esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 3 Proposed Research Methodology
### Support Vector Machine  
Regression analysis and classification are two applications for the Support Vector Machine (SVM) technique. The method uses labeled data as input and searches for the optimum hyperplane to maximize the margin--the distance between the hyperplane and the closest data points from each class--while separating the various classes [29].  
SVM can handle complex datasets very well since it can determine the decision boundary that maximizes the separation between distinct classes. Additionally capable of handling high-dimensional data, SVM may separate data that is not linearly separable in the original space by transforming the input data into a higher-dimensional space using a variety of kernel functions. The following are SVM's primary features:  
* Maximum margin: SVM seeks to identify the hyperplane with the greatest margin between classes. The gap between each class's nearest data points and the hyperplane is known as the margin. Through margin maximization, SVM may minimize overfitting and improve generalization performance.
* Kernel trick: To separate data that is not linearly separable in the original space, SVM can apply various kernel functions to transform the input data into a higher-dimensional space. SVM can handle complex datasets thanks to a technique called the kernel trick.
* Robustness against outliers: Because SVM only takes into account data points that are closest to the decision border, it is less susceptible to outliers than other classification methods.
* Binary classification: SVM is limited to classifying data into two groups because it is a binary classification system. It can, however, be expanded to address multi-class classification issues by utilizing strategies like one-vs-all or one-vs-one.  
SVM is known for its versatility and effectiveness in handling complex decision boundaries. This can enhance the accuracy of the detection of a complex data pattern. Theattractive characteristics of SVM have been demonstrated in the number of IDS developments [25; 26], particularly those using the intrusion detection's AWID dataset [30].
# High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
## 2 HATS for Atoms
### THz Benchmark for the Long and Short Trajectories of HHG
Intuitively, recombined electrons dominate the emission of high harmonics, thus the amount of the escaping and forward-rescattered electrons decreases, leading to the reduction of the electronic current for THz generation. As shown in Figure 5a and discussed in Ref. [67], the phase for the maximal THz intensity varies when the gas jet is positioned around the laser focus. Here, each subplot in Figure 5a labeled by the position of the gas jet is normalized to its absolute value of the maximal THz amplitude under this specific gas jet position. Each spectrum along the horizontal direction within each subplot indicates the THz waveforms under different relative phases of the dual-color field when the gas jet is placed at the specific position relative to the laser focus. The dual-color phase dependent THz yields allow the reconstruction of the Gouy phase, which comes from the propagation of the focusing laser [68]. The Gouy phase shift also has an influence on the optimal phases of HHG, which can be found in Figure 5b-d. Note that the relative phase in Figure 5 does not correspond to the value of the absolute phase between the fundamental and the second harmonic pulse, while the absolute phase could be determined by the optimal phases of HATS (as demonstrated in Section 2.1). There exist two advantages in detecting the long and short trajectories of the electrons from HATS. On the one hand, the phase shift caused by the focusing of laser, such as the Gouy phase shift, could be eliminated straightforward when comparing to the dual-color relative phase dependent HATS, as illustrated in Figure 4c,e. On the other hand, dynamics of electrons contributing to the long and short trajectories for HHG could be revealed by the phase reference provided by the THz modulation, because the classical trajectories can be mainly categorized according to their ionization times. Figure 4: The modulations of high harmonics and THz yields with strong second harmonic (intensity ratio about \(A_{2}^{2}/A_{1}^{2}=0.1\) (**a,b**)) and weak second harmonic (\(A_{2}^{2}/A_{1}^{2}=0.005\)) (**c–f**). The laser condition of (**c,d**) is for phase-matching condition of the gas jet, while the gas jet is positioned closer to the focus of the laser in (**e,f**). Reproduced with permission from [59]. Copyright Springer Nature Publishing, 2016.
## 2 Related Work
### Embedding Method of TLE
In the second embedding layer, the TLE method calculates the second prediction error \(e_{2}=x_{c}^{(1)}-p_{2}\), and then uses Equation (3) to hide the secret bit \(s_{2}\in\{0,1\}\) into \(x_{c}^{(1)}\). \[x_{c}^{(2)}=\left\{ \begin{array}{ll}x_{c}^{(1)}-s_{2},&\text{ if }e_{2}=-1\\ x_{c}^{(1)}-1,&\text{ if }e_{2}<-1\\ x_{c}^{(1)},&\text{ if }e_{2}>-1\end{array} \right. \tag{3}\]  
where \(x_{c}^{(2)}\) is the stego pixel value after embedding in the second layer. The TLE method aims at the prediction error \(e_{1}=1\), and then the pixel value \(x_{c}\) on the original \(I_{H\!S\!B}\) image will add \(s_{1}\). If the prediction error \(e_{2}=-1\), then the pixel value on the \(I_{H\!S\!B}\) image will be the intermediation value \(x_{c}^{(1)}\) subtracting \(s_{2}\) after the first layer of the embedding procedure. In the TLE strategy, the design predictor \(p_{2}\) is always greater than the predictor \(p_{1}\), so the predictor (\(p_{2}\)) embedded in the second layer can be modified to \(p_{2}^{\prime}\) by Equation (4):  
\[p_{2}^{\prime}=\left\{ \begin{array}{ll}p_{2}+1,&\text{ if }p_{2}=p_{1}\\ p_{2},&\text{ otherwise}\end{array} \right. \tag{4}\]  
Step 7. The pixel of \(I^{\prime}_{H\!S\!B}\) is converted to binary, which is the original \((b_{7},\ \ldots,b_{n+2},b_{n+1},\)\(b_{n})\) and pixel of \(I_{LoS\!B}\) converted to binary, which is the original \((b_{n},b_{n-1},\ldots,b_{0})\) combined into a stego pixel, until all the pixels are combined into a gray-scale stego image \(I^{\prime}\). Step 8. Steps 2 to 7 are repeated to perform the TLE embedding method on the white pixel of \(I_{H\!S\!B}\). Some auxiliary information is needed. The auxiliary information includes the following: \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Predictors**} & **Predictor Methods** & **1** & **2** & **3** \\ \hline \(p_{1}\) & \(x_{\pi(1)}\) & \(\left\lfloor\frac{x_{\pi(1)}+x_{\pi(2)}+x_{\pi(1)}}{3}\right\rfloor\) & \(\left\lfloor\frac{x_{\pi(1)}+x_{\pi(2)}}{2}\right\rfloor\) \\ \(p_{2}\) & \(x_{\pi(4)}\) & \(\left\lfloor\frac{x_{\pi(2)}+x_{\pi(3)}+x_{\pi(5)}}{3}\right\rfloor\) & \(\left\lfloor\frac{x_{\pi(5)}+x_{\pi(5)}}{2}\right\rfloor\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three different predictor methods. Location map (LM): To avoid the overflow/underflow problem and to lossless extract the secret message and restore the image recovery, the following Equation (5) is used to pre-process the image to generate a location map (LM). If \(\ M1N\leq x_{c}\leq MAX\) where \(MAX=2^{(8-nt)}-1\) and \(MIN=0\), the location map \(LM_{i,j}\) is represented by 0; if not \(MAX\) or \(MIN\), \(LM_{i,j}\) is represented by 1. \[x_{i,j}=\left\{ \begin{array}{ll}x_{i,j}-1,&\text{if }x_{i,j}=MAX\\ x_{i,j}+1,&\text{if }x_{i,j}=MIN\\ x_{i,j},&\text{otherwise}\end{array} \right.\text{ and }\ \tag{5}\]  
\[LM_{i,j}=\left\{ \begin{array}{ll}0,i\text{ }x_{i,j}=MAX\text{ or }MIN\\ 1,otherwise\end{array} \right.\text{ }\]
## 4 Results and Discussion
### Comparison with State-of-the-Art Methods  
Demonstrating the model performance against the best available approaches is essential for performance evaluation. In classifying brain tumors, the performance of the proposed model is highlighted by a comparison with state-of-the-art methods. Such a comparison helps to determine whether the model is superior in critical aspects such as accuracy and explainability. The architectures of the models, the type of classification, the visualization of decisions with explainability method, and the accuracy rate are the criteria taken into consideration for comparing the CNN + XGBoost model with the state-of-the-art methods. Table 10 contains information about the methods according to these criteria. Most models did not use any XAI method for the explainability of the classification results. While the SVM model has the lowest accuracy rate, with 96.67%, among the other models the proposed CNN + XGBoost model has the highest accuracy rate, with 99.77%. The proposed model stands out from other models not only in terms of accuracy but also in terms of explainability using Grad-CAM. The EfficientNetB0 model fell behind the proposed model in terms of performance, with 98.72% accuracy when using Grad-CAM. The model that performed the closest to the proposed model was the NeroNet19 model, which was supported by the LIME explainability approach and had an accuracy rate of 99.30%. Therefore, by using Grad-CAM to visualize the decision mechanism, it not only achieves the best accuracy rate but also increases the model's reliability, making it the most successful model.
## 2 Materials and Methods
### Study Selection and Data Collection  
The study selection process involved several stages to ensure the inclusion of relevant and high-quality studies:  
* **Initial Screening**: Titles and abstracts of the retrieved articles were screened to exclude irrelevant papers. * **Full-Text Review**: The full texts of the remaining articles were reviewed to confirm their relevance and adherence to the eligibility criteria. * **Data Extraction**: Data from the selected studies were systematically extracted using a predefined data extraction form. The extracted information included study characteristics (e.g., author, year, publication type), research objectives, methodologies, key findings, abstracts, and conclusions. The authors independently screened the titles and abstracts, concentrating on the eligibility criteria outlined in Section 3.1. Any studies the authors disagreed upon were included in the review process. In total, twenty-seven (27) studies were included in this review, providing a robust dataset for analyzing the factors contributing to user susceptibility to phishing emails. Table 1 presents the selected studies, including their titles, publication years, and the user characteristics/factors that investigated in each study. Our contribution aims to extend the existing research by challenging the provided results, exploring additional samples, and conducting phishing campaigns and surveys to gather further insights. This approach not only synthesizes the current state of research but also enhances the understanding of phishing susceptibility through empirical evidence and practical applications. The primary criterion for the selection of articles was the timespan, with a particular focus on research conducted within the past decade. This period is of significant relevance due to the rapid advancements in the field of phishing email cyberattacks, which have evolved considerably over the years. Additionally, the increasing digitalization of everyday life has made cyberthreats, particularly phishing emails, more prevalent and impactful. These developments provide a solid foundation for the present research. Furthermore, the global COVID-19 pandemic has exacerbated the prevalence of malicious online activities, further emphasizing the need for timely and effective countermeasures in the field of cybersecurity. Articles that did not include the search terms in their title, abstract, or keywords were excluded during the initial filtering process. Lastly, articles published in languages other than English were excluded from the review, as English is the primary language of most prominent journals and conferences in the field.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 4 Ongoing Horizon Europe Projects Related to JPSAC  
Related to the emerging topics in JPSAC areas, we detail the ongoing research projects in Europe. In particular, the JPSAC-related research areas have garnered a particularly high level of interest in European research, and many projects, especially MSCA training networks, are working to address the challenges of ISAC and radio SLAM. We have summarized the ongoing European research in Table 3. All projects listed in Table 3 are ongoing Horizon Europe projects as of January 2025. It can be easily seen from this table that the key research areas listed in Table 3 correspond to most of the emerging topics discussed in Section 3. The international landscape of research and development projects is likely to resemble research developments in Europe to a significant degree, but we have not detailed it here due to a lack of user-friendly country-agnostic search interfaces for research projects at an international level. As an illustrative and partial comparison of EU JPSAC-related research with the research recently funded by the United States National Science Foundation (NSF) ([[https://www.nsf.gov/awardsearch/](https://www.nsf.gov/awardsearch/)]([https://www.nsf.gov/awardsearch/](https://www.nsf.gov/awardsearch/))), recent NSF themes include: learning-assisted integrated sensing, communication, and security for 6G Unmanned Aerial Vehicles (UAV) networks, dedicated waveform synthesis and performance assessment for ISAC, and AI-assisted waveform and beamforming design for ISAC.
# Vision Systems for a UR5 Cobot on a Quality Control Robotic Station
## 3 Vision-Based Measurement Algorithm
### Camera Calibration and Camera-to-Robot Calibration
#### 3.2.1 Camera Calibration  
The first step before image preprocessing is the process of calibrating the camera to remove image distortions caused by the optical path. For this purpose, the function cv.calibrateCamera() available from the OpenCV [63; 64; 65] libraries was used. For calibration, a chessboard-type calibration board was provided with the following dimensions: 270 \(\times\) 240 mm for the USB camera and 150 \(\times\) 150 mm for the Mako camera. Below (Figure 12a,b), the determined values of reprojection errors for individual calibration photos are presented. On this basis, photos that did not meet the assumed reprojection error threshold were rejected, and the matrices of internal and external parameters were re-calculated. The determined matrices and distortion vector values for both cameras are presented below.  
Mako camera--values of the estimated camera intrinsic matrix and distortion vector (radial and tangential).  
\[\text{Distortion Coefficients}=[-0.27496,\,4.08649,\,-0.00045,\,-0.00277,\,-0.00 043] \tag{1}\]  
\[\text{Camera intrinsic matrix}=\begin{bmatrix}4862.88707&0&592.17219\\ 0&4858.84719&437.38086\\ 0&0&1\end{bmatrix} \tag{2}\]  
Figure 11: Developed semi-finished measuring products with marked dimensions: (**a,b**) scheme/ engineering drawing, (**b**) features of the semi-finished product analyzed during measurements: 1–4: hole diameters; 5: outer diameter of the part; (**c**) image of the real element. Semi-product thickness: 5 mm.  
Figure 10: Semi-finished products developed for the purposes of functional testing of the stand (with reference dimensions marked during the stand’s operation). Semi-finished product thickness: 5 mm.  
Savio camera--values of estimated camera intrinsic matrix and distortion vector (radial and tangential).  
\[\text{Distortion Coefficients}=[-0.37186,0.13426,-0.00247,-0.0007,-0.02940] \tag{3}\]  
\[\text{Camera intrinsic matrix}=\begin{bmatrix}502.57943&0&313.29807\\ 0&502.95521&196.46255\\ 0&0&1\end{bmatrix} \tag{4}\]
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
## 3 Experimental Observations and Test Results
### Bearing Resistance and Deformation  
Figures 6 and 7 show the load-displacement curves of the specimens with varying end distances and edge distances, respectively. The curves of the load-displacement relationship in Figures 6 and 7 omit any possible slip phase and assume that the bolt bears directly against the back of the hole from the start of loading.  
The load-displacement curves in Figures 6 and 7 have an initial elastic region followed by a nonlinear hardening region. For the curves in Figure 6, the peak point of each curve was reached when the stress on the shear planes attained the ultimate strength of the steel material. The bearing resistance of each specimen is termed as \(F_{\text{u}}\), and \(\delta_{\text{u}}\) is the deformation, corresponding to \(F_{\text{u}}\). Both \(F_{\text{u}}\) and \(\delta_{\text{u}}\) increased with an increase in the end distance. For the curves in Figure 7, both \(F_{\text{u}}\) and \(\delta_{\text{u}}\) increased with an increase in the edge distance when the edge distance was no more than \(1.5d_{0}\). Beyond \(1.5d_{0}\), an increase in the edge distance had little effect on the bearing resistance of the specimens. The measured \(F_{\text{u}}\) and \(\delta_{\text{u}}\) are summarized in Tables 3 and 4.  
Figure 5: Failure modes of 10 mm thick specimens with different edge distances.  
Figures 8 and 9 present the relationships of the ultimate load versus the end distance and the ultimate load versus the edge distance, respectively. Figure 8 shows that, with an adequate edge distance, the bearing resistance was linearly proportional to the end distance when the end distance was in the range of \(1.0d_{0}\)-\(2.0d_{0}\). The scale factor between the bearing resistance and the end distance decreased when the end distance was increased to \(2.5d_{0}\). This may be caused by the decreased area of the shear planes after the fracture. Figure 8 shows that the bearing resistance of the specimens increased when the edge distance was increased from \(1.0d_{0}\) to \(1.5d_{0}\). Beyond \(1.5d_{0}\), the bearing resistance of the specimens stayed constant. Given the same end distance of \(1.5d_{0}\), the failure modes of a net-section fracture and shear-out were observed for the specimens with an edge distance of \(1.2d_{0}\) and an edge distance of \(1.5d_{0}\), respectively, and the bearing resistance increased by \(15\%\) when the edge distance was varied from \(1.2d_{0}\) to \(1.5d_{0}\).  
Figure 6: Load–displacement curves of specimens with varying end distances.  
Figure 7: Load–displacement curves of specimens with varying edge distances.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 4 Case Study
### Model-Based Vulnerability Assessment for the Human-Robot Collaborative Assembly System  
Our criticality study consists of two steps: (i) a criticality calculation using defined dependence rules and Equation (1), and (ii) a conversation with stakeholders to determine the weighting of criticality.  
In alignment with the US-CERT [66] asset management guidelines, We first identified assets and then evaluated the critical components within the HRC assembly system. We rank these components by their criticality, determined by how functionally dependent other components are on them. The top five critical components identified include _HRC_MainController_UnitController_ with a functional dependency (FD) score of 6, _HRC_Router_ and _HRC_MainController_OperatingSystem_, each with an FD score of 5, _HRC_MainController_DockerEngine_ with an FD score of 4, and _HRC_MainController_Drag&Bot_ with an FD score of 2. To validate and further refine the criticality of these components, we consulted with project members from _SYMBIO-TIC_. In addition to the initially identified components, they emphasized the significance of the physical PLC gripper and robot, as well as the data components exchanged among _RobotStudio_, _Drag&Bot_, _UnitController_, and _Cockpit_, underscoring a comprehensive view of system criticality.  
We collected configuration and component information for the HRC assembly system and subsequently formulated queries for our localized vulnerability database, focusing on 14 essential components as illustrated in Figure 9. This process yielded 41 documented vulnerability instances up to 24 February 2024, categorized into 5 critical, 30 high, and  
Figure 9: Human–robot collaborative system that integrates _SYMBIO-TIC_.  
6 medium severity vulnerabilities. Note that, here, we count vulnerabilities by their unified (_CVE_)-_IDs_.  
Table 1 presents the criticality levels, incorporating factors such as functional dependencies, the total number of identified vulnerabilities, and their average severity scores.  
In the APP layer, Docker containers bundle program codes and dependencies. A container is reasonably separated from other containers and its host system. Therefore, databases are not shared between computers. Nonetheless, several known Docker vulnerabilities, such as the container breakout vulnerability, allow an attacker to further exploit confined software through a backdoor. Table 1 suggests that the operating system of the _MainController_ can be given the highest prioritization.
## 2 Mechanism of Low-Frequency Pressure Fluctuation
### Resonance Mechanism of Low-Frequency Pressure Fluctuation
#### 2.2.1 Circuit and Helmholtz-Type Resonance  
[PERSON] et al. [7] presented a study on the suppression of low-frequency pressure fluctuations in the HAWT wind tunnel in South Korea. The results demonstrated excellent agreement between the measured frequency of the low-frequency pressure fluctuation and the theoretically calculated frequency of the circuit acoustic mode of a certain order, indicating that one of the modes within the wind tunnel circuit is responsible for exciting the low-frequency pressure fluctuations.  
The study conducted by [PERSON] et al. [26] identified three dominant frequencies in a 1:20-scale wind tunnel of the Audi Aeroacoustic Wind Tunnel (AAWT). Two of these frequencies were attributed to the circuit modes of the wind tunnel, while the third frequency corresponded to the Helmholtz frequency of the plenum. In contrast, three circuit modes were observed in the full-scale AAWT, two of which corresponded to those found in the scaled wind tunnel.  
The study conducted by [PERSON] et al. [50] also revealed that the edgetone feedback resonated with one of the modes presented in the pipeline circuit of the Jilin University open-jet wind tunnel.