esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index
### Node Importance Measurement Result Based on Passenger Flow of Urban Rail Network  
Taking the peak period (17:30-19:30) and the off-peak period (14:30-16:30) of 22 June 2016 and the full day inbound passenger flow as the measurement indicators, the importance of the nodes was ranked respectively. To facilitate data processing, the passenger flow data were normalized. Nodes of high importance in urban rail networks tend to have significant passenger flow characteristics. A ranking diagram of node importance by metrics of passenger flow is given, as shown in Figure 6. In the figure, the larger the diameter of the node and the darker the color, it means that the larger the passenger flow of the node and the higher the importance of the node.  
As shown in Figure 6a, the nodes with large passenger flow during the off-peak period are Beijing West Railway Station, Beijing Station (large hub station with multiple modes of transportation), and Dongzhimen, Xizhimen, Guomao, and other multi line transfer stations. As shown in Figure 6b, the nodes with large passenger flow in peak period are mainly concentrated in No. 2 ring line and No. 10 ring line, such as Xierqi, Guomao, Chaoyangmen, Sanyuanqiao, Liangmaqiao, etc. As shown in Figure 6c, the important nodes obtained by the full day inbound passenger flow are mainly concentrated in the ring road and residential areas, such as xierqi, Guomao, Chaoyangmen, Tiantongyuan, Tiantongyuanbei, etc.
## 3 Simulation Results and Discussion
### Dynamic Behavior of TB-TSS with Fixed-Length Tethers
Figure 6: Dynamic characteristics of TB-TSS with fixed-length tether in case 2; (**a**) libration angles of \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{1}-\theta_{2}\); (**b**) libration angles of \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{1}-\alpha_{2}\); (**c**) tether tension of tethers L\({}_{1}\) and L\({}_{2}\). Finally, a set of initial attitude angles was chosen: \(\theta_{1}=\theta_{2}=0^{\circ}\), \(\alpha_{1}=20^{\circ}\), and \(\alpha_{2}=-20^{\circ}\). In this case, the oscillation amplitudes of \(\alpha_{1}\) and \(\alpha_{2}\) were lower after one orbit, while the oscillation responses of \(\theta_{1}\) and \(\theta_{2}\) were enhanced, as shown in Figure 9a,b. Additionally, the in-plane oscillation becomes atrophic, while the out-of-plane oscillation begins to strengthen after two orbits. This observation aligns with the findings in Figure 8a,b. The tether tension in this case is depicted in Figure 9c. The maximum values of T\({}_{1}\) and T\({}_{2}\) are larger compared to those in case 4; however, the values remain below those in case 3. These results suggest that the tension characteristics are influenced by the initial attitude angles, and the amplitudes of tether tension elongate with different initial libration angles. The maximum tether tension in case 5 is nearly three times the value in case 4. Consequently, it can be concluded that the initial attitude angles significantly influence the dynamic characteristics of a fully deployed TB-TSS. Figure 8: Dynamic characteristics of TB-TSS with fixed length tethers in case 4; (**a**) libration angles of \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{1}-\theta_{22}\); (**b**) libration angles of \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{1}-\alpha_{2}\); (**c**) tether tension of tethers L\({}_{1}\) and L\({}_{2}\). In conclusion, the simulation results show that the initial attitude angles significantly influence the dynamic characteristics of a fully deployed TB-TSS. The peak value of tether tension is closely related to the initial attitude angles. Moreover, the amplitude is greater with different initial attitude angles than with the same initial attitude angles. Libration energy is not transferred from in-plane motion to out-of-plane motion when only in-plane libration occurs, while energy can be delivered from out-of-plane motion to in-plane motion when out-of-plane libration occurs, and energy transfer will reach dynamic equilibrium after a period of time.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 9 Conclusions  
In this work, the authors developed an energy-optimal trajectory planning algorithm for an emergency drone. This energy-optimal path can also save lives in case of emergency calls or assistance needed, such as in mountain tourism, when a smaller medical package must be delivered to those in trouble. To achieve this, the authors analyzed several path planning algorithms, from which the following conclusions could be drawn.  
Originally, this study focused on the PPL processes using evolutionary algorithms, such as NN, and GA-based PPL processes. Unfortunately, these methods are considerably limited in the case of dynamic weighting, because they are highly dependent on the training (or new population generation) time and the update frequency of the weights. Simply put, during training, the weights can change several times, and the path selected would not be the optimal and current one. Based on these studies, the dynamic and Real-Time PPL methods seemed to be most suitable, thus the heuristic A* algorithm was opted for.  
Essentially, a _modified A*_ algorithm was used, because in the classic algorithm the edges were weighted and the optimal path was identified based on a cost function. In this modification, not only the edges but also the nodes were weighted, and the algorithm stepped from one node to the next, selecting the branch to the next node based on the actual weight (which is calculated and evaluated) at each node. A good example can be seen in Figure 14 (see the yellow path), where the drone flew in a headwind and \"sailed\" between the nodes to achieve minimum energy use. Basically, this is a highly complex system, therefore it was divided into sub-programs (procedures) and their operations were interconnected and/or interlinked. The system relied on two Databases (DBs): 1. \"MeteoCloud\"--where the IoT data of weather stations is uploaded. The wind map for the selected Working Space was created from this DB; 2. \"PointCloud\" for geographical data of the selected WS. After partitioning the WS, the points were represented by the center-points of the cubes, and these points were also the nodes of the graph (of course, only in free-spaces). These points (nodes) were in fact vectors (arrays) containing coordinates and calculated weights, \(\overset{\rightarrow}{n}_{(i)}=\left[x,y,z,w_{(i)}\right]\).  
As Figures 12-14 reveal, the developed algorithm fulfilled the expectations of the authors. In the case of tailwind (Figure 12), the trajectory was an almost straight line to the goal, while in the case of cross- and headwinds, the drone drew a trajectory resembling that of a glider aircraft.
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 5 Conclusions  
The full-wave electromagnetic simulations showed that the end-fire antenna vertically embedded in the skull exhibited two times (3 dB) higher antenna gain than an ordinary case--a broadside antenna horizontally embedded below the skull. The proposed end-fire Vivaldi antenna was measured to have a small footprint of \(12\times 7\) mm\({}^{2}\) because of the novel comb-shaped slot arrays behind the main aperture. These slot arrays not only promote the antenna miniaturization but also enhance the gain and bandwidth. The geometry of the antenna was carefully optimized by full-wave simulations to operate in the 3-5 GHz IR-UWB frequency range. An antenna prototype was fabricated and a series of measurements were performed by embedding the antenna in an in-house-made seven-layer brain-tissue-emulating phantom to verify the antenna performance. The measurement results of the proposed Vivaldi showed that the resonant frequency was 2 GHz lower, and the gain was 2.6 dB higher than the conventional Vivaldi without slot arrays. Furthermore, the proposed Vivaldi can be a promising candidate for brain-to-outside wireless communication based on the link budget and SAR analyses. Based on our findings, it could be worthwhile for surgeons and medical professionals to identify pathways for implanting antennas in the skull during a brain surgery (e.g., craniotomy).  
Conceptualization, J.-Y.C.; methodology, L.S., S.H. and J.-Y.C.; validation, L.S. and S.H.; formal analysis, L.S. and J.-Y.C.; investigation, L.S. and J.-Y.C.; resources, S.H. and J.-Y.C.; data curation, L.S. and S.H.; writing--original draft preparation, L.S. and S.H.; writing--review and editing, J.-Y.C.; visualization, L.S.; supervision, J.-Y.C.; project administration, J.-Y.C.; funding acquisition, J.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Research Program funded by SeoulTech (Seoul National University of Science and Technology).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflict of interest.
## 2 An Overview of the HJC Model
### State Equation  
The state equation in the HJC model can be divided into three regions: the linear elastic region, the transition region, and the fully dense region.  
(1) Linear elastic region (\(0\leq\mu\leq\mu_{crush}\))  
Figure 1: Technique for removing the pressure effect.  
Figure 2: Determination of the strain rate constant.  
The pressure in the linear elastic region is expressed as follows:  
\[P=K_{elastic}\mu \tag{4}\]  
In this equation, \(P\) is pressure; \(\mu\) is volumetric strain, \(\mu=(\rho/\rho_{0})-1\); \(\rho\) and \(\rho_{0}\) are the current density and initial density, respectively; \(K_{elastic}\) is the elastic bulk modulus, \(K_{elastic}=P_{crush}/\mu_{crush}\), \(P_{crush}\) and \(\mu_{crush}\) are pressure and volumetric strain under the elastic compression limit.  
(2) Transition region (\(\mu_{crush}<\mu\leq\mu_{lock}\))  
In this region, the air is gradually compressed out of the holes of the rock material, and there will be a plastic volumetric strain in the rock material. At this time, the pressure of the material during loading and unloading is expressed as follows:  
\[P=P_{crush}+\frac{P_{lock}-P_{crush}}{\mu_{lock}-\mu_{crush}} \tag{5}\]  
\[P=P_{crush}+\frac{P_{lock}-P_{crush}}{\mu_{lock}-\mu_{crush}}(\mu_{0}-\mu_{crush })+[(1-F)K_{elastic}+F\frac{P_{lock}-P_{crush}}{\mu_{lock}-\mu_{crush}}](\mu- \mu_{0}) \tag{6}\]  
In this equation, \(\mu_{lock}\) and \(P_{lock}\) are volumetric strain and pressure, respectively, in the transition region; \(F\) is the unloading coefficient, which is expressed as follows:  
\[F=(\mu_{0}-\mu_{crush})/(\mu_{lock}-\mu_{crush}) \tag{7}\]  
(3) Fully dense region (\(\mu_{lock}<\mu\))  
In this region, the air is fully removed from the rock mass. At this time, the pressure of the material during loading and unloading is expressed as follows:  
\[P=K_{1}\overline{\mu}+K_{2}\overline{\mu}^{2}+K_{3}\overline{\mu}^{3} \tag{8}\]  
\[P=K_{1}\overline{\mu} \tag{9}\]  
In this equation, \(\overline{\mu}=\frac{\mu-\mu_{lock}}{1+\mu_{lock}}\) is the modified volumetric strain; \(\mu_{lock}\) is the volumetric strain of the rock material compaction point; and \(K_{1}\), \(K_{2}\) and \(K_{3}\) are rock material constants.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 4 Experimental Results
### Eclipse-Aware Routing Results
This performance degradation is attributed to differences in the two implementations. In the CUDA implementation, additional steps such as CSR transformation and data copying introduce overhead. Specifically, CUDA requires data transfers between the host (CPU) and the device (GPU), which are handled using the cudaMemcpy function. Since the CPU and GPU operate in separate memory spaces, this data transfer is essential but incurs significant memory overhead. In smaller network environments, this overhead can have a substantial impact on performance. However, when the network size was increased to 5000 nodes, the CUDA-based algorithm achieved  
Figure 9: Validation by comparing execution results PC with only CPU and OBC with CPU and GPU. a 3.081\(\times\) speedup compared to the CPU-based algorithm. This result demonstrates that CUDA-based KSP exploration is highly effective in performing parallel computations in large-scale graph environments.
## 2 Materials and Methods
### Data Source
#### 2.2.4 GOSIF Data  
The GOSIF datasets (W\(\cdot\)m\({}^{-2}\cdot\)um\({}^{-1}\cdot\)sr\({}^{-1}\)) consolidated the discrete Orbiting Carbon Observatory-2 (OCO-2) SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data, with high spatial and temporal resolutions (i.e., 0.05\({}^{\circ}\) spatial resolution and 8 day, monthly and annual temporal resolution) [32]. The GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record (from March 2000 to December 2020) obtained from the website ([[http://data.globalecology.unh.edu/data/GOSIF](http://data.globalecology.unh.edu/data/GOSIF)]([http://data.globalecology.unh.edu/data/GOSIF](http://data.globalecology.unh.edu/data/GOSIF))\({}_{-}\)v2, accessed on 20 April 2021). At present, GOSIF is useful for assessing terrestrial photosynthesis and ecosystem function and benchmarking terrestrial biosphere and Earth system models. In this study, the 0.05\({}^{\circ}\) monthly GOSIF data were employed to examine the performance of SIF in drought monitoring and the relationship between SIF and GPP.
## 4 Parameter Sensitivity Analysis
### Effect of Elastic Mdulus on Wave Behaviour  
In this work, both isotropic and orthotropic models are considered to study the influence of elasticity modulus on the wave propagation in the wood pole. The parameter values are selected within the range of elastic modulus for Spotted Gum, which are shown in Table 4. As all we know, the elastic modulus for orthotropic material is free of three orthogonal axes. As a consequence, all three directions are considered to find out the effect of change in elastic modulus in each direction on the wave velocity. Figure 12 shows the acceleration responses of wood pole isotropic models corresponding to three elastic modulus values 23,000, 21,000 and 18,000 MPa. It is noticeable that when other properties such as density and Poisson's ratio are determined, the lower elastic modulus will result in a lower wave velocity. Moreover, it is clearly noted that the delay of wave peaks appearing is more obvious within the three materials after the waves travel up and down the poles several times. The similar phenomenon can also be found in the orthotropic material model in the longitudinal direction, shown in Table 5 and Figure 13.  
Figure 11: Diagram of point vs time.  
For the orthotropic material model, the elastic modulus in the longitudinal direction plays a significant role in changing wave velocity, and the response phenomenon is quite similar to that of the isotropic material model. However, in the radial and tangential directions, the changes of elastic modulus provide slight effect on the wave velocity. The corresponding results are shown in Figures 14 and 15. It is clearly observed that the first peaks of the curves are totally overlapped, which means that the wave propagation will less influenced by the elastic modulus changes in the radial and tangential directions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elastic Modulus (MPa)** & **Change Percentage** & **Wave Velocity (m/s)** & **Change Percentage** \\ \hline
23,000 & Not Applicable (N/A) & 4920 & N/A \\
21,000 & \(-8.69\%\) & 4649 & \(-5.51\%\) \\
18,000 & \(-21.74\%\) & 4306 & \(-12.48\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Study on elastic modulus for isotropic material model.  
Figure 12: Effect of elastic modulus on wave behaviour in the isotropic material model.  
Figure 13: Effect of elastic modulus in the longitudinal direction on wave behaviour in the orthotropic material model.
# A Point Crack Source Location Method without Velocity Information in Anisotropic Plates
## 3 Experiments  
### Robustness  
This section is devoted to explaining the numerical experiment settings for testing the noise robustness of the location estimation method. For the numerical experiments, we generated the signal arrival time \(t_{i}\) using the following equation derived from Equation (1) for \(i\ =\ 1,2,\ldots,N\)  
\[:t_{i}=t+\sqrt{\frac{(x_{i}-x)^{2}v_{y}^{2}+(y_{i}-y)^{2}v_{x}^{2}}{v_{x}^{2}v_ {y}^{2}}} \tag{6}\]  
where \(N\) is the number of sensors, assuming that the crack occurrence point \((x,y)\) and the position \((x_{i},y_{i})\) of the sensors are given on the \(xy\) plane and the propagation velocities \(v_{x}\) are \(v_{y}\) of the elastic wave in the \(x\) and \(y\) directions are given.  
Figure 3 shows the numerical experiment settings in the domain \([-4,4]\times[-2,2]\). The source location in the figure is marked with a red dot at (1,0), and various crack locations at (1,0), (2,0), (3,0), (1,1), (2,1), and (3,1) were tested in the numerical experiments. For ease of viewing, only one sensor (blue dot) is shown in the figure, but numerical experiments were performed with varying numbers of sensors (4, 6, 8, 10, and 12). Here, for a variable number of sensors, we always spaced them equally at the boundary of the domain. For the wave propagation, we used the propagation velocities \(v_{x}=3\) and \(v_{y}=1\) (i.e., the elastic wave in the \(x\) direction was three times faster than that of \(y\) direction).  
To check the robustness of the algorithm against noise, we added Gaussian random noise \(n_{i}\) to the time data \(t_{i}\). Here, \(t_{i}\) is generated by computing Equation (6), and \(n_{i}\) is a pseudorandom value drawn from the normal distribution with a mean of 0 and standard deviation \(\sigma\). To experiment with varying the magnitude of the noise according to the magnitude of the time data, we adopted the alternative definition of the SNR [29,30], denoted as ASNR, which is the ratio of the mean to the standard deviation of the signal arrival time \(\{t_{i}\}_{i=1}^{N}\), as shown below:  
\[\text{ASNR}=\frac{\mu}{\sigma} \tag{7}\]  
where \(\mu\) is the mean of \(\{t_{i}\}_{i=1}^{N}\). In this paper, we use \(\text{ASNR}=10\), \(30\), \(50\), and \(\infty\). Here, for notational simplicity, it is expressed as \(\text{ASNR}=\infty\) when there is no noise. For each noise level, the average of the relative errors from a total of \(M=100\) experiments is used. The relative error of the source location is defined as the ratio of the distance error to the diameter of a circle having an area equal to the area of the domain, and the distance error is the distance between the crack location and the estimated location.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 5 Results
### Effectiveness and Complexity
#### 5.4.1 Effectiveness
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{8}{c}{\(E_{b}/N_{0}\)} \\ \cline{2-11}  & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
2 & 0.9860 & 0.4804 & 0.1249 & 0.8961 & 0.1589 & 0.0107 & 0.0909 & 0.6328 & 0.0478 & 0.1157 & 0.2613 \\
4 & 0.9988 & 0.9459 & 0.9930 & 0.8216 & 0.8027 & 0.0900 & 0.0763 & 0.7385 & 0.6534 & 0.9313 & 0.2785 \\
8 & 0.0002 & 0.0010 & 0.0001 & 0.0002 & 0.0002 & 0.0003 & 0.0003 & 0.0001 & 0.0001 & 0.0001 & 0.0022 \\
16 & 0.1520 & 0.9662 & 0.1189 & 0.6116 & 0.8844 & 0.1877 & 0.0227 & 0.0960 & 0.0034 & 0.0086 & 0.0014 \\
32 & 0.3196 & 0.0080 & 0.0001 & 0.0011 & 0.0001 & 0.0001 & 0.0001 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Significance level test results.
## 2 Materials and Methods
### Spectrophotometric Determination of Antioxidant Activities of Alkaloid Extracts
#### 2.4.5 Superoxide Anion Radical Scavenging Activity  
To generate the superoxide anion radical, a mixture comprising 3 mL of 0.1 M Na\({}_{3}\)PO\({}_{4}\) buffer (with a pH of 7.4), 156 M nicotinamide adenine dinucleotide (NADH) (Merck), 52 M nitro blue tetrazolium (NBT) (Merck), and 20 M phosphate-buffered saline (PBS) (Thermo Fisher Scientific, Waltham, MA, USA) was prepared. One mL of varying concentrations of alkaloid extracts (0, 50, 100, 150, 200, and 250 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) was then incorporated. After a 5 min incubation period at 25 \({}^{\circ}\)C, the solution's absorbance was assessed at 560 nm using a microplate reader in comparison with a blind sample. The suppression of superoxide anion radical formation in the samples was determined using the equation: Inhibition (%) = [(A\({}_{\mathrm{control}}-\) A\({}_{\mathrm{sample}}\))/A\({}_{\mathrm{control}}\)] \(\times\) 100. Here, A\({}_{\mathrm{control}}\) is the absorbance value when all elements are present, excluding the test compound, and A\({}_{\mathrm{sample}}\) is the absorbance of the test substance. As a reference, ascorbic acid (0.5 mg/mL) was employed [34; 35].
Performance Evaluation of Buried Concrete Pipe Considering Soil Pressure and Crack Propagation Using 3D Finite Element Analysis  
[PERSON]  
1 Department of Civil Engineering, Kangwon National University, Samcheok 25913, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Architecture, Kumoh National Institute of Technology, Gunti 39177, Korea 2* 2  
not only affected by backfill materials but also by other environmental conditions, such as the loading type, the presence of a groundwater table, and voids that form around buried pipes. These environmental conditions are not fully considered in existing pipeline analysis procedures.  
In addition to experimental studies, several analytical and numerical investigations [6; 7; 8; 9; 10; 11; 12; 13] have also been conducted. However, the simulations were performed under conditions of a 2D plane strain using elastic models for soil and pipe materials. To analyze the influence of inelastic soil behaviors on the pressure distributions around pipes, the elastoplastic behavior of surrounding soil must be considered.  
Therefore, in this study, a nonlinear elastoplastic model for backfill materials was developed to obtain more accurate soil pressure distributions around buried concrete pipes and analyze crack propagation in pipes. The specific objectives of this study can be summarized as follows.  
* Use an advanced soil model to explore the load-transfer mechanisms through which a buried pipe may benefit from the soil arching effect.
* Examine material-specific soil pressure distributions and crack propagation in pipes related to external loading, groundwater tables, and voids.
* Derive significant insights based on the simulation results for the environment-related design and analysis of buried concrete pipes.