esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Research  
### Generative Adversarial Networks (GANs)  
To counter machine learning-based mouse data attacks, this study proposes a mouse data protection technique utilizing Generative Adversarial Networks (GANs). GANs, first introduced in 2014, comprise two neural networks--a generator and a discriminator--that engage in a competitive learning process. The generator creates synthetic data that mimic real data, while the discriminator's task is to distinguish between the real and generated data. This iterative competition improves the performance of both networks, with the goal of generating highly realistic fake data. GANs are commonly employed in applications such as image generation and data augmentation, particularly when there is a lack of sufficient training data for machine learning models [12; 13].  
Generative Adversarial Networks (GANs) are increasingly being applied in the field of information security, particularly in generating synthetic attack data and enhancing security systems. One such study investigates how GANs can be utilized in cybersecurity to create synthetic attack data, and this is crucial for training intrusion detection systems. Since acquiring realistic cyberattack data can be challenging due to privacy concerns, GANs provide a solution by generating diverse and realistic attack data that simulate a wide range of cyber threats. This synthetic data can be employed to improve the training of security models without compromising the privacy of real-world data [14; 15; 16]. Another application of GANs in information security is enhancing encryption methods for digital image protection. By using GANs to generate encrypted versions of images, researchers aim to make it more difficult for unauthorized users to access sensitive information. This approach also incorporates a customized super-resolution network for reconstructing these images, ensuring secure data handling even when faced with potential adversarial attacks [17; 18]. These studies underscore the growing interest in leveraging GANs to improve data security, particularly in areas like attack simulation and encryption, where the generation of synthetic data plays a critical role in advancing cybersecurity defense mechanisms.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Time Synchronization Requirements
#### 2.4.2 Scalability  
Deployment of a large number of sensor nodes is required by many sensor network applications. Synchronization protocols should be designed to work well with the scale of the network size and/or density [35]. For example, the TPSN requires a topology hierarchy and does not adopt with dynamic changes in the network structure [31]. However, the FTSP is the best scalable network protocol, as it selects its root node dynamically, which provides high precision, but it increases energy consumption [30]. It is necessary to design a protocol with scalability because different numbers of sensor nodes are required for different tasks or different applications.
## 4 Discussion of Simulation Results
### Self-Propulsion Simulation
#### 4.1.4 Distribution of Self-Propulsion Point Flow Field  
Through simulation analysis, it is found that the velocity distribution of the flow field at each self-propulsion point is roughly the same under the four operating conditions of speed 6 kn, 5 kn, 4 kn, and 3 kn. Therefore, taking the operating condition of speed 6 kn as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Propeller Paddle Type** & **Kap00** & **Kap01** & **Kap02** & **Kap03** & **Kap04** & **Kap05** \\ \hline
6 kn speed & 1.1431 & 1.1416 & 1.1444 & 1.1437 & 1.1390 & 1.1341 \\
5 kn speed & 1.1415 & 1.1417 & 1.1459 & 1.1458 & 1.1436 & 1.1345 \\
4 kn speed & 1.1474 & 1.1469 & 1.1491 & 1.1482 & 1.1441 & 1.1384 \\
3 kn speed & 1.1496 & 1.1600 & 1.1629 & 1.1548 & 1.1467 & 1.1501 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hull efficiency \(\eta_{H}\) of self-propulsion point of hybrid-driven underwater glider.  
Figure 23: Wake fraction \(\omega\) in self-propulsion points.  
an example, the velocity distribution cloud diagram of the flow field at each self-propulsion point is shown in Figure 24. In the state of self-propulsion point, the flow field velocity distribution of each hybrid-driven underwater glider is basically the same. There is a high-speed zone on both sides of the bow turning point, both sides of the stern turning point, and the large radial position of the propeller wake area of the underwater glider, as well as a low-speed zone on the front side of the bow, near the stern, and near the center axial part of the propeller wake area.
## 3 Methods and Data Sources
### Spatial Correlation Analysis  
Global Moran's Index is one of the commonly used global indexes to measure spatial correlations [37; 38; 39]. The formula is as follows: The value range is (\(-\)1, 1), less than 0 means negative correlation, equal to 0 means irrelevant, and greater than 0 means positive correlation.  
\[I=\frac{n\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}wij(xi-\overline{x})(xj- \overline{x})}{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}wij(xi-\overline{x})^ {2}}=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j\
eq i}^{n}wij(xi-\overline{x})( xj-\overline{x})}{S^{2}\sum\limits_{i=1}^{n}\sum\limits_{j\
eq i}^{n}wij} \tag{4}\]  
In the above formula, \(I\) is the Global Moran's \(I\), \(x_{i}\) is the observation value of \(i\), \(w_{ij}\) is the spatial weight matrix. When Global Moran's \(I\) is significantly positive, it indicates that the research index has a positive spatial correlation, and the research unit with high or low value of the index shows a spatial agglomeration trend; conversely, it shows a spatial dispersion trend; when Global Moran's \(I\) is zero, the research unit shows space random distribution trend. Another commonly used index is the Geary's C. Unlike Global Moran's \(I\), the core component is \((x_{i}-x_{j})^{2}\). The value of Geary's C is generally between 0-2, greater than 1 means negative correlation, equal to 1 means no correlation, and less than 1 means positive correlation. The formula is as follows:  
\[C=\frac{(n-1)\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}\big{(}x_{i}-x_ {j}\big{)}^{2}}{2(\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij})[\sum \limits_{i=1}^{n}\big{(}x_{i}-\overline{x}\big{)}^{2}]} \tag{5}\]
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 5 Network Architectures and Transmission Technologies
### Wireless Networks
#### 5.2.3 Mesh Networks
Since luminaires are usually deployed along streets, the resulting topology is composed by linear segments and some branches, which eventually form a number of trees, rooted at the corresponding gateways. In actual deployments, IEEE802.15.4 transceivers have a transmission range that covers not only the adjacent light-poles but also a number of nearby luminaries. This makes the system intrinsically robust to possible failures, as a given streetlight is usually reachable by more than one neighbor. **Bluetooth mesh networking**: Bluetooth is one of the best known and most ubiquitous wireless communications technologies on the planet. It has been in existence since 2000 and is currently integrated into billions of devices. It operates in the 2.4 GHz ISM band and uses frequencies between 2402 and 2480 MHz. Initially, it was simply intended as a cable replacement technology, becoming soon the dominant solution for the connection of audio devices as well as of computer peripherals. However, it has been systematically improved with the addition of new functionalities, enabling it to keep pace with market requirements. Bluetooth mesh networking (BMN), conceived in 2015 and adopted on July 2017 [96; 97], is the latest chapter in this evolution path. It was designed to allow many-to-many communications by meshing together hundreds (or even thousands) of devices capable of handing off messages to each other, thus is gaining momentum for smart lighting applications. Similarly to the previously described IEEE802.15.4 technology, BMN establish a communication infrastructure where light fixtures can communicate with one another to relay commands, diagnostic information and possible data transmitted by nearby smart devices. The resulting mesh network architecture is the same depicted in Figure 4.
## 5 Conclusions  
Results of the present study showed that the aroma of raw, unprocessed lamb meat is the outcome of the combination of specific volatile compounds. Storage time proved to be a parameter that significantly affected the evolution of hexanal, heptanal, and nonanal (\(p<0.05\)). Furthermore,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aldehyde Ratio-MDA** & **Day 1** & **Day 5** & **Day 9** & **Pearson’s (r)** \\ \hline Hexanal–Nonanal & 2.02 & 2.44 & 3.49 & 1.00 \\ Heptanal–Nonanal & 0.45 & 0.31 & 0.62 & 0.87 \\ Octanal–Nonanal & 0.26 & 0.27 & 0.58 & 0.83 \\ Sum of (Hexanal, Heptanal, Octanal)–Nonanal & 2.73 & 3.02 & 4.70 & 0.89 \\ MDA (mg/kg) & 1.4 & 2.8 & 3.8 & \\ \hline \hline \end{tabular}  
Aldehyde ratios were obtained by dividing average values of each aldehyde presented in Table 1. MDA: malonic dialdehyde. [PERSON]’s correlation coefficient \(r\) was considered at the confidence level \(p<0.05\).  
\end{table}
Table 2: Aldehyde ratios and malonic dialdehyde development with respect to storage time (days).  
the evolution of hexanal, heptanal, octanal, and nonanal during storage under refrigeration, along with the Hex/Non, Oct/Non, and Hex + Hept + Oct/Non ratios could be proposed as overall indicators of lamb meat freshness and degree of oxidation, since they were strongly correlated with shelf life and TBA test data.  
Special attention should be given to hexanal to nonanal ratio, which could serve as a unique marker of freshness and overall quality, since was perfectly correlated with the results of degree of oxidation (TBA test). Therefore, this ratio value--equal or lower than 2.45--is proposed as an indicator of lamb meat freshness and overall quality.  
As an executive summary, the present study, unique in the literature, aids the fast quality control of raw lamb meat that enters the market by proposing specific aldehydes or aldehyde ratios as markers of raw lamb meat quality, in support of some previous studies on other food products [17; 48; 49].  
The author would like to thank [PERSON] who provided access to the Laboratory of Food Chemistry, Department of Chemistry, University of Ioannina, and [PERSON] for her technical assistance during preparation of samples for the GC/MS analysis. SVEKI SA is also acknowledged for the donation of lamb meat.  
The author declares no conflict of interest.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
## 1 Introduction  
Tibet, located at the top of the world, has a unique plateau climate. The territory comprises a total area of 1.23 million km\({}^{2}\). Nevertheless, the region is extremely mountainous, and only a small portion supports human settlement and farming [1]. The area used for agricultural purposes in Tibet amounts to 0.45 km\({}^{2}\), corresponding to 0.42% of the total area. Income in the agricultural sector is limited due to low land utilization and extreme climate. Improving the use of its limited arable land is critical for promoting sustainable development in the highlands and mountainous areas of Tibet.  
Tea made from the tender leaves of _Camellia sinensis_ is among the world's three major beverages. Tea's high polyphenol content, caffeine, theanine, and terpenoids collectively contribute to the charming flavors of tea and to its health benefits [2, 3]. Tea is categorized into three major types, green, black, and oolong, which are determined by the employed processing technologies [4]. Oolong tea has distinctive features; its sweet, floral and fruity aroma sets it apart from green and black teas [5, 6]. Traditional oolong teas originate from Fujian, Guangdong, and Taiwan. To meet the amplified demand of oolong tea, more and more varieties suitable for the manufacture of oolong tea have been introduced to other regions for China. 'Wuyedancong', originating from Chaozhou in Guangdong province, is an elite variety suitable for oolong tea that has been cultivated in Medog, Tibet since 2019 [7].  
Cultivation of crops on plateaus, which have dramatic changes in climate, modifies flavors profiles [8; 9]. For example, coffee and grapes cultivated at high elevation affected both the abundance of volatile organic compounds (VOCs) and the aroma profile of the processed products [9; 10]. Generally, tea grown at high elevation fetches a higher price than that grown in lowland regions, but few scientific reports have documented these effects. In particular, how the plateau climate affects the quality of tea planted on the highest elevation region on Earth, Tibet, is still unknown.  
In this study, tea cultivar 'Wuyedancong,' planted in its origin Chaozhou, Guangdong and in Medog, Tibet, were processed into oolong tea using the same technology. The aromatic components and taste substances of origin oolong tea (OOT) and Tibet oolong tea (TOT) were systematically analyzed and compared. The results will provide a reference for monitoring the introduction and cultivation of the crop into plateau areas, in an effort to maintain efficient and sustainable development in Tibet.
###### Abstract  
This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. The general aim is to highlight the existing literature on the subject, while the specific goal of the research is to attempt to answer research questions that emerge after the review and classification of the literature, which are aspects that have not previously been addressed. The methodology for retrieving relevant articles involved a keyword search in the Scopus database. The results from the search were filtered based on the selected criteria. The research spans a 40-year period, from 1984 to 2024. After filtering, 296 articles relevant to the topic were identified. Statistical analysis highlights fuzzy systems as the technique with the highest representation (163 articles), followed by neural networks (81 articles), with machine learning and genetic algorithms ranking next (25 and 20 articles, respectively). The main conclusions indicate that the primary sectors utilizing these techniques are industry, transportation, construction, and cross-sectoral models and techniques that are applicable to multiple occupational fields. An additional finding is the reasoning behind researchers' preference for fuzzy systems over neural networks, primarily due to the availability or lack of accident databases. The review also highlighted gaps in the literature requiring further research. The assessment of occupational risk continues to present numerous challenges, and the future trend suggests that fuzzy systems and machine learning may be prominent.  
risk assessment techniques; human factor analysis; safety management systems; machine learning; computational intelligence in safety; occupational hazard evaluation; risk analysis +
Footnote †: journal: Applied sciences  
1
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 2 Background: Non-Linear Control of DC Microgrid
### Control of \(V_{dc}\)  
The error between \(V_{DC}\) and \(V_{DC}^{*}\) is calculated following the equation:  
\[e_{V_{DC}}=(V_{DC}-V_{DC}^{*}) \tag{9}\]  
This is a reference value, which allows for controlling the voltage \(V_{DC}\).  
The integrator of the error is calculated, as follows:  
\[\dot{\alpha}_{7}=K_{7}^{\alpha}e_{V_{DC}} \tag{10}\]  
\[\dot{e}_{V_{DC}}=K_{7}e_{V_{DC}}-\overline{K}_{7}\alpha_{7} \tag{11}\]  
From (7) and (11), it can be written:  
\[(I_{L_{2}}+I_{L_{3}})^{*}=\frac{V_{DC}}{R_{LOAD}}-(1-u_{1})I_{L_{1}}- \overline{K}_{7}C_{\text{C}}\alpha_{7}-K_{7}C_{DC}(V_{DC}-V_{DC}^{*}) \tag{12}\]  
with \(K_{7}\), \(\overline{K_{7}}\), and \(K_{7}^{\alpha}\), positive gains, which are handled by imposing desired dynamics to the closed loop system. Then:  
\[\begin{bmatrix}\dot{\alpha}_{7}\\ e_{V_{DC}}\end{bmatrix}=\begin{bmatrix}0&K_{7}^{\alpha}\\ -\overline{K_{7}}&K_{7}^{\alpha}\end{bmatrix}\begin{bmatrix}\alpha_{7}\\ e_{V_{DC}}\end{bmatrix} \tag{13}\]  
Thus, the eigenvalues are:  
\[\lambda_{7.1}=-0.5(K_{7}+\sqrt{K_{7}^{2}-4\overline{K_{7}}K_{7}^{\alpha}}) \tag{14}\]  
\[\lambda_{7.2}=-0.5(K_{7}-\sqrt{K_{7}^{2}-4\overline{K_{7}}K_{7}^{\alpha}}) \tag{15}\]  
The transfer function \(H_{7}\) can be expressed, as follows, based on these eigenvalues:  
\[\dot{e}_{V_{C_{1}}} =-K_{1}e_{V_{C_{1}}}-\overline{K}_{1}\alpha_{1} \tag{27}\] \[\dot{e}_{I_{L_{1}}} =-K_{2}e_{I_{L_{1}}}-\overline{K}_{2}\alpha_{2} \tag{28}\]  
with \(K_{1}\), \(\overline{K_{1}}\), and \(K_{1}^{a}\) constants that allow changing the dynamic of the PV panel control. Based on Equations (1) and (22):  
\[I_{L_{1}}^{*}=\frac{V_{PV}-V_{C_{1}}}{R_{1}}+C_{1}K_{1}(V_{C_{1}}-V_{C_{1}}^{*} )+C_{1}\overline{K}_{1}\alpha_{1} \tag{29}\]  
Additionally, with (4) and (23), it obtains:  
\[u_{1}=\frac{1}{V_{DC}+(R_{02}-R_{01})I_{L_{1}}}[V_{DC}-V_{C_{1}} +R_{02}I_{L_{1}}\\ -L_{1}(K_{2}(I_{L_{1}}-I_{L_{1}}^{*})+\overline{K}_{2}\alpha_{2}- C_{1}\overline{K}_{1}K_{1}^{*}e_{V_{C_{1}}}+\\ (C_{1}K_{1}-\frac{1}{R_{1}})(K_{1}e_{V_{C_{1}}}+\overline{K}_{1} \alpha_{1}))] \tag{30}\]
## 5 Deployment Challenges
### Performance of the Surrogate  
The surrogate is the focal point of computing and processing in our solution. It will have to interwork with virtual clients and the underlying routers and switches. Therefore, the performance and the interworking capability of the surrogate will have the highest impact on the solution. The surrogate will have to maintain hundreds of sessions, connections and state information from different users. In grid computing, load balancing is a common technique to improve performance of remote servers and such infrastructure would lend itself to surrogate support. Moreover, distributed implementation of surrogates, wherever possible, should be studied. These are actually typical, well-studied scalability issues.
## 2 Data Collection and Preprocessing  
Two distinct types of data were employed to study the glacier recession by quantifying changes in the terminal point and area of Gorner and Franz Josef glaciers (Figure 3). These are two similar but spatially distant mountain glaciers. One set of data consists of satellite imagery, multiple images of spatial locations taken over time. The other data type is a univariate time series of environmental sensor recordings of multiple climate factors collected by various international agencies. Each of the data sets presents its own challenges from a data wrangling perspective given the diversity of values, measurement scales, and potential usefulness of the attributes unique to each data type.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods  
### Wavelet Soft Thresholding Denoising  
Wavelet transform can process the signal in a more subtle way, and can also better express some characteristics of the signal. There are also many denoising methods in wavelet analysis, and these different denoising methods will also have a great impact on the denoising effect of each signal. The Wavelet threshold denoising method is the most widely used method in engineering. The basic idea is as follows: a noise-containing one-dimensional signal model can be expressed as \(f(t)=s(t)+n(t)\), where \(s(t)\) is the original signal, and \(n(t)\) is Gaussian white noise with variance \(\sigma^{2}\), obeying \(N\big{(}0,\sigma^{2}\big{)}\). Wavelet transform belongs to linear transformation, and the wavelet coefficient obtained after discrete wavelet transform of \(f(t)\) is divided into two parts: signal and noise. The wavelet coefficients with larger amplitudes are useful signals. The wavelet coefficient with smaller amplitude is the energy of the noise signal. Therefore, an appropriate threshold \(\lambda\) can be found for distinguishing noise and signal, and the useful signal is retained with reference to this threshold. For this problem, we use the soft threshold method [26] for denoising, first transforming the actual signal into the wavelet domain using wavelet transform. Then the wavelet coefficients are processed by the nonlinear shrinkage rule; finally, the thresholded wavelet coefficients are subjected to inverse wavelet transform to obtain the denoising signal. The soft threshold function is defined as follows:  
\[\bar{w}_{i,j}=\left\{\begin{array}{cc}\text{sign}\Big{(}w_{j,k}\Big{)}\Big{(} \Big{|}w_{j,k}\Big{|}-\lambda\Big{)}&\Big{|}w_{j,k}\Big{|}\geq\lambda\\ 0&\Big{|}w_{j,k}\Big{|}<\lambda\end{array}\right. \tag{1}\]
# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 5 Conclusions  
The purpose of the presented study was to provide a comparative assessment of earth slope stability using benchmark learning classifiers. The objective of the study was to provide a more reliable and accurate procedure that can be used to estimate F.S for earth slope instability assessments. The MLP, SVM, DT, and RF algorithms were used to predict the F.S value as the primary variable or output of the models for the earth slopes. The primary database was prepared based on 100 records of earth slopes in Iran's Fars, Isfahan, and Tehran provinces. The dataset contains records and geotechnical investigations of the slopes that were used in stability analysis. The main dataset was randomly divided into training and testing sets, which included 70% and 30% of the primary dataset. The classifiers were learned with a training set and validated with a testing set. Models were controlled by LEM-based stability analysis and measured F.S with GeoStudio software. For performance and capability analysis of the machine learning algorithms, confusion matrix, loss function, and ROC curves were utilized for all models and implemented in the Python programming language. As modeling results showed, MLP reached the highest accuracy (0.901) and precision (0.900). SVM and RF with accuracy = 0.873/precision = 0.850; and accuracy = 0.835/precision = 0.850 are ranked as the second and third algorithms that are properly used in F.S prediction. DT obtains the lowest accuracy in the confusion matrix, with accuracy = 0.812/precision = 0.810. The results were approved based on overall accuracy with ROC. The estimated losses for the models revealed that MLP is associated with the lowest loss rate (0.298921) and DT is associated with the highest loss  
Figure 5: ROC curve analysis for different machine learning approaches.  
rate (0.622065). This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.  
[PERSON], Z.L. and J.C. ([PERSON]): writing--original draft preparation, methodology, investigation, resources; J.C. ([PERSON]) and S.A.: conceptualization, visualization, methodology; M.A. and R.D.: formal analysis, supervision, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank the anonymous reviewers for providing invaluable review comments and recommendations for improving the scientific level of the article.  
The authors declare no conflict of interest.