esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 6 Conclusions  
1. This paper proposes a smoothed JPS path planning method, combining the improved JPS algorithm with the smoothing method in this paper, adding angle indicators to the paths to exchange a smaller time sacrifice for a smoother path.
2. This paper combines the AP-JPS algorithm with the seventh-order polynomial optimization based on minimum snap while introducing a safety corridor to solve the safety problem of AP-JPS optimized paths by inequality constraints.
3. In this paper, we verify the path optimization effect of the AP-JPS algorithm in different complex map situations by three sets of experiments. First, the AP-JPS algorithm is tested by performing the AP-JPS algorithm under different sizes of random obstacle maps. From the test results, the AP-JPS algorithm shortens the total turn angle of the path by 13.7% to 88.4% and the path length by 1.1% to 5.2% compared with the JPS algorithm. Secondly, this paper uses some map data of three cities for algorithm comparison experiments. From the experimental results, the AP-JPS algorithm has shorter and smoother paths compared to the JPS algorithm. At the same time, the time complexity and space complexity of the AP-JPS algorithm are much smaller than that of the AP-Theta* algorithm. Finally, this paper simulates real indoor and outdoor scenes by using large maps with public datasets for path planning tests. From the experimental results, the AP-JPS algorithm can complete the task with better paths, and its generated paths are more consistent with the actual motion requirements of mobile robots than other algorithms.
4. The AP-JPS algorithm still has some drawbacks, as it sacrifices more computing time compared to the JPS algorithm, and the complexity of the algorithm is still high. As the size and complexity of the grid map increase, the efficiency of the algorithm decreases. In future work, we need to further optimize the computational efficiency and memory usage of AP-JPS algorithm, and we hope to apply AP-JPS 3D to UAV path planning [7].  
Author Contributions: software, J.L.; validation, Y.L. ([PERSON]); investigation, Y.L. ([PERSON]); resources, Y.L. ([PERSON]); data curation, Q.Q.; writing--original draft preparation, J.L.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Space radiation affects satellites by introducing anomalies such as single event effects (SEE), component degradation due to ionizing radiation dose, and surface and internal charging. Understanding the radiation environment is, therefore, important in order to design satellites that can withstand the possible anomalies. Space radiation sources include galactic cosmic rays (GCR), solar energetic particle (SEP) events, high energy particles trapped in the Earth's magnetic field and the continuous radiation background. In addition, the low-Earth orbit (LEO) region where most satellites reside is subject to unknown mechanisms that provide it with unpredictable energy variability in the spectra of particles [1; 2; 3]. This variability is particularly poorly understood for electrons, which can appear with energies higher than expected.  
The consequences of the presence of high-energy electrons, protons and ions for spacecraft developers vary depending on each mission design, epoch and class. The spacecraft design should account for the effects of ionizing radiation, as well as charging and discharging effects on satellite surfaces. For manned missions, mission duration [4; 5] and the associated life support systems need to be tuned to account for this unpredictability of particle populations.  
In recent years, different missions have been launched to explore the near-Earth region in order to provide direct measurements of charged particles, plasma, and the magnetosphere. Missions such as the Van Allen Probes (RBSP) [6; 7], THEMIS [8], MMS [9] from NASA, JAXA's ERG (ARASE) satellite [10], Proba-2 [11] and Swarm [12] from ESA have been launched into specific orbits to study space radiation around the Earth [13]. In addition to the mentioned missions with launch masses of hundreds of kilograms, dedicated small satellites offer an important complement to the measurements because they enable a wider, more comprehensive view of the space environment thanks to their reduced development time and cost. This class of satellites leverages commercial off-the-shelf (COTS) components to bring cost and time savings at the expense of an increased risk of failure. The small-satellite Ten-Koh has been developed to demonstrate the feasibility of providing space environment measurements with such low-cost platforms, as well as to provide readily usable data.  
This paper provides a comprehensive overview of the Ten-Koh mission design, presents its preliminary results and discusses the applicability of small, low-cost satellites to conduct space weather research. The specific objectives of the mission are described next, followed by a summary of the satellite platform and payloads. The main challenges in the development of the satellite are then summarized and the adopted solutions to them described. Even though the mission has been successful, which is demonstrated by briefly presenting data obtained in-orbit, it has suffered a number of failures at component, assembly and subsystem level. These are described in detail to help improve the design and implementation of future small satellites that take advantage of the COTS components.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Pearson Correlation Coefficient  
The Pearson correlation coefficient, indicated in (Equation (2)), is a measure of linear correlation that could analyze the relationship between two or more variables [48]. In this context, the Pearson correlation was adopted to express the degree of linear correlation between environmental parameters and biogas production.  
\[r_{xy}=\frac{\sum{(x-\overline{x})(y-\overline{y})}}{\text{N}\cdot S_{x}S_{y}} \tag{2}\]  
The formula given defines the correlation coefficient of the two variables. In the formula, \(N\) represents the total number of data samples, while \(x\) and \(y\) represent the mean values of the two sets of variable data. \(S_{x}\) and \(S_{y}\) represent the standard deviations of the respective variable data samples.
## 3 Experiments
### Determination of the Rotating Speed of the Rotary Base  
The rotating speed of the rotary base is another key working parameter of the trimming machine. To determine the optimal rotating speed, the rotary base was controlled to run at five different speeds: 7.5, 10.5, 13.5, 16.5 and 19.5 r/min. The tested shrub plant was _Fagraea celianica_, with its size before trimming being around 500 mm and the average moisture of its branches being 36.2%.  
To evaluate the performance of the trimming operation, the contour of the knife system and the contour of the plant after trimming were compared. To make a quantitative comparison, the trimming performance was recorded by a photography system (German PCO, series dimax HD+, 1920 \(\times\) 1440 resolution). The experimental setup is shown in Figure 7. A grid paper was put in the background, and a white paper with angle marks were stuck on the ground. The photography system was placed in front of the trimming machine. After the trimming was completed, the contours of the knife system and the trimmed plants were captured. To record a full view of the plant contours, the plant was put in 12 different angle positions, and the angle marks on the ground were used for alignment. Pictures of contours taken by the photography system are shown in Figure 8.  
Figure 8: Pictures of contours taken by the photography system (**a**) Knife system; (**b**) Shrub plant after trimming.  
Figure 7: Experimental setup.  
After trimming, the pictures were then uploaded to a PC with a MATLAB program for processing and analysis. The profiles of the knife system and the plant after image processing are shown in Figure 9. Feature points (\(x_{i},y_{i}\)) were extracted from the profile of the knife system, and (\(a_{i},b_{i}\)) were extracted from the profile of the plant.  
The overall relative deviation of the plant profiles from the profile of the knife system can be calculated by the following equation  
\[E_{S}=\frac{1}{m}\times\frac{1}{\hat{k}}\times\sum_{j=1}^{m}\sum_{i=1}^{\hat{k} }\left[\frac{\sqrt{\left(a_{i}-x_{i}\right)^{2}}}{x_{i}}+\frac{\sqrt{\left(b_{ i}-y_{i}\right)^{2}}}{y_{i}}\right] \tag{9}\]  
where \(E_{S}\) is the overall relative deviation, \(m\) is the number of pictures taken for each trimmed plant and \(m=12\), \(k\) is the number of feature points extracted in each picture, (\(a_{i}\), \(b_{i}\)) are the coordinates of feature points of the trimmed plant (mm), (\(x_{i}\), \(y_{i}\)) are the coordinates of feature points of the knife system (mm). The similarity \(S\) between profile of the trimmed plant and the profile of the knife system can be calculated by  
\[S=(1-E_{S})\times 100\% \tag{10}\]  
The second index for evaluating the performance of the machine was the time consumed for each complete trimming. During the experiments, the knife system was rotated for at least two circles. If the performance was not desirable, more rotations were carried out.
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 2 Brillouin Sensing Principle  
The properties of the Brillouin gain spectrum including the linewidth and its frequency shift relative to the pump--called Brillouin frequency shift (BFS)--can be studied to obtain information about the environmental parameters such as temperature and strain, as well as the geometry of the waveguide. Brillouin scattering can be observed as backward or forward scattering involvinglongitudinal and transverse acoustic waves, respectively. Transverse acoustic waves mean that their propagation is mostly in the transverse direction to the propagation of the optical waves in the waveguide.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 5 Simulation Results
Figure 4 shows the hard decision BER comparison of the proposed SDGS algorithm with NS, CG, GS and MMSE under a high fading scenario with \(128~{}\times~{}16\) antenna configuration. As can be seen from Figure 4 the BER of the proposed SDGS algorithm was better and followed the MMSE performance with increasing SNR and number of iterations. Moreover, due to high fading impact on the SNR, there was a gap between the proposed SDGS algorithm and MMSE algorithm at a high SNR level. Figure 5 shows the BER comparison of the proposed SDGS algorithm with NS, CG, GS and MMSE under a low fading level and \(128~{}\times~{}16\) antenna configuration. It can be seen from Figure 5 that all the algorithms showed lower BER and better performance as compared with the hard decision BER performance in Figures 2 and 4. Therefore, to keep the system performance in a suitable level, the  
Figure 3: Soft decision bit error rate (BER) performance comparison. (**a**) Analysis at \(64~{}\times~{}16\) antenna configuration; (**b**) Analysis at \(128~{}\times~{}16\) antenna configuration. fading level and number of iterations should be considered, which has an obvious impact on the system's overall performance. Furthermore, the proposed SDGS algorithm in Figure 5 had a close BER performance with MMSE which indicated that the SDGS algorithm showed better performance in the low fading level.
## 3 Particle Swarm Optimization
### Applications of PSO  
Due to advancements in and modifications of PSO, various applications have been found in the literature [70] due to its ability to efficiently search for optimal solutions. PSO can be applied to optimize mathematical functions with multiple variables. By exploring the search space, particles can locate the global minimum or maximum of a function. This application is particularly useful in fields such as engineering design, data analysis, and financial modeling.  
In image and signal processing, PSO has been employed for image and signal processing tasks [71]. It can optimize parameters in image reconstruction, denoising, feature extraction, and object recognition. PSO algorithms have shown promising results in optimizing parameters for image- and signal-processing techniques, enhancing the quality and efficiency of these processes [72].  
PSO can be used to train the weights and biases of neural networks. It has been employed as an alternative to traditional optimization algorithms, such as backpropagation, to improve the training process and avoid local optima. PSO-based training algorithms can enhance the convergence speed and accuracy of neural networks, making them more effective in pattern recognition, classification, and prediction tasks. They are also used to solve optimization problems in power systems [70]. They can optimize various aspects such as power flow, unit commitment, economic dispatch, and capacitor placement. PSO-based approaches enable efficient utilization of power resources, leading to improved power system operation, reduced costs, and enhanced stability. They can be used for feature selection in machine learning and data-mining tasks. By selecting a subset of relevant features, PSO helps in dimensionality reduction, improving classification accuracy and reducing computational complexity. This application is particularly useful in areas such as text mining, bioinformatics, and image recognition [73].  
PSO is also used to optimize vehicle-routing problems, including route planning, delivery scheduling, and fleet management. By considering factors such as distance, capacity, and time constraints, PSO algorithms can determine efficient routes and schedules, minimizing transportation costs and improving logistics operations. Another application of PSO in electronics is in the optimization of antenna design [74]. Antennas are crucial components in wireless communication systems, and their performance greatly impacts signal reception and transmission. Moreover, in radar waveform design, PSO can optimize the characteristics of radar waveforms, such as pulse duration, modulation schemes,and frequency characteristics, to enhance target detection, resolution, and interference mitigation. By iteratively adjusting particle positions representing waveform parameters, PSO can efficiently explore the design space and converge on optimal solutions that maximize radar performance. This enables radar systems to improve their capabilities in detecting and tracking targets, reducing interference, and enhancing overall operational efficiency.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## Abbreviations  
ADC analog-to-digital converter AM amplitude modulation AWGN additive white Gaussian noise BER bit error rate BPSK binary phase shift-keying DAC digital-to-analog converter DOA direction of arrival ETMA enhanced time-modulated array FM frequency modulation LBFN linear beamforming network LFM linearly frequency modulated MRC maximum ratio combining RF radio frequency SLL side-lobe level SNR signal-to-noise ratio SPDT single-pole double-throw SR sideband radiation SWC sum-of-weighted-cosine TMA time-modulated array TMRA time-modulated array TMRA time-modulated reflector array
## 3 Results and Discussion
### Dielectric Properties of the EL Coating
Variations of dielectric constant in (**a**) \(-\)100–20 \({}^{\circ}\)C, (**b**) \(-\)40–160 \({}^{\circ}\)C. Variations of dielectric loss in (**c**) \(-\)100–20 \({}^{\circ}\)C, (**d**) \(-\)40–160 \({}^{\circ}\)C.  
Breakdown field strength and flashover voltage along the surface are two other extremely important performance indicators of insulators. As shown in Figure 5c, the breakdown field strength is 36.48 kV/mm, meeting the required standards for effective high-voltage insulation. Additionally, experimental measurements of the flashover voltage of insulators coated with this material reveal a clear improvement in their performance, as shown in Figure 5d. When the insulator surface is coated with the paint, the flashover voltage increases from 35 kV for an uncoated insulator to 37 kV. This enhancement is indicative of the protective nature of the coating; by mitigating surface flashover, the coating significantly boosts the operational reliability of insulators under high-voltage conditions. As shown in Figure S1, the fracture surface exhibits excellent dispersion of ZnS:Cu particles within the epoxy matrix, with no observable agglomeration even at high magnification. The particles demonstrate uniform spatial distribution throughout the coating, which directly contributes to the material's homogeneous luminescent output and stable dielectric properties. The SEM images further demonstrate intimate interfacial contact between ZnS:Cu particles and the epoxy matrix, with no visible voids or delamination at the filler-matrix boundaries. This dense microstructure accounts for the coating's high breakdown strength and enhanced flashover resistance, as it prevents localized field concentration that could initiate electrical trees or surface discharges. This nanoscale characterization bridges the gap between material design and macroscopic performance, explaining why the coating achieves both reliable EL response and robust insulation properties. As for the aging life of the coating, while accelerated aging datasets require future validation, industrial evidence suggests the E51 epoxy-based coating retains structural/dielectric stability for 2-5 years in moderate outdoor environments [44; 45; 46]. In summary, the EL coating has stable \(\epsilon_{\rm r}\), low dielectric loss, high breakdown field strength, and flashover suppression capability within the working temperature range. These capabilities ensure the insulation reliability of the EL coating itself and make it suitable for insulator detection under high-voltage operation. Figure 5: Electric conductivity and insulation performances of ZnS:Cu-doped epoxy composite coating. Frequency dependent electrical conductivity in (**a**) \(-\)100\(-\)20 \({}^{\circ}\)C, (**b**) \(-\)40\(-\)160 \({}^{\circ}\)C. (**c**) The AC breakdown performances. (**d**) The AC flashover voltages of insulators.
## 4 Experiments and Results
### Other Visual and Numerical Comparison with the Previous Methods  
Figure 4 shows the standardized results of the proposed method, the original unified many-to-one method, the histogram matching method, and the joint histogram registration method on the volunteer MR image dataset while Figure 5 shows the logarithmic absolute error image between the standardized image and corresponding reference image. The results of the histogram matching method are deviated in terms of overall image intensity and contrast of gray matter and white matter. The joint histogram registration method has good overall results, but has some image intensity errors in the skull, muscle, and skin areas. Both the original unified method and the proposed method could produce the state-of-the-art image intensity standardization results. In Figure 5, the mean intensity value of the error image corresponding to the proposed method is relatively small, but the spatial distribution of the errors is more extensive. This may be mainly because the standardization results of the proposed method are highly similar to the original target MR image structure, while the original target image and the reference \"gold standard\" image still have difference due to scanning interval and registration accuracy. Therefore, such difference might be reflected in the error images.  
Table 7 shows the numerical comparison for these different methods. The proposed method outperformed the original unified many-to-one method based on CycleGAN in the overwhelming majority of metrics. Meanwhile, the proposed method had a significant effect improvement over the two conventional standardization methods.  
In terms of runtime, the histogram matching method is mainly piecewise linear functions, which takes only 0.5867 s to standardize an image slice. The joint histogram registration method requires b-spline fitting, so the amount of calculation is large, and the time taken for processing a slice reaches 2.1651 s. For the original many-to-one universal method and the proposed method, since the convolution is very suitable for parallel computing using the graphics processing units (GPUs), with the help of two NVIDIA TITAN Xp GPUs, the original many-to-one universal method only needs 0.2868 s to transform an MR image slice. Because the parameters are shared between layers in the cascading block and between the cascading blocks, the proposed method only needs 0.1978 s, which is fast enough for a preprocessing step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Group** & **Original Images** & \
\begin{tabular}{c} **Standardized Images** \\ **with the Original Method** \\ \end{tabular} &
\begin{tabular}{c} **Standardized Images** \\ **with the Proposed Method** \\ \end{tabular} \\ \hline Accuracy & 0.7143 & 0.8214 & 0.8571 \\ Sensitivity & 0.6471 & 0.7647 & 0.8824 \\ Specificity & 0.8182 & 0.9091 & 0.8182 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The result of HGG/LGG classification.