esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, an improved FTC scheme was developed for the attitude systems of a flexible variable structure spacecraft in the presence of disturbances, multiple actuator faults, and saturation. A fault detection and estimation approach is presented to detect the time of actuator faults occurrence and estimate the amplitude of unknown faults. On this basis, the improved adaptive FTC with variable structure harmonic functions was designed in the framework of the fast terminal sliding mode technique, which ensures that the faulty attitude system is asymptotical convergence in finite time. Thus, the proposed FTC scheme achieves unified compensation for multi-location (different actuator channels) multi-type (bias and complete failures) concurrent faults in nominal and variable structure flight modes. Simulation results verified the good fault-tolerant performance using the proposed active FTC scheme. However, the proposed FTC scheme did not consider the sensor fault problem, and how to design a FTC strategy to compensate the effects of sensor fault is our future research works.  
K.-Y.H. provided methodology, validation, and writing--original draft preparation; W.S. and C.Y. provided conceptualization, writing--review & editing, and supervision; and W.S. and K.-Y.H. provided funding support. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61922042, Grant 61873127, and Grant 61773201, the enterprise cooperation project between Nanjing University of Aeronautics and Astronautics, and AVIC 601 Research Institute under Grant 1003-KFB18233.  
Not applicable.  
The authors of this paper thank the fault diagnosis and fault-tolerance control team of Nanjing University of Aeronautics and Astronautics for their technical support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FTC Scheme** & **Convergence Time: \(\sigma\)** & **Steady Precision: \(\sigma\)** & **Convergence Time: \(\omega\)** & **Steady Precision: \(\omega\)** \\ \hline FTC without anti-saturation method & 34.5 & \(2\times 10^{-4}\) & 35.7 & \(4.5\times 10^{-4}\) \\ FTC in this paper & 5.1 & \(4\times 10^{-5}\) & 5.2 & \(6\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of performance with proposed FTC without anti-saturation method.
## 3 Data Preparation and Analysis  
### Overview of the Study Area  
The study area is located in Gengma, Cangyuan, and Shuangjiang counties in the southwestern part of Lincang City, Yunnan Province, with geographic coordinates between 98\({}^{\circ}\)52\({}^{\prime}\) and 99\({}^{\circ}\)43\({}^{\prime}\) E longitude and 23\({}^{\circ}\)04\({}^{\prime}\) and 24\({}^{\circ}\)01\({}^{\prime}\) N latitude (Figure 3). We primarily selected the study area in the region of the 1988 Lancang-Gengma earthquake, and we took the area extending 10 km outside the VII-degree isobar of the Gengma earthquake as the study area. A multitude of landslide geological hazards exist in the study area, and the study placed a major focus on the evaluation and analysis of the landslide geological hazards in the area extending 10 km beyond the VII isoseismic line. The overall terrain of the study area is high on all sides and low in the middle, belonging to the Hengduan Mountain Range with high mountains and valleys. Most of the mountains are north-south; the northeast side of the mountain form is high and steep, with the highest elevation of 2931 m; the southwest is characterized by gullies and ravines, with the lowest elevation of 676 m; the height difference of the entire study area is 2255 m; and most of the terrain slope is 15\({}^{\circ}\)\(\sim\)25\({}^{\circ}\). Located in the Indian plate and the Eurasian plate collision zone of the southern extension of the Himalayan rift, frequent earthquakes, the geological and tectonic landscape is more complex, and it is located in the southern section of the Hengduan mountain system, located in the low latitude of the Tropic of Cancer. Moreover, the sun's altitude angle is large, the range of change is small, and the intersection of the northern tropics and subtropical south of the combination of the Indian Ocean is mainly subjected to the impact of the warm and humid climate and the southwestern monsoon, as well as the formation of the dry and wet seasons. Thus, the vast majority of valleys and slopes are located in the soft rock slope zone, providing favorable topographic conditions for the occurrence of landslides [51].
## 4 Simulation Results  
### Encryption and Decryption Results  
The numerical simulation for the proposed encryption has been carried out on a PC with Intel (R) Core(TM) i5-7200U CPU 2.50 GHz and 8 GB memory capacity. Python 3.7 is used for this numerical simulation. The outer spherical \(R\), inner spherical \(r\) and the wavelength \(\lambda\) are set as \(500\times 10^{-3}\) m, \(50\times 10^{-3}\) m and \(250\times 10^{-6}\) m, respectively, as shown in Table 1. According to the sampling theorem in the spherical diffraction calculation [41], the pixel numbers in the azimuthal and vertical directions are at least 2514 and 1257, respectively. If we take 1/5 of the surface for spherical diffraction, the minimum number of pixels required in the azimuthal and vertical directions are 503 and 252, respectively. Therefore, the size of the original image is selected as \(512\times 512\) pixels. Figure 5a-d show the original images with \(512\times 512\) pixels for the proposed method. Figure 5e,f shows the private key of the first image \(P_{1}\) and the combined image \(f\), respectively. Figure 5g,h shows the ciphertext \(C\) and the private key \(PK\), respectively. Figure 5a1-d1 shows decrypted images with correct keys.  
To evaluate the decryption quality, the peak signal-to-noise (PSNR) and the correlation coefficient (CC) between the decrypted and original images are calculated. The formula of PSNR and CC can be given by  
\[PSNR(I_{o},I_{d})\ =\ 10\log_{10}\left[\frac{M\times N\times 255^{2}}{\sum_{ \forall x,y}[I_{o}(x,y)-I_{d}(x,y)]^{2}}\right], \tag{19}\]  
\[CC\ =\ \frac{E[[I_{o}-E[I_{o}]][I_{d}-E[I_{d}]]]}{\sqrt{E\big{[}[I_{o}-E[I_{o}] ]^{2}\big{]}}\sqrt{E\big{[}[I_{d}-E[I_{d}]]^{2}\big{]}}}, \tag{20}\]  
where \(M\times N\) is the size of image and \(E(\bullet)\) denotes the expected value of the function, \(I_{o}\) and \(I_{d}\) represent the original and decrypted images, respectively. The PSNR and the CC values between the original images and corresponding decrypted images are infinity and 1, respectively, which shows the decrypted images are lossless.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(R\) & \(r\) & \(\lambda\) & \(x_{n}\) & \(\mu\) \\ \hline \(500\times 10^{-3}\) m & \(50\times 10^{-3}\) m & \(250\times 10^{-6}\) m & 0.50 & 3.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The algorithm’s parameters.  
Figure 4: Process of decryption.
## 4 Experiments and Results
### Results Analysis  
#### 4.4.1 The Effectiveness of Data Augmentation  
In this study, we sought to enhance the performance of our classification model by expanding the original dataset through some methods of data augmentation. To evaluate the effectiveness of this pre-processing step, we conducted a series of ablation experiments. The classification model based on Triple-EfficientNet was used to conduct experiments on the training set and the test set before and after expansion. As can be seen from the results in Table 2, the application of data augmentation resulted in a significant increase in classification accuracy for both the training set and the test set. Specifically, the accuracy of the training set increased by 31.4%, while the accuracy of the test set increased by 22.4%. These results demonstrate that pre-processing a small sample dataset through data augmentation can improve the network's ability to extract more comprehensive rock features, thereby enhancing the model's overall generalization capability.
## 3 Finite Element Simulation
### Finite Element Model  
For the pit in a finite element simulation, there is no need to adopt complex random geometric shapes. A simple spherical shape is sufficient for accurate fatigue life predictions [11]. Based on the experimental results of [PERSON] et al. [17], a semi-ellipsoidal pit model was used to simulate the pit shape. On the basis of analyzing a single-pit wire, two relative position distributions of pits on the wire are discussed: along the \(Z\)-axis and along the \(X\)-axis, as illustrated in Figure 3.  
The accuracy and computational efficiency of the finite element model are influenced by the shape and density of the mesh. Therefore, it is crucial to partition the mesh reasonably. Considering the stress distribution characteristics of steel wire with pits, this paper adopts a regional partitioning method. More refined mesh partitioning is applied to the damage-sensitive pit areas to meet the requirements for accurate calculation of damage evolution analysis at the pit. The mesh size in the regions far from the pits on both ends of the wire can be appropriately increased to improve computational efficiency.  
After element mesh, the calculated elemental stress and nodal stress consistently differ. However, if the disparity is too significant, it indicates insufficient refinement in the meshing at that location [18]. Based on this concept, [PERSON] et al. [19] used the error values between calculated elemental stress and nodal stress to validate the adequacy of mesh refinement. To verify the correctness of the element mesh, different mesh accuracies are applied to partition the pit area of the steel wire, and calculations are performed separately. Finally, a comparison is made between the maximum element stress (\(S_{E}\)) and the maximum node stress (\(S_{N}\)). It is generally considered that the mesh partitioning is reasonable if the deviation between the two is within 5%. The calculation results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Accuracy (mm)** & \(S_{E}\) **(MPa)** & \(S_{N}\) **(MPa)** & \(S_{E}\)/\(S_{N}\) **(\%)** \\ \hline
0.4 & 968.24 & 1055.12 & 91.77 \\
0.2 & 996.95 & 1066.67 & 93.46 \\
0.1 & 1019.91 & 1062.04 & 96.03 \\
0.05 & 1041.12 & 1063.03 & 97.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of maximum element stress and maximum node stress under different meshes.  
Figure 3: The schematic diagram of corrosion pit. (**a**) Pit distribution along the \(X\)-axis on the surface of the steel wire; (**b**) Pit distribution along the \(Z\)-axis on the surface of the steel wire.  
Considering the trade-off between computational cost and accuracy requirements, a mesh size of 0.1 mm is chosen for the area around the pit, while the element size at both ends of the wire is set to 0.4 mm. The mesh of the element is illustrated in Figure 4.
###### Abstract  
It is imperative to understand the shear mechanical properties and pore evolution of granite under thermal shock to assess the fracturing of hot dry rock reservoirs. In this study, variable-angle shear tests were performed on coarse- and fine-grained granite samples following liquid nitrogen (LN\({}_{2}\)) cooling under different high-temperature conditions. The effect of thermal treatment temperature, particle type, and cooling method on the shear strength, cohesion, and angle of internal friction of granite was then analyzed. To this end, low field nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM) were used to investigate the pore size distribution and microstructural evolution of granite. The experimental results indicate that both the shear strength and cohesion of granite initially increase and then decrease with the rise in thermal treatment temperature. The maximum increases in shear strength and cohesion are 38.0% and 36.7%, respectively, while the maximum decreases reach 43.7% and 42.4%. Notably, the most pronounced thermal hardening effect is observed at 200 \({}^{\circ}\)C. In contrast, the internal friction angle exhibits a decreasing-then-increasing trend as the temperature rises, with a maximum reduction of 5.4% and a maximum increase of 14.5%. In addition, fine-grained granite exhibits superior shear strength and a more pronounced thermal hardening effect compared to coarse-grained granite. Furthermore, the damage effect caused by thermal shock increases with increasing heat treatment temperature, with the damage effect induced by liquid nitrogen cooling being particularly significant compared to water cooling. Furthermore, for both types of granite at the same shear angle, an increase in the heat treatment temperature results in a corresponding increase in the total fracture area, with the fracture area after liquid nitrogen cooling being more significant. The macroscopic failure mode changes from a mixed compression-shear failure mode to a direct shear failure mode with increasing shear angle. NMR testing shows that liquid nitrogen cooling can effectively increase the proportion of medium pores and large pores in the granite and increase the connectivity of internal pores; specifically, in coarse-grained granite, medium pores and large pores collectively increased by 10.5%, while in fine-grained granite, the total increase in medium pores reached 51%. As the heat treatment temperature increases, the type of crack that develops in granite changes from intragranular to transgranular. In addition, the fracture surface of granite is more prone to form micropores and small pores when cooled with liquid nitrogen, increasing the connectivity of the crack network. The results of this research will be useful for fracturing hot dry rock reservoirs.  
**Keywords:** liquid nitrogen cooling; hot dry rock; shear mechanical properties; pore structure
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 3 Analysis of OFDM/FHMA System Based on Mini-Slot Frame Structure
### Signal Analysis for Reliable OFDM/FHMA System
Next, \(q_{m}(t)\) is input into the correlator and multiplied by the conjugation of the channel gain of the desired user \(g_{m}^{(1)^{*}}\). Then, the output signal at the \(m\)-th branch \(Z_{m}^{\prime}\) is obtained as follows:  
\[ \begin{split} Z_{m}^{\prime}&=\frac{g_{m}^{(1)^{*} }}{T_{s}}\int_{0}^{T_{s}}q_{m}(t)dt=\left|g_{m}^{(1)}\right|^{2}X_{m}^{(1)} \sqrt{2P}\\ &+\sum_{k=2}^{N_{u}}\underbrace{\frac{g_{m}^{(k)}g_{m}^{(1)^{*}X_ {m}^{(1)}\sqrt{2P}}}{T_{s}}}_{I^{(k)}}\int_{0}^{T_{s}}e^{\beta \pi\left(c_{m,0}^{(k)}-c_{m,0}^{(1)}\right)f_{s}dt}dt}_{I^{(k)}}+\underbrace{ \frac{g_{m}^{(1)^{*}}}{T_{s}}\int_{0}^{T_{s}}v(t)e^{-\beta\pi\left(f_{m}+c_{m,0 }^{(1)}f_{s}\right)t}dt}_{v_{m}}\end{split}  \tag{7}\]  
In (7), the data of the desired node contributes to the first part of the above expression. The second part is the multi-node interference resulting from the other nodes. In the third part, the AWGN with mean 0 and variance \(N_{0}a_{m}^{(1)}/T_{s}\) contributes to \(v_{m}\). It is noted that multiple nodes' interference \(I^{(k)}\), the value of which is determined by the collision properties of the FH strategy, is the main factor resulting in the reliability reduction. We analyze \(I^{(k)}\) in the following two cases:  
1. When the hopped subcarrier of the desired node is orthogonal to other \(N_{u}-1\) nodes (i.e., collision-free among these nodes, \(c_{m,0}^{(k)}\
eq c_{m,0}^{(1)}\)), then we have \[\int_{0}^{T_{s}}e^{\beta\pi\left(c_{m,0}^{(k)}-c_{m,0}^{(1)}\right)f_{s}t}dt= 0,\text{ and }I^{(k)}=0\] (8)
2. When the hopped subcarrier of the desired node collides with the \(k\)-th user (i.e., \(c_{m,0}^{(k)}=c_{m,0}^{(1)}\)), then we have \[I^{(k)}=g_{m}^{(k)}g_{m}^{(1)^{*}}X_{m}^{(k)}\sqrt{2P}\] (9)  
For the completely random FH strategy where the instantaneous frequency bin is uniformly chosen over \(N_{h}\) frequency slots, the probability at least one collision with other nodes occurring is equal to  
\[p_{hit}=1-\left(1-\frac{1}{N_{h}}\right)^{N_{u}-1} \tag{10}\]  
and the probability of no collision occurring is equal to \(p_{\text{no-hit}}=1-p_{hit}\). It is noted that the collision properties of the FH sequences are the key factor impacting the reliability of the system. Finally, the signal \(Z_{m}^{\prime}\) is executed in the detector. For the BPSK mapping adopted in the transmitter, the decision variable is obtained by taking the real part of \(Z_{m}^{\prime}\), i.e., \(\text{Re}\{Z_{m}^{\prime}\}\), and the transmitted data are estimated by the following decision criterion:  
\[\widetilde{X}_{m}^{(1)}= \begin{cases}1,&\text{if }\text{Re}\{Z_{m}^{\prime}\}>0\\ 0,&\text{if }\text{Re}\{Z_{m}^{\prime}\}\leq 0\end{cases}  \tag{11}\]  
Based on Equations (7)-(10) concerning the derived decision variable and decision criterion, the reliability in terms of the BER of a multi-node OFDM/FHMA system, i.e., \(P_{b_{\text{mini-slot}}}\), can be computed statistically by Monte Carlo simulation.
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 2 Preliminaries
### Equilibrium Selection  
An equilibrium selection algorithm is a negotiation mechanism that allows players to determine a Nash equilibrium, which is the stable state in the game where all players reach an agreement on their collective action. A variety of equilibrium selection algorithms have been proposed, each considering wildly different approaches. [PERSON] [24] methodically categorized these approaches into three main categories--a learning process [13; 14; 15], a traditional constraint optimization [2; 12], or a heuristic search [22; 25; 26; 27].  
As seen previously, game-theoretic task allocation models tend to be impractical when considering large games due to high computational load. As such, the study on application of game-theoretic task allocation in crowded problems have largely been avoided. While some have attempted to propose algorithms that can accelerate this equilibrium selection, none seem to have convincingly tackled the problem at hand. [PERSON] [27] proposed a fast convergence algorithm whose convergence time is roughly linear, instead of exponential, with the number of agents but did not provide any answers to the more demanding issue--the dependency on the number of tasks.  
Distributed Stochastic Algorithm (DSA)  
DSA is a myopic, greedy, local search algorithm that employs a random parallel schedule, in which each agent will, with some probability, called the degree of parallel executions, change its action. The preferred action will be determined by the arg max decision rule. The motivation to implement such a schedule is to minimize the phenomenon known as \"thrashing\", where having all agents change their actions at the same time unintentionally leads to a suboptimal Nash equilibrium. However, a random parallel schedule cannot completely eliminate thrashing although it may be minimized. Furthermore, DSA almost surely converges to a Nash equilibrium in potential games due to the finite improvement property as there is a probability of moving towards the Nash equilibrium, which is an absorbing state, at every time step [24].
## 2 Experimental and Methods
### Fire Analysis
#### 2.2.3 Safety Criteria  
The life safety criteria in performance-based design are defined by parameters such as temperature, visibility, oxygen (O\({}_{2}\)), carbon monoxide (CO), and carbon dioxide (CO\({}_{2}\)), measured at a height of 1.8 m above the floor, which corresponds to the respiratory threshold for evacuees [25]. Temperature affects residents by causing thermal effects, and high temperatures of 60 \({}^{\circ}\)C or higher increase the risk of respiratory issues and skin burns [26; 27]. Visibility is a factor that can obstruct evacuation due to the blocking of occupants' view. Therefore, it is defined as safe when visibility is secured at more than 5 m [28]. Additionally, O\({}_{2}\), CO, and CO\({}_{2}\) values can be calculated using the Fractional Effective Dose (FED), which assesses the behavioral impact of gas concentration in the respiratory zone of the occupant. Equations (2)-(5) depict the calculation methods for FED [29].  
\[\text{FED}_{\text{tot}}=\text{FED}_{\text{CO}}\times\text{HV}_{\text{CO}_{2}}+ \text{FED}_{\text{O}_{2}} \tag{2}\]  
\[\text{FED}_{\text{CO}}=4.607\times 10^{-7}(\text{C}_{\text{CO}})^{1.036}\text{t} \tag{3}\]  
\[\text{FED}_{\text{O}_{2}}=\frac{\text{t}}{60\text{exp}\big{[}8.13-0.54\big{(}2 0.9-\text{C}_{\text{O}_{2}}\big{)}\big{]}} \tag{4}\]  
\[\text{HV}_{\text{CO}_{2}}=\frac{\text{exp}\big{(}0.1930\text{C}_{\text{CO}_{2} }+2.0004\big{)}}{7.1} \tag{5}\]  
where, t: unit time(s), C\({}_{\text{CO}}\): CO concentration (ppm), C\({}_{\text{O}_{2}}\): O\({}_{2}\) concentration (%), and C\({}_{\text{CO}_{2}}\): CO\({}_{2}\) concentration (%).  
The FED value serves as a criterion for assessing the impact of harmful gases resulting from combustion on the human body. An FED value of 1 indicates fatality for 50% of the occupants. Additionally, an FED value of 0.3 is considered an index of human body incapacitation [30; 31].  
In this study, the measurement criteria for fire risk assessment were divided into three categories: temperature, visibility, and FED. Table 4 outlines the life safety criteria utilized in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Limit** \\ \hline Respiratory limit line & 1.8 m \\ \hline Fractional Effective Dose (FED) & \textless{}0.3 \\ \hline Temperature & \textless{}60 \({}^{\circ}\)C \\ \hline Visibility & \textgreater{}5 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance criteria for life safety [25; 30].
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 4 Experimental Simulation 7
### System Reliability  
In order to verify the feasibility of the algorithm, we used the proposed constellation encryption algorithm to simulate QPSK and 16 QAM modulation and obtained the variation curve of the bit error rate under different signal-to-noise ratios, as shown in Figure 10:  
Figure 10: Simulation diagram of the bit error rate of QPSK scrambled constellation encryption algorithm.  
Figure 9: Constellation diagram of 16 QAM modulation of SNR = 15 dB, in which (**a**) is the unencrypted constellation diagram and (**b**) is the encrypted constellation diagram.  
It can be seen from Figures 10 and 11 that, under the condition of using the scrambled constellation encryption algorithm, if the communication receiver uses the traditional method to make judgments, even if the signal-to-noise ratio continues to increase, the bit error rate of QPSK will slowly decrease, but will eventually settle at around 0.25, and the bit error rate of 16 QAM will gradually approach one.  
This is because the illegal receiver can only judge that it is a traditional modulation through constellation identification. Therefore, even if the sender knows that the sender may have adopted a certain constellation scrambling algorithm, it cannot be effectively cracked. After using the corresponding demodulation judgment method, the bit error rate of the legitimate receiver is much lower than that of the illegal receiver. Simultaneous simulations show that the proposed encryption algorithm has almost the same bit error rate as the non-encrypted algorithm, so the proposed algorithm will not deteriorate the bit error rate performance.