esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Machine Performance Comparison  
The structures of four machines are optimized under two different winding configurations, and the optimal size parameters are obtained. This section compares eight cases of both FP and SP winding connections in terms of magnetic field distribution, back electromotive force (EMF), average torque, torque ripple, loss, and efficiency.  
Figure 8: Optimization results of the last generation (Pareto front) with SP winding configurations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Item** & **Notation** & **Unit** & **Structure I** & **Structure II** & **Structure III** & **Structure IV** & **Vibration** \\ \hline \multirow{2}{*}{Stator tooth width} & \multirow{2}{*}{\(\mathrm{W_{st}}\)} & \multirow{2}{*}{(mm)} & 7.74 & 4.37 & 3.89 & 5.05 & 3.5\(-\)7.5 \\  & & & 8.1 & 9.9 & 10.9 & 10.4 & 7.5\(-\)12.5 \\ \hline \multirow{3}{*}{Slot open size} & \multirow{2}{*}{\(\mathrm{R_{diff}}\)} & \multirow{2}{*}{(mm)} & 1.44 & 0.83 & 0.72 & 1.12 & 0.4\(-\)1.6 \\  & & & 14.4 & 10.2 & 6.73 & 8.11 & 5-20 \\  & \(\mathrm{H_{ss}}\) & (mm) & 1.6 & 0.97 & 0.21 & 0.28 & 0.1\(-\)1.8 \\ \hline \multirow{2}{*}{PMs size} & \multirow{2}{*}{\(\mathrm{t_{r}}\)} & \multirow{2}{*}{(mm)} & 2.98 & 3.56 & 3.14 & 3.69 & 2.5\(-\)5 \\  & & & 8.86 & 8.8 & 9.74 & 9.96 & 4-11 \\ \hline \multirow{2}{*}{Rotor inner radius} & \multirow{2}{*}{\(\mathrm{R_{r1}}\)} & \multirow{2}{*}{(mm)} & 16.08 & 7.5 & 9.4 & 12.32 & 5-20 \\ \cline{1-1}  & & & 29.5 & 32.6 & 33 & 36.3 & 25–35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal dimension parameter with SP wingding configurations.  
Figure 7: Optimization results of the last generation (Pareto front) with FP wingding configurations.
# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 4 Routing Strategy
For the node \(i\) between charging path \(r_{v}\) and charging path \(r_{v+1}\), \(\alpha_{i}\) is:  
\[\alpha_{i}=min[d_{is}-vd,\ (v+1)d-d_{is}]/0.5d \tag{10}\]  
among them, \(vd\leq d_{is}\leq(v+1)d\), \(d_{is}\) is the distance from node \(i\) to sink node. Equation (10) shows that the closer node \(i\) is to the charging path, the smaller \(\alpha_{i}\) is, then the more energy can be obtained by node \(i\) in the same charging time. For node \(i\) with a small value \(\alpha_{i}\), high energy consumption can get node \(i\) more energy in the charging cycle to slow down its energy consumption speed; thus, increasing \(C_{ij}\) properly will not have a great impact on the lifetime of node \(i\). On the contrary, for node \(i\) with a large value \(\alpha_{i}\), the energy obtained through charging stage may not be able to make up for high consumption, which may lead to node's death, thus energy consumption should be reduced as far as possible. Therefore, adjusting the next hop selection through \(\alpha_{i}\) can effectively improve the overall performance of the network and prolong the network lifetime. When \(0\leq\alpha_{i}<0.5\), the energy consumption of node \(i\) in the routing phase can be increased appropriately, i.e., the \(C_{ij}/\hat{E}_{i}\) increases. In this case, when node \(i\) chooses the node \(j\) closer to sink node, the overall energy consumption and the number of network hops can be reduced. When \(0.5\leq\alpha_{i}<1\), choosing a closer node \(j\) can extend the network lifetime effectively. Equation (9) shows that when \(\alpha_{i}\) is small, \(\alpha_{i}C_{ij}/\hat{E}_{i}\) can be adjusted to be small, and the part that affects the \(U(i,j)\) value is mainly \((1-\alpha_{i})C_{js}/\hat{E}_{j}\). The node \(i\) selecting node \(j\) closer to sink node is advantageous for lowering energy consumption of the node \(j\) and vice versa. In summary, node \(i\) will preferably select the node with smaller \(U(i,j)\) as the next hop in \(NF(i)\):  
\[j=\arg[min[U(i,k)]],k\in NF(i) \tag{11}\]  
In routing, node \(i\) selects node whose \(C_{ij}\) and \(C_{js}\) are small to be the next hop, ensuring that the total distance of the route approaches the linear distance from node \(i\) to sink node, thereby reducing the route consumption. In addition, the algorithm can adaptively select the next hop node according to the characteristics of different nodes. On the one hand, selecting the node \(j\) closer to the sink node for the node \(i\) with higher residual energy and better charging character in the charging phase can reduce the overall network average hop count and enhance energy utilization. For nodes with low energy, reducing their transmission distance helps to extend network lifetime. On the other hand, appropriately increasing the energy consumption speed of nodes with better charging character is beneficial to guiding the charging phase to allocate more charging time, thereby improving the replenishing effect of the charging phase.
# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 3 Model Performance Verification
### Performance Evaluation of the ISSA-ELM Prediction Model  
According to the evaluation criteria for the prediction models that we calculated, Table 6 displays the average prediction outcomes for the ISSA-ELM and SSA-ELM. The ISSA-ELM model achieves superior prediction accuracy, as evidenced by its reduced RMSE, MAPE, and MAE compared to the SSA-ELM model. In this case, it shows that the ISSA-ELM model can reliably and accurately predict the operational safety status of the terminal area control system.  
In predicting the probability of occurrence of an unsafe event in the operation of the terminal area control system, the RMSE is reduced from 3.1499 to 2.6008 for the model optimized by the ISSA compared to the traditional model without optimization, implying that the predicted probability of occurrence of an unsafe event is more connected to the probability of occurrence of the actual unsafe time. Similarly, the optimized MAPE value is lower than the traditional model, which indicates that the relative error between the predicted probability of occurrence of unsafe events and the actual probability of occurrence of unsafe events is smaller in the optimized model, and the accuracy and reliability of the prediction is higher. The smaller MAE means that the average deviation of the predicted takeoff time from the actual takeoff time is smaller, which proves that the model optimized by the ISSA is more accurate. The increase in R2 then proves that the ISSA-ELM is more capable of interpreting the data.
## 2 Materials and Methods
### Information Technologies Used in the Proposed Model
#### 2.2.3 Fundamentals of the \"Naive Bayes\" Algorithm  
Bayesian networks can be applied to classification problems using Bayesian classifiers. The simplest structure of the Bayesian Network is a classification algorithm called Naive Bayes, also known as the Bayesian classifier with k-dependence and the structure of Simple Bayesian Networks with K-Dependency [47].  
Therefore, Naive Bayes is a simple probabilistic algorithm based on [PERSON] theorem. This algorithm uses training data to form a probabilistic model based on evidence of specific characteristics in an extensive database. The algorithm assumes independence between the features of the model, implying the absence of relationships between a given quality and the other attributes of the database [47].  
A Naive Bayes classifier is structured in the calculation of the posterior probability distribution \(P\left(Y\mid X\right)\), where \(Y=\left(y_{1},y_{2},\ldots,y_{p}\right)\) is the random variable to be classified presenting categories \"k\", and \(X=\left(x_{1},x_{2},\ldots,x_{p}\right)\) is a set of discrete explanatory variables presenting \"\(p\)\". In addition, to calculate the conditional probability \(P\left(Y\mid X\right)\), this method assumes a probabilistic independence between the explanatory variables, which facilitates the computational approach following Equation (3):  
\[P(Y=y_{k}|x_{1},\ldots,x_{p})\propto P(Y=y_{k})\prod_{i=1}^{p}P(x_{i}|Y=y_{k}) \tag{3}\]  
Therefore, a Naive Bayes classifier is based on calculating the probability that a particular observation belongs to a specific category and classifies that observation into the most suitable variety, as shown in Figure 2.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 4 Hikari-2021 Generation Methodology
### Attacker Profile  
The attack traffic is generated synthetically, first by targeting a specific page for user login of the CMSs, and second by scanning their vulnerability. Both of the attacks are delivered via the HTTPS protocol. The attacks are delivered on different days with different scenarios (details in Section 4.5). The types of attacks are as follows:  
1. Brute force attack: this attack is the most famous for cracking passwords. The attacker usually repeatedly tries to gain the target over and over using all possible combinations using a dictionary of possible common passwords [64]. We developed a script that mimics a brute force attack, using a browser to deliver the attack. We intentionally added a user to the three different CMSs with the role as an admin and password, which we took randomly from [64]. The purpose is to make sure that the brute force attack is delivered successfully.
2. Brute force attack with different attack vectors: while the first type of attack uses the browser as the attack vector, the second attack uses a different attack vector, XMLRPC. We developed a script that accesses XMLRPC for gaining valid credential access.
3. Probing: this is also called vulnerability probing. This script scans the web applications, such as Joomla, WordPress, and Drupal to find their vulnerability. The tools for vulnerability scanning are publicly available. For this dataset, the scripts used these probing scripts: droopescan [65] for WordPress and Drupal, and joomscan [66] for Joomla.  
We provide the template script to customize the attack profile so researchers may use it for making custom attacks using different vectors. Distinguishing between an attack profile and benign profile is based on the source IP address, source port, destination IP address, destination port, protocol, and the day both of the profiles being generated. In addition, to determine benign traffic, any destination addresses in the Alexa list are considered benign.
###### Abstract  
In this study, a three-dimensional numerical simulation of a solid oxide fuel cell (SOFC) with dimensions of 6 cm \(\times\) 6 cm on the anode side and 5 cm \(\times\) 5 cm on the cathode side (active area) was conducted to determine the performance characteristics of the cell electrodes. The performance characteristics of each SOFC unit cell were investigated through numerical simulations. 3.5a COMSOL Multiphysics software was used to solve the model. The effects of the operating conditions, fuel concentration, and electrode porosity on the electrochemical performance of the SOFC electrodes were examined. In addition, an experiment was conducted to investigate the operating cell performance at 600, 700, and 750 \({}^{\circ}\)C. The results indicate that a higher electrode porosity can improve fuel mass transfer, resulting in an almost uniform H\({}_{2}\) concentration at a porosity of 0.75 when the model was investigated with electrode porosities of 0.25, 0.375, 0.55, and 0.75. The simulation results also reveal that the performance of the voltage distribution on electrode surfaces is improved when the input operating temperature of the fuel cell is increased at different temperatures (650, 700, and 750 \({}^{\circ}\)C). At the operating temperature of 750 \({}^{\circ}\)C, it can be seen from the experimental results that the highest current and voltage of the cell were 587.4 mA\(\cdot\)cm\({}^{-2}\) and 1.12 V, respectively.  
s pacs: solid oxide fuel cells; hydrogen concentration; electrode porosity; numerical simulation; electrochemical performance
# Logistics Space: A Literature Review from the Sustainability Perspective
## 3 Methods and Results
### Literature Overview
#### 3.4.4 Research Method of Logistics Space  
We have summarized the analysis method of logistics space and divided all methods into management perspective and geography perspective, as shown in Table 5. The geography perspective mainly includes average distance method, centrographic, standard deviation ellipse and kernel density estimation. The average distance can visually show the dispersion of logistics facilities through the change of distance, so it is often used to determine the logistics expansion. Centrographic and standard deviation ellipses are ideally suited to provide resources for their cartographic representations and to visualize the scope and direction of logistics expansion. The combination of the two methods works better. Kernel density estimation is suitable for the visual expression of point distribution patterns. Management perspectives include Gini coefficient, LQ, HCLQ, Moran's I, and LISA. These methods are mainly used to measure industrial spatial concentration and industrial spatial correlation. In addition, location mapping can simply reflect the distribution of logistics space and is often used to be an analysis aid. As a computer tool that can analyze and process spatial information, GIS (Geographic Information System) is used in geography research. [PERSON] used GIS as a platform to establish a decision-making information system for water resources management in the Zhenjiang section of the Yangtze River to analyze changes in water quality and monitor pollution sources [63]. [PERSON] used a combination of geostatistics and GIS to study the spatial distribution of soil organic matter and its influencing factors [64]. Similarly, GIS plays an important role in the study of logistics space.
# A REM Update Methodology Based on Clustering and Random Forest
###### Abstract  
In this paper, we propose a radio environment map (REM) update methodology based on clustering and machine learning for indoor coverage. We use real measurements collected by the TurtleBot3 mobile robot using the received signal strength indicator (RSSI) as a measure of link quality between transmitter and receiver. We propose a practical framework for timely updates to the REM for dynamic wireless communication environments where we need to deal with variations in physical element distributions, environmental factors, movements of people and devices, and so on. In the proposed approach, we first rely on a historical dataset from the area of interest, which is used to determine the number of clusters via the \(K\)-means algorithm. Next, we divide the samples from the historical dataset into clusters, and we train one random forest (RF) model with the corresponding historical data from each cluster. Then, when new data measurements are collected, these new samples are assigned to one cluster for a timely update of the RF model. Simulation results validate the superior performance of the proposed scheme, compared with several well-known ML algorithms and a baseline scheme without clustering.  
radio environment map (REM); random forest (RF); machine learning; clustering +
Footnote †: journal: Applied sciences
## 5 Data Products, Processing and Distribution
### G-LiHT Data Distribution  
NASA's Earth Science Program promotes the full and open sharing of data with all users in accordance with NASA's Data and Information Policy [32], and this includes G-LiHT data products. Every effort is taken by the G-LiHT instrument team to ensure that accurate, well-calibrated data is released in a timely manner, and data is distributed in common, readily usable file formats. ClassifiedLiDAR returns and feature heights are made available in ASPRS LAS file format (American Society for Photogrammetry and Remote Sensing, LASer file format), a non-proprietary, binary file industry standard [22]. This open data file format allows for the raw data and information specific to data collection to be recorded (e.g., scan angle, return number, classification, AGL height). Gridded LiDAR products are made available as GeoTIFF files that conform to established Tagged Image File Format (TIFF) interchange format for georeferenced raster imagery [33]. GeoTIFF files include geographic metadata formally, using compliant TIFF tags and structures. Multiband products from the imaging spectrometer will be distributed in standard Band Interleaved by Line (BIL) format. In addition to the data files, quick look images of key spatial variables (e.g., aircraft trajectory, DTM, CHM, true color image) created in Keyhole Markup Language (KML) [34] for visualization in Google Earth.  
The data archive for G-LiHT products can be accessed through the G-LiHT webpage, [[http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)]([http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)) or by directly connecting to the anonymous ftp site, fusionftp.gsfc.nasa.gov/G-LiHT. The G-LiHT website contains additional instrument specifications, a description of various airborne campaigns, software tools and links to open source data analysis and visualization software. G-LiHT has been used to collect more than 6,500 km\({}^{2}\) of data for NASA-sponsored studies across a broad range of ecoregions in the USA during 2011-2012 (Figure 12, [35, 36, 37]), with plans to collect data in Mexico and interior Alaska in 2013-2014. Our automated data processing and distribution system is designed to give scientists open access to both low- and high-level data products ([[http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)]([http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov))), which will stimulate community development of synergistic data fusion algorithms.  
Figure 12: G-LiHT coverage (show as red lines) as of September 2012 (**a**). Long transects are coincident G-LiHT data along ICESat GLAS LiDAR tracks, enlarged in (**b**), which capture fine scale heterogeneity in large, space-base LiDAR footprints (65 m diameter). Other collections in the USA include Forest Inventory and Analysis (FIA) plots and intensive study sites. The green color in (a) represents percent forest cover [35,36]; dark gray lines indicate country borders, and light gray lines delineate terrestrial ecoregions [37].
# Effects of Random Values for Particle Swarm Optimization Algorithm
## 2 Standard and Modified Particle Swarm Optimization Algorithms
### Modifications for Particle Swarm Optimization Algorithm
#### 2.2.3 Adaptive Inertia Weight Strategies  
The inertia weight is adjusted by using one or more feedback parameters to balance the global and local searching abilities of the PSO algorithm [52; 53; 54; 55]. These feedback parameters include the global best fitness, the local best fitness, the particle rank, and the distance to the global best position, etc.  
In each iteration, the inertia weight determined by the ratio of the global best fitness and the average of particles' local best fitness can be expressed as [17],  
\[w(iter)=1.1-\frac{p_{g}(iter)}{\frac{1}{N}\sum\limits_{i=1}^{N}p_{i}(iter)} \tag{6}\]  
where \(p_{i}(iter)\) and \(p_{g}(iter)\) represent the best previous positions of the \(i\)-th individual and all particles, respectively; \(N\) is the number of particles.  
The inertia weight updated by the particle rank can be expressed as [53],  
\[w_{i}(iter)=w_{\min}+\frac{rank_{i}}{N}(w_{\max}-w_{\min}) \tag{7}\]  
where \(w_{i}(iter)\) is the inertia weight of the \(i\)-th particle in current iteration; \(rank_{i}\) represents the position of the \(i\)-th particle when the particles are ordered based on their best fitness.  
The inertia weight adjusted by the distance to the global best position can be expressed as [54],  
\[w_{i}=w_{0}\bigg{(}1-\frac{dist_{i}}{\text{max\_dist}}\bigg{)} \tag{8}\]  
where the inertia weight \(w_{0}=rand(0.5,1)\); \(dist_{i}\) is the current Euclidean distance of the \(i\)-th particle from the global best, and it can be expressed as,  
\[dist_{i}=\left[\sum\limits_{d=1}^{D}\left(p_{g}^{d}-x_{i}^{d}\right)^{2} \right]^{\frac{1}{2}} \tag{9}\]and max_dist is the maximum distance of a particle from the global best in the previous generation.  
In recent research, the PSO algorithm with inertia weight adjusted by the average absolute value of velocity or the situation of swarm is proposed to keep the balance between local search and global search [56; 57; 58]. In addition, the adaptive population size strategy is an effective way to improve the accuracy and efficiency of the PSO algorithm [59; 60; 61; 62].  
It is obvious that the improvements of the PSO algorithm are generally implemented by adaptively adjusting inertia weight or population size. These methods can avoid falling into local optima by adaptively updating the velocity of particle to some extent. However, the effect of random values on the particle velocity has never been discussed. Therefore, the PSO algorithm with different types of random values will be studied in Section 3 in detail.
## 4 Methodology
### Pre-Processing of Speech Signals  
Before feature extraction, a pre-processing step evaluates, for each speech recording, the signal process by normalization, speech detection, noise removal, and framing to extract the desired features.
## 6 Conclusions  
In this paper, 3D discrete-element numerical software (3 DEC) is used to analyze the dynamic response and failure mechanism of anti-dip bedding rock slopes with different slope angles, joint angles, and angles between the joint and slope trends. The slopes were subjected to the action of natural seismic waves and sinusoidal waves loaded in three directions. The permanent displacement of the slope was also calculated using [PERSON]'s method. The following conclusions can be drawn:  
1. When the slope is dynamically loaded using a 3D seismic wave, the 3D acceleration magnification coefficient of the slope is not very regular; there is also no sign of obvious 'elevation' and 'appearance' effects. Instead, it shows rhythmicity. The peak acceleration amplification effect is more significant under the action of sinusoidal waves. As the angle between the joint and slope trends increases, the amplification effect of the slope first decreases and then increases.
2. As the seismic wave propagates within the slope, the amplitudes of the low-frequency parts of the seismic wave are generally amplified and the earthquake dominant frequency tends to decrease. The EDF in the \(Y\)-direction also diverges, forming 2 or 3 peaks.
3. When subjected to seismic action, the slope starts to fail in the lower part of the slope and failure progresses from bottom to top. The failure surface is step-like, showing the characteristics of flexure toppling. When the angle between the joint and slope trends is nonzero, the failure surface becomes more irregular. Moreover, as this angle increases, the depth of the failure surface increases and the scope of the unstable part of the slope becomes wider.
4. The permanent displacement of the slope increases as the slope and joint angles increase. Under the action of natural seismic waves, the displacement of the slope in the \(Y\)-direction develops slowly at first and then accelerates at a critical time \(T_{c}\). As the angle between the joint and slope trends increases, the permanent displacement of the slope increases and then decreases.
5. [PERSON]'s method only considers the action of seismic waves in one direction (i.e., the method is only useful for planar problems). The displacement results obtained using the method are much smaller than those obtained using the dynamic 3D discrete-element method. The results obtained using [PERSON]'s method are therefore overly conservative.  
Conceptualization and methodology, C.C. and C.S.; Data curation, Y.W.; Software, Z.R. and C.S.; Writing--original draft, Z.R.; Writing--review and editing, Z.R. and C.S. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by National Natural Science Foundation of China (Grant Nos. 12072358 and 12102443). We are grateful for the Foundation's continued support and also to our colleagues for their valuable help to this research.  
The authors declare that there are no conflict with this publication.