esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation and Application of PWV Derived from CPRAP
### Application of CPRAP-Derived PWV
#### 3.2.1 Application of CPRAP for Drought Monitoring  
The standardized precipitation conversion index (SPCI) was proposed by [PERSON] et al. [53] for drought monitoring, which is different from the standardized precipitation evapotranspiration index (SPEI). In SPCI, only the parameters of PWV and precipitation were used, and the SPCI value is easy to obtain. A corresponding study proved that the SPCI with 12-month scale is of good capacity for drought monitoring and the correlation coefficients between SPCI and SPEI are larger than 0.96 (\(p<0.05\)) [53]. Yunnan province is a representative arid area in China [54], making it ideal for drought monitoring. Thus, four stations (HLFY, HNMY, JLY) and JLCL) in this province were randomly selected to verify the performance of CPRAP-derived PWV for drought monitoring and the corresponding SPCI value was calculated using the precipitation and CPRAP-derived PWV at those stations over the period of 2012 to 2018. Furthermore, the corresponding SPEI was also calculated using the meteorological data at those four stations following the steps proposed in [PERSON] et al. [55]. Figure 10 gives the time series of SPEI and SPCI at four selected stations over the period of 2012 to 2018. It can be observed that the CPRAP-derived SPCI using GNSS observations (\(\text{SPCI}_{\text{GNSS}}\)) and ERA5 data (\(\text{SPCI}_{\text{ERA5}}\)) have good consistency with SPEI at four selected stations. Although a small fluctuation existed between SPEI and \(\text{SPCI}_{\text{GNSS}}\)/\(\text{SPCI}_{\text{ERA5}}\), the correlated coefficients at those four stations are larger than 0.8 (\(p<0.05\)) (Table 6). Statistical results show the averaged correlation coefficients of four stations are 0.875 and 0.865 between SPEI and \(\text{SPCI}_{\text{GNSS}}\)/\(\text{SPCI}_{\text{ERA5}}\), respectively, which shows the good performance of CPRAP-derived SPCI and proves the good ability of the established CPRAP for drought monitoring.  
Figure 9: RMS and Bias distributions of PWV differences between Sentinel-3A and GNSS/RS over the period of 1 March 2019 to 4 March 2020.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Implementation Solutions  
Technical Framework: Implement a cloud-based vehicle management system that leverages the 6G network to transmit OTA updates securely and efficiently. Vehicles could be equipped with a system that checks for updates during non-operational hours, downloads, and installs them autonomously while ensuring that the new software is compatible and stable.  
Addressing Challenges: Employ end-to-end encryption for data transmission to ensure security. Implement a robust testing and validation process for new software updates to ensure compatibility and stability. Additionally, schedule updates during off-peak hours to manage network load effectively.  
Practical Considerations: The practicality of implementing OTA updates via 6G also hinges on regulatory compliance, ensuring that software updates adhere to vehicular safety and operational standards, and managing user perceptions and trust regarding autonomous updates.
## 3 Results and Discussion
### Evaluation Index System Construction
#### 3.1.2 Evaluation Index Construction
The final service quality evaluation index system of fresh agricultural produce supply chain platforms, based on the coordination of four dimensions, was obtained through the application of these methods. After repeated screening and sorting, the service quality evaluation index system of the fresh agricultural produce supply chain platform was obtained, as shown in Table 4. The index system was designed to determine the operation level of fresh agricultural produce supply chain platforms and their service quality. Moreover, its criterion layer was built around the four dimensions of supply chain synergy, which include capital flow, logistics, business flow, and information flow; there were 17 specific index layers.
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 5 Hough-Transformed Energy Distribution Evaluation Scheme  
In order to understand the Hough-transformed energy distribution of the interfered GNSS signal in the \(\rho-\theta\) parameter space, in this paper, a metric known as zero Hough-transformed energy percentage is proposed to evaluate the properties of energy distribution concentration and cross-term suppression in the GNSS interference detection. The concept of zero Hough-transformed energy percentage is explained in Figure 6. The \(\rho-\theta\) parameter space \(S\) is a grid of cells where the Hough-transformed energy distribution is evaluated. The grid is with the two dimensions of \(\rho\) and \(\theta\); \(\Delta\rho\) is the cell size in the direction of the \(\rho\) axis, and \(\Delta\theta\) is the cell size in the direction of the \(\theta\) axis.  
Assuming that the peak of Hough-transformed energy distribution is located in a specific cell \(C\) within the parameter space \(S\), and then the cell \(C\) can be selected as the geometric center to construct a rectangular local neighborhood domain \(P\), where the main part of the Hough-transformed energy is concentrated; \(\delta_{\rho}\) is the size of the rectangular local neighborhood region \(P\) in the direction of the \(\rho\) axis, and \(\delta_{\theta}\) is the size of the rectangular local neighborhood region \(P\) in the direction of the \(\theta\) axis. In this way, the entire parameter space \(S\) is divided into two parts: the local neighborhood domain \(P\), which dominates most of the Hough-transformed energy, and the other remaining area (\(S-P\)), where the residual Hough-transformed energy is contained.  
In order to compare the properties of energy concentration and cross-term suppression among the Wigner-Hough transform, the pseudo-Wigner-Hough transform, and the smoothed pseudo-Wigner-Hough transform techniques used in GNSS interference detection, the concept of zero Hough-transformed energy percentage can be explained as:  
\[\alpha=\frac{N_{zero}}{N}\times 100\% \tag{20}\]  
Figure 6: The zero Hough-transformed energy distribution evaluation scheme for the Hough transform-based GNSS interference detection techniques.  
where \(N_{zero}\) is the number of cells in the region \((S-P)\), where the values of the Hough-transformed energy distribution are equal to zero, and \(N\) is the total number of cells contained in the \((S-P)\) area within the parameter space \(S\), which excludes the local neighborhood area \(P\).  
For any type of Hough-transformed technique used in GNSS interference detection, when the zero Hough-transformed energy percentage \(\alpha\) is larger, the Hough-transformed energy distribution is more concentrated in the intersection point of concurrent sinusoidal curves in the parameter space, and the residual Hough-transformed energy is effectively suppressed to a lower level even equal to zero in the other area \((S-P)\) except the local intersection domain \(P\), indicating that the cross-terms are more significantly mitigated.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 4 Experiment and Analysis
### Applicability Verification  
To validate the applicability of the improved fusion algorithm in environments beyond the fixed grid map used in the previous experiment, This paper also selects the underground roadway scene of a working face of Anhui Development Mining Co., Ltd. (Lu'An, China) as an example. and applied the enhanced algorithm to perform global path planning as well as static and dynamic obstacle-avoidance experiments. The resulting experiment outcomes are presented in Figures 15-17.  
Figure 13: Static obstacle avoidance experiment on the Zijin map.  
As illustrated in Figure 15, the refined A* algorithm generates a global trajectory that remains closely aligned with the underground roadway centerline. The path exhibits gentle curvature variations, a substantially lower waypoint (turning-point) count, and no abrupt angular discontinuities.  
Figure 16 substantiates the robustness of the integrated planning framework when confronted with newly introduced static obstacles. Although the global route is pre-computed, the mobile platform autonomously perceives emergent impediments and, at the three representative loci (1-3), performs on-the-fly local replanning to negotiate collision-free detours.  
Figure 14: Dynamic obstacle avoidance experiment on the Zijin map.  
Figure 15: Adaptive smoothing of the A* algorithm on the Ankai map.  
The dynamic obstacle experiment depicted in Figure 17 further underscores the system's adaptability. Upon detecting that an obstacle ahead possesses a forward motion trend, the planner extrapolates its future states via a trajectory-prediction module and subsequently invokes the Dynamic Window Approach (DWA) to synthesize a rapid circumvention maneuver, thereby mitigating collision risk while preserving progress toward the goal.  
Collectively, these three evaluations demonstrate that the proposed Refined A* algorithm and the Refined A* + DWA hybrid planner are not restricted to a single, idealized grid environment; rather, they exhibit strong generalizability and scenario adaptability across a diverse, real-world underground roadway map.
## 3 Materials and Methods
### Classification Algorithms
#### 3.5.3 Artificial Neural Networks (ANNs)  
An ANN is based on biological neural systems with nonlinearity, adaptability, responsiveness, and fault-tolerance characteristics. The inputs in ANNs are called interconnected neurons, and work together to search for the best configuration by modifying the network weights to solve a problem.  
A neural network receives as input a set of \(i=1,\ldots,n\) patterns in the form of vectors of dimension \(p\); each input vector is processed through the neurons of the \(I\) hidden layers according to the connections between the nodes. Each node contains an activation function \(f\), which obtains the node output value through a weighted sum of the node inputs and an additional bias value [22].  
In a feedforward ANN, there are three layers: input, output, and hidden. The input layer buffers the distribution of the input signals \(x_{n}\) (\(n=1,\ 2,\ 3,\ldots\)) and sends them to the neurons in the hidden layer. The neurons in the hidden layer aggregate the input signals, \(x_{n}\), after weighting them with their respective connectivity strengths, \(W_{nl}\), which are the inputs of the layer. Finally, the output \(Y\) is calculated as follows.  
\[y_{l}=f\left(\sum_{n=1}^{n}W_{nl}x_{n}\right) \tag{36}\]  
The number of neurons is \(I\). The activation function can have different functional forms, such as sigmoidal, radial, linear, hyperbolic tangent, etc. Using a similar approach, the outputs of the neurons are calculated.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Hyper-Parameters Analysis  
The impact of changes in the learning rate, output feature vector dimension, and similarity calculation method is tested, and the results are listed in Table 1.
## 3 Method and Process  
### Reservoir Facies Identification Method  
#### 3.1.1 Unsupervised Intelligent Clustering Method Based on FCM-DSDFP  
FCM-DSDFP is an improved method of the FCM unsupervised clustering method. FCM adopts mechanisms such as membership degree and cluster center and uses alternating optimization algorithms for intelligent solution and clustering. It has fewer parameters and is more advantageous in complex mapping relationships. However, the random selection of initial clustering centers and the local optimality of the Euclidean distance algorithm make FCM susceptible to noise interference. The clustering effect of FCM is unstable and ignores global consistency, resulting in a decrease in clustering accuracy. FCM-DSDFP  
Figure 1: (**a**) Location of Sichuan Basin (modified from [3]). (**b**) Location of Ediacaran Dengying Formation gas fields in this study. (**c**) Stratigraphic column of Ediacaran Dengying Formation.  
overcomes the shortcomings of FCM and achieves global optimal clustering by introducing density-sensitive distance and fuzzy entropy, which can be used to solve the prediction problem of complex reservoir facies.  
Based on fuzzy mathematics theory, FCM can divide the logging dataset X (\(x_{1}\), \(x_{2}\), \(x_{3}\)...\(x_{n}\)) into \(k\) classes (\(k_{1}\), \(k_{2}\), \(k_{3}\)...\(k_{k}\)), with an objective function of:  
\[Y=\sum_{i}^{n}\sum_{j}^{k}b_{ij}^{m}c_{ij}^{2} \tag{1}\]  
In the formula, \(b_{ij}\) is the membership degree of sample point \(x_{i}\) to the \(k_{j}\) of the cluster center, \(c_{ij}\) is the Euclidean distance between the sample point \(x_{i}\) and the \(k_{j}\) of the cluster center, and \(m\) represents the fuzzy factor (\(m>1\)).  
On this basis, FCM-DSDFP uses density-sensitive distance to replace Euclidean distance for measurement, enabling the function to perform global optimal interpretation. At the same time, fuzzy entropy is introduced to compensate for function partitioning defects, resulting in a new objective function \(G\), namely:  
\[\begin{array}{c}G=\frac{4}{e^{\prime}}\sum_{i}^{n}\sum_{j}^{k}b_{ij}\big{(}1 -b_{ij}\big{)}D_{ij}^{\rho}+\omega\sum_{i}^{n}\sum_{j}^{k}b_{ij}lnb_{ij}\\ \text{s.t.}\;\;p=log_{e}4n(e-1)-1\end{array} \tag{2}\]  
In this formula, \(e\) is the number of classifications. The parameter \(p\) is used to compensate for the defects in the partition coefficient and clustering function (generally \(p>0\)). \(D_{ij}^{\rho}\) is density sensitive distance. \(\rho\) is the scale factor (\(\rho>0\)). \(\omega\) represents the adjustment factor representing fuzzy entropy.  
The initial clustering center is no longer randomly selected, but is calculated through the average density sensitive distance, which can effectively solve the problem of noise interference and reduce the number of iterations. Its function is:  
\[D_{avg}^{\rho}=\frac{1}{C_{n}^{2}}\sum_{i=1}^{n}\sum_{j=1}^{i}D_{ij}^{\rho} \tag{3}\]  
In the formula, \(D_{avg}^{\rho}\) is the average density sensitive distance. \(C_{n}^{2}\) is the number of combinations of any two sample points selected from \(n\) samples.
# Method for Determining the Susceptibility of the Track
## 2 Experimental Testing on a Real Object
### Experimental Determination of the Vertical Stiffness Coefficient of the Crossroads  
Turnout tests were carried out at the Idzikowice station located on the Central Railway Line. A train was used at this station, consisting of a locomotive and wagons as shown in Figure 1 and Table 1. Displacements were measured using strain gauge transducers with a linear range of plus or minus 15 mm. The transducers were placed on elements that eliminated the effect of subgrade movement on the transducer. These tests were carried out to verify the analytical calculations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Turnout Type**} & \multicolumn{5}{c}{**Length of Section [m]**} \\ \cline{2-6}  & \(l_{1}\)\(-\)\(f_{2}\) & \(l_{2}\)\(-\)\(l_{2}\) & \(l_{2}\)\(-\)\(l_{3}\) & \(l_{3}\)\(-\)\(f_{3}\) & \(l_{3}\)\(-\)\(l_{4}\) \\ \hline \(\mathrm{R_{z}}\) 560-190-1: 9 & 19.6 & 1.8 & 2.5 & 1.3 & 4.3 \\ \hline \(\mathrm{R_{z}}\) 560-300-1: 9 & 25.6 & 1.9 & 3 & 1.4 & 5 \\ \hline \(\mathrm{R_{z}}\) 560-500-1: 12 & 33.3 & 1.7 & 3.5 & 1.5 & 5.9 \\ \hline \(\mathrm{R_{z}}\) 560-1200-1: 18.5 & 50.2 & 4.2 & 4.9 & 4 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Lengths of individual sections in the various S-60 ordinary turnouts.  
Figure 8: Course of variation of the vertical stiffness coefficient of the rail tracks in the turnout at different values of the ground coefficient: 1—inner track (with cross member), 2—outer track [15; 16].  
The stiffness measurements used the measurement train shown in Figure 1. Loads per wheel were taken as shown in Table 1.  
Deflections of three points of the cross member were recorded. The deflections were induced by the very slow passage of the measuring train.  
As the distance between the individual wagon axles was considerable, so the influence of neighbouring axles on the deflection of a given point was neglected. Measurements were also made of deflections of the track on the track under the influence of a passing train for which V = 5 km/h. Displacement sensors were placed on pegs driven 1 m deep into the subgrade to reduce the influence of ballast vibrations on the sensor readings. Displacements were recorded on a magnetic recorder.  
The average values of the cross-rail stiffness coefficients are given in Figure 9 and Table 7.  
The average stiffness value of the plain track rail was 76 MN/m.  
Figure 9: Vertical stiffness of selected points of crossbuck and plain track.  
The experimental results presented here coincide with the results of calculations with an assumed ground coefficient of 200 MN/m\({}^{3}\) (as shown in Figure 8). Of course, the differences are small, as it was impossible to determine what the ground coefficient was in a given turnout. In paper [10], the vertical stiffness of the crossing was determined to be 70 MN/m, while the vertical stiffness of the normal rail was assumed to be between 12 and 90 MN/m. No other study in this field was found in the available bibliography.
## 3 Results and Discussion
### PL Analysis  
Figure 6 shows the room temperature PL spectrum of different ZnO NPs, where the pristine and vacuum-treated ZnO both display strong UV and weak visible emission peaks, at 384 and 470 nm, respectively. The UV emission is attributed to the direct recombination of free excitons [5]. The blue-green (470 nm) emission implies that electrons trapped by the oxygen vacancies recombine with photo-generated holes [5; 29]. This further confirms the existence of oxygen defects on the surface of the ZnO photocatalyst. Compared with Sample A, Sample B, with a higher concentration of surface oxygen vacancies, shows stronger visible, and lower UV, PL signals, which indicates an increase in the separation rate of photo-generated electron-hole pairs by oxygen vacancies.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 2 Backgrounds
### Fire and Explosion Risk Analyses in GCT Transportation  
One of the most critical safety concerns in GCT operations pertains to the threat posed by fire and explosion risks. The objective of research in this area is to develop strategies to prevent potential hazards by analysing accidents in different systems. For instance, the fire and explosion risks associated with liquefaction processes in LNG Floating Production Storage and Offloading (LNG-FPSO) systems have been assessed using quantitative methods, and recommendations for safety improvements have been proposed [15]. Furthermore, an examination of accidents occurring in diesel and natural gas-powered ship engine rooms has been conducted using Fault Tree Analysis (FTA), thereby highlighting the significance of preventive maintenance strategies [16]. During research conducted on the potential hazards associated with the transportation of LPG, it was emphasised that mechanical failures represent a primary contributing factor to the heightened risk of fire and explosion [17].  
Analyses conducted to determine the necessary safety distances during the transfer of LNG from ship to ship contribute to modelling the variables that are effective in this process [18]. The Kannur LPG tanker accident, a significant case study concerning the transportation of hazardous materials, was analysed for its loss of life and environmental consequences. This analysis unveiled the comprehensive risk dimensions of the incident [19]. Furthermore, risk analyses of the regasification process specific to LNG-FSRUs (LNG-Floating Storage and Regasification Units) provide critical findings for enhancing system safety [20]. Finally, studies systematically analysing fire risks associated with loading and unloading operations at LPG filling facilities have developed methods to control potential hazards during these processes [21].
# NMR Evaluation of Shale Oil Mobility: Combined Pyrolysis and CO\({}_{2}\) [PERSON]
## 2 Materials and Methods
### Design of Pyrosis-NMR Experiment  
Firstly, a discussion on the temperature sequence of the heating process is necessary. The temperature sequence used in geochemical pyrolysis for hydrocarbon source rock analysis is divided into the following three stages: initially, the temperature is raised from the starting point to 90 \({}^{\circ}\)C and maintained for 2 min to retrieve S\({}_{0}\); secondly, it is held at 300 \({}^{\circ}\)C for 3 min to collect S\({}_{1}\); and finally, it is increased at a rate of 50 \({}^{\circ}\)C per minute to 600 \({}^{\circ}\)C, held for 1 min, to obtain S\({}_{2}\). Here, S\({}_{0}\), S\({}_{1}\), and S\({}_{2}\) correspond to the quantities of gaseous hydrocarbons, free hydrocarbons, and hydrocarbons produced from kerogen pyrolysis, respectively. However, this theory does not fit the realities of shale oil research. In shale oil research, samples contain both oil and water, necessitating the determination of partitioning temperatures for various water components. In the shale, water is categorized into free water, capillary water, clay-bound water, and structural water, requiring the establishment of temperature boundaries among these forms. [PERSON] et al. analyzed the thermal treatment between capillary water and clay-bound water, finding that capillary water desorbs completely below 60 \({}^{\circ}\)C, leaving only clay-bound water behind. Furthermore, a temperature of 300 \({}^{\circ}\)C can achieve the goal of dehydration without destroying the clay mineral framework, thus distinguishing between clay-bound and structural water [34]. Apart from the temperature gradient, the heating rate also influences shale pyrolysis. The conventional rate in geochemical pyrolysis is 50 \({}^{\circ}\)C per minute, ensuring experimental efficiency, but this can result in incomplete component release for large samples. Previous studies on sample heating rates have shown that the slower heating of rocks leads to a more comprehensive release of components [35].  
Considering the temperature characteristics of oil and water components, as shown in Figure 1, we chose the temperature ramp sequence of 60 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C for pyrolysis experiments on shale samples to perform shale component analysis. The procedure entailed gradually heating the shale samples at a rate of 5 \({}^{\circ}\)C per minute, and holding at each temperature for 3 h to ensure adequate release of components under that temperature condition. The experiment used parallel samples crushed to 60 mesh, and conducted multistage anaerobic pyrolysis experiments according to the temperature sequence. Following each temperature increase, 1D and 2D NMR spectra were recorded at the various stages. Pyrosis experiments combined with NMR analysis used 2D NMR spectra to distinguish oil and water, constructing a fluid component map that characterizes total oil-bearing and movable components.