esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Digital Twin in Handling and Transportation of Hazardous Chemicals
## 4 Application of DT in LNG Road Transportation
### Filling Stage  
(1) Rationality analysis of unit layout.  
Relying on the original design drawings and models of the charging area of the station, pipeline distribution, GIS data, distribution of hazard sources and firefighting equipment in the station, combined with the dynamic driving characteristics of LNG tankers and the distribution characteristics of personnel activity areas, the 3D models of pipelines and equipment in the charging area and the BIM models of ancillary buildings are constructed to realize dynamic supervision of charging vehicles and personnel in the station. It converts the previous non-intuitive methods, such as interpretation by intercom and instrument data, into a dynamic model that can be monitored intuitively, which facilitates station managers to consider the rationality of pipeline design and facility arrangement in the station from a more macro perspective and optimize the operation process.  
(2) Filling process optimization.  
Nowadays, most stations adopt the same conservative management model, for example, some stations set up buffer parking lots at the far side of the charging area, and many other stations follow suit, taking up more land. The location of each station and the nearby traffic conditions are different, and the distribution of vehicle arrival times is also significantly different. The same process will reduce the turnover rate and cause a burden on the surrounding road network. Through spatial-temporal analysis process optimization, improving the efficiency of filling can create benefits for enterprises.  
(3) Security Monitoring.  
The DT model is connected to the sensor signals in the on-site charging facilities and environment, making it possible to transfer the monitoring of the site to the visualization system, avoiding the previous situation of repeated communication through intercoms or video monitoring to monitor the production operation status of the site, and introducing the common instrumentation and DCS data into the system to monitor the abnormalities of the process in a timely manner.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 4 Analysis of Vibration Isolation Structure
### Model Parameters
In addition, the sliding limit can be defined by setting the end displacement ds, and the stiffness K\({}_{0}\) is used for deformations exceeding ds. By setting the preload displacement dc, the initial force required to start sliding during loading and to terminate sliding during unloading can be defined. The \"friction spring damper\" unit can accurately simulate the setting of the ring spring vibration isolator, so the friction spring unit is used for simulation. Under the standard conditions of extracting gravity load, the vertical reaction force at the bottom of the common structure column is about 1470 kN and five identical sets of ring springs can be used as a base vibration isolator simultaneously, arranged between the column and the foundation. The vertical section of the vibration isolation connection with the column bottom is shown in Figure 14.  
In the software simulation, the focus is only on the vertical performance of the vibration isolation system, so the horizontal nature of the spring isolator is set to fixed. Eight isolators are arranged at the bottom of the columns of the C and D axis of the middle two frames where the vertical vibration response is relatively unfavorable. Additionally, the relevant mechanical combination parameters are listed in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bearing** & **Preload** & **Ultimate** & **Loading** & **Unloading** & **Unloading** \\
**Capacity** & **Displacement** & **Deformation** & **Stiffness K\({}_{1}\)** & **Stiffness K\({}_{0}\)** & **Stiffness K\({}_{2}\)** \\ \hline
3330 kN & \(-16\) mm & \(16\) mm & \(90\) kN/mm & \(725\) kN/mm & \(40\) kN/mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: New ring spring isolator parameters. Figure 14: Vertical section schematic diagram of the vibration isolation connection at the column bottom. Figure 13: Force–deformation curve of friction spring damper.
# Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval
## 3 Experimental Results and Discussion
### Compared Methods and Evaluation Metrics  
To prove that the proposed TORBW method can achieve excellent ANN search performance, we compare the TORBW method against five hashing algorithms and two bitwise weight methods. Among them, LSH [7], AGH [8], KMH [9], Top-RSBC [25], and OCH [1] aim to generate excellent binary codes. WhRank [23] and QRank [24] learn bitwise weights to resort the tied ranking orders, and we use them to further boost the ANN search performances of LSH [7], AGH [8], and KMH [9].  
In this experiment, we use the criterions _recall_[31; 32] and _mAP_ to evaluate the ANN search performances. _recall_ represents the fraction of positive data that are successfully returned as defined in Equation (18).  
\[\textit{recall}=\frac{N_{positive}}{N_{all}}, \tag{18}\]  
where \(N_{positive}\) means the number of positive data that are retrieved and \(N_{all}\) is the number of the true nearest neighbors. We further use _mAP_ to exactly express which position the \(i\)-th positive data point locates in, and the definition is shown in Equation (19):  
\[mAP=\frac{1}{|Q|}\sum_{i=1}^{|Q|}\frac{1}{K_{i}}\sum_{j=1}^{K_{i}}\frac{j}{ \textit{rank}(j)}, \tag{19}\]  
where \(|Q|\) represents the number of query samples, _Ki_ is the number of the \(i\)-th query sample's ground truth and _rank_(\(j\)) is the ranking position of the \(j\)-th true positive sample in the retrieval results.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 2 Design and Analysis of Side Metal Structure for Metasurfaces  
The side metal structure used in this study is composed of two components: the side plate (SP) and the sidewall (SW). As illustrated in Figure 1, the SP is a square ring-shaped structure that is parallel to and surrounds the patch antenna. The proposed TA structure, also shown in Figure 1, consists of a four-layer TA, a source antenna, and an SP implemented using a PCB board, while the SW is made entirely of a copper plate. Section 2 focuses on analyzing the gain variations in the source antenna in relation to different SP configurations and examines the differences between the resonant frequency and the maximum gain frequency, as well as the gain changes resulting from the application of the SW structure. To integrate this design into a metasurface, a cross-type slot TA with a basic structure is developed and applied. The design and analysis of the electromagnetic characteristics are performed Finite Element Method (FEM) electromagnetic simulation with ANSYS HFSS 2021R2.
## 4 Network Performance Analyses
### Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sender Node** & **Receiver Node** & **Time (s)** & **Source Node** & **Receiver Node** & **Time (s)** \\ \hline
6 & 2 & 78.309 & **0** & **1(ack)**\(\tau\)**–5 ends & 0.613 \\
4 & 2 & 0.320 & 7 & 2 Rtx\({}_{2}\) & 0.684 \\
7 & 2 & 0.333 & 3 & 2 Rtx\({}_{2}\) & 0.684 \\
**2** & **6(ack)** & 0.344 & **2** & **1 Rtx moving 4\({}_{2}\)** & 0.684 \\
5 & 2 Rtx\({}_{2}\) & 0.355 & **1** & **2 (ack)** & 0.701 \\
**2** & **1 (moving frame from 6)** & 0.380 & 7 & 2 Rtx\({}_{2}\) & 0.714 \\
3 & 2 Rtx\({}_{2}\) & 0.380 & **1** & **0 (moving frame from 4)** & 0.740 \\
**1** & **2(ack)** & 0.397 & **2** & 7 (**ack)** & 0.746 \\
**1** & **0 (moving frame from 6)** & 0.421 & **0** & **1 (ack)**\(\tau\)**–4 ends & 0.764 \\
5 & 2 Rtx\({}_{2}\) & 0.421 & **2** & **1 (moving frame from 7)** & 0.780 \\
3 & 2 Rtx\({}_{2}\) & 0.463 & 3 & 2 Rtx\({}_{2}\) & 0.780 \\
**2** & **1 (moving frame from 5)** & 0.486 & **1** & **2 (ack)** & 0.792 \\
7 & 2 Rtx\({}_{2}\) & 0.486 & 3 & 2 Rtx\({}_{2}\) & 0.807 \\
**2** & **5(ack)** & 0.488 & **2** & **3 (ack)**\({}_{2}\) & 0.825 \\
**0** & **1(ack)**\(\tau\)**–6 ends & 0.488 & **2** & **1 (moving frame from 3)** & 0.860 \\
**1** & **2(ack)** & 0.500 & **2** & **1 Rtx moving 3\({}_{2}\)** & 0.877 \\
4 & 2 Rtx\({}_{2}\) & 0.513 & **1** & **0 (moving frame from 7)** & 0.877 \\
3 & 2 Rtx\({}_{2}\) & 0.524 & **0** & **1 (ack)**\(\tau\)**–7 ends & 0.913 \\
7 & 2 Rtx\({}_{2}\) & 0.535 & **2** & **1 Rtx moving 3\({}_{2}\)** & 0.929 \\
**2** & **4(ack)** & 0.547 & **1** & **2 (ack)** & 0.949 \\
**2** & **1 (moving frame from 4)** & 0.581 & **1** & **0 (moving frame from 3)** & 0.979 \\
**1** & **0 (moving frame from 5)** & 0.589 & **0** & **1 (ack)**\(\tau\)**–3 ends & 79.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transmission time for 2-Bytes from all the patients to the main node. Figure 11: Global network of this study. Data1 means that the message transmission from a mobile node to the main node has terminated. Data2 means that the corresponding node has not received the ACK message.
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 4 Three-Dimensional Slope Stability Analysis
### Analysis of Stress Simulation Results  
According to the stress cloud diagram of Case 1 (the static water level of the reservoir is 175 m) (Figure 10), when the reservoir increases to 175 m, the maximum value of the first principal stress is 2.10 \(\times\) 10\({}^{3}\) kPa, and the minimum value is \(-\)2.51 \(\times\) 10\({}^{3}\) kPa; the maximum value of the third principal stress is 2.12 \(\times\) 10\({}^{3}\) kPa, and the minimum value is \(-\)8.24 \(\times\) 10\({}^{3}\) kPa. The overall stress level is lower, the slope is relatively stable, and there is no obvious stress concentration phenomenon.  
In Case 2 (the water level slowly decreases from 175 m to 145 m) and Case 4 (the water level suddenly decreases from 162 m to 145 m) (Figures 11-13), the distribution of the first and third stresses of the slope body changed. The maximum value of the first principal stress decreased from 6.55 \(\times\) 10\({}^{3}\) kPa to 5.58 \(\times\) 10\({}^{3}\) kPa, and the minimum value increased from \(-\)3.83 \(\times\) 10\({}^{3}\) kPa to \(-\)4.13 \(\times\) 10\({}^{3}\) kPa for both conditions. Compared with the high water level (175 m), the stress level inside the slope decreased overall during the lower water level (162 m). This suggests that the decline in the high water level has a greater detrimental effect on slope stability, mainly because the high water level provides a greater reservoir level load, the stress redistribution is more intense when the water level declines,  
Figure 10: (a) First principal stress (kPa) under Case 1. (b) Third principal stress (kPa) under Case 1.  
When the water level drops and encounters rainfall in 1 of 50 years, the stress cloud maps (Figures 12 and 14) of Case 3 (the water level slowly drops from 175 m to 145 m, superimposed on rainfall in 1 of 50 years) and Case 5 (the water level suddenly drops from 162 m to 145 m, superimposed on rainfall in 1 of 50 years) are combined. The impact of Case 3 on the stress field is much larger than that of Case 5, with the maximum value of the first principal stress decreasing from 9.07 \(\times\) 10\({}^{3}\) kPa to 6.44 \(\times\) 10\({}^{3}\) kPa, and the minimum value of the third principal stress increasing from \(-\)2.21 \(\times\) 10\({}^{3}\) kPa to \(-\)1.2 \(\times\) 10\({}^{3}\) kPa, indicating that Case 3 is the most unfavorable case. The superposition effect of the rainfall leads to extremely significant tensile action within the slope body, while the high water level exacerbates the stress redistribution under the heavy rainfall. This leads to stress redistribution.  
Combined with the pore water pressure results, it can be seen that there is a coupling effect between the seepage field and the stress field. The change in the reservoir water level and rainfall together change the seepage field, which triggers the corresponding change in the stress field under different working conditions.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### Theoretical Impact of Buoy Movements on the FLS Horizontal Velocity Estimates
#### 4.2.1 Horizontal Velocity Bias from an Isolated LIDAR Beam Subjected to a Period of Rotation  
The study of motion-induced radial wind speed variability of an isolated beam provides an understanding of the dynamic behavior of the LIDAR during a _LOS_ measurement. It is the study of the geometric effect of motion on the velocity perceived by the wind LIDAR's beams. The translational movements of the buoy appear as an added component to the velocity vector measured by the LIDAR. The consideration of this phenomenon on the LIDAR measurement is presented in the model formulation in Section 3.1.1. As the modification of the measurement cone geometry of the LIDAR has a less intuitive effect on the measurements, we focus here on the effect of the buoy's rotations.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 4 Model Trajectories
### Particle Trajectories of the Albatros Drifters
In the present study, CPL experiments showed good agreement for these drifters with the observations. A deviation of the trajectory, and consequently an increase in the separating distance, was observed, but stayed within the same grid resolution. On the other side, the WD experiments deviations from the simulated drifter trajectories from the observations and separating distances were high. This result proves the importance of improving the Eulerian current simulations needed for Lagrangian transport modelling by implementing the sea state momentum forcing and Stokes-Coriolis forcing in the numerical model. We did not aim to assess all specific differences between individual drifters and discuss in detail the wind forcing and the North Sea circulation during the different periods of their deployment, as this was done by [27]. Neither did we aim to tune the model to find the optimal percentage of the contribution of either the Stokes drift or windage that can be taken into account in OpenDrift simulations (as was recently done by [64]. Additional errors of the drifter simulations can be due to the errors in the atmospheric forcing, but also the boundary or tidal forcing. Another reason may be errors in the vertical resolution and mixing and bottom friction parameterization; the latter is essential for shallow water dynamics in regions like the German Bight. By using the same model configuration as in the present work, the interactions between barotropic tides and mesoscale processes were studied by [65] showing that barotropic tides affect diapycnal mixing substantially. In our study, we aimed to investigate the potential of a different approach, namely the contribution of wave-induced processes in the NEMO model. It is important to stress here again that the North Sea circulation is very complex. The drifters were deployed in the shallow German Bight, a coastal ocean where the currents are dominated by tidal and wind forcing and are steered by the bathymetry and coastline. Other possible error sources are circulation features such as inertial oscillations, sub-mesoscale dynamics and baroclinic effects. Further studies are needed to quantify the combined effects of the mesoscale variability and resolution of the ocean and particle transport model, especially in the coastal areas of the German Bight (the Wadden Sea) as well as for other regions. For these aims, the GCOAST framework will be taken into account and tested for its best performance in terms of trajectory simulations over regions with different oceanographic properties (e.g., Baltic Sea, the North-East Atlantic). The advantage of the method here is that by coupling the circulation model to the wave model, no further sensitivity experiments on the percentage of the contribution of windage or Stokes drift to improve the skills of the Lagrangian model are needed.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 7 Challenges
### Bandwidth Limitation of the Light Source  
Since the limited bandwidth suppressed the speed of the VLC systems, in 2020, [PERSON] et al. successfully demonstrated a semipolar (20-21) 525 nm \(\upmu\)-LED that achieved 3 dB bandwidth up to 756 MHz and 1.5 Gbit/s under a current density of 2.0 kA/cm\({}^{2}\), through several improved approaches on epitaxy and chip processes, such as the introduction of atomic layer deposition (ALD) [61]. Furthermore, in 2021, [PERSON] et al. achieved beyond 5 Gbit/s OWC with a 2 \(\times\) 2 high-contrast grating (HCG) green  
Figure 19: VLC using in wireless local area networks. Figure reused from [5] under a Creative Commons Attribution Non-commercial Non-Derivative 4.0 International license (CC BY-NC-ND 4.0).  
\(\mu\)-LED operated under low current density, decreasing the power consumption of the transmission system [137].
# Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization
## 2 Methods
The initial configuration, illustrated in Figure 2, comprises a pipe arrangement intended to emulate the phenomenon of hot gas leakage through perforations. A 2 mm hole was drilled in a 25 mm internal diameter polytetrafluoroethylene (PTFE) tube. Air with a controlled temperature was introduced using a hot air blower. The unperforated sections of the PTFE tube were covered with 10 mm thick cylindrical urethane insulation, which was employed to exclude the natural convection occurring on the surface of the PTFE tube from the Schlieren images. The second leak simulation experiment employs a pipe assembly analogous to those utilized in operational NPPs. This assembly comprises a 100 mm outer diameter aluminum pipe encased in an aluminum jacket with insulation. The experimental setup is illustrated in Figure 3. The interior of the 250 mm outer diameter aluminum jacket is filled with fiberglass insulation. It is divided into two half-concentric cylinders that encase the internal pipe, secured together with clips. To simulate a gas leakage from the pipe and jacket, a 12 mm diameter hole was drilled into the pipe, and the aluminum jacket was configured with a gap approximately 12 mm wide, as illustrated in the right side of Figure 3. In contrast to the preceding PTFE tube leak simulation configuration, the thickness of the pipe and insulation necessitated an adjustment in the height of the Schlieren optical system by approximately 250 mm while maintaining the exact positioning. The pipe was positioned 200 mm in front of the mirror for both experimental setups. Figure 3: Schematic of experimental setup of the pipe covered by insulation and aluminum jacket with a gap. Figure 2: Schematic of experimental setup of the Schlieren imaging of leakage in a perforated PTFE tube. The hot air supplied to the experimental apparatus was delivered using a hot air blower (Hotwind Premium, Leister AG (Kagiswil, Switzerland)). The airflow rate was fixed at the minimum setting of 200 LPM. The temperature of the leaking air was adjusted from 20 to 100 \({}^{\circ}\)C in 10 \({}^{\circ}\)C increments, with the temperature monitored by a K-type thermocouple installed at the perforated hole. The experiment was conducted during an unheated winter period, at an ambient temperature of 14 \({}^{\circ}\)C. Schlieren images were recorded at one-second intervals using a camera, resulting in 20 images for each temperature of the leaking air. The brightness values from the regions exhibiting leakage in the acquired images were extracted to calculate the coefficient of variation. The brightness analysis was conducted within a rectangular region measuring 25 \(\times\) 12.5 mm, situated at the center of the flow emanating from the bottom center of the mirror, approximately 25 mm in height. Approximately 5000 pixels were analyzed to compute the mean and standard deviation, thereby facilitating a comparison of \(I_{E}\). The subsequent section presents the Schlieren flow visualization images and image processing results based on the leakage temperature in the simulation setup.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods
### Global MLC Methods Considering Only Positive Label Correlation  
Methods based on label correlations can be categorized into second-order and higher-order strategies, depending on the level of label correlations considered. Second-order strategies primarily focus on the correlations between pairs of labels. For instance, the Calibrated Label Ranking (CLR) [64] method transforms the MLC task into a pairwise ranking problem, aiming to ensure that the correct labels are ranked higher than the incorrect ones for each instance. The CLR method considers pairwise combinations of labels, which provides good generalization ability. However, in practical applications, the relationships between labels can be quite complex, making it insufficient to rely solely on pairwise relationships to accurately describe label correlations in real-world scenarios.  
Higher-order strategy methods aim to investigate the relationships among all labels or their subsets. For instance, the Classifier Chains (CC) method reformulates the MLC task into a series of binary classification problems by sequentially linking single-label classifiers. In this approach, each classifier's input includes the outputs of all previous classifiers, thereby considering the influence of all preceding labels on the current label. The Label Powerset (LP) algorithm [63] leverages higher-order correlations by focusing on subsets of labels, treating each unique label combination in the training data as a separate class, effectively converting the task into a single-label MLC problem. However, a key limitation of LP is its ability to only predict label sets that are present in the training data. As the number of labels increases, the potential combinations of labels grow exponentially, which can lead to sparse samples for each new class and result in class imbalance. To tackle this challenge, [PERSON] et al. [77] introduced the Random k-labelsets (RAkEL) algorithm, which integrates randomly chosen k label sets, ensemble learning, and the LP method to enhance its performance. Although these high-order strategy methods are more comprehensive, they significantly increase computational complexity, making them difficult to use for large-scale learning problems.