esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Post-Event Recovery and Summarizing  
After a deliberate attack occurs, some functions of IECPS are damaged, and the primary task of the system is to repair the compromised functions. To ensure precise implementation of the repair work, a comprehensive assessment of the damage caused by the attack is required to identify which physical infrastructure and information components need to be rebuilt. Once the system functions are restored, the next step is to investigate the method of the attack, deeply analyze the system vulnerabilities exploited by the attacker, and utilize this analysis as a breakthrough to further uncover other potential vulnerabilities within the system. Finally, a comprehensive evaluation of the defense measures is required to identify any weaknesses in the defense process and to implement corresponding improvements, thereby optimizing and enhancing the overall defense capability of the system.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 2 EMS Measurement and Prediction
### Proposed Method  
The data obtained through the EMS test has different formats and variation rules. When the UAV's data link is subjected to different types and intensities of EMI, its state parameters will be disturbed. Besides, the amplitude and phase characteristics of different EMI signals are significantly different, which can be analyzed by obtaining the I/Q signal.  
By visualizing the obtained heterogeneous data as the model input, the interpretability of the data is enhanced, which is in line with the visual sensory understanding of humans. Besides, a single image contains more effective information and has a strong noise immunity, which can improve the accuracy of the model. Therefore, the MIMT-CNN method is proposed in this paper. The output of the MIMT-CNN acts simultaneously as EMI signal identification and electromagnetic threat assessment. As shown in Figure 3, the proposed method includes the following steps:  
1. The original data are obtained through the EMS injection experiment, including the state parameters of the data link and the I/Q data of the received signal by the data link in the presence of interference.
2. The state parameters of the data link are converted to visualized performance parameters (VPP). Meanwhile, the I/Q data are transformed to STFT spectrograms and normalized density constellation (NDC), which denotes the time-frequency and phase information of the EMI signal, respectively.
3. The MIMT-CNN is constructed and trained on the training set. By using the Bayesian optimization, the hyperparameters of the network are optimized on the validation set.
4. The trained network is tested on the test set. According to the actual results of EMI signal classification and threat level prediction, the accuracy and generalization ability of the model are evaluated.  
Figure 3: Flow chart of proposed method.
## 5 Open-Loop Simulation
### Real-Time Performance  
This section evaluates the real-time performance of the DNN-LCvx algorithm by comparing the elapsed time of the proposed DNN-LCvx algorithm and the standard SCvx algorithm. The initial state variables are shown in Table 2. The discrete number is \(N\) = 30, and the general ECOS solver is used to solve the SOCP. The elapsed time and other information obtained from the test are shown in Table 5.  
It can be found that the DNN-LCvx has good real-time performance. In this example, the DNN-LCvx algorithm is 12.0 times faster than the SCvx algorithm, which is mainly caused by two factors:  
1. SCvx adopts the idea of solving problem in sequence, so it needs to solve sub-SOCPs many times, which significantly increases the elapsed time, while DNN-LCvx can be completed only once.  
Figure 16: Influence of the discrete precision on terminal mass.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **DNN-LCvx** & **SCvx** \\ \hline Elapsed time (ms) & 6.5 & 78.0 \\ Iterations & 1 & 5 \\ Elapsed time per Iteration (ms) & 6.5 & 15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of real-time performance between the SCvx and DNN-LCvx.  
In order to prevent the failure of the linearization and the artificial infeasibility, the SCvx algorithm needs to introduce a large number of trust region constraints and virtual control variables, thus increasing the complexity of the sub-SOCP.  
In summary, the DNN-LCvx algorithm proposed in this paper has excellent real-time performance compared with traditional algorithms. Next, we discuss the impact of the discrete number on real-time performance. The variation of the elapsed time with the discrete number is plotted, as shown in Figure 17. If the discrete number is the same, the computation speed of DNN-LCvx is about an order of magnitude higher than SCvx.
## 4 Error Performance Analysis
\[f(g)= \frac{(\alpha\beta)^{\beta}}{\Gamma(\alpha)\Gamma(\beta)}g^{\beta-1} \Bigg{[}\Gamma(\alpha-\beta)\Bigg{(}\sum_{k=0}^{\infty}\frac{\sqrt{\alpha\beta}g ^{2k}}{(1-v)_{k}k!}\Bigg{)}\ +\Gamma(\beta-\alpha)\Bigg{(}\sum_{k=0}^{\infty}\frac{\sqrt{\alpha\beta}g^{2k}}{ (v+1)_{k}k!}\Bigg{)}\Bigg{]} \tag{13}\] \[= \sum_{k=0}^{\infty}\frac{(\alpha\beta)^{k+\beta+1}}{\Gamma(\alpha )\Gamma(\beta)}\frac{\Gamma(\alpha-\beta)}{(1-v)_{k}k!}s^{k+\beta-1}+\sum_{k=0 }^{\infty}\frac{(\alpha\beta)^{k+\beta+1}}{\Gamma(\alpha)\Gamma(\beta)}\frac{ \Gamma(\beta-\alpha)}{(v+1)_{k}k!}s^{k+\beta-1}\] \[= \sum_{k=0}^{\infty}g_{1}(k)g^{k+\beta-1}+\sum_{k=0}^{\infty}g_{2 }(k)g^{k+\beta-1}\]  
\[ \begin{split}&\text{P}\big{(}o_{ij}|G\big{)}=\int_{0}^{\infty}f(g)Q \Bigg{[}\frac{\sigma^{2}\ln\frac{p_{X}(v_{i})}{p_{X}(v_{j})}+\left\|g(x_{i}-x_ {j})\right\|^{2}}{\sqrt{2\sigma^{2}}\left\|g(x_{i}-x_{j})\right\|^{2}}\Bigg{]} dg\\ &=\int_{0}^{\infty}\Bigg{(}\sum_{k=0}^{\infty}g_{1}(k)g^{k+\beta -1}+\sum_{k=0}^{\infty}g_{2}(k)g^{k+\beta-1}\Bigg{)}\left(\frac{1}{\Gamma 2}e^{-\zeta}e^{-\frac{1}{2}\left(\frac{\beta}{\sigma^{2}}+vg^{2}\right)}+ \frac{1}{4}e^{-\frac{4}{4}\zeta}e^{-\frac{2}{3}\left(\frac{\beta}{\sigma^{2} }+vg^{2}\right)}\right)dg\\ &=\sum_{k=0}^{\infty}\left[g_{1}(k)+g_{2}(k)\right]\left[\frac{1 }{\Gamma 2}e^{-\zeta}\mu^{\frac{\beta+k}{4}}v^{\frac{1}{2}(-\beta-k)}K_{\frac{\beta+k} {2}}(\zeta)+\frac{1}{4}e^{-\frac{4}{4}\zeta}\mu^{\frac{\beta+k}{4}}v^{\frac{ 1}{4}(-\beta-k)}K_{\frac{\beta+k}{2}}\left(\frac{4}{3}\zeta\right)\right]\end{split}  \tag{14}\]
# Aerodynamics Analysis of Speed Skating Helmets: Investigation by CFD Simulations
## 6 Results and Discussions
### Drag for the Bare-Head Case  
Table 1 presents both the total aerodynamic drag and the breakdown in terms of static pressure and friction of the bare head skater configuration. It is shown that the static pressure is the main contributor to the total drag of the skater, which is typical for bluff bodies at similar Reynolds numbers [35].  
Figure 10 presents the drag area breakdown of the individual body segments of the bare-head skater configuration in order to better understand where the drag is generated (note that \(HHN\) stands for head-helmet-neck). The individual body segments are illustrated in Figure 1b. The skater's total drag area without a helmet is \(AC_{D,skater}=0.1320\) m\({}^{2}\). The main contributors to the skater's total drag area are the legs, in fact, the upper left and lower right leg, which correspond to the regions of significant streamwise velocity deficit observed in Figure 9a. These body segments are oriented almost vertically, thus, having a large frontal area. In addition, their cross-sectional profile is similar to the one of a cylinder, leading to a wide separation region. Therefore, they induce a strong drag force. The next most relevant segment is the back followed by the HHN. The relative contribution of these body segments to the aerodynamic drag is similar to that of a cyclist [13].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & \(C_{D}[-]\) & \(C_{D,p}[-]\) & \(C_{D,f}[-]\) & \(C_{D,p}[\%]\) & \(C_{D,f}[\%]\) \\ \hline Bare-Head & 0.529 \(\pm\) 0.002 & 0.505 & 0.024 & 95.4 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drag coefficient characteristics of the entire skater without helmet as well as friction and static pressure drag breakdown.  
Figure 9: Distribution of (**a**) streamwise velocity and (**b**) vorticity with in-plane velocity field vectors at a vertical plane, \(x\sim\) 0.85 m distance downstream from buttocks.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.3.2 Standard Deviation and Confidence Interval  
To ensure the statistical significance and reliability of our experimental results, we compute the Standard Deviation (SD) and Confidence Intervals (CI) for the accuracy and F1-Score metrics. SD is a measure of the amount of variation or dispersion of a set of values:  
\[\text{SD}=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(x_{i}-\bar{x})^{2}} \tag{20}\]  
\(N\) is the number of observations, \(x_{i}\) represents each individual observation, and \(\overline{x}\) is the mean of the observations.  
CI provides a range within which to expect the true value of a metric to lie with a certain level of confidence. For instance, the 95% and 99% CIs for a metric are calculated as follows:  
\[\text{CI}=\bar{x}\pm z_{\alpha/2}\cdot\frac{\text{SD}}{\sqrt{N}} \tag{21}\]  
\(z_{\alpha/2}\) is the critical value from the standard normal distribution for a given \(\alpha\). The 95% (\(z_{0,025}=1.96\)) and 99% (\(z_{0,005}=2.576\)) critical values are used in the experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Metric** & **Warmup Steps** \\ \hline RQ1 & Accuracy, F1-Score & Evaluate the performance of the proposed model. \\ \hline RQ2 & F1-Score & Evaluate the effectiveness of the prompt template and modulation layer. \\ \hline RQ3 & Accuracy, F1-Score, Standard Deviation, Confidence Interval, Relative Improvement Rate & Evaluate the generalization of the proposed model in low-resource scenarios. \\ \hline RQ4 & Accuracy, F1-Score, Confidence Interval, Relative Decline Rate & Evaluate the generalization and robustness under the noisy training data conditions. \\ \hline RQ5 & F1-Score, Standard Deviation & Performance evaluation for various historical BERT checkpoints. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics for the experiments of research questions.  
The SD and CI are calculated for all parallel tests in the experiments to examine the fluctuation (dispersion) in the curves of the test results, and the robustness and generalization of the LePB-SA4 RE model across different noise conditions and datasets are validated.
## 2 Study Area and Data Sources  
### Study Area  
Jincheng City, a prefecture-level city, is located in the southeastern region of Shanxi Province. Its geographical coordinates span from \(112^{\circ}45^{\prime}10^{\prime\prime}\) E to \(112^{\circ}55^{\prime}08^{\prime\prime}\) E in longitude and from \(35^{\circ}24^{\prime}55^{\prime\prime}\) N to \(35^{\circ}35^{\prime}45^{\prime\prime}\) N in latitude. The city is surrounded by mountainous landscapes and features a relatively flat terrain in its central area (refer to Figure 1). Jincheng stands as a pivotal anthracite coal base in China, with its coal-bearing area encompassing 49% of the total territorial area. Its annual anthracite coal production comprises nearly 50% of the national aggregate output. Furthermore, Jincheng boasts over 110 active coal mines, predominantly located in Yangcheng, Zezhou, Gaoping, and the urban center, collectively contributing a substantial 93.3% to the city's total coal production capacity.  
Jincheng City, with a history of over 50 years of large-scale coal mining, is currently confronted with a series of severe environmental issues resulting from high-intensity mining activities. These issues primarily encompass ground subsidence and collapse, reduction in and pollution of water resources, as well as the accelerated degradation of vegetation and soil erosion [46; 47]. In this context, the efficient employment of remote sensing technology in assessing and monitoring the ecological environmental quality of Jincheng City is crucial in fostering the sustainable development of this resource-intensive urban landscape [48].
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques
### Electromagnetic Bandgap (EBG) Structure  
An electromagnetic bandgap (EBG) structure is designed on the ground plane by periodic arrangement of unit cells. This structure acts as a band stop filter and provides high attenuation, which helps in achieving reduction in mutual coupling between the antennas [27; 28]. Each cell in an electromagnetic bandgap structure looks like a mushroom inserted between two antenna elements. It can act as a band stop filter at different frequencies by adjusting the unit cell dimensions and its arrangement.
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup
### Application Layers On-Board the Underwater Vehicle  
Different application layers were used to transmit data from the HROV to the control station in order to simulate the data traffics required by each of the working modes described in Section 3. The application layers used in our simulations are listed in the following.  
1. MON_WP, a periodic application layer used to transmit monitoring packets with a size of 56 bytes every 6 seconds. This application layer also receives the way-point packets transmitted by the WP_TX application layer presented in Section 4.3 and sets the next way-point of the underwater vehicle accordingly.
2. JOY_RX, an application layer used to receive the joystick position control by the JOY_TX application layer presented in Section 4.3 and to move the underwater vehicle accordingly.
3. IMG, an application layer generating sporadic 4160 \(\times\) 2336 pixel JPEG images with a size of 2.5 Megabytes, divided in packets with size 2 kilobytes. The images are generated on average every 1800 s according to a Poisson process.
4. VHQC, an application layer transmitting a high-quality real-time video stream, with an average bit rate of 1.3 Mbps. The packets were generated according to a 705 \(\times\) 576 px at 25 fps video file with the instantaneous bit rate depicted in Figure 6(a): in our simulation setup, each packet contained a frame, and the transmission period was 0.04 s.
5. VHQG, an application layer transmitting a high-quality grey-scale real-time video stream, with average bit rate 0.6 Mbps. The packets were generated according to a 705 \(\times\) 576 px at 25 fps video with the instantaneous bit rate depicted in Figure 6(a): in our simulation setup, each packet contained a frame, and the transmission period was 0.04 s.  
Figure 6: Complete path that should be followed by the underwater vehicle (**a**) during the simulated mission: the control station position is marked with a red circle, while the positions of the acoustic relays are marked with red crosses. The zoomed-in section of the path in the proximity of the control station is depicted in (**b**), while the path that the vehicle should follow at a distance between 200 m and 1400 m from the control station is zoomed-in in (**c**).  
6. VLQ, an application layer transmitting a very low-quality low-rate video, with average bit rate of 13.68 kbps. The packets were generated according to a 200 \(\times\) 96 px at 5 fps video file with the instantaneous bit rate depicted in Figure 7b: in our simulation setup, each packet contained a frame, and the transmission period was 0.2 s.
## 2 Methodology  
### Description of the Problem  
The schematic diagram of a web printing machine is shown in Figure 1. This machine usually consists of four color units, namely, cyan, magenta, yellow, and black. The printing cylinder is continuously rotating, and the printed image of one plate is repeating [5]. Figure 2 shows a schematic diagram of the entire printed image of one plate. The registration marks on the two sides of the image are used to register the cyan, magenta, yellow, and black color units. Printing defect detection generally uses the entire printed image of a cylinder plate as the basic detection unit. The images captured by the line camera are compared with the standard reference template image, which requires image registration.  
Figure 1: Schematic diagram of web printing machine [5].  
Figure 2: Entire printed image of one plate and the color registration mark.  
Previous printing defect inspection systems utilize the cross-line registration marks on both sides of a printed image as the registration feature. They also inspect the entire printed image of the plate as a whole. However, with the increase in printing speed, the stretching, deformation, and vibration of printing materials cause inconsistencies in the physical size of printed images. Moreover, registration with a whole plate image may cause false detection. Therefore, an increasing number of printing defect inspection systems require partition detection of entire printed images, that is, a printed image is divided into several subregions for sub-area detection. At present, the printed images of each region are not necessarily the same, and the traditional manual method of selecting a registration area is inefficient. Meanwhile, the selection of the feature regions of every sub-area by human vision is unstable and prone to errors. Therefore, an intelligent and automatic registration feature region searching method is urgently needed to achieve an effective selection of the registration feature regions of subdetection area.  
Figure 3 presents the flowchart of the online printing defect detection system. We use printing defect visual inspection machine to capture the standard reference template images and other printed images in real time. During printed image defect detection, the image to be detected and the standard reference template image need to be registered initially. We propose an automatic feature region searching algorithm for image registration.  
In this work, a region with discriminative shapes is used as the registration feature region for aligning the printed image collected online with the standard template image. A good shape region requires significant contour features and can be highly differentiated from other regions, such as geometric shapes, text, and characters, in printed images.
## 5 Experimental Findings
### Spatial Distance-Based Metrics Segmentation Results
The Wilcoxon rank-sum test for pairwise result comparison was used with the alternative hypothesis that the samples do not have equal medians of DI values. A significance level of \(\alpha=0.05\) with a correction using the Bonferroni–Holm method. Notation: ***** \(p<0.0001\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAD** & **MaxD** & **HD** \\ \hline SMRG & \(2.00\pm 1.41\) & \(5.63\pm 3.81\) & \(2.32\pm 0.44\) \\ k-means & \(1.58\pm 0.99\) & \(4.22\pm 2.73\) & \(2.24\pm 0.42\) \\ FCM & \(1.58\pm 1.22\) & \(4.46\pm 3.12\) & \(\mathbf{2.21\pm 0.43}\) \\ sFCM & \(\mathbf{1.37\pm 0.90}\) & \(\mathbf{4.04\pm 2.87}\) & \(2.21\pm 0.44\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Spatial distance-based metrics achieved by the proposed segmentation approaches: the results are expressed as average value \(\pm\) standard deviation.
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 4 Data Analysis  
### FE Model  
As shown in Figure 9, the HMES wallboards were modeled in ABAQUS/Explicit with C3D8R elements. The C3D8R element is a general-purpose linear brick element, with reduced integration (1 integration point) [28]. The shape functions are the same as for the C3D8 element. In the force-control loading pattern, a uniform increasing pressure is imposed on the top face of specimens gradually until failure occurs. The load capacity of two insulation layers is neglected in the FE (Finite element) model.  
The elastic modulus of husk mortar was set as 4976 MPa according to the test results mentioned above. A preliminary FE model with elastic modulus of husk mortar was established to validate the results of HMES wallboards.  
Figure 10 shows that there is a relatively reasonable agreement between numerical and test values at the initial stage of the load displacement curve. Such results verify the accuracy of test methods and test results in the elastic range. However, the numerical and test results do not match well after the displacement reaches 6.41 mm. When the specimen 1-3 enters the plastic stage, the elastic constitutive model of husk mortar cannot reflect the plastic behavior of HMES wallboards. A reasonable constitutive model should be adopted for plastic analysis. This issue requires additional research.
# Bio-Inspired Real-Time Prediction of Human Locomotion for Exoskeletal Robot Control
## 3 Methods
### Assessment Measures  
To quantitatively evaluate the algorithms' prediction performance, the prediction ratio (\(PR\)) representing prediction errors, and the smoothness factor (\(SF\)) measuring predicted trajectory smoothness are calculated by the following equations [19]:  
\[PR(y(t),e(t))=1-\frac{RMS(e_{PR}(t))}{RMS(y(t))}\times 100\%, \tag{13}\]  
\[e_{PR}(t)=\hat{y}(t|t-\Delta t_{k})-y(t), \tag{14}\]  
where \(\hat{y}(t|t-\Delta t_{k})\) represents the predicted current position \(\hat{y}(t)\) at \(t-\Delta t_{k}\), which is obtained by shifting the predicted results with \(\Delta t_{k}\) and  
\[SF(\hat{y}(t))=\frac{1}{t_{End}(max(\hat{y}(t))-min(\hat{y}(t))}\int_{0}^{t_{ End}}|f(t)-\hat{y}(t)|, \tag{15}\]  
where \(t_{End}\) is the time when prediction ends and \(f(t)\) is the filtered result of \(y(t)\) by a zero-phase filter with a cut-off frequency of 5 Hz.  
A higher \(PR\) value means higher accuracy, while a lower \(SF\) value means smoother motion. In order to provide an overall relative comparison among different methods, \(PR\) and \(SF\) are combined into an overall performance index (\(OPI\)), which is computed with  
\[OPI=\alpha PR(i)/max(PR(i))+(1-\alpha)min(SF(i))/SF(i), \tag{16}\]  
Figure 7: Dataset for prediction method assessment (the joint angles of the knees and hips of a test subject in a sagittal plane collected by the inertial measurement unit-based motion capture system).  
where \(i\) represents the index of a specific method involved in the comparison. It should be emphasized that such an index is not an absolute criterion but a relative one. The preference over prediction accuracy and smoothness could be modified by the coefficient \(\alpha\).  
In addition, the adaptability of prediction methods to irregularities happening from time to time is very essential. Such capability could be assessed by isolating the irregularities, including the beginning and termination of walking and the transition between different motion speed and modes.