array with crystal structures by a three wave interference [24], the multi-beam interference method has often been exploited to generate optical vortex arrays with crystal and quasicrystal structures [25, 26, 23, 27]. Recently, a Fourier-transformed method with an amplitude mask was demonstrated for flexibly generating optical beams with crystal and quasicrystal structures [28, 29]. However, using the Fourier-transformed method to generate optical vortex arrays has not been explored in detail. It is scientifically interesting as a way to provide a convenient and robust approach for realizing various optical vortex arrays.  
In this work, we provide a novel and convenient approach for generating various optical vortex arrays using a combination of an amplitude mask and a vortex phase plate. A collimated coherent laser is employed to illuminate a stencil mask with multiple apertures systematically arranged on a ring. The arrangement of the multiple apertures is based on a numerical calculation. Then, the laser beam passes through a spiral phase plate with variable topology charges from one to eight. It turns out that by changing the vortex geometry of the spiral phase plate, such an optical structure can be converted into various optical vortex lattices. The experimental results were found to be in a good agreement with the theoretical calculations. The excellent agreement between the experimental and theoretical results not only validates the present method, but also manifests the structure of vortex lattices.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Contributions of Video Features to UES-SF
#### 4.3.1 Lighting Key  
Lighting Key is a video feature that represents the relationship between light and shadow in the video. The regression analyses of this feature for each of the engagement dimensions are shown in Table 5. Significant relations have been found in the case of linear models for the dimensions of Aesthetic Appeal (AE) and Reward (RW) as well as in the case of overall User Engagement Score (UES). Statistical evaluation also confirms the quadratic relation in the dimensions of Aesthetic Appeal (AE) and Reward (RW); although \(R^{2}\) values are slightly in favour here, upon visual inspection, one can identify little improvements in case quadratic factors are deployed.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Video Feature** & **FA** & **PU** & **AE** & **RW** & **UES** \\ \hline avgLightKeyAd & 0.488 & 0.174 & **0.029** & **0.043** & 0.192 \\ avgColVarAd & 0.218 & 0.795 & **0.032** & **0.017** & 0.064 \\ avgMotionMeanAd & 0.391 & **0.037** & 0.689 & 0.404 & 0.494 \\ avgMotionStAd & 0.330 & 0.220 & 0.270 & 0.805 & 0.705 \\ avgShotLenAd & 0.221 & 0.446 & 0.221 & 0.396 & 0.998 \\ \hline \end{tabular}
\end{table}
Table 4: Reported \(p\)-values of Kruskal–Wallis test with randomized effects, where the independent variables are the video features, and the dependent variables are the individual dimensions of the UES-SF scale.  
Figure 3: Examples of (**a**) scatter plot and linear model plot of a decreasing linear relation between Lighting Key and Reward (RW); (**b**) scatter plot and a quadratic model plot of relation between Color Variance and overall User Engagement Score (UES), indicating a U-shaped relation; (**c**) scatter and quadratic plot of the relation between Standard Deviation of Motion and Perceived Usability (PU) dimension. Please note that a small jitter was added to improve visualisation of overlapped values.
# Security Architecture for Cloud-Based Command and Control System in IoT Environment
## 2 Background
### Existing C4I System in Korea
#### 2.2.2 Interworking among the C4I Systems  
Currently, the C4I system of the Korean military is composed of the allied Korea joint command and control system (AKJCCS), Korean joint command and control system (KJCCS) and the army tactical command information system (ATCIS), Korea naval command control system (KNCCS), air force command control system (AFCCS) and the C4I system below the battalion level. The internal systems (authentication and authorization systems, etc.) operated by each C4I system are different. In addition, the army, navy, and air force C4I systems have limited direct interworking with each other, and can currently perform data sharing through interworking with the KJCCS. Security measures necessary for interworking with the KJCCS and ATCIS are analyzed in this subsection. In the current Korean military, users must receive a cryptographic module first and register it in the system. In addition, because the key management system for each C4I system is established, the ID, password and cryptographic module for each system must be used, and each system must have an interworking-router and interworking-server, separately.  
Therefore, for users of the ATCIS C4I system to access the KJCCS C4I system, they must be authenticated with the ATCIS ID, password and cryptographic module first, and additionally authenticated with the KJCCS ID and password. Such a process, which does not involve direct interworking among the army, navy, and air force C4I system, lacks security and convenience during operation owing to decryption when exchanging data for each C4I system.
# On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
## 4 Evaluation
### Experimental Set-Up  
We use an NVIDIA Jetson Xavier NX platform, designed to accommodate DNN applications. We use the _clang_ compiler with CUDA (both version 10) in an Ubuntu system. In order to minimize system interference, we employ the PREEMPT-RT patch and isolate the NVIDIA Carmel ARM core that executes the program with the highest real-time priority and configure it to run at the maximum frequency. We launch a single matrix-matrix multiplication stream to the GPU to avoid the uncertainty in the order of execution of the streams associated with several applications running simultaneously [27]. We employ the same matrix dimensions and nomenclature as in reference [14], seeking a fair comparison. In reference [14], the authors also include a catalog of diagnostic techniques, but based on a sequential and an AVX-based implementation. Table 2 shows the matrix-matrix multiplication dimensions, dividing them into two groups:  
* Square matrices: seek to evaluate the influence of matrix size. We represent the dimensions of input matrices \(A\) and \(B\) and output matrix \(C\) as \(N\times N\);
* Unbalanced matrices: focus on assessing the representativeness of the matrix dimensions in performance impact experiments and assessing the variability of the DC when modifying the relationship between rows and columns. We denote the matrix dimensions of \(A\) as \(M\times K\), \(B\) as \(K\times N\), and \(C\) as \(M\times N\). In the performance results, \(L91\) refers to a particular layer position extracted from a CNN. We follow the same notation in DC.  
In performance impact experiments, we run the matrix-matrix multiplication function 1000 times and measure the execution time with \(<\)time.h\(>\) library to reach a nanoseconds resolution and calculate the mean value. We disregard the initial 100 measurements to avoid the cold-start problems associated with caches and the delays associated with the initial kernel launches [28].  
In DC experiments, we perform a bit-exhaustive fault-injection campaign in both \(A\) and \(B\) matrices (as in reference [14]). This evaluation requires as many matrix-matrix multiplication executions as bits positions have those matrices. This is the reason for employing smaller matrix dimensions in DC in contrast with performance impact experiments. We have performed these bit-flips in the host before launching the kernels executing the matrix-matrix multiplication in the GPU.
## 3 Results and Discussion
### Total Phenolic Content (TPC) and Total Flavonoid Content (TFC)
#### 3.3.2 Total Flavonoid Content (TFC)  
Figure 5 shows the total flavonoid content (TFC) of the different bread types. As with phenolic compounds, there was an increasing trend in flavonoid content with increasing proportions of both vegetable powders. In contrast to TPC, TFC values showed less variability. The lowest flavonoid compounds were determined in the control bread (0.47 mg/g) and the highest in CB30% (2.99 mg/g). Higher TFC values were recorded for bread with CP (1.53-2.99 mg/g) compared to bread with PP (1.29-2.02 mg/g). However, these differences were only significant for PB10%, which contained fewer flavonoids than the other enriched breads (\(p<0.05\)). A similar trend was observed by [PERSON] et al. [72], indicating smaller differences in flavonoid content than polyphenols when increasing the proportion of PP in the bread. In our study, flavonoids in PP-enriched bread accounted for 11-19% of the TPC, depending on the percentage of powder, while in the study by [PERSON] et al. [72],  
Figure 4: Total phenolic content (TPC) in each type of bread. Abbreviations: WB—wheat bread, CB10/20/30%—bread with 10/20/30% addition of carrot powder, PB10/20/30—bread with 10/20/30% addition of pumpkin powder. Means with different letters (a, b, c, d, e) are significantly different at \(p<0.05\). Means a, b, c, d, e specify differences between the total phenolic content (TPC) of wheat bread and bread enriched with 10, 20, and 30% CP and PP.  
they accounted for about 15%. [PERSON] et al. (2017) report that flavonoids account for up to 60% of the phenolic compounds in pumpkin pulp powder. Differences in content are mainly due to the proportion of vegetable powder. In addition, differences may be due to the different types of solvents used to prepare the extracts (water, methanol) ([PERSON] et al., 2017).