esa-sceva/satcom-chunk-collection huggingface dataset:
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 3 PoAIR Algorithm
### Matching and Tracking Successive Rainstorm Objects in a Rainstorm Sequence  
To match instantaneous objects in successive snapshots and group them into rainstorm sequences, three steps are followed. In the first step, suitable features are selected to calculate the matching  
Figure 3: Three types of linkages with 8-neighborhood. (**a**) 8-neighborhoods of two rainstorm grid cells, (**b**) 8-neighborhoods of two rainstorm regions, and (**c**) the five independent grid cells and four spatial regions. similarities, in the second step, a matching algorithm is designed to determine which of the instantaneous objects in the current snapshot are matched with the objects in the next snapshot, and in the third step, a loop of the matching algorithm is performed until all the rainstorm objects at time snapshots have been analyzed. For the first step, according to the definition of a rainstorm sequence, all the rainstorm objects belonging to the same sequence have similar spatial structures and thematic features, that is, during their movements, the rainstorm objects do not split or merge. Thus, the matching and tracking algorithm should impose the conditions of invariance of rotation and scale on the rainstorm objects [25]. To match and track a rainstorm sequence, this paper selects spatial features, including eccentricity (\(E\)), rectangularity (\(Rt\)), soundness (\(Sd\)), and a shape index (\(SI\)), a temporal feature, i.e., the time of occurrence, and thematic features, i.e., the mean rainfall intensity (\(Rm\)). The details and formulas of these features are listed in Table 1.  
As the matching objects in the successive snapshots have the minimum differences in spatial and thematic features, PoAIR uses Equations (1)-(4) to calculate the differences between rainstorm objects and build their cost function. Then, the Hungarian method is used to take the minimum difference as an object function to match and track the rainstorm objects in two successive snapshots. The Hungarian method is an optimal way to solve the assignment problem [8]. \[F=w_{1}*Sd+w_{2}*Td \tag{1}\]  
\[Sd=w_{11}*d_{\mathrm{E}}+w_{12}*d_{\mathrm{Rt}}+w_{13}*d_{\mathrm{Sd}}+w_{14}* d_{\mathrm{SI}} \tag{2}\]  
\[Td=\left|Rm_{t+1}-Rm_{t}\right| \tag{3}\]  
\[\left\{ \begin{array}{c}d_{E}=\left|E_{t+1}-E_{t}\right|\\ d_{Rt}=\left|Rt_{t+1}-Rt_{t}\right|\\ d_{Sd}=\left|Sd_{t+1}-Sd_{t}\right|\\ d_{SI}=\left|St_{t+1}-St_{t}\right|\end{array} \right. \tag{4}\]  
where \(w_{1}\) and \(w_{2}\) are the weights of the spatial and thematic features and \(w_{11}\)-\(w_{14}\) are the weights of the spatial features. As all the features are of equal importance in the implementation, \(w_{1}\) and \(w_{2}\) are equal to 0.5 and \(w_{11}\)-\(w_{14}\) are equal to 0.25. When the third step is finished, all the instantaneous rainstorm objects are grouped into either rainstorm sequences or independent rainstorm objects. Independent rainstorm objects are ones which have not been matched to another object in previous or subsequent snapshots.
## 3 Passive Thermal Control Methods
### Surface Paints  
The satellite's passive thermal control system mainly uses specially prepared thermal coatings to maintain the subsystem temperature within safe operating ranges. In space, external satellite surface paints or coatings are greatly influenced by adverse environmental effects, namely, Atomic Oxygen (ATOX), molecular contamination, and Ultraviolet radiation (UV) [22]. Energy absorbed by the satellite surfaces depends on the external surface characteristics and area. The major concern in using paints is the degradation of physical and thermo-optical properties of the components and this degradation is dependent on mission duration and orbit altitude. Thermal radiation heat transfer on satellites can be controlled using materials that have specific thermo-optical surface properties, which are solar absorptivity (\(\alpha\)) and infrared emissivity (\(\epsilon\)). These properties are dependent on materials and processing techniques [23; 24]. \(\alpha\) governs how much solar incident heating a spacecraftabsorbs, while \(\epsilon\) determines how much heat a spacecraft emits to space [10]. By altering \(\alpha\) and \(\epsilon\), the overall temperature of the spacecraft can be controlled to some extent. There are numerous space qualified paints and coatings that are commercially available and most of them have space heritage. Radiative tapes also provide better performance. For example, second-surface fluorinated ethylyne propylene (FEP) provides better performance as radiator coatings [10] and for most small satellites, adhesive tapes or surfaces finishes (polishing, anodize, alodine) have been considered. End-of-life (EOL) properties are considered in the early design phase as the absorbed solar energy will gradually increase over the years due to degradation of surface properties. In general, satellite components will run cooler in the early phase of the mission life and additional heaters may be used to avoid temperature drops for the critical components [25].
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Description of the Trust Estimation Method  
The method published in [11] demonstrated the applicability of neural networks (NNs) for the evaluation of trust.  
We defined trust as the confidence that a given node will forward the data correctly [26]. To measure that, we use the _packet delivery ratio (PDR)_ metric. A node is considered trusted if its \(PDR\) is greater than a given threshold (\(THR\)). Otherwise, it is considered untrusted.  
A path between a source node and a destination node is an ordered sequence of intermediate nodes. Let \(P=N_{1},N_{2},\ldots,N_{p}\) be a path from source \(N_{1}\) to destination \(N_{p}\), where \(p\) is the length of the path. Paths are constructed by routing protocols.  
The \(PDR\) of a node \(N_{i}\) is defined as follows:  
\[PDR(N_{i})=\frac{\text{forwarded-by-}N_{i}}{\text{sent-through-}N_{i}}, \tag{1}\]  
where forwarded-by-\(N_{i}\) is the number of packets correctly forwarded by node \(N_{i}\) and sent-through-\(N_{i}\) is the total number of packets sent to node \(N_{i}\) that were supposed to be forwarded.  
The advantage of the NN-based trust estimation method is fast NN learning with generated data. To generate the data, we used the fact that \(PDR\) of a path \(P\) is defined as the product of \(PDR\)s of intermediate nodes along the path [11]:  
\[PDR(P)=\prod_{i=2}^{p-1}PDR(N_{i}) \tag{2}\]  
Let us describe the method. We have a network topology where nodes can measure the \(PDR\)s of the paths. Using NNs, we can learn the underlying dependencies and predict \(PDR\)s of nodes from \(PDR\)s of paths. A detailed example of the problem and its solution are described in [11], Subsection III-A.  
A formal mathematical definition of the NN-based trust estimation method is presented in [26]. The trust of a node is its estimated \(PDR\), and it is a real number between 0 and 1.  
The evaluation of method performance showed that in 98% of cases NN was successful in estimating the trust of a node in an ad hoc network.  
The advantage of the NN-based estimation of node trusts is that data can be incomplete or incorrect, and the NN still predicts trust with high success [26].  
However, for the NN-based trust estimation method to perform reasonably, network topology should change slowly. As the method proposed in this article is designed to run in symbiosis with the NN-based trust estimation method, we assume that the positions of all network nodes are fixed. Considering the communication model, the environmental conditions may change (e.g., some obstacles appear), so even with the fixed topology, _PDRs_ of nodes, and thus their trusts, may change in time.
## 2 Methods and Models
### Cumulative Cost Curve-S-Curve Method  
Presenting planned financial flows on a timeline using a cumulative cost chart is a simple and efficient tool for measuring the use of financial expenditures in a construction project [39]. The cumulative cost curve \"S\" illustrates the project's progression from the start of construction activities to their completion. It represents the total expenditure incurred by all allocated resources for each task. Graphically, the cumulative cost curve typically resembles the shape of the letter \"S\". By continuously collecting financial data, it is possible to generate and compare budgeted versus actual cost curves [40].  
Many tools have been used to map the shape of the cumulative cost curve, including: the theory of fuzzy sets [41; 42], the method of least squares and fuzzy regression [43], methods using elements of artificial intelligence [44; 45], and elements of BIM technology [46].  
The research also uses empirical methods of forecasting the course of the cumulative cost curve in various construction projects. The mathematical models of the cost curve existing in the literature are based on real, historical data concerning construction projects conducted, among others, in the UK [47], Iran [48], Taiwan [49], the United States [50], and Asian countries [51].  
Subsequent researchers have endeavored to depict the trajectory of the cost curve by formulating mathematical relationships involving variables such as time and cost. Figure 1 presents their graphical interpretation in the form of cumulative cost curve charts.  
The cumulative cost curves presented in Figure 1 determine the area of cash flows within the specified envelope. It was noticed that it is not possible to use one theoretical model or one empirical mathematical expression that illustrates the course of the cumulative cost curve. When planning and monitoring the cost curve, it is advisable to utilize the curve envelope.  
When comparing the mathematical models proposed by various authors to describe the shape of the cumulative cost curve, the most frequently used are a sixth-degree polynomial [41; 52; 53], a third-degree polynomial [49; 54; 55], and less commonly, a second-degree polynomial and a linear function [56].  
The cumulative cost curve is characterized by varying slopes, making it inappropriate to use first-degree polynomials (linear functions) or second-degree polynomials (quadratic functions) to accurately depict its course. Employing descriptions may lead to inaccuracies and produce an unreliable representation of the cost curve [56; 57].  
Therefore, it is justified to use higher-order polynomials, at least of the third degree, to describe the course of the cumulative cost curve. While a sixth-degree polynomial allows for a high correlation coefficient (close to unity, indicating a strong relationship and a good description of the phenomenon) and a low coefficient of variation (indicating low variability and homogeneity), its practical application may prove challenging and overly complex for decision-makers, including investors and contractors.
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 7 Conclusions  
The problem of finding bottlenecks in parallel workloads is important to consider to provide high performance and efficiency of the execution and becomes more challenging with the increasing scale of parallelization. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods for finding the critical path; however, most of them suffer a performance drop on a large scale.  
In this paper, we analyzed several methods of critical path finding based on the classical Dijkstra and Delta-stepping algorithms along with an algorithm based on topological sorting. While being slightly less generic compared to the classical algorithms, topological sorting can provide significantly higher performance at the expense of adhering to the Bulk Synchronous Parallelism (BSP) model and neglecting micro-communications that do not affect the overall view at the bottlenecks. At the same time, this approach removes some of the limitations of the classical methods, such as the acyclicity of the directed program activity graph. Even being designed with the BSP model in mind, this approach works for any program, showing actual bottlenecks related to computation without analyzing details of the communication patterns of the program.  
We presented the corresponding algorithms for each approach including additional enhancements for increasing performance (parallel splits, shuffle, load balancing, compression, and thresholding). We presented the implementation of the approaches for the OpenMPI library and results for several benchmark applications: the NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE. We presented the advantages and disadvantages of each approach and implementation and concluded with a few examples of bottlenecks that we found in real-world programs using the presented approach.  
Conceptualization, V.K., I.G., A.S. and V.S.; methodology, V.K. and I.G.; software, I.G., A.G., M.M., I.P. and D.T.; validation, V.K., A.S. and V.S.; formal analysis, I.G.; investigation, V.K., I.G., A.G., M.M., I.P. and D.T.; resources, V.K., A.S. and V.S.; writing--original draft preparation, I.G. and V.K.; writing--review and editing, V.K., A.S. and V.S.; visualization, M.M., D.T., V.K. and I.G.; supervision, V.K. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by Saint Petersburg State University, project ID 86066736, 94062114.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 MPPT Algorithms
### Artificial Neural Network (ANN)
#### 5.1.3 ANN Training Under PSC  
To enhance the model's performance and adaptability under PSC, the neural network was retrained using updated datasets that included PSC scenarios. The network architecture remained identical, with two hidden layers comprising 20 and 10 neurons, respectively, while the input layer was expanded to six neurons to accommodate additional input features. Notably, the training algorithm and hyperparameters were kept unchanged to ensure evaluation consistency with the baseline model.  
Figure 10: Simulink model of the implemented ANN for MPPT control.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Test Pt.** & **Irrad.** & **Temp.** & \(V_{mp}\) & **Pred.** & **Abs. Err.** & **Rel. Err.** & \(P_{mp}\) & **Pred.** & **Abs. Err.** & **Rel. Err.** \\  & **(W/m\({}^{2}\))** & **(°C)** & **(V)** & **(V)** & **(V)** & **(V)** & **(\%)** & **(W)** & **(W)** & **(W)** & **(\%)** \\ \hline
1 & 477.21 & 31.58 & 18.20 & 18.56 & 0.36 & 2.00 & 7.64 & 7.94 & 0.30 & 3.94 \\
2 & 163.71 & 23.19 & 6.52 & 6.44 & 0.08 & 1.25 & 0.98 & 0.96 & 0.02 & 2.13 \\
3 & 597.23 & 43.73 & 22.79 & 23.14 & 0.35 & 1.54 & 11.97 & 12.34 & 0.37 & 3.06 \\
4 & 433.10 & 42.56 & 16.71 & 16.99 & 0.28 & 1.70 & 6.44 & 6.65 & 0.22 & 3.35 \\
5 & 868.99 & 18.45 & 32.66 & 32.73 & 0.06 & 0.19 & 24.60 & 24.70 & 0.10 & 0.41 \\
6 & 531.06 & 32.95 & 20.60 & 20.58 & 0.02 & 0.07 & 9.78 & 9.78 & 0.001 & 0.01 \\
7 & 797.51 & 17.32 & 30.52 & 30.01 & 0.50 & 1.65 & 21.47 & 20.76 & 0.71 & 3.29 \\
8 & 856.85 & 23.42 & 32.94 & 32.47 & 0.47 & 1.43 & 25.01 & 24.32 & 0.70 & 2.78 \\
9 & 883.10 & 43.10 & 33.58 & 33.15 & 0.43 & 1.27 & 25.99 & 25.42 & 0.58 & 2.22 \\
10 & 294.77 & 44.72 & 11.80 & 11.66 & 0.13 & 1.13 & 3.21 & 3.14 & 0.07 & 2.22 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Real and predicted values of \(V_{mp}\) and \(P_{mp}\), with respective absolute and relative errors.  
The retrained network was integrated as a Simulink block within the MPPT control system, as illustrated in Figure 11.
###### Abstract  
Cross-linked polyethylene (XLPE) cable is a representative power transmission cable. XLPE has excellent mechanical properties, chemical and heat resistance, and insulation. However, XLPE insulation deteriorates during operation due to electrical, mechanical, and thermal stresses. Among these, thermal stress is a major factor and reduces insulation properties due to a change in molecular structure. Therefore, XLPE characteristic evaluation by heat exposure is essential for power cable condition evaluation. Herein, deteriorated XLPE samples were characterized by tensile strength, X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectroscopy after exposure to various temperatures and durations. Comparing the tensile strength with other analysis results yielded correlations. Each characteristic showed a linear relationship. The correlation between tensile strength and carbonyl index was the strongest, and the coefficient of determination, \(R^{2}\), was 0.9299. Therefore, these results will provide important information on chemical properties when establishing operational management standards for XLPE insulators in the future.  
XLPE; power cable; insulator; degradation; correlation
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 5 Experimental Results and Analysis
### Evaluation
#### 5.2.1 Image Segmentation Evaluation  
As shown in the figure, both the IoU and Dice coefficients steadily improve during training, showing the model's increasing ability to optimize segmentation tasks. In the beginning, especially in the first 50 rounds, both IoU and Dice values improve quickly, indicating the model is learning basic features. This is typical for segmentation models, where early training focuses on learning basic contours and features. As training progresses (around 150 rounds), both metrics level off. Although the model is close to convergence, the rate of improvement slows, and segmentation performance stabilizes. The model achieves an IoU value of 0.9406, showing better coverage and more accurate boundary recognition of the target region. The highest Dice coefficient of 0.9877, close to the ideal 1.0, shows that the model fits the overlapping area well and handles overlaps and boundaries effectively. The loss rates of the training set and the validation set are shown in Figure 12.  
Figure 11: Grid-CAM heatmap.  
Figure 12: IoU and Dice.  
To show the model's segmentation performance, the confusion matrix is displayed in the figure, with darker colors showing higher values. This helps identify any classification bias and shows how the positive and negative classes are distributed. The confusion matrix is shown in Figure 13.  
Accuracy is calculated as the ratio of correct predictions (true positives and true negatives) to the total number of samples. It is expressed as follows:  
\[\text{Accuracy}=\frac{\text{Predict the total number of correct samples}}{\text{Total sample size}} \tag{29}\]  
The number of correctly predicted samples is the sum of the diagonal elements in the confusion matrix:  
\(403+299+244=946\)  
\(\text{Accuracy}=\frac{946}{1000}=0.946\)  
\(\text{Precision}_{\text{p1}}=\frac{403}{403+8+1}=0.978\)  
The model performs well in overall classification with high accuracy, especially in recognizing joints accurately and consistently.
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 3 System Identification
### Identification Results  
Herein, the identification results of MCU-5X are introduced by our approach. We obtained system parameters (\(J_{m}\), \(B_{m}\), \(M_{t}\), \(C_{t}\), \(K\)) of axes feed drive systems and the corresponding transfer function in form of (3). For the velocity loop, the corresponding frequency responses of the actual system and estimation system are shown in Figure 7 (solid-line: experimental results; dotted-line: identification model). From Figure 7a,c,e, we observed that there existed an identification error in the high frequency region due to the identified results in the form of an ideal model which showed as Figure 7b. For servo tuning, we focused on the bandwidth; therefore, it was adopted to obtain the equivalent inertia and viscosity coefficient of the motor, respectively. This identification results were valid as long as they fit low-frequency characteristics and maintained the actual velocity loop system characteristics.  
Subsequently, the results of the velocity loop coefficients \(J_{m}\) and \(B_{m}\) were set (as obtained from the above process) for identifying the position loop. The FRF of the actual system and estimation results are shown in Figure 7. Obviously, the estimation results were suitable, and the corresponding frequency response and dynamic properties were almost the same. In summary, the parameters for velocity and position loop were identified and listed in Table 2. Herein, a time domain test where the input signal was generated by TNCOPT, was performed to demonstrate the identification results. The root mean square error (RMSE) of outputs corresponding to sine chirp signals are introduced in Table 3.  
To verify the model obtained from system identification, another illustration example was introduced. We changed the servo parameters \(K_{pp}\), \(K_{ei}\), and \(K_{pp}\) to have the FRF of the actual experimental results (machine tool results) and the identified results. Figure 8 shows the identification results of the \(X\)-axis, \(Y\)-axis, \(Z\)-axis, \(A\)-axis, and \(C\)-axis, respectively. The blue line in Figure 8a-e are the FRF of default servo parameters, which we employed in system identification. The black line in Figure 8a-e are the FRF of changed servo parameters which were obtained by MCU-5X. Both dotted red lines represent the simulation FRF of the default and change servo parameters. It was confirmed that the system identification process was reliable.  
Figure 8: \(Cont.\)  
Figure 7: Frequency responses of velocity loop and position loop. (**a**) \(X\)-axis: velocity loop. (**b**) \(X\)-axis: position loop. (**c**) Y-axis: velocity loop. (**d**) Y-axis: position loop. (**e**) \(Z\)-axis: velocity loop. (**f**) \(Z\)-axis: position loop. (**g**) \(A\)-axis: velocity loop. (**h**) \(A\)-axis: position loop. (**i**) \(C\)-axis: velocity loop. (**j**) \(C\)-axis: position loop.