esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Sedentary behavior among young women is increasingly associated with adverse metabolic and cardiovascular outcomes. The aim of this randomized controlled trial was to evaluate and compare the effects of three structured group fitness programs on anthropometric parameters, body composition, and lipid profile in overweight young women (N = 111, age 18-25, BMI \(\geq\) 25). Participants were assigned to mix aerobics (E1, n = 27), kickbox aerobics (E2, n = 28), step aerobics (E3, n = 27), or a control group (C, n = 29). Each intervention lasted 12 weeks, with sessions conducted three times per week, each lasting 60 min. The results were analyzed using repeated measures ANOVA. Significant reductions were observed in body weight (\(-\)4.8 kg in E1, \(p<\) 0.01), waist circumference (\(-\)5.3 cm in E1, \(p<\) 0.001), and body fat percentage (\(-\)3.6% in E1, \(p<\) 0.01). High-density lipoprotein (HDL) increased by 7.4 mg/dL (\(p<\) 0.01), while low-density lipoprotein (LDL), total cholesterol, and triglycerides decreased by 12.1 mg/dL, 18.6 mg/dL, and 19.4 mg/dL, respectively (all \(p<\) 0.01). The most pronounced overall improvements were found in the mix aerobics group. In contrast, the control group showed significant deterioration in most variables, including a 2.1 kg weight gain and a 6.3 mg/dL increase in total cholesterol (\(p<\) 0.05). These findings confirm the superior effectiveness of mix aerobics as a non-pharmacological intervention to improve body composition (notably through reductions in body weight, fat percentage, and waist circumference) and cardiovascular biomarkers (such as increased HDL and decreased LDL, total cholesterol, and triglycerides) in young overweight women. Compared to kickboxing and step aerobics, mix aerobics consistently achieved the greatest improvements across all measured parameters, making it the most comprehensive and effective option among the three programs tested.  
fitless; group programs; body composition; lipid profile; young women +
Footnote †: journal: Applied sciences  
Article
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 3 Non-Destructive Testing Methods for High-Speed Train Rails
### Multi-Sensor Fusion Methods  
Multi-sensor data fusion and decision fusion of multiple classifiers both belong to the category of information fusion, which originated from the sonar signal processing system funded by the US Department of Defense in 1973. At present, the level of information fusion can be divided into low level (data level or pixel level), middle level (feature level), and high level (decision level) based on the different levels of fusion objects, which facilitates the description of fusion behavior at all levels according to the fusion task.  
Research on data fusion technology in China started relatively late, and it was not until the late 1980s that reports on multi-sensor data fusion technology began to emerge. Among them, [PERSON] et al. studied multi-source features based on vibration signals in the time domain, frequency domain, and envelope spectrum and used decision fusion methods to construct a wind turbine bearing fault diagnosis model [100]. [PERSON] et al. studied a fault diagnosis method for self-propelled artillery gearboxes based on information fusion [101]. [PERSON] et al. studied the information fusion fault diagnosis method of Bayesian networks [102]. [PERSON] et al. studied a multi-sensor information fusion method for diagnosing vibration faults in aircraft engines [103].  
The theory of data fusion has been widely applied in numerous military and civilian fields, such as industrial control, robotics and intelligent instruments, air traffic control, ocean surveillance and management, and more. In these fields, the fundamental theoretical research of data fusion, the construction of a data fusion theoretical system, and the study and establishment of a basic theoretical framework for multi-sensor integration and information fusion are some of the research directions. In addition, for database management, introducing various artificial intelligence technologies into the field of data fusion, reducing computational complexity in the context of heterogeneous sensors on multiple platforms, and solving the expression and inference operations of uncertain factors are also key areas of future research. Relatively speaking, there are few examples of applying multi-sensor data fusion technology to rail track detection. Some scholars have proposed using machine learning methods to detect wheel crack states using multiple sensors [104], while others have proposed the idea of using multiple sensors to detect obstacles in railways [105]. Finding a detection method for rail cracks is gradually becoming an increasingly urgent need, and this project is based on the multi-sensor measurement of acoustic emission signals to detect rail cracks, which currently has great development prospects.
###### Abstract  
Centimeter-level or better positioning accuracy is needed in engineering surveying applications. When employing the Global Navigation Satellite System (GNSS) in engineering surveying, the high-precision real-time kinematic (RTK) positioning method must be used to achieve such positioning accuracy. Currently, precise point positioning (PPP) cannot reliably achieve the positioning accuracy needed for engineering surveying in real time. The high-precision RTK positioning method needs carrier-phase measurements and a reference station/network. Surveying may not need a GNSS receiver in their organizations/companies to act as the reference station. Continuously Operating Reference Stations (CORS), run by international/national organizations/agencies or private companies such as GNSS receiver manufacturers, let users freely access the raw GNSS measurements or corrections for real-time and post-processing applications. The positioning accuracy of the GNSS rover is affected by the data quality of the reference stations, including virtual reference stations (VRS). The International GNSS Service (IGS) currently provides the number of cycle slips and the L1 and L2 average pseudorange multipath errors per station daily. The US National Geodetic Survey (NGS) provides daily station coordinate residuals. Carrier-phase data quality of the CORS stations is not provided by their organizations/agencies. Nowadays, many CORS stations track multi-GNSS satellites. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality. The proposed approach is demonstrated with case studies on IGS/CORS networks in Hong Kong and Canada/USA. In other words, a strategy to obtain non-linear combined carrier-phase multipath errors and noise is proposed in this work. The data quality of a CORS station depends on the site environment, monument type and height, and GNSS receiver/antenna. An account of the data quality at some selected stations is given; the main focus of the paper is on the proposed data quality assessment approach.  
Continuously Operating Reference Stations (CORS); GNSS data quality; multipath errors; random errors; Hong Kong Satellite Positioning Reference Station Network (SatRef); IGS network 21 February 202329 March 20230 March 20233 April 20233 April 2023 by the authors[PERSON]; [PERSON] A Data Quality Assessment Approach for High-Precision GNSS Continuously Operating Reference Stations (CORS) with Case Studies in Hong Kong and Canada/USA.  
1
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 2 Methodology  
The double-differenced network solution is used in the proposed parallel multi-GNSS ultra-rapid orbit determination. After the data preprocessing, the basic procedure of multi-GNSS ultra-rapid orbit determination mainly includes three steps. First, the double-differenced normal equations are constructed based on un-differenced observation equations. Second, the normal equations are solved after eliminating the redundant parameters. Finally, the predicted orbits are obtained by extrapolation on the basis of initial orbit and model parameters using orbit integration. We optimize the above core processing to achieve higher computational efficiency using a parallel computing method based on MPI/OpenMP. Since the original observational model and stochastic model have not been changed, the approach retains the original correlation of the estimated parameters and the consistency of the ambiguity parameters.
## 3 Numerical Method
### Calculation of \(\mathbf{u}^{(n+1)}\) and \(p^{(n+1)}\)  
To calculate the velocity \(\mathbf{u}^{(n+1)}\) and pressure \(p^{(n+1)}\) fields, we use the incremental projection method by [PERSON] [33], that uncouples the mass conservation and momentum equations, given by Equations (5) and (6), respectively. This method allows one to obtain an intermediate velocity field \(\widetilde{\mathbf{u}}^{n+1}\) from Equation (11). In the HiG-Flow methodology, this Equation (11) can be approximated using an explicit Euler method, Runge-Kutta RK-2 or RK-4, or, the semi-implicit Euler methods, Cranck-Nicolson, and BDF2. One can also choose a spatial discretization orders of 2 or 4. One can use the the convective central schemes or Upwind (order 1), or, schemes of order 2 like the Cubista [34] and Quick [35].  
In this work an Implicit Euler scheme together with a second order spatial approximation and an Upwind Cubista scheme for the convective terms, is used:  
\[\frac{\widetilde{\mathbf{u}}^{(n+1)}-\mathbf{u}^{n}}{\delta t}+\mathbf{u}^{n} \cdot\
abla\mathbf{u}^{n}=-\
abla p^{n}+\frac{1}{Re}\
abla^{2}\widetilde{ \mathbf{u}}^{(n+1)}+\
abla\cdot\mathbf{S}^{n}+\mathbf{F}^{n} \tag{15}\]  
here, \(\delta t\) is the time step, \(n\) represents the known values of velocity, stress, and pressure at instant \(n\), and \(n+1\) represents the new velocity field values (unknown) to be obtained from the solution of the equation. At the inlet, (see Figure 3) we consider a constant velocity profile, \(u(y)=1\) (the stress components are set to 0) and at the outlet, we assume fully developed boundary conditions (Neumann boundary conditions) for the velocity and stress (the pressure is imposed). Finally, at the walls (\(y=0\) and \(y=1\)), we have the empirical no-slip boundary condition (\(u=0\)).  
Using the projection method, it is well known that the velocity field \(\widetilde{\mathbf{u}}^{n+1}\) obtained from Equation (15) may not satisfy the mass conservation equation. Therefore, in order to solve this problem, the equation for the potential \(\psi^{(n+1)}=\delta t(p^{n}-p^{(n+1)})\) is solved,  
\[\
abla^{2}\psi^{(n+1)}=\
abla\cdot\widetilde{\mathbf{u}}^{(n+1)}, \tag{16}\]  
and the Helmholtz-Hodge decomposition (see [31; 32; 36] for more details) is used to correct the previous non-conservative velocity field \(\widetilde{\mathbf{u}}^{(n+1)}\),  
\[\mathbf{u}^{(n+1)}=\widetilde{\mathbf{u}}^{(n+1)}-\
abla\psi^{(n+1)}. \tag{17}\]  
The new velocity field \(\mathbf{u}^{(n+1)}\) satisfies the mass conservation equation. Finally, the pressure is updated \(p^{(n+1)}=p^{n}+\frac{\psi^{(n+1)}}{\delta t}\).  
Figure 3: Dimensionless representation of the channel domain.
# SLR, GRACE and Swarm Gravity Field Determination and Combination
## 2 Materials and Methods
### Combination of Swarm and SLR on the Normal Equation Level
To assess the contribution of either Swarm or SLR to the combined solution, resolution matrices \(\mathbf{R}_{SLR}\) and \(\mathbf{R}_{Swarm}\) were determined from the individual normal equation matrices \(\mathbf{N}_{SLR}\), \(\mathbf{N}_{Swarm}\) and the inverse of the combined matrix \(\mathbf{N}=w_{SLR}\mathbf{N}_{SLR}+w_{Swarm}\mathbf{N}_{Swarm}\) following the approach described in [PERSON] [79]:  
\[\mathbf{R}_{SLR} =\mathbf{N}^{-1}\mathbf{N}_{SLR}, \tag{3}\] \[\mathbf{R}_{Swarm} =\mathbf{N}^{-1}\mathbf{N}_{Swarm}. \tag{4}\]  
The contribution numbers of the individual SHC are found on the main diagonals of the resolution matrices and are presented in triangle plots in Figure 2 for Swarm (left column) and SLR (right column). The contribution to the Sine-coefficients of the spherical harmonic spectrum are shown in the left hand part of the triangle plots, to the Cosine-coefficients in the right hand part, and the contribution to the zonal SHC (order 0) can be found in between. The contribution numbers vary between 0 (no influence) and 1 (determined by 100% from the corresponding observations). In the middle column of Figure 2, the mean contribution per degree is given. Figure 1: Ratios of monthly weights of the Swarm- with respect to the SLR-normal equations, as derived by variance component estimation. In case of a ratio of weights 100:1 (Figure 2, first row), only degree 2 SHC is significantly influenced by SLR, C\({}_{20}\) in fact is dominated by SLR. Applying relative weights of 10:1 (Figure 2, second row), still mainly degree 2 SHC are impacted by SLR. Considering the strength of the SLR-derived temporal gravity variations, the suppression of the contribution of SLR to the other SHC is not justified. A combination based on the weights derived by VCE (Figure 1) therefore is pointless. Decreasing the relative weight of the GPS observations further (Figure 2, rows three and four), the contribution of Swarm is step by step reduced to the sectorial (equal degree and order) and near sectorial SHC, where the sensitivity of GPS observations is strong [70], and to C\({}_{30}\) that in case of SLR is weakly determined due to correlations with other zonal coefficients [68].
## 5 Results and Discussion
### Estimation of Sensor Values
#### 5.1.2. Throttle Estimation
It is further supported by entries in Table 8. From Table 9, it can be noted that RSS, MSE, R\({}^{2}\), and adjusted R\({}^{2}\) values for NLR are much  
Figure 5: Estimated throttle values with PSO, GA, and NLR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **PSO** & **GA** & **NLR** \\ \hline Residual Sum of Squares (RSS) & 2.65 & 3.02 & 0.28 \\ \hline Mean Square Error (MSE) & 0.14 & 0.16 & 0.01 \\ \hline Coefficient of Determination (R\({}^{2}\)) & 0.996 & 0.995 & 0.999 \\ \hline Adjusted R\({}^{2}\) & 0.94 & 0.93 & 0.99 \\ \hline Standard Error of Estimation (SEE) & 0.40 & 0.42 & 0.10 \\ \hline Optimization Time (t) in seconds & 26.63 & 3.03 & 0.097 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Estimation statistics for throttle estimation. better than those for PSO and GA. It can be seen in Table 9 that the R\({}^{2}\) values for all three techniques are the same (0.99), but the adjusted R\({}^{2}\) of NLR is very impressive (0.99) as compared to GA (0.93) and PSO (0.94). PSO and NLR produce nearly the same results. Moreover, the SEE value for NLR is only 0.1 as compared to 0.4 and 0.42 in PSO and GA, respectively. It proves the higher accuracy of NLR over PSO and GA. To further justify the claim of the effectiveness of NLR, the optimization times are also calculated by using MATLAB, and it can be noted from Table 9 that the optimization time of NLR is shorter (0.097 s) as compared to PSO (26.63 s) and GA (3.03 s), which makes the NLR useful for time-sensitive applications and controllers with less computation power. Both PSO and GA produce identical results, but it is worth noticing that PSO consumes much more time as compared to GA. This fact makes GA the second-best option for this optimization, after NLR.
## 4 Applied Algorithm  
To simulate the communication between the sensor nodes and to obtain the results from the underwater channel transmissions data we have studied and simulated the DEADS routing algorithm (Depth and Energy Aware Dominating Set Based Algorithm) [20]. According to [21], there two kinds of localizations: distributed and centralized. The nodes in our study have a randomly distributed localization in underwater environment and they have a predefined transmission range. It is also supposed that at the end of the broadcast phase, all the nodes will have information about the depth and the remaining energy of all their one hop away neighbors. The nodes send information on regular intervals about their remaining energy. As it is shown in Figure 2, it is taken for granted the fact that source nodes are in the greater depth region and the relay and destination nodes are in the smaller depth regions, respectively.  
To avoid flooding and its drawbacks our proposed change in algorithm as in [20], uses a threshold value \(d_{th}\) which limits the outgoing links for every received packet. So, we calculate depth threshold as follows:  
\[d_{th}=(Nd/N)\cdot R \tag{23}\]  
where Nd is the number of alive nodes inside the node's transmission range, N is the total number of nodes in the network and \(R\) is the predetermined range of transmission for every node.  
Thus, our approach minimizes the data propagation delay compared to the case where no depth threshold is used, because data will be transmitted in a shorter range. The performance of the algorithm, as shown in Figure 3, is evaluated and the temperature, salinity and multipath effects are studied. The packet will be received by a node inside the \(d_{th}\) while all the other nodes outside of it will be considered in listening state (they listen to the information but do not receive it) so they will only spend energy in the listening state. The source nodes are selected based on the depth, by their highest remaining energy and the biggest number of neighbors. Then this node is selected in its depth threshold (\(d_{ths}\)), it is checked for the other nodes that have lower depth than the source node, with the longest distance from it and the highest remaining energy, then the node is marked as a relay node. The same procedure applies also to the destination node selection. In every other case, like the cases when the end of network's lifetime is approaching and nodes do not have the necessary energy to communicate, the standby energy is subtracted from every node's remaining energy.  
Figure 2: The position of nodes in the underwater network.
## 2 Modeling and the Proposed Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Cube Target Microwave Simulation Micro-Doppler Signal STFT Parameters**} \\ \hline Size & 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m & Wavelength & 0.03 m \\ Spin rate & 2 Hz & Sampling Rate & 200 kHz \\ Attitude angle & 45\({}^{\circ}\) & Frequency resolution & 100 Hz \\ Window function & Hanning & Time resolution & 0.01 s \\ Window length & 2000 points & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cube target microwave simulation micro-Doppler signal STFT parameters. Figure 1: Spin motion of cube-shaped target. (**a**) Geometry of radar and rotating target. (**b**) Rotations by Euler angles (\(\phi,\theta,\varphi\)). When the size of the target is already known, the algorithm of the pose measurement and motion estimation of the targets is mainly divided into the following steps:  
1. The received signal is collected by the laser radar. 2. The 2D TF image is obtained by using STFT. 3. Pretreatmentment of the 2D TF image is performed. 4. The micro-motion period is obtained by the 2D Fourier transform of the 2D TF image. Furthermore, the spin rate can be precisely acquired. 5. The pose measurement is obtained according to the relationship between attitude angle, maximum Doppler frequency shift, spin rate, size of the target, and the wavelength of the transmitted laser. The estimation of the pose feature depends on the precision of the maximum Doppler frequency shift and the spin rate. When the size of the target is unknown, the algorithm of the pose measurement and motion estimation of the target is slightly different. In Step 5, the two pose measurements are performed by extracting the edges of the two TF images. Finally, the size of the target is acquired based on the relationship between the target size and the attitude angle. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Cube Target Laser Micro-Doppler Signal STFT Simulation Parameters**} \\ \hline Size & 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m & Wavelength & 1.064 m \\ Spin rate & 2 Hz & Sampling Rate & 200 MHz \\ Attitude angle & 45\({}^{\circ}\) & Frequency resolution & 100 kHz \\ Window function & Hanning & Time resolution & 1 ms \\ Window length & 2 \(\times\) 10\({}^{4}\) points & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cube target laser micro-Doppler signal STFT simulation parameters. Figure 3: Time\(-\)frequency diagram of laser micro\(-\)Doppler echo signal of cube target. Figure 2: Time\(-\)frequency diagram of microwave micro\(-\)Doppler echo signal of cube target.
## 6 Simulation Results
### Reliability Properties  
The reliability and robustness are significant properties in the communication networks. We further compare the performance of PM-DQN with that of DQN, Q-routing and FP-Q-routing to evaluate the Packet Loss Ratio (PLR) and estimation error. Figure 10(a) shows the trend of PLR as a function of episodes in the network with 10 SU nodes. PLR declines as the learning process goes on, and Q-routing achieves the lowest PLR, followed by FP-Q-routing. PM-DQN based scheme holds a slight advantage over natural DQN. The reason for this is the same as it in Figure 8(a). Comparison of PLR in the network with 50 SU nodes is shown in Figure 10(b). PLR of Q-routing and FP-Q-routing is much lower than two DQN based schemes in the beginning, and after about 200 episodes PLR of PM-DQN based scheme achieves the least and keeps the advantage till the end. While after around 300 episodes, PLR of DQN based scheme is lower than Q-routing and FP-Q-routing. This is due to the  
Figure 10: Expected power consumption ratio vs. the number of episodes.  
Figure 9: Expected end-to-end delay vs. the number of episodes.  
fact that, since at first the end-to-end delay of Q-routing and FP-Q-routing falls faster than it of two DQN based algorithms as shown in Figure 8(b), the total number of episodes whose routing latency exceeds the threshold is less than it of DQN based methods so that its PLR is relatively low. However, the delay of Q-routing and FP-Q-routing drops slightly after that and therefore its PLR is higher than it of the other two schemes.  
In the learning process the agent may select some invalid actions such as non-neighbor nodes or unavailable channels. Estimation error denotes the proportion of steps choosing invalid actions in the total time steps. It is observed from Figure 11(a),b that PM-DQN based routing scheme achieves the least estimation error in the network with either 10 or 50 SU nodes. The primary reason for this is that the invalid actions result in very low target Q value (we set it to -100 in the simulation) corresponding to relatively large TD-error. In PM-DQN, the prioritized memories preserve most of these transitions so that the probability of selecting the invalid actions reduces sharply even in the small scale network. In Figure 11(a), we can see that estimation error of Q-routing and FP-Q-routing is much lower than it of DQN and is almost the same as PM-DQN method. However, estimation error of Q-routing and FP-Q-routing is significantly high in network with 50 nodes as shown in Figure 11(b). Network with larger state space results in more invalid actions. In Q-routing and FP-Q-routing, the current parameters determine the next data sample, which could make the parameters get stuck in a poor local minimum in large state space. This is what leads to the trend of the curves.  
Figure 11: Packet loss ratio vs. the number of episodes.  
Figure 12: Estimation error vs. the number of episodes.