esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection
### APK and API  
The Android platform is an essential factor in facilitating the expeditious growth of Internet of Things (IoT) applications. Concomitantly, the prevalence of malware in the Android operating system has increased, and the advent of virus strains incorporating highly advanced evasion tactics has been observed. An ensemble learning-based Android malware detection solution of high precision was developed by [PERSON] et al., 2015 [56]. The analysis of Android malware necessitates the examination of the software features obtainable from the APK. Utilizing Java-based APK analysis tools, one can extract the set of features from the existing app corpus. This study focused on extracting 65 features, comprising a range of API calls and Linux/Android command sets. The following Application Programming Interfaces (APIs) are included: SMS Manager API (utilized for sending, receiving, and reading SMS messages, etc.); Phone Manager API (for accessing device ID, subscriber ID, network operator, and SIM serial number, etc.); and the Package Management API (used to list installed packages) [56]. It has been asserted that the extraction of sensitive data streams from within the application can be an effective means of detecting malicious software. The authors developed DeepFlow, an Android malware detection tool, and implemented a technique involving the analysis of Android Application Programming Interface (API) codes with APK packages, the extraction of sensitive data streams, and the utilization of Deep Belief Networks (DBN) for classification. The results of an experiment conducted on 3,000 benign apps and 8,000 malicious apps revealed that DeepFlow achieved a noteworthy F1 score of 95.05% when appropriately configured [57].
## 2 Materials and Methods
### Overall Platform Architecture and Functional Module Design
#### (1) Data monitoring  
Data monitoring is the data support of the platform. The vehicle terminal obtains the monitoring data of the vehicle-monitoring sensor. It actively initiates a connection request to the platform server. It starts to send data remotely after the platform server confirms the request. The data is transmitted to the remote operation and maintenance platform. The platform remotely monitors the operation data or status of the combine harvester system.
# Low-Frequency Magnetic Field Exposure System for Cells Electromagnetic Biocompatibility Studies
###### Abstract  
The advancement in science and technology has resulted in the invention and widespread usage of many electrical devices in the daily lives of humans. The exponential use of modern electronic facilities has increased electromagnetic field exposure in the current population. Therefore, the presented article deals with designing, constructing, and testing a new applicator system developed for cells electromagnetic biocompatibility studies. The applicator system is intended for studying the non-thermal impacts of low-frequency magnetic field on cell cultures growth. Main attention is focused on increasing the capacity of the applicator and effectivity of the experiments. The key idea is to reach high level of the magnetic field homogeneity in an area of interest and the temperature stability during the biocompatibility studies. The applicator system is designed based on numerical simulations and its construction, measurements, and properties evaluation are also reported for proving the applicator's functionality. The new applicator allows performing five parallel experiments at the same time under the same conditions. The simulation together with the experimental results confirm that the magnetic field homogeneity reaches 99% in the area of interest and the maximum temperature instability is lower than 2% during the experiments. The effectiveness of new applicator is tested and proved during preliminary experiments with Saccharomyces Cerevisiae cells. The observed effects of MF exposure represent maximal stimulation of 74% and maximal inhibition of 49%. The reason why MF with the same parameters induces inhibition in one sample and stimulation in the other will be the subject of further research.  
low-frequency electromagnetic field; biocompatibility; applicator system; coil; ion parametric resonance theory; YPD medium +
Footnote †: journal: Physics Letters B
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Frequency-Dependent** \\ **ANN** \\ \end{tabular} & **Pretrained ANN** & **NARX** & **biLSTM** \\ \hline \multirow{3}{*}{Inputs} & Accelerations & \begin{tabular}{c} Pretraining with \\ eigenvectors \\ \end{tabular} & Accelerations as & \begin{tabular}{c} Accelerations as \\ time sequence \\ \end{tabular} & Accelerations as \\  & Frequency & \begin{tabular}{c} Accelerations for \\ training \\ \end{tabular} & Feedback & \begin{tabular}{c} Accelerations as \\ time sequence \\ \end{tabular} \\  & \begin{tabular}{c} Levenberg–Marquardt \\ backpropagation \\ \end{tabular} & \begin{tabular}{c} Levenberg–Marquardt \\ gradient \\ backpropagation \\ \end{tabular} & \begin{tabular}{c} Scaled conjugate \\ gradient \\ backpropagation \\ \end{tabular} & Adam optimizer \\ \begin{tabular}{c} Number of nodes for \\ 1 st theoretical case \\ \end{tabular} & 12 & 12 & \begin{tabular}{c} 12 neurons, \\ 3 delays \\ \end{tabular} & 12 \\ \begin{tabular}{c} Number of nodes for \\ 2 nd theoretical case \\ \end{tabular} & 19 & 100 & \
\begin{tabular}{c} 140 neurons, \\ 3 delays \\ \end{tabular} & 68 \\
\begin{tabular}{c} Number of nodes for \\ use case \\ \end{tabular} & 70 & 5 & 5 neurons, 1 delay & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Architecture of the different ANNs. Figure 6: Training plots of (**a**) the pretrained ANN and (**b**) the NARX model for the industrial use case. 100 steps proves to increase the quality of training tremendously. This does not make a difference for the feedforward and the biLSTM networks, as it only increases computation time.
###### Abstract  
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior information of the low-order gradient of the image. This study proposes an image-deblurring model (Lp_HOGS) based on the LOGS model to mine the high-order prior information of an infrared (IR) image amid salt-and-pepper noise. An Lp-pseudo-norm was used to model the salt-and-pepper noise and obtain a more accurate noise model. Simultaneously, the second-order total variation regular term with overlapping group sparsity was introduced into the proposed model to further mine the high-order prior information of the image and preserve the additional image details. The proposed model uses the alternating direction method of multipliers to solve the problem and obtains the optimal solution of the overall model by solving the optimal solution of several simple decoupled subproblems. Experimental results show that the model has better subjective and objective performance than Lp_LOGS and other advanced models, especially when eliminating motion blur.  
infrared image deblurring; overlapping group sparse total variation; Lp-pseudo-norm; salt-and-pepper noise +
Footnote †: journal: Applied Soft Matter  
1
Footnote †: journal: Applied Soft Matter
## 3 Results
### Sensitivity Analysis  
To verify which geometrical parameters are impacting the natural frequency and model area, a sensitivity analysis using the Spearman correlation factor was performed. The correlation factor expresses to which extent two continuous data tend to change together. The factor described both the strength of the correlation and the direction and is in the range from \(-1\) to \(+1\). The correlation analysis presented in the article used the rank correlation coefficient applicable to analyze the data with non-linear dependance according to Equation (27).  
\[\rho=1-\frac{6\sum_{i=1}^{n}\mathrm{d}_{i}^{2}}{n^{3}-n} \tag{27}\]  
where:  
\(\mathrm{d}_{i}\)--delta between x-variable rank and y-variable rank for each pair of data accordingly to the ascending order (see rank assignment example in Figure 14),  
\(n\)--number of pair data (in example below is equal to 10).  
Figure 14: Correlation coefficient interpretation: (a) No correlation \(\rho=0\); (b) Strong positive correlation \(\rho=1\); (c) Strong negative correlation \(\rho=-1\).  
Figure 14 shows the correlation coefficient (\(\rho\)) levels for three types of data representing no correlation (\(\rho\) = 0), strong positive (\(\rho\) = + 1) and negative (\(\rho\) = \(-\) 1) dependance between x and y tested variables.  
Correlation coefficients against two y-variables are evaluated. The first is the natural frequency of the system mode and the second is the 2D model area impact. As x-variables, each of the geometrical features is described in the parameter's definition section (Figure 12).  
A review of the correlation coefficients presented in Figure 15 for the analyzed case reveals the high contribution of the casing shell B in the response for both system mode frequency (\(\rho\) = 0.83) and model area (\(\rho\) = 0.64). Nozzle pendulum mode shape induces the bending condition into casing where the forward and aft rails are moving off-phase (up and down). The thickness change is increasing overall system stiffness and directly affecting the natural frequency of the system. From the design change perspective, interesting characteristics are forward hook leaning and aft hook position since they can be neural for weight. No correlation on the response shows the rear hook leaning and the forward hook position.
# Deep Learning-Based Classification of Weld Surface Defects
## 1 Introduction
It can learn the high-level features of the target from the sample data by supervised learning [15]. Recently, deep learning methods have been widely applied in weld defects detection. [PERSON] et al. [16] proposed a modified convolutional neural network (CNN) to implement classification in X-ray weld images. [PERSON] et al. [17] construct a deep convolutional neural network (DCNN) to extracted high-level features from X-ray images. [PERSON] et al. [18] introduced the idea of Gaussian kernel into deep learning. The purpose is to ensure that the main information of the image is extracted while minimizing the occurrence of noise and interference and improving the classification accuracy. In Reference [19], [PERSON] et al. utilized the VGG16 based fully CNN to inspect welding defect images using the idea of transfer learning. High precision classification is achieved with a relatively small data set. In Reference [20], [PERSON] et al. proposed a deep neural network based on sparse automatic encoder. It learns the feature information of the image in an unsupervised way, and then uses the softmax classifier with supervised learning to identify the defects such as pore and undercut. In Reference [21], [PERSON] et al. adopted feature pyramid network (FPN) and RoIAlign to realize defect detection of X-ray images of automobile aluminum casting parts. The above methods usually need to construct deep CNN architecture and utilize softmax to handle classification tasks. However, in the case of fewer training samples, there is a poor performance for softmax that the features are confusing. In order to solve the problems mentioned, an intelligent recognition method based on deep learning is proposed. What is more, random forest is selected as the final classification algorithm because random forest has good fault-tolerant ability and low generalization errors on the issue of dealing with classification. The overview of the proposed method is shown in Figure 1. It is mainly formed by two modules: CNN and forest random. The CNN architecture is constructed to automatically extract high-level features of weld surface defects images. Random forest is trained by using the high-level features to accomplish the classification task. In addition, the welding surface defects images including normal, overlap, spatter, and pore are preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images and is taken as input data. Finally, comparative experiments are carried out to verify the effective and practical of proposed method. The structure of this paper is organized as follows. The Section 2 gives specific architecture of the CNN module. In Section 3, the classification module applying random forests is described in detail. Weld defect image pre-processing and experimentation are given in Section 4. The Section 5 summarizes the article.
## 2 General Approach to Homogenization
However, in the isotropic scenario, the elements \(uu\), \(u\sigma\), \(\sigma u\) and \(\sigma\sigma\) of this matrix are defined as:  
\[\overset{=-1}{T}_{uu}^{-1}(\mathbf{G})=-\left[\frac{\overset{\leftrightarrow} {P}_{T}(\mathbf{k}+\mathbf{G})}{\frac{\omega^{2}}{l}\rho_{b}+i|\mathbf{k}+\mathbf{G}|^ {2}C_{44,b}}+\frac{\overset{\leftrightarrow}{P}_{L}(\mathbf{k}+\mathbf{G})}{ \frac{\omega^{2}}{l}\rho_{b}+i|\mathbf{k}+\mathbf{G}|^{2}C_{11,b}}\right], \tag{19}\]  
\[\overset{=-1}{T}_{\sigma u}^{-1}(\mathbf{G})=iS_{b}^{-1}K^{T}\overset{=-1}{T }_{uu}^{-1}(\mathbf{G}), \tag{20}\]  
\[\overset{=-1}{T}_{u\sigma}^{-1}(\mathbf{G})=\overset{=-1}{T}_{uu}^{-1}( \mathbf{G})K\,iS_{b}^{-1}, \tag{21}\]  
\[\overset{=-1}{T}_{\sigma\sigma}^{-1}(\mathbf{G})=iS_{b}^{-1}[I_{6}+K^{T} \overset{=-1}{T}_{uu}^{-1}(\mathbf{G})K\,iS_{b}^{-1}], \tag{22}\]  
where \(\overset{\leftrightarrow}{P}_{T}(\mathbf{k}+\mathbf{G})=I-\frac{(\mathbf{k}+\mathbf{ G})(\mathbf{k}+\mathbf{G})}{\left|(\mathbf{k}+\mathbf{G})\right|^{2}}\) and \(\overset{\leftrightarrow}{P}_{L}(\mathbf{k}+\mathbf{G})=\frac{(\mathbf{k}+\mathbf{ G})(\mathbf{k}+\mathbf{G})}{\left|(\mathbf{k}+\mathbf{G})\right|^{2}}\) correspond to the transverse and longitudinal projectors, respectively. As can be seen in Equation (6), it is not necessary to know all the elements of \(D^{-1}(\mathbf{k};\mathbf{G},\mathbf{G}^{{}^{\prime\prime}})\) but only the \(D^{-1}(\mathbf{k}=0;\mathbf{G}^{{}^{\prime}}=0,\mathbf{G}^{{}^{\prime\prime}}=0)\) block; therefore, when writing Equation (17) for \(\mathbf{G}^{{}^{\prime\prime}}=0\), we obtain a system of equations for the column block \(D^{-1}(\mathbf{k};\mathbf{G},0)\):  
\[D^{-1}(\mathbf{k};\mathbf{G},0)+\overset{=-1}{T}^{-1}(\mathbf{G})\frac{\Omega}{i} \sum\
olimits_{\mathbf{G}^{{}^{\prime}}}\Delta A(\mathbf{G}-\mathbf{G}^{{}^{ \prime}})D^{-1}(\mathbf{k};\mathbf{G}^{{}^{\prime}},0)=\overset{=-1}{T}^{-1}( \mathbf{G})I\delta_{\mathbf{G},0}, \tag{23}\]  
The form factor \(F(\mathbf{G})\) in Equation (14) varies very slowly when the filling fraction \(f\) (ratio between the volume occupied by the inclusion within the unit cell with respect to the total volume of the cell) is too small (small inclusions), being constant for large values of \(|\mathbf{G}|\) (\(|\mathbf{G}|\)\(\sim\)\(1/r\)\(\gg\)\(1/a\); \(r\) and \(a\) are the characteristic size of the inclusion and the lattice constant, respectively). To determine the block of the matrix \(D^{-1}(\mathbf{k};0,0)\), the matrix \(\Delta A(\mathbf{G}-\mathbf{G}^{{}^{\prime}})\) in Equation (17) can be replaced by \(\Delta A(-\mathbf{G}^{{}^{\prime}})\) (with \(F(\mathbf{G}-\mathbf{G}^{{}^{\prime}})\)\(\rightarrow\)\(F(-\mathbf{G}^{{}^{\prime}})\)) because the contrast for \(\mathbf{G}\) close to the origin is negligible. This consideration allows us to analytically calculate the column block \(D^{-1}(\mathbf{k};\mathbf{G},0)\).
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Parameter Determination
#### 2.4.2 Spatial Model Determination  
Considering the modeling data and spatial distribution of the modeling stations, we select the cross-validation of 8 modeling stations to determine the spatial reconstruction model. The specific process is shown in Figure 12. First, the TEC observations of the reconstruction station are removed from the modeling dataset. Then, the remaining observed TEC of the modeling stations (represented as the reconstruction dataset in Figure 12) were used to carry out spatial interpolation for the reconstruction stations, and the cycle was carried out successively until the TEC reconstruction of all stations was completed. And RRMSE was used as the evaluation standard to determine the optimal SF.  
Figure 11: An example of temporal regression coefficients at the Dourbes station.  
Figure 12: The flowchart of spatial cross-validation.  
Figure 13 shows the RRMSE of the spatial reconstruction results against different \(SF\). The minimum reconstruction error is 13.49%, and the corresponding \(SF\) is 1.15. Accordingly, the ionospheric distance between two ionospheric observation stations can be expressed as:  
\[d_{nj}=\sqrt{\left(\varphi_{n}-\varphi_{j}\right)^{2}+\left(1.15\cdot\left( \lambda_{n}-\lambda_{j}\right)^{2}\right)}, \tag{11}\]  
where (\(\varphi_{n}\),\(\lambda_{n}\)) and (\(\varphi_{j}\),\(\lambda_{j}\)) represent the longitude and latitude coordinates of the \(n\)th and the \(j\)th ionospheric observation station, respectively.  
After the definition of the ionospheric distance is determined, the Lagrange extreme algorithm can be applied to solve the variation function (8) and spatial interpolation (5), then the spatial reconstruction weight coefficients of each observation station can be determined. Figure 14 shows the spatial reconstruction weight coefficients corresponding to the 8 modeling stations used in this paper. It can be seen that different modeling stations have different weights on reconstructed TECs of the same reconstruction station. Specifically, the closer the spatial distance between the modeling station and the reconstruction station, the greater the corresponding weight coefficients of these stations.  
To sum up, we determine the temporal and spatial reconstruction model of TEC, as well as the harmonic function composed by them. To validate the correctness and effectiveness of the proposed model, we will take RMSE and RRMSE as the evaluation standard to test the model by comparing the observed TECs of the validation stations and the predicted TECs calculated by using the empirical model determined by the above process. Furthermore, the proposed model is compared with the IRI model to prove its superiority.  
Figure 14: The corresponding spatial reconstruction weight coefficients of the modeling stations.  
Figure 13: The RRMSE of spatial reconstruction against \(SF\).
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 3 An Overview on Common Process Engineering and ITSM Frameworks
### Cobit 5  
The next generation of ISACA (Information Systems Audit and Control Association) guidelines for IT management and governance is COBIT 5 (Control Objectives for Information and related Technologies) [61], which provides organizations with a comprehensive framework to be able to achieve their corporate IT management and governance goals. COBIT 5 helps companies gain the most benefits from IT by striking a balance between the results, by optimizing risks, and by leveraging resources based on the following five principles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Lifecycle Management** & **Benefits** & **Limitations** \\ \hline ALM & Comprehensive for software products and applications & - Not designed for complex system of systems \\ \hline ILM & Basis of information handling & - Does not answer how to develop applications or systems \\ \hline PLM & Useful for production and product tracking & - The scope is on the product-specific requirements. - Characteristics may be different for products. \\ \hline SDLC & Well-defined set of principles and methodologies for software developments & - Not applicable in every case, e.g., for small or evolutionary developments - Complicated to use in dynamically changing environments \\ \hline SLM & Useful for service operation management & - The scope is on the service-specific requirements. - Characteristics may be different for services. - The focus is on enhancing functional performance. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benefits and limitations of the analyzed lifecycle models.
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 2 Materials and Methods
### Signal Processing Methods  
For the evaluation of the thermographic data two typical signal processing methods were used: principal component analysis and contrast-to-noise ratio. In order to increase the detectivity of material inhomogeneities and to extract the desired information from the thermal three-dimensional raw data, PCA is adapted from Rajic [30; 31]. This method uses singular value decomposition to extract contrast information from the spatial and temporal information of the captured image sequence (3D matrix), which can then be visualized as an image. The singular value decomposition is represented as the product of three matrices:  
\[A=USV^{T} \tag{1}\]  
Here \(S\) is a diagonal matrix, which contains the singular values of the matrix \(A\) in the main diagonal. The columns of the matrix \(U\) consist of empirical orthogonal functions, which represent the spatial variations of the thermal data set, called spatial functions. The transposed matrix \(V^{T}\) describes the connection between the orthogonal functions [18]. When observing the cooling phase, the second spatial function is non-uniform and emphasizes areas of contrast [18; 24]. Therefore, the second spatial function is utilized throughout the analysis to make inhomogeneities in the material visible.  
To evaluate the visibility of potential defects, the contrast that exists between a defect and its surroundings is essential. To be able to compare the visibility of defects between  
Figure 1: Schematic representation of long-pulse thermography in reflection mode.  
different images, the contrast is related to the surrounding noise. The so-called contrast-to-noise ratio (CNR) is calculated from the absolute contrast  
\[C_{a,\text{A-B}}=|\hat{I}_{\text{A}}-\hat{I}_{\text{B}}| \tag{2}\]  
between the areas A and B of the pre-processed data and the corresponding standard deviation \(S_{\text{A-B}}=\sqrt{{S_{\text{A}}}^{2}+{S_{\text{B}}}^{2}}\):  
\[\text{CNR}=\frac{|\hat{I}_{\text{A}}-\hat{I}_{\text{B}}|}{\sqrt{{S_{\text{A}}} ^{2}+{S_{\text{B}}}^{2}}}\,. \tag{3}\]  
The absolute contrast \(C_{a,\text{A-B}}\) is calculated from the difference of the mean intensity values \(\hat{I}_{\text{A}}\) and \(\hat{I}_{\text{B}}\) of two areas A and B [28; 29].