esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Optimization-Based Energy Management Strategies
#### 3.2.4 Dynamic Programming  
DP is a class of algorithms for solving optimization problems. The main step is to break the original problem down into several smaller problems, solve each one recursively, and then combine the answers to all of the smaller questions to arrive at the original problem's solution. The algorithm is mainly applied to problems with overlapping sub-problems and optimal substructures [148, 149, 150, 151]. DP has the advantage of being effective in avoiding  
Figure 11: Schematic illustration of the multi-level MPC controller structure.  
double counting and solving large-scale problems. However, it requires a large amount of time and space to compute state variables and state transfer equations. Overall, the results of DP are often used as an optimal benchmark for other controllers, or as a basis for developing and improving other sub-optimal controllers [152; 153].  
In [154], an effort was made to develop an energy-efficient supervisory control method for the HEV to improve fuel economy and decrease emissions of exhaust gases. In order to achieve efficient power distribution, a global solution was obtained by means of deterministic DP. Similarly, [PERSON] et al. applied a controller based on action-dependent heuristic dynamic planning (ADHDP) to acquire ecological speed profiles and implement active distance control under normal driving conditions [155]. The ADHDP was able to adjust internal parameters online, thereby enabling the handling of systems with perturbations. Power distribution is achieved through ADHDP by enumerating the design shift commands. In [156], [PERSON] et al. provided benchmark results on how to balance conflicting objectives corresponding to identification and system efficiency through the DP strategy. This method monitored battery parameters and identified vehicle status to ensure battery safety and efficient operation of the HEV.  
For a given driving condition, DP can find the globally optimal EMS for a hybrid powertrain and calculate the optimal control variable results. Therefore, DP is often used in hybrid powertrain energy management analysis to obtain optimal fuel economy results and thus to analyze more rational power allocation strategies and power component behavior. At the same time, the hybrid powertrain energy management problem is typically a non-linear multi-disturbance control problem and is usually expressed as a multi-constraint non-linear optimization problem, which is then solved by DP to obtain the optimum fuel economy performance.
## Challenges and Potential Solutions
### Security versus Spectral Efficiency  
Achieving end-to-end, attack-proof wireless data communication requires many spectrum bandwidth preventive measures which results in a reduction in the available spectrum data available for data transmission [78]. Finding an acceptable scheme to address the security and spectral efficiency concerns of wireless communications would entail complex computations but can be addressed via three possible strategies. First, encryption algorithms designed by security experts are expected to be highly successful. With the encryption algorithm reaching the maturity stage, realizing such algorithms might be slightly cumbersome. Second, security experts should identify a strategy to implement PHY security technologies with a loss in spectrum spectral. Third, AI algorithms are equipped with the capacity to expertly discover network shortcomings and would become handy in 6G networks for designing an early warning strategy for security fortification.
## 5 Conclusions  
In most previous studies, scholars often studied the impact of single mining methods on faults, but the impact of co-mining on faults was rarely studied. In this paper, the following main conclusions are drawn through numerical simulation and theoretical analysis:  
Upon increasing the height of segments of the sublevel caving method without the sill pillar and the lower-layer filling method, the changes in shear stress and displacement showed similar patterns. The displacement and shear stress of the fault face near the mining side increased continuously, but the impact of the disturbance of the sublevel caving method without the sill pillar on the fault face was smaller than that of the lower-layer filling method.  
By analyzing the dynamic responses of 11 measurement points, it can be concluded that the instability was greatest with the sublevel caving method without the sill pillar within the fault, and a small slip may occur, but no slip phenomenon occurred at the other measurement points, so the fault is in a stable state as a whole. Additionally, the impact of the sublevel height change in the sublevel caving method without the sill pillar is larger than that of the filling method, which is more likely to put the fault into an unstable state.  
When the sublevel height of the sublevel caving method without the sill pillar increased from 10 m to 15 m, the changes in the shear stress and displacement of the fault surface did not show any rapid changes; however, when it grew to 20 m, the rate of displacement continued to increase, which increased the index of slip tendency, and the risk of activation of the fault became larger. When the sublevel height of the filling method increased from 15 m to 20 m, the shear stress, displacement and slip tendency indexes did not change much.  
When the mining face is close to the fault, the shear stress of some measuring points on the fault plane will increase by about 1.4 times, and the stress concentration phenomenon will occur, so when the working face is advanced to the fault, attention should be paid to the change of fault thickness and the change of surrounding rock stress, and preventive measures should be taken immediately when sudden changes occur to ensure the safe and efficient production of the mine.  
Writing--original draft, F.L.; Writing--review and editing, F.L.; Supervision, J.G. and F.M.; Investigation, G.L. and Y.S. All authors have read and agreed to the published version of the manuscript.  
The research received support from the National Natural Science Foundation of China (Grant No. 42072305). We appreciate the kind support.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Proposed Methods
#### 2.2.2 GLCM Feature Extraction  
Accumulated evidence has provided substantial support for the effectiveness of the gray-level co-occurrence matrix (GLCM) in capturing bone architecture properties. This approach has been successfully applied in various domains, including the assessment of bone quality [26], the diagnosis of osteosarcoma [27; 28; 29; 30], and the prediction of fractures [31]. In this study, the GLCM was computed for each sample, and significant texture features were extracted from each GLCM. These features were subsequently concatenated into a vector to represent the information within a specific ROI. The resulting ROI feature vectors were then generated and utilized as input for the FBLS model.  
Figure 1: Schematic of the analysis plan.  
The GLCM is a mathematical representation that offers significant information about the intricate characteristics of an image. As shown in Figure 3, each element within the matrix corresponds to the statistical probabilities of changes between various gray levels at specific displacement distances and angles, commonly \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\). For the classification task in this study, the following uncorrelated features were selected: contrast, angular second moment, inverse different moment, and correlation. The subsequent content will provide detailed explanations of these selected features.
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 6 Experiments
### Results
#### 6.3.4 Learned Representation  
To better investigate the learning capabilities of MKSC, we visually show the group structure of the representation matrix \(Z\) learned by MKSC. In particular, we show the results on the Jaffe, MNIST, ORL, and PIX data sets in Figure 6. It can be seen that the representation matrices have clear block diagonal structures, which correspond to the clusters of the data sets. Moreover, we visualize the data points of Jaffe and PIX in Figure 7, with t-distributed stochastic neighbor embedding (t-SNE) [36]. In Figure 7, the first and second rows are the original data and the t-SNE visualization of the representation matrix \(Z\), respectively. It is seen that the samples are overlapped with the original features and are well separated with the similarity graph. These observations visually confirm the effectiveness of MKSC in recovering the group structures of the data.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **ACC** & **NMI** & **PUR** & **FS** & **ARI** \\ \hline NSLLRR & 0.8900 & 0.5448 & 0.8900 & 0.8035 & 0.6045 \\ SSC & 0.6538 & 0.1837 & 0.6538 & 0.5618 & 0.1361 \\ SCLA & 0.6925 & 0.1121 & 0.6925 & 0.5759 & 0.1461 \\ SSRSC & 0.8475 & 0.3841 & 0.8475 & 0.7402 & 0.4817 \\ S-TLRR & 0.6575 & 0.0729 & 0.6575 & 0.5476 & 0.0970 \\ RT-TLRR & 0.6575 & 0.0729 & 0.6575 & 0.5476 & 0.0970 \\ TLSRR & 0.5025 & 0.1394 & 0.5025 & 0.3618 & 0.1029 \\ ETLRR & 0.6817 & 0.1539 & 0.6817 & 0.5537 & 0.2639 \\ EDESC & 0.7716 & 0.3384 & 0.7716 & 0.6071 & 0.3382 \\ PSSC & 0.8637 & 0.7034 & 0.8637 & 0.7418 & 0.5539 \\ MKSC & 0.9975 & 0.9773 & 0.9975 & 0.8134 & 0.7361 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Clustering results on moon data set.  
Figure 5: Time costs of different methods on various data sets.  
Figure 6: Examples of the learned representation matrix \(Z\).  
Figure 7: The t-SNE visualization of the Jaffe and PIX data sets, respectively. The first and second rows correspond to the t-SNE visualization of the original data and the reconstructed representation on Jaffe and PIX, respectively.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods  
### Study Area  
Gelephu (Figure 1) is one of the local government administrations (gewogs) in Sarpang district and covers an area of 54.50 km\({}^{2}\). According to the census 2017, the actual resident population is 4461 and consists of 1027 households [45]. Geographically, Gelephu lies between, 25 to 27\({}^{\circ}\) north and 90 to 91\({}^{\circ}\) east with an elevation between 168 m in the southern floodplain and 1799 m towards the north. Gelephu consists of one of the administrative units of the municipality, Gelephu Thromde, and covers an area of 10.66 km\({}^{2}\). The LULC dynamics in the study area have changed considerably due to recent urban planning and subsequent infrastructure development activities such as road network, building construction, automobile repair zones, and others, with most of the infrastructure development activities being concentrated in Gelephu Thromde.
## 3 Framework
### Path Distance-Weighted Attention (PDWA) Model
#### 3.5.1 Cross-Path Entity Matrix Construction  
The cross-path entity matrix \(\mathbf{M}\) organizes entity relationships across document paths. As described in Algorithm 1, the original relation matrix \(M\) is divided into \(N\times N\) sub-matrices corresponding to path pairs:  
\[M_{ij}\]  
represents entity relations between path  
Figure 2: The proposed Path Distance-Weighted Attention (PDWA) model.  
**Matrix dimensions:** Each sub-matrix \(M_{ij}\) has shape \(|E(p_{i})|\times|E(p_{j})|\), where \(|E(p_{i})|\) is the number of entities in path \(p_{i}\). When \(i=j\), \(M_{ii}\) captures intra-path relations; when \(i\
eq j\), \(M_{ij}\) captures inter-path relations.  
**Sparsity optimization:** To reduce computational overhead, we retain only the top 34% of entity connections based on relevance scores, which reduces the processing time to approximately 22 ms per sample while preserving critical bridge entity relationships.  
**Integration with PDWA:** The distance-weighted attention mechanism in Algorithm 1 operates on these sub-matrices, applying the computed distance weights \(\alpha_{ij}\) to adjust attention as \(H^{\prime}_{ij}=\alpha_{ij}\times H_{ij}\).  
```
0: Document paths \(P=\{p_{1},p_{2},\ldots,p_{N}\}\), target relation \(r_{ht}\), entity representations \(E\)
0: Enhanced relation representations \(\{r_{h_{i},i_{t}}\}_{i=1}^{N}\)
1: Step 1: Path-to-Relation Mapping
2:for each document path \(p_{i}\in P\)do
3: Extract sentence embeddings \(s_{i}\) from path \(p_{i}\)
4:\(s_{i}\leftarrow\text{Attention}(r_{ht},s_{i},s_{i})\)\(\triangleright\) Attention with relation as query
5:\(s_{i}\leftarrow\text{GumbelSoftmax}(\text{Linear}(s_{i}))\)\(\triangleright\) Select most relevant sentence
6:endfor
7:Step 2: Distance Matrix Computation
8:for each pair \((p_{i},p_{j})\) where \(i,j\in\{1,2,\ldots,N\}\)do
9:\(\text{Dis}(i,j)\leftarrow\frac{1}{\text{DetProductSimilarity}(s_{i},s_{j})}\)\(\triangleright\) Compute semantic distance
10:\(\alpha_{ij}\leftarrow\frac{\text{Dis}(i,j)}{\sqrt{\sum_{k=1}^{N}|\text{Dis}(i,k)| ^{2}}}\)\(\triangleright\) Normalize distance weights
11:endfor
12:Step 3: Attention Matrix Adjustment
13: Divide original relation matrix \(M\) into \(N\times N\) sub-matrices \(\{M_{ij}\}\)
14:for each sub-matrix \(M_{ij}\)do
15:\(H_{ij}\leftarrow\text{Attention}(M_{ij})\)\(\triangleright\) Compute attention for sub-matrix
16:\(H^{\prime}_{ij}\leftarrow\alpha_{ij}\times H_{ij}\)\(\triangleright\) Apply distance weighting
17:endfor
18:Step 4: Relation Representation Generation
19:for each path \(p_{i}\)do
20:\(r_{h_{i},i_{t}}\leftarrow\text{ExtractTargetRelation}(\hat{M^{\prime}}(l),p_{i})\)\(\triangleright\) Extract from final layer
21:endfor
22:return\(\{r_{h_{i},i_{t}}\}_{i=1}^{N}\)
```  
**Algorithm 1** Path Distance-Weighted Attention (PDWA).
# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 3 Experimental Data and Processing
### Evaluation Metrics  
In previous studies on medaka ventricular segmentation, image segmentation methods were commonly used, and thus the Intersection over Union (IoU) and mean Intersection over Union (mIoU) were widely adopted as the primary evaluation metrics. Among these, mIoU calculates the average IoU of all predicted regions and their corresponding ground truth annotations, and its formula can be expressed as follows:  
\[mIoU=\frac{1}{C}\sum_{c=1}^{C}\frac{TP_{c}}{TP_{c}+FP_{c}+FN_{c}} \tag{4}\]  
where \(C\) represents the total number of categories, and \(TP_{c}\), \(FP_{c}\), and \(FNc\) denote the number of true positives, false positives, and false negatives for the \(c\)-th category, respectively.  
However, to comprehensively evaluate the performance of our proposed video segmentation model, this study selected two metrics specified in the DAVIS dataset challenge during model training: \"Jaccard (J) per sequence\" and \"Boundary (F) per sequence.\" These metrics are variants of the IoU (Jaccard index) and F-Measure, focusing on the performance of each individual video sequence rather than the overall performance of the entire dataset.  
The formula for calculating Jaccard (J) per sequence is as follows:  
\[J=\frac{1}{T}\sum_{t=1}^{T}\frac{pre_{t}\cap mask_{t}}{pre_{t}\cup mask_{t}} \tag{5}\]  
where \(pre_{t}\) represents the predicted segmentation region for the \(t\)-th frame, \(mask_{t}\) is the corresponding ground truth annotation, and \(T\) is the total number of frames in the video.  
This metric quantifies the segmentation consistency of the model across the temporal dimension by calculating the average IoU between predicted and ground truth regions frame by frame. Its value range is [0, 1], and a value approaching 1 indicates that the model has excellent cross-frame stable segmentation capabilities.  
The formula for calculating Boundary (F) per sequence is as follows:  
\[\begin{array}{c}\text{Precision }=\frac{\text{TP}}{\left(\text{TP}+\text{FP} \right)}\\ \text{Recall }=\frac{\text{TP}}{\left(\text{TP}+\text{FN}\right)}\\ F=\frac{2\times\text{Precision}\times\text{Recall}}{Precision+\text{Recall}} \end{array} \tag{6}\]  
This metric focuses on the topological fidelity of segmentation edges by calculating the harmonic mean of precision and recall. Its computation involves constructing an \(\varepsilon\)-neighborhood (typically \(\varepsilon=3\) pixels) for both the predicted contours and the ground truth annotations and then measuring boundary alignment accuracy through a bidirectional contour matching algorithm. This approach is highly sensitive to dynamic interference factors such as motion blur and deformation.  
This dual-metric evaluation system constructs an evaluation space from two orthogonal dimensions: region coverage (J) and edge alignment (F). The J metric reflects the global accuracy of the segmentation results, while the F metric characterizes the fine-grained details of local edges.
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 5 Simulation Results and Analysis
### Cache Hit Ratio  
The node cache hit rate refers to the ratio of the content requests cached by the node to the total number of requests arriving at the node. In this article, we calculate the overall average cache hit rate. The cache hit rate is usually regarded as one of the most important metrics in a caching strategy.  
In this article, we have conducted a lot of experiments in three different topological networks to verify the effectiveness of our strategy. In the first three sets of experiments in Figure 7, we set \(\alpha=0.6\), and the cache space ratio was changed according to the settings in Table 1. From Figure 7a-c, we can see that the cache hit rate of all strategies is on the rise. Due to the low percentage of cache space, the overall cache hit rate is at a low level. However, from the figure, we can see that our strategy only needs about 0.04 of the cache space to achieve the best performance out of all other solutions, which saves almost 20% of the cache space.  
Moreover, as shown in Figure 7d-f, as the popularity of content requests increases, the cache hit rate of all methods has doubled. In all the test values, our strategy maintains the best performance and does not show significant attenuation.  
Through the experiments after the above indicators have been changed, our strategies have achieved the best performance. This time, it is worth mentioning that we can see the poor performance of EDGE in Figure 7, which is an edge cache that only considers the network topology. At the same time, it is also a reference method for many researchers to design edge caching solutions in mobile scenarios. We can see that with the increase in the cache space, the EDGE cache hit rate is very cached, and MINE can reach the EDGE hit rate of 0.05 cache space at a very low percentage of cache space. This is because based on the popularity and freshness of the content, the highest quality content is kept in the marginal area as much as possible.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Topology structure & GARR, WIDE, and GEANT \\ Replacement policy & LRU \\ Number of contents & \(3\times 10^{5}\) \\ Requests number for system warm-up & \(10^{6}\) \\ Total mobile user requests & \(6\times 10^{6}\) \\ Number of mobile users per second & 10 per/s \\ Cache size ratio & [0.004, 0.002, 0.01–0.05] \\ Skewness (\(a\)) & [0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] \\ Experiment run time for each scenario & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 2 Materials and Methods
### Experimental Equipment and Conditions
#### 2.3.1 Wavelet Technology  
Wavelet technology is the result of the development of the Fourier transform. It can realize local transformation in time and frequency domains at the same time, which has a better effect on the analysis of nonlinear processes. From a mathematical point of view, the wavelet technique is the expansion and translation of a function, which represents an orthogonal sequence square-integrable function. In this case, the continuous wavelet transform function is given as [41]  
\[W_{f}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}f(t)\overline{\varphi \left(\frac{t-b}{a}\right)}dt \tag{1}\]  
\(f(t)\) is the proposed transformed signal, \(\overline{\varphi(t)}\) is the complex conjugate of wavelet basis function, \(a\) is the scale factor, and \(b\) is the translation factor. When \(a>1\), the wavelet basis function expands and becomes more expansive in the time domain. On the other hand, the wavelet basis function contracts and becomes narrower, and \(b\) only affects the window's position in the time domain.  
However, the continuous wavelet transform is computed with a large amount of redundancy. Therefore, in practical applications, the scale factor \(a\) and translation factor \(b\) are often discretized. The discrete wavelet transform function in the time domain, \(t\), is given as  
\[W_{f}(j,k)=a_{0}^{\frac{-j}{2}}\int_{-\infty}^{+\infty}f(t)\overline{\varphi \left(a_{0}^{-j}t-k\right)}dt \tag{2}\]The Wavelet decomposition diagram is shown in Figure 4. As can be seen from the figure, an arbitrary sequence \(S\) can be decomposed into low-frequency components (\(cA_{1}\)) and high-frequency components (\(cD_{1}\)), and according to the demand, the low-frequency components can be decomposed even further to obtain the low-frequency components (\(cA_{2}\) and \(cA_{3}\)) and high-frequency components (\(cD_{2}\) and \(cD_{3}\)) on an arbitrary scale. Finally, the wavelet coefficients obtained are selectively reconstructed, and reconstruction sequences at different frequencies (\(cD_{n}\)) can be obtained.
## 4 Game Theoretic Client Selection Model
### Battery and Privacy Cost Estimation  
While exact values for battery drop and privacy degradation vary across devices, our model assumes that each client estimates the following:  
* \(\Delta B_{i}^{t}\): battery consumption expected if selected for training at round \(t\).
* \(\Delta P_{i}^{t}\): privacy degradation expected if selected for training at round \(t\).  
These cost estimations depend on the device's capabilities and sensitivity. For example, lightweight IoT sensors typically incur lower battery costs but may suffer greater privacy risks due to limited data protection mechanisms.
## 3 Results and Discussion
### Results and Discussion of Method Application
#### 3.1.2 Comparison of Liberec and Decin  
Although Liberec and Decin are comparable in terms of area, ecosystem function values differ substantially. Habitat-level biodiversity, expressed in HVM values, was almost 33% higher in Decin than in Liberec, as was the average connectivity value, which was 22% higher. Annual carbon production was 23% higher and evapotranspiration was 20% higher than in Liberec. These differences can be attributed mainly to the stronger population growth and the related faster development of the built-up area in Liberec, while, in Decin, the number of inhabitants (similar population to 100 years ago, Czech Statistical Office, 2006) and the development of the city have more or less stagnated; consequently, the current built-up area in Liberec is twice as large as in Decin. Accordingly, the share of forests in Liberec is lower (only 24%), while, in Decin, forests cover almost half of the administrative territory. However, the protection regime probably had a dominant influence on the fact that ecosystem functions in the territory of Decin reached significantly higher values than in Liberec, as most of the cadaster of Decin falls within two PLAs, while, in the case of Liberec, only a small percentage of the administrative territory belongs to the PLA.