esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristic Deformation and Associated Physical Processes at Piton de la Fournaise volcano
### Short-Term Rapid Variations and Typical Deformation Patterns Associated with Eruptions  
Volcanic eruptions at Piton de la Fournaise volcano are typically preceded by seismic crises indicating the escape of magma from the shallow reservoir and its propagation towards the surface. A seismic crisis usually lasts between a few hours to days [38] and is accompanied by sudden high rates of surface inflation that are associated with the magma ascent [22; 39; 40]. Amongst the main deformation phenomena observed at Piton de la Fournaise volcano (as discussed throughout Section 3), this type of deformation is the most straightforward to capture, discriminate, and quantify by means of SAR interferometry. In contrast to deformation that affects the central volcanic cone (Section 3.1), intrusions and eruptions oftentimes take place at locations along the volcano's rift zones, which are not as densely equipped with ground instrumentation. Therefore, InSAR is the essential technique allowing for imaging, quantifying, and analyzing intrusion- and eruption-related deformation patterns at Piton de la Fournaise volcano. This is especially true for distal eruptions as the GNSS ground deformation network is sparser further away from the central cone (cf. Sections 3.2.1 and 3.2.2, and references therein; Figures 2, 6 and 7). Because of their high frequency, their occurrence in very confined areas, their short (and known) duration, and their characteristically large amplitudes, eruption- and intrusion-related deformation signals are also the most commonly observed, described, modelled, and analyzed in the Piton de la Fournaise volcano InSAR literature.  
Figure 5: Line-of-sight (LOS) displacement velocities between 7 September 2011 and 14 June 2014 from ascending TSX SM data. Characteristic deformation processes observed during this quiescent period of the volcano are lava flow subsidence, flank sliding of about \(\sim\)3 cm of LOS subsidence per year, and the continuation of subsidence of the central volcanic cone upon the major 2007 eruption. Inset shows the time series for one selected point along the volcano’s mobile eastern flank.
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
## 3 Experimental Verification
### Simulation Experiments and Data Analysis
#### 3.2.2 Analysis of Experimental Results
The plotted results indicate that the operating efficiency of EVs was the highest for \(w=0\), and the curves achieved the maximum value near the CV flow rate of 1700 pcu/h. In Figure 13b, the length of the non-intrusive interval was \(L_{1}=15\) m, the flow input remained unchanged at 1300 pcu/h, and the buffer length changed from 30 m to 150 m. For \(w=0\), the operating efficiency of EVs changed the least, specifically when the buffer interval exceeded 120 m. The change did not exceed 5% of the overall change, and these results can be employed to guide actual emergency rescue missions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**(a)**} \\ \cline{2-7}
**CV Flow (puc/h)** & \multicolumn{6}{c}{**System Operation Efficiency Net Benefit (s)**} \\ \cline{2-7}  & \(w\) = 1 & \(w\) = 0.8 & \(w\) = 0.6 & \(w\) = 0.4 & \(w\) = 0.2 & \(w\) = 0 \\ \hline
600 & 10.99854 & 11.07883 & 11.15912 & 11.23942 & 11.31971 & 11.4 \\
700 & 12.4113 & 12.46904 & 12.52678 & 12.58452 & 12.64226 & 12.7 \\
800 & 12.74314 & 12.99451 & 13.24588 & 13.49725 & 13.74863 & 14 \\
900 & 13.9554 & 14.12432 & 14.29324 & 14.46216 & 14.63108 & 14.8 \\
1000 & 14.22697 & 14.42158 & 14.61618 & 14.81079 & 15.00539 & 15.2 \\
1100 & 14.54119 & 14.77295 & 15.00471 & 15.23648 & 15.46824 & 15.7 \\
1200 & 15.37011 & 15.69609 & 16.02206 & 16.34804 & 16.67402 & 17 \\
1300 & 14.88432 & 15.32746 & 15.77059 & 16.21373 & 16.65686 & 17.1 \\
1400 & 15.74119 & 16.17295 & 16.60471 & 17.03648 & 17.46824 & 17.9 \\
1500 & 15.67011 & 16.15609 & 16.64206 & 17.12804 & 17.61402 & 18.1 \\
1600 & 16.01714 & 16.51371 & 17.01028 & 17.50685 & 18.00343 & 18.5 \\
1700 & 12.95346 & 13.90277 & 14.85207 & 15.80138 & 16.75069 & 17.7 \\
1800 & \(-\)3.48919 & 0.008647 & 3.506485 & 7.004323 & 10.50216 & 14 \\
1900 & \(-\)17.9167 & \(-\)12.9333 & \(-\)7.949049 & \(-\)2.96666 & 2.01667 & 7 \\
2000 & \(-\)33.7431 & \(-\)27.4345 & \(-\)21.1259 & \(-\)14.8173 & \(-\)8.50863 & \(-\)2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of CV and EV operation on the net income of the system.
## 1 Introduction
Although microseismic monitoring provides high real-time accuracy in locating the seismic source and analyzing energy release characteristics, it can only indirectly reflect the fracturing process of coal and rock masses. This limitation makes it challenging to capture the degree and evolution of stress concentration in real time, thereby restricting the effectiveness of dynamic early warning for high-energy mining-induced earthquakes. The stress monitoring method offers advantages such as high precision, strong real-time capabilities, and high data directness, enabling it to sensitively capture internal stress changes within rock masses. This provides critical data support for identifying high-energy mining-induced earthquake precursors, making it particularly suitable for stress monitoring and stress evolution studies in key areas. It thus provides reliable technical support for coal mine safety management [24; 25; 26; 27; 28]. Based on an analysis of the load transfer mechanism between key strata and thick-impact coal seams, [PERSON] et al. [29] proposed a theoretical calculation model for abutment pressure on longwall panels in thick-impact coal seams,with validation from microseismic and stress monitoring results supporting the model's rationality. In further research, [PERSON] et al. [30] investigated the mechanism of the newly defined Overall Instability Induced (OII) rockburst and developed a direct OII rockburst risk assessment method based on actual load indicators using microseismic and stress monitoring. Although stress monitoring can capture stress changes in rock masses and identify concentration areas, it has limited ability to predict energy release from the seismic source and cannot fully reveal the disaster-causing mechanisms of mining-induced earthquakes. Existing studies have made advances in understanding seismic source mechanisms, spatiotemporal distribution patterns, and microseismic monitoring methods. However, most research lacks a comprehensive, multi-parameter fusion analysis, often focusing on individual parameters or regional characteristics, without integrating factors such as stress monitoring, microseismic monitoring, and source mechanisms. In particular, studies on the dynamic coupling of triggering factors for high-energy mining-induced earthquake events remain limited. To address the above issues, this paper focuses on the Yangcheng Coal Mine, integrating microseismic monitoring, dynamic stress analysis, and seismic source mechanism analysis to investigate the primary triggering mechanisms of high-energy mining-induced earthquakes in deep mines. Key precursor features, such as the silent period preceding high-energy mining-induced earthquake events, were identified. Based on the research findings, this paper proposes dynamic risk prevention and control measures that combine pressure relief boreholes and deep-hole blasting, providing an efficient strategy for managing mining-induced earthquake hazards in high-risk areas.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment One--MAUI Chat
#### 4.1.3 Findings  
The available capacity of the network must be carefully considered when employing encryption techniques for the variety of messages used for UxS communications. As such, this first experiment primarily focuses on understanding the average added overhead of a selected MLS ciphersuite for a common plaintext message. During this phase, we do not employ the full scope of the MLS protocol maintenance messages (such as update, add, remove, etc.) to better identify baseline metrics. Although SHA512 is double the cryptographic strength against a brute force attack as opposed to SHA256, the additional overhead is not ideal for our use case that has the potential to send an aggregate of thousands of messages per second. This is also highly dependent upon the UxS group size and the information the group is sharing.
## 3 Results
### Relationships between LDDI, Climate Factors and Anthropogenic Factors  
The relationships between LDD and the main climate factors are shown in Figure 4. Temperature had a positive effect on LDDI in the north and southwest parts of China and had a negative correlation with LDDI in south China (Figure 4a). From Figure 4b, we can find that precipitation positively correlated with LDDI in north China, especially in Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region and the Loess Plateau area; while temperature negatively correlated with LDDI in southwest China, such as the south Tibetan area and Yunnan Province. However, as can be seen in Figure 4c, the regression relationships between LDDI and climate factors were not significant, with overall \(p\) values higher than 0.5. The significant relationships were only found in the Daxinganling Mountain area and the south Tibetan area.  
Figure 3: The hot and cold spots of LDD.  
The residual represents the effects of anthropogenic activities on LDD. The increase in residual indicated that the effects of human activities on LDD processes had been strengthened. From Figure 5, we can see that the human factors became more important in driving the LDD processes in China during the study period. As shown in Figure 5b, the area experiencing increasing effects of human activities on LDD accounting for about 84% of China's mainland and was mainly distributed in the Loess Plateau area, Qinghai-Tibetan Plateau, north China plain and southwest China. About 6% of the total area underwent a decrease in human influences on LDD, which were located in the Yangtze River delta, the Pearl River Delta area and some places in the Xinjiang Uygur Autonomous Region.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 4 System Model
### Formal Problem Formulation  
With the formal specifications presented in the previous two subsections, we provide a formal problem formulation of the synthesis problem during the design of time-triggered real-time systems.  
The task of system synthesis is to find a valid implementation \(\mathcal{I}=(\mathbf{B},\mathbf{R}_{\text{route}}^{pkts},\mathbf{S})\), consisting of a binding  
\[\mathbf{B}\subseteq\left(\bigcup_{A_{i}\in\mathbf{A}}\mathbf{T}_{i}^{t} \right)\times\mathbf{R}_{pe},\]  
the routing of all packets \(\mathbf{R}_{\text{route}}^{pkts}\) on resources of the NoC, and a feasible global time-triggered schedule \(\mathbf{S}\) for all computational tasks and packets of the application model.  
A binding \(\mathbf{B}\) contains for each computational task \(t\in\mathbf{T}_{i}^{t}\) of an application \(A_{i}\in\mathbf{A}\) exactly one PE \(r\in\mathbf{R}_{pe}\) it is executed on. The routing \(\mathbf{R}_{\text{route}}^{pkts}\) contains the specific route \(\mathbf{R}_{\text{route}}^{p}\) of each packet \(p\in\mathbf{P}^{m}\) of each message \(m\in\mathbf{T}_{i}^{m}\) from its sending computational task to the receiving computational task on a set of connected resources of the NoC. Note that packets are not routed in the NoC if the sender of a message and the receiver of a message are bound on the same PE, i.e., \(t,\tilde{t}\in\mathbf{T}_{i}^{t},(t,r),(\tilde{t},r)\in\mathbf{B},(t,m),(m, \tilde{t})\in\mathbf{E}_{i}^{A}\rightarrow\forall p\in\mathbf{P}^{m}:\mathbf{ R}_{\text{route}}^{p}=\emptyset\). A time-triggered schedule \(\mathbf{S}\) contains the start times of all computational tasks on PEs and the start times of each packet on the resources of it route on the NoC. The time-triggeredschedule has to resolve all resource access conflicts of shared resources and ensures that the deadlines of all applications are met (see Section 6).  
In the following sections we will describe in detail how we solve the subproblems of binding, routing and scheduling in our coordinated system synthesis approach.
## 3 Results and Discussions
### Models
#### 3.1.1 Site-Specific Models
For non-numerical data such as soil type, classification machine learning models were used to determine the soil types of locations for which no data were available. For classification machine learning models, accuracy rates were used as performance indicators as compared to regression models. The nearest neighbor model had the highest rate of accuracy among competing models for predicting soil types based on proximity to an unknown layer, shown in Table 4. Typically, the estimate is based on a majority vote, which means that the soil type that is most frequently represented around the unknown data point is the estimate. Site-specific models have significant limitations despite their accuracy in predicting ground elevation, groundwater table elevation, SPT N-value, fines content, and soil type. Due to the large number of variables involved, including spatial relationships, there is no particular equation that can represent them. Therefore, there is no benefit to converting the internal models into equations. These trained site-specific models are therefore commonly referred to as black boxes.
## 2 Research on UAV Path-Planning Problem
### Environmental Modeling
#### 2.3.2 Urban Environment Model  
In urban terrain, the shape of buildings is usually highly irregular. To simplify the modeling process and highlight the obstacle avoidance feature in path planning, this paper adopts a rectangular body to abstractly simulate buildings in the city:  
\[b_{i}=\left\{(x,y,z)\ |\ b_{x(i)}\leq x\leq b_{x(i)}+w_{i},b_{y(i)}\leq y\leq b_{y (i)}+d_{i},0\leq z\leq b_{h(i)}\right\} \tag{10}\]  
The set of buildings is:  
\[b=\bigcup_{i=1}^{n_{c}}b_{i} \tag{11}\]  
In the expression, \(b_{i}\) corresponds to the area range of the \(i\) rectangular building; \(b_{x(i)}\) and \(b_{y(i)}\) represent the coordinates of the lower left corner \(x\) and \(y\) of the \(i\) building, respectively; \(w_{i}\), \(d_{i}\), and \(b_{h(i)}\) denote the extension (length, width, and height) of the \(i\) building's lower-left coordinates in the \(x\), \(y\), and \(z\) directions, respectively; \(n_{c}\) is the number of buildings and \(b\) is the set of all buildings.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dynamic Viscosity in 2 \({}^{\circ}\)C** & **Flash Point** & **Acid Number** & **Base Number** & **Water Content** \\ \hline  & **[m-Pa-s]** & [\({}^{\circ}\)C] & **[mgKOH/g]** & **[mgKOH/g]** & **[\%]** \\ \hline
**Fresh oil** & 596 & 212.4 & 2560 & 3.907 & 0.096 \\ \hline
**Fresh oil and colloidal** & 686 & 200.4 & 2.668 & 3.604 & above 1 \\
** nanosilver 2.5 mL** & 742 & 198.4 & 2.659 & 3.751 & above 1 \\ \hline
**Fresh oil and silver** & 698 & 206.9 & 2.682 & 3.479 & above 1 \\ \hline
**Fresh oil and silver** & 736 & 201.2 & 2.660 & 3.731 & above 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the parameters of fresh oil without additives with fresh oil with the addition of colloidal nanosilver and silver solution. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dynamic Viscosity in 2 \({}^{\circ}\)C** & **Flash Point** & **Acid Number** & **Base Number** & **Water Content** \\ \hline  & **[m-Pa-s]** & [\({}^{\circ}\)C] & **[mgKOH/g]** & **[mgKOH/g]** & **[\%]** \\ \hline
**Used oil** & 719 & 198.8 & 4.027 & 1.358 & 0.167 \\ \hline
**Used oil and colloidal** & 712 & 186.2 & 2.668 & 1.350 & above 1 \\
** nanosilver 5 mL** & 758 & 194.0 & 2.659 & 1.402 & above 1 \\ \hline
**Used oil and silver** & 796 & 191.6 & 2.682 & 1.037 & above 1 \\ \hline
**Used oil and silver** & 806 & 197.6 & 2.660 & 1.199 & above 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the parameters of waste oil without additives and waste oil with the colloidal nanosilver and silver solution. was obtained, while the viscosity of the oil used at this temperature was 645 mPa-s. This means that the best additive to improve the properties of used oil is microorganisms in liquid form.
## 2 Materials and Methods
### Whole-Genome Sequencing (WGS) and bioinformatic Analysis  
The genomic DNA extracted by the SDS method was quality-checked via agarose gel electrophoresis and quantified using Qubit fluorometry. Sequencing libraries (\(\sim\)350 bp in-sert size) were prepared and subjected to paired-end sequencing (PE150) on the Illumina HiSeq 2000 platform at Novogene (Beijing, China). Raw reads were filtered and base-called using CASAVA software-2.0.12, yielding FASTQ files containing sequencing and quality data. High-quality reads were aligned to the Mtb H37 Rv reference genome (NC_000962.3) using the Burrows-Wheeler Aligner with the BWA-MEM algorithm. SAMtools-1.20 was used for alignment analysis and variant calling, including single-nucleotide variants (SNVs), insertions, and deletions (InDels \(<\) 50 bp). Structural variants (SVs), including large insertions, deletions, inversions, and translocations, were identified using BreakDancer. Circos was used for the visualization of coverage and variation, while CNVnator v0.3 was used to detect copy number variations (CNVs). Library construction, sequencing, and downstream bioinformatics analyses were performed using in-house pipelines and tools at Beijing Novogene Bioinformatics Technology Co., Ltd. (Beijing, China). Quality control of paired-end reads was also conducted using proprietary in-house software. Synonymous mutations were excluded to focus on potentially functional variations related to PZA resistance.
## 3 SA-PSO Method for FKP
### Improvement by Simulated Annealing Process  
One shortcoming of PSO is that particles may appear premature, then the algorithm traps them into the local optimum. The result of local optimum is that the algorithm cannot find the optimal solution meeting the accuracy requirements. The SA algorithm is derived from the principle of solid annealing and has the ability to jump out of the local optimal trap [19]. The main idea of the SA is that the optimization problem is simulated by a thermodynamic system, the energy of the system is regarded as the objective function of the optimization problem, and the optimization process is simulated by the annealing process of gradually cooling the system to reach the lowest energy state. The SA algorithm has the ability of probability jump in the search process, and can effectively avoid the search process trapping into a local minimum. It has been proven theoretically that the SA algorithm converges to the global optimal solution with a probability of 1 under certain conditions [20].  
To avoid the local optimum, the SA algorithm is integrated into the selection of \(\mathbf{P_{best}}\) of PSO to improve the ability to jump out of the local minimum. For the proposed SA-PSO method, \(\mathbf{P_{best}}_{i}^{k+1}\) is individual optimum of particle \(\mathbf{i}\) in the \(\mathbf{k}+1\) iteration, and it is updated by:  
\[\begin{split}\mathbf{P_{best}}_{i}^{k+1}=\left\{\begin{array}{ll} \mathbf{x_{i}}^{k+1}&\quad fiti^{k+1}{<}fiti^{k}\ \ \text{or}\ \ Pro^{k+1}>&rand\\ \mathbf{P_{best}}_{i}^{k},&\quad fiti^{k+1}>fiti^{k}\ \text{and}\ Pro^{k+1}< \ rand\end{array}\right.\end{split} \tag{8}\]  
While:  
\[\begin{split} fiti^{k}:&\text{the fitness of the}\ \ \mathbf{P_{best}}_{i}^{k}\\ fiti^{k+1}:&\text{the fitness of the}\ \mathbf{x_{i}}^{k+1}\end{split}\]  
Figure 2: Space \(\mathbf{Q}\) and \(\mathbf{L}\) are linked by IK( ) and FK( ).  
\(\mathit{Pro}^{k+1}:\) the SA probability for the \(k+1\) iteration \(rand:\) random number range from 0 to 1  
The individual optimum is allowed to receive the non-minimum by a probability of \(\mathit{Pro}^{k+1}\),which makes the particles search the SA neighborhood. This kind of SA randomness can jump out of the local minimum. The important factor \(\mathit{Pro}^{k+1}\) conforms to Boltzmann distribution and it is calculated by:  
\[\begin{split}\mathit{Pro}^{k+1}&=e^{-\mathit{f} \mathit{i}^{k+1}/T^{k+1}}\\ T^{k+1}&=\kappa_{B}T^{k}\end{split} \tag{9}\]  
While:  
\(T^{k+1}:\) the annealing temperature of the \(k+1\) iteration \(T^{k}:\) the annealing temperature of the \(k\) iteration \(\kappa_{B}:\) the Boltzmann constant  
The initial temperature \(T^{0}\) depends on the complexity of the problem. Here, we calculated it with the fitness of the initial population: \(T^{0}=\Delta fitness*population\) size. \(\Delta fitness\) is the maximum difference in the fitness. As is shown in Figure 3, the SA searching increased the randomness and received non-optimal individual optimum. In this way, the SA neighborhood searching can help the PSO algorithm jump out of the local optimum.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 6 Conclusions  
The transformer was accompanied by different amplitude vibration signals under different operating conditions. The accuracy and rapidity of fault classification depended on the accuracy of feature extraction of the vibration signal periodic map spectrum and the optimisation degree of the classification model. In this study, the following conclusions were obtained:  
1. A method based on SCA-VMD mode decomposition was proposed. SCA was used to adaptively optimise the parameters, that is, the number of modes and penalty factor, to optimise the decomposition effect of VMD and improve the input quality of the prediction model.
2. The feature extraction method of the periodic map spectrum generated after the SCA-VMD was used. The periodic map spectrum feature map reflected the difference in the energy distribution of the different mode components at different frequencies to reflect the different fault types of transformers.
3. This paper proposes GoogLeNet optimised by an attention mechanism to realise transformer fault diagnosis. The spatial and channel attention mechanisms are combined with different layers of the periodic map spectrum for the corresponding weighting, which improves the accuracy of the GoogLeNet classification model.  
The datasets used in the experiments were usually limited and covered a certain number of fault modes, the diagnosis of unknown fault modes may be poor, and obtaining a sufficient number and diversity of fault data may become an expensive and time-consuming task. The research work following this paper will be devoted to introducing more data types to enable the improvement and optimisation of transformer fault diagnosis methods based on SCA-VMD and the improved GoogLeNet.  
Conceptualisation, K.Z. and Y.B.; methodology, K.Z.; software, K.Z.; validation, K.Z., Y.B. and Z.L.; formal analysis, K.Z.; investigation, K.Z.; resources, W.S.; data curation, K.Z.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z.; supervision, W.S.; project administration, K.Z. and Z.L.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the \"Research and Development of Key Technology for Integration of All Elements of Industrial Internet Logo Resolution\" (Grant No. 2022B01049).  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methodologies** & **Accuracy (\%)** \\ \hline Ref. [19] & 97.36 \\ Ref. [20] & 97.28 \\ Proposed programme & 99.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of test results of different methods.  
Data Availability Statement: The vibration signals collected from the experimental transformer are the property of the author's institution and may not be used publicly.  
The authors would like to thank TBEA for their support throughout the research project.  
The authors declare no conflicts of interest.