esa-sceva/satcom-chunk-collection huggingface dataset:
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
## 4 Experiments of the Multi-Electrode Sensor
### Defects Buried in the Fiberglass Board  
As shown in Figure 10, the non-conducting specimen (fiberglass board) contained three buried defects and the three buried defects from left to right were abbreviated to B1, B2 and B3. The diameters of these three defects were all 10 mm and the buried depths of these three defects from left to right were 3 mm, 2 mm and 1 mm respectively. In addition, the distance between any two adjacent defects was 80 mm and the length of the line scans was 220 mm, leaving 20 mm on each side of the boundary to avoid edge effects. The red line shown in Figure 10 was the inspection path using the multi-electrode sensor and the lift-off was 0.1 mm.  
Figure 8: Schematic diagram of a 12 mm thick fiberglass board containing flat bottomed holes of surface depth (from left to right) 3 mm, 2 mm and 1 mm.  
Figure 9: The signal strength diagrams with the multi-electrode sensor with 0.1 mm lift-off: (**a**) E21, (**b**) E31, (**c**) E41.  
Through position compensation, the experimental results of the multi-electrode sensor detecting three buried defects were shown in Figure 11a-c. As shown in Figure 11a, the voltage of E21 detecting B1 was greater than the reference value which was rough red line. In previous work, the voltage would become greater when the conventional CI sensor defecting the defects on the surface of the conducting specimen. This abnormal phenomenon of detecting the non-conducting specimen was caused by the negative MSDs of different regions. Unlike the depths of the surface defects, it was extremely difficult to acquire approximately quantitative information about the depths of three buried defects because of the negative MSDs of different regions. If Figure 11a was the only picture acquired by the conventional CI sensor, B1 could be mistaken for a defect on the surface of the conducting specimen. As shown in Figure 11b,c the voltages of E31 and E41 detecting B1 were smaller than the corresponding reference values, which meant that B1 was a defect of the non-conducting specimen. By comparing these three line scans of B1, it could be sure that B1 was a defect of the non-conducting specimen, which could eliminate the misjuddment which was caused a single picture. Besides that, it was found that B1, B2 and B3 were the defects of the fiberglass board and their buried depths were decreased from left to right in turn. For B3 in Figure 11c, the two troughs were unequal, which was again caused by the MSDs asymmetry induced by the floating potential in actual experiments.
## 2 Materials and Methods  
### Sample Preparation  
In this study, two types of granite, distinguished by their coarse and fine particle sizes, were used in the experimental setup. The coarse granite was obtained from Huang-bai Town, Miluo City, Hunan Province. The fine granite was obtained from Chenxiang Town, Changtai District, Zhangzhou City, Fujian Province. Thin sections of coarse- and fine-grained granite samples were observed with a M330P\({}_{-}\)HK830 polarizing microscope at 5\(\times\) magnification. Figure 1 presents the cross-polarized micrographs of the coarse- and fine-grained granite samples. Preliminary measurements from the imaging system indicated that in the coarse-grained granite, the particle size ranges for mica, feldspar, and quartz were 0.074-0.726 mm, 0.063-0.577 mm, and 0.085-0.635 mm, respectively, whereas in the fine-grained granite, the respective ranges were 0.048-0.415 mm, 0.074-0.225 mm, and 0.050-0.454 mm. In this study, the terms 'coarse-grained' and 'fine-grained' granite are employed to denote the relative differences in grain size between the two sample groups, as opposed to adhering strictly to the IUGS classification standards. This nomenclature is employed exclusively to differentiate the heterogeneity characteristics of the two groups for subsequent analysis. The polarized microscope images corresponding to each granite type are shown in Figure 1. X-ray diffraction (XRD) analyses showed that the main mineral compositions of both granite types include quartz, mica, and feldspar, with the coarse granite having a higher quartz content than the fine-grained granite. The mineralogical composition of the two granite types differs, with the coarse granite containing both sodium and potassium feldspar, whereas the fine granite is characterized by the presence of sodium feldspar alone (see Table 1). All samples were obtained from a homogeneous granite source and carefully ground into cubic samples measuring 20 mm \(\times\) 20 mm \(\times\) 20 mm, ensuring that the surface was flat during the polishing process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Granite**} & \multicolumn{4}{c}{**Mineralogical Composition (\%)**} & \multirow{2}{*}{**Natural Density (g/cm\({}^{3}\))**} \\ \cline{2-2} \cline{4-6}  & **Quartz** & & **Micas** & **Feldspar-Sodium** & **Potassium Feldspar** \\ \hline Coarse & 23.16 & 18.45 & 30.37 & 28.02 & 2.63 & 0.81 \\ Fine & 14.5 & 27.02 & 58.47 & 2.80 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coarse and fine-grained granite: main mineral components and physical properties.
## 2 Materials and Method
### Model Piles and Sand Tank
The steel pile and the timber pile were both constructed using St-37 steel and beech tree, respectively, a 50% fiberglass-included FRP pipe was used as the FRP pile, and PVC-U type cylindrical profile was used as the PVC pile. The four model piles used in this study are depicted in Figure 5. Since the main objective of this study is to investigate the friction capacity of mode piles, therefore four different materials were selected to study their friction capacity when used as pile foundations. In pile foundations, different construction materials offer unique advantages. Steel piles are favored for their high strength, allowing them to bear heavy loads in deep foundation applications such as tall buildings and bridges. Timber piles are suitable for smaller structures due to their ease of installation and cost-effectiveness, making them a viable option for residential and light commercial projects. FRP (Fiber-Reinforced Polymer) filled-with-concrete piles excel in corrosive environments, as they are resistant to chemical degradation, making them valuable in marine and industrial settings. PVC filled-with-concrete piles provide enhanced resistance to weathering and groundwater corrosion, making them a durable choice for foundations in certain environmental conditions. The selection of the most advantageous material for pile foundations depends on factors such as the project's scale, load requirements, site conditions, and long-term performance considerations [58; 59; 60]. The mechanical and physical properties of model piles are listed in Table 3. The 3D schematic diagram of the soil tank is as seen in Figure 6.  
Four different relative densities employed in this investigation were obtained using a hand compactor. Vibration equipment is generally used for compaction of sand [61]. Relative density experiments were performed in the laboratory; the different relative densities used in this study are (Dr = 10%, 40%, 65% and 90%). The summary of obtaining various relative densities of sand with the compaction technique corresponding to the dry unit weights and the void ratio is presented in Table 4. The water content measured under laboratory conditions is about 1%. Compaction process tests were carried out at this water content, and then the dry unit weight was calculated. To ensure the homogeneity of the sand in the soil tank during the tests, small cans were positioned in various areas within the soil tank to control the relative density of the sand. After each test, the small cans were carefully extracted from the soil tank and the density of the sample was calculated, as has been performed in the literature [62]. Figure 5: Model piles.
## 6 Prospects and Future Trends
### Harnessing Machine Learning and Edge Computing for Advanced EMS and Eco-Driving Solutions  
The previous literature analysis indicated that the level of sophistication for the cooperative optimization of EMS and eco-driving is gradually advancing. To address many human-factor-related issues, machine learning methods have become popular. These data-driven models integrate predictive models that utilize the driver's compliance with speed advice systems or other interactions with V2I and V2V services. By doing so, these models provide a more realistic representation of how these services may affect systems. In addition, leveraging empirical observations that encompass different driver types, vehicle types, and traffic conditions can enable accurate predictions regarding the performance of co-optimization applications. An example is [74], where deep learning was used to model state transitions based on an offline-trained graphical model, which significantly reduced the time complexity. Furthermore, two or more hybrid learning algorithms will be the direction of approaches for solving the cooperative optimization problem with more and more data sources. One of the application direction of the learning algorithm is to extract useful traffic information and perceive the behavioral characteristics of human-related factors from huge amounts of data, which makes the \"brain\" of connected HEV/PHEVssmarter, and another is application is the onboard implementation of the EMS, which makes its \"behavior\" more reliable.  
In addition, online control requirements need more powerful calculation and processing capacity. To improve working efficiency, cooperative optimization frameworks must be able to allocate computing resources reasonably according to different calculation tasks. With the advent of the Internet of Things and 5G communications, centralized mobile cloud computing has given way to Mobile Edge Computing (MEC) in recent years. The primary feature of MEC is to push mobile computing, network control, and storage to network edges, thereby enabling computation-intensive and latency-critical applications on resource-limited mobile devices [109]. Therefore, the analysis and study of cooperative optimization under the Mobile Edge Computing architecture represent another important research direction.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 4 Data Processing
### Ocean Current Inversion
(**b**) Percentage of points that meet convergence conditions with increasing number of iterations. Figure 12: (**a**) Sea surface velocity from LuTan-1. (**b**) Sea surface velocity reference data from CMEMS. Color map represents radial current velocity. Figure 10: M45 model ATI-SAR ocean surface retrieval process.
## 3 BM-RAGAM Retrieval Scheme in the Vertical Domain of LLMs  
The BM-RAGAM (BM25 retrieval-augmented generation attention mechanism) retrieval scheme proposed in this study is shown in Figure 2. In this scheme, a BM25 search is responsible for quickly matching query terms with keywords in the document, ensuring that the system can efficiently retrieve documents that have a high relevance score in the term frequency associated with the query. RAG, on the other hand, combined with a deep learning language model, can capture the context association between queries and documents, so as to identify the most relevant documents at the semantic level. An attention mechanism is used to calculate the score of the search-related knowledge documents and assign the attention weights as the ratio of the scores for the document. The scheme of the BM-RAGAM achieves a high balance between retrieval efficiency and generation quality in knowledge retrieval and information generation and further improves the accuracy of semantic understanding and matching. In addition, by further integrating and understanding the key documents retrieved, the LLM uses the ability of natural language generation to condense the important information in the documents into more explanatory responses. This keyword-guided generation method enables the system to provide explanations and contextual understanding that are more in line with users' query requirements when answering questions, significantly enhancing the accuracy and interpretability of the replies. The BM-RAGAM seamlessly integrates the strengths of BM25 and RAG, leveraging an LLM to semantically merge the generated results. This approach enhances the system's ability to generate more explanatory and relevant responses, catering to diverse query requirements while delivering more precise and comprehensive information to users.  
Figure 2: BM-RAGAM retrieval scheme.
## 3 Simulation Results
In particular, for the on-axis reference case, the singularity distribution was easily perturbed. We further investigated the effect of the cylindrical lenses angle on the CSD distribution. As shown in Figure 4, the CSD phase and amplitude were simulated, with \(\sigma_{\text{g}}=0.4\omega_{0}\), \(l=\pm\) 2, and different cylindrical lenses angles. When the angle decreased gradually from \(90^{\circ}\) to \(0^{\circ}\), the singularities in the CSD amplitude evolved gradually into stripes. In particular, between \(30^{\circ}\) and \(60^{\circ}\), the relationship between the number of stripes and the TC was N = 2 \(|l|\) + 1. In contrast to the amplitude, the CSD phase showed a dumbbell-like shape, and the total number of \"dumbbell pieces and handles\" (marked with arrows) obeyed the relationship N = 2 \(|l|\) + 1. In particular, when the angle was approximately \(30^{\circ}\), the phase relationship was more evident than that in the amplitude pattern, although the amplitude was shown on a log scale. Thus, the magnitude of the TC was easier to distinguish and determine from the CSD phase pattern with an angle of \(30^{\circ}\). Figure 3: Numerical simulation results for normalized distribution of \(\log\left[1+\left|W\left(\rho_{x_{1}},\rho_{y_{1}},0,0\right)\right|\right]\) of PCLG vortex beams with different TCs and coherence widths. CSD amplitude distributions for (**a**–**d**) low coherence, (**e**–**h**) medium coherence and (**i**–**l**) high coherence cases with four different TCs. The arrows mark the positions of stripes in the CSD amplitude. Figure 5 shows the numerical simulation results of the CSD amplitude and phase for different TCs and coherence widths when the included angle of two cylindrical lenses was 30\({}^{\circ}\). The number of stripes or rings in the CSD amplitude (Figure 5a-f) did not show a clear relationship with the TC. In comparison, the number of \"dumbbell pieces and handles\" in the phase pattern indicated a more reliable relationship, i.e., N = 2 \(|\)\(|\)\(|\) + 1. For the case of extremely low coherence, i.e., \(\sigma_{\rm g}=0.1\omega_{0}\), N can be determined by analyzing the dark rings in the CSD amplitude whose number equal to the TC. Additionally, CSD phase measurements for cylindrical lenses angles of 90\({}^{\circ}\) and 30\({}^{\circ}\) could be performed to double check the measurement of the TC. In fact, this is the main highlight of this study as the flexible and adjustable angle of the dual cylindrical lens system allows this scheme to be used in both extremely low coherence and extremely high coherence scenarios.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 5 Numerical Examples  
In this section, the presented approach is applied to four examples, which include a mechanical part, a reinforced concrete short column of a structure, a roof truss structure, and a wing structure. In addition, the Monte Carlo Simulation approach (MCS) is employed to verify the feasibility of the presented method. The number of random numbers of each parameter generated by MCS in this study is ns = 1 \(\times\) 10\({}^{6}\).
# Application of a High-Power Reversible Converter in a Hybrid Traction Power Supply System
## 5 Simulation and Experiment
### Coordinated Control Experiments
Traction and braking energy statistics of one substation are shown in Table 6. A and B denote traction energy provided by each 12-pulse rectifier, respectively, C denotes the traction energy provided by the reversible converter, and D denotes the braking energy recovered by the reversible converter. Figure 19 shows the histogram of the traction energy provided by the reversible converter and each 12-pulse rectifier for clarity. The reversible converter has provided nearly one third of the total traction power (the average proportion is 36.5%). However, it will be found that the traction power provided by the reversible converter is slightly more than that of each 12-pulse rectifier. The reason for this is that the DC output curve of the reversible converter is above that of the 12-pulse rectifier in the light load region, as shown in Figure 5b. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **12-Pulse** & **12-Pulse** & \multicolumn{2}{c}{**Reversible Converter**} & \multicolumn{2}{c}{**Total Traction**} & \multicolumn{2}{c}{**Percentage of**} \\  & **Rectifier 1/A** & **Rectifier 2/B** & **Rectifying/C** & **Inverting/D** & **Energy/A + B + C** & **C/(A + B + C)** \\ \hline
2016/6/18 & 4.432 & 4.482 & 4.699 & 0.974 & 13.613 & 34.52\% \\
2016/6/19 & 3.880 & 3.928 & 4.474  
In order to evaluate the energy-saving effect of the reversible converter used in the traction power supply system more clearly and intuitively, the average energy-saving percentage over a long period is defined as \(\eta\):  
\[\eta=\frac{\sum\limits_{i=1}^{n}E_{\text{inv\_i}}}{\sum\limits_{i=1}^{n}E_{ \text{rec\_i}}}\times 100\% \tag{23}\]  
where \(E_{\text{inv\_i}}\) denotes the inverting energy of the reversible converter every day, and \(E_{\text{inv\_i}}\) denotes the total rectifying energy of the reversible converter and 12-pulse rectifiers every day. In Table 6, regenerated energy and total traction energy change every day, but according to Equation (23), the average energy-saving percentage \(\eta\) (from 18 June 2016 to 30 June 2016) is calculated as high as 11.15%.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 2 Related Work
[PERSON] et al. [32] introduced a hybrid quantum CNN (HQCNN) for LoS/NLoS classification, showing excellent performance. Additionally, the attention mechanisms integrated into DL models have yielded promising results. [PERSON] et al. [33] combined attention mechanisms with Bi-directional LSTM (BiLSTM) and CNN for high-accuracy NLoS classification, while [PERSON] et al. [34] embedded an efficient channel attention (ECA) module into a residual network for greenhouse environments. [PERSON] et al. [35] combined gated recurrent units (GRU) with attention mechanisms to capture dynamic UWB signal information, reducing NLoS classification errors. All relevant methods have been summarized in Table 2. Despite the significant advancements in DL-based UWB NLoS research in recent years, most methods still focus on LoS/NLoS binary classification. There is limited exploration of NLoS multi-class classification, especially in complex indoor environments. Conducting research on NLoS multi-class identification can help systems better understand UWB signal propagation and obstacle types, thereby addressing specific ranging and positioning errors. While some advanced techniques have significantly improved model performance, incorporating attention mechanisms and complex model structures typically increase model size and computational requirements, leading to higher hardware costs. For UWB positioning tasks, it is crucial to design more lightweight and efficient models.
# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 5 Conclusions  
In this paper, we propose a new multitask learning network, MSC-Net, to simultaneously accomplish retinal vessel segmentation and centerline extraction. We design a channel multiscale spatial attention fusion module, CAS-FB, which is used to fuse and correct the features of different tasks and different scales. In addition, we use the clDice loss function to perform constraints between different tasks and improve the topological continuity of the results of blood vessel segmentation and centerline extraction. Comparison with other methods on three retinal datasets (DRIVE, STARE, and CHASE) and the ablation of each module prove the superiority and effectiveness of our proposed method. In the future, our work will focus on how to reduce the amount of network parameters and calculations and extend the proposed method to other 3D medical imaging datasets.  
Conceptualization, L.P. and L.H.; methodology, Z.Z. and S.Z.; software, Z.Z.; validation, Z.Z. and S.Z.; formal analysis, S.Z.; investigation, L.P.; resources, S.Z. and L.H.; data curation, S.Z. and L.H.; writing--original draft preparation, L.P. and Z.Z.; writing--review and editing, L.P., Z.Z., S.Z., and L.H.; visualization, L.P.; supervision, S.Z. and L.H.; project administration, S.Z. and L.H.; funding acquisition, S.Z. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Provincial Natural Science Foundation project (2021)02019, 2021)01578, 2019Y9070), and Fuzhou Science and Technology Project (2020-GX-17).  
Not applicable.  
DRIVE dataset can be found at [[https://drive.grand-challenge.org/](https://drive.grand-challenge.org/)]([https://drive.grand-challenge.org/](https://drive.grand-challenge.org/)) (accessed on 16 November 2021). STARE dataset can be found at [[https://cecas.clemson.edu/~ahoover/stare/](https://cecas.clemson.edu/~ahoover/stare/)]([https://cecas.clemson.edu/~ahoover/stare/](https://cecas.clemson.edu/~ahoover/stare/)) (accessed on 16 November 2021). CHASE dataset can be found at [[https://blogs.kingston.ac.uk/retinal/chaosedb1/](https://blogs.kingston.ac.uk/retinal/chaosedb1/)]([https://blogs.kingston.ac.uk/retinal/chaosedb1/](https://blogs.kingston.ac.uk/retinal/chaosedb1/)) (accessed on 16 November 2021).  
The authors declare no conflict of interest.