esa-sceva/satcom-chunk-collection huggingface dataset:
# Tolerance Synthesis of Delta-like Parallel Robots Using a Nonlinear Optimisation Method
## 2 Materials and Methods  
### Modelling the Delta Parallel Robot  
#### 2.1.1 Geometric Description  
A Delta parallel robot (cf. Figure 1) with 3 degrees of freedom (DOFs) has been the subject of several studies in the literature [22; 23; 24; 25]. It was developed by [PERSON] (and others) in Switzerland at EPFL [26]. A Delta parallel robot was explored as an example of a high-speed application (e.g., pick and place). This type of robot has exceptional performance in terms of speed (up to 10 m/s) and acceleration (up to 20 g), and it has achieved significant commercial success.  
Because a Delta parallel robot is symmetrical, only one of the three legs can be considered, as illustrated in Figure 2. The following coordinate systems can be established:  
\(O_{0}x_{0}y_{0}z_{0}\): The reference frame system also denoted by \(R_{0}\), where \(O_{0}\) is the centre of an equilateral triangle; defined by \(A_{i}\) (\(i=1,2,3\)); \(x_{0}\) is along \(OA_{1}\); and \(z_{0}\) is orthogonal to the base plane determined by \(O_{0}A_{i}\) (\(i=1,2,3\)).  
\(A_{i}x_{i}y_{i}z_{i}\): The coordinate system denoted by \(R_{i}\), where \(x_{i}\) is along \(OA_{i}\).  
\(A_{i}\) are the centres of the actuated links between the arms and the base \((i=1,2,3)\), \(C_{i}\) are the centres of the cardan links between the arms and the parallelograms, \(B_{i}\) are the centres of the cardan links between the parallelograms and the end-effector (centre of mobile platform), \(O_{0}\) is the barycentre of the points \(A_{i}\), and \(P\) is the barycentre of the points \(B_{i}\).  
The dimensional parameters of the Delta parallel robot are the following lengths: \(La_{i}=260\) mm for the arms, \(Lb_{i}=480\) mm for the front arms, and the difference between the radii, \(Ra=194\) mm and \(Rb=30\) mm, is denoted by \(R\).  
\(\theta_{i}\) is the angle between the \(i\)th arm and the plan of the fixed base, i.e., between \(x_{0}\) and \(x_{0i}\) around the axis \(z_{0}\). Its value is equal to \(\frac{2\pi(i-1)}{3};i=1,2,3\).  
Figure 1: CAD view of Delta parallel robot.  
Figure 2: Illustration of the main geometric parameters of a Delta parallel robot.
## 4 Analysis of Tubing Stress Changes under Eccentric Wheel Fatigue Loading
### Axial Stress Variation of Tubing V-Groove Section at Different Positions during Fatigue Loading  
In order to further study the variation law of tubing stress under eccentric wheel loading, four different points (point a, point b, point c and point d) near the outer surface of the tubing V-groove section are analyzed, as shown in Figure 10. Point b is the farthest from the eccentric wheel excircle axis, and point d is the closest to the eccentric wheel excircle axis.  
The variation of axial stress at different positions in the tubing V-groove section under eccentric wheel loading is shown in Figure 11. Under eccentric wheel cyclic loading, different locations in the tubing V-groove section are subjected to tensile stress and compressive stress. The magnitude and time of tensile stress and compressive stress at different locations are different.  
It can be seen from Figure 11a that at 0-0.36 s, the tensile stress on point a is the same as the compressive stress on point c. The tensile stress and compressive stress of point a and point c are both less than 400 MPa. At 0.36 s, the axial stress of point a and point c is 0 MPa. At 0.36-1 s, point a and point c change symmetrically because they have the same eccentricity relative to the eccentric die. In addition, point a and point c in the V-groove section are in the symmetrical position of the tubing, and their stress changes are symmetrical to each other.  
It can be seen from Figure 11b that at 0-0.62 s, the duration of tensile stress on point b is the same as the duration of compressive stress on point d. At 0.62 s, the axial stress of point b and point d is 0 MPa. The maximum compressive stress on point b is greater than the maximum tensile stress. In more detail, the maximum compressive stress is greater than 650 MPa, and the maximum tensile stress is less than 600 MPa. The compressive stress duration is less than the tensile stress duration. The maximum compressive stress on point d is greater than the maximum tensile stress. In more detail, the maximum compressive stress is greater than 750 MPa, and the maximum tensile stress is less than 700 MPa. The  
Figure 10: Sampling locations for stress analysis of tubing V-groove section.  
duration of compressive stress is greater than that of tensile stress. The eccentricity of point b and point d at different positions of the tube is different. According to the work hardening effect, the greater the eccentricity, the greater the axial stress. The minimum tensile stress and compressive stress are at point a and point c. Point d closest to the eccentric wheel excircle axis has the largest compressive stress.
## 4 Research on Critical Technologies
### Atmospheric Channel Modeling
#### 4.5.3 Negative Exponential Distribution  
For the case of strong turbulence with a link length of a few kilometers, the signal amplitude follows a negative exponential distribution of the signal strength with a probability density function with the following expression:  
\[f_{l,N}(I)=1/I_{0}\exp(-I/I_{0}) \tag{3}\]  
We believe that the future direction of atmospheric turbulence modeling research is as follows [30]:  
* Measurements of the actual atmospheric turbulence characteristics to obtain more statistical parameters of atmospheric turbulence and to promote the research and development of wireless optical communication in actual atmospheric turbulence conditions.  
* An analysis and comparison of the transmission characteristics of different light sources in different atmospheric turbulence conditions to explore the influence mechanism of atmospheric turbulence on optical signal transmission and improve the ability of these systems to suppress atmospheric turbulence.
* Research on high-accuracy atmospheric turbulence parameter measurement instruments and, at the same time, the development of high-sensitivity, high-resolution atmospheric turbulence characteristic measurement techniques and methods so as to improve the accuracy of atmospheric turbulence parameter measurement in the actual atmosphere.  
Having studied and analysed the key technology components in depth, we can draw the following summary. These technologies, both at present and in the future, play a crucial role. Table 1 shows a detailed summary of these key technologies.
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 3 Proposed Methods
### Abnormal Early Warning
#### 3.3.3 Data Sensitivity Analysis  
In essence, the proposed abnormal early warning model can be considered a joint composition of two independent networks, and both are trained using the same dataset. Due to the poor quality of the few data present in the dataset, both networks produce large errors, and the errors of both will further superimpose on each other, leading to serious degradation of the model performance.  
However, because the two networks are trained separately, the information between them is not interoperable, which leads to the fact that, although the final result error of both networks is small, the locations where the errors are generated are different, which significantly magnifies the small errors in the process of cross-network transfer, thus affecting the performance.  
To solve the above two problems, in this study, the loss function of the autoencoder with fixed weights is added as the judgment basis during the training of the LSTM network, that is, it is not only required that the prediction results obtained by the LSTM conform to the true distribution but also that the prediction results can be correctly coded and decoded by the autoencoder.
## 4 Results  
### Rotation Impact on the Baseline Model  
The baseline model was tested by using data augmentation, in particular, by applying rotations, from 0 to 180 degrees, on the frontal, longitudinal, and sagittal axis. Performances between the five cross-validation splits were observed. In particular, a minimum Kappa-score value of 0.87 and a maximal Kappa-score value of 0.92 were obtained when testing the baseline model with the original test set. Thus, it could be concluded that the model performance was not dependent on which recordings were included in the training set. Figure 4 reports the percentage of wrong classifications according to multiple axis and groups of activities. It is noticeable how the Y-axis, parallel to the participant's frontal plane and parallel to the Earth's gravity acceleration, was the least impacted axis by orientation changes. Moreover, because of the nature of the proposed activities, the static ones (Lying in bed, Left side, Passive wheelchair, Right side, Reading, Reclined, Sitting on the edge of the bed, Standing, Upright, Washing hands/brushing teeth) were the least affected compared to the dynamic ones.  
Figure 5 shows the percentage of misclassified segments of selected groups of activities when rotations were applied along the frontal, longitudinal, and sagittal axis (respectively, X-, Y-, and Z-axis) separately; the performance is shown for each applied rotation from 0 to 180 degrees (0, 5, 10, 20, 25, 30, 40,..., 180 degrees). Two groups of activities are reported: treadmill walking and static activities. As previously observed, major differences were  
Figure 4: Percentage of misclassified segments when testing the baseline model with rotations from 0 to 180 degrees applied to each axis separately. Misclassified segments correspond to the amount of false negative predictions for the specific label  
noticeable in relation to the type of activity being considered and the axis to which the rotation was applied. It is relevant to highlight that, within dynamic activities, the error percentage increased rapidly even for low values of applied rotations. Activity distribution from the WPS and SHS data, divided according to the label of our interest, were as follows:  
stars ascent 5.2%, stairs descent 4.9%, static 25.2%, walking 62.5%, wheelchair 2.3%.
# Statistical Modeling of Wall Roughness and Its Influence on NLOS VLC Channels in Underground Mining
## 3 Simulation Results
### Second VLC Simulation Position
#### 3.2.3 Third Case
\begin{table}
\begin{tabular}{c c} \hline \hline
**Random Distribution** & **Max Received Power (W/m\({}^{2}\))** & **Max CIR Peak** \\ \hline UNID & \(1.45\times 10^{-8}\) & \(8.2\times 10^{-9}\) \\ \hline EXPD & \(9.0\times 10^{-9}\) & \(4.0\times 10^{-9}\) \\ \hline GAMD & \(2.7\times 10^{-9}\) & \(1.71\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Max power and CIR peak in position 1 results (random incidence and irradiance angles). \begin{table}
\begin{tabular}{c c} \hline \hline
**Height [m]** & **Max Received Power (W/m\({}^{2}\))** & **Max CIR Peak** \\ \hline \(0\) & \(7.50\times 10^{-8}\) & \(6.00\times 10^{-9}\) \\ \hline \(1\) & \(4.00\times 10^{-7}\) & \(1.20\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Max power and CIR peak in position 1 results (random reflection coefficient). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Random Distribution** & **Max Received Power (W/m\({}^{2}\))** & **Max CIR Peak** \\ \hline UNID & \(1.45\times 10^{-8}\) & \(8.2\times 10^{-9}\) \\ \hline EXPD & \(9.0\times 10^{-9}\) & \(4.0\times 10^{-9}\) \\ \hline GAMD & \(2.7\times 10^{-9}\) & \(1.71\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Max power and CIR peak in position 2 results changing the height. Highly rough or variable surfaces require models with EXPD or GAMD distributions, which more accurately depict increased reflection dispersion. In contrast, smoother surfaces with more uniform reflection properties are better represented by uniform distributions, as they produce more consistent reflection patterns. In general, Position 1 exhibits higher channel losses due to the surrounding geometry and reflection scattering, while Position 2 maintains a more stable power level regardless of height. The reflection coefficient and angular distributions also impact propagation, with uniform distributions favoring higher received power and CIR peaks, while exponential and gamma distributions introduce greater dispersion effects. The correct selection of a roughness model is essential to accurately simulate the signal behavior in underground environments, as it directly influences the energy distribution, channel gain, and CIR characteristics.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 1 Introduction  
In recent years, 3D face modeling has received extensive attention due to its widespread applications in face recognition, animation and 3D video games. The usual way to obtain 3D face models is 3D scanning by some high resolution 3D scanners, such as Artec Eva and Minolta Vivid. However, these professional 3D scanners are expensive and have a high computational cost. For this reason, some consumer depth cameras, such as Microsoft Kinect and Intel RealSense, have drawn wide attention because of their low cost and easy integration. A depth camera is able to acquire the depth information of objects in a scene; i.e., the distances between the camera and the surfaces of objects. Depth information can be transformed into 3D information; i.e., corresponding 3D models of objects can be constructed from depth images. The emergence of consumer depth cameras makes a cheap and fast acquisition of 3D face model possible. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. How to acquire high precision and high resolution face models fast and cheaply still is a challenging task. Much research about 3D reconstruction based on depth cameras has been done [1; 2; 3], but the resolution is not high enough when involving the human face. Improving the precision and resolution of 3D face models acquired by consumer cameras, i.e., 3D face model super-resolution, is a valuable study.  
In this work, we built a database including 111 sets of 3D face models, where each set contains a low resolution 3D face model and the corresponding high resolution one of the same participant.  
The low resolution model was acquired by Kinect with the Kinect Fusion method [2], while the high resolution one was acquired by Artec Eva. With this database, we propose a 3D face model super-resolution method based on radial curves. In the method, we estimate the radial curves of the high resolution 3D face model from the corresponding low resolution one, and generate the high resolution 3D face model by deforming a high resolution face reference with the landmarks on the radial curves being control points. Experiments validated the proposed method.  
The rest of the paper is organized as follows: The related work is described in Section 2; radial curves' extraction and the 3D face super-resolution method are described in Section 3; experimental results are reported and discussed in Section 4; conclusions are given in Section 5.
## 5 Results
### Comparative Analysis of Graph-Based Models as Inputs  
Building on the comparison of various models, we also quantitatively evaluated the effectiveness of different graph-based models as inputs for predictive modeling of treatment outcomes. This assessment utilized metrics such as AUC-ROC, accuracy, F1 score, recall, and precision. The detailed results of this evaluation are presented in Table 5.  
The gene co-expression graph model achieved an AUC-ROC of \(0.7654\pm 0.0323\) and an accuracy of \(0.7210\pm 0.0546\), with the F1 score, recall, and precision at \(0.7856\pm 0.0899\), \(0.8231\pm 0.2007\), and \(0.7973\pm 0.1213\), respectively. The protein-protein interaction graph model exhibited an enhanced AUC-ROC of \(0.8519\pm 0.0321\), with the accuracy remaining consistent with the gene co-expression graph model. Notably, the recall reached a striking \(0.9846\pm 0.0211\), although the precision was \(0.7094\pm 0.0401\), indicating a high true positive rate but a moderate rate of false positives.  
Combining gene co-expression with protein interaction data into a multiview graph model resulted in the best overall performance, with an AUC-ROC of \(0.8724\pm 0.0511\), accuracy of \(0.7789\pm 0.068\), F1 score of \(0.8529\pm 0.0338\), recall of \(0.9231\pm 0.0719\), and precision of \(0.8038\pm 0.0955\). This integrated approach outperformed the individual graph models, underscoring the synergistic effect of combining multiple data views for a more accurate prediction of treatment outcomes.
## 2 Background Information
Its time interval is 1 h, and the spatial resolution is 2.5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) in the geographic latitude and longitude. The detailed information on the CODE GIM TEC can be found on the web ([[http://ftp.aiub.unibe.ch/CODE/IAR_README.TXT](http://ftp.aiub.unibe.ch/CODE/IAR_README.TXT)]([http://ftp.aiub.unibe.ch/CODE/IAR_README.TXT](http://ftp.aiub.unibe.ch/CODE/IAR_README.TXT)) (accessed on 21 September 2021), and references included in the document), as well as in the work of [PERSON] [44]. After half a year following the CSES launch, on 25-26 August 2018, there occurred a strong geomagnetic storm, with the geomagnetic indices varying abruptly (Figure 1). It shows that since UT18:00 on 25 August, the geomagnetic storm started with SYM-H index decreasing quickly, and at 07:00 on 26 August it was detected to the minimum value of \(-\)200 nT. At UT13:30, it recovered above \(-\)100 nT, and then continued to increase to be above \(-\)30 nT on 30 August (Figure 1). So the main phase of this storm took place on 25-26 August within a few hours, and the recovery phase maintained a few days till 30 August. The speed of solar wind began to increase after the quick recovery phase on 26 August and maintained higher values until 29 August. The interplanetary magnetic field and electric field, including the flow pressure exhibited clear variations at the initial phase on 25 August, with Ez increasing firstly and then decreasing quickly, while By and E decreased with small amplitude firstly and increased largely to the main phase. At the quick recovery phase on 26 August, these four parameters showed strong modulations, at least three times from positive to negative in Bz, By, and E during the second half day of 26 August. The SYM-H (the fifth panel in Figure 1) also presented the minimum value of \(-200\) nT on 26 August, still being \(-50\) nT on 27 August, and up until 29 August, it became bigger than \(-30\) nT, and also ASY-H (the bottom panel in Figure 1) descended to lower than 30 nT on 29 August. This strong geomagnetic storm provides a good opportunity to study the ionospheric response process at middle and lower latitudes on the basis of satellite observations.
## 5 Experimental Results
### Performance Analysis for Other Datasets
#### 5.5.2 NTU RGB+D Dataset (NTU)  
The NTU RGB+D dataset includes 946 videos labeled as falling under medical conditions. For this dataset, we applied STFT to extract 50 frames, specifically at moments when a fall occurred. We randomly extracted frames from videos without significant motion or fall events to simulate the lack of subtle movement typically associated with non-fall scenarios. This approach aimed to challenge the model's ability to discern true falls from low-activity states.  
The retraining of our model on this dataset excluded the use of the Gaussian module, focusing solely on the capabilities of STFT and the classification framework to evaluate its performance against sophisticated activities and subtle motions.  
The classification report indicates a slight misclassification rate in the fall category, primarily due to incomplete fall actions in some videos, which pose challenges in detecting definitive fall patterns as seen in Table 6. Nonetheless, the high precision and recall rates affirm the model's robustness and capability to handle complex scenarios in medical monitoring applications.
## 2 Problem Statement  
### Underwater Navigation Model  
The motion model of the AUV is shown in Equation (1) by using continuous process and discrete measurement equations, and the motion model of the AUV and underwater navigation system are shown in Figure 1.  
\[\left\{\begin{array}{l}d\mathbf{X}(t)=\mathbf{F}(t)\mathbf{X}(t)dt+\omega(t )dt\\ \mathbf{Z}(t_{k})=\xi(t_{k})+h[\mathbf{X}(t_{k})]+v(t_{k})=\xi(t_{k})+\mathbf{ H}_{k}\mathbf{X}(t_{k})+v(t_{k})\end{array}\right. \tag{1}\]  
where the state vector \(\mathbf{X}(t)=\begin{bmatrix}x_{t}&v_{x,t}&y_{t}&v_{y,t}&z_{t}&v_{z,t}\end{bmatrix}^ {T}\), \(x_{t}\) and \(v_{x,t}\) refer to the position and speed of the AUV in the east direction, respectively; \(y_{t}\) and \(v_{y,t}\) refer to the position and speed of the AUV in the north direction, respectively; \(z_{t}\) and \(v_{z,t}\) refer to the position and speed of the AUV in the up-direction, respectively; the subscript \(t\) indicates continuous time \(t\); \(\mathbf{F}(t)\) represents the state transition equation; \(\mathbf{Z}(t_{k})\) refers to the observation vector of discrete time \(k\); \(\omega(t)\) is the process noise obeying the Gaussian distribution with zero mean; the process noise matrix is \(\mathbf{Q}(t)\); \(v(t_{k})\) is the observation noise obeying the Gaussian distribution with zero mean; the observation noise matrix is \(\mathbf{R}_{k}\); \(\xi(t_{k})\) is the random variable at the discrete time \(k\); \(\mathbf{H}_{k}\) and \(h[\bullet]\) are the measurement functions of linear (**Case 1**) and nonlinear (**Case 2**) forms, respectively.  
Figure 1: The motion model of the AUV and underwater navigation.  
The observation vector is denoted as follows:  
\[\mathbf{H}_{k}\mathbf{X}(t_{k})+v(t_{k})=\begin{bmatrix}x(t_{k})&y(t_{k})&z(t_{k} )\end{bmatrix}^{T} \tag{2}\]  
where \(\mathbf{H}_{k}=\left[\begin{array}{cccccc}1&0&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&0&1&0\end{array}\right]\); \(x(t_{k})\), \(y(t_{k})\), and \(z(t_{k})\) indicate the measured position in all directions at discrete time \(k\).  
**Case 2:** The measurement function is nonlinear, including **Case 2.1**, **Case 2.2**, and **Case 2.3**.  
The observation vector is denoted as follows:  
\[h[\mathbf{X}(t_{k})]+v(t_{k})=\left[\begin{array}{c}\sqrt{\left(x_{t}-x_{0} \right)^{2}+\left(y_{t}-y_{0}\right)^{2}}\\ \sqrt{\left(x_{t}-x_{0}\right)^{2}+\left(y_{t}-y_{0}\right)^{2}}+\left(z_{t}- z_{0}\right)^{2}}\\ \arctan\Bigl{[}\frac{\left(y_{t}-y_{0}\right)}{\left(x_{t}-x_{0}\right)} \Bigr{]}\end{array}\right]+v(t_{k})\ =\begin{bmatrix}r(t_{k})&d(t_{k})&\theta(t_{k}) \end{bmatrix}^{T} \tag{3}\]  
where the elements of \(\mathbf{Z}(t_{k})\) are depicted in Figure 1. \(x_{0}\), \(y_{0}\), and \(z_{0}\) mean the position of the observation site in all directions; \(r(t_{k})\) is the horizontal distance between the observation site and the AUV measured at time \(k\); \(d(t_{k})\) is the three-dimensional Euclidean distance between the observation site and the AUV at time \(k\); \(\theta(t_{k})\) indicates the measured angle of the AUV with respect to the observation site at discrete time \(k\).