esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Numerical Methodology
#### 2.2.1 Structural Model
Therefore, this modification leads to the final set of equations of motion used in this investigation  
\[I\underline{\dot{q}}+\alpha_{1}C\underline{\dot{q}}+K\underline{q}=\psi^{ \text{T}}\underline{F}_{ext}+\psi^{\text{T}}\underline{F}_{s}-\alpha_{2} \text{Csign}(\underline{\dot{q}})|\underline{\dot{q}}|^{\beta_{2}}-\alpha_{3} \text{Csign}(\underline{\dot{q}})|\underline{\dot{q}}|^{\beta_{3}} \tag{5}\]  
where \(\underline{F}_{ext}\) and \(\underline{F}_{s}\) are the global vectors of applied external and internal sloshing forces, respectively, defined in Cartesian coordinates at the FE nodes. Equation (5) is recast in state space such that time integration can be performed in Matlab using the Runge-Kutta 45 pair implemented within the ODE45 solver [20]. Parameters \(\alpha_{1-3}\) and \(\beta_{2-3}\) define the nonlinear damping response of the dry MiniWoT model. These parameters were determined by solving an optimisation problem that minimises the differences between the numerical and experimental damping ratios obtained from the response time series following the free-release conditions whilst only considering \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Type** & **Nastran Element** & **Number of Elements** \\ \hline Plate & CQUAD4 & 10,147 \\ Beam & CBEAM & 1200 \\ Spring & CBUSH & 17 \\ Spring & CELAS1 & 6 \\ Mass & CONM2 & 55 \\ MPC & RBE3 & 22 \\ \hline Number of nodes & 10,903 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FE model elements and nodes. Figure 5: MiniWoT finite element model overview. the out-of-plane dynamics. The resulting damping parameters can be found in Table 2. This simplified equivalent nonlinear damping model is used to capture the global dissipative properties arising from significantly more complex nonlinear internal and external influences such as boundary conditions, joint interfaces and aerodynamics effects. Figure 6 shows the comparison between the experimental data and the calibrated model for the frozen mass case. On the left-hand side, the tank tip acceleration versus time normalised by the first bending mode period is shown, and on the right hand side, the acceleration amplitude of the first bending component is shown on a semi-logarithmic scale. There is a very good match overall, indicative of the fact that the model frequencies and damping correspond well with the experimental measurement. The logarithm of the acceleration envelope shows damping trends in the first signal component (calibrated). For the viscous damping case, such a curve is a line on a semi-logarithmic plot; this is not the case here, indicative of dry damping nonlinearity that is correctly captured by the numerical model.
## 4 Simulation Results
### Robustness to Parameter Variations  
Robust controllers are crucial for maintaining consistent performance in the face of varying system parameters. The robustness of the proposed controller was assessed by perturbing the key parameters \(D_{a}\), \(H\), and \(\beta\), which significantly influence CSTR dynamics, by \(\pm 20\%\) around their nominal values (\(D_{a}=0.072\), \(H=8\), and \(\beta=0.3\)).  
Figure 16 shows the system's behavior with increased parameters (\(D_{a}=0.0864\), \(H=9.6\), \(\beta=0.36\)), while Figure 17 depicts the system's response to decreased parameters (\(D_{a}=0.0576\), \(H=6.4\), \(\beta=0.24\)). Table 5 quantifies the performance metrics associated with these variations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{2}{c}{\(d_{1}=0\to 0.2\), \(d_{2}=0\to 0.2\)} & \multicolumn{2}{c}{\(d_{1}=0\to-0.2\), \(d_{2}=0\to-0.2\)} \\ \cline{2-7}  & \(M_{peak}\) & \(t_{ry}\) & \(IAE\) & \(M_{peak}\) & \(t_{ry}\) & \(IAE\) \\ \hline APID & 0.666 & 13.529 & 3.506 & 0.996 & 18.150 & 2.232 \\ \hline ANPID & 0.631 & 9.916 & 2.510 & 0.949 & 14.279 & 2.296 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Disturbance rejection performance comparison of the two methods.  
Figure 14: Disturbance performance when \(d_{1}=0\to\)0.2 and \(d_{2}=0\to\)0.2.  
Figure 15: Disturbance performance when \(d_{1}=0\to-0.2\) and \(d_{2}=0\to-0.2\).  
An analysis of the figures and table reveals that the outputs of both methods demonstrate a swift return to the setpoint following parameter variations. These findings support the efficacy of the adaptation mechanism in the two methods.
## 3 DI-distributed Localization (DIL) Algorithm
### Distance Measurement
The detail procedure of executing the localization algorithm is given in Table 2. Since there may be slight differences between the final coordinates, the error estimation and correction methods as described in Subsection 3.3 can be used to find the most accurate location of a normal node.
## 3 Results and Discussion  
### Characterization of RMFs and Laser Pretreated CFRP and Aluminum Surfaces  
Ni/Al multilayers were deposited onto the PI substrate, as shown in Figure 2. Alternating Ni and Al layers were detectable, and the total thickness achieved 37 \(\upmu\)m. The thickness of the Ni layer (white) was about 40 nm, and the thickness of Al layer (grey) was about 60 nm, thus the bilayer thickness of the Ni/Al RMF was about 100 nm. The atomic ratio of the Ni/Al RMF was kept 1:1. The XRD result in Figure 3a shows that the as-deposited film was pure Ni and Al without obvious intermixing between the layers. Figure 3b presents the DSC measurement of the Ni/Al RMF; the calculated heat release of the total reaction is about 1270 J/g. Figure 4 shows the screenshots of the self-propagating reaction of the Ni/Al RMF. The length and width of the film was 25 mm by 5 mm. The film was ignited from the left side and the self-sustained reaction wavefront spread from the left to the right side in 8 ms. The reaction process was accompanied by a visible flame and the spread rate was about 3.2 m/s. Figure 5 presents the surface of femtosecond-treated CFRP and aluminum. As seen in Figure 5a, the epoxy matrix on the top of the CFRP surface was completely removed, and the carbon fibers were exposed outside. The aluminum surface was patterned with grids, as shown in Figure 5b.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**RMF Thickness (\(\upmu\)m)** & **Pressure (MPa)** & **Ignition Method** & **Pretreatment Laser Power (W)** \\ \hline
32–96 & 5 & 13 V DC & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Reactive multilayers joining parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Materials** & **Length** & **Width (mm)** & **Thickness** & **Themal** & **Density** & **Heat** \\
**(mm)** & **Width (mm)** & **(mm)** & **Conductivity** & **(g/cm\({}^{3}\))** & **Capacity** \\
**(/g-K)** & **(/g-K)** & **(/g-K)** \\ \hline
**RMF** & 8.000 & 5.000 & 0.037 & 76.000 & 4.910 & 0.536 \\ \hline
**PC** & 8.000 & 5.000 & 0.100 & 0.220 & 1.200 & 1.300 \\ \hline \end{tabular}
\end{table}
Table 2: ANSYS simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pretreatment** & **Joining Laser** & **Joining Laser** & **Joining Laser Size** & **PC Thickness** \\
**Laser Power (mW)** & **Power (W)** & **Wavelength (nm)** & **(mm)** & **(\(\upmu\)m)** \\ \hline
300–800 & 50–150 & 806 & 5 \(\times\) 2 & 150 \\ \hline \end{tabular}
\end{table}
Table 3: Laser joining parameters.  
Figure 4: High-speed camera measurement of the RMF reaction speed.  
Figure 5: SEM images of (**a**) Femtosecond laser-treated carbon fiber reinforced plastic (CFRP) surface; (**b**) Femtosecond laser-treated aluminum surface.  
Figure 3: (**a**) XRD pattern of the as-deposited Ni/Al RMF; (**b**) differential scanning calorimetry (DSC) measurement of the Ni/Al RMF.  
Figure 2: SEM images of (**a**) the total Ni/Al RMF; (**b**) the bilayer thickness of the Ni/Al RMF (white is Ni and grey is Al).
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 5 Conclusions  
The proposed multi-sensor data fusion method based on the self-attention mechanism builds a novel multi-sensor data fusion model that effectively fuses and collaboratively learns data from binocular vision measurement, laser tracking, and depth camera systems. A multi-source and multi-modal data fusion network, CNN-SA, served as the core component of the multi-sensor data fusion model. By incorporating the self-attention mechanism, CNN-SA could adaptively learn the correlations and weighs between different modalities, thus improving the accuracy of data fusion by better utilizing multi-sensor data. A significant advantage of the CNN-SA network was its ability to handle data without a unified coordinate system. Furthermore, compared with the input data, the CNN-SA network achieved an accuracy improvement of 72.6%, surpassing the improvements of 29.9% for CNN-CBAM, 23.6% for CNN, and 11.4% for CNN-LSTM. These results demonstrate the significant accuracy improvement achieved by the CNN-SA network in multi-source and multi-modal data fusion tasks, providing strong support for further research and applications in the field of multi-sensor data fusion processing. In future, the application of a metaheuristic optimization algorithm to the proposed CNN-SA neural network is expected to improve the efficiency of multi-sensor data fusion.  
Conceptualization, X.L. and D.Y.; methodology, S.C. and D.Y.; software, S.C.; validation, S.C.; formal analysis, S.C. and D.Y.; investigation, S.C.; resources, X.L.; data curation, X.L.; writing--original draft preparation, S.C.; writing--review and editing, S.C.; visualization, S.C.; supervision, X.L., D.Y., L.G., Y.L. and L.L.; project administration, X.L. and S.C.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Deviation map. (**a**) CNN-CBAM deviation map, (**b**) CNN deviation map, (**c**) CNN-LSTM deviation map, and (**d**) CNN-SA deviation map.  
**Funding:** This research was funded by the Key Research and Development Project of the Jilin Province Science and Technology Development Program [No. 20200401019 GX] and the Zhongshan Social Public Welfare Science and Technology Research Project [No. 2022B2013].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that the data is only for internal communication within the laboratory.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# A Decentralized Multi-Agent-Based Approach for Low Voltage Microgrid Restoration
## 4 Proposed Decentralized Approach
### General Assumptions  
The goal of this paper is to develop a decentralized multi-agent-based approach to restore the MG loads and generations with a proper sequence of actions after a total blackout. The local controllers (MCs and LCs) and the MG communication infrastructure are so important for the successful implementation of the decentralized restoration scheme.  
Based on the abovementioned distributed averaging algorithm, it is assumed that each node of the graph can be considered as an agent. The edges of the graph can be considered as the communication links among the agents. It is assumed that each local controller is assigned to a specific agent. Thus, there are two kinds of agents: (i) MC agents and (ii) LC agents. Figure 7 shows the conceptual decentralized multi-agent based model for MG restoration. In this model, each MC agent has some local information such as the amount of the generation capacity of the corresponding microsource, connection situation of the corresponding microsource (connected or disconnected), its availability and preparedness for the restoration, and pre-defined priority for the restoration.  
Figure 5: A typical studied graph.  
Figure 6: Comparison of converging speed of different methods: (**a**) uniform; (**b**) metropolis.  
Figure 7: Conceptual decentralized multi-agent based model for MG restoration.  
The LC agents have similar local information of their corresponding loads. The agents don't have any direct access to the global information of the system. An agent is only able to communicate with its neighbors. By using the ACA based communication law, the agents are able to share the local information, to access the global information, and accordingly to take a common decision for restoring the loads or generation units.
## 2 Methodology
### Temporal Attention-LSTM Model  
The temporal attention mechanism [40] can improve the model performance in multi-step forecasting by identifying the more critical time points in the input sequence and assigning larger weights to these more critical points [41]. A temporal attention-LSTM model is developed in this study to achieve multi-ring shield attitude predictions. Figure 2 depicts the temporal attention-LSTM structure. Specifically, the data stream of shield construction parameters for different rings is fed into the input layer as the input sequence \(\mathbf{x}_{t}\). The data of each ring is processed through the LSTM layer, which captures the long-term dependencies in the time series and outputs a series of hidden states \(\mathbf{h}_{t}\). Then, the temporal attention layer assesses the hidden state of each ring and calculates its attention score \(\mathbf{s}_{t}\), which is normalized to weights between 0 and 1 by the softmax function, leading to the temporal weight \(\mathbf{w}_{t}\). The weights are then multiplied by the hidden states to obtain the context vectors \(\mathbf{c}_{t}\). All context vectors are combined into a global context vector \(\mathbf{c}_{\text{all}}\), which is processed through a dense layer to produce the final prediction \(\mathbf{Y}\). Through this process, the temporal attention-LSTM model can dynamically focus on the ring in the sequence which contributes more significatly to the final prediction, leading to a reasonable multi-ring shield attitude prediction.
## 2 Materials and Methods  
As depicted in Figure 1, the proposed model comprises three principal components: a Multi-Scale Mixed Preprocessor, a Cross-Channel Spatial Encoder, and a Multi-ScaleSpatial Decoder. In the context of agricultural land change detection, the Multi-Scale Mixed Preprocessor extracts local features across multiple scales, thereby enhancing sensitivity to changes. The Cross-Channel Spatial Encoder integrates features from various channels, employing a self-attention mechanism to prioritize critical change areas. Finally, the Multi-Scale Spatial Decoder maps the encoded features back to spatial dimensions, yielding precise change detection outcomes. This structural integration markedly enhances the model's performance and accuracy in agricultural land change detection.
## 1 Introduction
### Iceland Case  
[PERSON] et al., (2003) [4] reported natural emission and power plant emissions in several areas in an initial attempt to compare the two types of emission. According to their calculations, the Krafla area's natural emission (Figure 1) corresponds to 200 g m\({}^{-2}\) d\({}^{-1}\). For the entire area covered by Iceland, the author estimates that the maximum possible CO\({}_{2}\) flux from geothermal and volcanic systems to the atmosphere would be about 1.3 \(\times\) 10\({}^{9}\) kg/year (148 t/h) ([PERSON] et al., (2005)) [5]. The same author estimated that emissions from the three major geothermal power stations in Iceland correspond to more than 8-16% of the maximum annual total emission estimated, and therefore suggests that geothermal emissions cannot be neglected in Iceland. Observations in Reykjanes and Svartsengi geothermal areas indicate an increase in diffuse emission from the geothermal field after the beginning of power production. In the case of the Svartsengi plant, an increase in the emission factors has been observed due to the formation of a steam cap in the mid-1980s with high concentrations of CO\({}_{2}\). After this initial phenomenon, decreasing emissions have been observed as a result of a gradual decrease in the CO\({}_{2}\) concentration in the steam cap [4]. In the Krafla geothermal power plant (Figure 1), the CO\({}_{2}\) concentrations increased between 1975 and 1984 due to magmatic activity referred to as the Krafla fires. The increased emissions caused by a gas pulse due to magmatic intrusions increased the CO\({}_{2}\) content drastically. In 1999, the emission spiked again but that was due to increased power output when a second 30 MW unit was added. Since 2005, the emission has been gradually decreased, which clearly shows that both natural and developing processes can impact changes in emission due to geothermal operations [5,21]. In the Reykjanes field, an analogous increase has been observed and could be due to the field evolution, since the commissioning of the 100 MW plant, in 2006, where the CO\({}_{2}\) production from wells has indeed already started a slow decline ([PERSON], [PERSON], [PERSON] and [PERSON], 2010) [22]. Other studies in Hengill and Krafla in Iceland indicate similar results where the emission increase is very small and only a negligible amount would be counted as added emissions [19].  
It has been assumed that the impact of geothermal power plants may actually be near zero, as the CO\({}_{2}\) discharge has been enhanced in the initial phases, which are immediately followed by a long period of relatively low gas output and, thereafter, left to function, permitting a natural equilibrium to be re-established ([PERSON], [PERSON] and [PERSON], (2012)) [18].  
However, not many studies have been conducted with direct measurements of natural emissions, and this information rarely exists for currently operating power plants, as pointed out by, e.g., [PERSON] et al., (2005) [5] and [PERSON] et al., (2016) [23].
## 2 Materials and Methods
### Comparative Genomic Studies and Genome Mining of the Lentzea sp. JNUCC 0626  
To assess the genomic similarity between _Lentzea_ sp. JNUCC 0626 and its closely related species, digital DNA-DNA hybridization (dDDH) values were calculated using the Genome-to-Genome Distance Calculator (GGDC 4.0) available within the Type (Strain) Genome Server (TYGS) provided by the Leibniz Institute DSMZ [34]. Additionally, average nucleotide identity (ANI) values between _Lentzea_ sp. JNUCC 0626 and its nearest phylogenetic neighbors were computed using the ANI calculator provided by EzBioCloud, an online tool for prokaryotic genome sequence comparisons [19].  
For genome mining and the discovery of biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites, including pacidamycin, trioxacarcin, BE-54017, and benzastatin, several computational tools were employed. The identification and comparative analysis of the BGCs were carried out using Known ClusterBlast, ClusterBlast, SubClusterBlast, ActiveSiteFinder, and Cluster PFam analysis. AntiSMASH 7.0 was utilized to predict and annotate secondary metabolite BGCs across the entire genome [35]. The PRISM 4 (v4.4.5) and BAGEL 4 tools were employed to further analyze the BGCs, focusing on RiPPs (ribosomally synthesized and post-translationally modified peptides) and bacterioxins, with BAGEL 4 being specifically used for bacteriocin mining [36; 37; 38]. These programs operated with default settings to ensure consistency and accuracy in the analysis. The combination of these genome mining tools enabled a comprehensive analysis of the secondary metabolite biosynthesis potential encoded within the genome of _Lentzea_ sp. JNUCC 0626. This approach provided detailed predictions regarding the chemical structures and biological activities of the identified secondary metabolites, facilitating a deeper understanding of the strain's metabolic capabilities.
## 3 Case Study
### Data Transferring and Simulation  
Data transferring is strongly influenced by the software chosen to perform simulation. Among commercial software for human simulation, most of them have interfaces that enable them to directly connect external supported devices, such as Kinect(r) or XSens(r), to the simulation environment. Alternatively, several software packages allow implementing customized plugins to read data from external devices. To simulate the proposed working activity the software Tecnomatix Process Simulate by Siemens(r), version 15.0.1, was used. It is an excellent solution for industrial processes simulation, including a module of human simulation which allows performing very accurate simulation of manual tasks. Moreover, several codes able to evaluate ergonomics indexes are already implemented and it is also allowed creating customized routines to implement other codes, plugins or interfaces. In this regard, a custom plugin has been developed in Visual C# to load the collected data. The plugin allows reading data stored on a CSV file, transferring them to the DHM and, hence, creating compound operations, according to experimental data, by using the function \"CreateHumanCompoundOperation\".  
The pseudocode for data transferring can be found in [PERSON] et al. [4].  
For this study, data have been re-sampled in order to reduce the number of micro-operations and, hence, the computational cost. Figure 6 shows the interpolating curve of the sampled data related to the posture angles trends, shown in Figure 5.  
Motion data related to all the 60 investigated working cycles have been transferred and, hence, all the working cycles have been simulated.  
Figure 4: Wearable motion tracking system configuration.  
Figure 5: Posture angles trends over one working cycle for: trunk (**a**), arms (**b**) and elbows (**c**).
## 5 Influence of Rock Lining Degradation on Rock Pressure with Thinning Defects
### Genuine Pressure Analysis Under Degradation Conditions  
The genuine pressure distribution in shallow four-track HSR tunnels with thinning defects under conditions of lining or rock mass degradation is shown in Figure 20. The operational service times considered include 30, 60, 90, 120, and 150 years. When the tunnel lining deteriorates, the maximum genuine pressure increases with service time. At \(t_{\mathrm{c}}=0\), the maximum genuine pressure is 112.67 kPa; when \(t_{\mathrm{c}}=150\) years, it increases to 124.91 kPa--an increase of 10.86%. When only the rock deteriorates, a similar increasing trend is observed. At \(t_{\mathrm{r}}=150\) years, the maximum genuine pressure reaches 246.28 kPa, representing an increase of 118.59 kPa compared to the initial state. When both the rock and the lining deteriorate simultaneously in the presence of a thinning defect, the genuine pressure rises further with time. At \(t_{\mathrm{r}}=t_{\mathrm{c}}=150\) years, the maximum genuine pressure reaches 262.16 kPa, reflecting a total increase of 132.69 kPa. In summary, as service time increases, the degradation of the lining and rock significantly exacerbates stress concentration within the thinning-affected zone, potentially resulting in severe structural damage. Moreover, such degradation may cause the tunnel to transition from a genuine pressure state to a loosening pressure state, with the secondary lining shifting from a safety reserve to a primary load-bearing structure. Therefore, in shallow four-track HSR tunnels, early identification and timely remediation of lining defects are essential to ensure long-term operational safety.