esa-sceva/satcom-chunk-collection huggingface dataset:
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm
#### 3.3.2 Step 2--Coarse Time Synchronization  
Before starting the demodulation process, it is mandatory to be time synchronized at the beginning of the frame to avoid ISI. Nevertheless, thanks to the differential process, the DCSS modulation is more robust than CSS to time synchronization errors. Therefore, a time alignment that ensures a predominant cardinal sine in each FFT is sufficient to achieve accurate decoding performance. Based on this feature, we propose, in this step, to coarsely estimate \(n_{s}\), the frame beginning. Indeed, after estimating \(\hat{K}\) and considering the distribution of \(\frac{\Delta T}{T_{s}}\) in the set \([-\frac{M}{2},\frac{M}{2})\), the signal's beginning instant \(n_{s}\) will be in the range of \([a\times M,b\times M]\) with \(a=\hat{K}-\frac{1}{2}\) and \(b=\hat{K}+\frac{1}{2}\).  
Here, it should be noted that the maximum possible FFT magnitude is obtained in a perfect time alignment. Otherwise, the energy of the main peak will span over several bins and two cardinal sines may appear. Thus, the principle of our coarse time synchronization method is to search for the starting index that maximizes the magnitude of all FFTs in the preamble detected in step 1. Thereby, the function that we propose to maximize can be written as follows:  
\[H(\omega)=\sum_{p=\omega}^{\omega+N_{p}+N_{\omega}-1}max_{k}(|Y[k,p]|),\ \omega\in[a,b] \tag{26}\]  
To guarantee a symmetric property between \(H(a)\) and \(H(b)\) (i.e., in a perfect time synchronization, no peaks would appear in the FFT before the preamble and in the one after the _sync word_), the down-chirp of the SFD is inserted before the beginning of the payload. Moreover, a silence period can be considered instead of the SFD. However, the use of latter SFD is mandatory since it is also used in the estimation of \(K\) as presented in the previous paragraph. Indeed, this SFD can be seen as a guard interval since up- and down-chirps are orthogonal. Finally, \(\hat{n}_{s}\) the estimate of \(n_{s}\), is obtained by searching the index that maximizes the function \(H(\omega)\) as explained in the pseudo-code of Algorithm 1.  
```
Input \(\psi\), \(H\), \(a\), \(b\) begin \(I\leftarrow(b-a)\) \(N_{ii}=log_{2}(\frac{b-a)M}{\psi})\) for\(i\gets 1\)to \(N_{ii}\)do if\(H(a)>H(b)\)then \(b\gets b-\frac{1}{2}\). \(\hat{n}_{s}\gets a\times M\) else \(a\gets a+\frac{1}{2}\). \(\hat{n}_{s}\gets b\times M\). \(\hat{l}\leftarrow\frac{1}{2}\)
```  
**Algorithm 1**Proposed estimation of \(n_{s}\).  
To reduce the computational complexity of this step, we suggest implementing the maximum research by dichotomy. Furthermore, to estimate the start index of the received frame, we have to set the maximum permissible error of the Algorithm 1 to \(\psi=1\) sample, which gives a number of iterations \(N_{it}=log_{2}(M)=SF\).  
If we note \(r(n)\), the coarse time synchronized signal, we have:  
\[r(n)=y(n+\hat{n}_{s}),\ n\in I_{1} \tag{27}\]  
with \(I_{1}=\{0,\ldots,(N_{p}+N_{sw}+1+N_{s}-1)M-1\}\).
## 2 Configuration Design and Problem Formulation
### Problem Formulation
These two methods are equivalent, but in order to make the three-dimensional motion of HAUV more general [26], the former is selected as the state variable of the system-\(\alpha\) as the intermediate control variable. **Assumption 1**. : _All state variables of the system can be obtained for feedback._  
The speed \(v\) of an aircraft can be controlled to vary within a very small range, which can be seen as a fixed speed. For notational simplification, let \(x_{1}=\gamma,\ x_{2}=\alpha,\ x_{3}=q\), and define control input \(u=\delta_{c}\). Considering the various factors that interfere with the process of diving into the water, the uncertain parts of water and other disturbances are added to each state variable to simulate the actual process as much as possible. The strict-feedback form is as follows:\[\left\{ \begin{array}{l}\dot{x_{1}}=f_{1}(x_{1})+h_{1}x_{2}+\Delta_{1}(x,t)\\ \dot{x_{2}}=f_{2}(x_{1},x_{2})+h_{2}x_{3}+\Delta_{2}(x,t)\\ \dot{x_{3}}=f_{3}(x_{2},x_{3})+h_{3}u+\Delta_{3}(x,t)\end{array} \right. \tag{2}\]  
where \(\Delta_{i}\) represent the disturbance terms, which satisfy \(|\Delta_{i}|\leq\rho_{i}\), \(\rho_{i}>0,i=1,2,3\), \(h_{1}\), \(h_{3}\), \(f_{1}(x_{1})\), \(f_{2}(x_{1},x_{2})\), \(f_{3}(x_{2},x_{3})\) are unknown system parameters and functions to be designed, with the formulation as listed in Appendix A. Equation (2) is a form of backstepping applied, and the exact form is not desired for the controller design, so the disturbance terms can replace the interference during the water-entry process. All unknown parameters are bounded. \(f_{1}(x_{1}),f_{2}(x_{1},x_{2}),f_{3}(x_{2},x_{3})\) are all unknown continuous function. As for \(h_{1},h_{3}\), there are positive numbers \(h_{1m},h_{1M},h_{3m},h_{3M}\) satisfying \(h_{im}\leq h_{i}\leq h_{iM},i=1,3\). The research goal of this paper is to design an adaptive neural-network-based dynamic surface controller, so that the flight-path angle \(x_{1}\) in the model can better track the expected \(x_{1d}\). However, in the process of actual plunge-diving into water, changes in aerodynamic parameters due to sudden changes in medium, wing sweepback angle, etc., are unable to obtain accurately. Hence, neural networks are considered for approximation. \(x_{1d}\) is bounded and both first and second derivatives exist and satisfy \(x_{1d}^{2}~{}+~{}x_{1d}^{2}~{}\cdots\). \(+~{}x_{1d}^{2}\leq\Theta\), where \(\Theta\) is any positive real number.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 6 Conclusions  
This study presents a U-Net-based architecture for robust fetal R-peak detection in low-SNR FECG signals, addressing the critical limitation of conventional algorithms in noisy environments. The proposed method demonstrates high-performance metrics and exhibits excellent robustness by minimizing FP and FN across most recordings. Notably, impressive results are achieved on fetal ECG signals from the original A&D fetal ECG database, with a PPV of 100.00%, an SEN of 99.84%, and an F1-score of 99.92%. Similarly, on the original FECGSYN database, the method achieves a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94%.  
Figure 8: Impact of matching window tolerance on fetal R-peak detection performance. Evaluation metrics are assessed across **(a)** the A&D FECGDB database (AD_0) and **(b)** FECGSYN database (SYN_12) at tolerances of 50 ms, 30 ms, and 20 ms. For each method, scores at 50 ms (top of bar) and 20 ms (bottom of bar) are explicitly labeled, with the highest-performing method highlighted in bold and underlined.  
The robustness of the U-Net model is further evaluated using real datasets with SNRs of 0 dB, 5 dB, and 10 dB, as well as synthetic datasets with SNRs of 6 dB, 9 dB, and 12 dB. Additionally, the performance of the proposed method is compared with ten existing peak detection methods, revealing that U-Net outperforms all of them even in low SNR scenarios (0 dB and 6 dB). Further analyzing the impact of matching window tolerance, U-Net's consistent performance under tightened tolerances (20 ms) underscores its detection precision.  
The proposed U-Net method effectively solves the problem of inaccurate detection of traditional peak detection algorithms in low SNR fetal electrocardiogram signals. Its strong robustness and high accuracy highlight its potential as a reliable solution for R-peak detection in low SNR fetal electrocardiogram signals.  
Methodology, validation, and original draft preparation, P.Z.; review and editing and supervision, S.S. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The research data in this work are sourced from a public database provided by PhysioNet, available at [[https://physionet.org/about/database](https://physionet.org/about/database)]([https://physionet.org/about/database](https://physionet.org/about/database)) [accessed on 28 July 2025].  
This article is a revised and expanded version of a paper entitled \"Using U-Nets for Accurate R-Peak Detection in Fetal ECG Recordings\" [45], which was presented at the Computer Science & Information Technology conference (2024).  
The authors declare no conflicts of interest.
## 1 Introduction  
The interaction of intense laser pulses with solids has recently attracted considerable interest. This is largely due to its potential application for accelerating charged particles [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Tumor therapy with accelerated ion beams (see e.g., [23; 24; 25; 26; 27; 28]) would in particular benefit from the replacement of conventional accelerators by all-optical devices, which may become compact and inexpensive in future, allowing this form of cancer therapy to be accessible for more patients.  
In this article, we demonstrate the feasibility of generating ion beams by shining an appropriately chirped short laser pulse on a target. At sufficiently high laser intensities the electrons are quickly ionized, and the ions get directly accelerated by the laser field. Modulating the frequency of the pulse leads to efficient particle energy gain from the field, as it was shown before [29; 30; 31; 32]. Here we consider the case of short chirped pulses, when the time duration of the pulse is comparable to a single cycle. We found that acceleration by such short pulses may be more efficient than by long chirped pulses, _i.e._, the same final ion kinetic energies can be reached with a lower pulse energy.  
The acceleration scheme is shown in Figure 1. We assume a solid-density or underdense target consisting of carbon ions, typically, bare nuclei, which can be generated via ionization by an intense pre-pulse. Collective plasma effects become important when the target thickness \(L\) in propagation direction of the laser is longer than the wave length of the induced plasma wave, _i.e._, \(\lambda_{p}=2\pi c/\omega_{p}\) with plasma frequency \(\omega_{p}^{2}=4\pi n_{e}e^{2}/m_{e}\). In the last formula, \(n_{e}\) is the electron density, \(e\) is the electron charge and \(m_{e}\) is the mass of an electron. When interacting with the accelerating pulse, first the lighter electrons are accelerated; they are pushed in the forward direction, as shown previously in [29]. The electrons are followed by the carbon ions, which are directly accelerated by the chirped pulse.
## 2 Computational Model
### Erosion Model  
Modeling of erosion in complex geometries such as the case with the current work becomes complicated. This is because erosion is a compound function of particle parameters (size, roughness, shape, etc.), local flow characteristics, and complex turbulent fields along with multiphase effects (e.g., erosion shield due to solid accumulation and the restraining development produced by the liquid film, cavitation) along with the influence of local cavities due to material removal and properties of wall material.  
Referring to [PERSON] [34], erosion rates are a function of particle velocity (\(V_{p}\)) and impact angle (\(\gamma\)) and with reference to this they suggested a relationship that is given by Equation (8).  
\[E=kV_{p}^{n}\,f(\gamma) \tag{8}\]  
The term \(E\) denotes dimensionless mass value removed by the erosion phenomenon, \(V_{p}\) is the particle's velocity, and \(f(\gamma)\) is a function of the particle's impact angle. At the same time, the value of \(n\) for different metals ranges from 2.3 to 2.5. It is impossible to formulate a universal erosion model under various conditions, and there is always a need for experimental studies to improve the model's parameters. For the current work, [PERSON]'s [35] model was adopted with \(n=2\). Based on [PERSON]'s work, Equation (8) could be written as given below.  
\[E=kV_{p}^{2}\,f(\gamma) \tag{9}\]  
In Equation (9)  
\[\begin{array}{c}f(\gamma)=\frac{1}{3}\cos^{2}\gamma\;if\,\tan\gamma>\frac{1 }{3}\\ f(\gamma)=\sin 2\gamma-3\sin^{2}\gamma\;if\,\tan\gamma\leq\frac{1}{3}\end{array} \tag{10}\]
## 7 Conclusions  
In this paper, a Q-learning and fuzzy logic-based hierarchical routing approach (QFHR) was suggested for VANETs. In the first step, a traffic identification algorithm was presented so that each RSU obtains information about the traffic conditions of four roads connected to its intersection. Next, each RSU uses this traffic information to design the Q-learning-based routing algorithm to discover the most suitable path between different intersections. In the last step, the routing algorithm at the road level was introduced. It is a greedy routing algorithm that uses fuzzy logic to recover routes and solve the local optimum problem. QFHR was implemented using NS2. Then, the results were compared with IRQ, IV2 XQ, QGrid, and GPSR in two scenarios. The first scenario analyzes the result based on the packet sending rate (PSR). In this scenario, QFHR increases PDR by 2.74%, 6.67%, 22.35%, and 29.98% and reduces delay by 16.19%, 22.82%, 34.15%, and 59.51%, and lowers the number of hops by 6.74%, 20.09%, 2.68%, and 12.22% compared with IRQ, IV2 XQ, QGrid, and GPSR, respectively. However, it increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2 XQ, respectively. Additionally, the second scenario evaluates the results with regard to the signal transmission radius (STR). In this scenario, QFHR increases PDR by 3.45%, 8%, 23.29%, and 26.17% and decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% and reduces the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively. However, it has a higher overhead than IRQ (11.26%) and IV2 XQ (25%). These results show that QFHR has a good performance in terms of PDR, delay, and the number of hops. However, the proposed method has a greater routing overhead than IRQ and IV2 XQ. In future research directions, our focus is on reducing the routing overhead in QFHR. This can be achieved in two schemes: (1) Providing a clustering technique to increase scalability and reduce routing  
Figure 16: Routing overhead in different approaches based on PSR.  
Figure 17: Routing overhead in different approaches based on STR.  
overhead and (2) adjusting the hello broadcast interval dynamically in accordance with road traffic conditions.  
Conceptualization, M.S.Y., E.Y.; methodology, M.S.Y., E.Y., M.H.; validation, A.M.R., R.A.N., O.H.A.; investigation, A.M.R., R.A.N., K.S.; resources, A.M.R., O.H.A.; writing--original draft preparation, M.S.Y., E.Y., K.S.; supervision, M.H.; project administration, M.H., K.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Xiamen University Malaysia Research Fund (Grant Number: XMUMRF/2022-C9/IECE/0033).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
This research reports on the development of a model to measure the value of a given traffic strategy for freight truck traffic through the perspective of multiple involved agencies. The model was designed as an Analytical Hierarchy Process (AHP) model, wherein each perspective was broken down into sub-criteria in subsequent tiers. These sub-criteria would then be compared to each other to determine the overall weights of each perspective in evaluating different traffic control strategies. The model was created to determine the value of four specific perspectives: the Economic, Safety, Environmental, and Mobility Perspectives of a given traffic control strategy. These perspectives were tested through the surveying of 24 experts within the state of Florida involved in the field of transportation, who were consulted to determine the weights of each perspective and its criteria using pairwise comparisons. Once completed, the model was then tested to evaluate the overall priority of three alternatives: one in which no strategy was employed, one in which a strategy was employed at a speed of 55 mph, and one in which it was employed at 75 mph. The results showed that safety was selected as the most important perspective among all the consulted experts. Since the strategy of implementation at 55 mph was deemed the safest, it was calculated as the most sustainable alternative, despite its lower speed and efficiency lowering the economic and mobility priorities. This supports the implementation of the AHP model in upper-level decision making, as it allows management to determine the best decision that will placate all lower-level parties, preventing conflict and increasing overall workplace efficiency.  
analytical hierarchy process (AHP); perspective; economic; safety; mobility; criterion +
Footnote †: journal: Applied Sciences
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 6 Conclusions  
This paper studies the design, fabrication, and corresponding algorithms to compensate for the temperature drift in a new HGMA. The accelerometer structure uses a beam structure. The simulation results show that the resonant frequency of HGMA in the 1 st order mode is 408 kHz. The 2 nd mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, so the coupling movement between the two modes is small, and HGMA has better linearity. For the temperature compensation of HGMA, four algorithms are proposed: RBF NN, RBF NN + GA, RBF NN + GA + KF and the RBF NN + GA + KF method compensated by the temperature drift model. First, the temperature drift accuracy is improved by the RBF neural network. Second, GA is used to optimize the parameters of the RBF NN. Next, the Kalman filter algorithm is used to optimize the output value, and then the temperature drift model is used to compensate RBF NN + GA + KF. Finally, a new fusion algorithm of RBF NN + GA + KF combined with the temperature drift model is proposed. The fusion algorithm can effectively compensate the temperature drift of the HGMA. At last, the Allan variance coefficient shows a comparison of the three methods with the original data. The data shows that if the RBF NN + GA + KF method combined with the temperature drift model is used, acceleration random walking is from 17130 g/h/Hz\({}^{0.5}\) to 765.3 g/h/Hz\({}^{0.5}\), and bias stability is from 4720 g/h to 57.27 g/h.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data, and [PERSON] wrote the paper.  
This work is supported by National Natural Science Foundation of China (No. 51705477, No. 61703098 and No.61603353), and Pre-Research Field Foundation of Equipment Development Department of China No.61405170104. The research is also supported by a program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi, Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province, Shanxi Province Science Foundation for Youths (No. 201801D221195), Young Academic Leaders of North University of China (No. QX201809), the Open Fund of State Key Laboratory of Deep Buried Target Damage No. DXMBJ]2017-15, and the Fund for Shanxi \"1331 Project\" Key Subjects Construction.  
The authors declare no conflicts of interest.
# A Theory of Slope Shear Scouring and the Failure Mechanism of PFC3D on a Gangue Slope
## 3 Results
### Comparison between Theory and Simulation  
After the PFC simulation calculations, the final gully failure pattern was obtained. The model angle was adjusted and the failure form of the coal gangue slope near Panzhou Electric Field in Guizhou Province was compared and observed, as shown in Figure 12. It can be seen from the figure that the shape of gully no. 5 simulated by the PFC is similar to the on-site failure shape of the slope, and the failure conditions of the main gully and the tributary gully are basically consistent. We achieved the preliminary aim of a PFC simulation. In order to further verify the rationality of the model and the theory, based on the rainfall scouring shear failure theory previously developed by our team, two types of scouring intensity factors were considered in depth, and the scouring depth optimization theory was further developed in combination with the characteristics of the PFC time effect calculations. Through a field survey, the data of one point every 10 m were input into Formula (22) to obtain the failure depth of each point after rainfall scouring and we compared it with the site failure depth and PFC simulation depth, as shown in Figure 13. Figure 14 shows the final failure critical line under three scouring failure conditions. Combining the two figures, we can see that the depth curve trends of the PFC calculation, the optimization theoretical calculations, and the actual data are consistent. The maximum damage point of gully no. 5 is located within the 30-50 m area in the middle of the slope, and the actual damage depth can reach 12 m. However, the peak depth calculated via the PFC and the optimization theory is relatively large and the curves are relatively close to each other. It can be seen from the profile that the scouring has formed a three-stage ladder effect, which is concentrated at the top of the slope, the middle of the slope, and the bottom of the slope (0-25 m, 25-65 m, and 65-110 m). This step effect fully verifies the intermittent characteristics of the erosion failure of the coal gangue slope. Since the diversion and fault phenomena of rainfall scouring are concentrated at the position of 30-60 m in the middle of the slope, the erosion pit here is the deepest. Finally, it can be seen that the depth calculated via the depth optimization scouring theory, the gully depth under the PFC simulation, and the actual failure depth have a good consistency, which
## 2 Algorithm
### Svm
\(a_{i}\) is a Lagrange multiplier for each training sample, the sample is the support vector for which \(a_{i}\
eq 0\), lying on one of the two hyper-planes:\((w\cdot\mathbf{x}^{+})+b=+1\); \((w\cdot\mathbf{x}^{-})+b=-1\). According to the Karush-Kuhn-Tucker (KKT) condition, the optimization problem must satisfy the last one in Equation (5) [33]. When dealing with the nonlinear SVM problem, SVM introduces a kernel function to map the data and make data into a high dimensional space:  
\[\underset{\boldsymbol{\alpha}}{\text{max}}\ \sum_{i=1}^{N}\alpha_{i}-\frac{1}{2} \sum_{i=1}^{N}\sum_{j=1}^{N}y_{i}y_{j}\alpha_{i}\alpha_{j}K(\mathbf{x}_{i}, \mathbf{x}_{j}). \tag{6}\]  
The \(K(\mathbf{x}_{i},\mathbf{x}_{j})\) is the kernel function [34]. \(K(\mathbf{x}_{i},\mathbf{x}_{j})=\varphi(\mathbf{x}_{i})\cdot\varphi( \mathbf{x}_{j}\). When the training algorithm appears \(\varphi(\mathbf{x}_{i})\cdot\varphi(\mathbf{x}_{j})\), we can use the \(K(\mathbf{x}_{i},\mathbf{x}_{j})\) instead. The constraint is the same as Equation (4), and \(0\leq\alpha_{i}\leq C\). SVM is based on the study of the kernel. SVM is a kernel-based learning algorithm, the key to the algorithm is the kernel function, so the kernel function has an important position, and the selection of kernel function affects the generalization ability of the model directly. Commonly used kernel functions are polynomial kernel functions, the RBF function, etc. The Gaussian Radial Basis Function (RBF) is one of the most popular kernel functions, and the RBF kernel function is accounted for in the nonlinear problems in this paper:  
\[K(\mathbf{x}_{i},\mathbf{x}_{j})=\exp\{-g\big{\|}\mathbf{x}_{j}-\mathbf{x}_ {i}\big{\|}^{2}\}, \tag{7}\]  
where \(g\) is the kernel parameter to measure the width of kernel function in RBF. If \(g\) is inappropriate, the results of the algorithm may outfit or overfit the training data. This function is robust and can account for the nonlinear decision boundary. Based on the above analysis, the basic idea of SVM is that, when classifying the nonlinear samples, the samples of the original space are mapped to a high-dimensional feature space by a nonlinear mapping, so that the space becomes linear and then linearly classifies in the high-dimensional space, so as to realize the relative original space nonlinear algorithm.