esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Survivability of the Automatically Obfuscated Android Malware
## 1 Introduction
### Background
#### 1.1.2 Malware Analysis  
A malware analysis is the process of analyzing malware and studying the components and behavior of malware. The commonly used malware analysis techniques are static and dynamic analysis [12]. Static analysis is a process in which the analysis is performed without running the malware, and it is also more secure when compared to dynamic analysis. In contrast, dynamic analysis is a process of analyzing the malware by running the code, and typically the process should be performed in a more secure/isolated environment [13].  
Static analysis is a technique that involves viewing the APK file without inspecting the impact of actual instructions. This type of analysis can verify whether the data is malicious, present information about its functionality, and sometimes provide information to create some uncomplicated network signature. Malware detection here is divided into various phases such as detection, the pre-processing phase, the extraction phase, and the feature-analysis phase [14]. The feature-extraction phase extracts the critical information by parsing the application's source code to form patterns for classifying malicious applications.  
Dynamic Analysis: A few checks are typically run during this process, for example, API calls, system calls, network calls, etc. This technique of detection aims to evaluate malware in a natural environment by executing the program [12]. However, antivirus applications rarely perform this type of in-phone analysis as it may not be in the best interest of all parties since scans require significant amounts of memory and CPU power while mobile devices have limits on their computational resources.  
Advanced malware analysis tools may employ event generators and observe suspicious behaviour to flag potentially harmful applications. For example, MEGDroid proposed by [PERSON] et al. [15] can simulate an environment that triggers malicious payloads to express harmful behaviour. However, this analysis requires an antivirus tool to actually run the suspicious application in a sandbox and therefore cannot be implemented on user devices. This limitation means that antivirus tools rely mostly on feature-based static analysis which, in turn, explains the successful use of obfuscation techniques by malware developers.
## 3 The Algorithm
### Prospective Symbol Detection  
The prospective symbol detection consists of scanning the text looking for strings of exactly \(V\) characters. All \(V\)-long strings are considered as prospective symbols to join the previously constructed language \(\textbf{{B}}_{V-1}\) made of strings of up to \(V-1\) characters. The idea is to find all possible different \(V\)-long strings present in the message \(M\), which after complying with some entropy reduction criteria, would complement language \(\textbf{{B}}_{V-1}\) to form language \(\textbf{{B}}_{V}\).  
To cover all possibilities of character sequences forming symbols of length equal to \(V\), several passes are done over the text. The difference from one pass to another is the character where the initial symbol starts, which will be called the _phase_ of the pass. Figure 2 illustrates how the strategy covers all possibilities of symbol instances for any symbol size specification \(V\).
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 4 Numerical Analysis
### Structure Model  
The FEA model for UHPC with single TSF is established by using ABAQUS and illustrated in Figure 10a and its meshing is shown in Figure 10b. The model is composed of three parts, that is, concrete matrix, TSF and their interface. The TSF has an embedment length of 20 mm, a free end with a short length of 1 mm and a rectangular section of 0.53 \(\times\) 0.37 mm\({}^{2}\). To perform a dense meshing on the concrete area close to TSF, a solid column with a diameter twice as big as the equivalent diameter of TSF was partitioned.  
The interface between concrete matrix and TSF was modelled as interfacial transition zone (ITZ), the thickness of which is twice the equivalent diameter of TSF. The steel fiber element is C3D8 hexahedral element with average side length of 90 \(\upmu\)m. The concrete and ITZ elements are C3D4 tetrahedral elements and their side length increases from 50 \(\upmu\)m to 1.5 mm when they are away from the TSF. The meshing sensitivity was analyzed and the results show that this fine meshing used in this simulation is sufficient to produce stable pullout force results.  
Figure 10: The FEA model for UHPC with single TSF, (**a**) illustration, (**b**) meshing.  
Figure 9: Comparison between the testing values and theoretical values of pullout forces.
## 2 Supervisory Control and Data Acquisition Systems
### Evolution of SCADA  
SCADA systems originated in the mid-20 th century coinciding with Industry 2.0, developed as simple monitoring tools for industrial processes, relying on rudimentary hardware and limited communication capabilities. Early systems used 2D graphical representations to display data, with icons representing physical components such as pumps, valves, and pipelines. These systems were typically confined to local networks, with limited scalability and real-time capabilities [2]. The most important aspects in the evolution of SCADA are as follows.  
* Digitalization: The transition from analog to digital systems enabled more precise data acquisition and control [3].
* Network Expansion: The integration of wired and wireless communication networks (e.g., Ethernet, cellular, and satellite) extended the reach of SCADA systems to remote and geographically dispersed locations [7].
* Internet of Things: The incorporation of IoT has enabled real-time data collection and remote control, enhancing the scalability and flexibility of SCADA systems [8; 9].
* Cloud Computing: Cloud-based SCADA systems have emerged, offering centralized data storage, advanced analytics, and remote access capabilities [10].
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
###### Abstract
In pathological image classification, various networks (such as VGG and Resnet) have exhibited good diagnosis performance [26; 27; 28]. A challenge when employing deep convolutional neural networks (DCNNs) is the extremely large size of pathological images, which can be in gigapixels. To overcome this issue, [PERSON] et al. proposed the use of small patches of the given images for a convolution neural network (CNN) in breast cancer classification [29]. The original whole slide image (WSI) was divided into small patches to train the CNN, and the final decision was made by combining the results for each patch. Different decision methods were used to combine the patch-wise results; however, they provided similar results, and none surpassed the individual results. [PERSON] et al. improved the work of patch-based CNNs by using a support vector machine (SVM) to combine the patch-wise results [30]. By applying the inception architecture, [PERSON] et al. extensively compared the performance of different approaches, such as a multi-scale approach, color normalization, and the use of a pre-trained network on the ImageNet dataset [31], for breast cancer diagnosis; they resulted in a similar outcome [32]. The main contribution of the present study is a novel two-stage classification pipeline, that is, the serial connection of the segmentation network and the binary classification network. This study was inspired by the results of the breast cancer classification challenge conducted in 2018, where the accuracy of binary classification (carcinoma or not carcinoma) was reported to be higher than that of four-class classification (normal, benign, in situ carcinoma, or invasive carcinoma) [33]. As a preliminary experiment, we trained the network for three-class classification (MSI-L, MSI-H, or normal tissue) and two binary classifications (normal or tumor and MSI-L or MSI-H). The resultant confusion matrix demonstrated that the binary classifications outperformed the three-class classification in that the network sometimes misclassified MSI-H patches as MSI-L or normal tissue. We presumed that the dataset imbalance caused the misclassification; thus we propose the two-stage classification pipeline that enables to perform classification with a dataset in which the imbalance is significantly reduced. In this study, first we segmented the tumor area from the WSI using the Feature Pyramid Network (FPN) [34] to except the normal tissue area from the MSI-status classification. Then we classified the segmented tumor area as either MSI-L or MSI-H using Inception-Resnet-V2 [35]. The proposed two-stage classification method is presented in Section 2, followed by the experimental results in Section 3. We compared the performance of the proposed two-stage classification pipeline to that of the conventional classification algorithm, which simultaneously classified the patches into normal tissue, MSI-L, and MSI-H.
## 4 Discussion
### Displacement Inputs  
In line with previous investigations [16; 19; 28; 31], LVP \(D_{\text{in}}\) can vary between \(f_{\text{set}}\) and postures up to 0.4 mm and 0.3 mm, respectively (Table 1). Despite the relevance this may have because \(D_{\text{in}}\) affect directly the resultant acceleration (\(a_{\text{in}}=f_{\text{in}}\times D_{\text{in}}\)) [13], studies usually reported averaged data [21; 23; 25; 26; 29; 32]. For this reason, it has not been possible to compare \(D_{\text{in}}\) obtained in different postures in this investigation with previous studies. However, when the values of \(a_{\text{in}}\) are provided for each \(f_{\text{set}}\) and posture, the approximate value of \(D_{\text{in}}\) can be calculated. As the \(f_{\text{set}}\) increase from 20 Hz, the LVP showed well-maintained \(D_{\text{in}}\) at 25 Hz (\(-\)0.05 mm) and reductions up to 0.4 mm at 30 Hz (Table 1). Overall, the studies measured similar behaviour showing progressive and sometimes non-proportional reductions as the \(f_{\text{set}}\) increase [16; 27; 28]--in which it is possible to determine the corresponding \(D_{\text{in}}\)[18; 20; 21; 25; 26]. Distinctively, in two studies, the trend was reversed [29; 31]. In the literature, as the \(f_{\text{set}}\) increases, there are large reductions up to 0.9 mm [20; 27] and 0.5 mm [21; 29], analysing gaps of 10 Hz and 5 Hz of \(f_{\text{set}}\), respectively. However, these non-proportional behaviours appear at different settings, and are more likely to appear at high \(f_{\text{set}}\) and \(D_{\text{set}}\)[21; 27; 29]. Regarding postures, the LVP \(D_{\text{in}}\) showed differences up to +0.3 mm adopting TS and LU compared with ER (Table 1), as was advised previously [14]. However, only one study provided the data across different postures, which reported similar differences (0.4 mm) [19]. Otherwise, in this study, the opposite behaviour from standing (ER) to crouched postures was reported (1.5 mm, 1.2 mm, 1.2 mm, and 1.1 mm at 180\({}^{\circ}\), 165\({}^{\circ}\), 150\({}^{\circ}\) and 135\({}^{\circ}\) of knee flexion) differing from the LVP (Table 1). The LVP \(D_{\text{in}}\) was shown as a variable and specific parameter among \(f_{\text{set}}\)\(\times\) postures. These specific responses can be due to the specific characteristics of the LVP (i.e., materials, dimensions, and motors) in conjunction with body-weight distribution [12; 14]. These different reactions between trials suggest that the vibrating device and the human body work together. Therefore, the present results support the claims of these authors, who call for an analysis of the transmission of each of the vibration devices [12; 13; 14; 15].
## 4 Discussion
### Estimation Bias Based on Weather: Cloudy vs. Clear
Cloud cover and aerosols significantly influence the absorption and diffusion of solar radiation, emphasizing the importance of differentiating between clear-sky and cloudy-sky conditions when evaluating surface solar radiation and air-pollution dynamics [47]. Notably, clear days with significant pollutants can behave similarly to cloudy days due to the scattering effect of solar radiation caused by atmospheric pollutants. For instance, [47] reported that clear days with substantial particulate matter of size 2.5 \(\upmu\)m (PM2.5) or less result in more solar radiation scattering at the surface, compared to days when there are significant PM10 in the atmosphere. Absorbing aerosols decrease both direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI), while non-absorbing aerosols decrease DNI but increase DHI.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
## 2 Experimental Program  
### Materials  
Clean fine sand, coarse sand, and gravel were selected as the testing materials in this study. The fine sand particles are uniform, with most of the particle sizes concentrated between 0.2 mm and 0.5 mm. This fine sand is classified as poorly graded sand (SP) according to the Unified Soil Classification System (USCS). The particle sizes of the coarse sand are concentrated between 3 mm and 5 mm, and the particle sizes of the gravel are concentrated between 2.36 mm and 13.2 mm. The fine sand, coarse sand and gravel have friction angles of 28\({}^{\circ}\), 42\({}^{\circ}\) and 50\({}^{\circ}\), respectively, based on interpretation of the linear Mohr-Coulomb failure envelope. The friction angles of soil were obtained from direct shear tests. The friction angle of 50 degrees for gravel is at the high end. This is attributed to the strong angularity of the gravel, as shown in Figure 1. Dry soil was used to eliminate the influence of moisture content on the interface characteristics. The soil was compacted in layers using volume control to ensure consistent relative density of 70%. The soil compaction at the top and bottom of the box was kept the same. Interface shearing was applied after the vertical displacement became stable under the applied normal stress.  
Three biaxial geogrids (SS20, SS30 and SS40) and three triaxial geogrids (TX150, TX160 and TX170) with different tensile properties were used in the study. The tensile strength values of these geogrids for loading in different directions are shown in Table 1. For both biaxial and triaxial geogrids, tensile strength decreases with the direction of tensile loading, changing from 0\({}^{\circ}\) to 45\({}^{\circ}\), and then increases with the direction, changing from 45\({}^{\circ}\) to 90\({}^{\circ}\). This indicates that the tensile strength of geogrids is significantly influenced by the direction of tensile loading, and also shows anisotropic behavior in both types of geogrids, especially for the biaxial geogrids.
## 2 Results and Discussion
### Analysis of Cinnamon Volatiles by SPME/GC-MS
#### 2.1.5 Lactones  
The lactone-related compounds are not a common class of VOCs except in a few plant species as in apricot [37]. However, in the current study, lactones showed large variation among authenticated drugs ranging from 1.2-44.8%, being represented by only a few numbers of peaks, including prunolide (P42), hydrocoumarin (P50), and coumarin (P61), Table S1. Higher levels were detected particularly in CA and CT samples, owing to their richness in coumarin (P61) which accounted for 44.8% and 39.6%, respectively, Table S1. The presence of a high coumarin level in cinnamon essential oil has been previously reported, especially in _C. cassia_[9], in accordance with our results, and to extend herein by identifying CT as a potential source of coumarin in cinnamon species. Coumarin is considered a toxic metabolite in high doses, where the European Food safe authority has classified cassia cinnamon among hepatotoxic and carcinogenic food. About 0.02 mg/kg/day for a 60 kg consumer is the maximum daily human exposure to coumarin from dietary sources [38]. Hence, it is recommended for consumption of cinnamon species containing low content of coumarin as CI was detected at (1.2%), CV (6.8%), and CVM (8.0%). The low level of coumarin in _C. irens_ (CI) poses it as a potential flavoring agent among cinnamon species.  
In comparison with previous literature, the officially used authenticated _C. verum_ encompasses ca. 0.017 g/kg or \(17\times 10^{-4}\%\)[39], while essential oil derived from _C. cassia_ was at 0.7% [28]. Furthermore, commercial products showed comparable coumarin levels in CP-2 (11.4%) and CP-4 (9.2%), except in CP-1 at 19.1% and CP-3 at 3.3%. Interestingly, CP-3 showed the highest hydrocoumarin (P50) level detected at 6.4%, Table S1.
## 4 Experimental Results and Discussion  
### Results of the Estimation Obtained at Constant Current without Aging Effect  
The training data included 110,985 samples of current, voltage, temperature, and corresponding SoC values obtained from several charging and discharging processes of a Panasonic Sanyo UR18650 ZY commercial battery cell (PANASONIC & SANYO, Osaka, Japan) with a nominal voltage of 3.7 V and a nominal capacity of 2.6 Ah. The battery was charged using a constant current and voltage (CC-CV) profile and then fully discharged until the voltage reached 2.5 V. The manufacturer claimed a lifespan of 300 charge and discharge cycles under specified conditions. The main chemical specifications are indicated in Table 2.  
The collected data were then divided into three sets: 15% was used for testing, 15% for validation, and the remaining 70% for training the proposed neural networks. To extract  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Battery** & **Panasonic Sanyo UR18650 ZY** \\ \hline Cathode & \(LiNiCoAlO_{2}\) \\ Anode & Graphite \\ Rated capacity & 2.6 Ah \\ Nominal voltage & 3.7 V \\ Fully charged voltage & 4.2 V \\ Cut-off Voltage & 2.5 V \\ Initial cell temperature & 20 \({}^{\circ}\)C \\ Operating temperature & 20 \({}^{\circ}\)C to 65 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical characteristics of the battery.  
Figure 8: Effect of aging of a \(LiCoO_{2}\) battery for a variable charge/discharge current profile.  
these data during the charging and discharging process, an experimental device was established to collect all data. This setup consisted of a battery, a battery charger, a DC load (discharge), a current sensor (ACS712), a voltage sensor (from DCT electronic), a temperature sensor (LM35 DZ), a breadboard, and an Arduino Uno card, as represented in Figure 9.  
Figure 10 illustrates the precision of the proposed models in predicting the SoC. The results indicate that the neural networks accurately estimated the battery's SoC, with a maximum error of under 4% and an average error of less than 2%. Table 3 presents the SoC prediction errors for the various proposed models.
# A Master-Slave Separate Parallel Intelligent Mobile Robot Used for Autonomous Pallet Transportation
## 1 Introduction
It was designed with a compact driving unit, in addition to actual motions, such as linear motion, oblique linear motion, and zero-radius turn. It could also perform synchronous lifting and laying of the pallet. The slave robot follows the master robot synchronously and consistently. Each robot had two driving units. In order to synchronize the motion and trajectory of these two sub robots, a nonlinear control system was used [15]. The application of nonlinear control systems in robotics control was very common, from the simplest two-wheel driving self-balancing robot dynamics [16; 17], the robotic arm joint PD control [18], to the cluster control of UAV formations [19; 20; 21]. In the design we used the synchronic control strategy of slave robot dynamic adaptive following master robot. By analyzing the practical effects of sliding mode control [22] and high-gain observer [23] of the nonlinear control tools, we selected high-gain observer to obtain the following speed and state control of slave robot which was to ensure that the following errors converged and achieved synchronization of the master-slave robot motion. Compared with the manual and forklift truck, the separate parallel intelligent mobile robot can fulfill an autonomous pallet transportation. Compared with the traditional intelligent forklift AGV, it has the advantages of compact structure, small self-weight and large payload, high expandability and safety. It can implement zero-radius turn and requires small channel width. It can also be combined with automation equipment or storage system seamlessly. So it is very suitable for small and standardized logistics warehouses. The rest of this paper is organized as follows. Section 2 describes the mechanical structure of master-slave separate parallel intelligent mobile robot, such as compact driving units, communication modules, and safety protection modules. High-gain observer is adopted to ensure the consistency and synchronization of actual motions of two sub robots of intelligent mobile robot in Section 3. Experimental results and related analyses are given in Section 4, followed by conclusions in Section 5.
## 2 Materials and Methods
### Computational Modelling  
COMSOL Multiphysics FEM was used to simulate the modal analysis of the cantilever with integrated proof mass, as this could be used as a tool to determine which cavities to fill and what density filler to use to achieve the specified resonant frequency. The commercial cantilevers consisted of multiple stacked layers including FR4 (glass-reinforced epoxy resin used in printed circuit boards), PZT, and Cu layers. The overall cantilever consists of stacked films of FR4/Cu/PZT/Cu/FR4. The PZT layer is the piezoelectric film (190 \(\upmu\)m) which requires a top and bottom electrode consisting of Cu layers (30 \(\upmu\)m each), and the FR4 was used as a protective layer and to alter the stiffness of the cantilever. A cantilever without a proof mass was originally used to model the cantilever. Since the elastic modulus of each layer can vary significantly depending on how it is manufactured, the stacked beam was considered as a single hybrid layer. To create an accurate FEM, we experimentally determined the modal analysis frequencies of the cantilever, and then adjusted the elastic modulus of the cantilever in COMSOL until the FEM resonant frequency matched the experimental results. The elastic modulus of the stacked layers was determined to be 52 GPa, which makes sense since PZT has a modulus of 70-120 GPa, Cu's is 110 GPa, and the average for FR4 is 26 GPa. Due to laws of mixture, an elastic modulus of 52 GPa is a reasonable value that matches the experimental data. The cavity filler in the FEM was assumed to be a solid rectangle with a density that matched the particle density specified by the manufacturer. A tetragonal finer mesh was used with a minimum element size of 0.185 and the tethered end of the cantilever was anchored. The placement of the filler mass varied, and the 1 st resonant frequency mode was monitored and compared to the experimental data. Figure 2 illustrates the FEM of the cantilever and proof mass.  
Figure 2: COMSOL Multiphysics simulation displacement results for the cantilever and proof mass.