esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
###### Abstract
[35] proposed a machine-learning-aided approach to improve mechanics-based code-conforming formulation so as to obtain the shear capacity equation for reinforced concrete beams and columns. Via verification with experimental data, this method is not only beneficial to develop capacity equations, but also can enhance accuracy of the final predictions. [PERSON] et al. [36] utilized the machine-learning approach to analyze predictions of the shear strength of components and assess the regional damage of bridges. Through comparison, they identified the efficiency of proper implementation of this method and key influential variables, which was helpful to understand reasons for behaviors and predictions of the machine-learning model. Because of the excellent capacity of AI technology, more and more researchers have begun to introduce AI technology in various applications of structural engineering. To improve the efficiency of optimization calculation and arrangement of isolation bearings, a joint algorithm based on AI technology is proposed in this paper. In this joint algorithm by means of setting reasonable model parameters, a Hunter-prey optimization (HPO) algorithm proposed by [PERSON] [37] is applied to optimize sizes of isolation bearings; similarly, CNN (Convolutional Neural Network) is utilized to predict types of isolation bearings during the optimization process, which is manipulated by Python based on the Application Program Interface (API) interface provided by ETABS. Based on the considerations mentioned above, this paper introduces the AI algorithm to improve calculation efficiency during the optimization analysis process of isolation bearings and comprehensively considers the impact of economic cost, seismic decrease coefficient, and story drift ratio, as well as the constraint condition of eccentricity of the stiffness center of isolation bearings and the center of gravity of the superstructure, eventually forming an optimization method of isolation bearings based on the joint algorithm of CNN and HPO. The overall structure of this paper is as follows: Section 2 introduces the calculating parameters and optimization objective function investigated in this paper. Section 3 presents the joint algorithm in which the type of isolation bearings is predicted through CNN algorithm; the optimization selection of the bearing size is based on the HPO algorithm, as well as the overall process of optimization calculation of isolation bearings using the joint algorithm. Section 4 introduces a calculation model example of the seismic isolation structure utilizing the optimization method proposed in this paper. Section 5 discusses the influence of different weight coefficient combinations of the optimization objective function on optimization analysis. Section 6 gives conclusions and prospects of the whole study, summarizing the optimization method of isolation bearings proposed in this paper.
## 4 Procedure and Analysis of Range Differentiation in 3D MMW Imaging
### Image Optimization  
In the MMW imaging experiments, a series of plots were generated to visualize the range information captured by the GDD integrated with FMCW radar, providing insights into the depth of objects with varying dimensional profiles. The first image, displayed as a normal grayscale 2D image as shown in Figure 5, represents the object based on the voltage signal levels acquired from the GDD detection circuit. This serves as an important step in validating the operation of the GDD system, providing a basic visualization of the object's shape captured by the reflected MMW signals at a single point in time.  
In addition to the grayscale 2D image, a heat map representation as shown in Figure 10 was used to enhance the visual clarity of the range data. The heat map assigns color values to different range measurements, allowing for easier differentiation between depth variations. Warmer colors, including red and orange, represent shorter ranges, while cooler colors like blue and green indicate longer distances. This color-based representation is particularly effective for identifying subtle variations in range, especially when imaging more complex 3D objects. For example, the 3D \"H\" shape experiment, which involved a central projection of 25 mm, resulted in a 24.1 mm difference in the calculated range, which was clearly captured in the heat map as a distinct shift in color, confirming the sensitivity of the system to depth changes.  
To further refine depth perception and enhance data visualization, a 3D topographical plot was developed, as shown in Figure 11, which includes three perspective views of the object. This plot projects range data as elevation changes across a grid, with each cell representing a specific depth value derived from the FMCW radar analysis. By transforming depth data into a 3D surface format, the plot effectively maps the object's profile, allowing for easy visualization of depth variations and overall shape.  
The 3D \"H\"-shape object, with its central 25 mm projection, is clearly depicted in the plot, where the elevated section highlights the system's ability to capture subtle depth differences. The projection is evident from all three perspectives, visually affirming the system's capability to measure and display depth variations. This three-angle view enhances the clarity of the object's contours, making it easier to interpret structural details that are otherwise challenging to convey in a single view.  
This topographical representation not only provides an intuitive understanding of the object's surface but also marks an important step toward creating a comprehensive 3D MMW imaging system. By successfully demonstrating the GDD system's ability to differentiate small depth variations, this work paves the way for future advancements in 3D imaging. The clarity offered by multi-perspective visualization is particularly beneficial for complex objects, with potential applications in non-destructive testing, material characterization, and security screening.  
Figure 10: Heat map of range data showing depth variations.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 3 Cyberattack Scenarios on Autonomous Ships
### Artificial Neural Network Attack  
AI has evolved from classical and reactive methods to machine learning and further to deep learning, an advanced, intelligent decision-making algorithm [9]. ANN is the core technology of AI algorithms in autonomous navigation learning from data. In the case of autonomous vehicles, AI algorithms are based on neural networks, such as DNNs, CNNs, and RNNs, which are advanced forms of ANN [37]. As with other software, attacks such as brute forcing, buffer overflow, and malware injection make neural network algorithms insecure [38].  
As shown in Figure 9, apart from attacking the AI model dataset, a neural network attack that attacks the AI algorithm process can undermine AI stability. Allowing neural networks to misperceive and misjudge the real-world environment can be a big problem in safety-critical applications such as autonomous navigation. An attack on ANNs is an attack on automated navigation and berthing systems, a key technology for autonomous ships. If the process of taking sensor data as input and deriving output values is compromised due to an attacker, autonomous ships will not be able to continue normal navigation.  
As neural-network-based algorithms are widely used in high-stake applications, security issues have received widespread attention [39]. Securing the core technologies of autonomous navigation is equivalent to protecting not only the ship but also the environment and humans. [PERSON] et al. [40] proposed AppCon, a novel approach that aims to improve resilience against evasion attacks. [PERSON] et al. [41] studied the feasibility issue of well-established defense techniques against adversarial attacks and proposed guidelines for effective solutions.  
Figure 8: Attack scenario to deceive or degrade sensors.
## 2 Materials and Methods
### Data Processing  
The kinematics and kinetic data were analyzed using the Visual3D software (C-Motion, Rockville, MD, USA). A Butterworth 4 th order zero-phase-shift filter was used to smooth the reflective sphere trajectory and ground reaction force data. The noise was filtered using a 10 Hz low-pass filter [31]. Reflective markers were used to establish the lower limb segment and to determine the instantaneous joint angles (hip, knee, and ankle) during take-off and touchdown. The minimum position of the CoM, CoM \(=\frac{(BAS_{\perp}LAS)+(BS_{\perp}+LSI)}{2}\) and the maximum jump height (the highest point reached by the CoM minus the height of the CoM when the participant stood naturally) were also calculated. The lower limb joint angle of 0\({}^{\circ}\) was defined as the position when the participants were in a neutral standing posture.  
\"Instantaneous take-off angles of lower limb joints\" are the angles of each joint at the moment when the foot leaves the ground during take-off.  
\"Instantaneous touchdown angles of lower limb joints\" are the angles of the joints at the end of the vacancy period, when any foot touches the ground and enters the ground period.  
The kinetic data represent the joint moment at landing. In the landing period, when both feet stepped on the force plates and the ground reaction force reached the first peak, the instantaneous moments of all joints were calculated by inverse dynamics using Visual3D. Considering the weight differences between the participants, these values were normalized by personal weight in Newton-meter/kg (N\(\cdot\)m/kg). In addition, the ground reaction forces from the 1 st and 4 th force plates were divided by the total ground reaction force to calculate the contribution of the braking leg and the jumping leg at landing.  
The wireless EMG system was used to collect the muscle signals of 12 muscles, including the bilateral RA, ES, RF, BF, and TA and the medial GN, at a frequency of 1500 Hz. The signals were band pass filtered at 10-500 Hz and smoothed using the root mean square [27]. The trend of AMP and MDF with time for each participant was calculatedby linear regression to confirm the RA fatigue state [32]. In addition, normalization of the average amplitude of the MVIC was used to observe the degree of muscle activation during the take-off and touchdown stages.
## 5 Numerical Analysis
### System Parameter Optimization  
In this sub-section, we will analyze the influence of link channel on ABER to get the optimal signal parameters of communication link.  
Figures 8 show the variation of bandwidth-limited ABER of the POV carrier link using 4-QAM/256-QAM with a half beam-width of POV, topological charge \(m_{0}\) of initial signal modes and channel length \(z\). In Figure 8a we draw curves of bandwidth-limited ABER of POV communication link as a function of the half beam-width of the POV at the ends of four different channel lengths. Figure 8a,c show that there exists optimal with minimum bandwidth-limited ABER as the function of the channel length (see Table 2). Figure 8b,d indicate that \(\tilde{w}_{opt}\) is related to the OAM topological charge \(m_{0}\) of initial signal modes (see Table 3). Combining the conclusions drawn from Figure 8a-d, we conclude that optimization \(\tilde{w}\) is a function of channel length \(z\) and the OAM topological charge of signal modes, that is \(\tilde{w}_{opt}\). In the subsequent discussion of this paper, we will replace half beam-width with the optimization half beam-width.  
Figure 7: The bandwidth limited ABER of POV carrier link with M-QAM versus the SNR of optical system \(P_{TX}/N_{0}\).
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Results
#### 3.3.4 Experiment 4
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Target Class**} & \multicolumn{8}{c}{**Classification Class**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-11}  & **3** & **10** & **24** & **25** & **30** & **32** & **34** & **36** & **52** & **53** & **59** & **72** \\ \hline
3 & **19** & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 90.48\% \\
10 & 0 & **19** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0 & 90.48\% \\
24 & 3 & 1 & **16** & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 76.19\% \\
25 & 0 & 0 & 0 & **21** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
30 & 0 & 0 & 0 & 0 & **21** & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
32 & 0 & 0 & 0 & 0 & 0 & **21** & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
34 & 2 & 0 & 0 & 1 & 0 & 0 & **18** & 0 & 0 & 0 & 0 & 0 & 85.71\% \\
36 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & **17** & 0 & 0 & 2 & 0 & 80.95\% \\
52 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **21** & 0 & 0 & 0 & 100\% \\
53 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **21** & 0 & 0 & 100\% \\
59 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & **17** & 3 & 80.95\% \\
72 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 7 & **13** & 61.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of true classification of ECG beat data of person. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Target Class**} & \multicolumn{8}{c}{**Classification Class**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-11}  & **3** & **10** & **24** & **25** & **30** & **32** & **34** & **36** & **52** & **53** & **59** & **72** \\ \hline
3 & **3** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
10 & 0 & **3** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
24 & 0 & 0 & **3** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
25 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
30 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\
32 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 0 & 0 & 100\% \\
34 & 0 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 0 & 100\% \\
36 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 100\% \\
52 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 0 & 100\% \\
53 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 100\% \\
59 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **3** & 0 & 0 & 100\% \\
72 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **2** & **1** & 33.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix for true identification of person.
## 2 Materials and Methods
### Extraction Difficulty and Likelihood of IAN Injury Classification Model  
Using the ROI image as input, the classification model predicts the extraction difficulty and the likelihood of IAN injury. To prevent overfitting and improve performance, data augmentations such as horizontal flip, rotation, and left-right, and up-down shifts were used. We used a Vision Transformer, a Transformer-based image classification model that outperforms CNN, which is widely used in existing image classification tasks [48]. Among the various Vision Transformer models, we used R50+ViT-L/32, which is a hybrid Vision Transformer that includes ResNet-50 in ViT-Large. The resolution of the input image is 384 \(\times\) 384 because the Vision Transformer benefits from higher resolutions. The classification models were pre-trained using an ImageNet dataset. The classification models were trained using an Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\), a batch size of 8, and the cross-entropy loss function. The model was trained for 250 epochs, each of which was evaluated. The best performing model was selected using the evaluation set, and the final results were calculated using the test set.
## 3 Settings for Prediction Accuracy Tests
### Design of Experiment  
Constructing a surrogate model to discover the relationship between the input and output variables requires the value of the output variable at various sets of the input points. One crucial point here is how to efficiently represent the infinite space of input variables with a finite amount of data. There are many studies regarding the ways to create datasets inside input variables' space in the design of experiment step such as the D-optimal and factorial methods [46; 47]. Among them, the methods used in the current paper for the prediction accuracy comparison of surrogate models are the face-centered composite design (FCCD) method [48] and the discrete \(m\)-level-based method, which equally distributes \(m\) points at each input parameter [7]. The FCCD method is applied for its advantage of covering the whole space of input variables with a small number of data, while the discrete level-based method is chosen to test how prediction accuracy changes with respect to the number of datasets because a different number of datasets can be created in the discrete \(m\)-level-based method if the value of \(m\) is set differently, even for the same degree of freedom prediction accuracy test. (Note that the total number of datasets is equal to \(2^{n}+2n+1\) at the \(n\) degree of freedom prediction accuracy test in the FCCD method, but it becomes \(m^{n}\) when the discrete \(m\)-level-based method is used.) In the present paper, the different \(m\) values are used for the two types of prediction accuracy tests. In the prediction accuracy tests with geometric variables, the discrete 3-level-based method  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variables** & **Control Points** & **Ranges** \\ \hline Variable 1 & \(\mathbf{p}_{7}\) & \(-0.03\leq\Delta y\leq 0.055\) \\ Variable 2 & \(\mathbf{p}_{8}\) & \(-0.035\leq\Delta y\leq 0.095\) \\ Variable 3 & \(\mathbf{p}_{2}\) & \(-0.015\leq\Delta y\leq 0.12\) \\ Variable 4 & \(\mathbf{p}_{3}\) & \(-0.08\leq\Delta y\leq 0.045\) \\ Variable 5 & \(\mathbf{p}_{6}\) & \(-0.015\leq\Delta y\leq 0.02\) \\ Variable 6 & \(\mathbf{is used, while the discrete 3-, 4- and 5-level-based methods are used for the tests with geometric and operational variables. The higher discrete-level-based methods are tested in the latter types of tests because the aerodynamic performance (measured by lift-to-drag ratio here) of a wind turbine airfoil shows more complicated associations with the input variables than the one in the former case, requiring more data to build the surrogate models with reasonable accuracy.
## 4 Prediction of Distribution of Drones
### Model Training and Testing  
The latitude, longitude, altitude, speed in the \(x\), \(y\), and \(z\)-axis, heading angle, pitch angle, and roll angle of the drone are input into the neural network. The outputs are the predicted longitude, latitude, and altitude of the drone. The specific steps are as follows:  
Step 1: Data processing. The collected drone trajectory data are processed using a sliding window, and spatial reconstruction and normalization are performed to construct samples that can be input into the QDRGRU. Assuming that the number of drone trajectory data is \(k\), the input of a sample is a matrix of \(k\times 9\), for which the \(i\)-th row of \(X\) is \((x_{i},y_{i},z_{i},v_{xi},v_{yi},v_{zi},\varphi_{i},\theta_{i},\gamma_{i})\)  
Figure 3: Structure of the QRDGRU model.  
and the target matrix corresponding to \(X\) is \([x_{i+k+s},y_{i+k+s},z_{i+k+s}]\), where \(s\) is the prediction timestep.  
Step 2: Network training. The processed drone trajectory data set is divided into the training set and testing set. The training set is input into the QRDGRU\({}_{i}\) under different quartiles, and the optimal weight and bias parameters are obtained. The validation set is input into the trained QRDGRU\({}_{i}\). The hyperparameters of the network, such as the number of iterations and the number of neurons, are determined in the network.  
Step 3: Predict the drone location distribution. Based on the constructed QRDGRU\({}_{i}\) with determined hyperparameters, the outputs including latitude, longitude, and altitude are obtained under each quartile. The outputs are denormalized to obtain the predicted value of the QRDGRU\({}_{i}\). Then, the predicted position distribution of the drone is formed.  
Step 4: Model evaluation. Compare the predicted location distribution of the drone with the actual location for model evaluation.  
The experimental environment configuration is the same as that of the manned aircraft. It is finally determined that three hidden layers are used, the number of nodes in the input layer is nine, and the number of nodes in the output layer is three. The activation function is \"ReLU\". The learning rate is set to 0.0001. The number of training iterations is set as epoch = 100. For each batch, ten samples and nine feature variables are trained, generating time series data \(T\) for each variable. Therefore, the actual input data of a batch of samples are three-dimensional vectors with the shape \(B\times N\times T\), where \(B\) is the number of batch samples, \(N\) is the number of features, and \(T\) is the timestep.  
The hyperparameters of the network are determined according to MAE, MAPE and RMSE, including the number of neurons in a single layer \(c\) and the timestep \(T\). It is finally determined that the number of neurons in a single layer is 80, and the timestep of the QRDGRU model is \(T\) = 25.
###### Abstract
The proposed MalwD&C approach is a lightweight and efficient solution for detecting and preventing the installation of malware in PE files on Windows-based computer systems. The proposed approach uses machine learning techniques to learn from large datasets of benign and malicious software programs and accurately classify new files as benign or malware. The MalwD&C approach is designed to serve in two scenarios: malware detection and categorization. In the malware detection scenario, the proposed approach processes the download.exe file, extracts the representative features and applies machine learning classifiers to predict the file's state (benign or malware). In the malware categorization scenario, the proposed approach predicts the type of the predicted malware and classifies it into one of the 14 families. The proposed MalwD&C approach achieved high accuracy, with a maximum of 99.56% for the malware detection scenario and 97.69% for the malware categorization scenario on a publicly available (BODMAS) [14] dataset. Overall, MalwD&C is a promising solution for detecting and preventing the installation of malware in PE files on Windows-based computer systems. The main contributions of this paper are as follows:  
* The proposal of an accurate, lightweight, and quick machine-learning-based approach, MalwD&C, for detecting and categorizing PE malware on Windows-based computer systems;
* An experimental validation methodology on a publicly available dataset to evaluate the feasibility and effectiveness of the proposed MalwD&C approach in detecting and categorizing PE malware;* The identification of important features and their impact on the performance of MalwD&C in detecting and categorizing PE malware;
* An evaluation of multiple classifiers in terms of training and decision-making time, providing insights into the performance and efficiency of MalwD&C. This is the first study to report such results to the authors' knowledge. Overall, the proposed MalwD&C approach offers a promising solution for detecting and preventing the installation of malware in PE files on Windows-based computer systems. Paper organization  
The rest of the paper is organized as follows: Section 2 presents the related work. Section 3 explains the basic idea of our approach. Section 4 presents the methodology we adapted for our solution. Section 5 reports and discusses the evaluation results. Section 6 concludes the paper with a summary of our major findings and the potential future research directions.