esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
1. The implicit boundary equation of the borehole's surrounding rock is obtained. The two-way confining pressure ratio determines the distribution pattern and range of the plastic zone of the borehole surrounding rock. The distribution range of the plastic zone is affected by coal strength, gas pressure, and hole diameter. The distribution patterns of the plastic zone are mostly circular, elliptical, and butterfly.
2. As coal strength decreases and gas pressure and hole diameter grow, the permeability-increasing circle of borehole surrounding rock widens. The extension of the permeability-increasing ring promotes gas flow.
3. A honeycomb gas flow network channel structure based on the distribution morphology of the borehole's surrounding rock is presented. Realize the linkage of the coal seam gas flow enrichment region and promote gas flow communication in the surrounding rock of each borehole.
4. A honeycomb gas flow network channel structure may be formed by the borehole's surrounding rock with butterfly, oval, and round plastic zones. The butterfly form is linked by the extension of the butterfly leaf in the plastic zone of the surrounding rock of the neighboring borehole, while the circular and oval plastic zones are stretched horizontally and vertically in a convex shape.
5. The engineering test results demonstrate that the honeycomb gas flow network channel construction based on the plastic zone distribution pattern can effectively increase the gas extraction rate. It can help with borehole spacing design, position selection, and determining the direction for optimal gas extraction.  
Conceptualization, C.Y.; methodology, C.Y.; text editing, correction, L.-X.L.; numerical simulation, J.-J.W. and L.F.; engineering experiments, Y.-Y.X.; normal analysis, C.Y.; writing--preparation of the original, C.Y.; writing-review and editing, C.Y. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (52274080), Natural Science Foundation of Hunan Province (2021 JJ40211), Hunan Provincial Department of Education Outstanding Youth Fund Project (21B0486), The Open Foundation of Work Safety Key Lab on Prevention and Control of Gas and Roof Disasters for Southern Coal Mines (E22319).  
Not applicable.  
The authors declare no conflict of interest.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 2 Material and Methods
### Data Collection
#### 2.4.2 Establishment of Management Values  
The fact that side slope failures often take place after rainstorms has led to the belief in a certain relationship between side slope failures and rainfall. Indeed, this relationship between side slope disasters and rainfall has been noticed and proven by researchers such as [PERSON] [31], [PERSON] [32], and [PERSON] and [PERSON] [33]. Nonetheless, some other prior hydrological conditions are essential for the triggering of side slope failures. Pre-storm rainfall injects water into side slope surfaces and allows water to flow freely in the slope. In other words, the soil surface needs to be saturated to trigger the following rainstorm's side slope failure mechanism. The pre-storm rainfall required by side slope failures depends on the soil surface cover, the water conduction capacity of the soil, the seepage rate, evapotranspiration, and the hydrological condition of the side slope.  
Knowing that the influences of pre-storm rainfall on side slope stability have been studied for years, [PERSON] [32] discovered the impact that pre-storm rainfall has on side slope failures. In particular, he found out that higher pre-storm rainfall leads to worse side slope failures. He categorized side slope failures caused by rainfall into the following four groups:  
* Severe events that cause over 50 side slope failures per day;
* Serious events that cause 10-50 side slope failures per day;
* Minor events that cause less than 10 side slope failures per day;
* Independent events that cause only one side slope failure per day.
# Communication Manager for Hyper-Connected RPAS Environments
###### Abstract  
The revolution of Remotely Piloted Aircraft Systems (RPASs), both in the commercial and the research field, has accelerated the arrival of innovative and complex services to the civilian environment within non-segregated airspace. The extensive deployment of these services will still require solving relevant challenges in several topics, such as regulation, security, or diverse technical defiance. In particular, the services to be provided increasingly demand network resources and performance improvements. This scenario will be strongly exacerbated by the upcoming resources provided by the 5G/6G architectures, where Remotely Piloted Arcerafts (RPAs) will likely support multiple communication interfaces and will be able to establish multi-hop network connectivity with numerous devices leading to an unprecedented hyper-connected RPA environment. In addition, future RPASs will have to enhance the management of their connectivity capabilities to comply with the latest regulations, which demand an uninterrupted link for the Control and Non-Payload Communications (CNPC). This article presents a flexible Communication Infrastructure Manager (CIM) based on Software-Defined Networking (SDN) and virtualization technologies capable of handling the complexity inherent to this ecosystem and being adapted to different operation requirements to cope with all these communication challenges. Finally, the article shows several validation experiences to demonstrate the potential of the CIM versus the standard approach.  
RPA; RPAS; RPS; SDN; communications Article
## 4 Privacy and Security Challenges  
The literature of federated learning is focused on developing ways that protect the privacy of data by only sharing the training gradients among nodes instead of the actual dataset [18]. Although strong noise addition techniques prevent some privacy attacks, the security of federated learning still lacks means of security. With its strong technologies, Blockchain integration with federated learning addresses many security concerns [12]. Attacks of tampering with the model by altering its gradients or poisoning the training dataset are still present in such integration.
## 1 Introduction
[PERSON] [20] simplified the nonstationary simulation as the simulation of multiple stationary processes with the wavelet decomposition of the nonseparable EPSD. [PERSON] [21] further optimized the simulation by decoupling the nonseparable EPSD with the proper orthogonal decomposition (POD). In addition to the aforementioned typical treatments, some other schemes were developed to decrease the random variables in the simulation. [PERSON] et al. [22] proposed a dimension reduction SRM to simulate multipoint nonstationary ground motions based on the orthogonal expansion of stochastic processes. [PERSON] et al. [23] further developed the dimension reduction representation for simulating stochastic ground motion fields based on the given wavenumber-frequency spectrum. Although these methods are efficient, they are not tractable and the simulation accuracy may be affected by dimension reduction to some extent. In order to achieve a simpler and more efficient simulation of nonstationary ground motions, an enhanced interpolation approach is developed for simulating nonseparable nonstationary ground motions based on the SRM in this study. Two steps of interpolations are, respectively, utilized to simplify the matrix decomposition and the time-frequency decoupling. The simulation formula is further optimized to reduce the amount of FFT operations. Although a few errors are introduced, the presented approach is highly efficient. The rest of this article is arranged below as follows: Section 2 reviews the basic theory of the SRM-based nonstationary ground motions simulation. The presented approach containing two steps of interpolations and an FFT optimization is introduced in Section 3 in detail. Then, two numerical examples of a nonstationary ground motion simulation are shown in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Inherent Optical Properties (IOPs)
### General Definitions
#### 3.1.2 Henyey-Greenstein Scattering Phase Function  
[PERSON] and [PERSON] [13] proposed the one-parameter scattering phase function given by (oppressing the dependence on the position \(z\))  
\[p(\cos\Theta)=\frac{1-g^{2}}{(1+g^{2}-2g\,\cos\,\Theta)^{3/2}} \tag{10}\]  
where the parameter \(g\) is the asymmetry factor defined in Equation (7). The Henyey-Greenstein (HG) scattering phase function has no physical basis, but is very useful for describing a highly scattering medium, such as turbid water or sea ice, for which the actual scattering phase function is unknown. The HG scattering phase function is convenient for Monte Carlo simulations and other numerical calculations because it has an analytical form. In deterministic plane-parallel RT models it is also very convenient because the addition theorem of spherical harmonics can be used to expand the scattering phase function in a series of Legendre polynomials [3; 14], as reviewed in Section 4.1.1. For the HG scattering phase function, the expansion coefficients \(\chi_{\ell}\) in this series [see Equation (117) below] are simply given by \(\chi_{\ell}=g^{\ell}\), where \(g=\chi_{1}\) is the asymmetry factor defined in Equation (7). The HG scattering phase function is useful for scatterers with sizes comparable to or larger than the wavelength of light. Although the HG scattering phase function is easy to use, it is not as realistic as the Fournier-Forand scattering phase function discussed in Section 3.6.1.
## 2 Current Status of Natural Gas Industry in South Korea and Japan  
### South Korea  
After experiencing two oil crises in the 1970s, the South Korean government established the Korea Gas Corporation (KOGAS) in 1983 to diversify its energy sources. Retailing of NG has been carried out by individual city gas companies. KOGAS, a transmission system operator, has operated the import and wholesale business of NG exclusively. All NG imported into South Korea is traded in the form of LNG, as there are no pipelines connected to other countries. In 2005, the government allowed the direct import of LNG for private consumption, and as of 2020, 22% of total domestic LNG imports were imported directly without going through KOGAS. LNG accounts for the third largest share of South Korea's primary energy supply. As of 2021, the NG imports in South Korea are about 46 million tons. Moreover, South Korea's NG self-sufficiency rate is 0.6%, most of which depends on imports. Major LNG exporters are the Middle East (Qatar, Oman), Southeast Asia (Indonesia, Malaysia), Russia, Australia, and the United States. LNG imported into South Korea is stored at an LNG receiving terminal operated by KOGAS. KOGAS converts the LNG of the storage base back into a gaseous state and transports it to each city gas company and power plant through a nationwide network of NG pipelines [37].  
About 45% of NG imported to South Korea is used for power generation. According to the Ministry of Trade, Industry and Energy, the government intends to phase out coal-fired power plants and replace them with LNG-fired power plants to reduce particulate matter and greenhouse gas emissions [38]. Therefore, the amount of NG used for power generation is expected to increase in the future. In addition, South Korea's final consumption of NG is divided into industrial, building, and transportation sectors. Industrial NG is consumed in industries such as petrochemicals, assembly metals, and steel. Most of the NG for buildings is used as city gas for cooking and heating. Finally, NG for transportation is used mostly as fuel for compressed NG buses. In addition, the demand for NG in South Korea varies from season to season, and it is high in winter due to the increase in heating demand. For example, NG consumption from November to February, the coldest period of the year in South Korea, accounts for 45% of the total annual NG consumption [23].
## 6 Conclusions  
In this research, we aim to address the challenges of high memory usage and prolonged inferencing times in super-resolution reconstruction for remote sensing images. To tackle this, we propose the SRRepViT model by combining ECB and RepViT modules using structural re-parameterization techniques while introducing an additional channel attention module. The objective is to achieve superior super-resolution reconstruction performance while significantly reducing memory footprint and speeding up the inferencing process. Comparing SRRepViT with classic models like EDSR, SwinIR, RCAN, and HAT, our results reveal that SRRepViT maintains similar levels of reconstruction performance, as evidenced by classic evaluation metrics like PSNR and SSIM. Yet, it achieves substantial reductions in model parameters and inferencing time, providing clear texture details in the reconstructed high-resolution remote sensing images. Additionally, compared to other lightweight models, the use of structural re-parameterization in SRRepViT marginally increases inferencing time, while significantly reducing model parameters by 34%. These findings underline that SRRepViT strikes the optimal balance between performance and efficiency in reconstructing remote sensing images, making it promising for deployment in resource-constrained environments. In the future, we will explore a reparameterization strategy for more efficient feature extraction modules, enabling our model to be embedded in edge devices.  
Conceptualization, J.B. and Y.L.; methodology, J.B. and Y.L.; software, J.B. and Y.L.; validation, J.B. and Y.L.; formal analysis, J.B. and Y.L.; investigation, J.B. and Y.L.; resources, J.B. and Y.L.; data curation, J.B. and Y.L.; writing--original draft, J.B.; writing--review & editing, J.B. and J.C.; visualization, J.B. and Y.L.; supervision, J.C.; project administration, J.B., Y.L. and J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Key R&D Program of China (Grant No.2021 YFB2600300).  
Figure 7: Comparison of performance indicators between SRRepViT and other networks.
## 2 Immersed Boundary-Lattice Boltzmann Coupling Scheme
### The LB Method for the Fluid Flow  
#### 2.2.1 Basic Formulation of LBM with Multi-Relaxation-Time Collision  
In this work, the LB model with the multi-relaxation-time (MRT) collision operator is used for the fluid flow simulation. The most common two-dimensional LB model uses a square lattice with nine discrete velocity directions (denoted as D2Q9). The MRT-LB model with external force may be written as:  
\[f_{a}(\textbf{x}+\textbf{e}_{a}\delta t,t+\delta t)-f_{a}(\textbf{x},t)=-M^{- 1}\textbf{S}[m_{a}(\textbf{r},t)-m_{a}^{eq}(\textbf{r}_{i},t)]+\delta tF_{a} \tag{16}\]  
where \(\{f_{a}(x,t):\alpha=0,1,\ldots,8\}\) are the discrete distribution functions at position **x** and time \(t\); \(\{F_{a}:\alpha=0,1,\ldots,8\}\) is the external force term; and \(\{m_{a}(\textbf{r},t):\alpha=0,1,\ldots,8\}\) are the moments of the distribution functions and expressed as \(m=(\rho,e,\epsilon_{j},x_{q},x_{j},y_{q},y_{xx},p_{xy})^{T}\). The relation between \(m\) and distribution function \(f\) is expressed as \(m=Mf\), where M is the transformation matrix [20]; \(m^{eq}\) is the equilibrium moment, which is expressed as:  
\[m^{eq}=\rho\Big{(}1,-2+3u^{2},1-3u^{2},u_{x},-u_{x},u_{y},-u_{y},u_{x}^{2}-u_{ y}^{2},u_{x}u_{y}\Big{)}^{\mathbb{T}} \tag{17}\]  
The diagonal collision matrix **S** is given by:  
\[\textbf{S}=\text{diag}\Big{(}s_{\rho},s_{e},s_{e},s_{x},s_{q},s_{X},s_{q},s_{ v},s_{v}\Big{)}. \tag{18}\]  
The fluid density \(\rho\) and flow velocity **u** are defined as:  
\[\rho=\sum_{a}f_{a},\rho\textbf{u}=\sum_{a}\textbf{e}_{a}f_{a} \tag{19}\]
###### Abstract
In the present paper, we first study the application to the non-linear forced motions of a tower-like mechanical structure in the form of a standing (inverted) rigid pendulum with a non-linear restoring moment under a ground excitation of the earthquake-type and gravity. Structural parameters are taken from a real tower discussed in [17]. For small vibrations of the earthquake-excited pendulum with an inelastic spring considering the P-Delta effect, see [18]. For some other advanced applications of pendulum vibrations in the present context, see, e.g., [19; 20]. In the following, we consider two cases of excitation: a harmonic excitation, which in vertical direction is in primary resonance, and a real earthquake excitation from recorded acceleration data. At a smaller scale, the model of a standing pendulum is also of interest for damping ground-excited vibrations of mechanical structures, see [21]. In the present contribution, we utilize results of the fourth-order Runge-Kutta method [16] as guess in the first step in the extended Picard iteration. Very similar time recordings of the non-linear structural response of our method are observed for our method on a standard computer, when compared to the standard initial value problem (IVP) solver NDSolve [12], but with substantially smaller computation times. Since computation times are also much smaller than the real observation periods of earthquake with a long duration, the technique should be of interest for real-time applications, such as in automatic control, particularly also because of the underlying symbolic formulation. In the last part of the paper, as a first step towards applications to non-linear multi-body dynamic systems, we present the application to free vibrations of a hanging double pendulum, for which also exact solutions do exist. In this example, two explicit discrete-mechanics type solutions originally suggested by [PERSON] [15] are used and compared as starting guesses in the first Picard iteration. Symbolic computations are performed in Maple [22]. In order to show that the method can also be used conveniently for linear dynamic problems, we restrict to free vibrations, for which closed-form solutions do exist, and concentrate on accuracy aspects here. Excellent accuracy with respect to the exact solution is demonstrated, also for a comparatively large observation period, which proves that the method can be applied with high advantage for linear vibrations also.
## 3 Suppression Strategy of Synchronous Vibration and Multi-Stage-Blade Modal Vibration
### Bandwidth Factor Rectification Method of the ANF
According to the rotor displacement specified in the ISO14839-2 2004 standard, this research assumed that the amplitude of the rotor displacement \(x\) is 0.3, 0.4, 0.5, 0.75, and 1 times the protective gap \(p\). This assumption is used to explain the displacement stiffness \(k_{h}\) perturbation phenomenon caused by the change in rotor displacement \(x\), which causes a change in open-loop cutoff frequency \(w_{c}\) and brings instability to the phase margin \(\gamma\) of the MSR system and is not the ultimate control target. The open-loop transfer function \(G_{\rm ol}(s)\) of the AMB-rotor system without an ANF is \(G_{\rm ol}(s)=k_{s}G_{c}(s)G_{w}(s)G_{m}(s)\). Figure 9a shows the magnitude-phase frequency characteris  
Figure 8: Diagram of electromagnetic force \(f_{\rm X}\) and displacement stiffness \(k_{h}\). (**a**) Relationship between \(i_{\rm X}\), \(x\), and \(f_{\rm X}\). (**b**) Relationship between \(i_{\rm X}\), \(x\), and \(k_{h}\). tics of the open-loop system with different rotor displacement \(x\). Figure 9b shows the phase margin \(\gamma\) at the open-loop cutoff frequency \(w_{c}\). As listed in Table 1, at a rotational speed of 120 Hz, by the ANF bandwidth factor rectification method based on displacement stiffness \(k_{h}\) perturbation, according to (22), (26), (28), and (29), with a change in \(x\), \(k_{h}\) is perturbed, so the distance between \(w_{c}\) and \(w=\Omega\) is obviously different, and \(\gamma\) is different; finally, the critical values of \(\varepsilon_{\max}\), \(\Delta f_{\max}^{\text{ANF}}\), and \(Q_{\min}^{\text{ANF}}\) are also different. From the previous analysis in Section 3.1, although any value of \(\varepsilon\) obtained by this method less than \(\varepsilon_{\max}\) can ensure the phase stability margin \(\gamma\), a larger value of \(\varepsilon\) can improve the convergence speed of the algorithm and the mismatch tolerance of the notch frequency. Compared to existing algorithms with fixed \(\varepsilon\), this method not only can solve the problem that the consistent effect of co-frequency synchronous vibration suppression cannot be guaranteed with a change in rotor speed \(\Omega\) but also can avoid the problem that the phase-lag angle \(\varphi(\varepsilon)\) introduced by an inappropriate \(\varepsilon\) of the ANF affects the phase stability margin \(\gamma\) of the MSR system.
## 2 Mathematical Modelling
### Model Formulation
#### 2.2.3 Objective Function  
The target of vehicle scheduling is to arrange the most suitable vehicle for each departure task and form the most reasonable vehicle chain for each vehicle so that the system can operate efficiently and orderly with the lowest total operating cost. In a real-time scenario, vehicles need to be assigned in a coordinated manner because there are multiple departure time points in a time period, and the good or bad vehicle scheduling solution affects not only the length of vehicle mileage but also the size of vehicles. Therefore, the optimization objective of the model is determined as the total number of vehicles used and the total traveling mileage.  
The number of the system's vehicles is determined by the number of tasks that need to be completed during the time period, and the vehicle scheduling scheme also influences it. If 2 tasks can be directionally matched to each other, they can be completed with the same vehicle, reducing the number of vehicles needed by 1. The more vehicles are matched between task points, the more the vehicle size is reduced, and the fewer vehicles are required. This leads to the formula for calculating the required vehicle size, as shown in Equation (1).  
\[Z_{1}=\sum_{k\in K}\left(M_{k}-\sum_{i,j\in M_{k}}y_{ij}\right) \tag{1}\]  
2. Traveling mileage  
The total mileage during the time period includes two categories: fixed mileage and deadhead mileage. The fixed mileage refers to the vehicle mileage required between the departure station and final station of each task. The vehicle task is determined, so the fixed mileage is certain, and the reduction of the driving mileage only needs to consider the deadhead mileage. The formula for calculating the total deadhead mileage is shown in Equation (2).  
\[Z_{2}=\sum_{k\in K}\sum_{i,j\in M_{k}}s_{ij}y_{ij} \tag{2}\]  
The model's objective function is obtained by combining the two optimization objectives.  
\[\text{min}\ Z_{p}=Z_{1}^{\prime}+Z_{2}^{\prime}=\sum_{k\in K}\left(p_{k}(M_{k}- \sum_{i,j\in M_{k}}y_{ij})+q_{k}(\sum_{i,j\in M_{k}}s_{ij}y_{ij})\right) \tag{3}\]