esa-sceva/satcom-chunk-collection huggingface dataset:
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 2 Related Work  
### Android Malware Detection  
The openness of the Android system makes it easier for attackers to carry out malicious activities by its vulnerability. The general malware detection methods usually employ classification algorithms to deal with the extracted features. Machine learning algorithms such as Support Vector Machine, Decision Tree, K-Nearest Neighbor, and Naive Bayes are of mature development and have been widely used in malware detection.  
Malware feature extraction includes a static method and a dynamic method. The static method decompiles the executable APK file and extracts features from the file or the program instructions. Static extraction is easy to implement, but it is difficult to deal with the camouflage caused by code obfuscation and other technologies.  
The dynamic method runs applications in a monitoring environment and detects the changes in the environment. Thus it is necessary to define \"normal\" states of the system, in order to judge the malicious application according to differences or changes. The dynamic method monitors application behavior in the simulated environment and could obtain nearly real operation information. However, executing and monitoring applications in a dynamic environment will consume more system resources.  
Machine learning algorithms are most widely used in malware detection. A single classifier is difficult to take into account both detection precision and recall, but multiple models have to consider the result combination of different classifiers. [PERSON] et al. [7] established a malware detection model MARVIN by linear classifier and SVM. [PERSON] et al. [8] proposed SEDMDroid to detect Android malware which combined SVM and MLP. The integrated learning framework MFDroid [9] employed seven feature selection algorithms to process permissions, API calls, and opcodes. Then the results of each algorithm are integrated to construct a new feature set to train the classifiers.  
Along with the emergence and development of Deep Learning, it is also applied in Android malware detection because of its powerful data processing ability. [PERSON] et al. [10]used Deep Belief Network to deal with multiple category features. [PERSON] and [PERSON] [11] developed a Deep Neural Network malware classifier and achieved 95% detection accuracy and under 0.1% false alarm rate. These researches are beneficial attempts to improve the effectiveness of Android malware detection and have promoted the development of relevant fields.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Network Layer
#### 4.2.1 Converged Communication and Sensing  
The convergence of sensing and communication network has become one of the leading trends in 6G technology and services [93]. The 6G network is expected to be a fusion of mobile communication, sensing, and intelligent computing. Such a converged network refers to a system that has the capabilities of target positioning (ranging, speed measurement, angle measurement), target imaging, target recognition, and target tracking. The development of higher-frequency bands such as millimeter waves, terahertz, and visible light will have more and more overlaps with traditional sensing frequency bands. Wireless communication and wireless sensing show more and more similarities in system design, signal processing, and data processing. Therefore, the AI-propelled VLC technologies will be found to be efficient in sensing technologies. A pragmatic solution is to allow communication and sensing in the same frequency spectrum. Research endeavors must be devoted to the solution of avoiding interference, and improving spectrum utilization. The codesign of the sensing and communication waveform may be aided by AI tools. The functionalities of communication and sensing are obtained based on software and hardware resource sharing or information sharing, which can effectively improve spectrum efficiency, hardware efficiency, and information-processing efficiency.
## 4 Experiments
### Datasets and Evaluation Metrics
The commonly used evaluation indicators for F-score include F1-score, F0.5-score, and F2-score. Due to the higher accuracy requirements for recommendations in data visualization, F0.5-score was chosen as one of the evaluation indicators in this paper. The formulas are as follows, where \(TP\) represents true positive, \(FP\) represents false positive, and \(FN\) represents false negative:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
Figure 5: Distribution of visits in dataset DA. \[F-score=(1+\beta^{2})\frac{Precision*Recall}{\beta^{2}*Precision+Recall} \tag{8}\]  
The NDCG indicator stands for Normalized Discounted Cumulative Gain, which not only evaluates the accuracy of recommendation results, but also evaluates whether the order of recommendation results is more reasonable. The NDCG indicator consists of two parts, DCG and IDCG, and DCG is composed of CG and the loss value at the current position of the result. CG is the cumulative gain, which is the sum of the correlation scores of the recommendation results; DCG is the Discounted Cumulative Gain, which means dividing each CG result by the discount value at the current position, so that the higher ranked result has a greater impact on the final result; and IDCG is the maximum DCG value under ideal conditions. The larger the value of this indicator, the better the model's performance. The formula is as follows. \[DCG =\sum_{i=1}^{k}\frac{rel_{i}}{\log_{2}{(i+1)}} \tag{9}\] \[IDCG =\sum_{i=1}^{|REL|}\frac{rel_{i}}{\log_{2}{(i+1)}}\] (10) \[NDCG =\frac{DCG}{IDCG} \tag{11}\]
# Dynamic Load Test and Contact Force Analysis of the AERORail Structure
## 3 AERORail Dynamic Load Test and Analysis  
### Structural Fundamental Frequency  
Whether it is an improved time domain algorithm based on the method of moments or an original time domain identification algorithm [23], all require a series of basic parameters including the fundamental frequency of the structure to be obtained in advance. In this paper, the finite element method is used to establish models of three spans (Figure 8) and calculate the fundamental frequency of the structure. To accurately reflect the dynamic characteristics of the structure, the following three structural features were mainly considered in the modeling: the tensile force of the cable provides additional stiffness; the slippage of the cable in the channels at both ends of the single span and the size of the strut in the middle of the span.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**First Order**} & \multicolumn{2}{c}{**Second Order**} & \multicolumn{2}{c}{**Third Order**} \\ \cline{2-7}  & & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** \\ \hline frequency (Hz) & north & 1.57 & 2.51/4.7 & - & 6.27 & 4.86 & 7.99 \\ damping ratio (\%) & 3.72 & 2.12 & - & - & 2.62 & 6.31 \\ frequency (Hz) & 1.57 & 0.78 & - & 6.27 & 4.86 & 2.51/4.7 \\ damping ratio (\%) & 3.72 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modal parameters of 30 m span and five support AERORail.  
Figure 7: Finite element mode shape diagram. (**a**) Vertical mode, (**b**) Transverse mode.  
The method based on modal analysis needs to determine the order of the analyzed mode in advance, and this order can be determined according to the needs of calculation accuracy [8]. According to the actual structure situation, this paper chooses to use the first three fundamental frequencies of the structure to identify the dynamic load. The calculation results of the first three-order fundamental frequency of each structure under different working conditions are shown in Table 3.  
Using the above fundamental frequency calculation results, combined with the measured data of the test line, the dynamic load of the AERORail can be identified.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 2 Modelling the Network
### Simulation Model  
The simulation was carried out using Riverbed Modeler version 16.0.A. The model is simplified to minimize the effect of other parameters on system performance. A File Transfer Protocol (FTP) server and an FTP client were set up to simulate network traffic. Figure 1 shows the simulated network model.  
**FTP Client:** The FTP client uses the Riverbed simulation module \"ethernet_wkst_adv\" for client functionality. It connects to the IP cloud using an Ethernet cable. The FTP client uses the default TCP parameters, as shown in Table 2. The client supports the FTP Profile (bottom right box in Figure 1).  
**FTP Server:** The FTP server uses the Riverbed module \"ethernet_server_adv\" for setting up server functionality. The server is linked to the IP cloud using an Ethernet cable. The FTP server also uses the simulator default TCP parameters (Table 2), and it supports the FTP application, as shown in Figure 1 (bottom left icon).  
**IP Cloud:** The IP cloud uses the simulator module \"ethernet_slot_slot_adv\" to set up its functionality. It is used to simulate an IP network. The Packet Latency was set to 0.02 s and the Packet Discard Ratio was set to different values depending on the scenarios studied.  
**FTP Application:** The application simulates FTP traffic. The FTP parameters are set to:  
* Command Mix (Get/Total): 100%
* Inter-Request Time (seconds): constant (3600)  
Figure 1: The Network Model.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Application of the Optimized SPR Biosensor for SARS-CoV-2 Sensing
Despite this, \(\text{Sys}_{8}\) maintains a high degree of precision up to 400 mM, which is crucial for applications requiring accurate viral load quantification, such as patient monitoring or assessing infection progression. This level of accuracy reinforces \(\text{Sys}_{8}\)'s capability as a reliable diagnostic tool, especially within the concentration range where viral load is most clinically relevant. The figure of merit, which balances sensitivity with signal resolution, is highest at 54.66 \(\text{RIU}^{-1}\) at 150 mM and decreases to 21.55 \(\text{RIU}^{-1}\) at 525 mM, as detailed in Figure 8d and Table S10. The high QF at lower concentrations reflects \(\text{Sys}_{8}\)'s ability to maintain a sharp, well-defined SPR dip, essential for distinguishing slight variations in virus concentration. As concentration increases, however, the broadening of the SPR dip reduces QF, compromising resolution. The gradual decay in QF from 54.66 \(\text{RIU}^{-1}\) to 37.45 \(\text{RIU}^{-1}\) up to 400 mM still supports \(\text{Sys}_{8}\)'s effectiveness for low and moderate concentrations. This makes \(\text{Sys}_{8}\) especially valuable for scenarios where clarity and precision are crucial, such as detecting small shifts indicative of early infection or changes in treatment efficacy. In this context, the analyzed performance metrics of \(\text{Sys}_{8}\) position it as a versatile biosensor for SARS-CoV-2 detection across a range of clinically relevant concentrations. Its high sensitivity, especially at low to moderate viral loads, makes it a prime candidate for early diagnostic applications. Furthermore, the observed linearity in sensitivity up to 400 mM (as shown in Figure S5) suggests that \(\text{Sys}_{8}\) can reliably quantify viral load without the need for complex calibration models, which simplifies real-time monitoring in clinical environments.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart Grid Application in IoT

Table 1: Integration of IoT into smart grid and its impact on efficiency, reliability, cost savings, consumer engagement, and sustainability.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Brain Implant  
Figure 14 shows a miniaturized planar implantable antenna presented in Ref. [92] with an operation frequency of 2.4 GHz. The approximate size of the antenna is 10 mm \(\times\) 10 mm \(\times\) 1.5 mm, and it is manufactured with Taconic RF-35 with \(\varepsilon_{r}\) = 3.5 and tan \(\delta\) = 0.0018. The achieved bandwidth of the antenna is 14.9%, with the peak gain of \(-\)20.75 dBi.  
In Ref. [93], a 2.4 or 4.8 GHz planar implantable antenna with modified E-shape is presented, built with a Rogers TMM13i substrate with \(\varepsilon_{r}\) = 12.2 and tan \(\delta\) = 0.0019. The size of the implantable antenna is 10 mm \(\times\) 8.7 mm \(\times\) 0.76 mm. The maximum SAR achieved is 69 mW/Kg for 10 g of tissue.  
Figure 14: Miniaturized planar implantable antenna for a brain implant [92]. Reproduced with permission from an open-access article from _IEEE Access_ (CC-BY).  
Figure 12: Helical wire antenna for blood pressure monitoring implant [23]. Reproduced with permission from an open-access article from _Biomedical Microdevices_ (CC-BY).  
Figure 13: Smart stent antenna for intravascular monitoring [91]. Reproduced with permission from an open-access article from MDPI _Sensors_ (CC-BY).  
The antenna presented in Ref. [68] is also compatible with a pacemaker application for a brain implant.  
A novel flexible moon-shaped slot implantable antenna operating at 2.45 GHz frequency is presented for neural recording systems and brain implants in Ref. [94]. The size of the antenna is 8 mm \(\times\) 9 mm \(\times\) 0.2 mm, fabricated with a RO4003C substrate with \(\varepsilon_{r}\) = 3.48 and tan \(\delta\) = 0.0027. The peak gain achieved is approximately \(-\)13 dBi at 2.45 GHz. The maximum SAR achieved is less than 1 W/kg for 1 g of tissue.
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Projection Profiling Row Align
#### 2.2.3 Confidence  
Let \(\sigma_{o^{2}}\) be the standard deviation of the variance function \(\sigma^{2}(H(\theta))\) as discussed in Section 2.2.1. Let \(\sigma^{2}\) be the mean of the histogram variances for all \(\theta\in SASS\). If \(\theta_{skew}\in SASS\) is the angle which produces the maximum value of \(\sigma^{2}\), namely \(\sigma^{2}(H(\theta_{skew}))\), then we can define the confidence measure as follows:  
\[Confidence=\left(1-\frac{\sigma_{o^{2}}}{\sigma^{2}(H(\theta_{skew}))-\sigma^{ 2}}\right)^{\tau_{2}} \tag{3}\]  
As the denominator term represents the variance deviation for the found skew angle, we can see that as it increases, the confidence also increases. This is measuring how much of a difference the shift in the angle space has made for the variance when \(\theta_{skew}\) maximized it. \(\tau_{2}\) is a positive exponent used for fine-tuning the confidence in the voting process to match the capabilities of the other algorithms.
# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 3 Materials and Methods
### Loss Function  
During the training of the RPN [8], each anchor was assigned a binary class label and five parametric coordinates. To train the RPN, we needed to find positive and negative samples from all anchors. The positive samples anchors were needed to satisfy the following conditions: the Intersection over Union (_IoU_) [8] overlap between an anchor and the ground-truth is greater than 0.7, or an anchor has the highest IoU overlaps with a ground-truth. Negative samples were defined as: IoU overlap less than 0.3 to all ground truth boxes. Anchors that are neither positive nor negative were discarded. We use the multi-task loss to minimize the objective function, which is defined as follows:  
\[L(p_{k},b_{k})=\frac{1}{N_{gtbox}}\sum_{k}(L_{clas}(p_{k},p_{k}^{*})+\frac{ \lambda}{N_{anchor}}\sum_{k}p_{k}^{*}L_{reg}(b_{k},b_{k}^{*}) \tag{1}\]  
where \(k\) represents the index of an anchor in the mini-batch. \(N_{gtbox}\) denotes the number of ground truth boxes and \(N_{anchor}\) denotes the number of anchors. \(p_{k}\) is the predicted possibility that anchor \(k\) is the target object. \(p_{k}^{*}\) is a binary value asserting whether the anchor is positive. \(b_{k}\) is a vector that is used to represent the predicted bounding box while \(b_{k}^{*}\) denotes the ground-truth bounding box.  
In the multi-task loss, we have two parts of the loss function. The first one is the classification loss. Here, we use a cross-entropy function, which is defined as:  
\[L_{clas}(p_{k},p_{k}^{*})=p_{k}^{*}log(p_{k})\qquad(\text{Cross Entropy}) \tag{2}\]  
The second part is the regression loss. We need to force the distance between the predicted bounding box and the ground truth to be as close as possible. Here, we measure the distance by smooth L1-norm, which is  
\[L_{reg}(b_{k},b_{k}^{*})=\left\{\begin{array}{l}|b_{k}-b_{k}^{*}|,if\quad|b _{k}-b_{k}^{*}|<1\\ (b_{k}-b_{k}^{*})^{2},\text{otherwise}\end{array}\right.(\text{smooth L1}) \tag{3}\]  
The term \(p_{k}^{*}L_{reg}(b_{k},b_{k}^{*})\) means only when the anchors are positive should the regression loss function be activated, i.e., \(p_{k}^{*}=1\). The outputs of the classification and regression layers include \(p_{k}\) and \(t_{k}\), respectively. Then, they need to be normalized with \(N_{clas}\) and \(N_{reg}\), and balanced by weight \(\lambda\).  
For regression, the parameterizations of the four coordinates (top left corner, width, and height) are defined as:  
\[\left\{\begin{array}{l}b_{x}=\frac{x-x_{a}}{w_{a}}\\ b_{y}=\frac{y-y_{a}}{h_{a}}\\ b_{w}=log(\frac{y_{a}}{w_{a}})\\ b_{h}=log(\frac{y_{a}}{h_{a}})\\ \end{array}\right.\left\{\begin{array}{l}b_{x}^{*}=\frac{x^{*}-x_{a}}{w_{a}} \\ b_{y}^{*}=\frac{y^{*}-y_{a}}{h_{a}}\\ b_{w}^{*}=log(\frac{y^{*}}{h_{a}})\\ b_{h}^{*}=log(\frac{y^{*}}{h_{a}})\\ \end{array}\right. \tag{4}\]  
Here, \(x,y,w,h\) denote the four coordinates of the predict box; \(x_{a},y_{a},w_{a},h_{a}\) denote those of the anchor box; and \(x^{*},y^{*},w^{*},h^{*}\) denote those of the ground truth box.
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 4 The Construction Principles and Key Points of End-Around Taxiway Layout
### ICAO and FAA Recommendations for EAT Planning
If there is a certain height difference between the runway end and the taxiway, the terrain conditions can be fully utilized to shorten the distance between the runway end and the centerline of the taxiway. Note 1: This example illustrates the approach surface, which controls the location of the side taxiway segments of the EAT, while the departure surface controls the location of the transverse taxiway section of the EAT. Note 2: This example assumes all centerline elevations to be the same elevation as the runway end centerline. Note 3: This distance varies depending upon the ground elevation of the transverse EAT transverse section relative to the applicable runway surface elevation (Departure End of Runway or threshold elevation). Note 4: The point where the tail height equals the approach surface height is 1730 ft from the runway threshold, assuming no change in terrain elevation. Note 5: The distance between the taxiway centerline and the outer edge of the approach surface edge is a minimum of one-half of the taxiway safety area (e.g., 59 ft). The FAA's AC150 advisory circular delineates comprehensive specifications for EAT layouts, specifying the minimum distance of taxiways from runway ends and setting certain dimensions for taxiways based on the tail height of different aircraft models (Table 4). In addition, the regulations have studied methods for determining the position and height of visual obstructions and proposed the use of on-site survey methods to measure the height and width of such obstructions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Tail Height (m)** & **Wingspan (m)** \\ \hline I & \textless{}6 m & \textless{}15 m \\ II & \textless{}9 m & \textless{}24 m \\ III & \textgreater{}13.5 m & \textless{}36 m \\ IV & \textless{}13.5\textless{}18.5 m & \textless{}36\textless{}52 m \\ V & \textless{}18.5\textless{}20 m & \textless{}52\textless{}65 m \\ VI & \textgreater{}20\textless{}24.5 m & \textless{}65\textless{}80 m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Airplane design groups (ADGs). Figure 11: EAT—ADG-III (FAA AC No 150/5300-13B) [67]. Most of the EATs in the United States are designed based on a takeoff climb surface of 1:40, without considering the one-engine-inoperative surface in management regulations [16; 17; 36; 68]. However, most ICAO member states in Asia use takeoff climb surfaces of 1:50 or 1:62.5 to ensure unrestricted passage of aircraft taking off in front of the taxiway [72; 73].
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 5 Performance Analysis  
### Snr  
The instantaneous receiver SNR is defined as [36; 37]:  
\[\gamma_{\mathrm{s}}=\frac{2P_{\mathrm{t}}^{2}R^{2}h^{2}}{\sigma_{\mathrm{h}}^ {2}} \tag{11}\]  
Figure 3: Location of Neom City [35].  
and is random due to the fluctuation of h, while the average SNR is:  
\[\overline{\gamma}_{\rm s}=\frac{2\mathrm{P}_{\rm t}^{2}\mathrm{R}^{2}}{\sigma_{ \rm n}^{2}}\mathrm{E}[\mathrm{h}]^{2} \tag{12}\]  
where \(\mathrm{E}[.]\) is the statistical expectation.
## 2 Research Methodology
### Unbalance
Initially, the network was trained to recognize the imbalanced position based on linear characteristics such as fundamental and higher harmonic amplitudes. This approach yielded only partial success: the network distinguished unbalance in some cases, but the overall reliability was insufficient. The authors then improved the method by incorporating nonlinear spectral components, particularly the identified subharmonic oscillations and fundamental harmonics, as the ANN's input data. This modification significantly enhanced accuracy: the improved \"nonlinear\" ANN could unambiguously identify which specific disk was unbalanced (Figure 7). In the test bench experiments, the network achieved 100% correct classification of imbalance location across different rotational speeds, even in the presence of other faults. Also, the study demonstrated that the proposed approach could localize imbalance with high accuracy while requiring a minimal number of sensors. However, there are several limitations to the proposed methodology. The major limitation is that the method only allows for identifying where and how significant the imbalance is but identifies nothing regarding its reason, reducing tremendously the diagnostic ability of the innovative approach. The second important limitation is retraining the neural network, even with minimal system changes. In the study by [39], the authors analyze a three-variant Jeffcott rotor supported by bearings using a pure Monte Carlo scheme, treating material density and Young's modulus as gamma-distributed variables. Running 8000 finite element realizations, the authors derived the means, variances, and higher-order moments of critical speeds and stability thresholds, and by superposing Campbell diagrams together with baseline deterministic damping-factor curves versus spin speed, Figure 8 shows that density dominates the statistical spread. At the same time, modulus has only a minor effect. A convergence study indicates that fewer than 6000 samples already stabilize the results, keeping the total CPU time below ten minutes for this lightweight model. The approach is transparent and distribution-agnostic but becomes impractical for complex geometries, omits parameter  
Figure 7: ANN success rates for unbalance state prediction. Adopted from [26]. correlations, and delivers no worst-case guarantees; moreover, it ignores defect-induced nonlinearities, so its applicability to real turbomachinery remains to be demonstrated. Table 2 summarizes the results of a comparative analysis of the methods for rotor unbalance detection.