esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 2 Background Theories
### Particle Swarm Optimization (PSO)  
The underlying concept of PSO is adopted from observation of the collective performance of animal groups and their individuals towards the task of foraging food or avoiding predators [15]. Since its appearance in the early 2000s, PSO has become one of the most widely used optimization algorithms. In PSO, a particle or an individual in a group shares the information of both local and global optima with other members in moving around the search space, in which every particle is randomly assigned its position and velocity. Once iteration begins, all the particles are dispersed to search for optimal points by updating their current positions and velocities at every time step, based on the contest result of their function values from the previous step. The search process is formally illustrated by Equations (2) and (3):  
\[v_{i}^{t+1}=w_{i}^{t}v_{i}^{t}+\mathrm{c}_{1}r_{1}\big{(}p_{i}^{t}-x_{i}^{t} \big{)}+c_{2}r_{2}\Big{(}p_{i}^{all}-x_{i}^{t}\Big{)} \tag{2}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t} \tag{3}\]  
Here, the current position and velocity of the particle are denoted by \(x_{i}^{t}\) and \(v_{i}^{t}\), respectively. They are used to update the position of the particle in the next step or \(x_{i}^{t+1}\), as shown in Equation (2). The updated velocity \(v_{i}^{t+1}\) is derived by considering the current velocity (\(v_{i}^{t}\)), the weighted difference between the optimal positions of local and global (\(p_{i}^{t}\)), along with the current positions (\(x_{i}^{t}\)). Note that \(r_{1}\) and \(r_{2}\) are randomly varied between zero and unity, to impose an unbiased balance of the search direction towards the local and global optimum. Also, the level of influence or learning factors \(\mathrm{c}_{1}\) and \(c_{2}\) of local and global optima are usually set to 2, to achieve moderate convergence speed and relatively high accuracy. The coefficient \(w_{i}^{t}\) regulates the search speed of particles, so that a higher \(w_{i}^{t}\) allows extensive jumping of search ranges, while a smaller value limits the search boundary to the vicinity of the current position. Thus, a strategy of initially setting the coefficient to a large value for extensive search is recommended in the early stage. Then, we slowly let it decay to a small value for detail or local scrutiny, as the search process approaches its conclusion. It is known that for reliable search performance in PSO, Equation (4) provides an appropriate value of \(w_{i}^{t}\) in terms of the maximum iteration number \(iter_{max}\) and current step number \(iter_{t}\).  
\[w_{i}^{t}=\frac{iter_{max}-iter_{t}}{iter_{max}} \tag{4}\]
## 1 Introduction  
The role of new techniques applied in environmental treatments and the necessity of environmental system analysis have been outlined in recent years [1, 2].  
Spinning disc (SD) technology is one of the recognized technologies for process intensification [3, 4, 5], with applications in different physical and chemical processes.  
The working principle of the SD relies on centrifugal force acting on the liquid fed at the center of a spinning disc [6], resulting in a rippled thin liquid film characterized by an advanced turbulence and a high mixing intensity, which enables increased transport phenomena such as heat and mass transfers.  
Spinning disc technology has been used successfully for polymer production [7, 8], in the food and pharmaceutical industries [9, 10], for nanoparticle manufacturing [11], in enzymatic reactions [12], and in biodiesel synthesis [13]. In wastewater treatment technologies, the spinning disc with a nanostructured thin film of ZnO and TiO\({}_{2}\) photocatalysts has been used for the degradation of some pollutants, such as methyl orange and rhodamine B dye or 4-chlorobenzoic acid [14, 15, 16].  
Given the enhanced spinning disc reactor (SDR) flow characteristics, the current study has as its primary objective the investigation of the SD technology's efficiency in the treatment of a textile effluent without the initiation of any other mechanical-physical, chemical, or biochemical process, mainly regarding the suspended solids, turbidity, and color removal. To our best knowledge, no other investigation regarding wastewater treatment using spinning disc technology, as is described below, has been performed before. Moreover, loaded industrial wastewater treatment has become mandatory for each responsible industrial company's environmental management, which will select the most adequate wastewater treatment system for its effluents [17; 18; 19; 20; 21; 22; 23; 24; 25; 26].  
In addition, in the second part of this paper process modeling with classical optimization was proposed in order to find the optimal operating variables in terms of flowrate, rotational speed, and operating time for the highest color and suspended solids removals from the treated textile wastewater. Thus, after each studied variable variation field was established, an experimental design respecting the active central composite rotatable 2\({}^{3}\) design was applied in the laboratory spinning disc setup for textile wastewater treatment. Rigorous empirical modeling and monitoring methods are becoming very important in each industrial wastewater treatment system, as reported in several studies [27; 28; 29; 30; 31].
## 4 Relationship between \(K_{ic}\) and \(\sigma_{t}\)
### Empirical Relationship
Thus, when rocks are heated to room temperature, 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C, the linear relationship is more pronounced than when they are heated to between 300 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, and temperature exposure has a significant effect on the relationship between the mode-I fracture toughness and tensile strength of rock. To achieve better fits, we also considered exponential formulas relating mode-I fracture toughness and tensile strength. The expression of the exponential function is different at different temperatures. R\({}^{2}\)\(>\) 0.90 at 20 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C. Therefore, at these temperatures, the mode-I fracture toughness has a good exponential relationship with the tensile strength. Comparing the linear function with the exponential function, the exponential function achieves a better description of the relationship between the mode-I fracture toughness and the tensile strength at 20 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C, whereas the linear function achieves a better description at 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C. Specifically, for example, at 400 \({}^{\circ}\)C, the R\({}^{2}\) of the exponential function is significantly larger than that of the linear function, and therefore, it is more accurate to predict the \(K_{IC}\) value using an exponential function. From the above analysis, it can be concluded that although the mode-I fracture toughness is positively correlated with the tensile strength, their relational expressions differ at different temperatures. Through analysis of the abundant testing data available regarding both rock fracture toughness and rock tensile strength values, an empirical relationship has been established between the two parameters [18] that can be expressed by the following equation  
\[\sigma_{t}=6.88K_{IC} \tag{6}\]  
with a coefficient of determination R\({}^{2}\) = 0.94. A linear empirical relationship has also been identified between the \(K_{IC}\) and \(\sigma_{t}\) of a particular clay [19]  
\[K_{IC}=0.3546\sigma_{t} \tag{7}\]  
More broadly, the correlation between \(K_{IC}\) and \(\sigma_{t}\) can be expressed as  
\[K_{IC}=\alpha\sigma_{t} \tag{8}\]  
where \(\alpha\)=the proportionality coefficient. The proportionality coefficient is different for different soils or rocks and testing methods. Further analysis has indicated that the relationship between tensile strength and fracture toughness is actually of power-law type [20]  
\[K_{IC}=\alpha\sigma_{t}^{n} \tag{9}\]  
instead of the linear type shown in Equation (8) and that this relationship only becomes linear for the particular case where exponent \(n\) is equal to 1.
# Negative Poisson's Ratio Lattice Structure with Chiral and Re-Entrant Properties
## 3 Results and Discussions
### The Influence of Geometric Parameters on NPR
When the length ratio (_a/b_) was 0.343 and the deflection angle (\(\theta\)) ranged from 0\({}^{\circ}\) to 20\({}^{\circ}\), the structure exhibited its maximum NPR effect, with an NPR value of approximately \(-\)0.6. At the same time, it could be inferred that the impact of the deflection angle on the negative Poisson's ratio of the structure followed a gradual trajectory, whereas the influence of the length ratio on the negative Poisson's ratio exhibited fluctuations. Particularly when the length ratio fell within the range of 3.5 to 7, irrespective of variations in the angle, the negative Poisson's ratio demonstrated an amplified tendency. In order to validate the rationality of the numerical simulation findings, we consulted the studies conducted by other researchers and conducted a comparative analysis of the outcomes. One of the structures designed by [PERSON] [70], which was similar to the structure of our study, exhibited a negative Poisson's ratio as low as \(-\)0.6. This validation substantiates the findings of our research. At the same time, in contrast to prior research, this article provides a more comprehensive analysis of the impact of geometric parameters on the negative Poisson's ratio. It investigates the influence of various deflection angles and length ratios on the negative Poisson's ratio of structures.
## 4 Simulations and Results
### Simulation Analysis  
#### 4.2.1 Channel Description  
In this paper, we use Bellhop to simulate a channel that is as close as possible to the experimental conditions of the pool and bring it into the simulation experiments with the condition parameters of this channel (Figure 6):  
Here, the depth of the simulated water body is set to 10 m, and the sound velocity profile is a negative gradient sound velocity environment, in which the depth of 0 m results in the speed of sound at the surface of the water being 1510 m/s, and the depth of 10 m results in the speed of sound at the bottom of the water being 1500 m/s. It is assumed that the surface of the water is a flat water surface with no undulation, the upper part of it is a vacuum environment, the bottom of it is assumed to be the liquid seabed and the sound velocity is 1650 m/s.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 6 Simulation and Results
Figure 20 shows the LNDs of the seven protocols in 200 m \(\times\) 200 m networks with different number of nodes. Table 5 shows specific measurement data of LNDs of the seven protocols in 200 m \(\times\) 200 m networks with different number of nodes. As can be seen from Figures 19 and 20, in the network environments with different numbers of nodes, the LNDs of PUDCRP occurred significantly later than the other six protocols. The results show that PUDCRP has better scalability for network environments with different nodes and different sizes. This is due to the high energy efficiency of nodes and the balanced energy consumption of whole network achieved by the PSO-based uneven dynamic clustering method. Figure 18: The number of packets received by the BS in the 400 m \(\times\) 400 m network area. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **100** & **200** & **300** & **400** & **500** \\ \hline UCCGRA & 991 & 1040 & 1030 & 1024 & 1014 \\ Multi-hop EEBCDA & 974 & 916 & 888 & 867 & 856 \\ EEMRP & 1385 & 1441 & 1290 & 1251 & 1213 \\ CAMP & 767 & 888 & 935 & 959 & 985 \\ PSO-ECHS & 560 & 705 & 726 & 794 & 784 \\ PSO-SD & 691 & 957 & 1024 & 1074 & 1022 \\ PUDCRP & 1545 & 1547 & 1423 & 1311 & 1294 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LND of different algorithms in the 400 m \(\times\) 400 m network area. Figure 19: The nodes number scalability of the nodes number in 400 m \(\times\) 400 m network area. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **100** & **200** & **300** & **400** & **500** \\ \hline UCCGRA & 1043 & 1070 & 1086 & 1096 & 1102 \\ Multi-hop EEBCDA & 1043 & 1022 & 1011 & 1002 & 1003 \\ EEMRP & 1482 & 1129 & 1098 & 1055 & 1092 \\ CAMP & 1053 & 1064 & 1070 & 1088 & 1099 \\ PSO-ECHS & 726 & 792 & 803 & 839 & 826 \\ PSO-SD & 1029 & 1257 & 1296 & 1323 & 1336 \\ PUDCRP & 1881 & 1537 & 1398 & 1364 & 1408 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LND of different algorithms in the 200 m \(\times\) 200 m network area. Figure 20: The nodes number scalability of the nodes number in 200 m \(\times\) 200 m network area. Compared to multi-hop EEBCDA and EEMRP, PUDCRP has good performance in any shape (not just in a rectangular network) and size network environment. Multi-hop EEBCDA, EEMRP and other rectangular meshing clustering algorithms are more suitable for rectangular network environments. Compared with CAMP, in PUDCRP, the farther the distance between the nodes and the BS, the larger the size of the clusters, the better the hotspot problem can be alleviated. Compared with PSO-ECHS and PSO-SD, the distribution of CH nodes in PUDCRP is more reasonable, which is determined by the distribution of nodes. Among these protocols, only PUDCRP considers the distribution of nodes in the clustering process.
## 4 Conclusions  
1. Adding steel fibers and coarse aggregate to the infill UHPC can improve the specimen's bearing capacity. The content of steel fibers in the infill UHPC is typically 1-2%, and the content of coarse aggregate is 0-20% for the improvement of the bearing capacity. Under the condition of not changing the type of steel pipe, the steel fiber doping at 1% and the doping of 20% coarse aggregate in the UHPC have the best effect in improving the restraining capacity, and the overall bearing capacity improvement coefficient of this specimen is also the highest. 2. The damage mode of steel slag powder UHPCFST has a certain relationship with the length and slenderness ratio of the specimen, the steel content, and the admixture of additives in the concrete. Increasing the length and diameter ratio will shift the position of the bulging section. Increasing the steel content will cause the specimen to fail along a more horizontal shear damage line. The addition of steel fibers and coarse aggregate will change the degree of bulging on the surface of the steel pipe specimen to a certain extent. 3. The specimen load-deformation curve can be roughly divided into two types based on the presence or absence of a descending segment. When the thickness of the steel pipe is 6 or 8 mm, i.e., when the steel content is high, the specimen will exhibit an obvious secondary ascending segment after experiencing a descending segment. The incorporation of steel fiber can effectively enhance the overall ductility capacity of the specimen and significantly improve the energy consumption capacity. In contrast, the increase in the amount of coarse aggregate mainly affects the descending segment of the deformation curve.  
Conceptualization, S.G. and X.T.; methodology, S.G. and C.F.; software, B.H.; validation, X.T.; formal analysis, S.G. and B.Y.; investigation, S.G. and X.T.; resources, S.G.; data curation, B.H. and X.T.; writing--original draft preparation, X.T. and C.F.; writing--review and editing, S.G. and B.Y.; visualization, B.H.; supervision, X.T.; project administration, S.G.; funding acquisition, S.G. and X.T.; All authors have read and agreed to the published version of the manuscript.  
The work was supported by the 2021 Guangxi Key R&D Program Project (No.: Guike AB21220049); Key Research and Development Program of the Ministry of Science and Technology of China (No. 2019 YFC0507503); Zhongse Guilin Institute Innovation Fund (No.: KDYCXJJ202207).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Author [PERSON] was employed by the company of China Nonferrous Guilin Institute of Mineral Geology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experimental Stations
### Experimental Station Alvra
#### 4.1.3 Optical Laser System  
The femtosecond optical lasers available to the experimental stations have been described in detail in Section 3.4. The primary short-pulse excitation source anticipated for ESA is the 240-2600 nm output from the OPA (see Table 2), which covers the UV to IR range in which most molecular or biological samples absorb light. In addition to the femtosecond laser, ESA will also have access to a long pulse laser, which is tuneable from the UV to the IR. This laser will operate at 100 Hz, and can produce \(>\)1 mJ/pulse from 200 nm to 2 \(\upmu\)m with pulses from 3-6 ns in duration. This laser can be used to more efficiently excite samples and measure on timescales ranging from ns out to ms. These experiments will take advantage of the fact that SwissFEL will have more photons available in a single pulse (\(>\)10\({}^{11}\)) than a 3 rd-generation storage ring in 1 ms (\(\sim\)10\({}^{9}\)), making them better at time-resolved experiments even on longer timescales, under certain conditions. The approach of using long-pulse excitation has proven particularly attractive for measurements on biological samples, where the relevant timescales can often be slow (\(\upmu\)s to ms), but when performed at XFELs the measurements can take advantage of the room-temperature, damage-free conditions [115; 117; 118; 119].
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 3 Generalized Random Petri Net Model for Plant Pest and Disease Spread Diagnosis  
Based on the plant pest and disease early warning mechanism flowchart summarized above, it is evident that pest and disease management is a complex system involving multiple variables, stages, and high interactivity. In this context, the GSPN is used for its unique modeling capabilities to study the spread and diagnosis systems of plant pests and diseases. The Petri net structure can clearly depict and analyze the dynamic relationships  
Figure 1: System diagram of plant pest and disease early warning mechanism.  
between system states, where transitions represent changes in the system's state, and places represent the various states the system may be in. This approach not only effectively simulates the transmission paths and potential development trends of pests and diseases but also provides a detailed reflection of the key control points and potential risks in the plant pest and disease management process. Moreover, this method supports quantitative analysis and optimization of complex systems, helping to propose more precise and practical control strategies. By introducing randomness and time parameters, the generalized stochastic Petri net can describe and analyze the randomness and uncertainty of plant pest and disease spread, providing scientific decision support for the sustainable development of the plant industry.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### High-Precision Clutter Covariance Matrix Estimation Based on Dual-Platform SAR System
#### 2.3.2 Calculation of Clutter Covariance Matrix Based on Echo  
Following image registration, the next step is to calculate the CCM. The calculation principle of the CCM of the range gate to be measured is shown in Figure 8.  
Figure 7: Image registration diagram.
## 4 Trust Assessment Using Various Approaches  
Various trust models are employed to assess the trustworthiness of 6G networks. The models encompass statistical models, reasoning models, machine learning models, decentralized models, and hybrid models. Each category provides unique benefits and factors to examine, enabling stakeholders to select the most appropriate solution depending on the resources, data, and specific requirements of the trust evaluation process.  
Trust models used to assess the trust level of 6G networks go beyond the methodologies stated before. These models include reasoning models, statistical models, machine learning models, decentralized models, and hybrid models. Reasoning-based trust models employ deductive reasoning utilizing rule-based systems or knowledge graphs to determine trust relationships. These models have the capability to manage intricate interconnections, but they require manual input of domain knowledge and rule specifications. Statistical trust models employ methods such as regression analysis or Bayesian networks  
Figure 2: Outline of the study [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49].  
to quantitatively estimate trust levels based on historical data. However, these models require a significant amount of data in order to achieve accurate results. Trust models based on machine learning utilize methods like supervised learning to automatically detect trust indications and adjust to changing conditions, necessitating sufficient training data and careful model selection.  
Decentralized trust models distribute trust evaluations throughout a network, utilizing technologies such as blockchain to provide transparency and consensus, hence decreasing dependence on centralized authorities while introducing complexities [19; 50]. Hybrid trust models combine logic, statistical analysis, machine learning, and decentralized features to thoroughly assess trust in 6G networks. These models use several approaches to capture different aspects of trust, resulting in a comprehensive evaluation [51]. Stakeholders have the option to select the most appropriate method by taking into account the resources, data, and specific trust evaluation requirements at hand while examining the benefits and considerations associated with each trust model category.  
The trust models have played an essential role in enhancing trust in 6G networks by providing various methods to accurately assess trust levels. By using reasoning, statistics, artificial intelligence, decentralization, and hybrid methodologies, stakeholders can make well-informed choices to guarantee the reliability and security of 6G networks in a progressively interconnected and technologically advanced environment.
## 4 Experiment Results and Discussion
### Moment-Strain Curves of the End-Plate Joint Component
#### 4.2.2 Axial Force of Bolts  
The type selection and force characteristics of bolts directly affect the reliability and safety of joint connections. Therefore, it is necessary to study the mechanical properties of bolts. Figure 15 shows the relationship curves between the bending moment and axial force of the bolts, in which HSB-1-HSB-3 are the upper bolts and HSB-4-HSB-6 are the lower bolts.  
By analyzing the force of the bolts at different positions, it can be seen that the lower bolts bear a greater tensile load than the upper bolts. Among them, the axial force of the HSB-5 bolt at the limit state is 48.0% higher than the final pretightening force, which exceeds the yield strength by 3.3%. The axial force of the HSB-4 bolt and HSB-6 bolt are increased by 9.3% and 14.0%, compared to the final pretightening force, respectively. However, the axial force of the upper bolts is decreased slightly.  
In conclusion, the lower bolts are generally stressed, and the force of each bolt is in good condition without failure occurrence, which indicates that the bolt selection is reasonable.