esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Analysis of Multi-Process Machining Based on Information Entropy
## 5 Conclusions  
This paper introduces the concepts of information entropy and mutual information into the analysis of machining process reliability, thereby achieving the integration of machining error and error transmission. By establishing mathematical models, this study addresses the issue that the subjective factors of researchers in other related studies have an impact on the analysis results and provides a method for quantitatively analyzing the reliability of the machining process. First, the paper defines the directed-weighted process graph to present the relationship between different processes visually. Then, the paper analyzes the reliability of the machining process, and based on the analysis of error transmission, the paper establishes single-chain and multi-chain models. Finally, the correctness of the single-chain and multi-chain models is verified through Monte Carlo simulations. The simulation results show that the single-chain and multi-chain models can correctly reflect the relationship between the machining error of the process and the error transmission. Based on this paper, the proposed models have the potential to be integrated with machine learning and other techniques to predict the quality of machining, avoiding significant economic losses due to unqualified machining quality.  
Conceptualization, J.M. and D.Q.; methodology, D.Q.; software, J.M.; validation, Y.Z. and D.Q.; formal analysis, Y.Z. and Y.W.; investigation, J.M.; resources, D.Q.; data curation, J.M.; writing--original draft preparation, J.M.; writing--review and editing, D.Q., Y.Z. and Y.W.; visualization, J.M.; supervision, Y.Z.; project administration, D.Q.; funding acquisition, D.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementNot applicable.  
Figure 10: The simulation results Example 2.  
**Conflicts of Interest:** We have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Methodology
### Training and Modelling
#### 2.4.2 Modelling of Site-Specific Properties  
Using site-specific properties such as ground elevation and groundwater elevation, the process of estimating initial parameters for locations without data consisted of using site-specific properties such as ground elevation and groundwater elevation. As depicted in Figure 1, the latitude and longitude of each location were used as input variables to trainmodels using the geotechnical investigation report's source data. Then, hyperparameter tuning was performed to enhance the winning model. Once the ground elevation was determined, it was extended to a depth of 20 m below mean sea level, allowing for accurate estimation of ground properties in regions where data was initially lacking. Using a combination of data-driven modeling and geospatial analysis, this method produced accurate estimates for these crucial site-specific properties.  
As depicted in Figure 2, the procedure for estimating soil type for each layer in an area with no available data was similar to that used for determining ground and groundwater elevation. In this instance, however, classification models were employed rather than regression models, as non-numerical variables are typically assigned to soil types. Figure 3 depicts the estimation of additional site-specific geotechnical properties, such as SPT N-values, fines content, liquid limit, and plastic limit, using regression models. Notably, when an SPT N-value of 50 was encountered during the estimation process, the corresponding layer was classified as rock. The only distinction between this procedure and the modeling of ground and groundwater elevation is the inclusion of layer elevation in the input and training data.  
Figure 1: Modeling of site-specific properties.  
Figure 2: Modeling soil type using classification models.
## 4 Results and Discussion
The results indicate that the highest AIR of 10.23 Gbps can be achieved at the bandwidth of 1.5 GHz through PS bit loading. Besides, we also calculate the minimum NGMI among all subcarriers at different bandwidth, all of which are above 0.92, showing that higher AIR can be achieved in the existing system.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 4 Algorithm Design
### Design of Improved Genetic Algorithm Based on Dijkstra Coding  
Dijkstra's algorithmis an algorithm for finding the shortest path. This algorithm suits the shortest-path problem with a single source point on the graph \(G=\langle V,E\rangle\). During the algorithm's operation, there will be a set of points S. The shortest-path values of all vertices and source points in the set S have been determined. The algorithm repeatedly selects the estimated minimum point \(u\in V-S\). It adds this to S. This paper uses the Dijkstra algorithm to solve each packet along its shortest path to the endpoint and the calculation results.  
In some cases, congestion will occur in the network if each packet passes the shortest path from the target node. In this case, the global delay is higher than that of some packets passing through non-shortest paths. Therefore, in the process of searching the shortest path in the Dijkstra algorithm, the non-shortest path should also be appropriately searched. These non-shortest paths should have the characteristics of 'a cyclic', and the shortest path difference is not too large.  
Based on this idea, the Dijkstra algorithm is improved. In the search by the Dijkstra algorithm, the addition of non-minimum points is appropriately allowed in the process of repeatedly selecting the estimated minimum point. For example, the current distance is the shortest, and the worst case is not more than 5. This value can be adjusted according to the size of the graph. Through this improvement, the path derived from the Dijkstra algorithm can also appropriately contain non-minimum paths with little difference.  
Genetic algorithms usually include genetic, mutation, hybridization, natural selection, and other processes. During the whole search process, it is always inclined to retain individuals with higher fitness. In the genetic algorithm, each needs to be encoded. In this problem, the individual is a specific case. In this case, each packet selects a path from the alternative set, and all packets select a feasible path. This constitutes a case of the individual of the problem. The case of each packet selecting a path constitutes the coding of the problem.  
The Dijkstra algorithm can only maintain a single shortest path to a certain point, but only maintaining a single shortest path often falls into a local optimal solution, and it is difficult to find a better solution. For this problem, the key to improving the Dijkstra algorithm is to design rules for generating alternative paths. Considering that the greedy algorithm finds it difficult to use the shortest path in the early stage (or when the sample is small), the simple use of the Dijkstra algorithm will lead to multiple packets using the same path, which will lead to congestion. In this problem, the set of nodes that are touched in each step, and, in some cases, include the accessed nodes, is updated to the path set. Some of the additional alternative paths will also be updated with the Dijkstra algorithm update.
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 5 Numerical Study
### Results Analysis
#### 5.2.2 Trade-Offs of Multiple Objectives
According to the relevant description in the past literature [26], the idle time variance is calculated by \(\sum_{g=1}^{G}\sum_{j=1}^{F}S_{j,g}^{2}\), where \(S_{j,g}\) refers to the last idle time at gate \(g\) until the arrival of the flight \(j\). Figure 4 presents the variance of idle time corresponding to each allocation under different clusters. It can be clearly deduced that the variance of idle time can be large or small, which fluctuates in the range of 2400-3300. In addition, the Pearson correlation coefficient between the variance of idle time and the cluster ID is 0.11, indicating that there is no significant correlation. Since the variance of idle time can reflect the sensitivity of gate allocation to variations in flight schedule (that is, the robustness of gate allocation), this performance indicator has been regarded as an important optimization objective in previous studies [24; 25; 26]. However, this paper focuses on the three stakeholders of passengers, airlines, and airports, and it is only used as an auxiliary performance metric for selecting weight combinations. When the value of each objective function is the same, the allocation result with the smallest variance of idle time is given priority to balance the idle time of each gate and alleviate the adverse effects caused by flight delays in actual operations. In the following experiments, the weight combination corresponding to the minimum idle time variance is selected, and its optimal solution is taken as the final gate allocation result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster ID**} & \multicolumn{3}{c}{**Centroid of Each Cluster (*)**} & \multicolumn{1}{c}{**Number of Weight**} \\ \cline{2-5}  & **Walking Cost (m)** & **Taxi Cost (min)** & **Gate Cost** & **Combinations** \\ \hline
1 & 18,913,266 (0.49) & 500,631.6 (0.26) & 4.0 (0.00) & 11 \\
2 & 19,764,024 (0.74) & 493,906.6 (0.10) & 4.0 (0.00) & 9 \\
3 & 18,008,472 (0.22) & 517,368.3 (0.67) & 4.4 (0.11) & 3 \\
4 & 17,849,523 (0.18) & 509,429.6 (0.48) & 4.8 (0.23) & 3 \\
5 & 20,461,502 (0.94) & 491,195.4 (0.03) & 4.8 (0.23) & 2 \\
6 & 17,307,339 (0.02) & 520,802.9 (0.76) & 5.7 (0.49) & 3 \\
7 & 18,002,704 (0.22) & 500,897.8 (0.27) & 5.6 (0.46) & 5 \\ \hline \hline \end{tabular}
* Note that the values inside the parentheses are the corresponding normalized values.  
\end{table} 
Table 5: Cluster analysis results of different weight combinations.
## 4 Experimental Results
### Results of Step 1 by Type  
#### 4.1.1 News  
The results of the survey conducted after the subjects watched the video clips containing accidents (news) are as follows.  
For the first question (\"On which screen were the information delivery elements arranged most efficiently?\"), as shown in Figure 10, 14 subjects selected clip no. 4, 5 subjects selected clip no. 3, and 1 subject each selected clip no. 1 and no. 2. For the second question, (\"On which screen were the information delivery elements arranged most properly and harmoniously?\"), 15 subjects selected clip no. 4 and 5 selected clip no. 3. For question no. three, (\"On what screen is the sign language interpreter most clearly visible?\"), 13 subjects selected clip no. 4, 7 subjects selected clip no. 3, and no one selected clip no. 1. This suggests that the majority preferred to have a large sign language interpreter on screen without a background.  
For the fourth question (\"Which screen do you want to continue to use?\"), 14 subjects selected clip no. 3, 5 subjects selected clip no. 4, and 1 subject each selected clip no. 1 and no. 2. For the fifth question, (\"Which screen was the most difficult to understand the content?\"), the overwhelming majority of the subjects chose clip no. 1, which presented the layout with a background and a sign language interpreter in small size. Considering that the majority of subjects selected clip no. 1 and no. 2, it was found that the size of the sign language interpreter was a more important consideration than the presence of a background.  
To summarize, as shown in Figure 11, the average value of news type preferences showed that the best screen layout for the news type was clip no. 4 with a large-sized sign language interpreter and no background of the interpreter, and the worst screen was clip no. 1, which is similar to the one currently shown on TV programs today.  
Figure 10: Results with news cases.  
In an individual interview after the preferences survey, as presented in Table 6, the subjects presented the following opinions on the screen components of news type and the proposed screen layout.  
Figure 12 shows the result of the gaze-tracking hit map in news cases. In the subtitled and most preferred layout, gaze was mostly focused on the subtitles and sign language interpreter. The subjects often identified professional words from the subtitle since sign language is limited in the sufficient delivery of the information. In the interview, a subject mentioned that it is essential to read subtitles because sign language does not contain many professional words used in news broadcasts. On the other hand, subjects least preferred watching the main content and the subtitled layout rather than watching the subtitles. If the presentation of the interpreter is too small, the subjects gave up trying to figure out the full content and, thus, watched fewer subtitles.
## 4 Performance Analysis
### Analysis of Node Density Change  
This subsection analyzes the change of node density caused by the sensor movement and examines how mobility affects a cluster-based infrastructure. Since a high density distribution change means an unstable network infrastructure, the rate of the density change may be considered as an indicator of topology change. The following analysis investigates the characteristics of the link available time distribution between the clusterhead and a cluster member. Furthermore, the Lindeberg theorem [49] is applied to abstract the node density dynamics in a cluster and to examine the behavior of network operation.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### GAN Model Construction
#### 3.5.4 GAN Reliability Test  
After the GAN model is built, the next step is to generate audio, and use the voiceprint recognition model built earlier to test the accuracy. After 10 constructions and trainings, the models with the highest accuracy were compared one-by-one for further use.  
One key aspect of GEC-VR is the iterative reduction of GAN-generated samples to find the optimal number of synthetic data points that improve the model's performance without causing overfitting. By adjusting the number of GAN samples, the algorithm can be expected to ensure that the model generalizes well to unseen data.  
Adaptive optimizers like Adam, combined with MFCC feature extraction, improve the efficiency and accuracy of training voiceprint recognition models by leveraging advanced signal processing and optimization techniques. However, careful monitoring of GAN over-reliance is necessary, as generating too many synthetic samples can degrade model performance. This algorithm provides a balanced and systematic approach that addresses real-world challenges in voiceprint recognition with limited and diverse datasets.  
Traditional GAN evaluation metrics, such as Frechet inception distance (FID), are designed for visual domains and lack standardization for evaluating synthetic audio. Consequently, this study assesses the GAN indirectly by analyzing its contribution to the voiceprint recognition system's performance, measured through accuracy, precision, recall, and F1 score.
## 2 Related Works  
The increasing availability of location-based data from mobile devices enables novel analyses across domains like transportation, urban planning, and economics [12; 13; 14]. Researchers are taking advantage of this rich source of information to study a wide range of topics, from studying population behavior to recovery efforts from COVID-19 [15; 16]. However, findings on the local economic effects of stadium investments are mixed--some studies even suggest focusing funds elsewhere, given modest impacts and opportunity costs [17; 18; 19]. Though multiple analyses find negligible links between sports infrastructure spending and growth [20; 21], others show potential for increased consumer spending, employment, and housing market uplift [22].  
Machine learning is increasingly applied to spatiotemporal data across sports and other domains, like using cell data to predict COVID-19 spread [23]. Techniques also estimate attendance, optimize player health, analyze team performance, and more [24; 25]. In economics, machine learning evaluates housing price spillovers from development programs [26] and regional innovation impacts [27].  
Additionally, ref. [28] used machine learning models to predict the attendance demand in European football games, with features such as the performance of the home team, the performance of the visiting team, the day of the game, distance, and uncertainty of outcome as independent variables. However, no study uses mobile data and machine learning to quantify the localized spillover effects of sporting events.  
Tree ensemble methods effectively handle mixed data types and capture complex relationships, making them suited to sports economics tasks [29]. In this study, we extract features such as game day and time, which exhibit intricate interactions with the target-variable-amount-spent locally. Tree-based algorithms are suitable nonlinear modeling techniques that can hierarchically estimate the importance of predictor variables in classification and regression tasks [30]. These models have proven to be effective in predicting outcomes, such as visitor attendance and revenue generation, and have been applied to various sports events [31].  
The flexibility of machine learning provides new means to evaluate economic trends, though applications estimating sports-related impacts remain limited [32]. Despite its potential, there has been limited research that utilizes machine learning techniques to estimate the economic impact of specific events, such as baseball games. This research responds to these gaps through a data science approach, uniting spatiotemporal data and sports economics.
## 1 Introduction  
Pulmonary hypertension (PHT) is a complex pathophysiological disorder characterized by an elevation in pulmonary arterial pressure (PAP). This diagnosis is given when mean PAP exceeds 25 mmHg at rest using invasive right heart catheterization [1]. In early stages of the disease, elevations in PAP and architectural remodeling typically induce dilation and overload-induced hypertrophy of the right ventricle. Over time, this results in impairment of cardiac function, eventual right heart failure, and secondary pathophysiology such as right atrial dilation [2]. Although two-dimensional (2D) Doppler echocardiography is commonly used to provide evidence of the disease hallmarks in the right ventricle, and right ventricular catheterization is needed for a conclusive diagnosis. Thus, other non-invasive diagnostic imaging biomarkers would be beneficial in clinical settings [3]. Magnetic resonance imaging (MRI) and angiography (MRA) could address this unmet need. Relative to 2D Doppler echocardiography, MRI possesses two advantages.  
First, it can perform multiplanar analysis allowing for accurate volumetric measurement of the vasculature. Second, it can evaluate hemodynamic changes in the pulmonary pathway via 4D-flow MRI. In both cases, volume and flow measurements demonstrate low variability and high test-re-test reliability [3; 4]. Although MRI has improved the characterization of right ventricular pathophysiology, it has also identified potential biomarkers of the disease in the pulmonary artery (PA). Patients diagnosed with PHT often exhibit hemodynamic changes such as decreased vorticity, peak velocity, and wall shear stress (WSS) [5; 6; 7]. Furthermore, anatomic remodeling, including PA dilation and medial thickening, can be simultaneously observed [8]. Although promising, these novel biomarkers and their relationship must be investigated.  
The purpose of this study was to test the hypothesis that hemodynamic changes within the pulmonary artery are associated with remodeling of the surrounding vascular architecture. In addition, we aimed to further characterize PHT biomarkers in the PA by investigating novel 3D flow biomarkers and bifurcation distances.
# Order-Based Identification of Bearing Defects under Variable Speed Condition
## 3 Validation of the Proposed Features
### Results and Discussion
#### 3.3.3 Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{6}{c}{**(a), Accuracy = 0.32**} & \multirow{2}{*}{**Actual**} & \multirow{2}{*}{**(b), Accuracy = 0.79**} \\ \cline{2-2} \cline{5-12}  & \multicolumn{6}{c}{**Predicted**} & & & & & \multicolumn{6}{c}{**Predicted**} \\ \hline class & H & ORD & IRD & BD & CD & class & H & ORD & IRD & BD & CD \\ \hline H & 4 & 0 & 4 & 21 & 1 & H & 26 & 0 & 1 & 3 & 0 \\ ORD & 0 & 3 & 6 & 18 & 3 & ORD & 1 & 23 & 0 & 6 & 0 \\ IRD & 3 & 1 & 20 & 6 & 0 & IRD & 0 & 23 & 1 & 6 \\ BD & 4 & 5 & 0 & 19 & 2 & BD & 0 & 2 & 24 & 2 \\ CD & 0 & 2 & 22 & 4 & 2 & CD & 0 & 0 & 0 & 7 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrices for the case of raw data: (a) using conventional features; (b) using updated features. Figure 11: Space representation of the tested 50% of raw data as predicted by the (MSVM) classifier: **(a)** using the non-updated features; **(b)** using the updated ones. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{6}{c}{**(a), Accuracy = 0.37**} & \multirow{2}{*}{**Actual**} & \multirow{2}{*}{**(b), Accuracy = 0.87**} \\ \cline{2-2} \cline{5-12}  & \multicolumn{6}{c}{**Predicted**} & & & & \multicolumn{6}{c}{**Predicted**} \\ \hline class & H & ORD & IRD & BD & CD & class & H & ORD & IRD & BD & CD \\ \hline H & 11 & 9 & 0 & 8 & 2 & H & 28 & 0 & 1 & 1 & 0 \\ ORD & 7 & 13 & 0 & 9 & 1 & ORD & 2 & 26 & 0 & 2 & 0 \\ IRD & 0 & 5 & 13 & 9 & 3 & IRD & 0 & 0 & 24 & 2 & 4 \\ BD & 6 & 10 & 0 & 12 & 2 & BD & 0 & 0 & 0 & 26 & 4 \\ CD & 5 & 12 & 0 & 6 & 7 & CD & 0 & 0 & 3 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices for the case of pre-processed data: (a) using conventional features; (b) using updated features. classification only reached 37% for the pre-processed data and 32% for the raw data. Confirming the performance of the updated features in identifying bearing defects in variable speed and the necessity of the pre-processing step.