esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 4 A Parallel Algorithm of Mesh Processing Based on \(3\)-dmaps  
In this section, we illustrate the interest of using a distributed combinatorial map by proposing a parallel algorithm for mesh processing. For this purpose, we consider an adaptive hexahedral mesh refinement. Indeed, by using the independent blocks of an \(n\)-dmap, a parallel version of a mesh processing algorithm is simple to achieve. Moreover, critical darts in the \(n\)-dmap give us all information required to ensure the global consistency of the different blocks. This aspect is important to guarantee the same final result when using an \(n\)-dmap with a parallel process, as when using an \(n\)-map with the corresponding sequential process.  
Many works exist on parallel mesh processing and adaptive hexahedral mesh refinement (see for example [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]) which shows the interest of this field of research. Several libraries are available that provide efficient parallel adaptive mesh refinement, for example libMesh[43] or PUMI[44]. However, these libraries are not generic, they are only defined in a fixed dimension, and do not allow us to consider any type of meshes. For example, authors of PUMI say in their paper: _\"the mesh must be composed of entities which belong to a small set of topological types... Regarding polyhedral meshes, it should be possible for the MDS structure to accept new topological types at runtime, though its performance will degrade as the number of types grows large. Good performance can be found using about 10 types or less, but when more than 100 types are involved, this may no longer be the optimal structure.\"_ In libMesh, it is only possible to represent basic objects (hexahedron, tetrahedron, prism, pyramid).  
Our goal in this paper is not to define a new method of parallel adaptive hexahedral mesh refinement better than previous works, but to illustrate the interest of our distributed combinatorial maps with a mesh processing example. Moreover, we remind that the main interest of our solution is to be generic. With our data structure, we can use many different algorithms to compute characteristics and to modify \(n\)D meshes, which is often not possible when using non generic data structures, for example allowing us only to represent some specific types of cells.  
In Section 4.1, we start by giving a generic parallelization of the considered algorithm. This parallel algorithm can be executed in sequential or in parallel, with a multi-threaded version or a distributed version. This generic algorithm uses our distributed combinatorial maps, and uses messages to update critical darts in the different blocks. There are several possibilities in order to transfer messages between the block, two of them are given in Section 4.2.
### Study on the Influence of Fault Dip Angle on Fault Activation  
To analyze how the fault dip angle affects the fault activation, this paper considers the situation when the fault cuts into the mining area, the numerical geometric model schematic diagram is shown in Figure 13a, in which \(\alpha\) is the fault dip angle, the intersection A of the fault and the mining area left boundary serves as the monitoring point, and the fault dip angle \(\alpha\) is determined to be \(30^{\circ}\), \(40^{\circ}\), \(50^{\circ}\), \(60^{\circ}\), \(70^{\circ}\), and \(80^{\circ}\), respectively.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 8 Implementation Setup and Results
### Multi-Media/Multi-Band Tested Results  
Based on the experimental setup described in Section 8.2, the first test was done with pure water inside a water tank of 1 m in height and 1.8 m in width. More than 20,000 packets where received at distances of 1 m and 1.5 m. The second test was done with salty water inside the water tank; in this case, the tank was of 1 m in height and 8 m in width. Around 20,000 packets were received at distances of 4 m and 6 m. The final test was done in open seabed with highly turbid water. The seabed was constructed with a depth of 1 m depth and width of 12 m. Around 20,000 packets were collected distances of 2, 4, 6, 8, 10, and 12 m. In recent tests, the signal strength was collected only for VLC and IR.  
Figure 14 shows the signal strength received by the multi-media/multi-band modem using pure water and the small water tank. The signal strength received for the VLC medium was 100 and 98.98 for 1 m and 1.5 m respectively, and that for the IR medium was 100 for both 1 m and 1.5 m.  
Figure 15 shows the signal strength received using salty water and the large water tank. The signal strength received for the VLC medium was 29.2 and 80.1 for 4 m and 6 m respectively; that for the IR medium was not noted.  
Figure 16 shows the signal strength received using the turbid water in open seabed test. In the case of 2 m, the signal strength received for the VLC medium was 100 for 1 m, 1.5 m, and 2 m, and that of IR is 100 for 1 m and 1.5 m. In the case of 4 m, the signal strength received for the VLC medium was 100, 100, 95, 19, 0.07 for 1 m, 1.5 m, 2 m, 2.3 m, 3 m, and 3.8 m respectively, and for IR
## 3 Results  
From the 33 subjects initially randomized, one participant did not show up for the assessments. Thus, 32 patients completed the follow-up assessments and were included in the analysis: seven subjects in the control group (seven women; 22.0 \(\pm\) 2.0 years old), eight in the placebo group (eight women; 21.8 \(\pm\) 0.9 years old), nine in the manual manipulation group (eight women and one man; 22.0 \(\pm\) 1.3 years old), and eight in the mechanically assisted manipulation group (six women and two men; 22.4 \(\pm\) 1.2 years old). The groups were similar regarding age (F\({}_{(3,31)}\) = 0.279, \(p\) = 0.840) and sex distribution (\(\chi\)2 = 3.883, \(p\) = 0.274), as well as in visual analogue scale, disability (neck disability index), Patient Global Impression of Change Scale, pressure pain threshold, pain pressure perception, and tone, elasticity, and stiffness of the trapezius and biceps at baseline (Table 1).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Manual** & **Placebo** &
\begin{tabular}{c} **Instrument-** \\ **Assisted** \\ \end{tabular} & **Control** & \(p\) **Value** \\ \hline VAS (cm) & 3.38 \(\pm\) 1.08 & 4.00 \(\pm\) 1.19 & 4.15 \(\pm\) 0.87 & 3.94 \(\pm\) 1.04 & 0.789 \\ NDI (score) & 10.50 \(\pm\) 4.56 & 10.13 \(\pm\) 3.56 & 12.25 \(\pm\) 3.41 & 11.86 \(\pm\) 3.44 & 0.643 \\ PGIS (score) & 3.78 \(\pm\) 1.30 & 2.00 \(\pm\) 0.93 & 2.13 \(\pm\) 0.99 & 1.71 \(\pm\) 0.76 & 0.789 \\ \hline Pressure pain threshold (kg/cm\({}^{2}\)) & & & & & \\ \hline right upper trapezius & 3.30 \(\pm\) 0.91 & 2.75 \(\pm\) 0.72 & 3.20 \(\pm\) 0.70 & 3.01 \(\pm\) 0.65 & 0.485 \\ left upper trapezius & 3.20 \(\pm\) 0.75 & 2.60 \(\pm\) 0.63 & 3.15 \(\pm\) 0.77 & 3.06 \(\pm\) 0.56 & 0.292 \\ right biceps brachii & 2.47 \(\pm\) 0.49 & 2.54 \(\pm\) 0.59 & 2.27 \(\pm\) 0.59 & 2.50 \(\pm\) 0.52 & 0.397 \\ left biceps brachii & 2.34 \(\pm\) 0.34 & 2.19 \(\pm\) 0.69 & 2.22 \(\pm\) 0.62 & 2.43 \(\pm\) 0.57 & 0.836 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline comparison between groups.
###### Abstract  
Inexpensive (<$300) real-time particulate matter monitors (IRMs), using laser as a light source, have been introduced for use with a Wi-Fi function enabling networking with a smartphone. However, the information of measurement error of these inexpensive but convenient IRMs are still limited. Using ESCORTAIR (ESCORT, Seoul, Korea) and PurpleAir (PA) (PurpleAir U.S.A.), we evaluated the performance of these two devices compared with the U.S. Environmental Protection Agency (EPA) Federal Equivalent Monitoring (FEM) devices, that is, GRIMM180 (GRIMM Aerosol, Germany) for the indoor measurement of pork panfrying or secondhand tobacco smoking (SHS) and Beta-ray attenuation monitor (BAM) (MetOne, Grants Pass, OR) for outdoor measurement at the national particulate matter (PM\({}_{2.5}\)) monitoring site near an urban traffic hotspot in Daejeon, South Korea, respectively. The PM\({}_{2.5}\) concentrations measured by ESCORTAIR and PA were strongly correlated to FEM (r = 0.97 and 0.97 from indoor pan frying; 0.92 and 0.86 from indoor SHS; 0.85 and 0.88 from outdoor urban traffic hotspot). The two IRMs showed that PM\({}_{2.5}\) mass concentrations were increased with increased outdoor relative humidity (RH) levels. However, after applying correction factors for RH, the Median (Interquartile range) of difference compared to FEM was (14.5 (6.1\(\sim\)23.5) %) for PA and 16.3 (8.5-28.0) % for ESCORTAIR, supporting their usage in the home or near urban hotspots.  
Keywords:PM\({}_{2.5}\); sensor; correction; pan frying; secondhand smoke; urban traffic +
Footnote †: journal: Applied
## 5 Intelligent Diagnostic Technology for Pile Inspection
### Comparative Analysis of AI Models  
Three artificial intelligence models, namely feature-based machine learning, end-to-end deep learning, and hybrid methods, display distinct characteristics in PIT, with  
Figure 15: CNN model [106].  
significant differences in performance across key evaluation metrics, including data dependency, accuracy, and interpretability.  
Feature-based machine learning models rely on manually designed feature engineering processes and exhibit significant advantages when data is limited. For example, [PERSON] [43] employed 923 field test signals and extracted key features from low-strain signals using domain knowledge, yielding an 88% accuracy rate in defect pile screening. However, the model's performance is strongly dependent on the quality of the feature engineering process. [PERSON] and [PERSON] [104] reported that single machine learning models achieved less than 70% accuracy in identifying multi-defect piles due to manually selected spectral features failing to capture complex signal patterns. However, these models are computationally efficient and highly interpretable, rendering them suitable for small datasets and projects or applications requiring interpretability.  
In contrast, end-to-end deep learning models exhibit exceptional performance when processing large, complex datasets. [PERSON] [25] attained a classification accuracy of 99.17% for multi-defect piles with 1200 datasets. [PERSON] [105] modeled defect locations in piles with a total of 17 \(\times\) 7200 datasets. However, these models demand significant computational resources and exhibit limited interpretability, restricting their utility in scenarios where result traceability is critical.  
The hybrid method achieves a balance between these two paradigms. [PERSON] [98] integrated multi-domain feature extraction (time, frequency, and time-frequency domains) with CNN, with a training set of 9 \(\times\) 100 samples, a validation set of 9 \(\times\) 40 samples, and a test set of 9 \(\times\) 20 samples, yielding an accuracy rate of 97.8% for defect identification under layered soil conditions. This integration leverages manual features (such as spectral entropy) to reduce data dependency while employing CNNs to capture complex feature interactions, thereby bolstering noise robustness.  
In summary, feature-based machine learning is optimal for small datasets and tasks requiring interpretability; end-to-end deep learning is well-suited to large-scale, highly complex scenarios; and hybrid methods offer a practical approach to medium-sized datasets in contexts involving complex geological conditions.
## 3 Experimental Results and Analyses
### Experiment in Waters with Flat Seabed Topography
#### 3.1.2 Experiment in Constant-Depth and Fixed-Point Modes
By comparing tests conducted in fixed-depth fixed-point mode and constant-altitude fixed-point mode, it was observed that in flat terrain conditions, constant-altitude fixed-point mode is the preferred mode for vertical movement. This mode has strong applicability for establishing lines' direction, spacing, and overlap degree. In flat waters where depth changes are minimal and unrestricted, there is no need to consider depth changes. However, combining fixed-depth mode with parallel zone edges can yield poor results.
## 5 Experimental Results and Discussion
### Verification Test of the Performance of the Working Circuit System
#### 5.1.2 Comb Motor Performance Test  
For the comb motor, the speed sensor and torque sensor were installed on the motor output shaft. The brake was installed on the other side to simulate the load torque. The motor speed was observed by adjusting the tension of the brake to change the motor load, and the speed test results of the comb hydraulic motor were recorded five times continuously, as shown in Table 3.
## 5 Discussion
### The Characteristics of PDEC Framework in Performance Improvement  
Based on the above experimental results, our experimental results lead to several key observations:  
* PDEC generally enhances the reasoning performance of KGC's baseline model. When compared with the baseline methods, PDEC demonstrates consistent improvements across different datasets. This observation aligns with our objective of developing a more effective approach for KGC tasks.
* The performance improvement of PDEC is more significant on larger datasets. When examining the performance improvement of PDEC on different datasets, we observe that the larger the dataset, the more significant the improvement. This trend suggests that PDEC is particularly effective in capturing relationships and patterns present in larger KGs. It further supports our hypothesis that PDEC's ability to model polysemous predicates effectively enables it to handle the complexity and diversity found in larger datasets.
* The performance improvement of PDEC is more significant for old-fashioned methods. Our experiments also reveal that the performance improvement of PDEC is more significant for older methods like TransE and DistMult. These methods are known to be more susceptible to polysemous predicates, which are common in KGs. This observation aligns with our theoretical analysis presented in Section 3.6, where we discuss how PDEC addresses the limitations of traditional methods by effectively modeling polysemous predicates.
# Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot
## 4 Experiments  
The sweeping robot experiment uses the Turtlebot2 experimental platform, as shown in Figure 8, which is a tiny, low-cost, ROS-based mobile robot. The camera of this experimental platform captures images and transmits the image information to the cloud server through the Wi-Fi module. The signal range of the Wi-Fi is within 40 square meters, and the signal delay is within 50 ms. The result of the cloud calculation is returned to the microprocessor through the Wi-Fi module. The microprocessor generates instructions based on the calculation results, and the robot starts to perform the task. The hardware and software used for the experiments are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Description** \\ \hline Wi-Fi & TP-LINK \\ Micro-processor & SZ02-232-2K, S202-NET-2K \\ Camera & Logitech C270c \\ GPU & NVIDIA GTX 1080 TI Single video memory 11G, memory 32G \\ CPU & AMD Ryzen 5 5600X 6-Core Processor 3.70 GHz \\ Platform & Turtlebot2 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental hardware environment.  
Figure 7: Superpixel segmentation and non-obstacle area extraction based on the depth map: (**a**) Original scene image, (**b**) Depth map, (**c**) Segmentation result, (**d**) Non-obstacle area.
## 6 Performance Evaluation and Results
### RSVTM Properties and Operation
The latter is evident from both Figure 7a,b where, in the first one, we notice that the number of visitors deviating from their corresponding choices for ground truth increases as the learning rate \(b\) increases while, as shown in Figure 7b, the average QoE decreases and the corresponding average visiting time increases. It is worthwhile pointing out that an increase in the visiting time implicitly contributes to an increase in the museum congestion. On the other hand, as the learning rate \(b\) increases, visitors select their preferred recommendation more quickly without exploiting their available options in detail. Thus, the RSVTM algorithm converges faster at the final recommendation selections for visitors (please notice the lower running times in Figure 7a with increasing values of parameter \(b\)). Based on the above discussion, we conclude that there exists a tradeoff between the learning rate of visitors and the accuracy of the optimal values for the achieved visitor QoE, visitor recommendation selection, and visitor visiting time. Figure 6: Average QoE and average visiting time for all the visiting styles.
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
## 3 Results and Discussion
### Batch Equilibrium Adsorption
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Heavy Metals**} & \multicolumn{2}{c}{**Langmuir Model**} & \multicolumn{2}{c}{**Freundlich Model**} & \multirow{2}{*}{\(\quad\)} \\ \cline{2-2} \cline{4-6}  & \(Q^{o}_{max}\) **(mg/g)** & \(K_{L}\) **(L/mg)** & \(R^{2}\) & \(K_{F}\) **(mg/g)/(mg/L)n** & \(n\) & \(R^{2}\) \\ \hline \multicolumn{6}{c}{**Single-metal experiments**} \\ \hline Mn & 0.710 & 0.154 & 0.951 & 0.143 & 0.459 & 0.874 \\ \hline Zn & 0.745 & 0.121 & 0.952 & 0.124 & 0.477 & 0.894 \\ \hline Cd & 0.771 & 0.154 & 0.980 & 0.157 & 0.431 & 0.936 \\ \hline Pb & 1.305 & 0.131 & 0.923 & 0.222 & 0.476 & 0.833 \\ \hline As & 1.113 & 0.455 & 0.993 & 0.220 & 0.426 & 0.843 \\ \hline \multicolumn{6}{c}{**Mixed-metal experiments**} \\ \hline Mn & 0.370 & 0.217 & 0.975 & 0.100 & 0.350 & 0.854 \\ \hline Zn & 0.447 & 0.263 & 0.955 & 0.109 & 0.402 & 0.949 \\ \hline Cd & 0.484 & 0.338 & 0.982 & 0.136 & 0.229 & 0.910 \\ \hline Pb & 1.059 & 1.913 & 0.902 & 0.577 & 0.261 & 0.870 \\ \hline \end{tabular}
\end{table} 
Table 3: Adsorption isotherm parameters for As and heavy metals.
## 2 Materials and Methods
### Statistical Tests  
To investigate the behavioral differences under varying CD, IV, TC, and TL and to comprehend their interaction effects, this study employs three distinct statistical tests:  
1. Friedman test: Employed as a non-parametric method, the Friedman test [43] is utilized to detect significant differences across related groups. It is particularly useful in assessing the impact of independent variables on CF and LC characteristics.
2. Multivariate Analysis of Variance (MANOVA): Applied when dependent variables display consistent variance, MANOVA [44] detects significant differences among these variables and explores their interactions and combined effects on behavioral indicators.
3. [PERSON]'s test: Utilized for pairwise comparisons, this test assesses significant variations among dependent variables in different environmental scenarios, effectively highlighting specific differences arising from diverse environmental combinations.  
These statistical approaches provide a comprehensive framework to thoroughly evaluate the effects of diverse independent variables on CF and LC behaviors, establishing a solid basis for in-depth analysis.
## 4 Experiments
### Comparison Experiments
#### 4.2.3 Ablation Experiments  
In this section, corresponding ablation experiments are described, which were used to verify the advantages of MKD and two-channel matching network. Ten pairs of images were randomly selected from the QXS-SAROPT dataset as test data. The specific experimental setup was as follows. The ablation experiment results can be seen in Figure 13.  
* Performance of MKD  
We chose the traditional keypoints detection method SIFT as well as the advanced network method D2-Net with MKD for the ablation experiments. At the same time, experiments were also set up to verify the superiority of MKD's strategies: (1) not using peak values as the keypoints measurement, i.e., MKD (w/o peak values); (2) not implementing the weighted combination, i.e., MKD (w/o weighted combination).  
Different keypoints detection strategies were used on the same image patch matching network (Matching Module, MM). _Repeatability_ was used as the evaluation criterion [48], the higher the repetition rate, the higher the probability that extracted keypoints are correct matching points. The results are shown in Table 4, and the experimental results with best performance are marked in bold.  
* Performance of the two-channel matching network  
To verify the superiority of the two-channel network structure (2 ChN), we conducted ablation experiments with two commonly used image patch matching network structures, namely Pseudo-Siamese network (PSN) and Siamese network (SN). They were trained on the same dataset from part of the QXS-SAROPT dataset. _Precision_ was used as the evaluation criterion. The results are shown in Table 5, and the experimental results with best performance are marked in bold.  
Figure 13: Ablation experiment results.  
When not using peak values for keypoints detection or conducting weighted combination for feature layers in MKD, the experimental results were unsatisfactory. Compared with D2-Net, which uses the maximum response values as the detection condition, MKD had better results overall.  
As for two-channel network structure, we performed patch matching comparison tests with two other Siamese-type network structures (i.e., pseudo-Siamese network and Siamese network). They used the same loss function and were trained on the same dataset. From the experimental results, the two-channel network on optical and SAR test images had higher matching precision overall.
###### Abstract  
Permanent magnet thrust bearings have garnered significant attention due to their high rotational speeds, low noise levels, and excellent vibration-damping performance. However, existing designs of these bearings often suffer from low load-carrying capacity and are tailored to specific machines, which limits their broader applicability. To address these limitations, this paper proposes a novel modular multi-unit cell structure for permanent magnet thrust bearings. The load-carrying performance of this design is validated through theoretical analysis, simulation, and experimentation. The inspiration for this design comes from bionics and honeycomb structures, emphasizing modularization and the combination of multiple unit cells. The unit cell consists of four permanent magnets, and multiple unit cells can be connected to form a structure that replaces the traditional design of directly embedding a permanent magnet ring into the bearing structure. Moreover, the designed unit cell structure can expand in both axial and radial directions, allowing for the creation of various nested or cross structures tailored to specific usage requirements. With this modular approach, the theoretical model of the bearing structure can be extended from the traditional single-layer cross-nested structure to an arbitrary number of nested cross-nested configurations using the equivalent magnetic circuit method. The bearing's performance is validated through finite element simulations and experimental testing. The results demonstrate that the bearing with a four-layer cross-nested structure achieves a maximum load capacity of 48.45 kN, with a deviation of 7.3% from the theoretical value and 4% from the simulation results. By leveraging the generalization of the unit cell, the maximum axial load capacity across various configurations ranges from 6.78 kN to 288.9 kN, significantly enhancing the bearing's adaptability to diverse operational scenarios.  
permanent magnet thrust bearing; bionic design; multilayer nested crossover; theoretical model; finite element analysis; experimental verification 2025 IEEEexample: accepted: 22 March 2025