esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna's parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing.  
GNSS spoofing detection; antenna; GLRT; direction of arrival; intersection angle 2024241116. [[https://doi.org/10.3390/s24041116](https://doi.org/10.3390/s24041116)]([https://doi.org/10.3390/s24041116](https://doi.org/10.3390/s24041116))  
1
## 5 Discussion
### Advantages and Limitations  
In this study, spatiotemporal dimensional information is innovatively introduced into the risk assessment of track maintenance operations, enabling precise identification of the location and time of risk occurrences within individual maintenance activities. This facilitates the development of differentiated risk response strategies, thereby significantly enhancing the relevance and effectiveness of risk prevention and control measures. The constructed risk assessment index system transcends limitations related to specific operation types and is adaptable to a variety of track maintenance scenarios, including line maintenance and turnout inspections. To optimize the weighting of risk factors, the GTCWM was employed. This approach effectively integrates expert experience with objective data, addressing the limitations of purely subjective or objective weighting methods. To overcome the limitations of conventional linear weighted average methods, particularly their tendency to dilute the influence of high-risk factors, a nonlinear FCE method was introduced. By incorporating prominent influence coefficients, the evaluation process preserves the impact of critical risk factors, ensuring that the final risk assessment more accurately reflects actual risk levels. This enhances both the precision and reliability of the overall risk assessment.  
To further improve the accuracy and reliability of the risk assessment results, future research can be optimized in the following three areas.  
1. Correlation analysis of risk factors: Develop a correlation analysis model to explore the coupling mechanism among various risk factors in greater depth. At the same time, expand the identification scope and continuously improve the evaluation index system.
2. Intelligent data collection and quantification: Address the subjective limitations of expert scoring by developing an intelligent data acquisition system capable of dynamically and automatically capturing risk factor values. A scientific risk factor quantification model can be established by combining multi-source data such as industry standards, historical data, and real-time monitoring information.
3. Expansion of sample data for objective weighting: Collect a larger volume of sample data through multiple channels to enhance the accuracy of calculating objective weights by the EWM.  
The risk assessment method proposed in this study has been integrated with real-world track maintenance scenarios, forming a practical solution that directly supports the optimization of maintenance operation processes.
## 2 Materials and Methods
### Sensory Integration  
The combination of sensory inputs was adopted from the Modified Clinical Test of Sensory Integration and Balance (mCTSIB), which is widely used to evaluate an individual's ability to maintain balance under various sensory conditions. The mCTSIB systematically alters the availability of visual, proprioceptive, and vestibular information, assessing the individual's reliance on each sensory system for postural control and stability [24]. The test consists of four conditions, each with a unique combination of sensory inputs (Figure 1):  
1. Eyes open, firm surface: This condition, hereafter referred to as \"Basic\", provides the individual with full visual, proprioceptive, and vestibular information, enabling them to rely on all three sensory systems to maintain balance.
2. Eyes closed, firm surface: In this condition, referred to as \"Visual\", visual input is eliminated, forcing the individual to rely primarily on proprioceptive and vestibular information for balance.
3. Eyes open, foam surface: This condition, referred to as \"Proprioception\", challenges the individual's proprioceptive system by providing an unstable surface while still allowing access to visual and vestibular information.
4. Eyes closed, foam surface: This condition, referred to as \"Vestibular\", presents the greatest challenge to the individual, as both visual and reliable proprioceptive inputs are removed, leaving only the vestibular system to maintain balance.  
For the foam surface, we used the Airex Balance Pad Cloud(r) (Airex AG, Sins, Switzerland).  
Figure 1: A representation of the testing conditions.
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 4 Development Stage
### Establishment of Numerical Wind Tunnel
#### 4.1.1 CFD Setup for Numerical Wind Tunnel
As shown in Figure 8, the dimensions of the numerical wind tunnel were identical to those of its physical counterpart, and all passive devices available for this study, as shown in Figure 3, were also modeled numerically. As for the boundary conditions, the computational domain employed a velocity inlet and a pressure outlet, the floor was set as a rough wall, and all other surfaces, including the surfaces of the experimental devices, were set as non-slip walls. A mixed-grid scheme was adopted for both the RANS and LES simulations. The structured grids were generated near the inlet and outlet, while unstructured grids were employed in the region where the passive devices were located. The unstructured cell region and the structured cell regions were connected by the interior face. As for the solution strategy for the RANS and LES methods, the Semi-implicit Method for Pressure-linked Equation (SIMPLE) algorithm was used to resolve the pressure-velocity coupling equation. For the RANS, the second-order upwind scheme was used to discretize the turbulence kinetic energy equation and the turbulence dissipation rate equation. For the LES, the second-order upwind scheme was applied to handle the non-linear convection term of the momentum equation, while the bounded central differencing scheme was used for the momentum equation. Additionally, the second-order implicit scheme was adopted for time discretization. To enhance computational efficiency and robustness, the RNG \(k\)-e model was initially utilized to obtain an initial result for the subsequent LES simulation. The time step in the LES simulation was set to 0.001 s, ensuring that the Courant number remained less than 1 throughout the entire simulation. A hyper-threading workstation with 96 CPUs and an Intel Xeon Platinum 8269 @2.50 GHz processor at the School of Civil Engineering, Chongqing University, was used for running the CFD simulations. The calculations were conducted with the finite volume method (FVM) based on ANSYS FLUENT 19.2.
## 5 Simulation
### Example 3  
A more practical simulation with external disturbance is conducted in this case. The followers' dynamics and topologies are the same as Example 2. The leaders' outputs are modified as piece-wise functions to simulate the transition of different types of tasks.  
\[Agent\ 5:w_{5}(k)=\left\{\begin{array}{ccc}-1.2&\text{if}&0<k\leq 250\\ 0.6&\text{if}&250<k\leq 500\\ (0.5+0.001k)sin(\pi k/150)&\text{if}&k>500\\ \end{array}\right.\] \[Agent\ 6:w_{6}(k)=\left\{\begin{array}{ccc}-1.6&\text{if}&0<k\leq 250 \\ 0.8&\text{if}&250<k\leq 500\\ (0.5+0.001k)sin(\pi k/150)-1&\text{if}&k>500\\ \end{array}\right.\] \[Agent\ 7:w_{7}(k)=\left\{\begin{array}{ccc}-1.8&\text{if}&0<k\leq 25 0\\ 1&\text{if}&250<k\leq 500\\ 0.3 sin(\pi k/100)-0.5&\text{if}&k>500\\ \end{array}\right.\]  
The communication channels are assumed to be intervened with external disturbances.  
\[\hat{w}_{l}(k)=w_{l}(k)+\beta\cdot n_{l}(k) (l=N+1,...,N+M)\] \[\hat{g}_{i}(k)=y_{i}(k)+\beta\cdot n_{i}(k) (i=1,...,N)\]  
where \(\hat{w}_{l}(k)\) and \(\hat{g}_{i}(k)\) are the real information that the follower receives from leaders and its neighboring followers, respectively, and \(n_{i}(k)\) is the channel disturbance following the normal distribution with intensity \(\beta\).  
Moreover, a widely used protocol [12] is chosen as comparison.  
\[u_{i}(k)=a_{i}^{g}(k)\bar{\xi}_{i}(k) \tag{36}\]  
where \(a_{i}^{g}(k)\) is the time-varying gain. It is worth noting that \(a_{i}^{g}(k)\) is difficult to determine if the agent model is unavailable. In this test, via a trial-and-error method, the gain is tuned similarly to the form in [12] as \(a_{1}^{g}(k)=0.3/(1+0.001k)\), \(a_{2}^{g}(k)=1/(1+0.005k)\), \(a_{3}^{g}(k)=0.3/(1+0.001k)\), \(a_{4}^{g}(k)=0.3/(1+0.001k)\).  
Denote the integral square error (ISE) of global containment errors as the performance index:  
\[\text{ISE}=\sum_{k}\bar{\varepsilon}^{+}(k)^{2}+\bar{\varepsilon}^{-}(k)^{2} \tag{37}\]  
The received outputs and global containment errors of the MAS with MFACC (\(\lambda=0.5\)) are shown in Figure 10. It can be clearly seen that MFACC still works well under communication channel disturbances with different intensities, and the global containment errors converge, which also validates the theoretical results in this paper. The comparison results  
Figure 9: PPD estimations in Example 2.  
when using the protocol in [12] are given in Figure 11. One can observe the controller is stable, but the tracking performance is not satisfying facing unknown heterogeneous follower dynamics. In summary, the ISE results of two methods under various channel environment are listed in Table 2.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 6 Conclusions  
In this paper, we propose a TR-MC-DFE method that uses subarray processing for high-rate underwater acoustic communication. This method couples passive TR processing and MC-DFE. We used simulation and processed data collected in the DJK15 lake experiment to measure the performance of the proposed method. The results show that the TR-MC-DFE has the potential to improve the performance of conventional TR-DFE. Based on the lake experimental data, the output SNR can be increased by up to 3.4 dB. In these data, the output SNR gap between MC-DFE and TR-MC-DFE for lake experimental data is very small. However, the complexity of MC-DFE is much higher. Sparse channel estimation using OMP and IPNLMS algorithms can be further superior to existing systems. Trade-off between subarray length and DFE order was also investigated. The results show that the proposed method provides a possible solution for communicating with an underwater platform with 6-8 sensor elements.  
**Acknowledgments:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 61471298, and No. 61531015). The authors would like to thank all the participants of the lake experiment.  
**Author Contributions:** All of the authors contributed extensively to the preparation of this manuscript. [PERSON] and [PERSON] conceived the work and designed the algorithms. [PERSON] prepared the channel estimation methods, [PERSON] analyzed the experiment data, and [PERSON] designed the lake experiments. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Nuclear Extracts and EMSA  
EMSA assay was used by modified method [29]. T24 cells were incubated with indicated concentrations of PSEFS (0, 400, 600, and 800 \(\upmu\)g/mL) for 24 h. Collected cells were cleaned and resuspended in a buffer composing 10 mM HEPES (pH 8.0), 0.1 mM EDTA, 1 mM DTT, 10 mM KCl, 0.5 mM PMSF, and 0.1 mM EGTA. Cellular pellets were maintained on ice for 15 min and lysed using 0.5% NP-40. Thereafter, nuclear extracts were harvested and extracted utilizing an ice-cold high-salt buffer (20 mM HEPES pH 7.9; 400 mM NaCl; 1 mM each of DTT, PMSF, EDTA and EGTA) at 4 \({}^{\circ}\)C for 15 min. Finally, the supernatant including the nuclear extract was obtained by performing centrifugation. The concentrations of proteins were determined via use of BCA protein assay reagent kit (Thermo Fisher Scientific). The nuclear extracts (20 \(\upmu\)g) were pre-reacted at 4 \({}^{\circ}\)C for 30 min utilizing a 100-fold excess of unlabeled oligonucleotide probes, which is consist of the \(-\)79 position of the MMP-9 cis-element of interest. Sequences of oligonucleotides were as follows: Sp-1, GCCCATTCCTTCCGCCCCAGATGAAGCAG; AP-1, CTGACCCCTGAGTCAGACCTT; and NF-kB, CAGTGGAATTCCCAGCC. The oligonucleotidescorresponding to the DNA-binding motif of the MMP-9 promoters were reacted with Klenow end-labeled [\(\gamma\)-P \({}^{32}\)] ATP utilizing T\({}_{4}\)-polynucleotide kinase (50,000 cpm/ng). Nuclear extracts were incubated with 2 ug poly dI/dC and \({}^{32}\)P-labeled DNA probe in a buffer (25 mM HEPES pH 7.9; 0.5 mM each of EDTA and DTT; 50 mM NaCl; 2.5% glycerol) at 4 \({}^{\circ}\)C for 20 min. Finally, the DNA-protein complex was separated by electrophoresis using a 6% polyacrylamide gel at 4 \({}^{\circ}\)C, and then, the gel was imaged by exposing the X-ray film. The value of the blots was estimated using ImagePro Plus 6.0 software (Media Cybernetics, Rockville, MD, USA).
## Theory
### The Basic Theory of Blind Source Separation
Next, the SVD operator is applied to \(R_{x}\) and the following formula can be obtained:  
\[R_{x}=V_{s}\Lambda_{s}V_{s}^{\text{T}}+V_{b}\Lambda_{b}V_{b}^{\text{T}} \tag{12}\]  
where \(\Lambda_{s}=diag\{\lambda_{1}\geq\lambda_{2}\ldots\geq\lambda_{n}\}\) is the principal eigenvalues in descending order and \(\Lambda_{b}\in diag\{\lambda_{n+1},\lambda_{n+2}\ldots,\lambda_{M}\}\) contains \(M-n\) eigenvalues of noise components. Therefore, the dimension of the noise subspace can be determined by judging the number of the smaller eigenvalue of the correlation matrix under the assumption that the eigenvalue corresponding to noise components is relatively small. However, the threshold of eigenvalues between the useful signal and noise components cannot be accurately estimated, so the dimension of the noise subspace is hard to determine. Finally, in order to solve the problem of threshold setting, Bayesian information criterion (BIC) [30] is used to estimate the dimension of useful signal and noise subspace in this paper. Figure 3: The typical model of blind source separation (BSS). BIC can be used to estimate the source number of non-Gaussian signal, and has a potential for mechanical multi-fault signal separation. BIC establishes the method of source number estimation based on the Bayesian Minaka selection model and can be expressed as:  
\[\text{BIC}(k)=(\prod\limits_{j=1}^{k}\lambda_{j})^{-N/2}\overset{\sim}{\sigma} _{k}^{-N(l-k)/2}\ \ N^{-(d_{k}+k)/2} \tag{13}\]  
where \(\overset{\sim}{\sigma}_{k}^{2}=(\sum\limits_{j=k+1}^{l}\lambda_{j})/(l-k)\), \(d_{k}=lk-k(k+1)/2\), \(1\leq k\leq l\), \(l\) is the number of non-zero eigenvalues. The objective of BIC is to identify the number \(k=m\) of the maximum of the cost function. This implies that \(m\) corresponds to the estimated number of source signals.
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.2 Google Earth Platform  
The Google Earth platform plays an important role in the interpretation and identification of geological disasters. [PERSON] et al. (2021) organized existing landslide data near the Langma-Baiya fault zone and the Bailongjiang fault zone in the Western Qinling Mountains and interpreted it using 1 m high-resolution optical images from the Google Earth platform, identifying 2765 landslides [17]. Field verification showed that this method has a high interpretation accuracy. Since the platform can visually and clearly display landslides, many researchers use it for interpretation. The Google Earth platform provides high-precision, multi-temporal optical imagery, which enables the comparative analysis of images from different time periods within the study timeframe to identify potential landslide hazards, thereby ensuring the quality of landslide inventory. In this study, due to the lack of sufficient field-monitoring data for on-site verification, the Google Earth platform was used to confirm and verify suspected geological disaster points from various image interpretation results, thereby enhancing the accuracy of identification.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 2 Materials and Methods
### Efficiency of Model  
The efficiency of the hydrological model should be investigated on the degree of precision and consistency. To test the performance of the model, the NSE (Nash-Sutcliffe Efficiency), R\({}^{2}\) (coefficient of determination), and PBIAS (Percent Bias) coefficient are recommended [42]. The R\({}^{2}\) designates the strength of the observed and simulated relations and the value ranges from 0 to 1. The NSE is the degree of fitness of plots and varies from negative infinity to 1.0, whereas 1.0 indicates better agreement between the flow streams. The percent bias is a measure of tendency of the simulated data. A positive and negative value of bias percent indicates a model toward underestimation and overestimation respectively. The flow parameters were tested and the influential sensitive parameters were selected and used for further analysis based on the performance measurements of stream flow simulation (Table 3).  
\[\text{R}^{2}=\frac{\left[\sum_{i=1}^{\text{n}}\left(\text{qsi}-\text{\ \overline{q}s}\right)\left(\text{qoi}-\text{\ \overline{q}o}\right)\right]^{2}}{\sum_{i=1}^{\text{n}}\left(\text{qsi}- \text{\ \overline{q}s}\right)^{2}\sum_{i=1}^{\text{n}}\left(\text{qoi}-\text{\ \overline{q}o}\right)^{2}} \tag{7}\]  
\[\text{NSE}=1-\frac{\sum_{i=1}^{\text{n}}\left(\text{qoi}-\text{qsi}\right)^{2 }}{\sum_{i=1}^{\text{n}}\left(\text{qoi}-\text{\ \overline{q}o}\right)^{2}} \tag{8}\]  
\[\text{PBIAS}=\frac{\sum_{i=1}^{\text{n}}\left(\text{qoi}-\text{qsi}\right)*100 }{\sum_{i=1}^{\text{n}}\left(\text{qoi}\right)} \tag{9}\]  
where; qsi: simulated streamflow in m\({}^{3}\)/s, qoi: observed streamflow in m\({}^{3}\)/s, \(\text{\ \overline{q}s}\): mean simulated value, \(\text{\ \overline{q}o}\): value of mean of the observed.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Random Vibration Testing
#### 4.2.2 RC Accelerometer
Figure 12: Random vibration results from the RC accelerometer in the Y-direction: (**a**) 0.5\({}^{\prime\prime}\) cut, (**b**) 0.25\({}^{\prime\prime}\) cut, (**c**) 0.1\({}^{\prime\prime}\) cut, and (**d**) no-cut BARC systems. found in the FEA. Table 6 depicts the dominant peaks found from the random vibration testing using the RC accelerometer for mode 1 and mode 2 in the Z-direction. Looking at the dominant peaks from each central-cut width, certain trends are found. Once again, nonlinear softening is present when looking at these results. In fact, nonlinear softening is again the highest in the 0.5\({}^{\prime\prime}\) cut case, similar to what is found in Table 5. Unlike Table 5, the nonlinear softening difference between 0.25\({}^{\prime\prime}\) and 0.1\({}^{\prime\prime}\) is very small, only 0.02%.
# Gaussian Process Modeling of Specular Multipath Components
## Appendix B Predicted Variance  
The predicted variance \(\mathbb{V}[y(\phi_{k}(\mathbf{p}^{*}))|\mathcal{D}_{k},\mathbf{\theta}_{k}]\) at position \(\mathbf{p}^{*}\) approximates the variance of the SMC amplitude values, which relates to the DMC term in (8) for position \(\mathbf{p}^{*}\). Assuming the Gaussian approximation to hold for \(|\hat{\alpha}_{k}(\mathbf{p}^{*})|\), we have  
\[\mathbb{V}[\psi(\phi_{k}(\mathbf{p}^{*}))|\mathcal{D}_{k},\mathbf{\theta}_{k}]\approx \frac{1}{2}\big{(}S_{v}(\tau_{k};\mathbf{p}^{*})T_{p}+N_{0}\big{)}\big{(}d_{k}( \mathbf{p}^{*})\big{)}^{2}. \tag{10}\]  
While the PDP \(S_{v}(\tau_{k};\mathbf{p}^{*})\) decreases with delay \(\tau_{k}=\frac{1}{c}d_{k}(\mathbf{p})\), the squared distance \(\big{(}d_{k}(\mathbf{p}^{*})\big{)}^{2}\) will be increasing, implying a counteracting effect. The left-hand side is independent of the distance. We can thus re-write the equation to illustrate the shape of the PDP as a function of \(\tau_{k}\) at some angle \(\phi_{k}^{*}\),  
\[S_{v}(\tau_{k};\phi_{k}^{*})T_{p}\approx\frac{2}{\tau_{k}^{2}c^{2}}\mathbb{V} [\psi(\phi_{k}^{*})|\mathcal{D}_{k},\mathbf{\theta}_{k}]-N_{0}. \tag{11}\]  
This result indicates a reciprocal squared decay in contrast to the usually assumed exponential decay.  
On the other hand, from Appendix A, by neglecting measurement noise, we have:  
\[\mathbb{E}\left\{\left|v_{k}(\mathbf{p})+w_{k}(\mathbf{p})\right|^{2}\right\}\approx \mathbb{E}\left\{\left|v_{k}(\mathbf{p})\right|^{2}\right\}=T_{p}S_{v}(\tau_{k}; \mathbf{p}) \tag{12}\]  
So, the predicted variance obtained from GPR can be validated by comparing with the DMC power, c.f. Equations (10) and (11).