esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 3 Problem Description
### System Model with Novel Configurations
With respect to low power output, the transmission power of each channel, denoted as \(P_{\text{s\_t,channel}}^{\text{(low)}}\), is set as:  
\[P_{\text{s\_t,channel}}^{\text{(low)}}=\frac{P_{\text{s\_t}}^{\text{(low)}}}{N _{\text{s}}} \tag{6}\]With respect to high power output, the transmission power of each channel, denoted as \(P_{\text{s\_t\_channel}}^{\text{(high)}}\), is set as:  
\[P_{\text{s\_t\_channel}}^{\text{(high)}}=\frac{P_{\text{s\_t\_}}^{\text{(high)} }}{N_{\text{s}}} \tag{7}\]  
However, according to the data from the aforementioned study [15] by [PERSON] et al., the maximum transmission power of the MBS (i.e., \(P_{\text{m\_max\_t}}\)) (equal to 20 W) and the maximum transmission power of the SBS (i.e., \(P_{\text{s\_t\_}}^{\text{(high)}}\)) (equal to 0.05 W) can differ by as much as 400-fold. In order to avoid unnecessary energy consumption, the transmission power of any MBS channel is consumed only when it is used; if a channel is not used, it does not consume energy. Specifically, the transmission power consumption of any used MBS channel, denoted as \(P_{\text{m\_t\_channel}}\), is set as:  
\[P_{\text{m\_t\_channel}}=\frac{P_{\text{m\_max\_t}}}{N_{\text{m}}} \tag{8}\]  
Furthermore, in order to improve the operating efficiency of network infrastructure in different environments, it is assumed that the data center has precise information and data collected via big data mining (methods for which include big data preprocessing, storage management, and deep learning data analysis), allowing adjustments to network throughput requirements and improvements to resource allocation efficiency. To provide clarity to the illustrations in later figures, the graphical representations of the SBSs in all possible states, as well as the accompanying descriptions for these representations, are shown in Figure 1. Last, but not least, a conceptual construction of the system model from a systemic point of view is presented in Figure 2.  
\[P_{\text{s}}^{\text{(high)}}=P_{\text{s\_basic}}+P_{\text{s\_t}}^{\text{(high)}} \tag{2}\]0 Small cell base stations (SBSs) in all possible states: (**a**) an SBS with low power output that is placed at a point outside of coverage hole centers; (**b**) an SBS with high power output that is placed at a point outside of coverage hole centers, providing coverage to nearby areas, excluding coverage holes, that need it; (**c**) an SBS that is disabled; (**d**) a disabled SBS that is placed at a point outside of coverage hole centers and unable, in its high power output state, to provide coverage to nearby areas that need it; (**e**) an SBS with high power output that is placed at a point outside of coverage hole centers, providing coverage to the coverage hole closest to it; (**f**) an SBS that is placed in a coverage hole center, and that is functioning in the low power state.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### Discrimination-Aware Decision Tree  
[PERSON], [PERSON], and [PERSON] [6] proposed an approach that directly integrates fairness constraints into the Decision Tree learning process by modifying the splitting criterion to balance accuracy and fairness. In addition to the conventional information gain (IG) with respect to class labels, they introduced information gain with respect to a protected attribute, leading to the development of three novel splitting strategies. The first strategy, \(IGC-IGS\), subtracts discrimination gain from accuracy gain, while the second, \(IGC/IGS\), balances the trade-off between accuracy and discrimination. The third strategy, \(IGC+IGS\), promotes homogeneity across both class labels and the sensitive attribute. Furthermore, the authors incorporate a post-processing relabeling technique that adjusts Decision Tree leaf labels to mitigate discrimination. This relabeling process is formulated as a knapsack optimization problem, aiming to maximize fairness while minimizing accuracy loss. The proposed method is evaluated on three benchmark datasets--the Census Income dataset, Dutch Census 1971 dataset, and Dutch Census 2001 dataset--demonstrating strong performance in both accuracy and fairness. Notably, the combination of the \(IGC+IGS\) splitting criterion and the relabeling technique outperforms existing fairness-aware classification methods.
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 3 Implementation of the Proposed Approach  
The extended fusion method to predict SST for sparse data proposed in this paper is mainly divided into four stages. The first stage is data association calculation. This stage mainly uses the time series classification method of deep learning to classify different data points, and then performs association calculations on the classification results between different data points. The second stage is the cluster analysis of the data. The result of the association calculation is used as the distance between the data points, and then K-means clustering algorithm is used to cluster the data points, where the data center point is a heterogeneous data point. The third stage is to continue the assimilation calculation of the data through the clustering results. Each type of data is assimilated based on the clustering center and the association relationship. The fourth stage is the data prediction. By using the assimilated data for data training, the pros and cons of the assimilation methods are judged by comparing the data prediction effects before and after the assimilation. Detailed descriptions of the implementation are discussed as follows.
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### FTA Qualitative Analysis of Repairable Systems
#### 2.6.2 Component Importance for RDFTA  
The component importance for RDFTA is the quantification of the importance of each component within the system, including the probability importance and structural importance [46]. Generally, the probability importance is calculated using Equation (15):  
\[I_{R}(j)=\frac{\partial h(R)}{\partial R_{j}},j=1,\ldots,n, \tag{15}\]  
where \(I_{R}(j)\) is the probability importance of component \(j\), \(h(R)\) is the reliability function of component \(j\), and \(R_{j}\) is the minimal path set of component \(j\).  
Through the probability importance ranking results, the weak links of reliability in the system design scheme can be found and hence, corresponding compensation measures can be put forward to support the formulation of a reliability growth scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & \(x_{1}(t)\) & \(x_{2}(t)\) & \(y(t)\) \\ \hline
2 & 0 & 1 & 1 \\
3 & 1 & 0 & 1 \\
4 & 1 & 1 & 0 \\
5 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The quantitative operation rules of the priority AND gate.
###### Abstract  
The objectives of this study were to compare the effects of two in-season velocity loss training methods (VBT) on performance outcomes to evaluate the effects of velocity-based training compared to traditional resistance training (TRT) on performance outcomes in young elite soccer players. VBT utilized the same relative load but varied in the extent of velocity loss during the set: 15% (VL15%) and 30% (VL30%). Thirty-four players were recruited and randomly distributed into three groups: the VL15% group (n = 12; age = 18.50 \(\pm\) 0.67 years; stature = 183.41 \(\pm\) 4.25 cm; body mass = 75.08 \(\pm\) 5.57 kg), the VL30% group (n = 11; age = 17.91 \(\pm\) 0.60 years; stature = 181.21 \(\pm\) 6.56 cm, body mass = 73.58 \(\pm\) 6.22 kg), and the traditional strength training group TRT (n = 11; age = 18.14 \(\pm\) 0.74 years; stature = 182.17 \(\pm\) 5.52 cm; body mass = 74.86 \(\pm\) 6.68 kg). Alongside regular soccer sessions and matches, the groups underwent a four-week (2 sessions per week) resistance training intervention with back squats involved. Changes in leg strength (SQIRM), 20 m sprint time (SPR 20 m), counter-movement jump height (CM), reactive strength index (RSI), and change of direction (COD) from before and after were evaluated using a 3 \(\times\) 2 ANOVA. While no significant interaction was found for SQIRM and SPR20, all of the groups showed significant pre to post improvements. Significant interactions were observed for CMJ (F = 38.24, \(p\) = 0.000), RSI (F = 8.33; \(p\) = 0.001), and change of direction agility test (COD) (F = 3.64; \(p\) = 0.038), with a post hoc analysis showing differences between the VL15 (6.0%) and TRT (1.7%) groups (\(p\) = 0.034); VL15 (12.2%) and VL30 (3.2%) groups (\(p\) = 0.004); VL15 and TRT (0.4%) (\(p\) = 0.018); VL15 (2.4%) and VL30 (1.5%) (\(p\) = 0.049); and between the VL15 and TRT (0.4%) groups (\(p\) = 0.015). Four weeks of VL15% training during the season induced similar strength increases to VL30% and TRT, superior improvements in RSI and COD compared to VL30%, and superior improvements in CMJ, RSI, and COD tests compared to TRT. Thus, incorporating the VL15% training method may be recommended to improve power-related performance metrics in elite young soccer players.  
v 2024 14, 9192. [[https://doi.org/10.3390/app14209192](https://doi.org/10.3390/app14209192)]([https://doi.org/10.3390/app14209192](https://doi.org/10.3390/app14209192))
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
###### Abstract
Furthermore, with the emergence of Software-Defined Wide-Area Network (SD-WAN [7]), when Internet Service Providers plan to deploy new services (such as cloud-based and on-demand applications) across the global network, it provides better adaptation and resilience that prevents inefficiency disruption on network communication. Similar to SD-WAN, utilizing SDN-based solutions [8; 9] at Internet eXchange Point (IXP) has become more and more popular. It allows multiple Internet Service Providers to peer and exchange their respective traffics with BGP protocol. Since ASes are interconnecting through IXP, some exchange operators are considering the use of SDN to develop a versatile control for manipulating traffic with precise manner [10], which eventually illustrates the concept of Software-Defined eXchange (SDX). By integrating RPKI with SDX, it is possible to enhance the security at the exchange point by installing forwarding entries with different policies [11]. Meanwhile, it also helps to mitigate the poisoning issue that some traditional networks may meet. Hence, the motivation of this research is to utilize softwarized-control advantages of SDN for enabling route validation in IXP, trying getting more security approaches in BGP route advertisement. To mitigate route exchange vulnerabilities in BGP with SDN method, this paper proposes an alternative SDX development that aims to establish a flexible validation scenario with RPKI validation. In design, the validating application built in the SDN controller is capable of investigating routing information and controlling hybrid SDN environment to manage non-SDN BGP neighbors to get trusted routes and drop suspicious ones. By integrating ROV with RPKI, SDX is able to provide security protection that against deliberate or accidental routing events to its members. This implementation aims to reduce the time of stopping poisoned router spreading unauthorized routes and avoiding it hijacking the traffic from legitimate routers. It is expected to enhance the security of peering procedure in route exchange. The paper organization is as follows. Section 2 reviews the background information and related works of BGP hijacking. Section 3 explains how security breaches happened in BGP routing and what the strategy could be to mitigate such attack. Section 4 presents the proposed idea and its corresponding development, SD-BROV, an SDX-based BGP Route Origin Validation mechanism. It also shows the evaluation result of SD-BROV, which is experimented in an overlay network testbed based on Research and Education Networks (RENs). Finally, the conclusions are given in Section 5.
## 3 Methodology
### Prediction Model  
#### 3.2.1 Preprocessing: Missing Data Elimination  
Because motion sensors generate a large amount of data, it is vital to store them on servers. Some missing values occurred due to data storage and retrieval, resulting in such spaces being filled up with hashes or question marks. There will be a decrease in categorization accuracy due to the employment of these symbols. To avoid a situation such as this, we combed through all of the data looking for similar symbols (see Figure 1).  
When the error is discovered, the line with the missing value (represented by symbols) is erased from the file. Because of the massive amount of data and because of the lines representing Cartesian coordinates that are formed every minute, there is no need to be concerned about losing out on any information. As a consequence, deleting a single line will result in no information loss.  
The detection of falls should always serve as the basis for motion classification. This endeavor is driven by the rising need for accurate fall prevention technology, which is especially crucial considering the limitations of the solutions that are now available. It is necessary to improve the quality of the classifier to make fall detection more reliable. Simply following the instructions listed here will take you to your destination.  
To commence, the data are transformed, labeled, and concatenated to fit the format required by the model and to convert to coded form and analysis. Ultimately, all data are divided into test data and data trains (see Table 1).  
Figure 1: Missing values elimination program.
## 4 Results
### Model Performance and Evaluation
#### 4.3.2 Feature Importance Based on SHAP Values  
In Figure 7, we present the influential variables for predicting bridge deck maintenance across various wearing surface types. In the following subsection, we offer a detailed analysis of our results with respect to all wearing surfaces. Additionally, we delve into the interaction among the most influential factors as identified by LightGBM (Light Boosting) using partial dependency plots (PDP). Our discussion further extends the insights from PDP results to understand how variable interactions affect the decision boundaries of the LightGBM model. Furthermore, we validate the LightGBM model's PDP results through data exploration and a visual examination of variable distributions in relation to the target variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1 Score** & **AUC** & **Kappa** \\ \hline \multicolumn{6}{c}{Bituminous} \\ \hline LR & 0.647 & 0.172 & 0.256 & 0.565 & 0.089 \\ DR & 0.741 & 0.227 & 0.312 & 0.179 & 0.179 \\ XGB & 0.811 & 0.375 & 0.461 & 0.745 & 0.326 \\ LB & 0.750 & 0.684 & 0.456 & 0.811 & 0.445 \\ \hline \multicolumn{6}{c}{Low Slump Concrete} \\ \hline LR & 0.743 & 0.666 & 0.603 & 0.779 & 0.435 \\ DT & 0.820 & 0.727 & 0.774 & 0.821 & 0.626 \\ XGB & 0.884 & 0.851 & 0.821 & 0.862 & 0.747 \\ LB & 0.871 & 0.851 & 0.821 & 0.840 & 0.721 \\ \hline \multicolumn{6}{c}{Other} \\ \hline LR & 0.718 & 0.437 & 0.341 & 0.581 & 0.136 \\ DT & 0.700 & 0.357 & 0.594 & 0.180 & 0.180 \\ XGB & 0.772 & 0.500 & 0.390 & 0.831 & 0.258 \\ LB & 0.772 & 0.571 & 0.410 & 0.776 & 0.207 \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._  
Figure 7: Influential variables in predicting bridge deck maintenance with various wearing surface types: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Slump Concrete, and Others. Influential sorted by the sum of SHAP feature importance value.
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## 2 General Description  
### Project Overview  
Although the space-geodetic sensors are located at the same station, they are physically different from one another. Therefore, the connection between two different sensors can only be accomplished by ground surveying, such as slant distance and horizontal and/or vertical angles, using total stations and/or theodolites. For IERS to accurately combine solutions obtained from both GNSS and VLBI techniques, the coordinate vector between their respective antennas (called the local tie vector) must be precisely determined. As the VLBI antenna rotates in any direction, its point of rotation remains fixed in location, which is the antenna's IVP. Thus, we must determine accurate coordinates of the VLBI IVP.  
The IVP location (coordinates) with respect to the continuously operating reference station (CORS) antenna was determined from measurements made during local ground surveying (see Figure 1). However, because the IVP is physically located inside the antenna structure, its coordinates cannot be directly estimated from ground surveys. Thus, a mathematical model should be developed to connect the IVP and the targets attached outside the antenna.  
The main task of the project is to connect the GNSS and VLBI antennas by measuring and adjusting the ground survey data. In the following, we often refer to these two antennas, or their locations, as \"GNSS antenna\" (or just GNSS) and \"VLBI antenna\" (or just VLBI), respectively. We developed a new model that relates all target coordinates to a single IVP; thus, every single point in the 3D space is connected to the IVP. In addition, the set of coordinates for each target is obtained from the ground survey; thus, each component of the coordinate set has an intrinsic error that should be considered in the adjustment process. Therefore, we applied the TLS approach based on the Gauss-Helmert model (GHM) to account for all error components in the target coordinates [16; 17; 18].
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction
#### 2.3.5 Ensemble Models  
To further enhance model performance, this study employed various ensemble methods, including the gradient boosting regressor, AdaBoost regressor, and voting regressor. The gradient boosting regressor improves predictive accuracy by incrementally fitting new models [37]. The learning rate was set to 0.2 (learning_rate = 0.2) and 200 iterations were used (n_estimators = 200). The AdaBoost regressor increases the model's accuracy by adjusting sample weights to handle difficult-to-predict samples more effectively [38], with 50 weak learners (n_estimators = 50) and a default learning rate (learning_rate = 1.0). The voting regressor enhances overall model performance by combining the predictions of three base learners: ridge, random forest, and gradient boosting. Each base learner was individually tuned, with ridge set to \(\alpha\) = 0.01, random forest using a random state, and gradient boosting with a learning rate of 0.2 and 200 iterations. This approach leverages the strengths of multiple models, making it suitable for complex regression problems that require a balance between bias and variance [48]. Figure 4 provides a visual representation of the ensemble methods used in this study.  
Figure 4: Overall architecture diagram of multi-model ensemble methods.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 4 Case Study  
The experiment selects Reduced Vertical Separation Minimum (RVSM) airspace in Guangzhou during the period from 1 September 2020 to 7 September 2020, which belongs to CAAC Central and Southern Regional Administration. This RVSM airspace ranges vertically from 29,000 feet to 41,000 feet, which is preferred by airlines for its fuel-efficient feature.  
The research data include sector data and Automatic Dependent Surveillance-Broadcast (ADS-B) data. The sector data includes geographical boundaries, flight level, routes information, and separation rules. ADS-B data include flight callsign, operating altitude, latitude, longitude, speed, et al. The specific format of ADS-B data is shown in Table 1.  
ADS-B data are filtered by geographical boundaries information and flight level information firstly, and the airspace structure and trajectory information are shown in Figure 6. Then, we fill in missing records by averaging ones in the data cleaning process, and we take 1 min as the unit time. Finally, 261,231 pieces of ADS-B data are processed in this paper, which are in 10,080 time slices.  
Taking the traffic parameters on 1 September as an example, the statistical distribution of traffic parameters is shown in Figure 7.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**CallSign** & **Longitude** & **Latitude** & **Height** & **Speed** & \(\ldots\) & **Time** \\ \hline SKW4310 & \(-\)104.685 & 38.826 & 2324.10 & 333.35 & \(\ldots\) & 22:25:09 \\ SKW4310 & \(-\)104.516 & 38.862 & 3581.39 & 629.67 & \(\ldots\) & 22:27:39 \\ SKW4310 & \(-\)104.474 & 38.592 & 5577.83 & 685.23 & \(\ldots\) & 22:30:21 \\ SKW4310 & \(-\)104.500 & 38.323 & 7132.31 & 740.79 & \(\ldots\) & 22:32:57 \\ \hline \end{tabular}
\end{table}
Table 1: The format of ADS-B data.  
Figure 6: Area of Guangzhou AR01.
# Ising-Based Kernel Clustering +
###### Abstract  
Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data. To overcome this problem, this paper proposes an Ising-based kernel clustering method. The kernel clustering method is designed based on two critical ideas. One is to perform clustering of irregular data by mapping the data onto a high-dimensional feature space by using a kernel trick. The other is the utilization of matrix-matrix calculations in the numerical libraries to accelerate preprocess for annealing. While the conventional Ising-based clustering is not designed to accept the transformed data by the kernel trick, this paper extends the availability of Ising-based clustering to process a distance matrix defined in high-dimensional data space. The proposed method can handle the Gram matrix determined by the kernel method as a high-dimensional distance matrix to handle irregular data. By comparing the proposed Ising-based kernel clustering method with the conventional Euclidean distance-based combinatorial clustering, it is clarified that the quality of the clustering results of the proposed method for irregular data is significantly better than that of the conventional method. Furthermore, the preprocess for annealing by the proposed method using numerical libraries is by a factor of up to 12.4 million \(\times\) from the conventional naive python's implementation. Comparisons between Ising-based kernel clustering and kernel K-means reveal that the proposed method has the potential to obtain higher-quality clustering results than the kernel K-means as a representative of the state-of-the-art kernel clustering methods.  
kernel clustering; simulated annealing; Ising model; constraint function Article
###### Abstract  
Dwell time is a critical factor in constructing and adjusting railway timetables for efficient and accurate operation of railways. This paper develops dwell time estimation models for a Shinbundang line (S line) in Seoul, South Korea using support vector regression (SVR), multiple linear regression (MLR), and random forest (RF) techniques utilizing archived real-time metro operation data along with smart card-based passenger information. In the first phase of this research, the collected data are processed to extract boarding and alighting passenger counts and observed dwell times of each train at all stations of the S line under the current operational environment. In the second phase, we develop SVR, MLR, and RF-based dwell time estimation models. It is found that the SVR-based model successfully estimates the dwell times within 10 s of differences for 84.4% of observed data. The results of this paper are especially beneficial for autonomous railway operations that need constructing and maintaining dynamic railway timetables that require reliable dwell time predictions in real-time.  
s 2020 10 476 10.3390/app10020476