esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 3 Simulation of Hard Roof Blasting
### Analysis of Simulation Results
#### 3.3.4 Hole Wall Pressure Analysis  
When an uncoupled charge is used, the energy generated after the explosive is detonated first acts on the air in the borehole and then contacts the rock through the air. The surrounding rock of the borehole reaches the yield failure instantaneously due to the shock wave, and the rock unit cannot directly reflect the detonation pressure. Therefore, selecting the air unit between the explosive and the rock can effectively analyze the pressure change acting on the rock after the explosive is detonated, as shown in Figure 6. A total of 6 monitoring points were set up from the cartridge to the borehole wall, and interval space \(w\) of each monitoring point was 5 mm.  
Figure 5: Impact pressure curve at the monitoring point.  
Figure 6: Layout of monitoring points for borehole interstice.  
The monitoring point data of different charge diameters were extracted to create the pressure and velocity change curves shown in Figure 7. With the propagation distance increase, the shock pressure linearly decreases. However, under the influence of the air medium, the explosive is diffuse in the open space, reducing the density of the cartridge (the detonation wave velocity will be reduced (Figure 7b monitoring point b2)). Then, as the detonation wave propagates, the air density increases, and the velocity also increases in the gap between the cartridge and the borehole wall. On the whole, the impact velocity increases as the diameter of the cartridge increases, and the impact pressure and speed also increase. The maximum impact pressure of the 60 mm diameter cartridge can reach 1488 MPa, almost 8 times that of the 20 mm cartridge. The maximum impact speed can reach 2 times that of the 20 mm diameter cartridge.
tunnel construction without the influence of nonlinear factors. [PERSON] et al. [9] established a mathematical analysis model of shield tunneling movement. Robust theory was applied to shield attitude control by [PERSON] et al. [10]. Based on the force balance condition of the shield, [PERSON] et al. [11] determined the relationship between the advance load and the earth pressure, and then connected the earth pressure with the shield attitude, thus establishing the theoretical model of the shield advance load. [PERSON] et al. [12] and [PERSON] et al. [13] proposed a shield attitude and trajectory automatic control system based on the sliding mode robust controller and a shield attitude dynamic coordination control system based on the dynamic model of the shield attitude adjustment process. [PERSON] et al. [14] believed that it is crucially important to select a proper TBM and define its basic specifications such as installed cutter-head torque and TBM thrust capacities. [PERSON] et al. [15] established a predicting model of the thrust and torque for the total load that fully reflects the influences of geological, operating, and structural parameters. In summary, the current research on shield attitude is more about the application of control theory, while ignoring the basic problems such as shield-soil interaction and shield tunneling mechanics.  
With the necessity to accurately predict performance of machines in different ground conditions, many researchers have worked to develop new prediction models or adjustment factors for the common existing models [16]. [PERSON] et al. [17] established a model to predict specific energy requirement of constant cross-section disc cutters in the rock cutting process by using fuzzy logic method. [PERSON] et al. [18] proposed a model named Q\({}_{\text{TBM}}\) that uses many input parameters (such as RQD, joint condition, Stress condition, intact rock strength, quartz content and TBM thrust). The modified CSM model was added rock mass properties as input parameters into the model [19]. In hard rock formations, such models have often shown results that it would not expand too much. However, in the soft soil layer, the interaction model for shield tunneling is still in its infancy.  
In view of the above deficiencies, the influence of the shield attitude parameter on the shield-soil interaction was studied. The shield-soil interaction was represented by an equivalent spring, and the interaction force between the shield and the soil was solved and analyzed according to the ground reaction curve. Based on this, the theoretical calculation method of shield attitude was proposed and compared with the actual project.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 5 Experimental Results and Analysis  
To verify the performance of the HSI inpainting algorithm proposed herein, experiments were performed using simulated and real hyperspectral data. The experimental results were analyzed and compared with the effective HSI inpainting algorithms that are widely used today. The performance of the algorithm was evaluated in terms of subjective effects and objective quality. The experimental environment was the MATLAB R2018a software, the operating system was a 64-bit Windows 10, the hardware platform was an Alienware R15 laptop containing a 2.80 GHz eight-core Intel Core i7-7700 HQ CPU and 16 GB RAM.  
To validate the effectiveness of the algorithm, we compared the performance of the proposed algorithm with that of the 3D data compatible discrete partial differential equation method (PDE) [30; 31], unmixing-based denoising (UBD) [32], a denoising method based on local low rank matrix recovery and global spatial-spectral total variation method (LLRSSTV) [33], the BPFA, and other image inpainting algorithms.
# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 3 Proposed Finger-Vein Identification System
### Multi-Layer Edge Detection  
The Sobel operator is a common edge detection system that is used in image processing. It uses the algorithm for surrounding pixels' gray level weighting and performs edge detection using the most extreme edge pixel values [14]. The operator uses two 3 \(\times\) 3 masks, a horizontal operator \(S_{x}\) and a vertical operator \(S_{y}\) to allow convolutional computation by Equation (5).  
\[G_{x}=\begin{bmatrix}1&0&-1\\ 2&0&-2\\ 1&0&-1\end{bmatrix},\ G_{y}=\begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix} \tag{5}\]  
The calculated values for \(G_{x}\) and \(G_{y}\) are combined using Equation (6) to complete the Sobel edge detection process.  
\[G=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{6}\]  
Figure 2(a) shows the bilateral image after processing using a Sobel operator. The pixel and the weighting result between the Sobel operator and the bilateral filter's processed images are shown in Figure 2(b). This method contrasts and enhances the finger outline and increases the effect of the Canny edge detector. It is difficult to achieve denoising and accurate edge detection so smoothly edged images are less accurate. However, increasing the sensitivity of edge detection increases the perceived noise signals. To address this issue, a Canny edge detector [15] is used to balance denoising and detection accuracy. This algorithm gives precise detection, a low error rate and produces detailed edge outlines, so the Sobel operator's results are processed using Canny edge detection. The multi-layer edge detection method gives stable finger outline information, as shown in Figure 2(c). An image that is processed using Canny edge detection still contains small cracks and unevenness so a closing operator from morphology studies is used to complete the finger edge outline while maintaining the finger position and shape, as shown in Figure 4.
# Adaptive Antenna Array Control Algorithm in Radiocommunication Systems
## 5 Conclusions  
The use of adaptive antennas in mobile telephony systems allows the avoidance of the imperfections of using antennas with omnidirectional radiation characteristics in base stations. In modern mobile telephony systems in areas with high traffic density, base stations are used, consisting of a large number of adaptive sector antennas with a small sector angle (e.g., 12 antennas, each with a radiation sector of 30\({}^{\circ}\)). This results in more effective management of the allocated frequency range, which allows for the servicing a larger number of mobile subscribers at the same time. Moreover, the radiation characteristics of such sector antennas are more accurate and are characterized by low power emission values in undesirable directions.  
The algorithm used is only a proposal that can be implemented in mobile telephony systems. It allows the problem of adapting the radiation pattern of an adaptive array to be solved based on the knowledge of the signals reaching the antenna. It is not required to know the angle of arrival of the useful signal, only its statistical parameters. For this reason, it is suitable for use in mobile telephony systems. In these systems, the exact location of the mobile station is not known, but it is known what the signal parameters are and in which time slot the signal will be transmitted.  
This algorithm is also useful because it minimizes the radiation characteristics of the array in the direction of the disturbance. This phenomenon is particularly useful in very densely populated areas, where the distances between individual base stations are very small, which increases mutual interference between them. Moreover, in such areas there are usually several different networks operating, which may interfere with each other.  
The proposed simulation model allows for a graphical illustration of the adaptation process of the adaptive antenna's radiation pattern. It includes the presentation of changes in radiation characteristics as a result of changes in the location of mobile subscribers for a given number of subscribers served, as well as the directions and amounts of interference occurring. This model can be very easily configured for various assumptions, such as the number of active elements in the array, the number and locations of mobile stations and interference signals, the movement of mobile stations and interference signals, and their powers. Therefore, it can be considered universal for the issues of adaptive algorithms.  
This research was funded by Military University of Technology, grant number [24 450].  
**Data Availability Statement:** The data that support the findings of this study are available from the 363 corresponding authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Numerical Analysis  
To verify the effectiveness of the MENC algorithm, a three-story building was installed with the AMD (in Figure 1), which was used to reduce the response of the structure under seismic load. The structural properties and simulation parameters of the previous neuro-controller system were used to compare its capabilities [6]. The parameters of the structure and AMD system are shown in Table 1. The sampling time was 0.005 s, and the delay time was assumed to be 0.0005 s. The equation of motion was integrated at every 0.00025 s using MATLAB/Simulink software. The MENC algorithm is used to calculate the accurate control command to be exerted on the AMD system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Quantity** & **Value** \\ \hline \(m_{i}\) & Mass of each floor & 200 kg \\ \(m_{a}\) & Mass of AMD & 18 kg \\ \(k_{i}\) & Stiffness coefficient of each floor & \(2.25\times 10^{5}\) N/m \\ \(\xi_{1}\) & Damping ratio of first mode & 0.6\% \\ \(\xi_{2}\) & Damping ratio of second mode & 0.7\% \\ \(\xi_{3}\) & Damping ratio of third mode & 0.3\% \\ \(g_{1}\) & Valve gain & \(321.4\times 10^{2}\) m\({}^{3}\)/s \\ \(g2\) & Valve gain & \(0.28\times 10^{-6}\) m\({}^{3}\)/s \\ \(\tau\) & Time constant & 0.1 \\ \(a_{r}\) & Piston effective area & \(1.52\times 10^{-3}\) m\({}^{2}\) \\ \(V\) & Volume of the cylinder & \(4.56\times 10^{-4}\) m\({}^{3}\) \\ \(c_{l}\) & Leakage coefficient & \(1.0\times 10^{-11}\) \\ \(\beta\) & Compressibility coefficient & \(2.1\times 10^{9}\) \\ \(\alpha\) & Contribution to restoring force (for nonlinear) & 0.5 \\ \(d\) & Constant (for nonlinear) & 0.01 \\ \(\rho\) & Constant of hysteretic behavior & 1.0 \\ \(\mu\) & Constant of hysteretic behavior & 0.5 \\ \(\sigma\) & Constant of hysteretic behavior & 0.5 \\ \(p\) & Constant of hysteretic behavior & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural model parameters of the AMD device control [6].
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
###### Abstract  
To improve the measurement accuracy of unmanned aerial vehicles (UAVs), in this study, we proposed a method for measuring the attitude angle of a UAV by the combination of a magnetometer and satellite positioning system. Based on the measurement principle, we established a combined measurement error model, analyzed the root cause of the measurement error, and calibrated the magnetometer using the least-squares algorithm. For handling external or system interference effects, we designed a compensation algorithm that does not depend on environmental information. For handling measurement errors, a \"current\" statistical model was established, and the continuous-discrete Kalman filter algorithm was used to eliminate measurement errors. The test results showed that after using the compensation algorithm, the measurement error was reduced from 17.80% to 6.86% compared to the average absolute error of the local magnetic field intensity, and the absolute measurement error was reduced by 61.49%. The absolute measurement error was compared after using the compensation + filtering algorithm, and the error after compensation was further reduced by 42.36%. Hence, we proved the feasibility and effectiveness of our method.  
U  
A 4 May 2022  
28 May 2022  
10 June 2022  
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## 3 Methodology
### Dry Beach Length Measurement
#### 3.2.2 Three-Dimensional World Coordinate Conversion  
According to the principle of photogrammetry, the transformation relationship between the three-dimensional space point coordinates and the pixel coordinates in the image can be expressed by Equation (1). The diagram is shown in Figure 7.  
Figure 6: Demarcation line identification. (**a,i**) Original image; (**b**) removal of reflections from water surface; (**c,j**) contrast enhancement and Gaussian filtering; (**d,k**) superpixel segmentation to select seed points (red dots); (**e,l**) seed points located in dry beach areas; (**f,m**) regional growth; (**g,n**) mask for non-dry beach areas; (**h,o**) boundary detection.  
\[s\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=K[R|t]\begin{bmatrix}X_{W}\\ Y_{W}\\ Z_{W}\\ 1\end{bmatrix}=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{1}\\ r_{21}&r_{22}&r_{23}&t_{2}\\ r_{31}&r_{32}&r_{33}&t_{3}\end{bmatrix}\begin{bmatrix}X_{W}\\ Y_{W}\\ Z_{W}\\ 1\end{bmatrix} \tag{1}\]  
In the formula, the point \(p(u,v)\) is projected by the point \(P(X_{W},Y_{W},Z_{W})\) in the world coordinate system into the pixel coordinate system; \(K\) is composed of the internal parameter matrix, where \(f_{x}\) and \(f_{y}\) are the focal lengths in the \(x\) and \(y\) directions, respectively; \(c_{x}\) and \(c_{y}\) are the main point coordinates where the optical axis intersects the image plane; \([R|t]\) is the external parameter matrix. That is, the rotation matrix and the shift matrix between the world coordinate system and the camera coordinate system.  
The pixel coordinates of the boundary in the image are obtained by performing the boundary identification step. For each image, the transformation relationship between its pixel coordinates and world coordinates was obtained by performing the steps of feature matching and bundle adjustment in the 3D reconstruction process. Therefore, the edge pixels of the boundary can be converted into three-dimensional coordinates using the transformation relationship of the corresponding image.
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
## 2 Experimental Section
### The Concept of Fault Tolerance  
[PERSON] and [PERSON] proposed an extended visual secret sharing scheme [1] in 1995. When they encrypt the secret image, each pixel on the secret image will be expanded into \(m\) subpixels. [PERSON] and [PERSON] proposed an extended visual secret sharing scheme to show that the fault tolerance mechanism can be achieved [14]. They encrypted the secret image into two expanded shares. Each pixel in the secret image will produce a diamond-like \(7\times 7\) subpixel pattern in the two shares (one is small and the other is big), as shown in Figure 2. Note that their scheme dealed with gray level image, so there is the gray color in Figure 2. Even though there is a slight deviation when stacking, the primary  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S\) & **Probability** & \(G_{1}\) & \(G_{2}\) & \(G_{1}\oplus G_{2}\) & **T(\(G_{1}\oplus G_{2}\))** \\ \hline \(\square\) & \(1/2\) & \(\square\) & \(\square\) & \(1/2\) \\ \(\blacksquare\) & \(1/2\) & \(\blacksquare\) & \(\blacksquare\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The transmittance of the Algorithm KK1.  
color (black or white) still can be restored. Their design will allow some space for fault tolerance. However, due to the expansion, the size of the restored image will be 49-times the original secret image. Hence, this paper will focus on designing shift-tolerant VSS schemes for black/white secret images without pixel expansion.
## 2 Analysis of Previous Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & I[10] & [11] & \multirow{2}{*}{I[12]} & \multirow{2}{*}{I[13]} & I[14] & \multirow{2}{*}{I[15]} & \multirow{2}{*}{**Ours**} \\  & **HiDDeN** & & & & & & \\ \hline Domain & spatial & frequency & spatial & spatial & spatial & spatial \\ Host resolution & specific & no result & no result & no result & specific & specific & general \\ adaptability & (FC layer) & & & & (FC layer) & (FC layer) & general \\ Host resolution & specific & general & specific & general & general & general & general \\ adaptability & & circular & AE using & & adversarial & adversarial & Simple CNN \\ Embedding & adversarial & loss & and DCT layer & residual block & & loss & and average \\  & global & & & global & & pooling \\ Extractor & pooling and & DCT layer & residual block & CNN & pooling and & FC layer & Simple CNN \\ network & FC layer & & specific attack & & Frobenius & variety attack & specific attack & variety attack \\ Embleder & specific attack & and strength & and strength & norm & and strength & and strength & and strength \\ Attack & one kind & one kind & all kinds & - & one kind & one kind & all kinds \\ per mini-batch & & differentiable & & & losses before & & differentiable & two-stage \\ Training & differentiable & & & residual block & and after & & training & - \\ characteristic & JPEG attack & DCT layer & & embedding & & & \\ Invisibility– & & & & & & & \\ robustness & \(\times\) & & & & \(\times\) & \(\bigcirc\) & \(\bigcirc\) \\ controllability & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of state-of-the-art networks and ours. of CNN layers with average pooling layers. In its attack simulation, all the attacks are included in each mini-batch, with random strength, but maintain a balanced distribution. Moreover, randomly generated data is used as the WM information in the training process that any data can be used as the WM. This method also includes a strength factor which adjusts the tradeoff relationship between invisibility and robustness. The next section describes the proposed deep learning network architecture for watermarking to achieve our three goals.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Evaluation of the Input-Type Recognition Layer  
The evaluation of the efficiency and accuracy of the input-type recognition layer was conducted by implementing a comparative experiment using three lightweight traditional (shallow) ML classifiers: DT, Logistic Regression, and RF. A synthetic dataset of 10,000 total samples, consisting of 5000 image-based files and 5000 tabular records, was generated and used as input to simulate structural differences in typical cybersecurity inputs. Each sample was characterized by features such as file size, entropy, and encoded file extension. Additionally, to preserve label balance across the evaluation process, the dataset was split using stratified 70/30 train-test partitions, respectively.  
As shown in Table 10, all models achieved perfect accuracy (1.00) and F1-scores, ensuring that the process of the input-type recognition layer is highly separable using minimal features. However, the Decision Tree classifier significantly outperformed the others in terms of inference time, classifying inputs in just 6.04 milliseconds. In contrast, Logistic Regression and Random Forest required 94.45 ms and 147.96 ms, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Fusion Strategy** & **Approach Description** \\ \hline Majority Voting & Picks most common class across branches \\ OR Rule & Flags if any branch detects a threat \\ Confidence-Weighted Avg. & Averages scores weighted by confidence \\ Dempster-Shafer & Combines belief from branches \\ Bayesian Inference & Probabilistic posterior inference \\ \hline \end{tabular}
\end{table}
Table 9: Summary of Evaluation Fusion Approaches.  
As a result, these findings lead to the selection of the Decision Tree-based input classifier, balancing high performance with minimal computational cost. This validates the design choice of employing a shallow ML model to ensure real-time routing throughout the (first) input layer of the suggested pipeline.  
A visual comparison of the inference times of the model compared above is illustrated in Figure 2, highlighting the advantage of the Decision Tree Classifier in terms of the efficiency in time-reference routing scenarios.