esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool
## 3 Methods  
### ICA Module and BiFPN Mechanism  
In contrast to channel attention, which focuses solely on reweighing the importance of different channels, the coordinate attention (CA) module encompasses the encoding of spatial information [28; 29]. This enables the CA module to more precisely locate the exact position of the object of interest, thereby enhancing the overall model's recognition capabilities. Our research identifies that targets on the surface of swimming pools are susceptible to deformation and posture changes, generating more non-linear features that increase the complexity of model recognition. Leveraging the sigmoid-weighted linear unit (SiLU) proves beneficial in capturing non-linear characteristics and considering non-linear factors, facilitating the capture of spatial dependency relationships and target position information [30]. The integration of the CA module and SiLU helps the whole model to recognize deformed targets and posture better. Subsequently, the convolutional layer, batch normalization layer, and SiLU activation function are used to further activate and integrate the features; then, the ICA module is proposed, as shown in Figure 1a. The ICA module contributes to enhanced feature learning and increased attention to the feature information of swimmers' behaviors. Its lightweight characteristic is suitable for mobile networks, and its capacity to analyze non-linear factors enhances the model's learning of deformed targets and posture changes.  
The bi-directional feature pyramid network (BiFPN), depicted in Figure 1b, integrates bidirectional cross-scale connections and fast normalized fusion [31]. It introduces learnable weights to learn the importance of different input features. BiFPN seamlessly fuses the original feature information from the backbone feature extraction, mitigating the loss of original feature information. Consequently, it achieves higher-level feature fusion, leading to a further improvement in algorithmic accuracy.  
In Figure 1a, the structure of the ICA module is depicted, highlighting areas of improvement. The \"X Avg Pool\" represents average pooling in the X direction, while the \"Y Avg Pool\" indicates average pooling in the Y direction. \"Conv2d\" denotes standard convolution, and \"BatchNorm\" refers to batch normalization. Figure 1b illustrates the structure of the BiFPN. The top-down blue lines signify the transmission of high-level semantic features, while the bottom-up green lines convey precise positional information. The purple line represents the fusion of peer characteristic layers [32]. \"Layer 1-3\" refers to the feature layers, and \"output 1-3\" denotes the resulting feature layers. \"BIF-Add2\" signifies the characteristic fusion layer combining two inputs, whereas \"BIF-Add3\" combines three inputs.
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 4 Navigation Performance
### Real-Time Orbit Determination Accuracy  
In order to improve the navigation accuracy, a real-time orbit determination filter based on an extended Kalman filter (EKF) was used with GNSS pseudorange observations and dynamic models. In the absence of observations, continuous navigation results can be calculated through orbit propagation.
## 2 Methodology
### Force Field  
The molecular force field includes the molecular interaction potential, van der Waals potential, and Coulomb potential, as shown in Equations (1)-(8).  
\[U_{total}=U_{coul}+U_{vdw}+U_{stretch}+U_{bend}+U_{torsion} \tag{1}\]  
Coulomb's action potential is based on [PERSON]'s law.  
\[U_{coul}=\frac{q_{i}q_{j}}{4\pi\epsilon_{0}r_{ij}} \tag{2}\]  
where \(q_{i}\) and \(q_{j}\) are the charge of atom \(i\) and atom \(j\), \(\epsilon_{0}\) is the dielectric constant, and \(r_{ij}\) is the distance between the two atoms.  
Van der Waals forces are calculated using the 12-6 Lennard-Jones potential function.  
\[U_{vdw}=4\epsilon_{ij}\left[\left(\frac{\sigma_{ij}}{r_{ij}}\right)^{12}- \left(\frac{\sigma_{ij}}{r_{ij}}\right)^{6}\right] \tag{3}\]  
where \(\epsilon_{ij}\) is the well depth for short-range interactions and \(\sigma_{ij}\) is the core diameter of the L-J potential.  
The L-J parameters between different atoms are obtained by mixing rule calculations, and the common mixing rule, the Lorentz-Berthelot mixing rule [29], is shown in Equations (4) and (5).  
\[\sigma_{ij}=\frac{\sigma_{i}+\sigma_{j}}{2} \tag{4}\]  
\[\epsilon_{ij}=\sqrt{\epsilon_{i}\epsilon_{j}} \tag{5}\]  
The molecular interaction potential includes bond stretching potential, bond angle bending potential, and dihedral angle torsion potential. In this study, harmonic is used to describe the bond stretching and bond angle bending potentials, and opls is used to describe the dihedral angle torsion potential. The bond stretching potential is shown in Equation (6).  
\[U_{stretch}=K_{r}(r-r_{0})^{2} \tag{6}\]  
The bond angle bending potential is shown in Equation (7).  
\[U_{bend}=K_{\theta}(\theta-\theta_{0})^{2} \tag{7}\]  
The dihedral angle torsion potential is shown in Equation (8).  
\[U_{torsion}=V_{0}+V_{1}(1+\cos\psi)+V_{2}[1-\cos(2\psi)]+V_{3}[1+\cos(3\psi)] \tag{8}\]  
where \(r_{0}\), \(r\) represent the known bond length and equilibrium bond length, \(\theta_{0}\), \(\theta\) represent the known bond angle and equilibrium bond angle, \(K_{r}\), \(K_{\theta}\), and \(V_{n}\) represent bond stretch potential constant, bond angle bending potential constant, and dihedral angle torsion potential constant.  
The non-bonded and bonded parameters for each of the force fields used in this work are listed in Tables 1 and 2.
# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 5 Evaluation
### Overall Performance Experiments
#### 5.2.2 Market Trading Simulation  
To further evaluate the effectiveness of the proposed important trading point prediction framework, backtesting is conducted to calculate the cumulative profit of each method by simulating stock trading for all of the test sets from January 2016 to December 2019. When the fund simulation is conducted, all of the methods trade at a daily frequency. If the methods provide a purchase signal, the trading system purchases the recommended stock at the closing price of the current date and holds it for one day. If the trading system no longer provides a purchase signal on the next trading day, it automatically sells the stock at the closing price of the second day; otherwise, it continues to hold the stock. Based on these signals, a straightforward portfolio construction strategy is to invest in all of the recommended stocks evenly. To approximate a real trading environment, the transaction costs per operation are roughly assumed to be 0.2%. In addition, to assess the performance of these methods, we calculate the average earning rate of the stock market by evenly holding every stock belonging to the dataset to indicate the overall market trend.  
To comprehensively measure the performance of each method, we use two widely accepted criteria in the evaluation. The first is the annualized return, which is the ratio of the cumulative profit that can be obtained per year; the second is the Sharpe ratio, which is an important indicator that is widely used in measuring the risk-adjusted performance of investment portfolios and is calculated by the following formula:  
\[\text{Sharpe ratio }=\frac{R_{P}-R_{f}}{\sigma_{P}}, \tag{23}\]  
Figure 6: The F1 score results for different models. RF: random forest; RNN: recurrent neural network; ITPP: important trading point prediction; LSTM: long short-term memory; HCRNN: hybrid convolutional recurrent neural network.  
where \(R_{P}\) represents the return of the portfolio, \(R_{f}\) is the risk-free rate--we use 3% as the estimate of \(R_{f}\)--and \(\sigma_{P}\) denotes the standard deviation of the portfolio's excess return. The comparison results of each method are listed in Table 4. In addition, the cumulative profit curves of the different methods are plotted over four years, as shown in Figure 7. It is shown that the proposed important trading point prediction framework based on our HCRNN model is able to obtain the highest profit with relatively low risk. The detailed comparison and analysis of each method are presented in the next subsection.
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 2 Methods
### Track-Bridge Model  
The track-bridge model consists of the CRTS-II slab track and the simply supported beam bridge, as shown in Figure 2. The track structure includes the rails, fasteners, track slab, CA mortar, base slab, and slab mat layer. The bridge structure contains the bridge bodies and bridge piers, which are connected by bridge bearings. In order to depict the physical and geometric characteristics, except that the fasteners, the slab mat layer, and the bridge bearings were regarded as spring damper elements; the rest of the components in the track-bridge model were all simulated by solid elements, which can take deflection and shear deformation into account.  
The kinematic equation of the track-bridge model is expressed by Equation (2):  
\[\mathbf{M}_{t}\cdot\ddot{\mathbf{Z}}_{t}+\mathbf{C}_{t}\cdot\dot{\mathbf{Z}}_{t }+\mathbf{K}_{t}\cdot\mathbf{Z}_{t}=\mathbf{F}_{t}, \tag{2}\]  
where \(\mathbf{M}\)t, \(\mathbf{C}\)t, and \(\mathbf{K}\)t denote the mass, damping, and stiffness matrices of the track-bridge model, which are extracted and assembled by the mode superposition method; \(\ddot{\mathbf{Z}}_{t}\), \(\dot{\mathbf{Z}}_{t}\) and \(\mathbf{Z}_{t}\) refer to the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Mass of car body/bogie/wheelset & kg & 40,200/2056/1627 \\ Car body pitching/yawing/rolling inertia & kg\(\cdot\)m\({}^{2}\) & \(1.88\times 10^{6}\)/\(1.85\times 10^{6}\)/\(1.06\times 10^{5}\) \\ Bogie pitching/yawing/rolling inertia & kg\(\cdot\)m\({}^{2}\) & 7200/6800/3200 \\ Wheellet pitching/yawing/rolling inertia & kg\(\cdot\)m\({}^{2}\) & 120/1210/1210 \\ Lateral/vertical/longitudinal stiffness of primary suspension & MN/m & 31/0.49 \\ Lateral/vertical/longitudinal stiffness of secondary suspension & MN/m & 0.24/0.4/0.24 \\ Lateral/vertical/longitudinal damping of primary suspension & kN-s/m & 0/45/0 \\ Lateral/longitudinal damping of secondary suspension & kN-s/m & 30/50/500 \\ Radius of wheel & m & 0.46 \\ Distance between bogies & m & 17.375 \\ Distance between wheelsets & m & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the CRH3 vehicle.  
Figure 2: The track–bridge model.  
acceleration, velocity, and displacement vectors of the track-bridge model; and \(\mathbf{F}_{t}\) represents the load vector. The parameters of the track-bridge model are listed in Table 2.
## 6 Finite Element Method
### FEM Used to Determine the Field of Strain and Stress in a RUC
\begin{array}{l}\overline{\sigma}_{11}=C_{11}\overline{\epsilon}_{11}+C_{12 }\overline{\epsilon}_{22}+C_{12}\overline{\epsilon}_{33}\\ \overline{\sigma}_{22}=C_{12}\overline{\epsilon}_{11}+C_{22}\overline{\epsilon }_{22}+C_{23}\overline{\epsilon}_{33}\\ \overline{\sigma}_{33}=C_{12}\overline{\epsilon}_{11}+C_{23}\overline{ \epsilon}_{22}+C_{22}\overline{\epsilon}_{33}\\ \overline{\sigma}_{23}=\overline{\tau}_{23}=(C_{11}-C_{23})\overline{\epsilon }_{23}=\frac{1}{2}(C_{11}-C_{23})\overline{\tau}_{23}\\ \overline{\sigma}_{31}=\overline{\tau}_{31}=2C_{44}\overline{\epsilon}_{31}= C_{66}\overline{\tau}_{31}\\ \overline{\sigma}_{12}=\overline{\tau}_{12}=2C_{44}\overline{\epsilon}_{12}= C_{66}\overline{\tau}_{12}\end{array}  \tag{132}\]The last equation of Equation (132) offers the following:  
\[C_{44}=\frac{\overline{\sigma}_{12}}{2\,\overline{\epsilon}_{12}}=\frac{ \overline{\pi}_{12}}{\overline{\tau}_{12}}=G_{12}=G_{13}=G_{1} \tag{133}\]  
which represents the shear modulus in a plane normal to the \(x_{2}x_{3}\) plane. Subtracting the third equation from the second in Equation (132), we have  
\[\overline{\sigma}_{22}-\overline{\sigma}_{33}=(C_{22}-C_{33})(\overline{ \epsilon}_{22}-\overline{\epsilon}_{33}) \tag{134}\]  
which can replace the fourth relation in Equation (132). So, there exists a system of six linear equations, from which four are independent, but they contain five unknowns, and only four of the elastic constants can be determined. The fifth relation can be considered the rule of mixture to compute the longitudinal Young's modulus \(E_{11}\):  
\[E_{11}=E_{f}v_{f}+E_{m}v_{m}. \tag{135}\]  
With \(E_{11}\) thus obtained, one can replace the redundant fourth relation with the following:  
\[E_{11}=C_{11}-\frac{2C_{12}^{2}}{C_{22}+C_{23}}. \tag{136}\]  
Adding the second and third relations in Equation (132), we have  
\[\overline{\sigma}_{22}+\overline{\sigma}_{33}-\frac{2C_{12}\overline{\epsilon }_{11}}{\overline{\epsilon}_{22}+\overline{\epsilon}_{33}}=C_{22}+C_{23}. \tag{137}\]  
From the first relation in Equation (132), we have  
\[C_{11}=\frac{\overline{\sigma}_{11}-C_{12}(\overline{\epsilon}_{22}+\overline {\epsilon}_{33})}{\overline{\epsilon}_{11}}. \tag{138}\]  
Substituting into that of \(E_{11}\), the following is obtained:  
\[E_{11}=\frac{\overline{\sigma}_{11}}{\overline{\epsilon}_{11}}+C_{12}\frac{ \overline{\epsilon}_{22}+\overline{\epsilon}_{33}}{\overline{\epsilon}_{11}} -\frac{2C_{12}^{2}(\overline{\epsilon}_{22}+\overline{\epsilon}_{33})}{( \overline{\sigma}_{22}+\overline{\sigma}_{33}-2C_{12}\overline{\epsilon}_{11} )}. \tag{139}\]  
and it is possible to obtain the coefficient \(C_{12}\). Other methods to compute these coefficients can be found in [232; 233].
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### Classification Results
#### 4.5.2 Test in Inter-patient Scheme  
The second experiment is based on inter-patient scheme, detailed numbers are listed in Table 14.  
Table 15 shows the confusion matrix of the classification results on the testing set in inter-patient scheme. 1371, 1643 and 296 Class N samples are misclassified into other three classes separately. Class N is still the main misclassification destination for other three classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Average(s)** & **SD(s)** \\ \hline \hline Bispectrum & 0.0482 & 0.0004 \\ \hline Bispectrum+2D-GFT & 0.2648 & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computational complexity analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ground Truth**} & \multicolumn{5}{c}{**Classification Results**} \\ \cline{2-5}  & **Class N** & **Class S** & **Class V** & **Class F** & **Total** \\ \hline Class N & 32,287 & 1371 & 1643 & 296 & 34,903 \\ \hline Class S & 63 & 824 & 42 & 8 & 937 \\ \hline Class V & 116 & 87 & 1882 & 3 & 2088 \\ \hline Class F & 28 & 3 & 6 & 369 & 406 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Confusion matrix of ECG heartbeats classification results after bispectrum +2D-GFT on inter-patient scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Proposed Method**} & \multicolumn{2}{c}{**Bispectrum**} & **Bispectrum+2D-GFT** \\ \hline \multirow{3}{*}{Class S} & _Sen_ & 41.0 & 71.5 \\ \cline{2-5}  & _Spe_ & 99.0 & 99.3 \\ \cline{2-5}  & _Ppr_ & 74.3 & 82.6 \\ \hline \multirow{3}{*}{Class V} & _Sen_ & 49.8 & 76.7 \\ \cline{2-5}  & _Spe_ & 98.5 & 99.2 \\ \cline{2-5}  & _Ppr_ & 77.2 & 86.9 \\ \hline \multirow{3}{*}{Class F} & _Sen_ & 19.0 & 46.2 \\ \cline{2-5}  & _Spe_ & 99.5 & 99.9 \\ \cline{1-1} \cline{2-5}  & _Spe_ & 54.6 & 91.0 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Classification results on class S, class V, and class F.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **N** & **S** & **V** & **F** & **Total** \\ \hline Training Set & 33,091 & 1640 & 2161 & 378 & 37,270 \\ Testing Set & 34,903 & 937 & 2088 & 406 & 38,334 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The number of ECG beats used for training and testing sets on inter-patient scheme.  
Results quoted in Table 16 are the comparison of the results conducted by [PERSON] et al. [7], [PERSON] et al. [42], [PERSON] et al. [45] and the proposed method. The overall accuracy is improved to 92.2%, higher than any other related literature. Besides, _Spe_ and _Ppr_ get a remarkable improvement in this experiment. For instance, the specific of S beats is 99.7%, much higher than the methods proposed by [PERSON] et al. [7] and [PERSON] et al. [45], and 5.8% higher than method proposed by [PERSON] et al. [42]. It demonstrates that the proposed method in this paper is a reliable tool in detecting the arrhythmia signals.
## 5 Discussions
### Estimation of the Source-Time Function  
In many studies on a MS location and natural seismic source parameter investigation, the second-order Gaussian wavelet (Ricker wavelet) or simple Gaussian function is often used as the source-time function to generate synthetic waveforms. However, the time domain Gaussian or Ricker function is simply distributed in a symmetrical shape with respect to the main peak, and it has limited fitting performance to asymmetric or more complex waveforms. For the fractional-order Gaussian function proposed by [PERSON] [48], better waveform fitting performance is obtained through searching for the best fractional-order derivate \(u\) and dominant frequency \(w_{0}\) through normalized cross-correlation coefficients between the modeled waveforms and target data waveforms. However, its application is still focused on the monitoring operation with densely distributed stations and highly similar waveforms. The stations in a mine MS monitoring network are usually sparsely distributed and have large waveform variation with wave propagating distances, which result in more complex and dissimilar waveforms. In this paper, we introduced the fractional-order derivative and time scaling factor into a source-time function estimation procedure based on DD-SVD-FD wavelet. This technique fully considers variability and complexity of target-observed waveforms and can better fit the recording waveforms in a mine. The waveform-based grid search location method proposed here is a kind of source scanning algorithm by performing grid search strategy to evaluate the matching between the recording and synthetic waveforms, and the misfit minimization corresponding event location. Such that, an improved source-time function estimation and corresponding forward modeling is important for precisely computing misfit function value for each candidate source grid.
# Parametric Analysis of Tensegrity Plate-Like Structures: Part 1--Qualitative Analysis
## 4 Examples
### Single Modified Quartex Module  
The first considered structure is the single modified Quartex module (Figure 6). The module consists of sixteen elements (\(n=16\)), i.e., four struts and twelve cables, and eight nodes (\(w=8\)). Its shape is based on a regular prism. The projection of the top surface of the modified Quartex module is inscribed into the bottom one, allowing for the easy connection of single units into multi-module structures [13; 72; 73; 74; 23; 24; 25]. The considered module's dimensions allow it to fit into a unit cube. The coordinates of the nodes and the numbers of elements are shown, respectively, in Tables 2 and 3. Figure 6: Geometry of the single modified Quartex module. The module with sixteen degrees of freedom (\(m=16\)) is analysed. The blocked displacements are \(q_{1}\), \(q_{3}\), \(q_{8}\), \(q_{9}\), \(q_{13}\), \(q_{15}\), \(q_{20}\), \(q_{21}\) (Figure 7). As the number of elements and the number of degrees of freedom are equal (\(n=m=16\)), the compatibility matrix \(\textbf{B}(\in\mathbb{R}^{16\times 16})\) is square, and, in turn, this means that the matrices \(\textbf{B}\textbf{B}^{T}\) and \(\textbf{B}^{T}\textbf{B}\) are equal and their eigenvalues are the same:  
\[\textbf{H}=\textbf{L}=diag[2.72\ 2.71\ 2.71\ 2.7\ 1.39\ 1.34\ 1.34\ 1.29\ 1.0 \ 0.96\ 0.96\ 0.88\ 0.23\ 0.1\ 0.1\ 0.0] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Node** & \(\boldsymbol{x}\) & \(\boldsymbol{y}\) & \(\boldsymbol{z}\) \\ \hline
1 & \(a\) & 0 & 0 \\ \hline
2 & 0 & 0.5\(a\) & \(a\) \\ \hline
3 & \(a\) & \(a\) & 0 \\ \hline
4 & 0.5\(a\) & 0 & \(a\) \\ \hline
5 & 0 & \(a\) & 0 \\ \hline
6 & \(a\) & 0.5\(a\) & \(a\) \\ \hline
7 & 0 & 0 & 0 \\ \hline
8 & 0.5\(a\) & \(a\) & \(a\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coordinates of the nodes of the single modified Quartex module. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **No. of** & **No. of First** & **No. of Second** \\  & **Element** & **Node** & **Node** \\ \hline \multirow{4}{*}{struts} & 1 & 1 & 2 \\ \cline{2-4}  & 2 & 3 & 4 \\ \cline{2-4}  & 3 & 5 & 6 \\ \cline{2-4}  & 4 & 7 & 8 \\ \hline \multirow{4}{*}{bottom cables} & 5 & 1 & 3 \\ \cline{2-4}  & 6 & 1 & 7 \\ \cline{2-4}  & 7 & 5 & 7 \\ \cline{2-4}  & 8 & 3 & 5 \\ \hline \multirow{4}{*}{middle cables} & 9 & 3 & 6 \\ \cline{2-4}  & 10 & 1 & 4 \\ \cline{2-4}  & 11 & 5 & 8 \\ \cline{2-4}  & 12 & 2 & 7 \\ \hline \multirow{4}{*}{upper cables} & 13 & 2 & 8 \\ \cline{2-4}  & 14 & 2 & 4 \\ \cline{1-1} \cline{2-4}  & 15 & 4 & 6 \\ \cline{1-1} \cline{2-4}  & 16 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numbers of elements of the single modified Quartex module. There is one zero eigenvalue in both matrices, thus one self-stress state and one mechanism is identified for the single module.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 4 Discussion
### Mean Standard Deviations  
The last graph (Figure 15) shows the mean standard deviations. Such an indicator was used to express in the best way the average quality and reliability of the measurement with a given GNSS receiver. This indicator is calculated from the formula:  
\[MSD\ =\ \sqrt{\frac{\sigma_{\chi}{{}^{2}}+\sigma_{Y}{{}^{2}}+\sigma_{H}{{}^{2}}} {3}} \tag{6}\]  
where:  
\(\sigma_{\chi}\), \(\sigma_{Y}\), \(\sigma_{H}\)--standard deviations of the determined coordinates.  
All receivers had the smallest MSD value at Pt 1 and the highest at Pt 7. These values were 1.0 cm and 4.6 cm for Leica GS18T, 1.5 cm and 7.5 cm for u-blox ZED-F9P + Leica AS10, and 1.1 cm and 25.0 cm for u-blox ZED-F9P + ANN-MB-00, respectively.  
On the chart below (Figure 15) the increasing tendency of the MSD values can be clearly seen. This is a result of measurement difficulties at subsequent stations (more and more obscured horizon, access to less satellites of a given constellation, as well as more reflected signals and interferences). This proves a good choice of successive measurement stations in order to increase the difficulty of measurement.  
Figure 14: Sum of X, Y and H absolute differences [m] on each station point.