esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Melting of Metals Explained via the Theory of Quasi-Liquid Layer
## 3 Conclusions  
Thus, the reason for such rapid contact melting of metals is that a quasi-liquid layer is located on the surface of one of the metals. When the metals come into contact with each other, the atoms in the second metal dissolve sufficiently quickly, as a result of which the next layer becomes liquid (in the case of a phase diagram with a eutectic). Following the motion of the layer, diffusion also occurs. Thus, the contact melting rate is limited by the rate of mutual diffusion in liquid metals, although both metals are in a solid state before contact.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Materials Used in the Experiment and Their Melting Melting Points, K [2,6,7] & **Experimental Temperature, K** & **Maximum Pre-Melting Temperature (First Metal)** & **Minimum Pre-Melting Temperature (Second Metal)** & **Where is the Quasi-Liquid Film Located?** \\ \hline Pb (601)–Sn (505) & 463 & 501 & 421 & Sn \\ Sn (505)–In (430) & 400 & 421 & 358 & In \\ Tl (577)–Bi (545) & 483 & 480 & 454 & Tl, Bi \\ Tl (577)–Sn (505) & 455 & 480 & 421 & Sn \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental contact melting temperature and quasi-liquid layer temperature for some metals.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 2 Phase Calibration Method
### Time Domain Gating
Since zeros remain in the transformed frequency domain due to data padding, the data were cut to match the frequency domain used for the measurement. Also, since the frequency domain's tick width is different from the \(\Delta f\) of the measured reflection coefficient, then linear interpolation yields \(S^{\prime}{}_{11}(\omega,n_{\text{tx}})\). \(S^{\prime}{}_{22}(\omega,n_{\text{rx}})\) is also calculated.
# Hybrid A-Star Path Planning Method Based on Hierarchical Clustering and Trichotomy
###### Abstract  
Aiming to improve on the poor smoothness and longer paths generated by the traditional Hybrid A-star algorithm in unstructured environments with multiple obstacles, especially in confined areas for autonomous vehicles, a Hybrid A-star path planning method based on hierarchical clustering and trichotomy is proposed. This method first utilizes the Prewitt compass gradient operator (Prewitt operator) to identify obstacle boundaries and discretize boundaries. Then, it employs a single linkage hierarchical clustering algorithm to cluster obstacles based on boundaries. Subsequently, the clustered points are enveloped using a convex hull algorithm, considering collision safety for vehicle expansion. This fundamentally addresses the ineffective expansion issue of the traditional Hybrid A-star algorithm in U-shaped obstacle clusters. Finally, the expansion strategy of Hybrid A-star algorithm nodes is improved based on the trichotomy method. Simulation results demonstrate that the improved algorithm can search for a shorter and smoother path without significantly increasing the computational time.  
autonomous vehicle; path planning; hierarchical clustering; Hybrid A-star algorithm +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Measurement and Result Analysis
### Bending Analysis for the Proposed Design
From Figure 24, we can observe that with the simulation of a 15-degree bent unit cell, the obtained frequencies for S\({}_{11}\) are 9.83 GHz and 11.47 GHz, with S\({}_{11}\) amplitudes of \(-\)30.98 dB and \(-\)21.90 dB, correspondingly, while the obtained frequencies for the S\({}_{12}\) are 8.01 GHz, 8.47 GHz and 11.09 GHz, with S\({}_{12}\) amplitudes of \(-\)33.41 dB, \(-\)37.51 dB and \(-\)26.79 dB, correspondingly. For a 30-degree bent unit cell, the frequencies for S\({}_{11}\) are 9.92 GHz and 11.46 GHz with S\({}_{11}\) amplitudes of \(-\)31.60 dB and \(-\)23.13 dB, correspondingly, while the obtained frequencies for the S\({}_{12}\) are 8.02 GHz, 8.46 GHz and 11.10 GHz, with S\({}_{12}\) amplitudes of \(-\)33.41 dB, \(-\)37.13 s dB and \(-\)25.42 dB, correspondingly. For a 45-degree bent unit cell, the obtained frequencies for S\({}_{11}\) are 9.87 GHz and 11.46 GHz with S\({}_{11}\) amplitudes of \(-\)31.68 dB and \(-\)23.29 dB, correspondingly, and the obtained frequencies for the S\({}_{12}\) are 8.02 GHz, 8.5 GHz and 11.09 GHz, with S\({}_{12}\) amplitudes of \(-\)33.40 dB, \(-\)37.23 dB and \(-\)25.79 dB, correspondingly. In the measurement process for the fabricated prototype, which is shown in Figure 19, the corresponding S\({}_{11}\) obtained frequencies for the 15-degree vertically bent prototype are 9.88 GHz and 11.31 GHz with S\({}_{11}\) amplitudes of \(-\)17.25 dB and \(-\)16.78 dB, correspondingly, while the obtained frequencies for the S\({}_{12}\) are 7.87 GHz, 8.51 GHz and 11.16 GHz, with S\({}_{12}\) amplitudes of \(-\)24.31 dB, \(-\)20.78 dB and \(-\)15.28 dB, correspondingly. A 30-degree bent for the proposed prototype, the corresponding S\({}_{11}\) frequencies are 9.88 GHz and 11.43 GHz with S\({}_{11}\) amplitudes of \(-\)17.25 dB and \(-\)17.32 dB, correspondingly, and the obtained frequencies for the S\({}_{12}\) are 7.99 GHz, 8.68 GHz and 11.09 GHz, with S\({}_{12}\) amplitudes of \(-\)16.90 dB, \(-\)20.57 dB and \(-\)24.02 dB, correspondingly, and for a 45-degree bend for the proposed prototype, the corresponding S\({}_{11}\) frequencies are 9.72 GHz and 11.41 GHz with S\({}_{11}\) amplitudes of \(-\)16.02 dB and \(-\)15.78 dB, correspondingly, while the obtained frequencies for S\({}_{12}\) are 7.99 GHz, 8.50 GHz and 11.33 GHz, with S\({}_{12}\) amplitudes of \(-\)16.93 dB, \(-\)20.05 dB and \(-\)20.06 dB, correspondingly. From Figures 21-23, we can say that the field distributions are also not much affected even with the vertical bending of the structure. From Table 3, it can be noted that the present work is based
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 3 Methodology
### The Construction of LCS Based on Geometric Saliency  
In 3D space, the point is composed of (\(x\), \(y\), \(z\)), which is just position information and no normal vector information recorded, and refined voting results is decomposed by eigenvectors \(\left(\begin{smallmatrix}\rightarrow\\ \epsilon_{1},\overrightarrow{e}_{2},\overrightarrow{e}_{3}\end{smallmatrix}\right)\) and related eigenvalues \((\lambda_{1},\lambda_{2},\lambda_{3})\), where \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}\). The saliency of geometric feature that encoded in each dimension can be computed based on Equation (7):  
1. in 1-dimensional normal space \(S_{1}=kron\left(\begin{smallmatrix}\rightarrow\\ \epsilon_{1},\overrightarrow{e}_{1}\end{smallmatrix}\right)\), the geometric feature turns out to be \"surface\", since there is only the 1D stick-shaped normal vector, and geometric saliency is \(s_{surface}=\lambda_{1}-\lambda_{2}\);
2. in 2-dimensional normal space \(S_{2}=kron\left(\begin{smallmatrix}\rightarrow\\ \epsilon_{1},\overrightarrow{e}_{1}\end{smallmatrix}\right)+kron\left( \begin{smallmatrix}\rightarrow\\ \epsilon_{2},\overrightarrow{e}_{2}\end{smallmatrix}\right)\), the geometric feature turns out to be \"line\", since there is the 2D surface-shaped normal vector, and geometric saliency is \(s_{line}=\lambda_{2}-\lambda_{3}\);
3. in 3-dimensional normal space \(S_{3}=kron\left(\begin{smallmatrix}\rightarrow\\ \epsilon_{1},\overrightarrow{e}_{1}\end{smallmatrix}\right)+kron\left( \begin{smallmatrix}\rightarrow\\ \epsilon_{2},\overrightarrow{e}_{2}\end{smallmatrix}\right)+kron\left( \begin{smallmatrix}\rightarrow\\ \epsilon_{3},\overrightarrow{e}_{3}\end{smallmatrix}\right)\), the geometric feature turns out to be \"point\", since there is the 3D ball-shaped normal vector, and geometric saliency is \(s_{point}=\lambda_{3}\).  
In above descriptions, the higher geometric saliency value of the point, the more credible the geometric feature category it belongs to; for example, if the point with line geometric saliency \(s_{line}\) higher than the saliencies of other dimensions, it can categorize into \(LCS\). However, if the point with surface saliency \(s_{surface}\) lower than some threshold value, it can also be taken into the \(LCS\). Besides, the point with high point saliency \(s_{point}\) turns out to be the corner structure of geometric framework, hence, it can also be taken into the \(LCS\). With these assumptions, the \(LCS\) can be constructed based on threshold values \(\left\{\sigma_{surface},\sigma_{line},\sigma_{point}\right\}\) for the saliency in different dimensions, as dented in Equation (10).  
\[LCS=\left\{p\left(x,y,z,s_{surface},s_{line},s_{point}\right)\in S\Big{|}s_{ surface}\leq\sigma_{surface}\cup s_{line}\geq\sigma_{line}\cup s_{ point}\geq\sigma_{point}\right\} \tag{10}\]
## 2 Reference Current Calculation  
### The Working Principle of a Compensation Device  
The compensation device active control method is based on power electronics technology, which is mainly divided into two parts: reference current calculation and compensation current output. The former can be regarded as a signal generator, where the reference current is generated according to the grid compensation requirements. The latter is used to control the power of the electronic switching device so that compensation current generation follows the reference current. A schematic diagram of the switching compensation device system is shown in Figure 1.  
In Figure 1, \(u\) is the power supply voltage, \(i_{\text{s}}\) is the power supply current, and \(i_{\text{L}}\) is the load current. After the algorithm calculates the compensation current based on various power theories, the reference compensation current \(i_{cc}^{z}\) can be obtained. In power theory analyses, the instantaneous reactive power based on the rotating coordinate system and FDB power theory, derived from Fryze power theory, is commonly used. However, references [16; 26] pointed out that the explanations for some of the power phenomena in power systems by these two theories are not accurate, which thus affect the calculated compensation current.  
In this paper, we attempted to use the CPC power theory, proposed by [PERSON], to find another method of calculating compensation current. The load current is decomposed by CPC power theory, and then the harmful current component \(i_{cc}^{*}\) in the power grid can be obtained. At the same time, during the operation of the compensation device, the voltage stability of the energy storage capacitor on the \(DC\) side needs to be maintained, so the voltage-stabilizing current \(i_{cu}^{*}\) on the \(DC\) side also needs to be calculated. In Figure 1, \(u_{DC}\) is the real-time value of the \(DC\)-side capacitor voltage of the switching compensation device, \(u_{DC}^{*}\) is the reference value of the \(DC\)-side capacitor voltage, and the regulated current value \(i_{cu}^{*}\) can be obtained using the \(DC\)-side voltage control circuit. The sum of the reference compensation current \(i_{cc}^{*}\) and the \(DC\)-side voltage-stabilizing current \(i_{cu}^{*}\) is the calculated reference current \(i_{c}^{*}\).  
The main circuit in Figure 1 is the inverter. Under the reliable control algorithm, the inverter can be regarded as an ideal linear power amplifier, which can follow the reference compensation current in real time to generate the \(i_{cc}^{*}\) compensation current \(i_{c}\), which behaves like a controlled current source.
## 5 Simulation Results and Discussion
### Some Future Research Directions  
SCNs are vital to the success of SGs as they provide real-time monitoring capabilities to the marvellous power system. In this paper, an approach was presented in the resiliency evaluation paradigm. Some of the future research directions to further augment the resiliency paradigm are enumerated below:  
* The dynamic characteristics of all the nodes in terms of failure, repair, etc. can be studied for resiliency analysis of the SCN;
* The effect of variable bandwidth on all channels can be studied to observe the performance of the SCN in terms of resiliency parameters;
* A more complex SCN can be modelled and the presented work can be extended on such a complex SCN for resiliency analysis;
* A more peculiar SCN model can be developed with the objective of designing a testbed for SG design, implementation, and performance evaluation;
* Last, but not least, more comprehensive definitions and resiliency metrics can be proposed in future work.
## 3 Materials and Methods
### Study Design
When the participants were sure they had completed the second task, they had to submit their solution via the online system and fill out a questionnaire to evaluate the implementation of task two with the selected tool. After participants completed and submitted both task results and answered the task-related surveys, users were asked to answer the tool survey. With the tool survey, we captured additional quantitative data on user perceptions of usability and UX when using the tool for qualitative data analysis. After completing the survey for evaluating the first tool (Taguette), the process continued with the same tasks and surveys for evaluating two selected AI-based tools. When users evaluated the ChatGPT tool, they conducted the qualitative data on the ux.txt file, and with the Gemini, participants conducted the qualitative data analysis of the data provided with the structure.txt file. The tasks were designed to reflect key activities in QDA and align with the study objectives of comparing the usability and UX of AI-based and non-AI-based tools. The first task focused on data annotation, a foundational QDA activity (e.g., [2]). Participants categorised and tagged qualitative data based on themes or patterns, simulating realistic coding activities that require interpretation, pattern recognition, and thematic categorization. This task highlighted the tools' flexibility and ability to support subjective judgment, which is critical in qualitative research. The second task introduced sentiment analysis, requiring participants to categorize data as either positive or negative. This task was chosen to test the tools' efficiency and usability in guiding users through structured decision-making processes. Sentiment analysis reflects a common extension of QDA, where researchers  
Figure 2: Methodology for user evaluation of Taguette, ChatGPT, and Gemini. analyze emotional tones in textual data (e.g., [7; 24]). These tasks were intentionally chosen to examine the tools' ability to handle open-ended and structured qualitative analysis, covering varying levels of cognitive complexity and decision-making processes. To ensure consistency and avoid familiarity bias, the datasets for each tool were specifically assigned: the content.txt file was analyzed using the traditional tool (Taguette; [30]), while the ux.txt and structure.txt files were analyzed using AI-based tools (ChatGPT and Gemini). The task design supports the study's primary research questions, particularly comparing usability, UX, trust, and mental workload across different tools during realistic QDA scenarios. Relevant usability criteria, including effectiveness, efficiency, and satisfaction (e.g., [7; 13]), informed the task design to ensure alignment with theoretical foundations and prior research.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experiment C: Cross-Dataset, Cross-Camera Testing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Recovery AE**} & \multicolumn{5}{c}{**Reproduction AE**} \\ \cline{2-13}  & **Mean** & **Med.** & **Tri.** & **B25\%** & **W25\%** & **Max** & **Mean** & **Med.** & **Tri.** & **B25\%** & **W25\%** & **Max** \\ \hline GW [4; 5] & 4.36 & 2.73 & 3.11 & 0.61 & 10.82 & 39.54 & 5.45 & 3.63 & 4.09 & 0.77 & 13.11 & 42.57 \\ WP [6] & 3.62 & 2.37 & 2.59 & 0.55 & 8.93 & 20.65 & 4.42 & 3.02 & 3.33 & 0.71 & 10.53 & 24.99 \\ SoG [7] & 4.48 & 2.66 & 3.20 & 0.47 & 11.39 & 22.91 & 5.31 & 3.36 & 4.03 & 0.60 & 13.02 & 25.28 \\ GE1 [8] & 4.02 & 2.80 & 3.14 & 0.55 & 9.58 & 20.31 & 5.05 & 3.61 & 4.03 & 0.67 & 11.82 & 24.59 \\ GE2 [8] & 4.40 & 2.55 & 3.07 & 0.60 & 11.24 & 24.58 & 5.22 & 3.26 & 3.94 & 0.78 & 12.86 & 27.53 \\ PCA-based [21] & 4.98 & 2.99 & 3.55 & 0.75 & 12.35 & 26.80 & 5.85 & 3.88 & 4.50 & 0.94 & 13.83 & 27.84 \\ GGW [8] & 3.28 & 1.97 & 2.26 & 0.44 & 8.49 & 20.40 & 4.07 & 2.57 & 2.95 & 0.57 & 10.20 & 26.87 \\ LSR [44] & 5.25 & 4.15 & 4.58 & 1.34 & 10.79 & 17.17 & 6.51 & 5.51 & 5.84 & 1.57 & 13.09 & **20.80** \\ \hline \multicolumn{13}{c}{**Proposed**} \\ \hline ClrM & 2.64 & 2.13 & 2.25 & 0.61 & 5.50 & 26.98 & 3.68 & 2.87 & 3.06 & 0.81 & 7.89 & 36.12 \\ \(CM_{19}\) & 2.30 & 1.69 & 1.84 & 0.55 & 5.06 & 22.16 & 3.06 & 2.22 & 2.44 & 0.70 & 6.78 & 27.69 \\ \(CM_{34}\) & 2.52 & 1.98 & 2.11 & 0.62 & 5.32 & 18.27 & 3.61 & 2.54 & 2.83 & 0.81 & 8.07 & 113.87 \\ CDF & 49.20 & 6.49 & 40.98 & 1.36 & 173.44 & 179.68 & 50.28 & 8.62 & 41.39 & 1.85 & 171.63 & 179.64 \\ IIF & 2.50 & 1.78 & 1.95 & 0.57 & 5.74 & 21.02 & 3.64 & 2.30 & 2.60 & 0.71 & 8.89 & 110.91 \\ RGBuv & 2.04 & 1.45 & 1.59 & 0.43 & 4.63 & 28.63 & 2.79 & 1.96 & 2.15 & 0.54 & 6.42 & 44.16 \\ ChengSF & 2.44 & 1.59 & 1.83 & 0.47 & 5.83 & 34.98 & 3.34 & 2.13 & 2.45 & 0.58 & 8.12 & 47.04 \\ RGBuv+ChengSF & 2.00 & 1.43 & 1.57 & 0.43 & 4.49 & 22.39 & 2.71 & 1.95 & 2.12 & 0.53 & 6.20 & 32.05 \\ RGBuv+ChengSF+IIF & 1.88 & **1.41** & **1.50** & **0.41** & 4.16 & **20.35** & 2.54 & **1.82** & **2.00** & **0.51** & 5.77 & 26.24 \\ RGBuv+ChengSF+IIF+\(CM_{34}\) & **1.87** & 1.44 & 1.53 & 0.42 & **4.14** & 20.52 & **2.53** & 1.86 & 2.02 & **0.51** & **5.74** & 27.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance metrics between the proposed method and some conventional methods for Canon EOS 550D.
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 1 Introduction
Clustering algorithms can be static and dynamic, centralized or distributed [29; 30]. Static clustering relies on a predetermined fixed base station cluster. Each static clustering algorithm utilizes different strategies to determine the efficient cluster formation. The network then decides on base station clusters. Dynamic clustering adapts to network changes, where the usual methods are designed based on centralized control on the network, which requires extensive information sharing. In our study, we only consider static clustering based on channel state information (CSI). The techniques mentioned above are essential to improve the overall spectral and energy efficiencies and also increase the throughput and coverage gains, when compared to traditional cellular networks [31]. Motivated by the work above, in this paper we provide a study on MU-MIMO systems, where GSM symbols are transmitted simultaneously to multiple users (differences between the proposed approach and a conventional MU-MIMO assumed as reference are shown in Table 1). To increase the SE of the transmission, different modulated symbols are sent on different (virtual) antennas, where high-order M-QAM constellation with sizes reaching \(M=1024\) symbols are considered. To remove inter-user interference and transform the MU transmission into several independent SU links, a BD precoder is applied at the BS, while a modified and improved version of the low-complexity SU GSM detector presented in [7] is used at the receiver. The influence of imperfect channel estimation on the performance of this massive MIMO GSM-based system is also analyzed. Link level simulations show that the presented GSM MU-MIMO approach can provide substantial performance gains over conventional MU-MIMO. Additionally, system level simulations show that deployments based on cloud-RAN (C-RAN) comprising several radio remote units (RRUs) can achieve large throughput and coverage gains over traditional cellular networks. The paper is organized as follows: Section 2 presents the model for the MU GSM system, Section 3 presents the transmitter and receiver structure followed by the numerical results obtained in Section 4. Finally, the conclusions are outlined in Section 5. Notation: Matrices and vectors are denoted by uppercase and lowercase boldface letters, respectively, (. )\({}^{\rm T}\) and (. )\({}^{\rm H}\) denote the transpose and conjugate transpose, \(\lfloor.\rfloor\) is the floor function, \(\binom{N}{k}\) denotes the number of combinations of N symbols taken k at a time, and supp(**x**) returns the set of indices of nonzero elements in x (i.e., the support of x).
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 5 Simulation Studies
### Computational Performance  
A 1.0-s simulation study for the test system in Figure 9 was executed on a PC with 3.6 GHz Intel core i7-7700 HQ with 32 GB RAM under Microsoft Windows 10 operating system. Different numbers of SMs per arm were employed in the simulation studies to represent different complexities of test systems. The simulation execution times for the three models are summarized in Table 3.  
Figure 13: AC fault. (**a**) MMC1 active power; (**b**) DC voltage; (**c**) DC current; (**d**) SMs capacitor voltage; (**e**) Current in the upper arm in phase \(a\); (**f**) AC voltage in phase \(a\) at valve-side.  
From Table 3, the DEM required longer simulation execution times, as the number of SMs per arm increased, since all SMs were considered separately and all individual capacitor voltage ripples were recorded. However, the proposed AEM requires the same execution time regardless of the number of the SMs per arm, as all SMs do not have to be considered separately, due to the arm equivalence. The proposed AEM was about only 45% slower than the AVM, and was more than three times faster than the DEM in terms of simulation efficiency. Hence, Table 3 demonstrates that the proposed model can be efficiently suitable for large-scale MMC-HVDC grids. In conclusion, the proposed AEM not only has an accuracy consistent with DEMs, but also has a satisfactory simulation efficiency compared to the AVMs.