esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Physical Models of Lane Change
#### 2.2.1 Kinematic Models
They carried out experimental observation of the state for \(S_{t}=(d_{lon}(t),\upsilon_{lon}(t),\ \Delta\upsilon_{lon}(t),d_{lat}(t),\ \upsilon_{lat}(t),\ \Delta \upsilon_{lat}(t))\), using a natural driving dataset of sampling time step \(t\), where \(d_{lon}(t)\) and \(d_{lat}(t)\) are the longitudinal and lateral distancesbetween this vehicle and the leading vehicle, \(v_{lon}(t)\) and \(v_{lat}(t)\) are the longitudinal and lateral velocities between this vehicle and the leading vehicle, and \(\Delta v_{lon}(t)\) and \(\Delta v_{lat}(t)\) are the relative longitudinal and lateral velocities between this vehicle and the leading vehicle, respectively. The action \(A_{t}=(\hat{a}_{lon}(t),\hat{d}_{lat}(t))\) generated by the agent consists of longitudinal acceleration \(\hat{a}_{lon}(t)\) and transverse acceleration \(\hat{a}_{lat}(t)\). In order to satisfy the interaction between the agent and the environment, the kinematic model of the state update is constructed as follows:  
\[\hat{v}_{lon}(t+1)=v_{lon}(t)+\hat{a}_{lon}(t)\Delta t \tag{1}\]  
\[\Delta\hat{v}_{lon}(t+1)=\Delta v_{lon}(t)-\hat{a}_{lon}(t)\Delta t \tag{2}\]  
\[\hat{d}_{lon}(t+1)=\hat{d}_{lon}(t)+(v_{lon}(t)+\Delta v_{lon}(t))\Delta t- \frac{v_{lon}(t)+\hat{v}_{lon}(t+1)}{2}\Delta t \tag{3}\]  
\[\hat{v}_{lat}(t+1)=v_{lat}(t)+\hat{a}_{lat}(t)\Delta t \tag{4}\]  
\[\Delta\hat{v}_{lat}(t+1)=\Delta v_{lat}(t)-\hat{a}_{lat}(t)\Delta t \tag{5}\]  
\[\hat{d}_{lat}(t+1)=\hat{d}_{lat}(t)+(v_{lat}(t)+\Delta v_{lat}(t))\Delta t- \frac{v_{lat}(t)+\hat{v}_{lat}(t+1)}{2}\Delta t \tag{6}\]  
In (1) to (6), \(\hat{v}_{lon}(t+1)\), \(\Delta\hat{v}_{lon}(t+1)\), \(\hat{d}_{lon}(t+1)\), \(\hat{v}_{lat}(t+1)\), \(\Delta\hat{v}_{lat}(t+1)\), and \(\hat{d}_{lat}(t+1)\) are the longitudinal velocity, longitudinal relative velocity, longitudinal distance, transverse velocity, transverse relative velocity, and transverse distance of the vehicle and the preceding vehicle, respectively, at the time step \(t+1\). The \(t\) update interval is 0.1 s. Table 3 gives a summary of the kinematic models.
## 3 Methods
### Dbscan  
DBSCAN requires two parameters: min points and epsilon. In the proposed method, the min points and epsilon are determined from the voxel size, \(Z_{mid}\), and \(Z_{max}\). Figure 6 shows an example of the point arrangement of a wall downsampled to a voxel size of 0.05 m as an ideal case. The height of the input point cloud data was determined as the difference between \(Z_{mid}\) and \(Z_{max}\), and the points were arranged at 0.05 m intervals. When viewed from the top (x-y plane), a cluster is formed in which an integer number of points divided by 0.05 m from the height of the input point cloud data gather at one point on the x-y plane.  
Figure 4: Concept of voxel-grid downsampling in the Open3D library.  
Figure 5: Plots of the x–y planes with the x- and y-coordinates extracted from the point cloud obtained with voxel-grid downsampling. (**a**) Plot of the x–y plane with x- and y-coordinates extracted between \(Z_{min}\) and \(Z_{max}\); (**b**) plot of the x–y plane with x- and y-coordinates extracted between \(Z_{mid}\) and \(Z_{max}\).  
Therefore, the ideal epsilon value of DBSCAN operating in the 2D projection x-y plane was set to 0.05 m, which is the same as the voxel size in the proposed method, as shown in Equation (4). The ideal min point value was an integer value that eliminated the decimal point of the value after the division of the height of the input data (arranged in 0.05 intervals). In this study, the authors used an integer value obtained by subtracting two from the ideal min point value to account for possible omitted points, as shown in Equation (5). This approach allows for a more robust clustering result, even when some points may be missing from the data. Figure 7 shows the results following the use of the min points and the epsilon determined based on the height of the input data and the voxel size. The red points in Figure 7 indicate the data determined to be outlier points, and the black points include the core points and border points. The proposed method defines the structural element candidates as the core points and border points, which are the result of DBSCAN. The authors utilized DBSCAN algorithm in scikit-learn library (ver. 1.2.0).  
\[\textit{epsilon}=\textit{voxel size} \tag{4}\]  
\[\text{min}\textit{points}=\left\lfloor\frac{|Z_{max}-Z_{mid}|}{\textit{ voxel size}}\right\rfloor-2 \tag{5}\]
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction
### Structure  
The remainder of this paper is organized as follows. Section 2 presents the considered system model, while Section 3 outlines the unsupervised machine-learning-based methods for the UFBS placement procedure. Next, the data collection, data pre-processing, learning, validation, and testing procedures, and the performance metrics of the proposed ANN-based placement scheme selection are outlined in Section 4. Finally, simulation results are given in Section 5, followed by conclusions and future directions in Section 6.
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 3 Network Performance Analysis
### Network Performance  
LSM-UVCN relies on the connection matrix to achieve SDM. Frequent changes in the network topology can render the connection matrix no longer representative of the actual network topology, leading to transmission failures. The main impacts are as follows:  
1. As nodes move, the communicable links in the connection matrix may be disrupted, causing nodes to establish incorrect transmission paths.
2. The non-interfering transmission paths established through multiple rounds of competition based on the connection matrix may cross due to node mobility. Interference may occur during the data transmission process, resulting in transmission failures.
3. During the neighbor discovery process, nodes may experience transmission failures of neighbor discovery control frames due to errors such as bit errors. This can lead to the establishment of incorrect connection matrices in the nodes, preventing the nodes from functioning properly during subsequent data transmission cycles.
## 2 Analysis of EV Controllability Considering Users' Travel Patterns
### The Controllable Capacity Forecast of Large-Scale EVs
Namely, the sequence diagram for different charging scenarios shows that the conditions for judging whether a single EV is in a controllable state is \(\overline{T_{i}}>0\), and the definition of \(\overline{T_{i}}\) is as follows:  
\[\overline{T_{i}}=t_{r,i}-t_{a,i}-n\Delta T-\frac{(SOC_{r,i}-SOC_{n,i})\times C _{i}}{P_{ch,i}\eta_{ch}} \tag{7}\]  
where \(SOC_{n,i}\) is the state of charge after completing \(n\) dispatching orders of EV \(i\), \(P_{ch,i}\) is the charging power of EV \(i\), \(\eta_{ch}\) is the charging efficiency, \(C_{i}\) is the battery capacity of EVs. When \(\overline{T_{i}}>0\), the individual EV is considered to be controllable. To summarize, the number of controllable EVs that can participate in the micro-grid dispatching in the same time period is taken as a set \(\Omega_{sch}\), whose definition is expressed by Equation (8). This set contains EVs which can participate in the dispatching of power system under all scenarios in the time period \(k\). Therefore, the expression of the controllable capacity of EVs in the time period \(k\) is shown in Equation (9). \[\Omega_{sch}(k\Delta T)=\left\{ \begin{array}{c}i\left|SOC_{n,i}+\int_{u,i+n \Delta T}^{t_{r,i}}P_{ch,i}\eta_{ch}dt/C_{i}\\ \geq SOC_{r,i},i\in\Omega_{free}(k\Delta T)\end{array} \right.\right\} \tag{8}\]  
\[C_{dis}(k\Delta T)=\sum_{i=1}^{\Omega_{sch}(k\Delta T)}P_{ch,i} \tag{9}\]  
where \(k=1,2,3,\ldots,NT\), \(NT\) is the total number of time periods in a day and less than the 96 time periods. Figure 2: The sequence diagram for uncoordinated charging and coordinated charging of EVs.
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 3 Experimental Results
### Relationship between Mechanical Parameters and Moisture Content of Coal Samples  
By analyzing the measured data of the uniaxial compression test of the coal samples, the specific values of mechanical parameters (compressive strength, Young's modulus, and peak strain) of coal samples with different water content can be obtained.  
Figure 7 shows the fitting curves for the mechanical parameters and moisture content of the coal samples in four parallel test groups. It was discovered that the coal samples in each test group can be fitted by the same functional relationship with a high degree of accuracy. Therefore, it can be considered that the loading rate does not change the type of fitting function for each of the mechanical parameters.  
The function expressions of uniaxial compressive strength \(\sigma_{c}\), Young's modulus \(E\), peak strain \(\varepsilon_{c}\), and moisture content \(\omega\) of coal samples are as follows:  
\[\begin{cases}\sigma_{c}=a_{1}+b_{1}\omega+b_{2}\omega^{2}+b_{3}\omega^{3}\\ E=a_{2}+b_{4}\omega+b_{5}\omega^{2}+b_{6}\omega^{3}\\ \varepsilon_{c}=\varepsilon_{0}+k\omega\end{cases} \tag{3}\]  
In Equation (3), \(a_{1}\sim a_{6}\), \(b_{1}\), \(b_{2}\) and \(k\) are fitting parameters, and \(\varepsilon_{0}\) is the peak strain of a dry coal sample.  
Figure 6: A diagram of the water filling and compression failure of the coal micro-pore structure. (**a**) Water filling of pore structure; (**b**) compression failure of pore structure.  
Figure 7: _Cont._
## 3 Materials and Method
### Materials  
The primary data used in this study consist of:  
1. A digital geological map with a scale of 1:25,000 established by the Central Geological Survey (CGS), including distributions of geological formations, faults, and folds.
2. DEM with a resolution of 30 m \(\times\) 30 m provided by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM).
3. Remotely sensed Landsat imagery captured on 6 November 1994; 9 June and 2 December 2015; and 27 June and 4 December 2016.
4. 118 drilling samplings (100 m in depth) and well yield data analyzed from 72 pumping tests (40 m in average depth) were implemented by the CGS and Sinotech Engineering Consultants, incorporated.
5. Post-processing production: 1. Landsat imagery * The image of first plane of principal component (PC-1) derived from principal component analysis (PCA) on 6 November 1994. * Map of lineaments greater than 1 km in length extracted by the LINE module of PCI Geomatica. * LST, NDVI, and SMI maps calculated from the function of Band Math in ENVI. 2. DEM * SD, FA, TWI, and TPI maps analyzed from the function of Raster Calculator in ArcGIS. * The major drainage in each watershed was extracted by a threshold value of FA. 3. HGU map delineated from the geological map. 4. Density map of lineament density (LD) and DD analyzed from the function of line density in ArcGIS.
## 3 Experiments and Discussions
### Algorithm Performance with Changes in Labeled Data Proportion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Ratio**} & \multicolumn{8}{c}{**Acc**} \\ \cline{3-10}  & & **FMDR** & **KNN\({}_{semi}\)** & **KMeans\({}_{semi}\)** & **RPCA** & **NMF** & **LE** & **ABNMTF** & **DANMF** \\ \hline \multirow{4}{*}{\(\text{ORL}\)} & 5\% & **52.50** & 48.89 & 22.50 & 35.83 & 45.56 & 35.28 & 51.39 & 52.22 \\  & 10\% & **55.83** & 46.11 & 33.00 & 35.83 & 46.11 & 36.11 & 52.22 & 53.61 \\  & 15\% & **63.86** & 53.87 & 32.75 & 46.69 & 60.76 & 49.07 & 58.33 & 58.77 \\  & 20\% & **71.91** & 66.77 & 42.25 & 46.46 & 51.23 & 30.88 & 57.99 & 59.63 \\  & 25\% & **68.55** & **68.55** & 45.50 & 59.45 & 64.60 & 60.48 & 61.19 & 62.66 \\  & 30\% & **70.44** & 64.56 & 45.75 & 58.48 & 64.97 & 60.63 & 62.77 & 63.84 \\ \hline \multirow{4}{*}{\(\text{AR}\)} & 5\% & **74.80** & 53.20 & 22.31 & 30.00 & 30.40 & 30.40 & 47.60 & 44.00 \\  & 10\% & **92.61** & 74.03 & 25.77 & 46.98 & 41.30 & 43.53 & 53.04 & 49.00 \\  & 15\% & **94.57** & 75.11 & 27.31 & 49.33 & 48.87 & 50.45 & 62.53 & 51.67 \\  & 20\% & **97.14** & 77.00 & 38.46 & 50.23 & 52.80 & 50.23 & 67.62 & 61.27 \\  & 25\% & **96.94** & 81.63 & 38.85 & 57.25 & 57.95 & 57.00 & 73.85 & 56.36 \\  & 30\% & **96.26** & 81.05 & 40.38 & 60.21 & 64.25 & 58.51 & 72.13 & 75.44 \\ \hline \multirow{4}{*}{\(\text{YaleB}\)} & 5\% & **52.00** & 41.33 & 16.67 & 25.33 & 25.33 & 25.33 & 32.00 & 20.67 \\  & 10\% & **56.00** & 45.33 & 22.03 & 24.00 & 24.67 & 24.67 & 35.33 & 25.67 \\  & 15\% & **58.52** & 45.26 & 25.39 & 38.52 & 33.58 & 35.04 & 39.71 & 28.53 \\  & 20\% & **64.44** & 45.26 & 28.48 & 32.85 & 31.85 & 31.85 & 41.30 & 33.60 \\  & 25\% & **57.02** & 48.36 & 28.27 & 38.52 & 45.60 & 40.32 & 44.63 & 41.20 \\  & 30\% & **62.50** & 51.22 & 30.60 & 38.21 & 37.90 & 38.02 & 41.94 & 42.80 \\ \hline \multirow{4}{*}{\(\text{YaleB}\)} & 5\% & **53.93** & 24.29 & 13.67 & 22.70 & 27.59 & 22.02 & 19.95 & 22.53 \\  & 10\% & **60.09** & 32.42 & 21.13 & 26.72 & 29.17 & 26.16 & 26.35 & 25.76 \\ \cline{1-1}  & 15\% & **68.45** & 41.03 & 24.36 & 31.91 & 34.47 & 33.11 & 42.40 & 28.08 \\ \cline{1-1}  & 20\% & **76.18** & 43.30 & 32.14 & 31.38 & 32.63 & 28.07 & 45.56 & 29.05 \\ \cline{1-1}  & 25\% & **77.08** & 50.11 & 37.30 & 37.38 & 41.03 & 37.90 & 49.02 & 31.50 \\ \cline{1-1}  & 30\% & **77.32** & 52.86 & 40.36 & 40.16 & 43.19 & 40.20 & 53.72 & 32.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy with different proportions of labeled data. Figure 7: _Cont_.
## 2 Literature Review
### Exploratory Factor Analysis (EFA)  
After systematic clustering, although it helps to reduce dimensionality, there may still be a large number of influencing factors, making further targeted improvements difficult. In such cases, dimensionality reduction is achieved using a secondary dimensionality reduction method, exploratory factor analysis [30]. It can help to further extract potential factors and to better understand the potential relationships between influencing factors, thus providing more targeted guidance for improving and optimizing products and services.  
Figure 1: Flow diagram of hierarchical clustering.  
Factor analysis is a statistical method designed to simplify complex data structures, reduce the number of variables and preserve the intrinsic connections of the original data [31]. Specifically, it aggregates multiple variables into a few unique shared factors that reflect the main characteristics of the original variables and the intrinsic links between them. Exploratory factor analysis focuses on determining the number of factors affecting the observed variables and their correlation with the variables [30]:  
Step 1: Standardize the data by using the Z-Score method, which uses the standard deviation as a yardstick to measure how far a raw score deviates from the mean in order to determine where it falls within the overall data.  
Step 2: Carry out a KMO test and a Bartlett test.  
Step 3: Calculate the eigenvalues of the sample correlation matrix and the total variance dilution and determine the principal factor by the cumulative variance dilution.  
Step 4: Calculate the factor component matrix and weights and filter the core influence factors according to the weights.
# Integration Between Serious Games and EEG Signals:
## 3 Foundations of Serious Games in EEG-Based BCI Systems
### Brain-Computer Interfaces (BCI) and Electroencephalography (EEG) Signals
Furthermore, EEG-based devices have become fundamental elements in the design and development of serious games, allowing them to meet consumer demands in terms of wearability, price, portability, and ease of use [51]. Electroencephalography (EEG) is a technique for recording electrical activity or voltage changes resulting from ionic currents within brain neurons [52]. EEG signals are sinusoidal waves with amplitudes typically between 0.5 and 100 \(\upmu\)v (peak-to-peak) [53]. EEG signals are generally described in terms of rhythmic and transient activity. On the other hand, active or passive electrodes placed on the scalp are used to measure the EEG signal, considering international systems or standards for their location [54,55]. There are several EEG signal processing techniques for evoked, spontaneous, and hybrid signals [49,56], along with software tools for their analysis, thus offering different forms of interface and processing style [57]. In conclusion, the signal generated through brain activity occurs as a result of thoughts or intentions [58,59], and that signal acquired using EEG has useful information that can be converted into commands in a serious game that is the subject of interest of this article.
# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 6 Conclusions  
The aim of this study was to create a model of mobility able to predict the vehicle numbers at intersections in the city of Zilina during the COVID-19 pandemic using artificial intelligence methods (machine learning). The method of neural networks was employed to build this model, whose main advantage is its high prediction accuracy. In this study, we achieved correlations between the prediction results and actual values ranging from approximately 0.70 to 0.95 for individual sensors and types of vehicles. In interpreting the study results, we focus not only on the predictions but also on interpreting how the pandemic affected mobility in the city. By examining the importance of individual variables in the created model, we found that the pandemic, quantified using internationally acknowledged variables describing its status and governmental responses in the form of implemented anti-pandemic measures, significantly impacted vehicle numbers in the city.  
Individual types of vehicles, directions, and sensors are predicted separately, capturing the overall mobility in the city. By aggregating the predicted vehicle numbers, the total vehicle numbers can be easily obtained regardless of their type or the sensor through which they pass. Moreover, since the task was approached using a data-mining approach and the models were created using data-mining software IBM SPSS Modeler, the created solution is easy to update. The model captures mobility in the city during the pandemic but is easily adaptable and updatable, which we consider its significant advantage. The model is data-open and can make new predictions after importing new data.  
Additionally, with new data, the model can be easily retrained to predict the number of vehicles under normal circumstances in a situation without a pandemic. This will subsequently enable quantifying the impact of the pandemic ex-post. Finally, the model should be easily adapted for predicting mobility in any other city, as it can be retrained on a new training sample containing the variables describing mobility circumstances specific to that city.  
Conceptualization, K.H. and K.C.; methodology, L.S. and M.D.; software, L.S.; validation, L.S., K.C. and K.H.; formal analysis, K.H.; investigation, K.H.; resources, K.H.; data curation, K.C. and L.S.; writing--original draft preparation, L.S., M.D. and K.C.; writing--review and editing, L.S. and K.C.; visualization, L.S.; supervision, K.H.; project administration, K.H.; funding acquisition, K.H. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project EU Interreg V-A Slovak Republic \"Implementation of innovative sensor networks in cross-border regions\" co-financed by the European Regional Development Fund. The APC was funded by the Slovak Grant Agency for Science (VEGA) under Grant No. 1/0509/24.
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### COVID-19 Detection Using Deep Learning  
As [PERSON] et al. [7] point out, for COVID-19 detection using medical images, the majority of previous works used transfer learning for the prediction of labels. For instance, [PERSON] et al. [28] used FC-DenseNet103 for feature extraction of the lung and heart contour and ResNet-18 for the classification of X-rays trained on a compilation of images from various sources, namely JSRT and NLM. Their method was able to classify Bacterial Pneumonia, Tuberculosis, Viral Pneumonia, COVID-19 and others. Even with the small dataset size, the authors were able to achieve 88.9% accuracy with segmentation mask and 79.8% accuracy without mask. Another such instance is seen in the work of [PERSON] et al. [29], who repurposed the CheXNet model [12] that identified pneumonia to detect COVID-19. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Year** & **Dataset** & **No. of Data Points** & **Domain** & **Model** & **Results** \\ \hline [PERSON] et al. [14] & 2017 & ChestX-ray8 & \begin{tabular}{c} Pneumonia: 1062 \\ Normal: 84,312 \\ \end{tabular} & Pneumonia & ResNet-50 & AUC for Pneumonia: 0.63 \\ \hline [PERSON] et al. [12] & 2017 & ChestX-ray14 & \begin{tabular}{c} Pneumonia: 105,408 \\ Normal: 6712 \\ \end{tabular} & \begin{tabular}{c} Multi-class lung \\ diseases \\ \end{tabular} & \begin{tabular}{c} CheXNet \\ (adaptation of \\ DenseNet) \\ \end{tabular} & \begin{tabular}{c} AUC for Hernenia: \\ AUC for Pneumonia: \\ 0.7680 \\ \end{tabular} \\ \hline [PERSON] et al. [16] & 2019 & ChestX-ray14 & \begin{tabular}{c} Pneumonia: 1431 \\ Normal: 110,689 \\ \end{tabular} & \begin{tabular}{c} Multi-class lung \\ diseases \\ \end{tabular} & ResNet-38 & \begin{tabular}{c} AUC for Hernenia: 0.937 \\ AUC for Pneumonia: \\ AUC for Pneumonia: \\ 0.714 \\ \end{tabular} \\ \hline [PERSON] et al. [17] & 2020 & \begin{tabular}{c} ChestX-ray14, \\ CheXpert (only for testing) \\ \end{tabular} & 112,120 CXRs & Pneumonia & DenseNet121 & \begin{tabular}{c} Training AUC: 0.71 \\ Testing AUC: 0.76 \\ \end{tabular} \\ \hline [PERSON] et al. [24] & 2020 & \begin{tabular}{c} Guangzhou \\ women’s and \\ children’s \\ hospital dataset \\ \end{tabular} & Pneumonia: 1000 & Pneumonia SSL & SGAN + VGG & Accuracy: 94.73\% \\ \hline [PERSON] et al. [27] & 2021 & ChestXRay2017 & 5856 CXRs & \begin{tabular}{c} Pneumonia, \\ Tuberculosisosis \\ \end{tabular} & \
\begin{tabular}{c} C3 schema \\ feature extractor \\ + ResNet50 \\ \end{tabular} &
\begin{tabular}{c} AUC for Pneumonia: \\ 0.98 \\ AUC for Tuberculosisosis: \\ 0.923 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of work using deep learning for Pneumonia classification through CXRs.
## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks  
In this section, we present some of the technologies that empower UAVs to achieve high EE when used in cellular networks. Any technique that enables the UAV to process less data, focus its energy more, or extract energy from the ambient environment will prolong its operational lifetime and is therefore considered energy-efficient. Below, we discuss such technologies and show how they enable UAVs to achieve energy savings.