esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Architecture
### Class Anchors  
After training and validation of the SNN, the feature vectors of images belonging to each class are extracted and averaged, resulting in the generation of class anchors. During the testing phase, an input image is passed through one of the sub-networks of the SNN, which encodes it into a feature vector. In the lower-dimensional feature space, the feature vector of the test image is compared with the feature vectors of all the training samples using distance measures. This distance measure is utilized to plot a radar chart, providing a visual representation of the initial characteristics of the image. When a second image is inputted into the SNN, another plot is generated on the same radar chart, visually indicating the progression of the disease.  
\[Anchor\ classx=\frac{1}{n}\sum_{k=1}^{n}x_{k}=x_{1}+x_{2}+\cdots+x_{n-1}+x_{n} \tag{3}\]  
With the class anchors established, the subsequent step involves calculating the similarity between a new DFU image and the class anchors. The next section elaborates on this process.  
Similarity Function between Test Image and Anchors  
To assess similarity, both cosine distance and Euclidean distance are employed between the feature vector of an image and the class anchors, as depicted in Figure 5. The cosine similarity function (5) is utilized, and the calculation of distance includes both cosine distance (4) and Euclidean distance (6). These measures highlight the disparity between the feature vector of the test image and the feature vector of the anchor class.  
\[Cosine\ Similarity(A,B)=\cos(\theta)=\frac{A\cdot B}{\|A\|_{2}\|B\|_{2}} \tag{4}\]  
\[Cosine\ Distance(A,B)=1-CosineSimilarity(A,B) \tag{5}\]  
\[Euclidean\ Distance(A,B)=\sqrt{\sum\limits_{i=1}^{n}(B_{i}-A_{i})^{2}} \tag{6}\]  
Figure 6 gives a visual explanation of the cosine similarity and Euclidean distance. The cosine similarity represents the angle between two vectors, and the Euclidean distance represents the distance between two points in Euclidean space.  
Now that the entire proposed process has been explained, it is imperative to dive into the rationale underlying the incorporation of class anchors within the framework.
## 6 Strengths and Limitations of SCD
Future effort will be focused on obtaining more samples of real time series that have more complex changing patterns (_i.e._, more than three changes) to improve the training process. The SCD also needs further validation with more study sites. A limitation of the SCD algorithm is that the minimum time period between two neighboring change dates is one year, because we always compare the records on the same dates in two years. Specifically, when a change date is detected in one year, only the records on the dates later than the change date in that year and the following year will be compared, which generates at least a year difference between two change dates. This limit may affect the detection accuracy when actual land-cover changes are more frequent than a year, which, however, is a rare case in the real world. In addition, observations recorded in the same seasons of two years are necessary for SCD to detect the change date in the second year, because SCD compares the seasonal pattern recorded in the two years and searches the change point at which the seasonal pattern started to represent a different land cover. Such requirement on time series can reasonably determine a land-cover change rather than inter-annual variation. Although it is rarely observed, persistent cloud cover would affect the accuracy of the result. In fact, observation records spanning more than six months (_i.e._, satisfying Nyquist frequency of a year) are necessary to generate meaningful results in the interpolation process. Year-to-year seasonal changes (or inter-annual variation) commonly exist in NDVI time series, such as changes in lengths of seasons from one year to another, possibly due to drought, floods, different snow onset time, or other harsh conditions. Such inter-annual variations are considered as noise in the sub-annual change detection process and should not be confused with land-cover change. For instance, the results on the stable time series in Table 2 showed small errors in false changes detected by SCD, as well as the fewer false changes in Table 4 compared to BFAST. Because SCD is designed to differentiate abrupt land-cover change from inter-annual variation, it is less influenced by inter-annual variations within stable time series. Nevertheless, certain inter-annual variation could bring about challenge in sub annual change detection and affect the detection accuracy when the variation is not observed in reference data or determined as abrupt land-cover changes (e.g., due to slow process of modification).
## 3 Results and Discussion
### Characterization of the Extract
#### 3.1.2 Total Phenol (TPC) and Flavonoid (TFC) Contents  
Nevertheless, phenolic acid derivatives were not found in significate amounts in the hydroethanolic extracts, where its full quantification was performed using the fast, cheap and conventional Folin-Ciocalteu assay; chalcones can also be quantified with this methodology [30]. These results are shown in Figure 5, as well as the total content for flavonoids. The high TPC and TFC for both region extracts corresponded to the hydroethanolic solutions (50%) of the EF. However, even with lower differences, the highest concentration corresponded to EF from plants in region A.  
The amount of TPC in flowers from the two different regions ranged from 201.28 to 313.04 ug gallic acid/mL for region B and region A, respectively, among the various statuses of maturation. The results obtained are in agreement with other authors, who obtained, for example, a concentration of 206.4 mg gallic acid/g sample in the Acacia flowers [31]. [PERSON] et al. [32] obtained higher results for TPC with an ethanol extraction for _A. confusa_ (544.4 mg/g of flower). In the aforementioned study, it was also found that the TPC was higher when ethanol was used as extract solvent. They also obtained high values for the antioxidant bioactivity due to this content in phenolic compounds.  
The flowers harvested in region A presented a higher value of total phenol and flavonoid content, where this difference represented 24.4% of the total variation for the TPC and 37.4% for the TFC (Table 1). The maturation status was also of key importance where the higher values were obtained for the early flowering status (55.5% of the total variation for the TPC and 27.3% of the total variance for the TFC). This data was in accordance with the function of chalcones as precursors of flavonoids.  
Figure 5: Total phenol and flavonoid contents (**(a)**) TPC and **(b)** TFC) of _A. dealbata_ flower extracts with different maturation stages from two central regions of Portugal. A—region A, B—region B; EF—early flower, MF—mid flower, LF—late flower. Different lowercase letters (a, b, c) denote significant differences between values (Tukey’s test; \(p<0.05\)).  
One of the goals in this work was to understand whether the early flowering status had better potential as a source of antioxidant compounds, or at least equal to the bioactivity of the compounds in the matureted flowers in the ripening stage, in order to collect them earlier and not give the possibility for the pollen to disperse. In this case, the results showed better activity than was expected, certainly due to the highest amounts of chalcones in this phase of the vegetative cycle of Acacia. These values were in line with the data obtained for the antioxidant activities. Studies from various authors with other _Acacia_ spp. also showed that the TPC can vary according to the plant organ, solvent used in the extraction procedure and season [33,34], which were in accordance with the data here discussed for _A. dealbeta_ extracts.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 2 Materials and Methods
### Spline-Based Deterministic Refinement of TCA
The coefficients \(a_{i},b_{i},c_{i},d_{i}\) are computed to ensure continuity of the function and its first and second derivatives across intervals. Once the spline is constructed, the TCA is estimated by solving  
\[t_{TCA}\ =\underset{t\ \in\ [t_{0},t_{f}]}{\text{arg min}}\ s(t) \tag{10}\]  
where \(s(t)\) is the interpolated spline function. The minimum is found by evaluating the derivative \(s^{\prime}(t)\) and solving for its roots, followed by selecting the point corresponding to the global minimum distance. This step improves temporal resolution and reduces the number of required propagation samples.
# Characterization and Prediction of Air Transport Delays in China
## 3 Data and Methods
### Prediction Models  
Beyond standard statistics analyses, the relevance of the aforementioned features is tested through data mining models--see Section 5.2. Three standard algorithms have been considered:  
1. _Random Forests_ (RF). Combinations of Decision Trees predictors, in which each tree is trained over a random subset of features and records; the final classification forecast is then calculated through a majority rule. Random Forests are especially appreciated for their precision and low tendency of overfitting [32].
2. _Stochastic Gradient Descent_ (SGD): meta-algorithm in which multiple linear Huber loss functions are combined and optimized [33].
3. _Multi-Layer Perceptron_ (MLP): based on the structural aspects of biological neural networks, MLPs are composed of a set of connected nodes organized in layers. Each connection has a weight associated to it, which is tuned through the learning phase [34]. When more than two layers are included in the model, it can be proven that MLPs can classify data that are not linearly separable, and in general approximate any non-linear function.  
All three models have been implemented through the corresponding function of the Scikit-learn Python package [35]. Parameters used were: 2000 estimators for RF; a modified Huber loss and a maximum of 2000 iterations for SGD; and 3 layers with 40 neurons in the hidden one for MLP. Additionally, all presented results have been obtained through a Leave-One-Out Cross-Validation, in order to reduce the risk of overfitting [36]. This strategy involves selecting one single instance as test data, train the model using all remaining data, and evaluate the prediction on the initial instance; this process is finally repeated over all records, to obtain a final averaged score.
## 2 Overview of BIPV System
#### Issues/Challenges in the BIPV Systems  
The issues of BIPV systems that are discussed in the previous studies are detailed and presented. The primary issues of the BIPV system are:  
* The system attains a high temperature during the sunshine hours, which reduces power generation. It also affects the building envelope temperature and the building thermal comfort. For preventing the heating of the modules, an uncontrolled high-power forced ventilation is used, which consumes much energy leading to an overall reduction in system efficiency.
* In a large BIPV system, different types of optical, electrical, and non-classified faults occur, depending on the ambient condition, type of integration, and adjacent shadings on buildings that need to be analyzed regularly.
* Regular and safe cleaning of dust-accumulated PV modules in large buildings, roofs, facades, and fenestration is challenging, which reduces the overall efficiency of the BIPV system and daylight availability in buildings.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Scheduler Module Technical Implementation
#### 3.2.3 Optimization Algorithm  
The scheduler combines CH and LS algorithms. The CH phase uses a first-fit decreasing algorithm that orders tasks by allocation difficulty, ensuring the most constrained tasks are scheduled first. The LS phase employs a variety of algorithms such as simulated anneal ing or tabu search with a dynamic tabu list to avoid local optima and explore the solution space effectively. The algorithm uses lexicographic ordering combined with weighted sum methods to handle the multi-objective nature of the problem. The pseudo-code for this optimization approach is presented in Algorithm 1.  
```
Input:\(P\) = scheduling problem instance containing tasks \(T\), satellites \(S\), time windows \(W\), constraints \(C\) Output: Optimized schedule solution \(Sol\) // Phase 1: Construction Heuristic Initialization FunctionConstructInitialSolution(\(P\)): \(Sol\leftarrow\emptyset\) ; // Initialize empty solution \(V\leftarrow\text{ExtractPlanningVariables}(P)\) ; // Extract tasks, satellites, time assignments \(V_{ordered}\leftarrow\text{OrderVariables}(V,CH_{criteria})\); // Order by difficulty, priority, resources foreach variable \(v\in V_{ordered}\)do \(domain\leftarrow\text{GetFeasibleDomain}(v,Sol,C)\); // Feasible satellites, time slots if\(domain\
eq\emptyset\)then \(value\leftarrow\text{SelectValue}(domain,CH_{selection\_rate})\); // e.g., first-fit, best-fit \(Sol\leftarrow\text{AssignVariable}(Sol,v,value)\); else \(Sol\leftarrow\text{MarkUnassigned}(Sol,v)\); // Handle infeasible assignments  end if returnSol; // Phase 2: Local Search Refinement FunctionLocalSearchOptimization(\(Sol_{initial}\), \(P\), \(termination\_criteria\)): \(Sol_{current}\gets Sol_{initial}\); \(Sol_{best}\gets Sol_{initial}\); \(score_{best}\leftarrow\text{EvaluateSolution}(Sol_{best},C)\); whilenot TerminationMet(\(termination\_criteria\))do  Neighbors\(\leftarrow\text{GenerateNeighborhood}(Sol_{current},LS_{strategy})\) // LS strategy determines move types: swap, relocate, etc. Candidates \(\leftarrow\emptyset\); foreach neighbor \(\in\) Neighborsdo ifIsFeasible(neighbor, \(C\))then  score \(\leftarrow\text{EvaluateSolution}(neighbor,C)\);  Candidates.add(\(neighbor,score\));  end if  end for ifCandidates \(\
eq\emptyset\)then \(Sol_{current}\leftarrow\text{SelectNextSolution}(Candidates,Sol_{current},LS_{criteria})\); // LS criteria: greedy, probabilistic, tabu conditions, etc. \(Sol_{current}\gets Sol_{next}\); if\(Score(Sol_{current})>score_{best}\)then \(Sol_{best}\gets Sol_{current}\); \(score_{best}\leftarrow\text{Score}(Sol_{current})\);  end if  end if UpdateSearchState() ; // Update tabu list, temperature, etc.  end for return\(Sol_{best}\); // Main Scheduling Algorithm \(Sol_{initial}\leftarrow\text{ConstructInitialSolution}(P)\); \(Sol_{optimal}\leftarrow\text{LocalSearchOptimization}(Sol_{initial},P,termination\_criteria)\); return\(Sol_{optimal}\);
```  
**Algorithm 1** Two-phase task-scheduling optimization
## 4 Results and Discussion
### Hydrogeochemical Simulation and Analysis
#### 4.4.1 Mineral Saturation Index Analysis  
The saturation index of minerals can visualize the dissolved equilibrium state of components in groundwater. Three representative water samples were selected for this study: the severely contaminated water sample S54 with hexavalent chromium concentration of 257 mg/L, the moderately contaminated water sample S01 with hexavalent chromium concentration of 60.3 mg/L, and the lightly contaminated water sample S43 with hexavalent chromium concentration of 0.729 mg/L. For the water sample S54, the saturation index of hexavalent chromium minerals was CaCrO4 > Na2 CrO4 > MgCrO4 > K2 CrO4 > Na2 Cr2O7 > K2 Cr2O7 > CrO3, and the saturation index was less than 0, which indicated that hexavalent chromium minerals were easily dissolved in the groundwater, and the proportion of chromium oxide was the largest among chromium minerals. The saturation index of trivalent chromium minerals was ordered as Cr2O3 > MgCr2O4 > Cr(OH)3 > CrCl3, and the saturation index of Cr2O3, MgCr2O4 and Cr(OH)3 were greater than 0, while the saturation indices of CrCl3 were less than 0. It can be concluded from the saturation index of trivalent chromium minerals that most of the trivalent chromium minerals were insoluble in groundwater, and CrCl3 made the greatest contribution to the concentration of trivalent chromium ions. For groundwater sample S01, the saturation index of hexavalent chromium minerals and trivalent chromium minerals showed the same variation pattern with S54. For groundwater sample S43, the saturation index of hexavalent chromium minerals was ranked as CaCrO4 > Na2 CrO4 > MgCrO4 > K2 CrO4 > CrO3 > K2 Cr2O7 > Na2 Cr2O7, and the saturation index of hexavalent chromium minerals was less than 0, indicating that hexavalent chromium minerals were easy to dissolve into groundwater, but the order of mineral saturation index had changed, and the Na2 Cr2O7 component had the largest proportion in chromium-containing minerals. The saturation index of trivalent chromium minerals showed the same change pattern as S54 and S01(Figure 8a).  
By comparing the saturation index of chromium-containing minerals at S54, S01 and S43, and it can be concluded that the higher the chromium ion concentration in the aquifer, the greater the saturation index of chromium-containing minerals, the smaller the tendency for hexavalent chromium minerals to dissolve and the greater the tendency for trivalent chromium minerals to precipitate.  
Figure 8b showed the simulation results of the saturation index of the main minerals. The results showed that anhydrite, gypsum and halite were under unsaturated state in groundwater, and halite was the most prone to dissolution, and calcite, aragonite and dolomite were under saturated state, and dolomite was the most prone to precipitation.
## 4 Implementation
### Collaborative Framework
#### 4.2.1 Master's Discovery Process  
During the discovery process, the joining node sends HELLO messages as broadcasts and waits for the master's answer. If there is a master on the network, it sends a response via TCP to the user. This process is illustrated in Figure 2, where, as an example, the interaction among three nodes during the discovery process is depicted. Solid lines represent TCP packets, and dotted lines represent UDP packets. It should be noted that all nodes receive the HELLO message, but the slaves ignore it, while the master responds to it.
## 4 Conclusions  
This study proposes a thermo-responsive hydrogel-based ecological soil to resolve the trade-off between fluidity and cohesion in high-slope construction. The key findings are summarized as follows:  
(1) A kind of thermo-responsive hydrogel stabilizer (HSZ) for ecological soil was designed and prepared.  
(2) After the addition of HSZ, the 7 d unconfined compressive strength of ecological soil reaches 1.55 MPa. The PH of ecological soil was maintained between 6.5 and 8.0. At 10 d, tall rescue and alfalfa leaves were longer than 8 cm, and at 30 d, the leaf density of tall rescue had greatly risen. The strength development matched root growth, which not only ensures plant growth but also improves slope protection performance.  
(3) Upon thermal activation, HSZ-modified eco-soil exhibited significant rheological changes (reduced spreadability; increased consistency). Optimal thermal stimulation achieved 79% strength conversion efficiency.  
(4) Conductivity and viscosity analyses confirmed precise thermo-responsive carrier release in both aqueous and hydrogel systems.  
This thermo-responsive hydrogel system enables dynamic rheological regulation, showing promise for high-slope spray applications requiring tunable fluidity-to-strength transitions.  
Conceptualization, X.Z. and W.Z.; methodology, P.Y.; software, Z.L.; formal analysis, P.Y. and Z.L.; investigation, W.Z.; resources, X.Z.; data curation, P.Y.; writing--original draft preparation, J.Z. and Y.G.; writing--review and editing, X.Z. and W.Z.; visualization, J.Z. and H.C.; supervision, Y.G. and H.C.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Three Gorges Jinsha River Chuanyun Hydropower Development Co., Ltd. Yibin Xiangjiaba Power Plant (No. Z422302058).  
Data Availability Statement: Data will be made available on request.  
Conflicts of Interest: Author [PERSON], [PERSON], [PERSON], and [PERSON] are employed by the China Yangtze Power Co., Ltd. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. The authors declare that this study received funding from Three Gorges Jinsha River Chuanyun Hydropower Development Co., Ltd. Yibin Xiangjiaba Power Plant. The funder was not involved in the study design; in the collection, analysis, or interpretation of data; in the writing of this article; or in the decision to submit it for publication.  
Figure 12: Controlled release of responsive carriers in hydrogel system.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 5 Experimental Design and Results
### Experimental Results  
#### 5.2.1 Ndt-Cuda  
In most cases, embedded upper-level computers use ARM chip architectures, which typically lack high computational performance. However, for larger maps, the amount of point cloud data needing to be handled is massive. Real-time point cloud matching consumes significant computational resources, which is not in line with the expectations of a reasonable localization system.  
In our experiments, the NDT calculation thread was offloaded to the GPU; comparative experiments with field matching algorithms (Direct1, Direct7) were conducted to  
Figure 4: The ground scene as displayed using images of the Wish3d-Earth and the PCD map as displayed using Rviz software (version: 1.24.20).  
Figure 3: Multi-modal sensor platform.  
validate their performance. The point cloud map had a resolution of 0.2 m and two sets of parameters (0.1 m and 0.2 m) were selected for a downsampling comparison. The Direct7 matching algorithm performs more extensive field searches compared to Direct1, demanding more computational resources. In Table 1, the performance of NDT-OMP with Direct7 is relatively poor, though it is not specifically recorded. The Drift parameter evaluates rotation offsets greater than 45\({}^{\circ}\) and translation distances exceeding one meter, serving as an indicator of the point cloud matching stability in the same environment.  
The CUDA-based NDT-D2D algorithm exhibits efficient resource utilization and minimal increase in GPU memory usage, making it a resource-efficient choice.
## 3 Methods
### Testing of the Evaluation Results  
#### 3.4.1 F1-Scores  
The F1-score is a statistical measure of the precision of a binary grading model, which is defined as the harmonic mean of precision and recall [26]. The F1-score ranges from 0 to 1, with higher values indicating better model prediction performance. The F1-score is particularly suitable for evaluating landslide susceptibility mapping because it considers both the likelihood that a predicted landslide is accurate (precision) and that an actual landslide is correctly predicted (recall).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{16}\]  
\[\text{Recall}\ =\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{17}\]  
\[\text{F1}=\frac{2\times\ \text{Precision}\ \times\ \text{Recall}}{\text{ Precision}+\ \text{Recall}} \tag{18}\]  
where TP denotes the number of actual positive samples (correctly identified landslides), TN denotes the number of actual negative samples (correctly identified non landslides), FP denotes the number of false positive samples (predicted landslides that are nonland-slides), and FN denotes the number of false negative samples (predicted nonlandslides that are landslides).