esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 4 Results
### Hybrid Constellation  
We assumed that a hybrid constellation with \(N+M\) satellites can be divided into two constellations, each of which is a polar-orbiting or walker constellation, with different periods. The conflict matrix of the first constellation \(\{v_{1}^{1},v_{2}^{1},\ldots,v_{N}^{1}\}\) with \(N\) satellites is \(R_{N\times N}^{1}\), while the conflict matrix of the other constellation \(\{v_{1}^{2},v_{2}^{2},\ldots,v_{M}^{2}\}\) with \(M\) satellites is \(R_{M\times M}^{2}\). Through many experiments, we found that if there is no special design for the orbital period or orbital altitude, as time increases, the conflict matrix \(R\) tends to  
\[R_{N+M,N+M}=\begin{bmatrix}R_{N\times N}^{1}&1_{N\times M}\\ 1_{M\times N}&R_{M\times M}^{2}\end{bmatrix} \tag{14}\]  
where 1 is a matrix with all elements equal to 1.  
This shows that any satellite in the first constellation conflicts with any satellite in the other constellation, so a hybrid constellation can form two independent sets. Therefore, the minimum PRN code number for a hybrid constellation is the sum of the two constellations. From Figure 15, it can also be seen that if the two satellites do not have a common period, the two satellite signals are likely to be observed by users near the ground where the orbital planes intersect, so the two satellite cannot share the same PRN code. In the same way, a constellation composed of multiple polar-orbiting or walker constellations also has the above characteristics.  
In summary, for hybrid constellations, we recommend that, after calculating the minimum number of PRN codes for each polar-orbiting or walker constellation separately, the minimum PRN code number of the hybrid constellation should be equal to the sum of the minimum PRN codes in the partial constellations that make up the hybrid constellation.  
Figure 14: Graph of the orbital inclination and approximate minimum number of groups. The blue line shows the approximate minimum number of PRN codes, and the orange line shows the number of conflicts. This is a walker constellation similar to Globalstar, which is divided into 8 orbits with 11 satellites each. The orbital altitude is 1414 km. The number of conflicts is equal to the number of instances of 1 in the conflict matrix \(R\).
## 2 Materials and Methods
### Experimental Equipment and Scheme  
The main components of an artificial rainfall system include a control host, a pump, a bucket, a number of sprinklers, and a rain bracket, as shown in Figure 3. The system pressurizes water through the pump to form a spray that is ejected from the nozzle. After testing, the rainfall uniformity of the artificial rainfall system is more than 0.87 and the raindrop diameter is between 1.2 mm and 2.8 mm, which can simulate real rainfall. In Wuhan, rainfall is primarily concentrated in the summer and is characterized by short duration, high intensity, and abundant precipitation. For this experiment, a duration of 4 h and a constant rainfall intensity of 60 mm/h were selected as the input conditions for the simulated rainfall.  
Each slope is equipped with 10 moisture monitoring instruments. These monitoring instruments are high-precision soil volumetric water content sensors (SWR-100) with a range of 0-100% and an accuracy of 0.1%. They are positioned along the central axis of the lateral slope at depths of 20 cm, 40 cm, 60 cm, and 100 cm below the slope surface. The monitoring instruments are divided into three sections: w\({}_{\text{u1}}\) to w\({}_{\text{u4}}\), w\({}_{\text{m1}}\) to w\({}_{\text{m4}}\), and w\({}_{\text{d1}}\) to w\({}_{\text{d2}}\). These sections are located at 1/4, 1/2, and 3/4 of the slope length perpendicular to the slope. The specific layout of the sensors can be seen in Figure 4. The soil water content at different depths of each slope was continuously monitored in real time. Under conditions of natural evaporation after the end of rainfall, the dynamic changes in soil water content in both the bare slope and the vegetated slope were compared and analyzed at different slope ratios.  
The meteorological monitoring instrument was a PH automatic weather station located on the model slope within the experimental area, as shown in Figure 5. This station allows for real-time monitoring of rainfall, wind speed and direction, atmospheric temperature, and humidity within the test site.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Plastic Limit (\%)** & **Liquid Limit (\%)** & **Natural Density (g/cm\({}^{3}\))** & **Maximum Dry Density (g/cm\({}^{3}\))** & **Optimum Water Content (\%)** \\ \hline
23.0 & 41.0 & 1.50 & 1.750 & 20.0 \\ \hline \end{tabular}
\end{table}
Table 1: Physical property indexes of the test soil.  
Figure 3: Sprayer and water pipe.
## 4 MODECP Approach
### Network Partition Module  
The purpose of the network partition module is to determine the number of network partitions according to the node characteristics of different network environments. In other words, in order to make various network performance indicators as good as possible, a reasonable number of controllers should be placed in the network. In this paper, the MODECP approach uses the affinity propagation algorithm to create a delay matrix, a security matrix, and a load matrix based on network characteristics.  
The affinity propagation algorithm is a semi-supervised algorithm [41] that does not need to determine the number of clusters and the center of clusters in advance. The clustering foundation of the affinity propagation algorithm is the similarity between the network nodes. The preference \(p\) can be determined through the similarity matrix, and the number of clusters presents different results based on the value of preference \(p\), which directly affects the selection of the number of clusters.  
In order to obtain a reasonable number of controllers, which makes the network run for as long as possible without failure and congestion, we minimized the controller placement cost based on the improved affinity propagation algorithm, which includes network delay, nodes security, and load according to the model established in Section 3. The improved affinity propagation algorithm takes the negative value of the controller placement cost as its similarity matrix, as in Equation (23), where preference \(p\) selects the mean value of the similarity degree of each switch.  
\[X=-\mathbf{D}\times\mathbf{S}\times\mathbf{L}, \tag{23}\]  
Here, \(X\) is the similarity matrix, \(\mathbf{D}\) is the reachable path delay matrix from each switch to other switches, \(\mathbf{S}\) is the security matrix of the switch, and \(\mathbf{L}\) is the load matrix of the switch.  
The affinity propagation algorithm updates responsibility and availability through each iteration, which are denoted in Equations (24) and (25), respectively, and finally obtains a reasonable number of clusters. This pseudocode is described in Algorithm 1.  
\[r(i,k) \gets x(i,k)-\underset{j\
eq k}{max}\{x(i,j)+a(i,j)\}, \tag{24}\]  
\[\begin{split} a(i,k)&\leftarrow min\{0,r(k,k)+ \underset{k}{\sum}max\{0,r(j,k)\}\},\\ a(k,k)&\leftarrow max\{0,r(j,k)\},\end{split} \tag{25}\]  
Here, \(a(i,j)\) is the availability of switch \(j\) to switch \(i\), \(x(i,j)\) is the similarity between switch \(j\) and switch \(i\), \(r(i,k)\) is the responsibility of switch \(k\) and switch \(i\), \(a(i,k)\) is the availability of switch \(k\) to switch \(i\), and \(r(k,k)\) is the responsibility of switch \(k\).  
Figure 1: MODECP Approach.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 4 Result Calculation and Analysis
### Effect of Different Lubricant Viscosities on the Fatigue Life of Bearings  
As analyzed in the preceding context, the oil film thickness formed during bearing operation exerts considerable influence on the overall fatigue life of the bearing. The initial viscosity of the lubricating oil undoubtedly has a significant impact on both the oil film thickness and pressure.  
Table 7 presents the effects of progressively increasing the initial lubricant viscosity from 0.013 to 0.223 on the bearing's overall fatigue life when applying the parameters of cylindrical roller bearings listed in Table 2 and the operating condition parameters specified in Table 3.  
As can be observed from Table 7, (1) since the De Mul model does not consider the influence of lubrication factors, variations in the initial lubricant viscosity have no effect on the bearing fatigue life calculated based on the De Mul model, and (2) as the proposed the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rotational Speed/(r/min )** & **De Mul Model** & **Our Model** & **ISO/TS 16281:2008** \\ \hline
500 & 100.9 & 92.1 & 88.4 \\
600 & 100.9 & 90.7 & 87.1 \\
700 & 100.9 & 89.1 & 85.5 \\
800 & 100.8 & 87.4 & 83.8 \\
900 & 100.8 & 85.3 & 81.8 \\
1000 & 100.7 & 83.2 & 79.7 \\
1100 & 100.7 & 80.9 & 77.7 \\
1200 & 100.6 & 79.1 & 75.9 \\ \hline \end{tabular}
\end{table}
Table 6: Fatigue life of bearings at different speeds (\(\times 10^{6}\) r).  
Figure 12: Results and relative deviations of the three models at different rotational speeds.  
oretical model in this study incorporates elastohydrodynamic lubrication effects, the center pressure of the elastohydrodynamic oil film decreases while the overall oil film thickness gradually increases with a progressive increase in initial lubricant viscosity, consequently leading to a gradual reduction in the bearing fatigue life calculated by the model.  
Figure 13 illustrates the bearing fatigue life and relative deviations computed using three different calculation methods.
###### Abstract
[23] further refined the image motion solution at the focal plane; however, its computational complexity made it unsuitable for high-dynamic response compensation in airborne platforms. Additionally, [PERSON] et al. [24] applied an orthogonal decomposition compensation method, utilizing double-angle relationships and optical reversibility. This method used two separate reflective mirrors for LOS stabilization and image motion compensation. A 45\({}^{\circ}\) pitch mirror compensated for forward image motion and pitch variations, while a compensation mirror corrected scanning image motion and roll variations, ensuring a lightweight, compact system design. Although computationally simple, this method was limited by coupling constraints. At large pitch angles, the equivalent compensation axis became non-coaxial with the aircraft roll axis, rendering it unsuitable for backward-squint whisk-broom imaging. Therefore, in backward-squint whisk-broom imaging, the coordinate transformation method is limited by computational complexity, while the orthogonal decomposition method is inapplicable. To address the limitation of vertical downward whisk-broom cameras in acquiring complete 3D information of target scenes, this paper proposes a backward-squint wide-swath whisk-broom imaging scheme. In addition, we propose a LOS path planning method and a novel non-coaxial image motion compensation method based on the aircraft coordinate system to overcome the limitations of traditional image motion compensation methods under the backward-squint scanning conditions. First, we plan the LOS path in the navigation coordinate system to compensate for forward image motion. Next, considering attitude disturbances, we determine the roll angle of the two-axis camera and the pitch mirror angle in the aircraft coordinate system. Finally, we establish a non-coaxial image motion compensation model under backward-squint conditions and propose a high-precision method for correcting non-coaxial image motion. We conducted theoretical analysis and real-time simulations of the LOS path and ground projection trajectory to validate the accuracy of the compensation method under backward-squint conditions. The findings of this paper provide significant support for advancing airborne area-array whisk-broom imaging technology in wide-swath backward-squint imaging and 3D modeling applications.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 4 Preliminaries
### Hole Detection  
The first step of hole-filling is to identify all the holes in one given incomplete model. A hole is usually a closed loop of boundary edges (see Figure 2a). As mentioned in Section 3, the holes are partitioned into three categories to make our hole-filling method faster and more robust when handling holes with different sizes. The specific process of hole identification is conducted as follows: given a seed boundary edge, its next boundary edge can be easily checked depending on the halfedge data structure. Then we proceed to identify the next boundary edge of the just detected next boundary edge. By repeating the same way, all boundary edges can be automatically detected by checking the number of their 1-ring triangles until a closed loop is reached.
## 4 Results and Discussion  
The results of the ECTSA method are discussed in this section. The implementation and simulation of this ECTSA-based cross-layer routing are done using a Network Simulator-2.34 (NS-2.34), where the system is operated with an i5 processor with 6 GB of RAM. The routing path issue can be overcome by deploying intermediary nodes in conjunction with routing. The MANET's ensemble relationships are built on the mobile nodes' unique motion patterns. The mobility model is divided into two versions based on characteristics such as size, mobility magnitude, unpredictability, regional restrictions, and target alignment. The main objective of this research is to perform an energy-efficient cross-layer routing over the MANET. Here, the protocols are developed according to the reference structure's rules. The higher layer protocol is designed in the layered architecture, which utilizes the lower layer's services. The developed higher layer protocol does not have a constraint on the details of service delivery. Moreover, the services offered by the lower layer are not dependent on the higher layer's details. In this research, an IEEE 802.11b MAC layer protocol is used because it is cost-effective, easy-to-set-up, and extensively maintained by manufacturers. The Random Waypoint (RWP) mobility model is used because it is an elementary model which describes the movement pattern of independent nodes in simple terms. Additionally, this mobility model is a simple and straightforward stochastic model that describes the movement behavior of a mobile network node in a given system area, which results in a higher effective speed. Therefore, the Random Waypoint mobility model is used in this research. The simulation parameters of this research are mentioned in Table 1.  
The performance of the ECTSA method is analyzed by means of energy consumption, packet delivery ratio, end-to-end delay, and routing overhead. Existing research on techniques such as CEELBRP [24] and EECRP-PSO [25] is used to evaluate the efficiency of the proposed method.
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.6. GRU  
The GRU, proposed in [28], is a simpler variation of LSTM and has fewer parameters than LSTM. The LSTM has update, input, forget, and output gates and maintains the internal memory state, whereas the GRU has only update and reset gates. It combines the forget and input gates of LSTM into a single update gate and has fewer tensor operations, resulting in faster training than LSTM.  
The GRU merges the cell and hidden states. It performs well in sequence learning tasks and overcomes the problems of vanishing or exploding gradients in vanilla RNNs when learning long-term dependencies [29]. It also tends to perform better than LSTM on fewer training data, whereas LSTM is more efficient in remembering longer sequences [30; 31; 32]. The following equations describe how memory cells at each hidden layer are updated at each time step [33]. The reset gate \(\mathbf{r}_{t}\) controls the influence of \(\mathbf{h}_{t-1}\) and is computed as  
\[\mathbf{r}_{t}=\sigma(\mathbf{W}_{\mathbf{r}}\cdot[\mathbf{h}_{t-1},\mathbf{x }_{t}]), \tag{7}\]  
where \(\mathbf{x}_{t}\) and \(\mathbf{h}_{t-1}\) are the input and the previous hidden state, respectively.  
The update gate \(\mathbf{z}_{t}\) specifies whether to ignore the current information \(\mathbf{x}_{t}\) and is computed as  
\[\mathbf{z}_{t}=\sigma(\mathbf{W}_{\mathbf{z}}\cdot[\mathbf{h}_{t-1},\mathbf{x }_{t}]). \tag{8}\]  
The computation of candidate activation \(\mathbf{h}_{t}\) is similar to that of the traditional recurrent unit, that is,  
\[\mathbf{h}_{t}=\mathbf{z}_{t}\odot\mathbf{h}_{t-1}+(1-\mathbf{z}_{t})\odot \widetilde{\mathbf{h}}_{t}, \tag{9}\]  
where  
\[\widetilde{\mathbf{h}}_{t}=\tanh(\mathbf{W}_{\mathbf{h}}\cdot[\mathbf{r}_{t} \odot\mathbf{h}_{t-1},\mathbf{x}_{t}]), \tag{10}\]  
\(\mathbf{W}_{\mathbf{r}}\), \(\mathbf{W}_{\mathbf{z}}\) and \(\mathbf{W}_{\mathbf{h}}\) are weight matrices which are learned.
###### Abstract  
This study aims to improve the electrical-mechanical performance of traditional epoxy/graphite composites for engineering applications. The improvement in the properties of these composites depended on the incorporation of different sizes of graphite particles of the same type and controlling their curing process conditions. The thermal properties and microstructural changes were also characterized. A maximum in-plane electrical conductivity value of approximately 23 S/cm was reported for composites containing 80 wt% G with a particle size of 150 um. The effect of combining large and small G particles increased this value to approximately 32 S/cm by replacing the large particle size with 10 wt.% smaller particles (75 um). A further increase in the electrical conductivity to approximately 50 S/cm was achieved due to the increase in curing temperature and time. Increasing the curing temperature or time also had a crucial role in improving the tensile strength of the composites and a tensile strength of \(\sim\)19 MPa was reported using a system of multiple filler particle sizes processed at the highest curing temperature and time compared to \(\sim\)9 MPa for epoxy/G150 at 80 wt.%. TGA analysis showed that the composites are thermally stable, and stability was improved by the addition of filler to the resin. A slight difference in the degraded weights and the glass transition temperatures between composites of different multiple filler particle sizes was also observed from the TGA and DSC results.  
e pacs: 62022
## 4 Results
### Simulation Results
#### 4.1.3 CoMTUS Imaging Depth  
Figure 7 compares the one probe with the CoMTUS (gap \(\sim\)45.3 mm) images at two different imaging depths (100 mm and 155 mm). Image degradation with depth is clearly observed in all the cases. However, at larger depths, the one probe shows a greater level of degradation. At the maximum imaging depth shown (155 mm), the point targets and the lesion can still be clearly identified in the CoMTUS image while they are barely discernible in the one probe image.  
Figure 6: (**a**) Lateral point-spread-function (PSF) extracted from Figure 5 at the depth of peak point intensity and in the principal direction. Corresponding computed quality metrics as a function of the effective aperture size in CoMTUS (solid line) compared with the one probe system (dashed line): (**b**) lateral resolution (LR); (**c**) contrast; and (**d**) contrast-to-noise-ratio (CNR).  
The computed image metrics as a function of the imaging depth are summarized in Figure 8. As expected, in both systems, all image metrics worsen at larger imaging depths. Nevertheless, results show that their dependence on the imaging depth is different. LR is always larger (worse) with one probe and increases more rapidly with depth (Figure 8a). While at reduced imaging depths (\(<\)100 mm), contrast and CNR seem to be affected in a similar way in both systems, the loss in contrast metrics is less accentuated in the CoMTUS system at depths larger than 100 mm. In those cases, the CoMTUS method exceeds the performance of the one probe system not only in terms of resolution, but also in contrast, despite in the used configuration, the gap in the aperture is not minimum and leads to greater side lobes (Figure 5d). The extended effective aperture created by CoMTUS consequently increases the sensitivity of the imaging system, particularly at large imaging depths.
# Inspection of Coated Hydrogen Transportation Pipelines
## 4 Evaluation of Current Pipeline Inspection Methods for Hydrogen Pipelines
### Coatings
#### 4.2.3 The Effect of Coatings on the Choice of Inspection Method  
Given the different requirements of each NDT method, some challenges will be experienced due to the coatings when selected NDT methods are used to inspect the pipeline. These include:  
* Thickness of coating: The distance between the sensor and metal surface due to the coating layer (known as lift-off) reduces the sensitivity of inspection [106]. The lift-off itself may vary due to the uneven thickness of the coating, complicating matters further [106]. This is likely to affect inspections of pipeline conducted using EC due to its dependence on lift-off [15].
* Conductivity of Coating: Coating conductivity may again interfere with methods such as EC [107]. Additionally, since MFL works best in metals that are easily magnetised, if the coating possesses this property, it may interfere with MFL inspections of the pipeline.  
Therefore, it becomes apparent that, depending on the coating, some NDT methods have to be ruled out. The material of the coating would also determine the NDT method used to inspect the coating itself. Table 10 attempts to provide an overview of how the choice of NDT method would change depending on the coating type and the subject of inspection.
## 2 Materials and Methods
### Drifter Ocean Surface Current Data  
The Global Drifter Program (GDP) maintains a network of approximately 1300 surface drifters that provide ocean current data worldwide, with temporal resolutions of 1 h [32] and 6 h [33]. Between 5 November 2017 and 31 October 2023, a total of 487 drifting buoys collected surface current measurements in the SCS. The 1 h resolution data are available through 31 October 2022, while the 6 h resolution data extend to 31 October 2023. Accordingly, the Sentinel-1 SAR Doppler shift observations were matched with the 1 h drifter data up to 31 October 2022, and with the 6 h drifter data thereafter. Both datasets are publicly available from the National Oceanic and Atmospheric Administration (NOAA) GDP website. Figure 1 illustrates the trajectories and corresponding surface current velocities of the drifting buoys. A strong coastal current is evident along the southeast shelf of Vietnam, with velocities occasionally exceeding 2 m/s, primarily driven by summer upwelling in this region [34]. Additionally, the combined effects of the coastal topography and wave-induced motion generate pronounced longshore currents near the Pearl River estuary. The figure also reveals the distinct signatures of the Kuroshio current and a prominent mesoscale eddy southeast of Taiwan, both of which are further analyzed in the case studies presented later in this paper.