esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of Bow-Tie Microstrip Antennas  
The proposed bow-tie microstrip structure, as shown in Figure 1, is presented to improve the antenna performance. The CST Microwave Studio program is used as a main program to design and simulate the antennas. AutoSketch software, which is an inclusive electrical structure drawing software, is used to draw the designed antennas with 1:1 scale in order to print the desired antenna on laser paper for fabrication. The purpose of this program is to avoid any error and mistakes in the scale of the fabricated structures.  
Material selections are one of the important issues that should be carefully considered during any design process. The material thicknesses can also have a crucial influence on the microstrip antenna design, hence it should be precisely selected. In the design of the bow-tie microstrip structure, RT/Duroid 5880 and RT/Duroid 5880 LZ, from the Regress Company, are used as raw materials. These materials are chosen because they are cheap and easily obtained. They are available under different substrate thicknesses. The cladding layer of these materials is copper with constant thickness of 35 micrometres. The same material type and thickness are modelled and used in the CST simulation.
## 5 Experimental Data Analysis
### Analysis of Circular Pressure Sensor Data at the Base of the Support Pillar  
In this experiment, two circular pressure sensors were installed at the base of the simulated single support pillar in the opening cut. However, the left-side sensor was damaged. Data from the right-side single support pillar, collected through 108 channels, were organized to generate a pressure variation curve, as illustrated in Figure 15.  
From Figure 15, it can be observed that during the first 16 loading cycles, the force increment on the single support pillar remained stable, with no visible changes to the exterior of the simulated pillar. However, after the 17 th loading cycle (13 kN), the force on the pillar increased significantly, resulting in severe deformation of the single support pillar. By the 19 th loading cycle (15 kN), the force reached a peak of 4.0 kN. Following this peak, the pillar experienced a fracture, leading to a rapid decrease in pressure to 2.0 kN as the pillar unloaded. This event also triggered a large-scale collapse of the overburden rock  
Figure 15: Pressure variation curve of the simulated single support pillar.  
layer above the model. These observations indicate that the single support pillar plays a crucial role in supporting the roof in the opening cut.  
Prior to the fracture of the individual prop, severe rib loosening accompanied by localized rib spalling/sloughing was observed, whereas no significant roof subsidence was detected. Following prop failure, the overlying strata exhibited catastrophic structural collapse, resulting in complete loss of load-bearing capacity of the roof system. This critical instability mechanism ultimately triggers a high-risk roof fall hazard due to the abrupt stress redistribution within the surrounding rock mass.
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods
### Point Cloud Data Preprocessing
#### 2.2.2 Transition down with Voxel Grid  
After the pass-through filter reduces the noise, the point cloud data were still relatively dense. To improve the speed of subsequent point cloud processing, the point cloud was transited down with a voxel grid. This approach can retain the basic contours and features of the point cloud, while effectively reducing the amount of point cloud data.  
This process involves establishing a 3D voxel grid of the point cloud, which consists of several small grids. The data point closest to the center of gravity of the grid will be used to replace all data points, and other data points are eliminated. This approach allows for sparse data, while being simple and efficient, without the need to build complex topologies. The center of each small grid is calculated as  
\[\begin{array}{l}x_{c}=\sum\limits_{i=1}^{n}x_{i}/n\\ y_{c}=\sum\limits_{i=1}^{n}y_{i}/n\\ z_{c}=\sum\limits_{i=1}^{n}z_{i}/n\end{array} \tag{9}\]  
In the above equation, \(n\) denotes the number of points in the small grid, and \((x_{i},y_{i},z_{i})\) denotes the points in the small grid. The point cloud dataset in this paper is large, and a cube with a voxel volume of 1 m is created when the size of the raster is set, which here indicates the size of the voxel in the \(X\)-\(Y\)-\(Z\) direction. The results of the transition down of the point cloud with the voxel grid is reported in Figure 6.  
The number of point clouds before transiting down was 153,265, and afterwards the number was 33,094. As reported in the above figure, after transiting down the point cloud, the basic contours and features are preserved. In addition, the size of the small raster can be controlled to adjust the balance between the number of point clouds and the point cloud contour information.
## 4 Architecture
### Network Architectures  
#### 4.1.1 Facial Information Extraction Module  
As is shown in Figure 6, a state-of-the-art model illustrated in [27; 28] is adapted to recognize emotions, which takes facial image as an input and calculates the first two probabilities and makes a process of data adjustment to guide new font generation with a specific style. The probabilities of seven kinds of expressions including 'anger,' 'disgust,' 'fear,' 'happiness,' 'neutral,''sadness,' and'surprise' are calculated by the expression recognition module. Considering that the Fer2013 [29] expressions dataset is manually annotated and have certain errors, to make sure the generated fonts of Chinese character exactly reflects emotions, we select the first two probabilities (\(c_{1},c_{2}\)) of the expression recognition module's results to make sure that the two data \(c_{1},c_{2}\) are adjusted into the standard data \(c^{\prime}_{1}\)= \(c_{1}/c_{1}\)+\(c_{2}\) and \(c^{\prime}_{2}\)= \(c_{2}/c_{1}\)+\(c_{2}\) usage of regularization as combined style labels for the process of font generation.  
Figure 4: The training procedure of the font generation module. (**a**) The generator, G, tries to generate high-quality images to fool the discriminator; (**b**) the discriminator, D, is trained to recognize the generator’s outputs as well as possibilities. The font generation module is trained by paired images (consists of source font images x and target domain images y that are ground-truth).  
Figure 5: The diagram of the proposed font generation system. 1° and 2° imply the learning processes of the discriminator model with {source font image x, generated image} and {source font image x, real image} tuples, respectively. First, the system trains the font generation module until the discriminator cannot distinguish between real fonts and generated fonts. Then, the pre-trained model is used to recognize the facial expression of the input image, so as to guide the generator to generate a new font with specific emotions.
# The Sonic Resonance Method and the Impulse Excitation Technique: A Comparison Study
## 4 Conclusions  
Samples made from different materials (metal, ceramics, and glass) and with two shapes (a prism with a rectangular cross-section and a cylinder with a circular cross-section) were examined using the sonic resonant method (SRM) and impulse excitation technique (IET) to confirm the equality of the two methods for measuring the resonant frequency \(f\) of flexurally vibrating samples. The mean values of the resonant frequencies and standard deviations were compared using the t-test and the F-test. The tests showed that both methods produced the same values of the resonant frequency. Small differences were within the scope of measurement error. This was also confirmed using the graph of the function \(f_{IET}\)(\(f_{SRM}\)), which was a line with the slope of 0.9993 \(\approx\) 1. [PERSON]'s moduli calculated for the tested samples were compared with those published in the literature and good agreement was found.  
Conceptualization, LS.; methodology, T.H., I.S. and A.T.; investigation, T.H., F.O., J.O. and A.T.; writing--original draft preparation, I.S., T.H. and A.T.; writing--review and editing, T.H., F.O., J.O., I.S. and A.T.; visualization, A.T.; supervision, I.S. and A.T.; project administration, A.T.; funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Education of Slovak Republic, grant number KEGA 027 UKF-4/2019 and by RVO: 11000.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Sample Material** & **\(E\) [GPa] (SRM)** & **\(E\) [GPa] (IET)** & **\(E\) [GPa] (Ref.)** \\ \hline
1 & Aluminum & 73.61 & 73.65 & 70 [27] \\ \hline
2 & Stainless steel & 212.31 & 212.32 & 168–206 [28] \\ \hline
3 & Carbon steel & 226.64 & 225.92 & 207 [29] \\ \hline
4 & Kaolin ceramics 1  & 30.27 & 29.93 & 29 [30] \\ \hline
5 & Kaolin ceramics 2  & 20.57 & 20.59 & 29 [30] \\ \hline
6 & Alumina porcelain & 56.60 & 56.62 & 55–85 [31, 32] \\ \hline
7 & Soda-lime glass & 70.06 & 70.07 & 73 [33] \\ \hline
8 & Corundum ceramics & 246.75 & 246.72 & 154–377 [34] \\ \hline
9 & Silicon carbide & 94.00 & 93.91 & 100–400 [35] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Young’s modulus of used materials determined by SRM and IET methods, and Young’s modulus from the literature.
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 6 Experimental Validation and Analysis
### Digital Twin Robot Monitoring Synchronization Experiment  
To comprehensively monitor the robot's operational status in the ROS system, the Transform plugin was utilized within ROS visualization (rviz) to configure the display of the robot's motion coordinates. This facilitated the creation of a user interface for displaying robot information, allowing for real-time data collection and updates at a frequency of 2 ms. To validate the consistency of the system's virtual-to-real synchronization and the accuracy of the data, four specific time points \(\{t1=200\;\text{s},t2=500\;\text{s},t3=800\;\text{s},t4=1100\;\text{s}\}\) were selected during the robot's motion. At these time instants, the positional coordinates and orientation (angle between the robot's direction and the horizontal direction) of both the virtual and real robots were recorded, and the error range was computed. The final calculated results are presented in the table below.  
From Table 3, it can be observed that, in the digital twin system, the robot's horizontal coordinate error range at the four-time points had a maximum absolute value of around 0.122, the vertical coordinate error range had a maximum total value of approximately 0.092, and the full error range in motion direction was about 0.162. This indicates that the system exhibited good real-time performance, meeting the requirements for virtual-to-real synchronization tasks. It could accomplish online virtual monitoring tasks, demonstrating the feasibility of the approach proposed in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Clearance** & **Original Algorithm** &
\begin{tabular}{c} **Improved Algorithm** \\ **with Digital Twin Collaboration** \\ \end{tabular} \\ \hline Motion Time/s & 942 & 926 \\ Hover Time/s & 244 & 43 \\ Total Time /s & 1186 & 969 \\ Path length/m & 73.26 & 72.35 \\ Site coverage\% & 100 & 100 \\ Power consumption/J & 155,053.26 & 128,826.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Metric Comparison of Robots under Different Algorithms.
## 3 Results and Discussion
### Subsystem Evaluation Findings and Analysis
#### 3.2.3 Findings and Analysis of the Social Climate Assessment  
The social climate evaluation result is shown in Figure 6 and Table 8. The Baltic countries continue to perform the best overall with an average score of 0.1764 in 2018, which is higher than 0.1314 of the Visegrad Group and Slovenian segment, and 0.0919 of the Balkans. Estonia scores 0.2170 in 2018, which has achieved the leading position in this indicator for 3 years. With scores in third and seventh place in 16 countries, Lithuania and Latvia have a stable overall performance and are in the middle to upper range of CEE countries. Within the Visegrad Group and Slovenia segment, the Czech social resource market is the most prominent performer, with a score of 0.1554 that places it in the fifth position of CEE countries in 2018. Although most Balkan countries have relatively low scores in this indicator, Albania has a favorable labor market climate, with a score of 0.1512 in 2018, ranking it sixth in 16 countries and a mean score of 0.1604 between 2009 and 2018, moving it to the second place among the 16 countries. Meanwhile, the Balkan countries outperform other regions in terms of indicator improvement, with Romania, Bulgaria, Serbia, and Montenegro achieving social resource market optimization. Thus, the improvements positively influence the further reduction of the differences in the labor market climate in CEE countries and promote regional coordination.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Ave.**} & \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Relative Change**} & \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Ave.**} & \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Relative Change**} \\  & & & & & & & (2009/2018) \\ \hline Albania & 0.0681 & 14 & 133.6\% & Lithuania & 0.1283 & 7 & 14.2\% \\ Bosnia & 0.0348 & 16 & 38.7\% & Montenegro & 0.1187 & 8 & 9.0\% \\ Bulgaria & 0.1117 & 9 & 9.0\% & Macedonia & 0.1072 & 11 & \(-\)54.0\% \\ Croatia & 0.1009 & 13 & \(-\)25.3\% & Poland & 0.1552 & 5 & 16.9\% \\ Czech & 0.1717 & 3 & \(-\)5.4\% & Romania & 0.1073 & 10 & \(-\)14.9\% \\ Estonia & 0.2014 & 2 & 33.7\% & Serbia & 0.0551 & 15 & 1.5\% \\ Hungary & 0.1024 & 12 & \(-\)22.9\% & Slovakia & 0.1330 & 6 & \(-\)32.2\% \\ Latvia & 0.1672 & 4 & 22.8\% & Slovenia & 0.2703 & 1 & \(-\)0.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average Evaluation results of economic climate in CEE countries.  
Figure 5: Evaluation results of economic climate in CEE countries from 2009 to 2018.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 4 Map Construction and Test
### Construction of 3D Map and Test
#### 4.3.3 Mapping Test Based on Fusion of Laser and Vision  
Although the mapping accuracy of laser SLAM is high and the computational complexity is low, the map constructed is a sparse point cloud, lacks semantic information, and has large linearization errors. In contrast, the environmental information obtained by the camera is much more than that obtained by LiDAR, and the semantic information is rich. Moreover, the RGB-D camera has the advantage of not being affected by the change in ambient light and texture, but its mapping accuracy is affected by the cumulative error. Based on the above considerations, the two mapping schemes are complementary. Specifically, in the dynamic environment of a coal mine roadway with strong interference, the mapping scheme based on the laser and vision fusion has a stronger robustness.  
The RTAB-MAP framework provides an interface between the camera and LiDAR [19], as shown in Figure 17. First, the laser and visual data are entered, then the multi-source information is initialized and aligned, and finally a map of the environment is output.  
The mapping result of the simulated roadway, based on the fusion of laser and visual data, is shown in Figure 18. The map is in a composite form, and includes a dense 3D point cloud and a 2D occupied grid. Compared with Figure 14, it can be seen that the SLAM mapping scheme that integrates laser and vision has more advantages in detail processing of the point cloud, and the map is more refined. Additionally, the point cloud can be used for 3D reconstruction of the environment after gridding and texture mapping in the later stage, and the 2D occupancy grid map can be used for roadheader navigation after being converted into a cost map.  
Figure 17: The RTAB-MAP mapping process based on vision and laser.  
Figure 18: Composite map based on visual and laser data.
## 3 Methods
### Model for Storing Trajectory Data
#### 3.1.2 Optimization of the Data Storage Model  
The taxi trajectory data simultaneously encompasses the trajectory point data generated by all currently operational taxis in Xiamen City. If one intends to retrieve all trajectory point data for a specific period, indexing each eligible data row based on time as a retrieval criterion is essential. This experiment has refined its storage structure to augment retrieval efficiency. It continues to utilize encoding in \"minutes\", \"hours\", and \"days\". This approach enhances the efficiency of data retrieval by aggregating all trajectory points generated at the same instance into a singular cell unit. Since the time encoding is unique, it serves as the row key. The improved storage model is shown in Table 5. This model is capable of accommodating around 8400 data points within each cell set when adopting \"minutes\" as the unit, approximately 100,000 data points when adopting \"hours\" as the unit, and a remarkable 1.4 million data points when utilizing \"days\" as the unit.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Row Key**} & \multirow{2}{*}{**TIMESTAMP**} & \multicolumn{4}{c}{**TAXIDATA**} \\ \cline{3-6}  & & **TIME\_Code** & **LAT** & **LNG** & **CAR\_NO** \\ \hline
1 & T1 & TIME\_Code1 & LAT1 & LNG1 & CAR\_NO1 \\
2 & T2 & TIME\_Code1 & LAT2 & LNG2 & CAR\_NO2 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ N & Tn & TIME\_Code & LATn & LNGn & CAR\_NOn \\ \hline \hline \end{tabular}
\end{table}
Table 4: Original storage model of taxi trajectory data at different time levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Level** & **Coding Rule** & **Coding Result** \\ \hline Minute & Minutes from 0:00 on 1 January 1970 & 27,237,600 \\ Hour & Hours from 0:00 on 1 January 1970 & 453,960 \\ Day & Days from 1 January 1970 & 18,915 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Encoding example.
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 2 Related Work
### Skeleton-Based Annotation Tools  
Accurately annotating hand skeletons in video is labor-intensive, and usually, researchers rely on general-purpose annotation tools. For instance, Label Studio [15] is a versatile web-based platform supporting multiple annotation formats, including key points, bounding boxes, and text labels. While it integrates machine learning-assisted annotations, its landmark labeling lacks native support for skeleton visualization, making pose estimation cumbersome. LabelImg [16] is a lightweight and offline tool commonly used for object detection annotation. Although it allows landmark labeling, it does not inherently support joint connections, requiring external post-processing to structure skeletons. VIA (VGG Image Annotator) [17] is a simple, browser-based tool designed for efficient manual annotation, offering point-based labeling but without built-in pose structures or automation. Despite their utility, these tools present limitations for skeleton annotation. Label Studio's configurability comes at the cost of complexity, requiring custom setups for skeletal connections. LabelImg and VIA, though easy to use, lack automation and structured skeleton representation, making frame-by-frame annotations time-consuming.  
On the other hand, advances in computer vision have led to approaches like MediaPipe [12], which provides automatic hand landmark detection using deep learning, significantly reducing manual annotation effort. The easy-to-use and high-performance real-time hand tracking makes this approach a widely employed tool to generate skeleton-based annotations for hand gesture recognition datasets [10; 11; 18; 19; 20]. However, MediaPipe often suffers from inconsistencies, especially in complex poses or motion-blurred frames (as shown in Figure 1), necessitating manual correction.  
In summary, none of the reviewed tools provide efficient and user-friendly tools tailored explicitly for video-based hand skeleton annotation. Therefore, we introduce an annotation tool that integrates automatic pose detection with manual refinement and interpolation. Unlike existing tools, our approach reduces annotation effort by leveraging automatic landmark detection while enabling precise adjustments and smooth transitions between frames.
## 1 Introduction
Regarding alternative dimensioning methodologies, stochastic models for improving the decision making are developed in [16], where the methodologies are applied to reach the optimal BESS investment for industrial buildings. As identified in the previously mentioned papers, there is no detailed battery ageing analysis, so the influence of this variable is underestimated. The BESS dimensioning studies mentioned above employ different selection criteria. Those criteria are based only on technical issues, or on techno-economic issues but using generic ageing data to calculate the BESS lifespan. Moreover, they analyse the complete compensation of technical issues under study, e.g., feed an isolated load with a combination of a BESS together with photovoltaic or eliminate the consumption peaks via smart charging of electric vehicles, which may not be the most feasible scenario faced by residential or industrial consumers. As a main contribution and further development, this paper proposes a BESS dimensioning methodology for diminishing the fixed term of the electricity access rate in real industrial consumption, employing a techno-economic criterion for selecting the optimum solution. The proposed methodology includes a detailed evaluation of the BESS ageing, which is included in the cost-benefit analysis as a key component. This improved BESS ageing assessment highly influences the time for a BESS replacement and, hence, the yearly cost (BESS cost and fixed electricity access fee). Hence, the applicability of this methodology encompasses industrial consumers with an irregular consumption accounting for several peaks of consumption, as well as BESS manufacturers aiming for specific developments for industrial consumption. The applicability of the developed methodology is exemplified in this paper for an existing industrial consumption and on a typical day in summer and in winter, considering a commercial battery and standard prices for the economic analysis. The paper is organized as follows: the BESS dimensioning methodology is described in Section 2, including a series of steps to be followed. The different simulation models used along the different steps determined by methodology are detailed in Section 3, including a power grid model, an industrial consumption model, a power converter model, and a battery model. At last, a case example with real industrial consumption data, commercial BESS and standard prices is presented in Section 4, while conclusions are drawn in Section 5.
## 2 Methodology
### Analysis of Observation Space
#### 2.4.1 Visualization  
To qualitatively assess the similarity of explored observation spaces, the high-dimensional observation data (raw pixel frames) was visualized in a 2D space using t-SNE. For each environment, observations from all five agents were combined. From each agent's collected observations, 5000 frames were randomly subsampled. These observations were then flattened into vectors before being projected into two dimensions using t-SNE. The perplexity hyperparameter for t-SNE was set to 30, a choice informed by a trustworthiness analysis (presented in Section 3.5) to ensure high-quality embeddings. The resulting scatter plots were color-coded by agent ID to reveal patterns of overlap and separation.