esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault-Tolerance Control Strategy  
The fault-tolerant control technique is designed to ensure the safe driving of unmanned fully line-controlled distributed drive electric vehicles when the steering motor fails during track tracking. Figure 12 depicts the line-controlled steering control strategy diagram.  
As displayed in Figure 12, the fault detection module monitors the status of the line-controlled steering system of an unmanned fully line-controlled distributed drive electric vehicle in real time.  
1. When there is no fault in the steering by wire, the vehicle is driven normally, and the cornering angle value of the vehicle is solved by the track following controller, and the solved steering angle is input to the steering controller established in MATLAB/Simulink. The steering controller transmits the target torque to the steering motor through calculation, and then the steering motor is input to the simulated vehicle model in CarSim 2019.
2. When there is a fault in the steering by wired control, the fault detection module transmits the number and position information of the faulty motor to the fault-tolerant decision module. If the single motor fails, the fault-tolerant decision will control the differential drive block to control the car's yaw torque, so that the unmanned fully line-controlled distributed drive electric vehicle can maintain stability during the track following process until the vehicle stops. If there is multi-motor failure, the vehicle's steering-by-line system will fail more seriously, which will cause a particularly serious accident if the trajectory tracking continues, so the vehicle will then need to execute emergency braking to make the vehicle stop in the shortest time to ensure the vehicle is secure.
## 2 Materials and Methods
### Calculations of Optical Waveguides with EIM
However, the slab-likeness will vary for each configuration of waveguide materials and cross-sections. The simulation accuracy depends on the direction of polarization of the electric field. In order to determine the polarization fraction of radiation along the transverse axes, Equation (4) was used [19]:  
\[\gamma_{TE}=\frac{\iint\left|e_{x}\right|^{2}dxdy}{\iint\left(\left|e_{x}\right|^ {2}+\left|e_{y}\right|^{2}\right)dxdy}. \tag{4}\]  
A finite-difference frequency-domain (FDFD) method was chosen as the reference method for the effective indexes and the mode polarization fraction calculation since it solves the full-vector wave equation without any approximation, except for the finite difference partial derivative approximation. The results obtained with EIM were compared with the results obtained with FDFD and the relative errors of the effective indexes of modes were calculated. The FDFD calculations were carried out using Difra Lab software, which allows the simulation of diagonally anisotropic waveguides. The size of the calculation window was 4.1 \(\upmu\)m in width and 3.6 \(\upmu\)m in height; a uniform grid with 300 cells for each axis was used for the simulation. The refractive indexes for LN and silicon oxide (SiO\({}_{2}\)) were calculated using the dispersion equations:  
\[ \begin{array}{l}n_{LiNbO_{3}}^{o}=\sqrt{1+\frac{2.6734\lambda^{2}}{\lambda^{ 2}-0.018}+\frac{1.229\lambda^{2}}{\lambda^{2}-0.059}+\frac{12.614\lambda^{2}} {\lambda^{2}-4.64}},\\ n_{LiNbO_{3}}^{e}=\sqrt{1+\frac{2.9804\lambda^{2}}{\lambda^{2}-0.0205}+\frac{ 0.5981\lambda^{2}}{\lambda^{2}-0.066}+\frac{8.9543\lambda^{2}}{\lambda^{2}-4. 608}},\\ n_{SiO_{2}}=\sqrt{1+\frac{0.6966\lambda^{2}}{\lambda^{2}-0.0684^{2}}+\frac{ 0.0407942\lambda^{2}}{\lambda^{2}-0.1162\lambda^{2}}+\frac{8.9543\lambda^{2}}{ \lambda^{2}-9.8962}},\end{array}  \tag{5}\]  
where \(n_{LiNbO_{3}}^{o}\) and \(n_{LiNbO_{3}}^{e}\) are the ordinary and extraordinary refractive indexes of LN, respectively, and \(n_{SiO_{2}}\) is the refractive index of silicon oxide [34]. The simulation was carried out for a wavelength range from 1.5 \(\upmu\)m to 1.6 \(\upmu\)m.
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 2 Design Mechanism  
This section describes the design mechanism based on three antennas [7]. They are single-, discrete double-, and connected double-loop antennas, which are shown in the insets of Figure 2a, Figure 2b, and Figure 2c, respectively. Each antenna has a single feed at a corner \(F\) with the same perimeter \(4L\) and spacing \(S\) at the same height \(H\) above the ground plane. The antenna was analyzed using our computer program that was developed based on the moment method [10] at a test frequency of \(f_{0}=3\) GHz.  
The discrete and connected double-loop antennas have double and wideband characteristics, as shown in Figure 2b,c. This fact means that the wideband can be attributed to the double bands. In other words, double bands merge to form a wideband once two loops are connected, which is the design mechanism of this study. Note that the wideband in [7] is 3% when using loops with a perturbation segment (a stub of the loop side). In contrast, the wideband in this study reaches 31% in Section 5 when using loops with quasi-two sources.
## 4 Experiment and Results
### BIMCoder Validation
#### 4.2.1 Exact Match Validation on Fuzzy Query Dataset  
Based on the results presented in Table 8 and Figure 4, the performance of each model was further evaluated on colloquial and fuzzy queries, where the characteristics of the queries tend to be more informal and complex.  
ERNIE 3.0 maintained strong performance, outperforming other models in most cases. It demonstrated significant advantages in categories such as T6, T7, T8, T9, and T11. Even when confronted with the most complex fuzzy query, ERNIE consistently achieved high precision in matching and Execution Accuracy.  
The performance of the SQLCoder on fuzzy queries was less stable than its performance on precise queries. Notably, its precision matching rate and Execution Accuracy decreased significantly in categories such as T5, T7, and T10.  
Figure 4: Comparison of Exact Matching Performance on Fuzzy Queries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Query Type** & **BIMCoder** & **ERNIE3.0** & **Llama-13B** & **SQLCoder** \\ \hline T1 & 92\% & 100\% & 46\% & 65\% \\ T2 & 90\% & 95\% & 100\% & 50\% \\ T3 & 79\% & 95\% & 0\% & 60\% \\ T4 & 91\% & 63\% & 100\% & 45\  
Llama continued to exhibit substantial variability in performance on fuzzy queries. Many categories showed very low accurate matching, underscoring the model's limitations in handling informal or complex natural language queries.  
The proposed BIMCoder model, which combines ERNIE 3.0's semantic understanding capabilities with SQLCoder's structural language generation strengths, demonstrated exceptional performance on fuzzy queries. It achieved higher accurate matching than any individual model across almost all categories. In particular, the fusion model significantly improved success rates in categories such as T5, T7, T8, T9.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 3 LuTan-1 Interferometric Experiment
### Wind Field Data  
Wind fields are the primary driving force of upper ocean dynamics and are closely linked to various oceanographic parameters, including sea surface current velocity. In this study, wind field data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. This dataset represents the institution's fifth-generation reanalysis, providing comprehensive global wind speed data. We extracted the due-east u10 and due-north wind field components at a height of 10 m above the study sea area [30]. The ERA5 dataset used in this study had a temporal resolution of 1 h and a spatial resolution of 0.25\({}^{\circ}\) Lat/Lon. Figure 5a shows the wind field data corresponding to the study region.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 3 ACF and PSD Derivation Verification of OFDM Navigation Signal Modulation System
### ACF of OFDM Navigation Signal  
Starting from the definition of the autocorrelation function, the autocorrelation function \(R_{p}(t,\tau)\) of the OFDM navigation signal can be obtained using:  
\[\begin{array}{ll}R_{p}(t,\tau)&=E(s_{p}(t)\overline{s_{p}(t-\tau)})\\ &=E\Big{[}(x_{u}(t)+x_{g}(t))(\overline{x_{u}(t-\tau)+x_{g}(t-\tau)})\Big{]}, \\ &=R_{u}(t,\tau)+R_{g}(t,\tau)+R_{ug}(t,\tau)+R_{gu}(t,\tau)\end{array} \tag{5}\]  
where \(E(t)\) is the expected value of the autocorrelation function, \(\tau\) is the delay of the received signal and the local signal, \(R_{u}(t,\tau)\) is the autocorrelation function of the OFDM symbol, \(R_{g}(t,\tau)\) is the autocorrelation function of the protection interval, \(R_{ug}(t,\tau)\) is the cross-correlation function of the OFDM symbol and the protection interval, and \(R_{gu}(t,\tau)\) is the cross-correlation function of the protection interval and OFDM symbol.  
As can be seen from Equation (5), the autocorrelation function of the OFDM navigation signal can be decomposed into the autocorrelation function of the OFDM symbol, ACF of the protection interval, and cross-correlation function of the OFDM and protection interval.  
The ACF of the OFDM symbol can be expressed as:  
\[\begin{array}{ll}R_{u}(t,\tau)&=E\Big{[}x_{u}(t)\overline{x_{u}(t-\tau)} \Big{]}\\ &=E\Bigg{[}\frac{1}{K^{2}}\sum\limits_{m,m=0}^{N-1}\sum\limits_{k,l=0}^{K-1}c _{n,k}\mathbb{g  
where * is convolution function and \(\Gamma_{K}(\tau)=\sum\limits_{k=0}^{K-1}e^{\beta 2\pi k\Delta f/\tau}\).  
The zero-padding mode is a special cyclic prefix mode without the loss of generality. In the following calculation, we first calculate in the cyclic prefix mode, where the zero-padding mode can thus be easily obtained according to the derivation process of the cyclic prefix mode. According to the above method, you can find \(R_{g}(\tau)\), \(R_{ug}(\tau)\), and \(R_{gu}(\tau)\) in turn.  
\[R_{g}(\tau)=\frac{\Gamma_{K}(\tau)}{K^{2}T_{s}}\mathsf{g}_{2}(\tau)*\overline{ \mathsf{g}_{2}(-\tau)}, \tag{10}\]  
\[R_{ug}(\tau)=\frac{\Psi_{K}(\tau)}{K^{2}T_{s}}\mathsf{g}_{1}(\tau-T_{g})* \overline{\mathsf{g}_{2}(-\tau)}, \tag{11}\]  
\[R_{gu}(\tau)=\frac{\Phi_{K}(\tau)}{K^{2}T_{s}}\mathsf{g}_{2}(\tau)*\overline{ \mathsf{g}_{1}(-\tau-T_{g})}, \tag{12}\]  
where \(\Gamma_{K}(\tau)=\sum\limits_{k=0}^{K-1}e^{\beta 2\pi k\Delta f/\tau}\), \(\Psi_{K}(\tau)=\sum\limits_{k=0}^{K-1}e^{\beta 2\pi k\Delta f/(\tau+T_{s}-T_{s})}\), and \(\Phi_{K}(\tau)=\sum\limits_{k=0}^{K-1}e^{\beta 2\pi k\Delta f/(\tau+T_{s}-T_{g})}\).  
Combining the above formula, the average ACF \(R_{p}(\tau)\) of the correlation function \(R_{p}(t,\tau)\) is obtained:  
\[R_{p}(\tau)=R_{u}(\tau)+R_{ug}(\tau)+R_{gu}(\tau)+R_{g}(\tau), \tag{13}\]
## 1 Introduction
We evaluate the impact of the HSR based on the mobile phone data recorded at an HSR station one year before and after its opening. Mobile phone data provide information, such as home location and all spatial locations of mobile phone users, as well as their gender and age group, regardless of whether they make a phone call. This information is used in the impact analysis of the HSR in terms of the volume of mobile phone users, geographical expansion of access areas, and urban demographic dispersion. In our study, we investigate the Honam Korea Train Express (KTX) line introduced on April 2, 2015. We focus on three cities--Iksan, Jeongup, and Mokpo--which are located at the south approximately 200 km, 250 km, and 330 km, respectively, away from Seoul. We geographically explore the impact of influence area with respect to the distance from the capital city. We extracted all mobile phone data recorded at the three Honam HSR stations for a one-week period in March 2015 and March 2016 for a before-and-after study. We perform basic statistical analysis of mobile phone data in terms of the gender and age group to investigate the volume changes and test whether the volume of age group by gender changes statistically. We then calculate the access distance for each HSR station based on the home (of the phone users) and HSR station locations. We then compare the geographical distribution of access locations. Further, we compare hotspot changes and compactness of hotspots, distance from station, to investigate how the HSR changes the hotspots in a city and attracts people near the stations. This study will be useful as it shows the potential use of mobile phone data in conducting the impact study of HSRs.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 4 NSGA-III Algorithm
### Conception Introduction
#### 4.1.2 Reference Point Setting  
The NSGA-III algorithm is applicable to the problem with a large number of objective functions. In the multi-dimensional space formed by the objectives, the reference point is introduced to preserve the non-dominated individuals in the population that are close to the reference point so as to maintain the diversity of the population.  
In order to preserve the diversity of the population, [PERSON] [PERSON] et al. [39] and [PERSON] et al. [40] proposed a method to set reference points on the standard plane. They divided each component of the standard plane in the n-dimensional space into P segments and selected reference points on the standard plane at equal intervals. In the optimization problem with \(M\) objective functions, the number of reference points (\(|REF|\)) set by this method is:  
\[|REF|=\binom{M+p-1}{p}, \tag{36}\]  
We take the three-dimensional space with three objectives as an example, as shown in Figure 5:  
Figure 4: Pareto level.  
In the multi-objective optimization problem with three objectives (\(M=3\)), the yellow triangle is a standard plane, and the analytic representation is:  
\[x+y+z=1,x,y,z\in[0,1] \tag{37}\]  
The coordinates of the reference points meet the following requirements:  
\[x+y+z=1,x,y,z\in\left\{\frac{0}{p},\frac{1}{p},\frac{2}{p},\cdots,\frac{p}{p}\right\} \tag{38}\]  
After each sub-vector of the standard plane is divided into six segments (\(p=6\)), the reference points are shown as the green points in the figure. The number is:  
\[|REF|=\binom{3+6-1}{6}=28 \tag{39}\]  
\(p=6\), and the coordinates of the reference points are:  
\[x+y+z=1,x,y,z\in\left\{\frac{0}{6},\frac{1}{6},\frac{2}{6},\cdots,\frac{6}{6}\right\} \tag{40}\]
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 2 Data
### Discriminant Parameters and Sample Dataset
The values of the above 22 parameters of the 1600 events were calculated with the help of the built-in software of the IMS system after the P-wave arrival picking and S-wave arrival picking performed manually on the recorded events, and their distribution is shown in Figure 3. The correlation coefficients between 22 seismic parameters are shown in Figure 4. Among them, the correlation coefficients between \(E_{0}\) and \(E_{s}\), \(M_{0}\) and \(P\), \(M_{0}\) and \(P_{s}\), \(M_{0}\) and \(P_{p}\), \(P\) and \(P_{s}\), \(P\) and \(P_{p}\) are 0.99, 1, 0.96, 0.95, 0.96 and 0.95, respectively, which are greater than the threshold value (0.85) suggested by [PERSON] et al. [34]. Maintaining one of the two or more parameters with a high correlation coefficient can reduce the amount of calculation and avoid overfitting. So, \(E_{0}\), \(P\), \(P_{s}\), and \(P_{p}\) were discarded, and the remaining 18 seismic parameters were adopted as the final characteristic parameters for discrimination in this paper. In addition, many researchers [35; 36; 37] suggest that the ratio of training data to testing data can be 8:2. Additionally, in this paper, 80% of the sample dataset was randomly allocated to the training set and the remaining 20% to the testing set. Figure 4: Heatmap of the correlation coefficients between 22 seismic parameters. Figure 3: Violin plots of the distribution of the 22 seismic parameters in the sample dataset. The box in each plot indicates the lower (Q1) and upper (Q3 ) quartiles, and the red circle indicates the median value. The value of Q3 minus Q1 is defined as the interquartile range (IQR ). The range of whiskers at both ends of the box represents the upper and lower bounds, which are defined as Q3 \(+\) 1.5 * IQR and Q1 \(-\) 1.5 * IQR, respectively. Cyan represents the distribution of data points.
## 3 Results and Discussion
### Validation of Parameters and Constitutive Models  
The fatigue life of the fractured granite specimen is defined as the number of cycles it undergoes before fatigue failure. The relationship between the irreversible strain and the degree of fatigue completion (the ratio of the current number of cycles to the fatigue life) is shown in Figure 14. The irreversible strain of the specimen gradually increases after various freeze-thaw cycles. This development can be divided into three stages as follows: the initial stage, the constant velocity stage, and the accelerated stage. The constant velocity stage lasts the longest, but the accumulated strain in this stage is only three-quarters of the total.  
The parameters \(m\) and \(F_{0}\) of the constitutive model, calculated from Equations (27) and (28), are shown in Figure 15. After various freeze-thaw cycles, the model parameters exhibit significant changes in the early stages and tend to stabilize later. However, as the number of freeze-thaw cycles increases, the range of these parameters gradually decreases. This indicates that the parameters of the fatigue constitutive model reflect the impact of freeze-thaw cycles on rock damage.  
Figure 16 shows the measured and theoretical values of the first fatigue load, 40% fatigue life, and 100% fatigue life of the double-fracture granite specimen under uniaxial fatigue load after various freeze-thaw cycles. The established fatigue constitutive model accurately reproduces the test results at various stages. As the number of cyclic loads increases, the range of hysteretic cycles in the stress-strain curves gradually increases. The model also reflects the strength state and structural effects of fractured rocks under cyclic  
Figure 14: Relationship between irreversible strain and the number of freeze–thaw cycles.  
loads, fully proving the validity of the defined damage variables, model parameters, and constitutive model.  
Figure 16: Theoretical fitting curve and experimental data. **(a)** F-T 0, first fatigue loading. **(b)** F-T 0, 40% fatigue loading. **(c)** F-T 0, 100% fatigue loading. **(d)** F-T 40, first fatigue loading. **(e)** F-T 40, 40% fatigue loading. **(f)** F-T 40, 100% fatigue loading. **(g)** F-T 80, first fatigue loading. **(h)** F-T 80, 40% fatigue loading. **(i)** F-T 80, 100% fatigue loading.  
Figure 15: Relationship between irreversible strain and model parameters \(m\) and \(F_{0}\).
## 3 Characteristics of the Waveforms
### Contrasts between the Different Tracking Packages  
The trackers on the satellites had adopted different tracking algorithms for the various targets. For example, in the ERS-1, two different models (a seawater model and a sea ice model) were used. An SMLE was used for the seawater model, and an OCOG was used for the no-oceanic surfaces [22]. The HY-2A/B provided the data for the two different tracking packages as they also used two different methods.  
Figure 5a,b show the typical waveforms of the quasi-specular surfaces. The two different tracking algorithms had both maintained the waveform characteristics of the quasi-specular surfaces. It was observed that the maximum value of the OCOG was larger than that of the SMLE. Meanwhile, the maximum value of the C band was larger than that of the Ku Band.  
As can be seen in Figure 6, the tracking results of the two tracking algorithms were similar. Both had maintained the waveform characteristics of the diffuse surfaces, and only minimal differences were observed between the SMLE and OCOG. However, the trailing edge slopes of the Ku Band were found to be greater than those of the C Band.
# The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
## 6 Conclusions  
In the study of existing positioning algorithms, a simple substitution of measured distances is often used in the estimation of the target node's position. In actual positioning, the ranging error model is more complicated, and it is often related to the actual ranging distance, as the experiments using SX1280 exemplified in this paper. Since the squared value of distance measurement is not an unbiased estimator of the squared value of the actual distance, when using the traditional multilateral positioning algorithm to solve the target node, directly substituting the distance measurement data often deviates from reality. Based on this, this paper adds the correction of the squared value of biased ranging to the existing positioning algorithm, making it an unbiased estimator. Both theoretical simulation experiments and actual experiments using SX1280 show that using an unbiased estimator can improve the positioning accuracy. It can also be concluded from the theoretical data that the improvement of the algorithm is more obvious when the error variance is larger. Theoretically, the method in this paper is suitable for the positioning algorithm involving the square value of the ranging, which is a further improvement of the accuracy of the existing algorithm.  
Writing--original draft preparation, Y.Y., S.S., A.C., S.Y., Y.S. and D.S.; writing--review and editing, Z.L. and D.S. All authors have read and agreed to the published version of the manuscript.  
Science and Technology Development Program of Jilin Province: 20200401089 GX.  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Proposed Distributed Bi-Behaviors Crow Search Algorithm
### The Distributed Bi-Behaviours Crow Search algorithm (DB-CSA)
#### 3.2.2 The Second Variant: DB-CSA for DMOPs, DB-CSA-II  
The second variant (DB-CSA-II) of the proposed DB-CSA algorithm has the same optimization steps as the first variant (DB-CSA) and the main difference is in its investigating the dynamic optimization process to manage the time-varying change that occurs when solving DMOPs. The dynamic handling mechanism starts with the extraction of the population of crowds of both the current iteration (\(t\)) and previous iteration (\(t-1\)), denoted, respectively, as _POS(\(t\))_ and _POS(\(t-1\)_). Then, the objective values are compared using the Pareto dominance operator [80]. Pareto dominance is a useful mechanism in multi-objective optimization to compare the evolution and deterioration of two solutions \(Solution_{p}\) (\(t\)) in _POS(\(t\))_ and \(Solution_{q}\) (\(t-1\)) from _POS(\(t-1\)_) based on their objective function vectors: _F_(\(Solution_{p}\) (\(t\))) = \(f_{1}\) (\(t\)),...,\(f_{M}\) (\(t\))_ and _F_(\(Solution_{q}\) (\(t-1\))) = \(f_{1}\) (\(t-1\)),...,\(f_{M}\) (\(t-1\)). During the optimization process, solution \(Solution_{p}\) (\(t\)) dominates \(Solution_{q}\) (\(t-1\)) if both dominance conditions are verified:  
* Check dominance condition (2): the \(Solution_{p}(t)\) is strictly better than \(Solution_{q}(t-1)\), _F_(\(Solution_{p}(t))>F(Solution_{q}(t-1))\) for at least one objective.
* Check the dynamic change: if the current solution \(Solution_{p}(t)\) dominates the previous \(Solution_{q}(t-1)\) based on both conditions (1) and (2), so the dynamic change is successfully detected. The next step aims to effectively react to the dynamic change and to enhance all deterioration in the time-varying search space.
* React to the dynamic change: all non-dominated \(Solution_{p}(t)\) are considered to create the next population at iteration (\(t+1\)), and all deteriorated solutions \(Solution_{p}(t)\) are randomly re-initialized. Then, all non-dominated solutions in the archive (A) are updated. This process aims to enhance the convergence and diversity of rows in a dynamic search space. The general flowchart of the second version of DB-CSA algorithm is shown in Figure 2.