esa-sceva/satcom-chunk-collection huggingface dataset:
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm
### Improvement of the A* Algorithm
#### 2.2.1 Determining the Variables Included in \(h(n)\)  
First, it is necessary to identify the variables in \(h(n)\). For an eight-directional search, the traditional A* algorithm's heuristic function \(h(n)\) often utilizes either the Chebyshev distance or the Euclidean distance. Among these, the Chebyshev distance is more effective in measuring the number of moves in an eight-directional grid, while the Euclidean distance better represents the actual distance. Therefore, we use both distance metrics simultaneously to enhance the accuracy of the heuristic function \(h(n)\) in distance evaluation.  
Additionally, considering that when a node is surrounded by many obstacles, the path passing through that node may increase in length due to the need to avoid obstacles, we also incorporate the obstacle density into the heuristic function \(h(n)\). This addition enables the search process to possess a certain level of obstacle avoidance capability.  
Thus, we modify the heuristic function \(h(n)\) as follows (1):  
\[\left\{\begin{aligned} & h(n)=\omega_{1}*d_{Chebyshev}+\omega_{2}*d_{ Euclidean}+\omega_{3}*\rho_{obstacle}\\ &\rho_{Obstacle}=\frac{n_{obstacle}}{8}\end{aligned}\right. \tag{1}\]  
In this context, \(d_{Chebyshev}\) represents the Chebyshev distance from the current node \(n\) to the goal node, \(d_{Euclidean}\) denotes the Euclidean distance, \(\rho_{obstacle}\) represents the obstacle density in the 8-neighborhood of the current node \(n\), and \(n_{obstacle}\) refers to the number of obstacles in the 8-neighborhood of node \(n\) (as illustrated in Figure 1). \(\omega_{1},\omega_{2},\omega_{3}\) are weight coefficients.
## 3 Results and Discussion
### Signal Location Analysis
The same procedure was performed for the computation of AE event location and characteristics in each cell. Figure 12 depicts the observed dependence. The vertical axis represents the different units corresponding to each value on the horizontal axis such as the number of AE events, amplitude in dB and peak frequency in kHz. The average value of crack density for samples No. 1 to No. 12 and for an investigated sample is also presented on each graph as red and black lines, respectively. Figure 11: Image-gridding method. Figure 10: AE source location analysis: (**a**) Amplitude and Frequency of AE events in sample No. 5. (**b**) Amplitude and Frequency of AE events in sample No. 8; (**c**) Amplitude and Frequency of AE events in sample No. 9; (**d**) Amplitude and Frequency of AE events in sample No. 11; (**e**) Amplitude and Frequency of AE events in sample No. 12. As can be seen, sample No. 5 has low cracking damage concentrated only on the right side of the sample (Figure 12a). The respective cells show a high enough value of amplitude and peak frequency, especially in cell No. 6 where relative crack density is maximum. In contrast, other samples have considerable damage degrees distributed on the whole sample area. In sample No. 8, the amplitude is distributed at the same level of about 50 dB whereas the peak frequency ranges more significantly that demonstrate the different crack growth rates in each part of the sample. Pre-existing cracks in cell No. 4 contribute a higher  
Figure 12: Counts of AE parameters and crack density in the grid: (**a**) sample No. 5; (**b**) sample No. 8; (**c**) sample No. 9; (**d**) sample No. 11; (**e**) sample No. 12.  
amount and their development is slower which is reflected by the low peak frequency AE signals content. In contrast, in adjacent cells of No. 5 and No. 7, the increase in AE events number, amplitude and peak frequency can indicate the crack development in these directions. The distribution tendency of AE events and their characteristic in samples No. 9 and No. 12 have similar features: a significant number of AE signals in central cell No. 5 of high amplitude (more than 50 dB) and peak frequency (about 80 kHz) correspond to active fracture processes occurrence in this area. Sides of the specimens in terms of adjacent cells No. 4 and No. 6 also show the considerable parameters of AE events that can be related to the main failure pattern as side expansion. It can be mentioned that the peak frequency in samples No. 9 and No. 11 demonstrate higher average values than in others which can be related to the crack's orientations. From Figure 5, the vertical cracks are prevalent in both these samples, whereas in samples No. 8 and No. 12, the horizontal cracks number is dominant. It was found that the crack propagation velocity parallel to the loading axis is faster than that of the perpendicular one to the loading axis [46]. Therefore, a high peak frequency can be expected in concrete samples with vertically oriented cracks.
## 4 Conclusions and Future Work  
As described throughout this article, the designed platform has been successfully implemented. The robot was assembled and has already been tested in indoor environments. The current implemented control techniques allow the robot to move with controlled \(v_{r},~{}v_{n_{r}},~{}\) and \(\omega_{r}\) speeds, as well as its height (\(z_{r}\)).  
The models for the motors used and for the improved joints were obtained and included in the robot's simulation model developed with the SimTwo simulator. The simulation results were compared with the experimental data, revealing a high similarity.  
The low errors obtained allowed us to proceed with the implementation of \(x-y\) control of the robot and a trajectory tracking algorithm in the simulated environment. Regarding the latter, its performance was assessed and, from the small errors observed, it can be concluded to be a viable strategy for the motion of the vehicle in its workspace.  
In the future, as the simulation has been validated and proved to be realistic, further work will continue in order to include \(z_{r}\)-axis control of the robot, testing algorithms for trajectory control and planning (with obstacle detection and avoidance), and developing the gait planning framework of the robot.  
Concerning the real vehicle, to perceive the surrounding space, it is necessary to add LiDAR sensors to this platform, acquire their data, and use them to recognize obstacles and contours such as steps and walls. It will then become possible to continue exploring the robot, namely, for stair climbing, reactive or predictive obstacle avoidance, as well as gait planning--particularly trotting. Furthermore, interesting data to collect includes the robot's maximum achievable speeds and energy consumption in each mode.  
A video presenting the experiments is available online at [[https://youtu.be/0](https://youtu.be/0) xVQSwRMrDs]([https://youtu.be/0](https://youtu.be/0) xVQSwRMrDs). Conceptualization, V.H.P. and P.C.; methodology, V.H.P. and P.C.; software, P.C., V.H.P. and I.N.S.; validation, V.H.P., I.N.S., M.R., J.G., J.L. and P.C.; investigation, V.H.P., I.N.S. and M.R.; writing--original draft preparation, V.H.P. and I.N.S.; writing--review and editing, V.H.P., I.N.S., J.G., J.L. and P.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average** & **Maximum** \\ \hline  & **Line** & \\ \hline Distance (m) & 0.018 & 0.097 \\ Angular position (rad) & 0.014 & 0.129 \\ \hline  & **Curve** & \\ \hline Distance (m) & 0.027 & 0.138 \\ Angular position (rad) & 0.060 & 0.514 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Absolute errors in distance and angular position in relation to the trajectory.  
**Acknowledgments:** This work is financed by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia, within project UIDB/50014/2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 4 Results
### DL-Based Dual-Tracer Separation Results
The average NRMSE values of the DL-based separation results are at a similar level for all \(\Delta t\), which indicates that the separation performance of simultaneous injection is comparable to \(\Delta t\geq 5\) min using the DL-based method. This is because the DL-based method uses both temporal and spatial information for dual-tracer separation, which effectively improves the separation performance even when simultaneous injection (\(\Delta t=0\) min) is considered.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
###### Abstract  
Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture, leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF). The test was conducted at the Mead site during the growing season (1 May to 30 September) in 2003, 2004, and 2005. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, then predicting one future month) are adapted to evaluate the influence of assimilation on subsequent prediction against Noah-MP open-loop simulation (OL). On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to open-loop run, respectively. The influence of assimilation on prediction can reach around 60 days and 100 days for H (LE) and SM, respectively. Our findings indicate that data assimilation can improve the accuracy of land surface variables in a short-term prediction period.  
sensible heat flux; latent heat flux; short-term prediction; ensemble Kalman filter; Noah-MP
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 3 Impacts of GNSS Variability
While a simulation allows for repeated measurements, unlike the unpredictable nature of real-world, time-varying positioning, a receiver's response still presents uncertainties. However, if we consistently initialize the receiver to a known state and the GNSS simulator precisely replicates the signals, we can statistically analyze multiple measurements under identical conditions--an advantage impossible in live testing. Therefore, to thoroughly assess a receiver's vulnerability to jamming, an automated system is essential. This system must feature the ability to reset the receiver to a defined initial state, a multi-frequency GNSS simulator capable of accurately reproducing all satellite constellation signals, a controllable jamming signal source, and an automated measurement processing software with statistical analysis code (the latter is detailed in Appendix A).
## 4 Overview of GNSSs
### Gps  
GPS is a space-based positioning and navigation system that provides three-dimensional positioning with accuracy to within a few meters, anywhere on or near Earth. It was designed for military use to provide the greatest possible accuracy in locating objects in space. The development of the system has made it possible to use GPS for commercial applications as well. Since the discontinuation of selective availability, civilian users have been able to use a signal whose predicted position accuracy has improved with each successive generation of satellites used. GPS satellites emit a signal that contains the transmission time, the satellite's position, and its identifier, along with other information. The signal transmission time determines the distance between the GPS receiver and the transmitting satellite. A receiver that is within range of four satellites is able to determine the receiver's three-dimensional position while eliminating the receiver's clock error [77; 78].
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 4 Analysis  
The access time for the proposed scheme to improve XML querying over wireless broadcast channels is determined by several factors, including the size and complexity of the XML data stream, the number of wireless broadcast channels, the query type and complexity, and network conditions. To calculate the access time for the proposed scheme, the following parameters were used:
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 3 Results
### Construction of the Spectral Feature Library with Disease Symptom Proportions
#### 3.2.2 Statistical Analysis of Narrow-Band Vegetation Indices  
Narrow-band indices employed in this research included the common narrow-band ones such as the sensitivity to anthocyanin content, the indices independent of pigments, the chlorophyll absorption ratio index, the index related to photosynthetic efficiency, the and nitrogen reflectance index. According to the maximum, minimum, and coefficient of variation values shown in Figure 4, the vegetation indices, the narrow-band normalized difference vegetation index (NBNDVI), the nitrogen reflectance index (NRI), photochemical/physiological reflectance index (PRI), the transformed chlorophyll absorption and reflectance index (TCARI), the red-edge vegetation stress index (RVSI), and the plant senescence reflectance index (PSRI), can basically separate the healthy tissue from the diseased tissue. Throughout the structure of these vegetation indices, it was found that the wavelengths used in these variables include 500, 531, 550, 570, 670, 680, 700, 712, 732, 750, 752 nm, etc. These bands are consistent with the above sensitive characteristic wavelengths presented in Figure 2.
## 5 Safety Evaluation
### Safety Evaluation Based on Reset Monitoring Starting Point Data  
According to the conclusion obtained in Section 5.1, the final setting time is taken as the starting point of effective deformation of each measuring point, and the strain conversion is carried out by Formula (10). The strain of each point is shown in Figure 7.  
From Figure 7, the compressive strain of each measuring point increases rapidly after pouring, reaches a peak within one month, and then gradually decreases. This is because the concrete is about 1 month old from the beginning of early pouring to the end of hydration heat release. During this period, a large amount of hydration heat is released by cement hydration, and the temperature rise expansion of the concrete is restrained to produce a large compressive strain. The peak range of the compressive strain at measuring points 1-5 is 182.47 \(\mu\varepsilon\)-335.98 \(\mu\varepsilon\). The peak range of the compressive strain at measuring points 6-10 is 182.47 \(\mu\varepsilon\)-335.98 \(\mu\varepsilon\). As the temperature gradually decreases, the heat release rate accelerates. Under the combined action of self-heat release and environmental temperature in the middle and late stages, the concrete cools and shrinks, and the compressive strain continues to decrease.  
In general, the strain of the central section is smaller than that of the edge position. The pressure changes of measuring points 5 and 10 near the outside reaches the peak earlier, and  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measuring Point** & **R-Stage1** & **R-Stage2** & **R-Stage3** \\ \hline
2 & 0.964 & 0.823 & \(-\)0.995 \\ \hline
8 & 0.997 & \(-\)0.068 & \(-\)0.997 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation coefficient of temperature and strain.  
Figure 7: Strain change of measuring points.  
the subsequent strain decline rate is significantly higher than that of the other measuring points. This is because the external measuring point is more susceptible to environmental impact and releases heat faster, and its temperature deformation is more intense. This is in line with the general conclusion that the surface position has a higher risk of cracking. The compressive strain of measuring point 10 decreases rapidly after reaching the peak value, and the tensile strain appears in a short period of time and increases rapidly. The cause may be a sensor failure or there is a risk of cracking at this location. At this time, the surface cracks should be checked timely.
## 3 Estimation of Reinforcement Strain  
### Assumption and Crack Width  
According to the results of the steel reinforcement strain and crack width measurement, the difference in behavior between the integral and modular specimens was identified. To analyze the steel reinforcement strain quantitatively according to the measured crack width, the following two assumptions were made.  
1. There was no slippage of the steel reinforcement in the modular specimen, especially at the discontinuity interface.
2. The compressive strength of the joint concrete in the modular specimen was equal to that of the precast module.  
Six crack width calculation formulas were employed to estimate the strain of the steel reinforcement according to the measured crack width, as shown in Table 4. Four formulas were based on the material model considering the tension stiffening effect in Eurocode2, and two formulas were based on the mean steel reinforcement strain for convenience of calculation in the Euro-Design Handbook [26; 27; 28; 29; 30; 31]. The stress of steel reinforcement, \(f_{so}\), was considered the unknown value to derive the strain. The same calculation procedures and formulas were applied to both specimens.  
Where, \(w_{k}\) is the crack width, \(S_{r.max}\) is the maximum crack spacing, \(\varepsilon_{sm}\) is the mean strain in the steel reinforcement under the relevant combination of loads, \(\varepsilon_{cm}\) is the mean strain in the concrete between cracks, \(c\) is the cover to the longitudinal reinforcement, \(k_{1}\) is a coefficient that takes account of the bond properties of the bonded reinforcement (0.8 for high bond bars), \(\beta_{1}\) and \(k_{2}\) are coefficients that takes account of the distribution of strain (0.5 for bending), \(d_{b}\) is the bar diameter, \(\rho_{e}\) is the effective ratio of steel reinforcement, \(\beta\) is the coefficient with relationship between mean crack width and design crack width (1.7 for maximum crack width), \(f_{so}\) is the stress in the steel reinforcement assuming a cracked section, \(n\) is the ratio \(E_{s}/E_{c}\), \(E_{s}\) is elastic modulus of steel reinforcement, \(E_{c}\) is elastic modulus of the concrete, \(\beta_{t}\) and \(\beta_{2}\) are factors dependent on the duration of the load (1.0 for short term loading), \(f_{ctm}\) is the mean value of the tensile strength of the concrete (\(0.3f_{ctm}^{2/3}\)), \(f_{cm}\) is the mean value of the compressive strength of the concrete (\(f_{dc}+4\sim 6\) MPa), \(f_{scr}\) is the stress in the steel reinforcement when cracked (\(ny(M_{cr}/I_{cr})\)), \(y\) is the \(d-x\), \(d\) is the effective depth, \(x\) is the distance from top of compressive zone to neutral axis, \(M_{cr}\) is the cracking moment, and \(I_{cr}\) is the cracked moment of inertia [32].
# Stability of MSW Landfill Slopes Reinforced with Geogrids
## 3 Results and Discussion
### Unreinforced Analyses
#### 3.1.3 Effect of Unit Weight  
Analyses were carried out for different unit weight values of the MSW landfill to investigate the effect of unit weight (\(\gamma\)) on slope stability. In the analyses, the other parameters were chosen as c = 10 kPa, \(\psi\) = 0\({}^{\circ}\), E = 6500 kN/m\({}^{2}\), \(\gamma\) = 0.45, \(\phi\) = 15\({}^{\circ}\), and \(\beta\) = 18.43\({}^{\circ}\) (1V/3H). Table 4 and Figure 6 show the FS values for different internal friction angle values obtained as a result of the FEM and LEM analyses.  
Figure 5: Variation in FS with \(\phi\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Internal Friction Angle, \(\phi\) (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Factor of Safety (FS)**} \\ \cline{2-3}  & **FEM** & **LEM** \\ \hline
5 & 0.81 & 0.93 \\
10 & 1.17 & 1.19 \\
15 & 1.52 & 1.55 \\
20 & 1.84 & 1.91 \\
25 & 2.21 & 2.27 \\
30 & 2.60 & 2.64 \\
35 & 3.00 & 3.06 \\
40 & 3.45 & 3.52 \\
45 & 3.88 & 4.05 \\
50 & 4.40 & 4.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis results for different internal friction angle values.  
From the analysis results, it can be seen that with the increase in the \(\gamma\) value, the FS of the slope decreased. It is thought that this linear decrease is due to the increase in the shear stresses in the MSW landfill. In addition, it is seen that the FS values obtained by LEM are higher by about 2% than the values obtained by FEM.
# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
## 4 Actual Case Analysis
### Case Analysis
#### 4.2.5 Construction of a Stable TSMDM Model  
On the basis of the satisfaction matrix \(\overline{U^{P}}\) and \(\overline{U^{Q}}\), a two-objective optimization model is established with the goal of maximizing the overall satisfaction of both parties:  
\[\begin{array}{c}\max Z_{1}=\sum\limits_{i=1}^{5}\sum\limits_{j=1}^{6}\overline {u_{ij}^{P}}x_{ij}\\ \max Z_{2}=\sum\limits_{i=1}^{5}\sum\limits_{j=1}^{6}\overline{u_{ij}^{Q}}x_ {ij}\\ \sum\limits_{j=1}^{6}x_{ij}\leq 1,\;i=1,2,\ldots,5\\ \sum\limits_{i=1}^{5}x_{ij}\leq 1,\;j=1,2,\ldots,6\\ \sum\limits_{\overline{u_{ii}^{P}}>\overline{u_{ij}^{P}}}x_{ih}+\sum\limits_{ \overline{u_{ij}^{Q}}>\overline{u_{ij}^{Q}}}x_{kj}+x_{ij}\geq 1,\;\forall \left(P_{i},Q_{j}\right)\in\Omega\\ x_{ij}\in\{0,1\},\;i=1,2,\ldots,5,\;j=1,2,\ldots,6\end{array} \tag{67}\]  
Let \(\omega=0.5\); then, convert the two-objective optimization model into a single-objective optimization model, as follows:  
\[\begin{array}{c}\max Z=0.5\sum\limits_{i=1}^{5}\sum\limits_{j=1}^{6} \overline{u_{ij}^{P}}x_{ij}+0.5\sum\limits_{i=1}^{5}\sum\limits_{j=1}^{6} \overline{u_{ij}^{Q}}x_{ij}\\ \sum\limits_{j=1}^{6}x_{ij}\leq 1,\;i=1,2,\ldots,5\\ \sum\limits_{i=1}^{5}x_{ij}\leq 1,\;j=1,2,\ldots,6\\ \sum\limits_{\overline{u_{ii}^{P}}>\overline{u_{ij}^{P}}}x_{ih}+\sum\limits_{ \begin{subarray}{c}\underline{u_{ij}^{Q}}>\overline{u_{ij}^{Q}}\\ \overline{U}>\overline{u_{ij}^{Q}}\end{subarray}}x_{kj}+x_{ij}\geq 1,\; \forall\left(P_{i},Q_{j}\right)\in\Omega\\ x_{ij}\in\{0,1\},\;i=1,2,\ldots,5,\;j=1,2,\ldots,6?\end{array} \tag{68}\]  
The optimal solution of model (68) is \(X^{*}=\left(x_{ij}^{*}\right)_{5\times 6}\), expressed by the matrix  
\[X^{*}=\left(\begin{array}{ c c c c c}0&0&0&0&1&0\\ 0&0&1&0&0&0\\ 0&0&0&1&0&0\\ 1&0&0&0&0&0\\ 0&0&0&0&0&1\end{array}\right) \tag{69}\]Using the above matrix, the optimal matching results between five electric power customers and six power sales packages are obtained: \(\mu=\{(P_{1},Q_{5}),(P_{2},Q_{3}),(P_{3},Q_{4}),(P_{4},Q_{1}),(P_{5},Q_{6})\}\). Thus, no electric power customers match power sales packages \(Q_{2}\).