esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 2 Related Work
### Intelligent Optimization and Collaborative Scheduling  
In addition to traditional optimization methods, researchers have combined intelligent computational methods to solve the problem of balancing resource competition in multi-objective optimization in task decomposition [23; 27; 28; 29; 30; 31; 32]. For example, [PERSON] et al. [23] improved resource utilization by balancing power and rate through a two-stage optimization framework. Similarly, [PERSON] et al. [29] proposed a resource allocation method based on ACO, which improves task execution efficiency and real-time performance by optimizing computation and network resource allocation. Additionally, [PERSON] et al. [32] introduced the PA-LBIMM algorithm, which integrates user-priority-based Min-Min scheduling to enhance load balancing in cloud computing environments, offering better performance in terms of task completion time under multi-objective optimization. However, non-convex optimization solutions lack stability, so further research is needed on weight-adaptive allocation methods for high-dimensional objective spaces.  
For the coupling problem of task relevance in convex optimization, some researchers have proposed task aggregation and graph network methods based on the concept of intelligent computing [33; 34; 35; 36]. Typically, [PERSON] et al. [33] reduced task conflicts through task network graph aggregation. [PERSON] et al. [36] proposed a task decomposition and resource scheduling method based on the network flow algorithm, which optimizes task offloading and resource allocation in cloud-edge collaborative computing through LSTM prediction and hierarchical scheduling. Although these methods effectively optimize task scheduling, they still face problems such as poor real-time performance, high computational complexity, and insufficient flexibility in resource scheduling.  
In the decentralized scheduling scenario, researchers adopt multi-agent collaboration frameworks [37; 38]. For example, [PERSON] et al. [37] reduces task latency through deep reinforcement learning. However, the communication overhead and policy consistency between intelligent agents are still bottlenecks, so designing lightweight collaboration protocols and heterogeneous verification mechanisms is necessary. Faced with the challenge of limited resources, [PERSON] et al. designed a cross-layer resource allocation model [28; 39]. However, existing methods have not been able to solve the problem of the dynamic matching of satellite ground resources, so combining them with digital twin technology is necessary to achieve virtual real linkage optimization. Although these methods have made some progress in task decomposition, scheduling, and resource optimization, they still face challenges in real-time performance, stability, and intelligence.
## 6 Discussion
There was conformity between the cross-sectional model from the gravity results and the resistivity model from the MT inversion, as shown in Figure 11. The resistivity model on profiles A-B can provide a clear picture of the graben structure, which was marked by the contrasting resistivity values. In addition, near the surface, conductive layer C2 could be interpreted as a clay cap, which is an alluvium deposit that has been altered, giving it a more conductive value. Below this layer, the presence of a reservoir zone was observed with moderate resistivity values. The integration results based on gravity and the MT method were used to reconstruct a conceptual model of the geothermal system in the Limboto Lake--Pentado area (Figure 12). The geothermal system included several parameters: reservoir zone heat sources, cover layers, hot fluid sources, and the hydrological cycle [18; 19; 20]. The model illustrated that the recharge area for the geothermal system in the study area was the upper slope of the Gorontalo graben zone, as an infiltration zone of rainwater that enters the ground into meteoric water and is heated by the dike system below the graben zone until it is trapped in the geothermal reservoir system. The geothermal fluid flow cycle, which is limited to the Gorontalo graben zone, undergoes forced convection until it finally arrives on the Earth's surface to form hot springs. Before reaching the surface, the fluid condenses at a depth to form steam-heated water, an indication of meteoric water heated by steam in the system. The appearance of the Pentadio hot water is an up-flow zone representing the geothermal reservoir zone below it and the geothermal prospect area on the surface. The  
Figure 11: An integrated geophysical interpretation in sections A–B: (**a**) density model of A–B constrained by the MT result and geological studies; (**b**) incorporated 3D resistivity profile of A–B. Limboto Lake--Pentadio geothermal reservoir was identified based on a resistive layer at 60-80 \(\Omega\)m recorded from a depth of \(-750\) msl, with a thickness varying between \(-750\) msl and \(-1250\) msl in the andesitic lava rock group. The lateral dimensions of the geothermal system reservoir in the study area formed a separate lenticular pattern (lens) in several segments. This condition was interpreted as the influence of the subsurface geological structural system on the Gorontalo graben zone.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios
### SVS Indices Implementation
#### 3.2.2 Csi  
In six defined scenarios, \(S_{l}\)s and \(S_{l}\)s are computed for selected buses and \(CS1\) for different time intervals are obtained where the results are shown in Figure 5. It is obvious from Equations (3)-(6) that, in unstable cases, \(S_{l}\)s and \(S_{l}\)s for system buses are near one and then for \(CS1\).  
For cases in which voltage variation is in the range acceptable and voltage magnitude is higher than 0.8 pu in the time interval, \(S_{l}\) and \(S_{l}\) for buses are equal to zero. In contrast, if the voltage dip exceeds 0.2 pu, the values' range of \(S_{l}\)s- and \(S_{l}\)s is \([0,1]\). Thus, if \(CS1\) has a value more than zero, then voltage magnitude has significant variations.  
Figure 4: System LE evolution in case (**a**) with \(N=60\).  
According to Figure 5, for instability detection, it can be said that, when \(CS1\) is more than a certain threshold like 0.6 pu after 2 s from clearing time, the occurred case is an unstable condition. For times less than 1.5 s, threshold can be reduced to 0.5 pu.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.8 Peru Forest Aboveground Carbon Density Map  
[PERSON], et al. [93] combined field plot and GLAS data, as well as vegetation indices, cover data, and topographic and climate data using the Random Forest model and generated a Peru forest carbon density map at 1-ha resolution. Validation of estimated results with field plot data showed a R\({}^{2}\) of 0.74.
## 3 Results and Discussion
### Thermal Imaging Test Results  
According to the average temperature at different times, the time-temperature curve was plotted as shown in Figure 13. It can be seen that within 100 min, the temperature increased rapidly from below 30 \({}^{\circ}\)C to above 45 \({}^{\circ}\)C. From 100 min to 300 min, the temperature rose slowly. Being affected by complicated working conditions, the temperature would  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(Y\) & \(m_{0}\) (g) & \(m_{f}\) (g) & \(m\) (g) & \(w\) & \(L\) (min) & \(v\) \\ \hline
**S-1** & 140.96 & 134.42 & 6.54 & 4.64\% & 1139 & \\
**4** & 140.65 & 137.86 & 2.79 & 1.98\% & 1648 & 44.69\% \\
**13** & 139.17 & 136.60 & 2.57 & 1.84\% & 1519 & 33.36\% \\
**S-2** & 144.82 & 139.51 & 5.31 & 3.66\% & 1161 & \\
**22** & 144.56 & 141.98 & 2.58 & 1.78\% & 1721 & 48.23\% \\
**25** & 143.62 & 138.72 & 4.90 & 3.41\% & 1333 & 14.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of wear and life tests.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(D\) (mm) & & & \(H\) (mm) & & & \(N\) & \\ \hline \multirow{3}{*}{\(F+\) (N)} & 1.0 & 1.5 & 2.0 & 0.5 & 1.0 & 1.5 & 36 & 24 & 18 \\  & 147.95 & 131.79 & 141.52 & 132.94 & 139.18 & 149.14 & 121.55 & 153.74 & 145.97 \\ \(F-\) (N) & 185.52 & 162.44 & 180.59 & 177.09 & 174.01 & 177.46 & 139.98 & 189.38 & 199.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(F+\) and \(F-\) when \(D\), \(H\) and \(N\) are unchanged.  
fluctuate with time and be in a relatively stable state after 300 min. During the continuous and stable operation of the mud pump (300-600 min), compared with the standard piston, the temperature of the friction pair of the piston with cylindrical pit arrays was significantly reduced. The temperature curves indicate that the curves of the textured pistons were consistently below those of the corresponding standard pistons. The temperature of piston S-1 was obviously higher than that of pistons 4 and 13. Similarly, the temperature of piston S-2 was significantly higher than those of pistons 22 and 25. In comparison with the corresponding standard pistons, the temperature of nearly all textured pistons was reduced by around 5-10 \({}^{\circ}\)C. The higher the temperature, the more severe the friction and wear of the piston, and the shorter its service life will be.
## 3 Realization of Adaptive Square Root Cubature Kalman Filter Algorithm in Vector Receiver
### Realization of ASRCKF Algorithm in Vector Tracking Loop  
#### 3.4.1 ASRCKF Vector Tracking Algorithm Implementation Specific Process  
The receiver uses the RF front-end to down-convert the received signal into an intermediate frequency signal. After mixing, correlation, and other processes, the receiver outputs six coherent integration results of early, instant, and late, that is \(I_{E},Q_{E},\ I_{P},Q_{P},I_{L},Q_{L}\), then uses the arctangent phase detection method for carrier to output carrier frequency difference and uses the noncoherent early minus late amplitude method for C/A code to output code phase difference. They are shown in Formula (31). The carrier frequency difference and code phase difference are used as measurements to input the navigation filter to monitor the loop tracking, and the filter uses the ASRCKF algorithm to calculate the position, velocity, acceleration, and other parameters.  
\[\varnothing_{carrier}=arctan\frac{Q_{P}}{I_{P}},\ \delta_{C/A}=\frac{1}{2}\frac{E-L}{E+L} \tag{31}\]  
The receiver vector tracking loop (VTL) includes two parts: C/A code vector tracking and carrier signal vector tracking. Whether it is C/A code vector tracking or carrier signal vector tracking, it is necessary to first obtain the control amount of local pseudo code and carrier signal from the navigation filter. From the measurement in Equation (2), it is known that the matrix H is determined by the observation vector from the receiver to the satellite, which means that the position of the receiver and the satellite must be known to determine the matrix. Therefore, the receiver is first started in the traditional way; after obtaining the corresponding control amount through the code phase/carrier frequency estimator, it switches to the vector tracking mode. Through the tracking process of the vector loop, it can be known that if the signal of a certain channel suddenly disappears, because of the assistance of other channels in the C/A code vector tracking loop, the position error estimation of the C/A code loop through the user's position feedback will always exist, and according to the estimated value, the phase of the local code is kept updated, so the loop will not lose lock in a short time. On the contrary, the scalar tracking loop will immediately enter the capture state to start the capture algorithm at this time, so the vector loop not only enhances anti-interference ability but also reduces a certain amount of calculation [16].  
The Table 1 shows the initial settings of the relevant data of the vector loop.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 4 Principles and Optimization of the YOLOv5 Model
### CA Mechanism  
The bearing images captured by industrial cameras have the characteristics of large size and numerous small defects. The pixel size of small defects is relatively small in the entire bearing image. Therefore, the feature information of these small defects is prone to being lost in multiple convolution and pooling operations. This can lead to the network inadequately learning the feature information of small defects and can result in the phenomenon of missed detection. Therefore, in the C3 module of the backbone network, this paper integrates the coordinate attention (CA) mechanism [17] to enhance the focus on the feature information of small defects, thereby improving the detection accuracy of small defects.  
The channel attention mechanism was initially applied to improve deep learning models by adaptively learning the importance of each channel through compressing and exciting input features [18]. This mechanism can enhance the model's performance to some extent. However, the channel attention mechanism neglects the importance of positional information, which is a crucial aspect the network needs to focus on learning.  
Figure 6: YOLOv5 network structure.  
Figure 7: Improved YOLOv5 model.  
The CA mechanism embeds positional information into the channel attention, allowing the network to simultaneously consider encoding inter-channel information and positional information. The schematic diagram of the CA mechanism is shown in Figure 8.
## 1 Introduction
Uncertainty quantification mechanisms enhance trust in automated systems by providing confidence intervals for predictions [28]. As research in this domain continues to evolve [29], key focus areas include real-time implementation [30], scalability, and the integration of emerging sensor technologies to further enhance structural diagnostics and decision-making [31]. Future advancements will likely focus on improving the interpretability of deep learning models in structural mechanics applications [32], optimizing computational efficiency, and incorporating domain-specific constraints into neural architectures [33]. Interdisciplinary collaborations between structural engineers and AI researchers will drive the development of more reliable and adaptable prediction models [34]. The combination of multi-sensor fusion, deep learning, and physics-informed models represents a paradigm shift in structural health monitoring and predictive maintenance strategies [35]. Despite significant progress in stress-strain prediction models, existing approaches still face several key challenges. Traditional finite element methods and classical constitutive models struggle to capture the complex nonlinear stress-strain behaviors of heterogeneous materials, leading to limitations in accuracy. Data-driven machine learning models, while improving predictive performance, often lack physical interpretability and generalizability due to their reliance on empirical training data. Computational efficiency remains a concern, as high-fidelity simulations demand significant processing power, limiting real time applications in structural analysis. To address these limitations, this study introduces the Stress-Strain Adaptive Predictive Model (SSAPM), which integrates multi-sensor image fusion with a domain-aware deep learning framework. By bridging the gap between physics-based modeling and machine learning, our approach establishes a more robust and scalable framework for stress-strain prediction in structural mechanics. We summarize our contributions as follows:  
* A hybrid modeling approach that combines mechanistic stress-strain relationships with data-driven corrections to improve accuracy while maintaining physical interpretability. * A hierarchical multi-sensor fusion module that effectively integrates thermal, acoustic, and visual imagery for enhanced material behavior prediction. * An adaptive optimization strategy that ensures computational efficiency while preserving model precision across diverse structural scenarios.
## 4 Numerical Model of the Retrofitted Joints
### Model Validation  
The numerical model was verified by extracting the load-displacement results of the beam through the FE analysis. As shown in Figure 8a, the comparison was made with the experimental testing output, and the results showed very close agreement, confirming the verification for both CS1 and RS1 models. As expected, the FE numerical solution resulted in a stiffer response for both models, showing higher initial stiffness. Following the expected initial discrepancy, trends of the numerical and experimental curves are similar up to 20 KN, followed by a stiffer response from the finite element model. However, the numerical analysis predicts the ultimate capacity relatively accurately, with a slightly higher resistant force, proving that the FE model and the performed analysis were reliable for both considered models (before and after retrofitting).  
Accordingly, the results presented in the graph of Figure 8a depict that the maximum strength achieved for both CS1 and RS1 joints through finite element analysis was 45.4 kN and 59.13 kN, respectively. In comparison, the maximum strength capacity for these joints obtained from the experimental tests was 44.8 kN and 56.5 kN, respectively. Therefore, these results validate the FEM simulation with a 1.2% error for CS1 and a 4.3% error for RS1. The higher error for the frame retrofitted with CFRP is mostly due to the assumption of full bonding between CFRP and concrete during FEM modeling, which is unlikely to occur in practical construction operations. To increase the accuracy of the results, it is recommended to insert the action of an adhesive in the model by using proper contact elements or interface elements to simulate the realistic behavior of CFRP and concrete bonding.
## 3 Results and Discussion
### Computational Study  
#### 3.3.1 Geometry Data  
The mean absolute error, for the (12) bond distances and the (28) bond angles are listed in Table 4. The MAE values range from 0.022 to 0.002 for the complex bond distances. The complex bond angles have MAE values below 2\({}^{\circ}\). B3 LYP was utilized to obtain these MAE values.
## 4 Results and Discussion
From our experimental result, we also find that Grobid takes the most time to convert 10 files, 3.65 s on average for a single file, and Pdftotext takes 0.196 s on average per file. \begin{table}
\begin{tabular}{l c} \hline \hline
**Package** & **Runtime (Second)** \\ \hline Tika & 3.35 \\ Pypdf2 & 10.9 \\ Pymupdf & 1.23 \\ Pdfminer.six & 26.1 \\ Pdftotext & 1.96 \\ Grobid & 36.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Runtimes in second of different PDF-to-text extraction packages (a lower score is better). Figure 3: Comparison results of memory usage in Mega Bytes of different PDF-to-text extraction packages (a lower score is better). To analyze the trend of memory usage and runtime of the PDF-to-text extraction packages, runtime and memory usage are calculated for 50 and 100 documents. The results are shown in Tables 7 and 8 for runtime and memory usage, respectively. The calculation results show that runtime and memory consumption for 50 and 100 documents follow a similar trend as for 10 documents. The Pymupdf package requires the least runtime while Pdfminer.six requires the highest runtime for converting text from PDF documents for 50 and 100 documents. Figure 5 presents the runtime comparison for all packages. On the other hand, Grobid consumes the least memory, while Tika requires the highest memory for converting text from PDF documents for both 50 and 100 documents, as shown in Figure 6. The experiment with 50 and 100 documents is performed in the same hardware and software setup mentioned in Section 3  
Following this analysis, and given the precision and f-1 values obtained, it is proposed to use Grobid for text extraction from scientific PDF documents. Moreover, the memory consumption of Grobid in converting text content from PDF documents is the lowest among all the compared packages, and Grobid also takes less time in converting a larger number of documents. This is evident from Table 8 and Figure 5. For converting 10 documents, Grobid takes the highest runtime of 36.5 s. However, when the number of documents is increased to 50 and 100, the Pdfminer.six package takes the highest runtime to complete the conversion process, and Grobid falls behind the Pdfminer.six package.
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### The Overall Flowchart of The Proposed Algorithm
#### 3.1.2 Spatial-Temporal Context Correlation Filter  
Traditional discriminative correlation filters use cosine windows to handle the boundary effects of tracking targets due to the circulant assumption, which causes limited contextual information for DCFs based trackers. It is easily result in tacking drift when target objects experience complex scene changes such as occlusion, background clutter and fast motion. In order to learn a powerful filter that produces a high response value for the target image block and a near zero response value for the context region, the CACF framework [13] combined the background information around the target into the learned filter. In our work, we utilize the hierarchical features of convolutional layer as feature representation and employ CACF framework as our spatial-temporal context model for visual tracking, reference [13].  
In this framework, we aim to get an ideal correlation filter \(w\). For all training samples \(U_{0}\) generated by circular shifts using a sliding window on three convolution layer, utilizing the nature of the circular matrix [6] will better dealt better with the ridge regression trouble.  
\[\min_{w}\lVert U_{0}w-y\rVert_{2}^{2}+\lambda_{1}\lVert w\rVert_{2}^{2}. \tag{1}\]  
In Equation (1), the data matrix \(U_{0}\) represents all circular shifts of the vectorized image patch \(u_{0}\), and \(w\) denotes the learned correlation filter. The regression target \(y\) represents a vectorized image of a 2D Gaussian, \(\lambda_{1}\) represents regularization weight parameters.  
We use the learned correlation filter \(w\) to convolve with the image block in the next frame in order to predict the position of the target. The maximum response value of all training sample response vectors \(y_{p}(z,w)\) is the estimated position of the target. Given an image block \(z\), the output response value is derived from the following formula:  
\[f(z)=\mathcal{F}^{-1}(z\odot w)=\hat{z}\odot\hat{w}. \tag{2}\]  
In Equation (2), \(\mathcal{F}^{-1}\) represents the inverse Fourier transformation, and \(\odot\) represents the convolution operation. Then update filter model by employing following equations:  
\[\hat{w}_{i}=\eta w_{i}+(1-\eta)\hat{w}_{i-1}, \tag{3a}\] \[\hat{x}_{i}=(1-\eta)\hat{x}_{i-1}+\eta\hat{x}_{i}. \tag{3b}\]  
The subscript \(i\) represents the sequence number of current frame, and \(\eta\) represents the learning rate parameter, and \(\hat{x}_{i}\) represents the target appearance model.