esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Unified Reliability Calculation Procedure Considering Aleatoric and Epistemic Uncertainties  
The step by step flowrate to calculate reliability index considering aleatoric and epistemic uncertainties with uncertain distribution parameters is listed as follows, and the solution flowchart is shown in Figure 1.  
Step 1: The uncertain distribution parameter \(\xi\) is represented using the maximum likelihood estimation method based on insufficient data from Source 1, the probability density function \(f_{\xi}(\xi|\theta_{k})\) considering insufficient data of distribution parameter are calculated using Equations (1) and (2).  
Step 2: The PDF \(f_{\xi}(\xi)\) of distribution parameter \(\xi\) in Equations (3) and (4) is calculated using evidence theory based on available uncertainty information from Source 2.  
Step 3: The design domain \([l,u]\) of \(\xi\) is determined based on sampling information from Source 1 and Source 2 using Equations (5)-(8).  
Step 4: The fusion model \(G\) is constructed to minimize the total deviation \(L(\mathbf{w},\mathbf{p})\) between \(G\) and \(G_{1}\), \(G_{2}\), the weight ratios \(\mathbf{w}\) and fusion probability density values \(p_{i}\) at discrete points are calculated using Equations (9)-(11), and the uncertainty distribution parameter \(\xi\) is represented considering uncertainty information from two sources simultaneously.  
Step 5: The uncertainty distribution parameters \(\xi\) are dispersed according to their probability density function \(f_{\xi}(\xi)\), the epistemic uncertainty variable \(y\) is probabilistically represented using a weighted sum of probability variables \(\mathbf{u}^{j}\) in Equation (12).  
Step 6: The approximation model of performance function \(g(\mathbf{x},\mathbf{y})\) considering aleatoric uncertainty variables \(\mathbf{x}\) and epistemic uncertainty variables \(\mathbf{y}\) is constructed around the most probable point (MPP) \((\mathbf{x}^{*},\mathbf{y}^{*})\), as follows:  
\[\hat{g}(\mathbf{x},\mathbf{y})=\alpha_{0}+\sum_{i=1}^{nx}\alpha_{i}x_{i}+\sum _{j=1}^{ny}\beta_{j}y_{i}. \tag{14}\]where \(\alpha_{0}\), \(\alpha_{i}(i=1,\cdots,nx)\), \(\beta_{j}(i=1,\cdots,ny)\) and \(\Gamma_{k}(k=1,\cdots,nz)\) are coefficients that are determined using the optimum Latin hypercube sampling method.  
Step 7: The actual reliability probability \(p_{G}\) is calculated according to the approximation model \(\hat{g}(\mathbf{x},\mathbf{y})\), which is defined in Equation (15):  
\[p_{G} =\Pr\{\hat{g}(\mathbf{x},\mathbf{y})\leq\psi\}\] \[=\Pr\biggl{\{}(\alpha_{0}-\psi)+\sum_{i=1}^{nx}\alpha_{i}x_{i}+ \sum_{j=1}^{ny}\beta_{j}y_{j}\leq 0\biggr{\}}. \tag{15}\]
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 2 The Composition of SRM Drive System
### Mathematical Model of SRM
The position sensorless control provides the rotor position signal for the control algorithm, and the control algorithm can also provide the required physical quantities for some rotor position estimation methods. Meanwhile, we can discover new methods in these essential electromagnetic and mechanical equations to improve the performance of the motor drive system, which will be reflected in many position estimation methods. The most closely related to the rotor position is the flux linkage and inductance, which motivates a large number of magnetic-model-based methods. At the same time, the magnetic-model-based method is also undergoing in-depth development to solve the difficulty involved in magnetic model establishment.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
### Complicated  
The map was square-shaped, with a structure in the centre and more rooms, entrances, and obstacles than the previous maps. Compared with the average of 176,671.1 nodes visited with the conventional image map for pathfinding to a destination, when the same pathfinding algorithm was applied to the preprocessed topology map, an average of 200.8 nodes were visited (decrease of 99.89%). Since more obstacles are encountered due to the central structure when generating a path, the time required for pathfinding through the image map is greatly increased. When A* was applied to the image map, it took an average of 775.5 ms, and when the preprocessing was performed through the CNA, the average time was 280.8 ms. When the same pathfinding algorithm was applied to the topology map generated by the CNA, it took an average of 6.7 ms; this represents a a 98.48% decrease when preprocessing and pathfinding were performed together versus alone.
## 1 Introduction
* **Concept Drift Detection Method Based on Variance Feedback (VFDDM)**: We develop the Concept Drift Detection Method Based on Variance Feedback (VFDDM). This method can be viewed as a drift detection framework that can be combined with various drift tests. It provides more suitable statistical tests and relevant parameters for different data streams based on the estimated variance, which is a relatively unique solution in the current research. * **Design of Drift Detectors and Experimental Validation**: Building upon VFDDM, the article presents three distinct drift detectors--VFDDM_H, VFDDM_M, and VFDDM_K--each employing different statistical testing methods. Comprehensive experiments conducted on synthetic and real datasets demonstrate the efficacy of the proposed drift detection method, showcasing its strong performance across various metrics. The rest of the article is organized as follows: Section 2 provides a brief overview of related work, Section 3 describes the proposed algorithm in detail, Section 4 describes the experimental setup and analyzes the experimental results, and Section 5 conducts the research of this paper. summarized and proposed future work directions.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Performance Metrics for Classification
#### 3.3.1 Accuracy  
In medical image classification, particularly when identifying lung nodules indicative of potential malignancies, accurate detection is paramount. True Positives (TPs) represent correctly identified nodules, reflecting the ultimate goal of such diagnostics--to ensure no potential threat goes unnoticed. Conversely, False Positives (FPs) occur when non-nodules are mistakenly classified as nodules, and False Negatives (FNs) are actual nodules that are overlooked, misclassified as benign. True Negatives (TNs) correctly identify non-nodules. The importance of distinguishing between these categories is underscored by the imperative to detect real nodules accurately, which can suggest the presence of cancer:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
While accuracy calculates the proportion of true results (both TPs and TNs) among the total number of cases examined, it may not always be the best metric, especially in scenarios where the dataset is imbalanced. This imbalance is typical in medical imaging, where positive cases (actual nodules) are significantly outnumbered by negatives. Relying solely on accuracy can lead to misleadingly high-performance metrics, suggesting effective model performance when, in reality, the model may be inadequately identifying critical positive cases. Therefore, in medical diagnostics, where the cost of missing a True Positive is very high, other metrics such as sensitivity (recall) or the F-score, which balance precision and recall, are often more informative and indicative of a model's practical utility in clinical settings.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 4 Proposed Protocol
### Packet Queuing Model  
During simulation, the first-in, first-out (FIFO) strategy was used to manage the transmission and reception of data packets. When the queue length exceeded the pre-set value, the queue performed packet loss operations on subsequent packets through the tail loss strategy. The data packet is discarded as follows:  
\[t_{now}\geq t_{in}+t_{expire} \tag{3}\]  
In Formula (3), t\({}_{\text{now}}\) is the current time; t\({}_{\text{in}}\) is the time when the packet enters the queue; and t\({}_{\text{expire}}\) is the allowed survival time for data packets. When the conditions were met, expired packets in the queue were deleted.  
The W-PAGPSR proposed in this paper was a location-based V2V routing protocol, the purpose of which was to improve the greedy and perimeter forwarding strategies of PA-GPSR through link stability.
# Human Milk Fat Substitutes from Lard and Hemp Seed Oil Mixtures
## 1 Introduction
According to various scientific studies, this very stereoisomeric specificity of breast milk triacylglycerlecols contributes to the enhancement of fat absorption from food, diminution of insoluble calcium salt formation, and prevention of the excretion of excessive calcium and magnesium from the body [15]. The chemical composition of breast milk is stable within some ranges. The content of selected chemical components, including fatty acids, changes depending on a number of factors, i.e., lactation phase, daytime, and mother's diet [16; 17]. Although breast milk is the most beneficial kind of food for feeding infants, breastfeeding is not always possible for economic or health-related reasons. Unfortunately, in both developing and industrialized countries, the number of breast-feeding women is getting lower and lower. Many infants are fed with animal milk--mainly cow's milk or baby-food products [12]. The elaboration of modern technologies for the production of modified milk designated for feeding infants, mimicking the original breast milk as natural food in a more and more sophisticated way, is very interesting for food manufacturers. Also, it constitutes a challenge for scientists conducting research studies on the profile of fatty acids in human breast milk and their essential role in the feeding of infants [18; 19]. Lard is a fat that mimics breast milk fat with respect to the distribution of fatty acids in triacylglycerecol molecules. Compared to breast milk fat, lard has a similar content of palmitic and oleic acids but features a lower percentage of essential fatty acids [19; 20; 21]. On the other hand, hemp oil can be used as a health-oriented product due to its high percentage of essential fatty acids. It is composed of 80% polyunsaturated fatty acids. The main fatty acid is linoleic acid, a chemical compound belonging to the family of \(\omega\)-6 acids. The second most abundant fatty acid contained in hemp oil seeds, required for the normal functioning of the human body, is \(\alpha\)-linolenic acid, a representative of the \(\omega\)-3 family. Moreover, hemp oil contains approximately 1-5% \(\gamma\)-linolenic acid (GLA), which is rarely found in seed oils. Next, this particular oil is characterized by an optimum \(\omega\)-6/\(\omega\)-3 ratio (3:1). Such a property makes it a wholesome oil of a very high nutritive value [22; 23]. The aim of our study was to acquire a substitute for human breast milk fat through intereesterification of lard and hemp seed oil mixtures and to monitor the properties of the resulting substitutes via calorimetric and chromatographic methods. We used enzymatic intereesterification with lipolytic enzymes as catalysts, because this method is considered one of the best choices for the modification of fat properties.
# The Algorithms of Image Processing and Analysis in the Textile Fabrics Abrasion Assessment
## 5 Discussion  
As a result of the evaluation of image analysis algorithms in scope of their potential sensitivity to surface alterations during the abrasion process it was found that:  
* Among all algorithms investigated within this work, the best outcomes gave calculations based on the brightness level histogram. The skewness, measured for CIELAB channels *a and *b showed sensitivity to surface alterations, such as protruding fibres, pills, relocated and thinning threads, missing places in the fabric as a result of broken thread and colour changes;
* In most cases, when skewness changed significantly, especially its sign, the surface texture was altered, e.g., pilling occurred, threads were relocated and became thinner or damaged;
* Effects of image global transformation--FHT transform, wavelet transform--did not allow for evaluating surface modifications caused by the abrasion process;
* Implementation of Gabor filtering, despite its similarity to human eye perception, did not give a sharper image of the abraded samples, with better visible signs of abrasion-caused damaged. However, beyond the visual effect, further attempts to express surface changes numerically did  
Figure 9: Skewness graph—Fabric 6.  
not give positive results. Gabor filtering was effective in pile fabric surface evaluation only, because of typical surface changes for this structure (smoothing, abrading of pile, colour fading).
# Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications
## 4 Results and Discussion
The correlation coefficient for the communication signal gradually increases from an initial value of 0.6-0.7 and stabilizes around 0.75 after 100 iterations (Note: The previously reported value of 0.886 results from sorting the separated signals and performing sign correction.). In contrast, the correlation coefficient for the interference signal remains stable near 0.975 with minimal fluctuation. Comprehensive analysis reveals that the iteration count significantly impacts the algorithm performance: intense parameter adjustments in the early stage lead to large errors and poor signal separation; as iterations proceed, errors decrease and separation improves; with sufficient iterations, the algorithm converges rapidly and achieves stable separation performance. Therefore, the algorithm can effectively separate signals given  
Figure 8: Correlation coefficients of separated signals versus initial ISR. ing distinct convergence characteristics when processing communication signals versus interference signals. Figure 10: The variation of the iteration error \(\Delta\)W with the number of iterations. Figure 9: Post-separation SIR and interference ISR versus initial ISR.
###### Abstract  
The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as _Hv_ (high valley value of the 14 May, 26 May, and 7 June SAR data), _Tb_ (difference between the 7 June and 14 January SAR data), _Cr_ (high valley value of the 13 July, 25 July, and 6 August SAR data), and _Lo_ (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.  
growth stage features; decision tree; fruit tree species; spatial mapping; synthetic aperture radar (SAR) +
Footnote †: journal:  
0000-0001-9199-22010
## 5 Modeling Results
### Influence of Variable Section Thickness of Sleeve Arch on Reinforcement Performance  
According to the experimental results, the material used at the variable section of the arch is steel fiber concrete. To further explore the impact of the thickness of the variable section on the reinforcement effectiveness of the steel fiber arch while adhering to building constraints, the thickness varied (50 mm, 60 mm, 75 mm, 85 mm, and 100 mm) with all other parameters held constant.
## 2 Constellation Station Maintenance Analysis
### The Content of Satellite Constellation Station Keeping  
The phase angle deviations between satellites in a constellation need to be calculated for constellation station holding. Therefore, a satellite needs to be selected as the reference basis for constellation phase holding. There are generally two methods for selecting the reference basis of constellation station position keeping. One method is to select a fixed satellite (the first satellite of the first orbital plane or the nominal orbit of the constellation in general) among all the satellites of the constellation as the reference basis and then calculate the phase angle difference between the other satellites and the reference satellite [15]. The other method is to dynamically select any satellite from all the satellites of the constellation as the reference basis and calculate the phase angle difference between the remaining satellites and the reference satellite [21].  
1. Fixed-reference-star method.  
In the constellation, a designated satellite (typically the first satellite in the initial orbital plane or nominal orbit of the constellation) is chosen as the reference star. This satellite, denoted as \((1,1)\), serves as the phase control basis for the constellation, while the reference satellite \((1,1)\) remains uncontrolled, with its standard phase denoted as \(\lambda_{1,1}\). If the actual phase of any other satellite \((i,j)\) in the constellation is \(\lambda_{ij}\), then \(\Delta\lambda=\lambda_{ij}-\lambda_{11}-\Delta\lambda\) represents the phase difference between satellite \((i,j)\) and the reference star, where \(\Delta\lambda\) denotes the designed theoretical value of the phase difference between satellites \((i,j)\) and \((1,1)\). When the deviation \(\Delta\lambda\) in the phase angle exceeds the permissible limits, it becomes necessary to implement phase-maintenance control for satellite \((i,j)\).  
2. Dynamic-reference-star method.  
The procedure for referencing the dynamic reference star is as follows. A satellite \((j,i)\) is dynamically chosen as the reference star from all the satellites in the constellation. The phase angle difference between other satellites \((i,j)\) and the reference star \((j,i)\) is denoted as \(\Delta\lambda=\lambda_{ij}-\lambda_{11}-\Delta\lambda\), where \(\Delta\lambda\) represents the designated value of the nominal phase difference between satellite \((i,j)\) and satellite \((j,i)\). When the phase angle deviation \(\Delta\lambda\) exceeds the permissible boundary value, it becomes necessary to implement phase-maintenance control for satellite \((i,j)\).
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 3 Survey Plan
### Issues Identified in Highway Guide Signs  
#### 3.4.1 Configuration or Placement Problems  
A review of guide signs along the G2 Expressway in the Beijing-Hebei section identified several configuration and placement issues, especially with distance and exit direction signs.  
(1) Improper placement of distance signs  
According to the standards, distance signs should be placed 1 km before the end of the acceleration lane in an interchange and must be repeated at least every 5 km [39]. However, the investigation identified the following issues: (a) Insufficient distance between signs: Some signs are only spaced 1 km apart, failing to meet the 5 km requirement. (b) Incorrect placement: Some signs are installed near ramp exits, which can distract drivers entering the mainline, posing safety risks.  
(2) Incomplete configuration of exit direction signs  
Standards stipulate that exit direction signs should be placed at intervals of 2 km, 1 km, 500 m, and at the exit point along the deceleration lane of expressways or urban roads [39]. However, the following problems were noted: (a) Missing levels: Many locations lack the 500 m level signs. (b) Insufficient pre-warning: Some places only provide 500 m signs and action point markers, but omit the 2 km and 1 km signs, posing safety hazards.  
(3) Incomplete entrance direction signs  
The standards recommend placing entrance direction signs on roads connecting to expressways, at 2 km, 1 km, 500 m, and at the reference point [39]. However, in certain cases, signs are missing, particularly at the 500 m mark, where only those of other places are present.  
(4) Incomplete service area signs  
Service area signs are required at 2 km, 1 km, 500 m, and at the start of the deceleration lane [39]. However, some service areas lack 2 km signs, leading to insufficient information for drivers.
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 1 Introduction
inaccurate weakly supervised signals that cannot directly offer pixel-level supervision for matting, the inherent semantic consistency among trimaps of different granularities can help to learn more robust semantic representations. Based on the motivation above, we propose a semi-supervised deep matting method based on the semantic consistency of trimaps (Tri-SSL). Specifically, Tri-SSL is a single-stage semi-supervised algorithm that includes a supervised branch and a weakly supervised branch in each training iteration. The supervised branch is consistent with ordinary supervised matting methods. In the weakly supervised branch, an unlabeled image has two trimaps of different labeling granularities. The trimap provides weakly supervised signals for unlabeled images, and the two trimaps are concatenated with the image as input. We apply the directional consistency constraints on the alpha predictions of different-granularity trimaps and the intermediate features, enabling the network to learn more information from unlabeled data. Tri-SSL leverages the consistency among the trimaps to improve the robustness of the learned semantic representations, resulting in high-quality alpha mattes with limited labeled data. In general, the main contributions of our work are as follows:  
* We propose the first semi-supervised learning framework for deep image matting (Tri-SSL). Tri-SSL utilizes the inherent semantic correlations of different-granularity trimaps to conduct consistency regularization;
* We apply directional consistency constraints to learn better semantic representations from the unlabeled samples using only trimaps;
* The experimental results demonstrate that our method can effectively utilize the unlabeled data to improve model performance.