esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.3 Hybrid NOMA
The overlaid signal is also captured by a back-scatter device in the RSU coverage area, which modulates its own data to reflect them toward moving vehicles.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 3 Materials and Methods
### Spherical Congruence Projection
#### 3.2.1 Spherical Pixel Segmentation  
For the QUVS image, the number of twelve pentagonal pixels remains constant. These pentagonal pixels are uniformly distributed across the six directional axes of the pole pixel. According to the distribution characteristics of pentagonal pixels, the entire spherical pixel space can be partitioned into six distinct subregions. With the north origin pixel \(O_{N}\) and the south origin pixel \(O_{S}\) of \(\tilde{\mathcal{S}}_{L}\), two hexagonal coordinate systems can be constructed with axes \(\omega_{N}^{1}\) to \(\omega_{N}^{6}\) and \(\omega_{S}^{1}\) to \(\omega_{S}^{6}\) separately, as shown in Figure 5, where \(\omega_{N}^{1}\) though \(\omega_{N}^{6}\) correspond to the directional vectors from \(O_{N}\), and \(\omega_{S}^{1}\) though \(\omega_{S}^{6}\) correspond to the directional vectors from \(O_{S}\). For each coordinate system, the direction of each axis points to the center of the adjacent hexagonal pixel. The following outlines the construction of the boundary pixels for the spherical segmentation area.  
Figure 3: The procedure of converting the original panoramic image into the QUVS image, whose pixels are evenly distributed on the spherical surface.  
Figure 4: The flowchart of spherical congruence projection.  
Figure 5: The procedure of spherical pixel segmentation. (1) to (6) in the upper row represent the process of extending and constructing boundary pixels along \(\omega_{N}^{1}\) to \(\omega_{N}^{6}\) respectively. Eventually, the \(\mathcal{S}_{L}\) could be converted into six subregions.  
In the first step, the boundary pixels begin at \(O_{N}\), extend along each axis until reaching the encountered pentagonal pixel, and repeat the above process in the same manner from \(O_{S}\). The second step begins with the pentagonal pixels encountered in the previous step. For the pentagonal pixels extending from \(O_{N}\), the boundary pixels are further extended to the lower left. For the pentagonal pixels extending from \(O_{S}\), the boundary pixels are further extended to the upper right until the two groups of pixels intersect. The boundary pixels effectively partition \(\tilde{\mathcal{S}}_{L}\) into six distinct subregions. By cropping along the boundary pixels and flatting pixels, six 2D images \(\{\tilde{\mathcal{S}}_{\mathcal{C}}|C=1,\cdots,6\}\) can be generated that preserve the topological relationships of the spherical pixels, as shown in the bottom row of Figure 5. To ensure that each subregion includes its own boundary area, the boundary pixels used for segmentation are duplicated across adjacent subregions. This duplication allows each subregion to maintain a clear boundary, facilitating independent processing or analysis.
## 4 Results
### System Configuration and Implementation  
We set up the batch training and testing systems on a system that included a CPU i7-10870H with 32 GB installed physical memory and a GPU Nvidia RTX 3080 Laptop with 8 GB dedicated memory, which served as our local cloud server. The software package that is compatible with our system includes a parallel computing platform (CUDA) version 11.5 and deep-learning acceleration (cuDNN) version 8.2., and we ran batch training on the deep-learning framework with PyTorch version 1.11, Torchvision 0.13.0, Numpy 1.23.5, and Chamferdist 1.0.0.  
When pre-processing data, we considered PIFu pre-processed data for the garment dataset with 3000 anchor points. According to our training procedure, we set up benchmarks with three approaches: raw input without a regularization term, Anchor-UDF, and CAP-UDF for recruiting methods that produce flawlessly consistent results or completeness of the result object. During training, we used the Adam optimizer with a 0.001 starting learning rate and a cosine learning rate schedule with 1000 warm-up steps. We set for full training iterations at 80,000 steps for all approaches for the fairness of examination. After the previous level converged, we began training for the following step. With mutual understanding or fair findings, we measured the discrepancy or completeness of the 3D-regenerated work to ground truth with the Chamferdist measure, as previously mentioned, which is an evaluation metric for two-point clouds. It considers the distance between each point. CD selects the nearest point in the other point set to each point in the cloud and adds the square of distance up to test the performance of the workpiece. We measured both L1 and L2 normalization distances as seen in Equation (1) from the training phase and Equation (2) from the testing phase, respectively, which represents how to measure the L1 Chamfer distance. Moreover, we also evaluated the traditional metrics like F-score with thresholds of 0.005 and 0.01.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **The Number of** \\ **Instances** \\ \end{tabular} & **Object Styles (Class)** & **Data Types** \\ \hline ShapeNet Cars & 1 & 1 & 3D Point clouds \\ DeepFashion3D & \(\sim\)2078 & 10 & 2D garments and 3D \\  & & & point clouds \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of instances for ShapeNet cars and DeepFashion 3D datasets.  
Figure 5: The exemplary ShapeNet cars and Deep Fashion 3D datasets.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
###### Abstract  
In the domain of food science, apple grading holds significant research value and application potential. Currently, apple grading predominantly relies on manual methods, which present challenges such as low production efficiency and high subjectivity. This study marks the first integration of advanced computer vision, image processing, and machine learning technologies to design an innovative automated apple grading system. The system aims to reduce human interference and enhance grading efficiency and accuracy. A lightweight detection algorithm, FDNet-p, was developed to capture stem features, and a strategy for auxiliary positioning was designed for image acquisition. An improved DPC-AWKNN segmentation algorithm is proposed for segmenting the apple body. Image processing techniques are employed to extract apple features, such as color, shape, and diameter, culminating in the development of an intelligent apple grading model using the GBDT algorithm. Experimental results demonstrate that, in stem detection tasks, the lightweight FDNet-p model exhibits superior performance compared to various detection models, achieving an [EMAIL_ADDRESS] of 96.6%, with a GFLOPs of 3.4 and a model size of just 2.5 MB. In apple grading experiments, the GBDT grading model achieved the best comprehensive performance among classification models, with weighted Jacard Score, Precision, Recall, and F1 Score values of 0.9506, 0.9196, 0.9683, and 0.9513, respectively. The proposed stem detection and apple body classification models provide innovative solutions for detection and classification tasks in automated fruit grading, offering a comprehensive and replicable research framework for standardizing image processing and feature extraction for apples and similar spherical fruit bodies.  
apple; quality grading; stem detection; image segmentation; artificial intelligence; machine learning; model compression; structural re-parameterization +
Footnote †: journal: _foods_  
Article
## 4 Results
### Statistical Analysis of Magnitude Distribution and Focal Depth  
In terms of magnitude distribution, the dataset predominantly consists of moderate-to-strong earthquakes (Mw 5.5-8.0), with low-magnitude events (Mw < 5.5) deliberately excluded. This exclusion criterion is guided by inherent limitations in InSAR technology. For earthquakes with Mw 5.5, atmospheric delays often dominate the signal in individual interferograms, significantly impeding the reliable extraction of coseismic deformation [41]. Although advanced techniques--such as stacking [42] and the SSC method [43]--have been developed to improve the detection of low-magnitude events, this study directly employs interferograms from the COMET LiCSAR archive without additional refinement. Processing such data is computationally demanding and requires frequent parameter tuning, making fully automated and efficient data generation difficult. Furthermore, the resulting interferograms may not align with the standardized requirements of this study.  
Figure 5: Spatial distribution and statistical characteristics of the earthquake events. (**a**) Geographical distribution of earthquake epicenters; (**b**) annual statistics of earthquake occurrences and raw interferogram acquisitions; (**c**) distribution of earthquake types and magnitude classes.  
Therefore, to ensure consistency, quality, and suitability for deep learning applications, InSAR data corresponding to low-magnitude earthquakes were deliberately excluded from the final dataset.  
Furthermore, statistical analysis of focal depth distribution indicates that all earthquakes in this dataset are shallow-focus events, occurring within the upper crust at depths ranging from 0 to 30 km. The effectiveness of InSAR in monitoring coseismic deformation largely depends on factors such as the spatial extent of the deformation field and the signal-to-noise ratio (SNR). For earthquakes of comparable magnitudes, shallow-focus events (0-30 km) typically produce more pronounced surface deformations, which are more readily detected by InSAR. In contrast, deeper earthquakes tend to produce smaller surface displacements that are often obscured by noise, making their detection significantly more difficult. Consequently, this dataset primarily includes shallow-focus earthquakes to ensure that the corresponding InSAR interferograms exhibit high SNR, thereby enhancing their reliability, consistency, and suitability for deep learning applications.
# Design of 4 \(\times\) 1 Power Beam Combiner Based on MultiCore Photonic Crystal Fiber
## 2 Principle of the Work
However, this condition depends on the geometrical parameter values of the multicore PCF structure (z, \(d\), \(d^{\prime}\), and \(\Lambda\)). Therefore, optimization of the key parameters was done, in order to fulfill the necessary condition. In addition, this model can be duplicated, in order to design a higher ratio power such as 8 \(\times\) 1 combiner.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experimental Setup and Performance  
All experiments were conducted on a system equipped with an Intel(R) Xeon(R) Silver 4316 CPU at 2.30 GHz, running Ubuntu 20.04.6 LTS. An NVIDIA GeForce RTX 3090 GPU with 24 GB of memory was used for the computationally intensive training and rendering tasks required by M2 Mapping.  
For keypoint detection, we selected the AKAZE [26] and ORB [27] feature detectors, as both are computationally efficient and widely used in real-time SLAM systems [3; 4; 5; 28]. To further optimize performance, keypoint detection in both our online and offline methods is parallelized. The input camera image is divided into three horizontal regions, and a  
Figure 2: Examples of LiDAR reconstructions in the dataset. (**a**) HITGraffitiWall dataset. (**b**) HKU-Landmark dataset. (**c**) SYSU dataset. (**d**) CBDBuilding dataset. (**e**) Garden dataset. (**f**) RedSculpture dataset.  
dedicated thread is used to compute keypoints in each region simultaneously. This multi-threaded approach allows the main tracking loop to achieve approximately 30 FPS when using AKAZE features and up to 80 FPS with the faster ORB detector.  
In the Online Rendering and Tracking approach, we employed a thread for image rendering and three threads for keypoint detection. For the Offline Keypoint-Map Tracking method, three threads were employed for keypoint detection. This highlights the fundamental difference in runtime load: the first method requires a dedicated, continuous computational effort for rendering, which is entirely eliminated during the tracking phase of the second method.  
For the point-based rendering method, we developed an optimized algorithm that runs at 16 FPS. This optimization relies on a preprocessing step where the 3D LiDAR point cloud is organized into a grid of 10 cm voxels. At runtime, the system first performs a fast check by projecting only the centers of these voxels onto the image plane. A full projection of all points within a voxel is only performed if its center falls within the current camera's field of view. This hierarchical culling strategy significantly reduces the number of points that must be processed for each frame, making real-time rendering feasible. Nevertheless, a GPU-based rendering could significantly speed up this process even further.  
The selection of the system's parameters was guided by an empirical analysis to balance performance and quality. The 2 mm distance threshold for point filtering represents a good trade-off between removing sensor noise and preserving the geometric fidelity of surfaces, ensuring the point cloud is clean for rendering. Similarly, the 10 cm voxel grid used for rendering optimization was chosen to maximize view-frustum culling efficiency without introducing the computational overhead of a finer grid or the precision loss of a coarser one. Together, these values enable the generation of high-quality synthetic views at high frame rates.
## 2 Method
### The Magnetic Dipole Model for Tracking System
#### 2.2.1 The Magnetic Dipole Model in Vacuum  
Considering that the propagation distance \(r\) is much larger than the coil length, the coil can be considered as a superposition of \(N\) magnetic dipoles, while \(N\) is the number of turns of the transmitting coil. Therefore, in the Cartesian coordinate system, the radiated magnetic field of the transmitting coil can be rewritten as (1) :  
\[\dot{\mathbf{H}}=\frac{N\mathrm{i}S}{4\pi}(\frac{1}{{{(x^{2}+y^{2}+z^{2})}^{2.5}}}+\frac{\mathrm{j}k}{{{(x^{2}+y^{2}+z^{2})}^{2}}})\Big{[}3 xze_{\mathbf{x} }+3 yze_{\mathbf{y}}+\Big{(}2z^{2}-x^{2}-y^{2}\Big{)}\mathbf{e}_{\mathbf{z}} \Big{]}, \tag{1}\]  
where \(\mathbf{e}_{\mathbf{x}}\), \(\mathbf{e}_{\mathbf{y}}\) and \(\mathbf{e}_{\mathbf{z}}\) are the unit vectors in the Cartesian coordinate system and \(I\) is the excitation current in the transmitting coil. The wavenumber is \(k=2\pi f\sqrt{\epsilon_{0}\mu_{0}}\), while \(f\) is the frequency of the excitation current. \(\mu_{0}\) is the permeability of free space, \(\epsilon_{0}\) is the permittivity of free space and \(S\) is the cross section area of the transmitting coil. It is worth noting that the radiated magnetic field in (1) is the near-field of the MDM, because the burial depth of the pipeline is usually several meters. \(kr=\frac{2\pi r}{\lambda}\ll 1\), only the higher powers of \(\frac{1}{kr}\) in the radiation magnetic field of the MDM in vacuum need to be retained. Furthermore, the nomenclature of the variables in the equations is listed in the Appendix A.
###### Abstract  
Optimal power allocation (PA) is a decisive part of the power domain non-orthogonal multiple access (PD-NOMA) technique. In PD-NOMA, users are served at the same time and using the same frequency band, but at differing power levels. In this paper, the optimization problem for PA is formulated with distance (\(d\)), signal-to-noise ratio (\(SNR\)), and foliage depth (\(d_{f}\)) constraints. A fuzzy inference system (FIS) addresses the optimization problem by allocating the optimal power factors (power levels) to each user in the vicinity of a 5G base-station (gNodeB). The proposed system incorporates a cooperative relaying technique at the near-user to assist the far-user facing signal degradation and greater path losses. A realistic 5G micro-cell is analyzed for downlink PD-NOMA where superposition coding (SC) is used at the transmitter side, a successive interference cancellation (SIC) scheme at the near-user, and a maximum ratio combining (MRC) technique at the far-user's receiver, respectively. For both simple PD-NOMA and cooperative relaying PD-NOMA, the presented technique's bit-error-rate (BER) performance is evaluated against various SNR values, and it is concluded that cooperative PD-NOMA outperforms simple PD-NOMA. By combining the presented FIS system with cooperation relaying, the proposed FIS method guarantees user fairness in PD-NOMA systems while also significantly improving performance.  
PD-NOMA; SIC; cooperative relaying; fuzzy inference system Article
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 2 Background
### Vowel's Pronunciation Problems  
In general, children learn their mother tongue from family members and their communities. At school age, they are trained by teachers based on the curriculum that has been approved by the Ministry of Education. Past works show that children with speech organ problems from diseases such as autism or from birth defects encounter first language learning problems. Children with speech sound disorders (SSD) struggle to produce vowel sounds in comparison with normal children [19], while speakers with Down syndrome (DS) have difficulty pronouncing corner vowels (/\(\alpha\)/,/\(\alpha\)/,/i/,/u/) [20].  
People communicate worldwide with different groups for a variety of reasons: trade, transport, medical aid, and education. Accordingly, foreign languages are learned as second languages and third languages. The differences in phonemes between one's mother tongue and the new language are essential factors in pronunciation practice. Previous studies [21; 22; 23; 24; 25] discovered that learners have difficulty with the pronunciation of vowels when learning second and third languages.  
This research focuses on vowel sounds. Vowels are the core of syllables (nucleus) and are a significant part of speech [26; 27]. Vowels, at least, are distinguished by three characteristics: the nature and position of the tongue, the duration, and the rhythm of speech or melody. Vowels are important phonemes that affect meaning and language behavior. Vowels can be trained in perception (listening) and production (speaking) with a phonetic approach. A vowel pronunciation test cannot be used as an articulation test like consonants [28]. Vowel pronunciation is more problematic than consonant pronunciation [29]. The practice of short-long vowel pronunciation is a problem for learners for whom in their mother tongue, the duration of the vowels does not affect the meaning of words [30]. Practicing vowels that do not exist in the mother tongue is more difficult than practicing consonants since the movement of the tongue heavily affects the pronunciation of a vowel. Since learners cannot know where exactly their tongue is, those who are practicing pronunciation should have an expert to guide them and teach them the correct pronunciation. The most popular description of vowel sounds involves describing the position of the high-low level, the front-back of the tongue, the area of the tongue, and the appearance of the lips [27]. However, this description is still difficult to understand and can often confuse learners.
## 2 Models and Methods
### Numerical Setup
#### 2.2.1 Validation Model  
[PERSON] conducted the experiment and performed the 2D VOF simulation which received satisfactory flow patterns comparable with the experiment results [19]. However, the 2D simulation was closer to a situation where a cylindrical drop of infinite length falls on an infinite-high cylinder, which is far from reality. Therefore, to better verify the accuracy of this method, the 3D simulations were performed.  
The mesh plot of 3D verification model and measuring positions are shown in Figure 1. The simulation scales are the same as those in [PERSON]'s experiments [19]. The validation experiment was conducted under atmospheric pressure and at a temperature of 273 K. A particle of 10 mm in diameter was positioned in a cylinder region whose diameter and length are 20 mm and 25 mm respectively. A water droplet of 4 mm in diameter was patched 5 mm above the particle and slightly offset from the central axis (about 2.82 mm). For the region within 17 mm of the particle and droplet surface, meshes with the highest resolution were used (0.07 mm for 3D cases). For the other region, the largest-scale nonstructural meshes were adopted. The top entrance was defined as the velocity inlet for the gas phase and the exit was defined as pressure outlet conditions. The time step used in the simulation was \(1\times 10^{-5}\) s, and the initial velocity for the liquid droplet was set to 0 so that the droplet fell down only by gravity. The simulated results were recorded every 5 ms, and the liquid film thickness was measured at different positions of the particle surface at different time intervals.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gas and Liquid Properties** & **Value** \\ \hline Liquid viscosity \(\mu_{\text{L}}\) (Pa-s) & \(8.8871\times 10^{-4}\) \\ Gas viscosity \(\mu_{\text{G}}\) (Pa-s) & \(1.85508\times 10^{-5}\) \\ Liquid density \(\rho_{\text{L}}\) (kg/m\({}^{3}\)) & 1000 \\ Gas density \(\rho_{\text{G}}\) (kg/m\({}^{3}\)) & 1.225 \\ Surface tension coefficient \(\sigma\) (N/m) & 0.072 \\ Contact angle \(\theta\) & 45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gas and liquid properties employed in both verify and DEM-CFD simulations.  
Figure 1: Mesh plot of 3D verification model and different measuring positions.
## 3 Evaluation of Experimental Results
### X-ray Diffraction  
X-ray diffraction (XRD) analysis characterized the secondary phases precipitated during solidification and solid-state transformations. The investigations were conducted using a Panalytical X'Pert Powder X-ray spectrometer equipped with a graphite monochromator for Cu-K\(\alpha\) radiation (\(\lambda=1.54\) A), at room temperature. The scanning details applied in these analyses were: 2 Theta range from 20 to 100 degrees, with a step size of approximately 0.03 degrees and counting at each step for 2 s. Cu K\(\alpha\) radiation was used, and the power of the X-ray generator was set to 40 kV and 30 mA. For indexing the interference peaks, the X'Pert High Score software version 5.0 and its database were used. Figures 9 and 10 show two characteristic X-ray diffraction patterns for the substrate and the first layer of deposited metal. It is noteworthy that in the substrate, the two characteristic phases of any duplex stainless steel (austenite and ferrite) are present, while in the vicinity of the first deposited layer, additional particles of complex carbides of alloying elements are observed.  
Figure 8: SEM image and the concentration profiles of the alloying elements on one side and the other of the interface between the deposited metal and the base metal.