esa-sceva/satcom-chunk-collection huggingface dataset:
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 4 Offline Keypoint-Map Tracking
### Online Tracking with the Pre-Generated Map  
During the online phase, the robot localizes itself utilizing IMU data and the pre-built map to achieve localization. Given a refined pose from the previous frame, the algorithm first integrates IMU measurements to generate a predicted pose \(\mathbf{T}_{t}\) for the current frame. This prior is then refined as follows:  
1. Keyframe Retrieval: The algorithm first identifies the most relevant keyframe(s) from the map.This is achieved by finding the keyframe \(\mathcal{F}_{k^{*}}\) whose position is closest to the IMU-predicted position \(\mathbf{t}_{t}\): \[k^{*}=\arg\min_{k\in\{1,\dots,M\}}\|\mathbf{t}_{t}-\mathbf{t}_{k}^{p}\|\] (7) The set of 3D keypoints associated with this keyframe, \(\mathcal{M}_{k^{*}}=\{(\mathbf{X}_{j},\mathbf{d}_{j})\}\), is retrieved as the set of candidate points for matching.
2. Projection and Matching: The candidate 3D points \(\{\mathbf{X}_{j}\}\) are projected onto the current real camera image plane using the current pose estimate \(\mathbf{T}_{t}\): \[\mathbf{p}_{j}=\pi(\mathbf{R}_{t},\mathbf{t}_{t},\mathbf{X}_{j})\] (8) Simultaneously, keypoints are detected in the real image \(I_{t}^{r}\). A search is performed to find correspondences between the projected map points \(\{\mathbf{p}_{j}\}\) and the detected real keypoints \(\{\mathbf{u}_{i}^{r}\}\). A match is established if a real keypoint is found within a search radius of a projected point and their descriptors (the precomputed descriptor \(\mathbf{d}_{j}\) and the new descriptor \(\mathbf{d}_{j}^{r}\)) are sufficiently similar.
3. Pose Estimation: The resulting set of \(N\) 3D-2D correspondences, \(\{(\mathbf{X}_{i},\mathbf{u}_{i}^{r})\}_{i=1}^{N}\), is used to compute the updated camera pose \(\mathbf{T}_{t+1}\). As with the first method, this is achieved by using a PnP algorithm within a RANSAC scheme to solve the reprojection error minimization problem defined in Equation (3). The refined pose \(\mathbf{T}_{t+1}\) is the final output for the current frame and serves as the starting point for the next IMU integration cycle.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 3 Methods  
We aim to develop a hybrid architecture to weaken the negative effect of noisy labels. This architecture could automatically estimate the label quality of slices without extra clean labels. As shown in Figure 1, the novel hybrid framework contains four major modules: (1) a 3D segmentation module, which generates whole-volume segmentation probability maps in one shot; (2) a 2D slice-level label-quality awareness module, which predicts label quality scores for each slice; (3) a shape-awareness regularization loss for introducing prior information; (4) a final re-weighting module, which assigns different weights for different slices to construct the final loss.  
Figure 1: The architecture of the proposed hybrid noise-robust architecture. It includes a 3D segmentation module, a 2D label-quality awareness module, a shape-awareness regularization loss, and a loss re-weighting module. The input is whole-volume 3D medical images, which consist of multiple slices (e.g., CT and MRI data). For the 2D label-quality awareness module, the input is the concatenation of the image, and the foreground and background for every slice. The outputs of the LQAM are the score set of \(Q_{LQAM}\). The shape-awareness regularization loss \(L_{R}\) is constructed with distance transform maps (DTMs). The final loss \(L_{final}\) is constructed with the idea of a re-weighting strategy. \(L_{seg}\) means the segmentation loss, and \(L_{total}\) is the weighted sum of \(L_{seg}\) and \(L_{R}\). The ground truths have noisy information.
# A Division Algorithm in a Redundant Residue Number System Using Fractions
## 2 An Approximate Method for Determining the Positional Feature of the Modular Number  
### Residue Number System  
In the RNS, a positive integer is represented as a bank of residues to selected co-prime bases. This approach allows one to replace operations with large numbers by operations with small numbers, which are represented as residues of the division of large numbers by earlier selected relatively prime modules \(p_{1},\ p_{2},\ \ldots,\ p_{n}\). Let  
\[A\equiv\alpha_{1}(\text{mod}p_{1}),\ A\equiv\alpha_{2}(\text{mod}p_{2}),\ \ldots,\ A\equiv\alpha_{n}(\text{mod}p_{n}). \tag{1}\]  
Then, an integer \(A\) can be associated with the set \((\alpha_{1},\ \alpha_{2},\ \ldots,\ \alpha_{n})\) of the least non-negative residues over one of the corresponding numbers. This correspondence will be one-to-one until \(A<p_{1}p_{2}\ldots p_{n}\), according to the CRT. The set \((\alpha_{1},\ \alpha_{2},\ \ldots,\ \alpha_{n})\) can be considered as one of the methods of the representation of the integer \(A\) in a computer, i.e., the modular representation or representation in the RNS.  
The main advantage of this representation is the fact that the addition, subtraction, and multiplication operations are implemented very simply by the formulas:  
\[A\pm B=(\alpha_{1},\ \alpha_{2},\ \ldots,\ \alpha_{n})\pm(\beta_{1},\ \beta_{2}\ \ldots,\ \beta_{n})=((\alpha_{1}\pm\beta_{1})\text{mod}p_{1},\ (\alpha_{2}\pm\beta_{2})\text{mod}p_{2},\ \ldots,\ (\alpha_{n}\pm\beta_{n})\text{mod}p_{n}) \tag{2}\]  
\[A\times B=(\alpha_{1},\ \alpha_{2},\ \ldots,\ \alpha_{n})\times(\beta_{1},\ \beta_{2},\ \ldots,\ \beta_{n})=((\alpha_{1}\times\beta_{1})\text{mod}p_{1},\ (\alpha_{2}\times\beta_{2})\text{mod}p_{2},\ \ldots,\ (\alpha_{n}\times\beta_{n})\text{mod}p_{n}) \tag{3}\]  
These operations are called modular, since, for their execution in the RNS, it is sufficient to fulfill one cycle of processing numerical values. In addition, this processing occurs in parallel, and the information value in each modulo channel does not depend on the other modulo channels.  
Thus, there are three main advantages of RNS [1].  
1. There is no carry propagation between RNS arithmetic units. Large numbers represented in the form of small residues that leads to faster data processing.
2. When using the RNS, large numbers are encoded into a set of small residues, which reduces the complexity of the arithmetic units and simplifies the computing system.  
RNS is a non-positional system with independent arithmetic units; therefore, an error in one channel does not apply to others. Thus, the processes of error detection and error correction are simplified.  
However, such operations as sign detection, comparison, division, and some others are time-consuming and expensive in the RNS [4].
###### Abstract  
Offering real-time precise point positioning (PFP) services for global and large areas based on global navigation satellite systems (GNSS) has drawn more and more attention from institutions and companies. A precise and reliable satellite orbit is a core premise for multi-GNSS real-time services, especially for the GPS and GLONASS, which are undergoing modernization, whereas the Galileo, BDS and QZSS have just fulfilled the construction stage. In this contribution, a real-time precise orbit determination (POD) strategy for the five operational constellations based on the hourly updated ultrarapid orbit prediction method is presented. After combination of 72 h arc through three adjacent 24 h arc normal equations, the predicted orbits are finally generated (hourly updated). The POD results indicate that the mean one-dimensional (1-D) root mean square (RMS) values compared with the Deutsches GeoForschungsZentrum (GFZ) final multi-GNSS orbits are approximately 3.7 cm, 10.2 cm, 5.8 cm, 5.7 cm, 4.1 cm and 25.1 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. The mean 1-D RMS values of the hourly updated ultrarapid orbit boundary overlapping comparison are approximately 1.6 cm, 6.9 cm, 3.2 cm, 2.7 cm, 1.8 cm and 22.2 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. The satellite laser ranging (SLR) validation illuminates that the mean RMS values are approximately 4.53 cm and 4.73 cm for the four MEOs of BDS-3 and four BDS-2 satellites, respectively.  
real-time precise orbit determination; multi-GNSS; hourly updated; satellite laser ranging: solar radiation pressure model 2022  
[PERSON], [PERSON], [PERSON], [PERSON], J. Real-Time Multi-GNSS Precise Orbit Determination Based on the Hourly Updated Ultra-Rapid Orbit Prediction Method 2022144412[[https://doi.org/10.3390/rs1474412](https://doi.org/10.3390/rs1474412)]([https://doi.org/10.3390/rs1474412](https://doi.org/10.3390/rs1474412))  
1
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Use Cases of Human DTC
#### 5.5.3 Future Prediction and Growth Support  
Human digital twins are digital replicas of real-life human beings that can be used to simulate and analyze different scenarios and outcomes. They are created by collecting data from various sources such as wearable devices, medical records, and genetic information and using Artificial Intelligence and Machine Learning algorithms to build a virtual model of a person. The potential for human digital twins is vast, and it is predicted that they will play a significant role in various industries such as healthcare, education, entertainment, and even space exploration. Here are some future predictions and growth support measures for human digital twins [64]:  
Healthcare: Human digital twins can be used to create personalized treatment plans for patients based on their individual genetic makeup and medical history. This can lead to more effective treatments and better outcomes for patients;  
Education: Human digital twins can be used to create virtual simulations of real-life scenarios that students can learn from. This can provide a more immersive and engaging learning experience;  
Entertainment: Human digital twins can be used to create virtual avatars that can interact with real people in virtual environments. This can lead to more personalized and engaging entertainment experiences;  
Space exploration: Human digital twins can be used to simulate the effects of long-term space travel on the human body. This can help researchers develop better ways to keep astronauts healthy during extended space missions.  
As the technology for creating human digital twins continues to advance, we can expect to see more applications for this technology in various industries. However, there are also potential ethical and privacy concerns that need to be addressed to ensure that human digital twins are used responsibly and for the benefit of society.
# Three-Dimensional (3D) Model-Based Lower Limb Stump Automatic Orientation
## 3 Results
Selection of points with maximum coordinates: calculate Rmax, Rmin according to Equations (1) and (2)  
Estimation of the rotation angles: calculate matrix M-array RotMat(3, 3) for direction (Rmin, Rmax)  
according Equation (8) and recalculate data according to Equation (7)  
Arrangement points in the cross-section: calculate array DataOrd(3, N) according to Equation (9),  
calculate central points of sections by averaging CentralData(3,K), where K-number of sections  
Fitting a regression line: for CentralData calculate vectors bVec and dVec according to Equation (12)  
Estimation of the rotation angles: calculate RotMat(3, 3) for direction bVec and recalculate Data according to Equation (7)  
**If** 3D model axis does not coincide with Z axis, **then go to** line 2  
Setting cross-section under control by the operator  
3D model centering  
**If** 3D model inverted, **then** flip over the model  
Data export (e.g., saving to file)  
Figure 5: Comparison of models obtained after orientation: (**a**) an expert oriented 3D-model, (**b–f**) 3D-models oriented by the proposed algorithm. The presented dependence function can be reasonably fit by a second order polynomial. It was found that significant simplification of the original model leading to the six-fold reduction of the number of polygons from 120,000 to 20,000, the algorithm's operation time is reduced by approximately 1.5 orders of magnitude. Thus, reduction of calculation time can be achieved by any measures that lead to the reduction of the polygon number, such as smoothing of the 3D-model surfaces.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 4 Phytochemical Composition
### Tocopherols  
Recent studies highlight the presence of tocopherols and tocotrienols in its oils, known as forms of vitamin E. Tocotrienols are the main form of vitamin E in the seeds of the Arecaceae family. In the oils of _Arocomia aculeata_, all tocotrienols and \(\alpha\)-, \(\beta\)-, and \(\delta\)-tocopherol were identified in the mesocarp [22]. The presence of tocopherols in significant amounts in the fruits of _Arocomia aculeata_ depends on the ripening stage, with \(\alpha\)-tocopherol concentration progressively increasing during ripening [7; 17]. Comparatively, the concentrations of \(\alpha\)-tocopherol in _Arocomia aculeata_ fruits are notably higher than in other fruits such as apricots and avocados. Differences in vitamin E profiles in macauba oils are attributed to genetic variation among wild populations and climatic conditions.  
The pulp oil of macauba can present a higher vitamin E content when grown in regions with higher precipitation rates, relative humidity, and temperature. This is an indication that this crop may be suitable for agroforestry systems in areas where it has not been considered due to climatic conditions [17; 22]. The pulp oil of macauba showed a tocopherol content of 212.95 mg/kg [4]. The most common form of vitamin E, \(\alpha\)-tocopherol, was found in the analyzed fruits [17].  
In summary, _Arocomia aculeata_ is a promising palm with potential for food, therapeutic, and industrial applications. Its oils are rich sources of vitamin E and carotenoids, with antioxidant and nutritional benefits. Variations in vitamin E profiles can be attributed to genetic variation and cultivation conditions [23].
## 3 Path Feature Recognition
### Path Classification  
The inherent mechanism of the back-propagation neural network is to propagate the error between the actual output value and the target value from the output layer of the network back to the input layer. At each layer, the neural network corrects the connection weights among neurons and the thresholds of each neuron, based on the error transmitted from the outer layer. The correction process is repeated continuously until the error has been converged to an acceptable level, or the operation of the algorithm reaches a maximum iteration steps preset.  
The structure of a 3-layer BP neural network with a single hidden layer is adopted in this paper. Theoretically, this network can implement any nonlinear mapping with arbitrary precision. The number of the input nodes of the BPNN is the dimensionality \(s\) of the samples after the reduction by the KPCA. The sigmoid function is selected as the transfer function for the hidden layer. Since each group of samples corresponds to one path type, the number of the output nodes is defined as 1. The number of the hidden-layer nodes is determined according to the empirical formula.  
\[p=\sqrt{s+q}+a \tag{11}\]  
where \(s\) is the number of the input nodes, \(q\) is the number of the output nodes, and \(a\) is a constant between 1 and 10.  
The traditional BP learning algorithm is sensitive to the learning rate. In order to deal with this drawback, the adaptive momentum BP learning algorithm is adopted here [34]. The learning rate is adaptively adjusted in the light of the local error surface during the training process, which makes the learning algorithm achieve a fast and still stable convergence. For the BPNN, the initial learning rate, the target error, and the maximum iteration steps are set as 0.05, 0.001, and 1000, respectively. Then, the samples after the KPCA dimensionality reduction are provided to train the BPNN. The BPNN trained can adaptively adjust the network weights for any new input data, thereby having strong self-learning and self-adaptive ability. Moreover, this network also has some fault-tolerant capability, i.e., it can still work normally when the network is damaged partially.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Proposed Method for Placing Multiple Markers
### Preprocessing Step
#### 2.1.3 Applying ROI  
If the marker is placed too high or too low, it is difficult to observe through AR devices. In consideration of this, a person's knee height (hereafter \(h_{knee}\)) to eye height (here after \(h_{eye}\)) is set as the ROI. The \(h_{knee}\) and \(h_{eye}\) are the median measurements of males and females of ages 20s-50s from the Korean anthropometric survey conducted by the Korean Agency for Technology and Standards (Tables 1 and 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Age** & **Male** & **Female** \\ \hline
20s & 1615.147 & 1489.162 \\
30s & 1595.316 & 1464.283 \\
40s & 1571.064 & 1451.561 \\
50s & 1547.258 & 1434.260 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean eye heights of males and females in their 20s–50s (unit: mm).  
Figure 3: Example of inner and outer surface determination.  
If the vertical value (generally y) of all \(\mathbf{v}^{\prime}\)s constituting \(\mathbf{f_{i}}\) is within the ROI, \(\mathbf{f_{i}}\) is maintained. Otherwise, \(\mathbf{f_{i}}\) is removed and new \(\mathbf{f^{\prime}s}\) are created using \(\mathbf{v}^{\prime}\)s within the ROI and positions (\(p^{\prime},\ p^{\prime\prime}\)) at the boundary, as shown in Figure 4a. The creation of new \(\mathbf{f^{\prime}s}\) depends on the number of \(\mathbf{v}^{\prime}\)s outside the ROI, as shown in Figure 4b. After removing the unused \(\mathbf{v}^{\prime}\)s, the preprocessing is complete.
## 4 Results and Discussion
### Discussion
This is because the enlargement of the step size may lead to skipping the baseline values of the start-end times during the coarse search phase, especially when judging angular relationships. In this experiment, the half-field angle is set to 30\({}^{\circ}\), resulting in an angle of about 2\({}^{\circ}\) for the time it takes for the satellite to move through this angle, which is approximately 90 s. Therefore, if the time step size exceeds this value, there is a risk of skipping critical values in the judgment. This indicates that when setting the step size, one should consider key factors such as the payload's half-field angle. It also illustrates the trade-off between algorithm accuracy and speed; within a certain range of time step sizes, accuracy can be maintained. Therefore, this paper, through the analysis of the relationship between step size, efficiency, and accuracy, demonstrates that choosing a 90 s step size is reasonable, optimizing the calculation step size.
# A Novel Ultrasonic Cavitation Peening Approach Assisted by Water Jet
## 4 Results and Discussion
### Microstructure of the Specimen Surface  
In order to observe the variations of surface profile, the microstructures of the specimen surfaces are captured at the vibration amplitude of 30 \(\upmu\)m, since the changes of the surface profile are easy to recognize at this vibration amplitude. The captured area is 150 \(\upmu\)m by 150 \(\upmu\)m. Figure 7a shows a polished specimen surface without any treatment. Figure 7b-d show the microstructures at the standoff distance of 0.1 mm, 0.6 mm and 1.0 mm, respectively. It can be seen that the surface without treatment is very smooth and without any large pits. In the case of the standoff distance of 0.1 mm, there are many small pits being generated on the surface. It is difficult to form bubble clouds in this small gap, which leads to a uniform distribution of the impacts caused by cavitation bubble collapse. By increasing standoff distance to 0.6 mm, strong cavitation increases in the small gap. The specimen surface profile changes severely and large pits are formed due to the violent impacts. When the standoff distance increases to 1.0 mm, the change of surface profile does not change more than that at the standoff distance of 0.6 mm. It is because that the impact on the treated surface is highly non-uniform. According to the contrast, it is deduced that no mass loss were produced. There are also no obvious cracks but some scratches due to polish.  
Compared to the traditional ultrasonic cavitation peening [18], the volume loss, hardness and roughness keep similar after treatment under the same investigation conditions. Therefore, this novel peening process not only takes the advantages of water jet assist but also has the similar treatment effects as the traditional ultrasonic cavitation peening process. Although there is no residual stress measurement, the plastic deformation and the increase of surface hardness indicate that the effectiveness of this novel peening process can still be evaluated.  
Figure 6: Variations of surface microhardness with error bar with the increase of standoff distance and vibration amplitude.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
###### Abstract  
Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.  
Keywords:model-based pose estimation; 3D pose estimation; homography decomposition; PnP problem; template tracking +
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