esa-sceva/satcom-chunk-collection huggingface dataset:
# EduTVA: Metadata Schema for Educational Audiovisual Contents in Digital Television Environments
## 3 Overview of the EduTVA Metadata Schema
### Base Metadata Schema Selection  
For several reasons, it is preferable to use or adapt existing standards (MPEG-7, TV-Anytime, EBUF/Meta, EBU Core, PBCore, and SMPTE) instead of developing our metadata schemas and vocabularies from scratch. Using a current standard can offer, in the first place, a cost-saving because it is unnecessary to create a new schema along with its usage patterns or vocabulary. Second, it can offer access to support and advice because a well-known standard probably has a user community that was built over time. The third is usability because, if users are already familiar with the metadata structure or terminology, they can use the collection of resources described with the standard faster and easier. Fourth, finding the resources make it easier to share the resources. Finally, sustainability, because common standards facilitate the sharing of a collection of resources between different entities (e.g., systems and people) [28].  
This work proposes a metadata schema for a digital television scenario that can reuse its productions (such as movies, news, documentaries, and animations) for an educational purpose. Consequently, it is necessary to build on an existing metadata schema to describe  
television content and adapt it to the characteristics of metadata for educational content. For this reason, TV-Anytime [16] is selected as the base standard for constructing the new metadata schema because it defines most of the metadata set in the H.750 [29] recommendation. In addition, the set of metadata that composes it is simple, so it allows a simple and easy-to-understand markup. Its structure and syntax are not complicated because it provides a temporal segmentation that responds well to the physical and operational constraints of broadcasting. In addition, it defines two metadata oriented to the educational description of the content, which means that the structure already contains a class of metadata intended for educational tagging. Finally, it is the internationally accepted schema in the field of consumption [30].
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 3 Method
### 3D Hand Pose Estimation  
The 3D hand pose can be estimated by _PosePrior_ by using 2D keypoints. After _PoseNet_, the score map is sent to the _PosePrior_ network to estimate the 3D hand pose.  
The _PosePrior_ network proposes to train the network to estimate coordinates within a canonical frame rather than to directly estimate absolute 3D coordinates. Additionally, it estimates the transformation from the relative 3D coordinates to the canonical frame during parallel processing, which is a 3D rotation matrix called the viewpoint. Two similar streams are used to estimate viewpoint and canonical coordinates. In the end, two estimates are combined to estimate the 3D coordinates.  
The 3D coordinates \(W\) are divided into a world coordinate system \(W^{world}\) and a camera coordinate system \(W^{camera}\). The camera rotation angle is introduced as \(R=\left(R_{x},R_{y},R_{z}\right)\) to convert the two coordinate systems:  
\[W^{world}=W^{camera}R. \tag{1}\]  
3D hand pose estimation can be divided into two tasks. One is to estimate the hand pose in the camera coordinate \(W^{camera}\), and the other is to estimate the angle of view, i.e., the camera rotation angle \(R\). Finally, the two results are fused to get the final coordinates \(W^{world}\). Thus, the 3D coordinate transformation network is divided into two subnetworks with the same architecture, as shown in Figure 3, and the task is divided into an above network and a below network. The above network estimates the coordinates of the 3D hand keypoint \(W^{camera}\) in the standard coordinate system. Meanwhile, the rotation angle \(R\) of the standard coordinate system relative to the camera coordinate system is estimated in the below network.  
Figure 3: The framework of our channel fusion attention mechanism (CFAM), where \(C\) represents six convolution operations, \(F\) and \(O\) represent data, and \(FC\) represents fully connected operations. The framework is divided into three parts: frontend, middle, and backend. Our proposed CFAM is highlighted in the figure. In the frontend, we use a cropped hand region to estimate the 2D score map. The features of the 2D score map and the cropped hand are extracted using a CNN. Then, in the middle, we concatenate the feature maps to obtain \(F_{i}\) and process \(F_{i}\) using a channel attention mechanism. Finally, in the backend, the fully connected layer is used to estimate the camera rotation angle and the 3D hand pose in the camera coordinate system, and the 3D hand pose in the world coordinate system is calculated.  
Although the above method performs well, it uses only 2D keypoints, and the RGB information is lost. The RGB image contains texture information and implicit spatial information, which are not included in 2D keypoints. The information in the RGB image is essential for ensuring the accuracy of 3D hand pose estimation. To overcome this drawback, we propose a CFAM (channel fusion attention mechanism; see Section 3.3 for details) which makes full use of the RGB image and takes the spatial information into consideration.
## Nomenclature  
\begin{tabular}{l l} \(A_{d}\) & Projection area drag force exerted (mm\({}^{2}\)) \\ \(a\) & Flaw distribution parameter \\ \(b\) & Radius of indentation circle (mm) \\ \(C_{d}\) & Drag coefficient \\ \(C_{e}\) & Mixing efficiency coefficient \\ \(d_{p}\) & Diameter of abrasive particle (mm) \\ \(d_{i}\) & Depth of erosion by single particle (mm) \\ \(E_{c}\) & Constant term concerning the erosion energy \\ \(E_{e}\) & Effective kinetic erosion energy (J) \\ \(E_{p}\) & Erosion energy of single particle (J) \\ \(F_{1}\) & External acceleration force (N) \\ \(F_{2}\) & Drag force (N) \\ \(f_{w}\) & Proportional factor for stress-wave energy \\ \(g\) & Gravitational acceleration (m/s\({}^{2}\)) \\ \(h_{0}\) & Potential head in the pipe tip section (mm) \\ \(h_{p}\) & Potential head in the pump section (mm) \\ \(H_{p}\) & Hardness of particle, Knoop (kg/mm\({}^{2}\)) \\ \(H_{t}\) & Hardness of target, Knoop (kg/mm\({}^{2}\)) \\ \(K\) & Resistance constant \\ \(k\) & Threshold velocity of impact (m/s) \\ \(k_{a}\) & Constant for kinetic structure of reaction \\ \(l_{p,t}\) & Distance traveled by the particle (mm) \\ \(m\) & Reaction order \\ \(\dot{m}_{a}\) & Abrasive flow rate (g/s) \\ \(m_{p}\) & Single-particle mass (g) \\ \(\dot{m}_{w}\) & Fluid (water) flow rate (g/s) \\ \(P_{f,0}\) & Pressure of fluid (water) in the pipe tip section (Pa) \\ \(P_{f,p}\) & Pressure of fluid (water) in the pump section (Pa) \\ \end{tabular}  
\begin{tabular}{l l} \(V_{D}\) & Displacement rate of target \\ \(v_{f,t}\) & Fluid velocity at time \(t\) (m/s) \\ \(v_{f,0}\) & Fluid velocity in the pipe tip section (initial fluid velocity) (m/s) \\ \(v_{f,p}\) & Fluid velocity in the pump section (m/s) \\ \(V_{p}\) & Volume of particle (mm\({}^{3}\)) \\ \(v_{p,t}\) & Particle velocity at time \(t\) (m/s) \\ \(V_{r}\) & Volume removed by single particle (mm\({}^{3}\)) \\ \(v_{t}\) & Terminal velocity (m/s) \\ \(W\) & Total stress-wave energy \\ \(\gamma\) & Fracture energy per unit area (J/mm\({}^{2}\)) \\ \(\varepsilon\) & Energy required to remove unit material volume (mm\({}^{3}\)/J) \\ \(\rho_{f}\) & Fluid density (g/mm\({}^{3}\)) \\ \(\rho_{p}\) & Particle density (g/mm\({}^{3}\)) \\ \end{tabular}
## 3 Hardware Development
### Sensors  
#### 3.1.1 Sensor Selection  
A key aspect of this development is the ability to identify the angular position of the magnetic field of the diametric magnet located under the removable dial. Thus, after several experimental evaluations with different affordable linear Hall-effect sensors, it was decided to use TI's DRV5055 linear Hall-effect sensors [24]. The DRV5055 responds proportionally to the magnetic flux density. It has two recommended operating voltage ranges: 3.0 V to 3.6 V and 4.5 V to 5.5 V, and it consumes 4 mA.  
The small size of the selected Hall-effect sensors (SOT-23 package) allows them to be placed under the dial, and this does not interfere with its function. It is proposed to use two Hall-effect sensors in quadrature, i.e., they form an angle of 90\({}^{\circ}\) with each other, as depicted in Figure 4a. As the magnet rotates, the analog outputs produce coincident sine and cosine waveforms with a phase shift of 90\({}^{\circ}\), as shown in Figure 4b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Percentage of Gas in Tank [\%]** & **Data Reading Periods [h]** \\ \hline
100—8 & 12 \\
7—0 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor data reading by tank fill percentage.  
The analog output signals of the two linear Hall-effect sensors (A and Z) shown in Figure 4b have been obtained using a mechanical LPG tank gauging system (see Figure 1b). Both sensors were supplied with 4.0 V and the mechanical float was moved manually, initially considering the tank empty and gradually moving the float upwards until simulating a full tank, and vice versa.  
Sensor A located at the bottom of the dial records the magnetic field that decays sinusoidally from 1220 mV to 840 mV as the dial needle rotates clockwise from 0% to 50%. Furthermore, again it starts to increase sinusoidally from 50% to 100%, where it again displays an analog output of 1220 mV for a full tank.  
On the other hand, sensor Z located on one side of the dial, at the beginning (empty tank) registers an output of 970 mV. Then, as the magnet rotates clockwise, it shows a minimum value of 870 mV when the tank is at 15% of liquid LPG. As the magnet continues to rotate, it increases sinusoidally to a maximum output of 1165 mV, when the dial gauge indicates 85%. Finally, the magnet continues to rotate until the dial gauge indicates 100% (full tank) and an output of 1040 mV is recorded.  
Figure 4b also shows the analog signals for counterclockwise rotation of the dial needle, that is, from a full tank to an empty tank.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 1 Introduction
### Motivation  
Wireless sensor networks (WSNs) are increasingly used in applications that demand real-time responsiveness, low power consumption, and robust communication across diverse environments. Hybrid communication systems combining visible light communication (VLC) and radio frequency (RF) offer a promising solution by leveraging the complementary advantages of both technologies. However, accurately estimating the channel in such systems remains a major challenge, especially under practical indoor conditions influenced by noise and fading.  
Traditional estimation methods, such as MMSE and LMMSE, while effective in idealized settings, struggle to adapt to the nonlinear and heterogeneous nature of hybrid VLC-RF channels. Furthermore, while machine learning (ML) models have been explored for channel estimation, most studies either overlook the hybrid VLC-RF scenario or apply complex models that are computationally expensive and poorly suited for the energy and latency constraints of WSNs.  
Tree-based machine learning models, especially decision tree regressors, offer a compelling alternative to more complex algorithms due to their simplicity, interpretability, and computational efficiency. Unlike other models, decision trees produce intuitive if-then rule sets, enabling transparent and traceable decision making, which is a critical feature in scenarios requiring fast and explainable results. They are capable of handling both categorical and continuous variables and can effectively model the nonlinear relationships between inputs and outputs. These advantages make them particularly suitable for real-time and resource-constrained applications. [10; 11]. Moreover, their potential in hybrid VLC-RF systems, especially under GFDM modulation, remains hugely unexplored. Additionally, a unified evaluation comparing their performance with both traditional and modern ML techniques across a wide SNR range is lacking.  
Motivated by these gaps, this study aims to develop and validate a lightweight, scalable, and accurate tree-based channel estimation framework tailored for GFDM-based hybrid VLC-RF WSNs. By emphasizing both predictive performance and inference speed, the proposed approach addresses the critical need for practical, deployable solutions in next-generation wireless networks.
## 4 Discussion  
In this paper, the responses of conventional bridge foundations and those with with drainage structures to sinusoidal waves was investigated by means of shaking table tests. The excess pore water pressure ratio, time-history curves of acceleration, acceleration amplification factors, bending moment and lateral displacement of sheet-pile walls, and lateral displacement of bridge superstructures were compared and analyzed for a test group and a conventional group.  
In previous studies on liquefaction-induced lateral spreading [40; 41; 42; 43], scholars have generally used the results of shaking table tests to elucidate the behavior of pile foundations subjected to liquefaction-induced lateral spreading; however, the question of how to reduce the damage to bridge foundations and superstructures has not yet been considered. The highlight of this paper is the design of a comparative test to verify that bridge foundations with drainage structures are more stable than conventional bridge foundations in a liquefaction-induced lateral spreading site.  
We found that as vibration excitation intensity increased, the differences in the excess pore water pressure ratio between the test and conventional groups were not as large overall as at PGA = 0.10 g when PGA = 0.20 g. Therefore, the increase in vibration excitation intensity reduced the function of the drainage structure. More shaking table tests with various scenarios should be conducted to investigate the liquefaction resistance of bridge pile foundations with drainage structures under the action of vibration excitation of different intensities. And the influence of soil structure and pore fluid on the development of excess pore water pressure was not taken into account during the test, further tests on this aspect can be carried out.  
In addition, due to the limited test conditions in this study, as well as the influence of artificial factors in the production of the model foundation, among other factors, a large number of test results are needed to verify the dynamic response law of the bridge foundations with drainage structures in shaking table tests.
## 3 Results and Discussion
### Case Study for North Brazil--R5
Figure 20: (**a**) Accumulated rainfall recorded during 29 April 2016 to 3 May 2016 over north Brazil (R5); (**b**) fraction of rain on days that had MCS records over the rain accumulated in five days; (**c**) number of MCS events recorded in five days; (**d**) fraction of the days that had MCS records; (**e**) analyzed region. Our findings show that COSCHg was the product with the indexes closest to perfect scores and performed better across regions of Brazil. Other studies have found similar results [14; 27; 59]. This product was adjusted by pluviometers (in situ measurements) and used information from IR and MW sensors. Generally, products with MW sensors describe the evolution of precipitating systems and characterize the hydrometer properties better than products without MW because the orientation and shape of hydrometeors affect the response in the transmitted signal [13; 60; 61; 62; 63]. COSCHg was developed specifically for South America; it is based on a combination of additive and multiplicative bias correction schemes to obtain the lowest bias when compared with the observed values [14]. These findings highlight that the characteristics of the region, the variability of the precipitation variable, and the development of the specific algorithm, which depends on specific mathematical and statistical methods, can influence the performance of a satellite product [59]:  
In general, satellite observations tend to underestimate precipitation when convective systems are shallow and overestimate when convective systems are deep [64; 65]. However, this is not always the case. Other regional factors, such as orography and local circulation patterns, must also be weighed When considering extreme rainfall values. According to [PERSON] et al. [27] and [PERSON] et al. [59], products without rainfall adjustments tended to underestimate the lowest values and overestimate the highest ones in the south and northeast regions of Brazil. Similar results were found by [PERSON] et al. [66] for East Africa and by [PERSON] et al. [26] for China. According to those authors, the performance of satellite products considerably improves when adjusted with in situ observations. [PERSON] et al. [67] show a tendency to overestimate precipitation in the cold semi-arid climate (Bsk) zone in the southeast of the Qinghai-Tibet plateau. A possible explanation is that the precipitation detected by satellites in arid zones may evaporate before reaching the surface and thus not be recorded by the gauges. Figure 21: Case of extreme precipitation occurred over north Brazil (R5) from 29 April 2016 to 3 May 2016 estimated by different products (**a–m**).
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 3 Parallel Adaptive Laplacian Mesh Smoothing on the GPU  
### Overview  
In this paper, we proposed a parallel adaptive Laplacian smoothing algorithm for improving the quality of large-scale tetrahedral mesh, the proposed algorithm consists of three main steps. (1) The first step is to determine the first-order domain of all nodes in the input tetrahedral mesh. This step includes searching for the first-order neighboring nodes of each vertex and initializing the mass of each adjacent tetrahedral element. (2) The second step is to determine the constraints, i.e., which nodes are internally smoothable and which are constrained. (3) The third step is to iteratively calculate the smoothing location of each node until the iteration converges, and the end of every iteration needs a quality judgment of neighboring tetrahedrons. The above steps are implemented in parallel on the  
Figure 3: Two common data layouts: AoS and SoA.  
GPU. The shortcomings and advantages are described in detail in Section 5.3. The entire workflow of the proposed algorithm is illustrated in Figure 4.
## 2 Study Area  
Indonesia is a country located in the ring of fire, an area of frequent geological disasters, including earthquakes, volcanoes, flash floods, landslides and tsunamis. These disasters are harmful, destructive and result in a huge loss of life. The most frequent disaster in Indonesia is a volcanic eruption. Indonesia currently has 129 active volcanoes, and 70 volcanoes have erupted in the last 400 years. The area of land threatened by a volcanic eruption in Indonesia is 16,670 km\({}^{2}\), and around 5,000,000 lives are threatened by volcanic eruptions [14].  
There have been several volcanic eruptions in Indonesia (Figure 1a) in the last 10 years. The focus of this research is Mount Sinabung (Figure 1b) and Merapi (Figure 1c), the eruptions of which have caused many casualties. The eruption of Merapi in 2010 was the largest eruption in the last 10 years and killed 347 people. Mount Sinabung has erupted two times in the last 10 years in 2010 and 2013-2017. The period of eruption of 2013 is still ongoing and has killed 16 people [14].  
Mount Sinabung is in the Karo Highlands, Karo District, North Sumatra Province, Indonesia. The geographical position of the peak of Mount Sinabung is 3\({}^{\circ}\)10\({}^{\prime}\) north latitude and 98\({}^{\circ}\)23.5\({}^{\prime}\) east longitude. The height of Mount Sinabung is 2460 m above sea level, and its volcano type is a stratovolcano [14]. Mount Sinabung was dormant for 400 years, from \(\sim\)1600-2010 CE. Mount Sinabung finally erupted in 2010, an activity that was predicted from the three earthquakes that struck Sumatra. The earthquakes measured 8.8, 7.9 and 8.4 on the Richter scale and struck in 2005, 2007 and 2007, respectively [15].  
Mount Merapi is located on the border of four districts; Sleman, Yogyakarta and Magelang, Boyolali and Klaten, in Central Java Province. Its geographical position lies at 7\({}^{\circ}\)32\({}^{\prime}\)30\({}^{\prime\prime}\) north latitude and 110\({}^{\circ}\)26\({}^{\prime}\)30\({}^{\prime\prime}\) east longitude. Based on its tectonic order, the mountain is located in a subduction zone, where the meeting of the Indo-Australian and Eurasian Plates controls the volcanism in Sumatra, Java, Bali and Nusa Tenggara. The height of Mount Merapi is 2986 m above sea level, and its type is a stratovolcano [14]. Mount Merapi is a very active volcano mountain. Since 1600, Merapi volcano has erupted more than 80 times, and an eruption occurs on average every 4-5 years. Merapi is a volcano cone composed of andesitic-basaltic magma with silica (SiO\({}_{2}\)) content ranging from 52-56%. The top morphology is characterized by a horseshoe-shaped crater, in which lava domes grow [16; 17; 18; 19].