esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 4 Results  
### Analysis of GDP Modeling Results  
#### 4.1.1 Spatial Regression Analysis  
Spatial modeling analysis found that the MNL, TNL, and CNLI are not highly correlated with the GDP, but they are highly correlated with the per capita GDP, so we used the NTL index and per capita GDP for modeling analysis. Table 2 shows the regression results of five models in 2013, including the linear regression model, quadratic regression model, exponential model, power function model, and logarithmic model, with the NTL index of 31 provinces as the independent variable and the per capita GDP as the dependent variable, taking mainland China as a whole, and taking the per capita GDP as the dependent variable. The results show that the \(R^{2}\) of each model of the MNL is between 0.421 and 0.795, with a high correlation; the \(R^{2}\) of the TNL is between 0.011 and 0.027, with a very low correlation; and the \(R^{2}\) of the CNLI is between 0.408 and 0.715, which is worse than the model of the TNL but not as good as the relevant model of the MNL, so the MNL and per capita GDP data were selected for fitting. From the regression results of each year, among the five function models, the quadratic function regression model has the best fit, and the \(R^{2}\) value is the highest in the model, so we choose the quadratic regression model to construct the function model.  
As shown in Table 3, the optimal function model constructed from 2013 to 2023, the \(R^{2}\) values of the functions are greater than 0.7, and the fitting effect is good. In addition, we calculated the RMSE from 2013 to 2023. The RMSE ranges from 0.91 to 1.95, and the average RMSE is 1.33. Among them, the RMSE of 6 years of data is less than the average. The smallest error is in 2013, the largest error is in 2023, and the error values from 2019 to 2023 are higher than the average. The overall error is within the allowable range, and the fit is higher and better.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 6 Performance Analysis
### Science Data Flow (DTNbox Approach)  
Now, let us examine the case of alternate delivery via DTNbox, which is the focus of this work. Although delivery results (Figure 10) are not exactly the same as before (Figure 9), which would have been impossible because the transmission mechanism is different, they are very similar. Comparing the two figures, it is evident that the delivery time penalty due to indirect delivery via DTNbox is negligible in space environments. In particular, the delivery of all 57 bundles is completed in roughly the same time as in the previous case (by inspection of numerical data we can see that it takes just 6 s more) although larger variations can be found on single bundles.  
In more details, it must be noted that the DTNbox processing time is essentially due to the fixed interval between updates (5 s), and thus can be safely assumed uniformly distributed in this range. Link intermittency, however, can either eliminate or expand this delay because bundles cannot be transmitted with continuity, but only when the link to the next node opens. Therefore, if the contact to the next node is closed, the DTNbox processing delay does not translate into an actual transmission delay (and eventually in a delivery time penalization). By contrast, in the unlucky opposite case, a small processing delay can result into a huge delivery time penalization, if it causes the missing of a contact. In brief, we can say that in an intermittent environment everything goes as in a journey consisting of  
Figure 9: Science data flow (bulk priority) from Lander to PDGS: time sequences of bundle generation and delivery. Direct delivery by means of DTNperf.  
multiple flights. If there is a delay in the landing of one flight, this can be either uninfluential if we can still take the connecting flight, or substantial, if, however small the delay, we miss the connection.  
Note that as actual penalizations depend on the contact plan, it would be impossible to derive statistical evaluations with a general scope from results achieved. However, as the numerical result obtained refer to a scenario quite representative of future interplanetary networks, the authors are reasonably confident that the evaluations reported here can have a general value.
## 4 Analysis of Fire Evacuation
### Analysis of Simulation Results
Figure 10: Distribution of people at different evacuation simulation times (disordered): (**a**) 20.4 s; (**b**) 200.1 s; (**c**) 300 s; and (**d**) 619.5 s.  
Figure 10.0 Relationship between flow rates for selected doors and time in cases of disorderly evacuation. Figure 10.1 Roadmap for orderly evacuation of each floor: (**a**) first floor; (**b**) second floor; (**c**) third floor; (**d**) fourth floor; (**e**) fifth floor; and (**f**) sixth floor. Comparing the simulation results of orderly evacuation and disordered evacuation, it is found that the time for personnel to leave the floor is much smaller than in disordered evacuation, which shows that orderly evacuation can greatly improve the efficiency of personnel evacuation under fire conditions. Specific data are shown in Figure 10.2  
The simulation shows that the evacuation time for the ordered evacuation is 309.8 s. Considering the evacuation preparation time and safety margin, the necessary evacuation time for orderly evacuation is 60 + 309.8 \(\times\) 1.2 = 431.76 s. Since the fire danger time is 709.2 s, the evacuation time for personnel evacuating in disordered evacuation must be 803.4 s, and the personnel cannot escape completely; in the case of orderly evacuation, the necessary evacuation time is 431.76 s, which is much smaller than the fire danger time, so in the case of orderly evacuation, the personnel can be safely evacuated from the teaching building. Under the Pathfinder2019 simulation environment, the total evacuation time of the disordered model was set to 803.4 s, whereas that of the ordered model was set to 431.76 s. The evacuation time was reduced by 371.64 s, and the evacuation efficiency was increased by 46.2%. Evidently, the orderly evacuation was beneficial to reducing the overall evacuation time in the present case. \begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **Floor** & **Disorderly Evacuation** & **Orderly Evacuation** \\ \hline  & 6 & 205.4 & 73.6 \\  & 5 & 406.2 & 84.8 \\ Time taken for people to & 4 & 429.8 & 202.0 \\ leave the floor(s) & 3 & 485.9 & 230.0 \\  & 2 & 554.5 & 267.2 \\  & 1 & 619.5 & 309.8 \\ \hline Total time spent(s) & & 619.5 & 309.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Evacuation time analysis for each floor. Figure 14: Relationship between flow rates for selected doors and time in case of orderly evacuation.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 4 Hydrogen Transportation and Distribution Technologies  
After the production and storage processes, hydrogen needs to be transported from the site where it was produced to the site where it will be used. This process is composed of two phases, namely the transportation phase, where large volumes of hydrogen are transported from the production plants to a city gate, and the distribution phase, which follows the transportation phase and guarantees that the hydrogen is delivered to the demand sites.  
There are three main options to transport and distribute hydrogen, namely by road, by pipeline, or by ship. Transporting and distributing hydrogen by road, using trucks, can be attractive for small volumes and short distances [79; 80; 81]. With further increases in volume or distance, pipelines present the most cost-effective way to transport hydrogen [82]. Depending on the desired volume, distribution pipelines with smaller or larger diameters can be used [82]. When considering long distances, especially transportation across water, ships become the most attractive option [82; 83].  
According to the International Renewable Energy Agency (IRENA) [82], it is not possible to highlight only one specific technology as the most cost-effective, either now or based on the predicted pathway to 2050. This is because this consideration not only depends on the technology performance but also on the specific volume to be transported and on the desired distance to be covered.
## 2 Methodology
### Theoretical Calculation Methodology
#### 2.3.3 Anti-Overturning Stability Calculation Method  
In elevated conditions, a WTIV is subjected to combined environmental loads, inertial loads, P-\(\Delta\) effects, and functional loads (predominantly crane operations). These loads collectively generate an overturning moment \(M_{o}\), which must be counterbalanced by the stabilizing moment \(M_{s}\) derived from gravitational forces. The design verification requires analysis of the most critical load combination, typically assuming concurrent environmental and functional load directions. The overturning safety criterion is expressed as  
\[\gamma\leq\frac{M_{s}}{M_{o}}=\frac{M_{so}-n\cdot P\cdot\Delta}{M_{ax}+M_{aw}+ M_{DAF}+M_{orange}} \tag{11}\]  
where \(\gamma\) is the safety factor against overturning, with \(\gamma\) = 1.1 being the minimum requirement for jack-up units according to ISO 19905-1 [25]: \(\Delta\) represents the total hull sway (lateral displacement) primarily induced by environmental loads; \(P\) denotes the average axial leg load attributable solely to functional loads, where hull-level analysis typically suffices; \(n\) indicates the number of legs; \(M_{ax}\) corresponds to the moment generated by combined wave and current loads; \(M_{w}\) stands for the wind-induced moment; \(M_{DAF}\) characterizes the inertia moment incorporating stochastic dynamic amplification factors (DAFs); and \(M_{orange}\) quantifies the operational moment from crane operations.
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 1 Introduction
[11] used LES to simulate the normal shock train in a 3D constant-area isolator, and confirmed that the isolator's side wall has strong 3D effects. Due to the LES computational cost, this study only conducted a relatively low Reynolds flow simulation. [PERSON] et al. [12] developed a LES solver based on the conservative finite difference method, and carried out LES simulations for a hypersonic inlet/isolator that was able to capture the large-scale features of the unsart process, nearly identical to the experiment results. Although the current computing power and storage capacity have been greatly improved, direct numerical simulation (DNS) of the Navier-Stokes equation is still limited to solving some extremely simple configurations with low Re, having no engineering application value. In 2014, NASA, together with Boeing, Pratt and Whitney and other aerospace industry giants, released a vision of the future development for CFD, which emphasized the use of LES for large-scale simulations within the predictable range of computer computing power development (before 2030) is still unrealistic [13], and the hybrid of RANS/LES algorithms provides a viable alternative, of which DES is a typical representative. To sum up, as far as the current level of computer development is concerned, RANS will continue to be considered as the main method for CFD, but most of the popular CFD commercial software's highest spatial accuracy stops at 2 nd or 3 rd order, such as Fluent, CFX, StarCCM++, and NUMECA. Only some of the in house CFD solvers can achieve higher order accuracy. Therefore, this research concentrates on this aspect and develops a high-performance CFD solver, which is based on multi-block structural grids with the finite volume method to solve the compressible RANS equations, mainly using the LU-SGS (steady-state)/dual-time step LU-SGS (transient-state) implicit time integration method. These methods are integrated with high-order reconstruction schemes such as 3 rd-order MUSCL and 5 th-order WENO/MP. A variety of flux computation schemes including FDSschemes (such as Godunov and Roe), FVS schemes (such as Steger Warming and HLLC), and the mixed schemes (such as AUSM and AUSM+) are also embedded. Combined with the \(k-\omega\) SST turbulence model, this solver can be used to accurately capture the turbulent flow details. For the purpose of improving computational efficiency, it is designed to be compatible with MPI/OpenMP parallel algorithms, realizing the high-precision and high-efficiency CFD simulation inside the supersonic inlet/isolator. In order to compare the solutions of RANS/DES/LES, this paper uses the mainstream CFD commercial software ANSYS Fluent 18.0 to achieve DES and LES simulations of this supersonic inlet/isolator with the aim of capturing the detailed vortex structures.
## 3 Analysis of Test Results
### Changes in Chemical Composition
In different temperature cycling intervals, the I\({}_{\text{S=O}}\) of SBS-modified asphalt mastic shows the same trend of change. As the number of temperature and humidity coupling cycles increases, the value of I\({}_{\text{S=O}}\) demonstrates a continual rise. Comparing this with the uncycled mastic, the I\({}_{\text{S=O}}\) content of the mastic after 12 cycles in the \(-20\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C, \(-20\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C, and \(0\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C intervals increased by 94.30%, 52.88%, 50.37%, and 53.12%, respectively, indicating an increase in the sulfoxide content of SBS-modified asphalt mastic after temperature and humidity coupling cycles. The increase in the number of temperature-humidity coupling cycles and temperature difference exacerbates the aging of SBS-modified asphalt mastic to varying degrees, which is consistent with the laws observed in previous conventional tests, viscosity tests, and mechanical tests. This is because, under the action of high temperature and oxygen, hydrogen peroxides are formed in the mastic. As an oxidant, it oxidizes sulfur-containing functional groups, such as sulfides and thiols in asphalt molecules, into sulfoxide functional groups. Meanwhile, the I\({}_{\text{B}}\) value decreases with the increase in the number of temperature-humidity coupling cycles, and the I\({}_{\text{B}}\) value of the mastic in the \(-20\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C range decreases the most by 58.89% during cycling, indicating that the SBS modifier undergoes degradation under the action of temperature and humidity. Water molecules interact with SBS polymer molecules, accelerating the cleavage of the C=C double bonds in the polybutadiene segments of the SBS modifier, thus disrupting the network structure formed by SBS.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 4 Problem Formulation and Analysis
### Objective Function  
From the perspective of the system, the LEO-ECN has limited energy resources, so it needs to minimize the energy consumption of all tasks, and on the other hand, the computing capacity is also limited, and we should properly allocate the computing resources of all satellites, balance system load and improve system resource utilization [5]. Therefore, the overall cost function could be formulated as Equation (16).  
\[\begin{split}\min_{n,m}\Theta&=\min_{n,m}\sum_{n\in \mathcal{N}}\sum_{m\in\mathcal{M}}E_{n,m}+\beta\sum_{m\in\mathcal{M}}(\sum_{ n\in\mathcal{N}}C_{n}x_{n,m}-\sum_{n\in\mathcal{N}}\frac{C_{n}}{M})^{2}\\ &=\min_{n,m}\sum_{n\in\mathcal{N}}\sum_{m\in\mathcal{M}}(P_{n} \frac{x_{n,m}D_{n}}{R_{n}}+\beta\sum_{i=2}^{hop}P_{i}\frac{x_{n,m}D_{n}}{R_{i }}+\epsilon(f_{m})^{2}x_{n,m}C_{n})\\ &+\beta\sum_{m\in\mathcal{M}}(\sum_{n\in\mathcal{N}}C_{n}x_{n,m }-\sum_{n\in\mathcal{N}}\frac{C_{n}}{M})^{2}\end{split} \tag{16}\]  
In (16), \(\Theta\) denotes the overall cost, and \(\beta\) is the weight of the load balance cost. With the definition and the constraints of (3)-(5) above, the objective function is a quadratic programming problem, which is convex, since \(x_{n,m}\) is continuous. We use opensource software CVPY [14] to solve the problem.
## 5 Potential Challenges
### Security Issues  
In NOMA, multiple user messages are superimposed in a single resource block (RB). The SIC receiver is used to decode the overlaid signals at the receiver. In the SIC receiver, the strong users are allocated with lower powers, and weak users are with strong powers for channel fairness. At the receiver, strong users have to subtract the low power signals to get their signals. But it causes some security issues, which have to be considered in NOMA. These security issues can be overcome using encryption and decryption schemes at the sender and receiver. This process increases the latency and processing requirements. But the basic idea of NOMA in 5G is to decrease the latency and to improve the data rates, spectral efficiency, and bandwidth. The NOMA implementation causes security issues such as the implementation of SIC, transmitted powers and outage probability. These security issues mainly occur in the physical layer. To overcome these security issues, optimal and analytical solutions must be thoroughly investigated.
# Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground
## 1 Introduction
Therefore, this paper proposes a method using inductive sensors to identify and locate anchor holes in steel strips. Inductive sensors are a type of metal-inductive linear device [20], whose sensing surface will generate an alternating magnetic field when electrified. When a metal object approaches the sensing surface, the metal will generate eddy currents, which absorbs energy from the oscillator and leads to a linear decrease in the output amplitude of the oscillator. Based on the changes in the decrease, the non-contact detection of the object is completed. The entire laid steel belt is scanned with inductive sensors. When anchor holes, grooves, or edges on the steel belt are scanned, the voltage output of the inductive sensors will change. Through the analysis of the voltage at different positions, the anchor holes can be identified and located. This method has no sliding contact points and will not be affected by non-metallic factors such as dust during operation, making it broadly applicable in coal mine roadways. This paper adopts the real-time operating system FreeRTOS. Tasks such as system operation, data collection, and data transmission are established in this system. Additionally, task switching is achieved by setting task priorities, which ensures the real-time performance of the system [21; 22].
## 4 Discussion
If the presence of coherent waves is confirmed, SMD may continue to fully process the data. The analysis of SMD results that was conducted in this subsection was not based on machine learning but instead on our understanding of the correlations between compressed data and attributes of the recorded waves. In the future, those strong correlations will be used with machine learning to train algorithms to accurately infer various properties of the source and the surrounding velocity model directly from compressed data. Additionally, application of data from marine surveys is difficult because of large amounts of interference between arriving waves. Thus, the SMD method may be more effective in different circumstances, such as in unconventional reservoirs monitored by distributed acoustic sensing. Thus far, we only considered application of SMD to two-dimensional seismic data, obtained by a single line of receivers. However, receivers may often be distributed over an area, in a large number of lines. In such case, SMD may still be applied individually, to the data collected from each line. However, there may be a lot of redundancy between SMD results from each of the lines of receivers. Similar redundancies can also occur by applying SMD to multiple files from different times, obtained by a same set of receivers. In future work, we will take advantage of the fact that SMD results may be similar for batches of data collected from different receivers or during different times. Dictionary learning could be applied to SMD results to further compress the data, although we can not yet recommend a method for learning the dictionary. Future work will therefore likely include further compression by applying dictionary learning to SMD results from multiple data files or multiple receiver lines.
## 3 Physical-Layer Network Coding Based on Block Markov Superposition Transmission
### Encoding Algorithm
for\(t=0\)to \(L+m-1\)do if\(t<L\)then  Split \(\mathbf{u}_{i}^{(0)}\in\mathbb{F}_{2}^{k(n-2)}\) into: 1. Left-most \(\mathfrak{a}\) bits \(\rightarrow\) Encode into \(\mathfrak{a}\) R codewords; 2. Remaining bits \(\rightarrow\) Encode into \(\beta\) SPC codewords;  Combine to form an RSPC codeword \(\mathbf{v}_{i}^{(t)}\in\mathbb{F}_{2}^{n(n-2)}\); else  Set \(\mathbf{u}_{i}^{(t)}=\mathbf{0}\) and \(\mathbf{v}_{i}^{(t)}=\mathbf{0}\); // Termination  end for for\(j=0\)to\(m\)do  Calculate \(\mathbf{w}_{i}^{(t,j)}=\mathbf{v}_{i}^{(t-j)}\Pi_{j}\);  end for  Calculate the \(t\)-th block \(\mathbf{c}_{i}^{(t)}=\sum_{\partial S\leq m}\mathbf{w}_{i}^{(t,j)}\). end for
```  
**Algorithm 1**Encoding algorithm for node \(N_{i}\), \(i\in\{1,2\}\)  
Figure 4: This is the schematic diagram for block Markov superposition transmission. Figure 5: This is the block diagram for a BMST system with memory \(m\). Because of the termination operation, the effective code rate \(\mathcal{R}^{\prime}\) of BMST-RSPC is reduced to \(\frac{L}{L+m}\) of the basic code rate \(\mathcal{R}\). Notably, when \(L\) is sufficiently large (i.e., \(L\gg m\)), the system's code rate asymptotically approaches \(\mathcal{R}\), rendering the rate loss negligible. Therefore, there is no need to worry about the code rate loss. Additionally, since the interleaving (from \(\mathbf{v}_{i}\) to \(\mathbf{w}_{i}\)) and the encoding (from \(\mathbf{u}_{i}\) to \(\mathbf{v}_{i}\)) are executed in parallel, the overall encoding latency of the BMST-RSPC system can be comparable to that of the basic code, provided that parallel processing resources are available.