esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
The two-stage COM-NOMA realizing the inter-cooperation between two MGs had improved coverage performance. However, in the second stage, some SUs were selected as URs with fixed ERSR, which is equal to the ETR of D2D. It resulted in low coverage efficiency. RDUR was proposed to solve this problem. The circular coverage range of BS was divided into several continuous AAs. Each AA was allocated corresponding ERSR. Expressions of UR selection ratio and coverage ratio are derived. The ERSR radius set that optimized system coverage efficiency was selected to perform UR selection. Main simulation results showed that for different ERSR radius sets, the analytical results of UR selection ratio and coverage ratio matched well with their simulated results. In comparision to other UR selection schemes, via RDUR, the capacity efficiency of the second-stage D2 MD had been improved. The coverage efficiency was at least 14% higher than the original UR selection scheme of COM-NOMA systems.  
Through RDUR, the coverage efficiency of COM-NOMA systems was improved. However, the radius of ERSR could only be chosen from several fixed values. It diminishes coverage efficiency of COM-NOMA system. In fact, in oder to maximize the coverage efficiency, USU should adaptively achieve the radius of ERSR according to his(her) own location information. In the future, a UR selection scheme with adaptive ERSR will be researched. Any distance smaller than 100 m can be chosen as the ERSR radius by USU.  
Conceptualization, Y.Z. ([PERSON]) and D.W.; methodology, Y.Z. ([PERSON]) and X.W.; software, Y.Z. ([PERSON]) and Q.Z.; validation, Y.Z. ([PERSON]) and Q.Z.; formal analysis, Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]).  
This research was funded by the National Natural Science Foundation of China under Grant number 61701038.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Capacity efficiency versus the number of users in MG, in different \(C_{1}\).
## 5 Numerical Applications  
### Comparative Study of the Accuracy of Complex-Step and Finite Difference Methods  
The effect of step size on the first derivative by finite difference and CS methods is investigated. Consider the following highly nonlinear function:  
\[f(x)=\frac{e^{x}}{\sqrt{\sin^{3}(x)+\cos^{3}(x)}} \tag{27}\]  
The four finite difference methods (first-order forward (FDM), second-order forward (FDM2), second-order central (CDM2), and fourth-order central difference (CDM4) approximations) and CS approximation discussed in Section 3 are utilized to compute the first derivative at \(x=-0.35\) with varying step sizes.  
The numerical results in Figure 2a show that the first derivatives by the CS method are not sensitive to the selection of step size. Finite difference methods, however, break down as the step size reaches the last value. Although higher-order finite difference methods result in fewer truncations errors, subtractive cancellation and their associate round-off errors affect the calculations. It should be noted that the number of function evaluations in higher-order finite difference methods is higher than one in the CS approach. Errors between the exact derivatives and approximated derivatives with varying step sizes are plotted in Figure 2b. The comparison results confirm that the CS approximation is more accurate for small step sizes, whereas the finite difference approach cannot achieve full accuracy.
## 2 Data Collection
### Physical Parameters Affecting Ionospheric Scintillation
An increase in the number of sunspots increases the intensity of the earth's magnetic field, and as a result, a large amount of energy is sent to the atmosphere known as solar flares [38]. The number of sunspots is measured by a parameter called the Wolf number and published daily on the National Geophysical Data Center at [[https://www.ngdc.noaa.gov](https://www.ngdc.noaa.gov)]([https://www.ngdc.noaa.gov](https://www.ngdc.noaa.gov)) (access on 27 March 2021) [37]. The international geomagnetic index \(K_{P}\) is a parameter for measuring the geomagnetic activity of the earth. The \(K_{P}\) index is another factor affecting ionospheric scintillation. The \(K_{P}\) index is the mean of the astronomical \(K\) index. The \(K\) index is the result of viewing elements such as H, D, and Z over eight 3-h periods for one day. The \(K_{P}\) index can be used to evaluate the quiet days or days with geomagnetic perturbations [37]. Values of less than 3 for this index indicate a quiet condition in terms of geomagnetic conditions [39]. Ionospheric irregularities and scintillations can be enhanced or inhibited during geomagnetically disturbed days which depends on geomagnetic parameters such as the \(K_{p}\) index, and the maximum d(Dst)/dt determined local time sector [40]. Furthermore, recent observations showed that plasma bubble irregularities and scintillations could be enhanced and inhibited at different longitude sectors during the same geomagnetic storms [41]. The \(K_{P}\) index values are published daily, as well as in 3-h intervals, and can be downloaded, for instance, from the website of Kyoto University at [[http://wdc.kugi.kyoto-u.ac.jp/kp](http://wdc.kugi.kyoto-u.ac.jp/kp)]([http://wdc.kugi.kyoto-u.ac.jp/kp](http://wdc.kugi.kyoto-u.ac.jp/kp)) (access on 27 March 2021). Other possible predictive attributes such as FoF2 and Dst related to the occurrence of ionospheric scintillation were used in the GANN input parameter. When adding the FoF2 parameter to GANN modeling, the results did not change dramatically. The sensitivity analysis demonstrated that FoF2 has a robust correlation with hmF2 and has a similar trend. These results were represented in the observations of other researchers such as [PERSON] et al. 2019 [42]. Furthermore, adding the Dst parameter to GANN modeling reduces the accuracy of the modeling and reduces the R-square parameter. The sensitivity analysis demonstrated that Dst has a weak correlation with the S4 and ROTI parameters. These results were represented in the observations of other researchers such as [PERSON] et al. 2019 as well [8]. The sensitivity analysis demonstrated a moderate correlation between each one of these five attributes and the measured ionospheric irregularity parameters, S4 and ROTI indices, with [PERSON]'s correlation coefficients of around 0.5.
# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 4 The Proposed Hybrid Control Architecture for Path Planning
### Path Planning Constrained by Equilibrium Traffic Assignment
Combined with the self-learning hidden graph dependencies, we propose the following multi-graph convolution operation:  
\[ \begin{split}\Upsilon^{(m)}=&\Gamma^{(MultiGraph)} ([\Lambda_{v}V\Theta_{v},\Lambda_{o}O\Theta_{o}]\cdot W^{(m)}).\end{split}  \tag{14}\]  
After obtaining the latent encoding features of the OD and background load graphs, we use the multi-graph fusion mechanism to retain the original spatio-temporal features as completely as possible [51]. At the end, the derived feature graph is projected to the generated equilibrium graph via a fully connected layer. The trained equilibrium mapping layer can be efficiently assigned traffic online. To train the input to output weights, we calculate the average error between the predicted traffic graph and the generated equilibrium graph. We expect the prediction results to be close to the ideal equilibrium graph by introducing the following loss function:  
\[ \begin{split} Loss(x,y)=&\left\|\frac{1}{n_{1}} \sum\limits_{r_{i}\in L_{1}}x_{r_{i}}C(r_{i})-\frac{1}{n_{2}}\sum\limits_{r_{j} \in L_{2}}y_{r_{j}}C(r_{j})\right\|.\end{split}  \tag{15}\]  
where \(x\) denotes the generated equilibrium traffic and \(y\) denotes the predicted assignment traffic. The \(n_{1}\) and \(n_{2}\) are the count of road sections contained in the OD path sets \(L_{1}\) and \(L_{2}\), respectively. This loss is equivalent to the mean square error when \(L_{1}\) and \(L_{2}\) contain the same section. Path Planning based on ST-ETA:As the background load and traveler evacuation volume change dynamically over time, the ST-ETA model should constantly update its traffic distribution to adapt the evolution, to guarantee the SO path planning from the origin at the current time step to the destination. The state correlations between the time steps are connected by the spatio-temporal characteristics of the traffic flow and the real-time OD trend features. Dynamic path decisions are stitched together by constantly giving the current step. The final output \(y\) represents the hypothesis of the travelers' trajectories in the network, interpreted as path planning based on the traffic equilibrium constraint. We adjust the road sections' rank according to the equilibrium topology configuration, which guides the path-planning method to use the potential traffic assignment sections for the routing decision. When a subject requests a path, the model performs path planning on the adjusted underlying graph, returning a path that meets the evacuation subject's demand and is consistent with system optimization.
## 1 Introduction
However, the formation of radiation frost is closely related to the complex micrometeorological data, especially the microclimate around the canopy. Meanwhile, multidimensional and nonlinear microgeological data increase the difficulty of radiation frost prediction. The support vector machines (SVMs) are originally developed for pattern recognition. It is a new general learning method based on the statistical theory of solving the quadratic optimization problem and seeking the global optimal solution [13]. SVMs are the algorithms that can be supervised learning, which mainly are employed for classification or regression problems. Kernel trick technique is used to transform the data. Based on these transformations, an optimal boundary is found out between these possible outputs [14]. Generally, micrometeorological data in a frost night are multidimensional andnonlinear, which increase the difficulty in prediction. In recent years, it has been applied in solving the issues of classification, function approximation and prediction related to precision agriculture, agricultural water management and bio-meteorology [14; 15; 16; 17; 18; 19]. Its successfully application in precision agriculture makes frost prediction reliable. While, the SVMs employed in frost prediction have not been completely attempted yet. This paper is the first attempt to evaluate the possibility of using the SVMs to predict the radiation frost in the Yangtze River region. Therefore, the objective of this study is using SVMs to develop the prediction models with different input combinations of meteorological data. The specific tasks are (1) to determine the effects of various input combinations of meteorological data for radiation frost prediction, (2) to develop the SVMs models based on three kinds kernel functions (linear function, radial basis function and polynomial function) for frost protection using the properly meteorological data and (3) to compare the prediction accuracy of four SVMs models and determine the optimal model and most related micrometeorological parameters in predicting the radiation frost.
## 1 Introduction
Split spectrum processing (SSP) can split the UGWs with multi-mode by separating the received signal into a sub-signal group in the frequency domain and recombining them in the time domain without the knowledge of dispersion and propagation distance [17; 18]. SSP was developed from the frequency-agile techniques in the radar field and then applied to conventional ultrasonic inspection to reduce the effect of coarse-grain scattering and improve the SNR [19], which is rarely applied in UGW testing. Only a few studies focus on the SNR enhancement of the T(0,1) UGW signal using SSP. [PERSON] et al. [7; 20] studied the parameters of an SSP filter suitable for T(0,1) UGW signals and compared the effects of different signal recombination methods through synthetic and experimental signals, and the results showed that SSP does have the ability to enhance the signal interpretation accuracy. However, to the best of the authors' knowledge, there is no study on the L(0,2) UGW signal processed by SSP. In addition, [PERSON] et al. only considered the dispersion as the cause of coherent noise where multi-mode should also be concluded, and they just applied SSP to UGW testing without any improvement. In order to improve the signal interpretation accuracy of UGW testing, SSP with raised cosine filters of constant frequency-to-bandwidth ratio (FBR-RC-SSP) was proposed by the filter bank design (Section 2). Based on the time-frequency analysis using the chirplet transform, the characteristics of dispersion and multi-mode of synthesized signal composed of L(0,1), L(0,2), F(\(n\),1) and F(\(n\),3) UGW were studied (Section 3). The effects of filter parameters on signal-to-noise ratio gain (SNRG) and defect-to-coherent noise ratio gain (DCRG) were explained (Section 4). Sections 5 and 6 evaluated the processing effect of FBR-RC-SSP on the simulated and experimental UGW signals with multi-defect, and Section 7 concluded the paper finally.
# Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data
## 2 Seismic Phase Theory Travel Time Calculation
\begin{array}{c}x=x_{0}+vsin\alpha_{0}\cdot\Delta\tau\\ z=z_{0}+vcos\alpha_{0}\cdot\Delta\tau\\ \alpha=\alpha_{0}-(v_{x}cos\alpha_{0}-v_{z}sin\alpha_{0})\cdot\Delta\tau\\ \end{array} \right. \tag{16}\]  
0 is the off-source angle of the ray at the point (Figure 2), \(v_{x}\), \(v_{z}\) are the velocity components of the seismic wave propagating to point P in the \(x\) and \(z\) coordinate axes, respectively, the off-source angle of the ray at point P (\(x\), \(z\)), and \(\Delta\tau\) is the time step of ray tracing. For the IASP91 Earth velocity model, it is more efficient to use the time-increment method for test shooting. The specific method is as follows: the source point S(\(x_{0}\), \(z_{0}\)) is placed on the X-negative axis of the two-dimensional circle model; the ray departs from the source with the off-source angle \(\alpha_{0}\), changes the size of the off-source angle \(\alpha_{0}\) from \(0^{\circ}\) to \(180^{\circ}\), and calculates the coordinates (\(x\), \(z\)) and the off-source angle \(\alpha\) of the ray arriving at point P after the elapsed time according to Equation (16). The corresponding ray path out of the surface point is regarded as the position of the detection point until the ray reaches the receiving position, completing a ray tracing process, and recording the corresponding travel time at different epicentral distances for different phases. According to [PERSON]'s law there are:  
\[\frac{sin\alpha_{0}}{v_{0}}=\frac{sin\alpha}{v(x,z)}=p \tag{17}\]  
At each point of the ray, the ray parameter is \(p\). \(v_{0}\) denotes the velocity of the ray at the epicenter \(S\). In this work, based on a simplified ray-tracing system in a two-dimensional medium (2D model is composed of concentric circles), ray tracing is performed using the time  
Figure 2: Schematic diagram of speed difference interface point recalculation. increment method [31]. When a ray enters an interface (the partition between two layers of homogeneous medium) at an off-source angle, the off-source angle corresponding to the ray after refraction should be calculated according to [PERSON]'s law before ray tracing. When a ray propagates in a layer of homogeneous medium, the off-source angle at each point is calculated according to Equation (16). This method is simple and convenient to adjust the speed of each layer directly.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Detailed Design
#### 3.4.5 Development Environment
* Common Methods In software development, various techniques and practices are essential for the development teams. These include serialization methods, compound data types, generic methods that can be employed in business logic, and structured objects that aid information management. The integration of these methods is crucial for the smooth functioning of software and efficient management of data throughout the system. * Settings In order for the platform to operate effectively, a configuration mechanism that can set parameters to initiate its operation is required. Although the connection string to the database is a common parameter, other configuration properties may be required for IT solutions, such as file directory paths, monitoring and logging schemes, connection parameters with external servers, session-opening parameters in external resources, and global business logic parameters. To address this issue, a uniform configuration mechanism that can be reused across different technologies, such as a group of text-based configuration files, can be placed in a base folder. This folder can be shared through multiple parallel deployments, and the files can be encrypted to prevent unauthorized access or changes. * Exception Control and Logging Exception handling can vary on different platforms. Therefore, it is important to have a unified control and logging scheme for all platforms. To achieve this, exception handling must be implemented in the transpiler language, and common objects for exception control should be defined in this layer. The logging mechanism must record exceptions in detail, including the execution stack, messages, lines of code, or other relevant data. This is particularly useful for analyzing technical events in production or test environments and identifying problems or areas for improvement. To ensure compatibility with different execution platforms, logs should be saved as files in a destination folder, with each file named with unique code for traceability purposes. Informative exceptions should be used for differentiation between business errors and other errors. * Runtime Methods To ensure compatibility and standardization across all platforms, base and attribute classes are necessary for business logic methods, which can then be unified using a single invocation method through external exposure services. Additionally, a router that serves as a gateway for external invocations should be incorporated to manage input and output serialization and dynamically invoke methods centrally with their respective parameters and return objects. This allows nest implementations to customize their invocation based on the input and output flows provided by standard communication while ensuring compatibility with back-end proxies.
## 4 Clustering and Routing Strategy
### Network Set-Up  
To study the scenario in this research, we are assuming a deployed WSN that comprises all of the sensor nodes of various services, \(X=S_{i}^{1}|S_{i}^{2}|\ldots S_{n}^{N}\) randomly dispersed in a coverage region (presumed smart area). Essential data sinks or BS are also considered in the sensing area. After the fixture of all sensing nodes and the required BS, they are presumed to be immobile. Furthermore, we categorize the heterogeneity of the sensor nodes based on their services in a smart environment. They are regular (R), periodic (P), and emergency (E)  
Figure 3: Contributions of previous surveys on clustering and routing protocols in WSNs [17; 19; 27; 35; 36; 37].  
services, which means they do not have equivalent abilities, such as reception/transmission range, processing, energy capability, and so on. By optimizing energy utilization, sensor nodes may employ power management to regulate the data transmission power level that depends entirely on the receiver's distance [40; 41]. Wireless connections are preferred for all communications. Communication is established among various deployed nodes only if the interested nodes are within the reachable, communicative range of others. In this model, because the transmitting power is predefined, each node can determine the distance of its neighbors based on the Minkowski metrics.
## 3 Results and Discussion
### Biochar Composites Characterisation
#### 3.2.3 Electrical Properties of PLA/PBAT/BC Composites  
In many industries (petrochemical, mining or power industries), personal protective equipment (PPE) is used in a dangerous explosive atmosphere. The main reason for the explosion is the charges accumulated on the surface of such PPE. Hence, it is desirable to use electrostatic dissipative, durable, sustainable and environment-friendly materials for such applications. The surface resistivity of the PLA/PBAT blends modified with different concentrations of biochar is shown in Table 4. When the biochar content in the composites increases from 0 wt% to 10 wt%, surface resistivity decreases by two orders of magnitude. Compared to other carbon-based composites, the decrease in electrical resistivity is not so drastic; however, the composite with 10 wt% of biochar shows a surface resistivity of \(\sim\)10\({}^{11}\Omega/\)sq, which is lower than the previously reported value (3 \(\times\) 10\({}^{12}\)\(\Omega/\)sq with 10 wt% of biochar) in the PLA/PBAT/biochar composites [3, 56]. This low electrical resistivity is due to the high concentration of sp\({}^{2}\) hybridised carbon in coconut shell biochar. However, the presence of functional groups and impurities may reduce the electrical conductivity of the biochar particles. Moreover, biochar particle-size, shape and orientation play major roles in attaining a continuous conductive network in the polymer matrix. It is worth noting that PLA/PBAT/BC (70/20/10) shows conductivity in the range of antistic material [57] making it suitable as 3D-printing material or film for packing, to prevent surface charge-accumulation. The packing material with surface resistivity of \(1\times 10^{11}\)\(\Omega/\)sq is sufficient for maintaining the static-dissipative properties [56, 58]. Previous electrical studies have demonstrated that biochar can be a potential material for electrical applications. However, the electrical conductivity of biochar depends on the amount of sp\({}^{2}\) hybridised carbon content and a low concentration of impurities [18, 59, 60]. Coconut shell biochar produced at 800 \({}^{\circ}\)C has a high sp\({}^{2}\) carbon content and negligible impurities, making it a suitable filler material for antistic PLA/PBAT composites.