esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification of Real-time Hybrid Control System
### Three-DOF Structural Dynamic Test with Steel Structure
#### 5.1.2 Linear Dynamic Test with Real-Time Hybrid Control System  
In this section, real-time hybrid tests were performed by adjusting the PGA (Peak Ground Acceleration) of the input seismic wave (Elcentro) within the linear displacement of the test specimen. The purpose of the linear dynamic tests was to evaluate the accuracy and stability of the overall system by comparing the numerical results with the hybrid test results. As shown in Figure 6, each actuator describes the horizontal and bending displacement at the end of the test specimen, and the response  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**H (mm)** & **B (mm)** & **t\({}_{\text{f}}\) (mm)** & **t\({}_{\text{w}}\) (mm)** & **r (mm)** & **Ag (mm\({}^{2}\))** & **lx (10\({}^{6}\)mm\({}^{4}\))** & **ly (10\({}^{6}\)mm\({}^{4}\))** \\ \hline
125 & 125 & 9 & 6.5 & 10 & 3031 & 8.47 & 2.92 \\ \hline \end{tabular}
\end{table}
Table 1: Section properties of the specimen.  
Figure 8: Accelerometer and strain gauge.  
Figure 6: Test set-up for steel frame and actuators.  
Figure 7: El Centro ground acceleration.  
values are reflected in the numerical analysis to derive the results of each step. As a result, as shown in Figure 9, the difference of the maximum displacement between the numerical analysis and the linear hybrid test was found to have almost the same results around 4% error. Figure 10 described the relationship of displacement and force in linear dynamic test. This hybrid system is stable and effective to predict the dynamic behavior of the structure.
# Decision Optimization for Power Grid Operating Conditions with High- and Low-Voltage Parallel Loops
## 2 Complex Network Theory Used for Opening Parallel Loops
### Modularity Indicator  
The GN algorithm can effectively find the communities contained in complex networks, however, it cannot estimate the practical significance of the communities obtained from the original network. To solve this problem, [PERSON] introduced an index, called modularity indicator, to evaluate the community separating result [20]. And [PERSON] proved that reasonable results can be obtained using this indicator when the scales of obtained communities differ in a relatively small range [21].  
Assuming the complex network can be divided into \(l\) communities, a symmetric matrix \(\mathbf{E}=(e_{ij})_{l\times l}\) with \(l\times l\) order can be defined, and \(e_{ij}\) represents the proportion of edges connecting community \(i\) and community \(j\). The summation of elements on the diagonal is taken as \(T\), which represents the proportion of edges connecting internal nodes of communities. The summation of elements in each row (column) is defined as \(a_{i}\), representing the proportion of edges connecting nodes of community \(i\). Then, the modularity indicator can be defined as:  
\[Q=\sum_{i=1}^{l}\left(e_{ii}-a_{i}^{2}\right)=T-\sum_{i=1}^{l}a_{i}^{2}. \tag{1}\]  
The definition of (1) is the expectation value of the proportion of the edges connecting community internal nodes in original network minus its corresponding proportion in another random network. The random network can be constructed by connecting different nodes randomly with the community property and node degree of each node unchanged. It indicates that the community separating result of original network is better if \(Q\) is closer to 1, which is the upper limit. If the proportion of the edges connecting community internal nodes in original network is smaller than its corresponding value of random network, \(Q=0\). In an actual network, the value of \(Q\) is usually between 0.3 and 0.7.  
In order to simplify the calculation, the equivalent equation of Equation (1) is derived as below [22]:  
\[Q=\frac{1}{2m}\sum_{v=1}^{n}\sum_{w=1}^{n}\left(A_{vw}-\frac{k_{v}k_{w}}{2m} \right)\delta(c_{v},c_{w}), \tag{2}\]  
\[A_{vw}=\left\{\begin{array}{ll}1,&\text{node $v$ is connected with node $w$}\\ 0,&\text{node $v$ is not connected with node $w$}\end{array}\right. \tag{3}\]  
\[\delta(c_{v},c_{w})=\left\{\begin{array}{ll}1,&\text{node $v$ and $w$ are in the same community}\\ 0,&\text{node $v$ and $w$ are in different communities}\end{array}\right. \tag{4}\]where \(n\) is number of nodes, \(m\) is number of edges, and \(k_{v}\) and \(k_{w}\) are the node degrees of node \(v\) and \(w\), respectively.
## 4 Emergency Maneuver Strategies and Blind 60 Spot Management
### Current Technologies and Algorithms for Emergency Response and Blind Spot Management
#### 4.2.2 Blind Spot Detection and Monitoring  
Radar technology plays a crucial role in blind spot detection due to its ability to function in various weather conditions. [PERSON] et al. [87] comprehensively reviewed automotive radars, highlighting their effectiveness in blind spot monitoring and collision avoidance applications.  
Camera systems offer high-resolution visual data for blind spot monitoring. [PERSON] et al. [88] developed a deep learning-based approach for blind spot detection using surround-view cameras, demonstrating improved accuracy in identifying vehicles and other objects in blind spots.  
Ultrasonic sensors provide reliable close-range detection, which is particularly useful for parking scenarios and low-speed maneuvering. [PERSON] et al. [82] proposed an integrated ultrasonic sensor system for enhanced object detection and classification in blind spots and other close-proximity areas.
# Cement Pastes with Hygyroscopic Polymeric Additions for Potential Building Applications
## 2 Materials and Methods  
### Materials  
Four different mixtures were prepared and tested during the early stage of aging. The samples were prepared by adding water to a pre-mixed powder, whose phases have been detailed in a previous paper [20], and then mixed for about 20 s. Although the technical data sheet of the pre-mixed powder provided by the manufacturer suggests a lower water/binder ratio for the preparation of the pastes, w/b = 0.4 was used to attain manually workable pastes. Superadsorbent poly-acrylate (SA-PA) and/or nylon fibers (Ny) [20; 35] were added to dry powder before mixing it with water.  
The compositions of the four sets of cement mortars are summarized in Table 1. Notably, for mixtures with the same workability, the percentages of Ny and SA-PA were halved in the material containing both, with respect to the proportions each of them represented in the sample in which they were present alone. All the samples were prepared using small quantities of mixture, not suitable for classic slump measurements. A spreading protocol was adopted: about 7.5 mL of mixture was poured into a cylinder 21 cm in diameter, vibrated for 30 s, and turned over onto a PET sheet, so that a diameter of about 5 cm could be measured.  
A single batch of commercial superabsorbent sodium polyacrylate (SA-PA) was used for all the experiments, accurately mixed before adding water, and then stirred for a few seconds before casting. The polymer was not brought to dryness, because being very hygroscopic it was preferred to use it in the condition reached in the jar already in equilibrium with the atmospheric humidity.  
All the fresh pastes were cast into molds--polyppropylene Petri dishes with 9 cm diameter and rubber molds of 1 cm \(\times\) 1 cm \(\times\) 0.3 cm--and vibrated for 30 s [20]. They were aged at different times at 20 \(\pm\) 2 \({}^{\circ}\)C and R.H. 50 \(\pm\) 5%. Each specimen was tested immediately after its curing process had been completed. In addition to the analyses carried out on fresh and cured samples, some pieces of the 28-day-aged samples were subjected to thermal treatment for further testing.
## 2 What Is Terrestrial Storage of Biomass (TSB)?
### Biomass Formation via Photosynthesis  
Biomass comprises complex carbohydrates, which are polymers of sugar monomers, as well as other compounds such as proteins, lipids, and, in the case of woody biomass,lignin. Plants derive all of their carbon from CO\({}_{2}\) in the atmosphere via photosynthesis. Photosynthesis involves a complex series of reactions that are catalyzed by the ATP enzyme in the presence of light ([PERSON] and [PERSON] [45]). A simplified representation of the photosynthesis reaction producing glucose sugar is as follows:  
\[6\;\mathrm{CO_{2}+6\;H_{2}O\to C_{6}H_{12}O_{6}+6\;O_{2}} \tag{3}\]  
where:  
\(C_{6}H_{12}O_{6}=glucose sugar.  
In subsequent reactions, the glucose may polymerize in a reaction that forms starch or cellulose and water, as follows:  
\[n(C_{6}H_{12}O_{6})\rightarrow(C_{6}H_{10}O_{5})n+nH_{2}O \tag{4}\]  
where  
\((C_{6}H_{10}O_{5})n=a\) starch molecule having n monomer units.  
Biomass in municipal landfills is known to undergo decomposition in several sequential distinct chemical phases. Biomass decomposition can first undergo aerobic decomposition followed by anaerobic decomposition. One of the anaerobic decomposition paths forms harmful methane.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Minimax Design of the 2D Sparse FIR Filter
#### 3.3.2 Step 2  
We again optimize \(\mathbf{h}^{s}\) in the weighted minimax sense in order to obtain the optimal solution for the 2-D sparse FIR filter. This optimization problem is expressed as  
\[\underset{\mathbf{h}^{s}}{\text{minimize }}\Big{[}\max_{(\omega_{x}, \omega_{ct})\in\mathcal{F}}W(\omega_{x},\omega_{ct})|\mathbf{c}(\omega_{x}, \omega_{ct})^{\mathsf{T}}\mathbf{h}^{s}\] \[-H_{d}(e^{j\omega_{x}},e^{j\omega_{ct}})|\Big{]}\] \[\text{subject to : }h_{i}^{s}=0\text{ for }i\in I_{\infty}, \tag{24}\]  
where \(I_{\infty}\) is the set containing indices \(i\) for which \(h_{i}^{s}=0\). Again, by introducing an upper bound \(\beta\) for \(W(\omega_{x},\omega_{ct})|\mathbf{c}(\omega_{x},\omega_{ct})^{\mathsf{T}} \mathbf{h}^{s}-H_{d}(e^{j\omega_{x}},e^{j\omega_{ct}})|\) over a finite \(M\) set of frequency grids \(\Omega_{d}=\{\omega_{i}=(\omega_{1}^{(i)},\omega_{2}^{(i)}),1\leq i\leq M\} \subseteq\mathcal{F}\), (24) is reduced to a tractable constrained problem as  
\[\underset{\text{subject to : }}\mathcal{W}(\omega_{i})|\mathbf{c}( \omega_{i})^{\mathsf{T}}\mathbf{h}^{s}-\] \[H_{d}(e^{j\omega_{1}^{(i)}},e^{j\omega_{2}^{(i)}})|\leq\beta \text{ for }1\leq i\leq M\] \[h_{i}^{s}=0\text{ for }i\in I_{\infty}, \tag{25}\]  
By defining vectors \(\mathbf{\hat{h}^{s}}\) and \(\mathbf{\hat{c}}(\omega)\) by deleting those entries of \(\mathbf{h^{s}}\) and \(\mathbf{c}(\omega)\) whose indices belong to set \(I_{\infty}\), we have \(\mathbf{c}(\omega)^{\mathsf{T}}\mathbf{h}^{s}=\mathbf{\hat{c}}(\omega)^{ \mathsf{T}}\mathbf{\hat{h}}^{s}\). Hence, (25) can be written as  
\[\underset{\text{subject to : }}\mathcal{W}(\omega_{i})|\mathbf{\hat{c}}( \omega_{i})^{\mathsf{T}}\mathbf{\hat{h}}^{s}-\] \[H_{d}(e^{j\omega_{1}^{(i)}},e^{j\omega_{2}^{(i)}})|\leq\beta \text{ for }1\leq i\leq M \tag{26}\]  
With \(\beta\) as an auxiliary variable, problem (26) becomes an LP problem of the form  
\[\underset{\text{subject to : }}\mathcal{\mathbf{A}}\mathbf{x}\geq \mathbf{b} \tag{27}\]  
where  
\[\mathbf{x}=\begin{bmatrix}\beta\\ \mathbf{\hat{h}}\end{bmatrix},\mathbf{f}=\begin{bmatrix}1\\ 0\end{bmatrix},\mathbf{A}=\begin{bmatrix}1&W(\omega_{1})\mathbf{\hat{c}}^{ \mathsf{T}}(\omega_{1})\\ \vdots&\vdots\\ 1&W(\omega_{M})\mathbf{\hat{c}}^{\mathsf{T}}(\omega_{M})\\ 1&-W(\omega_{1})\mathbf{\hat{c}}^{\mathsf{T}}(\omega_{1})\\ \vdots&\vdots\\ 1&-W(\omega_{M})\mathbf{\hat{c}}^{\mathsf{T}}(\omega_{M})\end{bmatrix},\]\[\mathbf{b}=\begin{bmatrix}W(\omega_{1})H_{d}(e^{i\omega_{1}^{(1)}},e^{i\omega_{2}^{ (1)}})\\ \vdots\\ W(\omega_{M})H_{d}(e^{i\omega_{1}^{(M)}},e^{i\omega_{2}^{(M)}})\\ -W(\omega_{1})H_{d}(e^{i\omega_{1}^{(1)}},e^{i\omega_{2}^{(1)}})\\ \vdots\\ -W(\omega_{M})H_{d}(e^{i\omega_{1}^{(M)}},e^{i\omega_{2}^{(M)}})\end{bmatrix}.\]  
By solving (27), the impulse response of the required 2-D FIR filter \(\mathbf{h}^{s}\) can be obtained.
## 3 Experiments and Analysis
### Comparisons on Mainstream Methods
#### 3.2.2 Results on LEVIR-CD+ Dataset
56.9% improvement on this subset. Comparable accuracy of RCCD, RCCD-B, and CDNeXt on the Small-VS subset suggests the overall improvement stems from the representation consistency learning, which mitigates visual style discrepancies. This highlights the crucial role of addressing such discrepancies for improved CD. Figure 6: Visualizations of different methods on LEVIR-CD+.
## 3 Results and Discussion
### Sensitivity Tests  
To test the applicable feasibility of a certain sample, one of the most important tests is the sensitivity test. For energetic materials, the impact sensitivity and flame sensitivity are two kinds of common tests to evaluate the safety of the materials for engineering application. The impact and flame sensitivities of Al/MoO\({}_{3}\)-E and Al/MoO\({}_{3}\)-M are listed in Table 3. The ignition height of the impact sensitivity was acquired by the up-and-down method, until the reaction probability of the sample reached 50%. And the larger the ignition height, the lower the impact sensitivity will be. As shown in Table 3, the ignition height for Al/MoO\({}_{3}\)-E was 23 cm, which was 8 cm shorter than that for Al/MoO\({}_{3}\)-M. It indicated the Al/MoO\({}_{3}\)-M was less sensitive to the impact stimulus than the electrosprayed one. On the contrary, in the case of flame sensitivity, the ignition distance of the former sample was 50 cm, which was 8 cm larger than the latter sample. It proved that the mechanically mixed sample was more sensitive to the flame stimulus than the electrosprayed sample. It indicated that Al/MoO\({}_{3}\)-E was more suitable for the application of mechanically initiated devices such as primer or percussion caps.
## 4 Discussion and Results
### System Reliability and Availability Analysis  
By substituting the value of the transfer rate in the Markov model into the derived formula, the reliability index of the wellhead connector can be obtained. The data are mainly obtained from OREDA, the WARs and expert knowledge. Using OREDA and expert knowledge to obtain the S\({}_{1}\)-S\({}_{18}\) transfer rates as shown in Table 5. The WARs collected the fault information of the wellhead connector and the time required for related tests. This article analyzes the reliability of the underwater connector based on these reliability data. Therefore, the values of every transfer rates are shown in Table 7.  
According to Equation (21), the reliability and transient availability of the system, based on whether maintenance is considered, can be obtained as shown in Figure 9. According to Figure 9, the transient availability of the wellhead connector decreased rapidly during the first 40 h. Then, as time increases, the transient availability decreases slowly and finally reaches a steady state value 0.928. When maintenance is not considered, system reliability continues to decrease with time. According to Equation (23), the MTTF of the wellhead connector can be calculated to be 4,637,100 h (529 years), which is basically consistent with the failure statistical data (543 years) from the OREDA [39].
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 5 Discussion
### Limitations and Future Work  
This study employed SVM and RF to assess landslide susceptibility in Hong Kong across four time periods. A limitation of this study is the random sampling method used to select non-landslide samples, which may have included areas with higher susceptibility, potentially affecting model accuracy. Additionally, the performance of SVM for the 2015-2019 period was slightly hindered by a limited number of landslide samples in certain years, impacting its ability to generate precise temporal susceptibility maps. However, visual inspection of the results showed good consistency with geological knowledge and existing datasets, supporting the model's reliability.  
To improve model performance, future research will integrate interferometric synthetic aperture radar (InSAR) technology to monitor ground subsidence and enhance the landslide sample database. Additionally, more advanced evaluation techniques, such as root mean square error (RMSE), mean square error (MSE), and confusion matrices, will be employed [53]. These methods will help assess model performance more robustly, optimize results, and facilitate the broader application of machine learning models in landslide susceptibility mapping.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 4 Experimental Results  
In this section, we conduct several experiments to show the effectiveness and applicability of the proposed scheme. We take eight grayscale images of size \(512\times 512\), namely, Lena, Jet, Baboon, Tiffany, Boat, Stream, Peppers and House, as the test images, as shown in Figure 4. These images can be obtained from the USC-SIPI image database [21]. We use the peak signal-to-noise ratio (PSNR) and bitrate to measure the performance. The PSNR is calculated by:  
\[\text{PSNR}=10\log_{10}\frac{255^{2}}{\frac{1}{n\times n\times N}\sum_{i=0}^{ n\times N-1}(x_{i}-{x^{\prime}}_{i})^{2}}, \tag{9}\]  
where \(x_{i}\) and \({x^{\prime}}_{i}\) represent the pixel values of the original and decompressed images, respectively. The bitrate metric is measured by the number of bits required to record each pixel (i.e., bit per pixel, bpp).  
In all of the experiments, we set \(\tau_{0}=4\) because the flat blocks under this setting show no apparent block boundary artifacts.
# Torsional Fatigue Performance of a Spot-Welded Structure: An XFEM Analysis
## 1 Introduction
The highest stress values and lowest fatigue life cycles are seen outside the nugget boundary in rigid and umbrella weld models. Conversely, with higher weld diameters, the critical region moves outside the nugget boundary with reduced stress and increased life. For smaller spot weld diameters, the solid element modeling technique results in maximum stress and minimal life inside the weld zone. The review articles on spot-welded structures [12; 13; 14; 15] and the literature survey indicate a need for an in-depth study of a spot-welded structure under torsional fatigue loading. It helps to increase fatigue resistance and increase the service life of spot-welded assemblies to fully understand the failure causes. It is crucial to understand the location of fracture initiation, how it spreads with increasing cycling loadings, the failure cycle, and how these factors depend on design parameters. The XFEM model for the spot-welded structure subjected to torsional loading is constructed in this study with this goal in mind. The XFEM approach is very useful for capturing complex crack patterns since it can simulate the propagation of cracks without changing the mesh, whereas most studies in the literature combined FE models with fatigue theories to determine the fatigue life of a spot-welded component without capturing intricate crack formations and their spreads. First, in this work, the experimental data for two distinct scenarios were used to validate the numerical model. Afterwards, an evaluation was conducted to determine how different model parameters, such as the nugget diameter, load ratio, and quantity of the applied loading, affected the fatigue life assessment of spot-welded components.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 2 System Model
The radius of the beam at any distance (\(z\)) is represented by _w(z)_ and determined in [11]; moreover, the laser-beam propagation can be modeled by assuming that lasers generate Gaussian beams or are operating in the fundamental transverse mode (laser's optical resonator) [18]. As a result, this paper follows this assumption and assumes that the laser beam used in this work has a Gaussian profile [19]. \[w(z)=w_{0}\sqrt{1+\left(\frac{\lambda z}{\pi w_{0}^{2}}\right)^{2}} \tag{1}\]  
where \(z\) is the distance between the sender and the receiver, \(w_{0}\) is the beam waist of the laser source at the transmitter, and \(\lambda\) is the wavelength denoted as 1550 nm (Typical values of the parameters are shown in Table 1). \[w_{0}=\frac{\lambda}{\pi\theta_{1/2}} \tag{2}\]
# Using Deep Learning for Collecting Data about Museum Visitor Behavior
## 3 Proposed Method
### Computation of the Exact Visitor Position  
To compute the visitor's spatial position from the bounding box of the detected badge, it is first necessary to calibrate the camera or cameras used. Generally speaking, the procedure of camera calibration consists of the estimation-with acceptable accuracy for the specific application-of the extrinsic (i.e., rotation matrix and translation vector) and intrinsic parameters (i.e., image center, focal length, skew, and lens distortion) of the camera [46]. This process is fundamental for most computer vision applications, especially when metric information related to the scene is required, as is our case. Once the camera has been calibrated, it is possible to determine the angular amplitude \(\alpha\) of each pixel of the camera [47]. This can be done with a simple computation consisting in counting the number \(m\) of pixels in a video frame (see Figure 3b) of a unit length yardstick put in front of the camera at a unit distance (see Figure 3a). Then, the angular amplitude \(\alpha\) of a single pixel can be expressed as follows:  
\[\alpha=\frac{2\arctan(0.5)}{m}. \tag{1}\]  
Knowing the angular amplitude of the pixel and the real dimensions of the badge (in our case they are \(L=10.4\) cm and \(H=14.0\) cm), it is straightforward to compute the distance \(\ell\) of the badge from the camera, which can be done as follows (see Figure 4a):  
\[\ell=\frac{H}{2\tan\Bigl{(}\frac{\alpha m_{y}}{2}\Bigr{)}} \tag{2}\]  
where \(m_{y}=|\mathbf{b}(y_{1})-\mathbf{b}(y_{2})|\) is the number of pixels of the height of the badge bounding box in the video frame (see Figure 4b). In Equation (2), we can also replace the term \(m_{y}\) with the term \(m_{x}=|\mathbf{b}(x_{1})-\mathbf{b}(x_{2})|\) and \(H\) with \(L\). As above, we can compute the angle \(\beta\) (respectively, \(\gamma\)) that the badge forms with the vertical (respectively, horizontal) centerline of the video frame. Thus, the triple \((\ell,\beta,\gamma)\) corresponds to the polar coordinates of the badge in the camera reference. The visitor position inside the museum can be obtained by adding the values of the camera coordinates in the museum reference. Knowing the video frame rate, we can also determine the exact time and length of the museum visit and all other temporal information such as how much time a visitor spent in front of a specific artwork and so on.