esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 2 Calculation of 2D Rayleigh Wave Phase and Z/H-Ratio Kernel
### D Full-Wave Sensitivity Kernels of the Traveltime Equation  
First, we briefly review the travel-time and amplitude-sensitivity kernels for single-component seismograms. Following the full-wave finite-frequency theory, the perturbations in the overall misfit \(\delta\chi\) are linearly related to the relative perturbations in the density (\(\delta ln\,\rho\)), P-wave velocity (\(\delta ln\,\,\alpha\)), and S-wave velocity (\(\delta ln\,\,\beta\)) :  
\[\delta\chi=\int\big{[}K_{a}(\mathbf{r})\delta ln\alpha+K_{\beta}(\mathbf{r})\delta ln \beta+K_{\rho}(\mathbf{r})\delta ln\rho\big{]}d^{2}x, \tag{1}\]  
In general, two primary measurements are used in adjoint tomography: the phase delay and the amplitude reduction. For the phase-delay measurements, the travel-time misfit perturbations \((\delta t)\) can be represented as follows:  
\[\delta t=-\frac{\int_{t_{1}}^{t_{2}}\partial\overline{s}(t)\delta s(t)dt}{ \int_{t_{1}}^{t_{2}}|\overline{s}(t)|^{2}dt}=\int\Big{[}K_{a}^{p}(\mathbf{r}) \delta ln\alpha+K_{\beta}^{p}(\mathbf{r})\delta ln\beta\,\,+K_{\rho}^{p}(\mathbf{r}) ln\rho\Big{]}d^{2}x, \tag{2}\]  
where \(\delta t\) is the travel-time anomaly; \(\delta s(t)\) is the perturbation of the waveform displacement, \(\overline{s}(t)-s(t)\); the tilde represents the synthetics based on the reference model; and t\({}_{1}\) and t\({}_{2}\) are the minimum and maximum values of the time window, respectively. \(\partial\) is the time derivative, so \(\delta t\) is equal to the lag time at the maximum of the cross-correlogram between the observed and synthetic waveform. \(K_{a}^{p},\,K_{\beta}^{p}\), and \(K_{\rho}^{p}\) are the travel-time Frechet kernels for the density, P-wave velocity, and S-wave velocity, respectively, which can be calculated using the adjoint-state method. The adjoint source of the cross-correlation travel-time is:  
\[f_{p}^{\dagger}(t,\omega)=-\frac{w(t,\omega)\dot{s}(t,\omega)}{\int w(t, \omega)\dot{s}^{2}(t,\omega)dt}\, \tag{3}\]  
where \(w(t,\omega)\) is the time window over which the measurement of the cross-correlation traveltime is made, s \((t,\omega)\) is the waveform recorded by a station, the dot represents the time derivative, and \(\omega\) is the central frequency of the Gaussian filter.
## 4 Machine Learning-Based Residual Error Compensator
### DI Dataset Construction Scheme  
As shown in Figure 2, the traditional ETE dataset \(\Phi_{ETE}\) contains reference input data \(\{u_{1},\ldots,u_{t},\ldots u_{T}\}\) and the corresponding residual error, where \(u_{t}\) includes the reference position, velocity, and acceleration of each joint and the robot EE at time \(t\). However, \(\Phi_{ETE}\) involves fewer nonlinear dynamic features of robots, which limits the performance of neural networks. Since the PDM describes the primary dynamic characters, the generated simulation data \(\Phi_{sim}=\{s_{1},\ldots,s_{t},\ldots s_{T}\}\) involve the motion knowledge that satisfy the dynamic constraints, where \(s_{t}\) denotes the simulated position, velocity, acceleration, and torque at time \(t\). Hence, \(\Phi_{sim}\) is proposed to integrate \(\Phi_{ETE}\) to construct the DI training dataset \(\Phi_{DI}\).  
Some latent features, \(\Phi_{lat}\), in reference trajectories are obviously related to the actual interpolation accuracy of robots, which also affects the prediction of the PDM. The curvature of the reference path is one of the critical factors. At the corner with large curvature, the contour error is always larger than the others. And the corresponding simulation results from the PDM might have bigger deviation. Hence, the curvature features and the corresponding velocity should be extracted to further enhance \(\Phi_{DI}\). In this study, the normal acceleration of the robot EE integrating curvature and velocity is calculated as \(\Phi_{lat}\) and added to \(\Phi_{DI}\). In addition, the direction and value of some physical parameters, such as velocity and acceleration, need to be considered simultaneously in the training dataset.  
Through the aforementioned analysis, \(\Phi_{DI}\) can be expressed as follows, and the detailed data categories are listed in Table 2.  
\[\Phi_{DI}=\{\Phi_{ETE},\Phi_{sim},\Phi_{lat}\} \tag{19}\]  
However, not all the data in \(\Phi_{DI}\) are valuable for model training. Hence, further extraction of \(\Phi_{DI}\) is required to increase the convergence, where the correlation analysis between \(\Phi_{ETE}\), \(\Phi_{sim}\), \(\Phi_{lat}\), and the residual error is performed in this study. To obtain the linear and nonlinear correlations, the distance correlation coefficient is adopted. It has a value range between 0 and 1. And a bigger value produces a stronger correlation. The distance correlation coefficient \(dCor(A,B)\) between random variables A and B can be given as follows:  
\[dCor(A,B)=\frac{dCov(A,B)}{\sqrt{dVar(A)dVar(B)}} \tag{20}\]  
where \(dCov(A,B)\) is the distance covariance and \(dVar(A)\) and \(dVar(B)\) are the distance variances of A and B, respectively. According to the correlation analysis and training  
Figure 11: Structure of LSTM with multi-memory units.  
experiments, the acceleration data including the last five items in Table 2 have little effect on prediction improvement, which should be removed from \(\Phi_{DI}\).
## 2 Materials and Methods
### Random Forest (RF)  
RF is an ensemble algorithm proposed by [PERSON] (1976). RF consists of multiple classification and regression trees (CART), and the average of these tree prediction results is the final prediction result. RF model has various advantages: (1) it can handle very high-dimensional data (that is, data with numerous features) and need no feature selection; (2) it can detect the interaction and importance of input factors; (3) it can be easily parallelized because trees are independent of each other during training; and, (4) it has high accuracy and can obtain good accuracy for the missing data problem.  
In this study, a k-fold testing approach was applied to assess the performances of XGBoost and RF models. The complete data set is divided into 5 parts, with 4 parts for training and 1 part for testing. Further, the importance of the input factors of each predictor is calculated by the XGBoost model. The gain represents the fractional contribution of each factor to the model based on the total gain of this factor's splits. A higher percentage means a more important predictive feature.
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 4 Experimental and Analysis of Results  
In this section, it is assumed that there is a single interference source in the metro environment, and the interference type is either single-tone interference, pulse signal, or chirp signal. The performance of the mitigation algorithm based on KL decomposition, and STFT and WPCT algorithms is compared under these three types of interference. The test environment shown in Figure 6 was set up to test and analyze the effectiveness of the interference mitigation algorithm. The interference signal injection is mainly composed of a communication positioning integrated base station and interference signal generator. The communication positioning fusion base station is mainly composed of a communication base station, a positioning base station, and a clock distributor. The communication base station is responsible for generating 5G communication signals, and the positioning base station generates the positioning signals mentioned above. The clock distributor provides reference clock signals and second pulses for communication base stations, positioning base stations, and signal sources. The interference signal generator is based on the Agilent N5181A signal generator, and the clock signal input comes from the time distributor. The interference signal is injected into the communication positioning fusion signal through the combiner, and the RF signal collector is used to transmit the data to the software receiver for analysis. The experimental parameters are configured as shown in Table 1. The 5G communication signal adopts 64-QAM modulation, the number of sub-carriers is configured to be 1200, and the signal power is 18 dB higher than that of the positioning signal. The positioning signal adopts weil code with a length of 10,230 chips, the code rate is 10.23 MHz, the center frequency of the communication positioning fusion signal is 3575 MHz, and the sampling rate of the positioning receiver is 50 MHz.  
We define the mean squared error (MSE) as a measure of the performance of the interference mitigation algorithm for CPIS [28]. As the MSE decreases, the signal quality improves. After performing the interference mitigation algorithm, the MSE of the positioning signal is estimated at the receiver output. It is calculated as follows.  
\[\text{MSE}=\text{E}\Big{\{}(\hat{\mathcal{Y}}(t)-y(t))^{2}\Big{\}} \tag{14}\]  
where \(\text{E}\{\cdot\}\) is average operation. \(y(t)\) is CPIS without interference. \(\hat{\mathcal{Y}}(t)\) is the signal after interference stripping.
## 1 Introduction  
At present, GNSS receivers with a single antenna are threatened by a serious jamming environment where many GNSS receiver failures occurred [1; 2; 3], limiting the GNSS applications. As a result, the techniques used to detect and mitigate interference effects have become an increasingly important issue and can be divided into the automatic gain control (AGC) method [4], digital signal processing methods [5; 6; 7; 8; 9] and receiver methods [10; 11] from GNSS signal processing chains.  
The AGC acting as an adaptive variable gain amplifier adjusts the input signal level to the analog-to-digital converter (ADC) input range [10; 12]. Therefore, the AGC gain variation can be utilized to detect interference, especially continuous wave interference (CWI) and pulsed interference (PI). However, there are not enough effective quantized bits for ADC to realize the gain variation range of AGC for a common GNSS receiver.  
From the GNSS receiver, the position accuracy and effective carrier to noise density ratio are commonly adopted to assess the impact of interference on GNSS receivers [13; 14]. However, both of them rely on a particular GNSS receiver performance. If the jamming power surpasses the spread spectrum gain of GNSS causing the GNSS receiver not to work, the corresponding algorithm will fail.  
For the digital signal processing methods, they include spatial domain methods [5], spatial-temporal domain methods [15; 16; 17], time domain methods [6], frequency domain methods [7], and time-frequency (TF) domain methods [8; 9]. Time domain techniques as well as frequency domain techniques cannot completely describe the nature of time-varying signals.  
Spatial domain techniques [5] and spatial-temporal domain methods assume that the GNSS signal angle of arrival (AOA) and the antenna model are known for the receiver. The antenna model is sensitive to AOA estimation error and can cause a high computational complexity. In addition, an antenna array is required, which is difficult to install on small devices.  
TF domain methods adopt the classical TF analysis including short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and RSPWVD [8], which illustrate that jamming signals usually focus their energy in a finite area of the TF plane, while noise power extends over the whole TF plane [9]. However, these TF methods cannot deal with the time-varying interferences with crossed frequency by the peak detection method [18]. In this paper, a joint method combining Hough transform and RSPWVD is depicted to detect chirp interferences with crossed frequency for GNSS receivers with a single antenna. The analytic expression of initial frequency estimation and chirp rate estimation is presented and the double threshold detection is proposed as well. In addition, the effect of sweep period on the estimation of initial frequency and chirp rate has been analyzed.
## 4 Results and Discussion
### Key Dielectric Parameters  
The key dielectric parameters have been investigated according to the above-mentioned standards and test conditions (Table 1) at Standard conditions B given by IEC 60212:2010. From the probability plots (Figure 12) is visible that the measured values show a low level of variability. There are also noted major statistical parameters, like average value (\(\upmu\)), standard deviation (\(\sigma\)) and coefficient of variation (\(\theta\)).  
For the comparison with the study of [PERSON] [28] is necessary also included minimum measured values (maximum value for dissipation factor). These values are shown in Table 2. From this comparison, it is clear that the synthesized material (marked A) exceeds in all cases commonly used composite materials contains mica paper (PET reinforced mica composite marked B, Glass reinforced mica composite marked C). It confirms the appropriate selection of individual components of the synthesized nanostructured micaless microcomposite.  
There is visible that the minimal dielectric strength of film reinforced composites (A, B) is relatively equal, but higher for synthesized nanostructured micaless microcomposite. It is caused by the compact character of the reinforced component, which fulfills the role of a dielectric barrier next  
Figure 11: Partial discharges measurement circuit according to IEC 60270 Std.  
to the reinforcing [59]. The positive effect of a polymer film on dielectric properties is also mentioned by the authors of the studies [60; 61].  
Other parameters are influenced by the internal structure of the composite, where primarily calcined mica paper contributes to dielectric losses by its strongly inhomogeneous structure, even though it is composed of smaller platelets than non-calcined mica paper [62]. By introducing the MgO into epoxy resin, the different energy levels are achieved. It resulting in the differences in dielectric losses and volume resistivity.
## 5 Conclusions  
An FD model of a CRTS II slab track and a DE model of a subgrade were established. DEM-FDM coupled simulations were realized by adopting interface elements for data interaction. The effects of differential subgrade settlement on the deformation of the slab track and contact forces of gravel grains in the subgrade were studied. The following conclusions can be drawn.  
1. The slab track has a smaller deformation amplitude and larger deformation wavelength compared to the subgrade. The contact force of the gravel grains near the boundaries of the settlement section is relatively large, which easily causes the plastic settlement of the subgrade and expansion of the settlement section.
2. The deformation wavelength of the slab track increases with an increase in the settlement wavelength of the subgrade. The slab track structure is in a suspended state when the settlement wavelength is short. When the settlement wavelength is 20 m and settlement amplitude is 10 mm, the maximum contact force can reach approximately 2.5 times that in the unsettled condition.
3. The deformation amplitude of the slab track increases nonlinearly with an increase in the settlement amplitude of the subgrade. The maximum contact force of the gravel grains increases significantly, even if the settlement amplitude of the subgrade is small.
4. The angular and faulting types of settlements cause severe deformation of the track structure. There is a clear gap between the concrete base and subgrade under a faulting type settlement, which is harmful to the service state of the railway line. Therefore, the faulting type settlement should be strictly controlled during maintenance.  
However, the DEM-FDM coupled model in this paper still needs validation by more tests for practical applications. In the future, the effect of the contact force of the gravel grains on the service life and maintenance of the subgrade will be further studied. The effect of external weather on the deformation of the track will also be investigated.  
Figure 15: Growth rates of contact force under other types of settlement.  
**Author Contributions:** Funding acquisition, B.D.; investigation, X.C.; methodology, R.Z. and B.D.; validation, G.G. and H.L.; visualization, R.Z.; writing--original draft, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Development Program of China Railway Corporation under Grant P2018G005-2, Research Project of Beijing-Shanghai High Speed Railway Co., Ltd. under Grant 2020-15, and Shenzhen Science and Technology Innovation Commission under Grant 827-000468.  
The authors declare no conflict of interest.
## 5 Potential 6G Applications in the Maritime Sector
### Emergency Signal Delivery and Latency-Critical Event Response  
In maritime settings, emergency events, such as equipment failure, fire, collision risk, or crew medical emergencies, demand instantaneous and reliable communication with nearby vessels, rescue authorities, or port infrastructure [79]. Sixth-generation supports ultra-low-latency, AI-prioritized network slicing, ensuring that emergency signals are delivered even during peak traffic or satellite congestion. Additionally, ISAC can detect abnormal patterns (e.g., abrupt vessel tilting temperature surges) and trigger autonomous distress alerts with precise location and context-aware metadata.
## 4 Discussion
### MOGP Modelling and Assessment  
We evaluated several MOGP kernels as well as the SOGP SM approaches aiming to select the best one for the spatiotemporal reconstruction of VWC maps. We presented a set of error metrics in Tables 2 and 3 for each MOGP kernel, but finally, we adopted the NRMSE for cross-model comparison. The poor capability of SOGP to reconstruct VWC in the absence of optical data can be easily observed for winter wheat ROI-1 and ROI-2. All latent values are remarkably underestimated, therefore, the true VWC profiles are entirely lost (see Figure 5). In the SOGP approach, the spectral mixture kernels are trained independently on each channel, which fails to account for the cross-spectral similarity between S1 & S2 time series. While it is true that other MOGP kernels such as CSM and SM-LMC reached an acceptably high performance for the 2020 winter wheat site, the CONV model was the only one able to tackle the VWC time series reconstruction for both averaged ROIs (see Figure 7). CONV makes use of the convolution theorem to model cross-channel dependencies in the S1 & S2 datasets by means of cross-convolution in the spectral domain. The cross-spectral similarity of the S1 & S2 time series, in this regard (see Section 4.1), is the backbone of the spectral mixture approach (see Figure 6). In terms of S1 & S2 dataset configuration, we assessed two or three-channel fusion models. Regarding the CONV model, superior results were found for both winter wheat averaged ROIs (see Tables 2 and 3) when the S1 & S2 dataset is fused with S1 RVI orbit 68 instead of S1 RVI orbit 141. We obtained \(NRMSE_{(S2\&S1,orbit168)}=14.97\%\) and \(NRMSE_{(S2\&S1,orbit141)}=20.05\%\) for winter wheat 2020 ROI-1 and a two-channel dataset. In the same way, \(NRMSE_{(S2\&S1,orbit168)}=14.61\%\) and \(NRMSE_{(S2\&S1,orbit141)}=15.68\%\) for ROI-2. When it comes to three-channel S1 & S2 datasets, the following scenarios wereencountered: (i) the accuracy of the CONV kernel increases for ROI-2 (wheat 2021), \(NRMSE_{(52651,orbits684141)}=10.1\%\) and; (ii) a slight accuracy loss is reflected by the CONV model over the ROI-1 (wheat 2020), \(NRMSE_{(52651,orbits68414)}=16.1\%\). These results are directly connected to the fact that the number of S2 cloud-free acquisitions over ROI-1 and ROI-2 is different. Furthermore, they are unevenly spaced, so the S2 GP VWC time series have different frequency spectra in each case.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Data Pre-Processing  
The European Space Agency (ESA) has developed the open-source software-SNAP (version: 8.0.0, [[https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1)]([https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1)), accessed on 2 September 2021)--for Sentinel satellite data, which also provides the Python interface (SNAPPY) for secondary development. In order to batch process the 455 scenes of Sentinel-1 SAR data, we developed a processing module (SARProcMod) based on SNAPPY and Python to process all the data, which supports products of type SLC and GRD. Figure 6 shows the flowchart of how to pre-process the Sentinel-1 SAR data. Finally, the 10 m-resolution Sentinel-1 SAR dataset was produced for water extraction.  
Figure 5: The flowchart showing the overall methods used in this study.  
Figure 6: The flowchart for the SARProcMod for Sentinel-1 SAR pre-processing engine.  
The workflow included nine steps. First, thermal noise removal was applied to all Sentinel-1 SAR data. Second, slice assembly was performed on the two consecutive slices. Third, the orbit files were applied to all Sentinel-1 SAR data to update the orbit state vectors in the abstract metadata. Fourth, radiometric calibration was applied to all Sentinel-1 SAR data to output radar backscatter bands (\(\sigma^{0}\)). Fifth, a Refined Lee filter was utilized to remove speckle noises. Sixth, multi-looking was applied to make the pixels square. Seventh, radar backscatter bands were orthorectified using the Range Doppler Terrain Correction algorithm with SRTM DEM data (with a spatial resolution of 1 s). Eighth, the backscatter coefficient (in dB) was acquired from the orthorectified radar backscatter band by the equation \(10\times\log_{10}\left(\sigma^{0}\right)\). Ninth, all the Sentinel-1 SAR data were clipped according to the extent of the study area. All the clipped images were processed by thresholding off values higher than 99.9% and lower than 0.01% from the histogram to remove the pixels with abnormal \(\sigma^{0}\). Then, the images were scaled to [0, 1] according the equation below:  
\[Image_{new}=\frac{Image_{ori}-minV}{maxV-minV} \tag{1}\]  
where the \(Image_{ori}\) and \(Image_{new}\) refer to the SAR images before and after the scale and \(maxV\) and \(minV\) refer to values corresponding to the 99.99% value and 0.01% value in each polarization channel for the image.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 2 Problem Formulation and System Modeling
### System Modeling  
The established integrated attitude measurement system consists of a vision module and inertial module as displayed in Figure 2. In the inertial module, the built-in gyroscope of the IMU is used for output angular velocity. Due to the inconsistent sampling frequency between the inertial and visual data, the multi-frequency problem exists in data fusion of the integrated attitude measurement system. Without visual data, the observation noise will trend towards infinity and the Kalman gain will approach zero. It can be easily seen that the integrated attitude system is forced into a pure inertial mode when vision data are unavailable, which may result in divergence during the intersampling of slow visual data. To enhance the fusion performance, a data fusion methodology based on CKF and RBF is developed. When the visual data are available, CKF is employed to fuse the inertial data and visual data. At the same time, the RBF model is trained by attitude errors between inertial and visual data. During the intersampling of vision data, the trained RBF can predict attitude errors and used to compensate the attitude calculated by inertial data. The specific process of the proposed RBF and CKF will be discussed in the next section.