esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Kinanthropometric Differences
#### 3.3.1. Kinanthropometric Differences According to Sex
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{6}{c}{**Study Groups**} \\ \multicolumn{1}{c}{**Analyzed**} & \multicolumn{3}{c}{**T**} & **B** & **H** & **W** & **R** & **C** & **S** & **G** & **V** \\ \hline \multirow{3}{*}{BMI} & T & \(24.24\pm 2.65\) & \(24.67\pm 2.12\) & \(24.25\pm 2.14\) & \(25.11\pm 3.21\) & \(22.90\pm 2.28\) & \(25.90\pm 2.79\) & \(23.48\pm 2.31\) & \(24.26\pm 2.58\) & \(23.20\pm 2.18\) \\  & M & \(24.85\pm 2.65\) & \(25.49\pm 2.12\) & \(25.09\pm 2.07\) & \(26.26\pm 2.86\) & \(23.06\pm 2.22\) & \(26.70\pm 2.55\) & \(23.77\pm 2.41\) & \(24.53\pm 2.07\) & \(24.49\pm 2.02\) \\  & F & \(23.28\pm 2.36\) & \(23.82\pm 1.79\) & \(23.24\pm 1.81\) & \(23.96\pm 3.17\) & \(22.22\pm 2.54\) & \(23.14\pm 1.54\) & \(22.5\pm 1.72\) & \(23.96\pm 3.07\) & \(22.34\pm 1.87\) \\  & \(p\leq 0.001\)\({}^{\star}\) & \(p=0.003\)\({}^{\star}\) & \(p=0.002\)\({}^{\star}\) & \(p=0.003\)\({}^{\star}\) & \(p=0.322\) & \(p\leq 0.001\)\({}^{\star}\) & \(p=0.061\)\({}^{\star}\) & \(p=0.547\) & \(p=0.001\)\({}^{\star}\) \\ \hline \multirow{3}{*}{PI} & T & \(41.65\pm 1.57\) & \(41.91\pm 1.31\) & \(41.83\pm 1.27\) & \(40.80\pm 1.76\) & \(42.45\pm 1.61\) & \(40.78\pm 1.47\) & \(42.12\pm 0.30\) & \(41.35\pm 1.51\) & \(41.95\pm 1.38\) \\  & M & \(41.63\pm 1.60\) & \(41.70\pm 1.45\) & \(41.94\pm 1.15\) & \(40.56\pm 1.65\) & \(42.51\pm 1.61\) & \(40.51\pm 1.47\) & \(42.22\pm 1.39\) & \(41.72\pm 1.03\) & \(41.60\pm 1.34\) \\ \cline{1-1}  & F & \(41.67\pm 1.52\) & \(42.11\pm 1.12\) & \(41.71\pm 1.42\) & \(41.03\pm 1.86\) & \(42.19\pm 1.66\) & \(42.17\pm 1.10\) & \(41.80\pm 0.95\) & \(40.97\pm 1.86\) & \(42.18\pm 1.39\) \\ \cline{1-1}  & \(p=0.781\) & \(p=0.257\) & \(p=0.538\) & \(p=0.290\) & \(p=0.092\) & \(p=0.017\)\({}^{\star}\) & \(p=0.255\) & \(p=0.173\) & \(p=0.205\) \\ \hline \end{tabular}
\end{table} 
Table 5: Kinanthropometric results of the participants. 3.2. Kiananthropometric Differences According to Sex in Different Sports Modalities between the sexes (Table 5):  
* Regarding the BMI, higher and more significant values were observed in the men in five sports categories (basketball, handball, walking, CrossFit, and volleyball). Significant differences did not appear among the other sports. * Regarding the PI, the highest and most significant values were observed in the women who practiced CrossFit. * Regarding the CI, higher and more significant values were observed in the men in the basketball and gym categories, while the women obtained higher values in the volleyball category. * Regarding the RILLs, higher and more significant values were observed in the men in two sports categories (basketball and gym). * Regarding the BDI, higher and more significant values were obtained for the women in the basketball, handball, walking, running, CrossFit, soccer, and volleyball categories.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 4 Methodology of the Design and Evaluation of the Classifiers  
Various metrics were used to assess the performance of the models and algorithms. The metrics were chosen based on the type of problem to be solved.  
The methodology proposed for ship classification using passive SoNaR signals consists of a structured pipeline involving several stages, as illustrated in the flowchart in Figure 5. The process begins with the acquisition of raw acoustic data and progresses through signal processing, dimensionality reduction, model training, and evaluation.  
Figure 4: Hyperparameter tuning followed by an accuracy assessment.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 1 Introduction  
Testing whether a point is enclosed by a polygon is a problem that many programmers in numerous fields, such as in computer graphics, geographic information systems, machine vision, or robotics, among others, have certainly come across more than once, as many authors have been asserting for a long time [1]. Indeed, the issue is very important and is the object of several concerns in Computational Geometry. It has already been studied and solved for practically all cases of polygonal regions, despite the fact that some earlier algorithms occasionally needed iterative computations, were ambiguous in special cases of on-boundary point locations, or show numerical instability in situations of extreme proximity to borders and vertices. Nonetheless, these limitations are often not very relevant in terms of practical implementation, and the algorithms are actually used on many applications in the software industry. What these algorithms are not aimed at, though, is the analytical evaluation of whether a point lies inside a more complex region bounded by mixed linear and curved segments. Actually, there is no published solution known to the author that indicates how it is possible, with the very same base algorithm and in a straightforward and scalable procedure, to determine analytically, i.e., without linearizing the contour or performing geometric approximations, whether a point is inside or outside regions such as those illustrated in Figure 1.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 6 Conclusions  
This study proposes a landslide early warning method based on the K-means-ResNet classification model. The effectiveness of the model was assessed by applying it to the Zhongma landslide and Shangmao landslide. The following conclusions are obtained.  
The proposed method uses K-means method to categorize and analyze the landslide deformation data and uses gray correlation analysis to select the corresponding input features, classify the landslide state by ResNet algorithm and finally achieve the early warning.  
The K-means-ResNet model is applied to the Zhongma landslide and Shangmao landslide. The results show that the landslide deformation can be classified into four states, which are low speed state, medium speed state, high speed state and super high speed state. The accuracy, precision and recall of the early warning are better than those of the MLP and LeNet models. The method was also compared with the traditional landslide threshold warning method, and the results show that this method reduces the false alarm and does not need to rely on the threshold parameters determined by the expert experience.  
The proposed K-means-ResNet model uses the K-means algorithm to cluster landslide states. Future research can take relevant geological and geotechnical information into consideration to classify the states of landslides, establish more accurate pre-training datasets and improve the accuracy of landslide warning. In addition, the landslide state is changing, so analyzing the influence of triggering factors on landslides is difficult. The model in this study could adaptively learn the relationship between rainfall anddeformation by deep learning. Future work can be focused on exploring the interpretability of the features learned by the ResNet algorithm, classifying landslide state and warning by both internal and external factors and establishing a more accurate landslide warning method to further improve landslide warning.  
Conceptualization, Y.W. and G.L.; methodology, Y.W.; software, Y.W.; validation, Y.W. and Y.L.; formal analysis, Y.W.; investigation, Y.W.; resources, Z.Z.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, G.L., Z.Z., D.B., X.Z., C.T. and Y.L.; visualization, Y.W., D.B., X.Z., C.T. and Y.L.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number: 41974148; the Natural Resources Research Project in Hunan Province of China, grant number: 2021-15; the Department of Transportation of Hunan Province of China, grant number: 202012; and the Key Research and Development Program of Hunan Province of China, grant number: 20205K2135.  
Not applicable.  
We would like to thank the editor and the reviewers for helping us improve the quality of the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system.  
Internet of Things; smart sensors; smart actuators; water metering; smart grid; real-world deployment; experimentation +
Footnote †: journal: Applied Soft
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 3 Air Quality Issues and Its Monitoring Network
### Air Pollutants and Recommendations  
Air pollution comes from several major sources, including inefficient energy consumption in households, industries, and the agricultural and transport sectors, as well as coal-fired power plants. In addition, in some areas, air pollution is exacerbated by sand and desert dust, waste incineration and deforestation. In addition, natural elements such as geographical features, meteorological conditions, and seasonal variations can affect air quality. Consequently, these pollutants have adverse effects on our health.  
Among the pollutants of greatest health concern are PM2.5, PM10, CO, O\({}_{3}\), NO\({}_{2}\), and SO\({}_{2}\). These pollutants can lead to health problems resulting from both short-term and long-term exposure. It is important to note that certain pollutants do not have specific thresholds below which no adverse effects occur. Therefore, even low levels of exposure to these pollutants can cause adverse health effects.  
The AQG by WHO [2] serves as a comprehensive global reference for defining thresholds and recommended limits for crucial air pollutants that pose health risks. These guidelines are developed using a rigorous and transparent methodology based on evidence-based decision-making. Alongside the guideline values, these guidelines also present interim targets, progressively moving from higher to lower concentrations, with the final target as the ultimate goal. A detailed overview of this guideline and the corresponding levels is shown in Table 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Pollutant** & **Guideline Value** & **Averaging Time** \\ \hline PM2.5 & 5 \(\mu\)g/m\({}^{3}\) & Annual \\  & 15 \(\mu\)g/m\({}^{3}\) & 24-h \\ \hline PM10 & 15 \(\mu\)g/m\({}^{3}\) & Annual \\  & 45 \(\mu\)g/m\({}^{3}\) & 24-h \\ \hline O\({}_{3}\) & 60 \(\mu\)g/m\({}^{3}\) & Peak \\  & 100 \(\mu\)g/m\({}^{3}\) & 8-h \\ \hline CO & 4 mg/m\({}^{3}\) & 24-h \\ \hline NO\({}_{2}\) & 10 \(\mu\)g/m\({}^{3}\) & Annual \\  & 25 \(\mu\)g/m\({}^{3}\) & 24-h \\ \hline SO\({}_{2}\) & 40 mg/m\({}^{3}\) & 24-h \\ \hline Formaldehyde & 0.1 mg/m\({}^{3}\) & 30-min \\ \hline Polycyclic aromatic & \(8.7\times 10^{-5}\) per ng/m\({}^{3}\) & \\ hydrocarbons & \(8.7\times 10^{-5}\) per ng/m\({}^{3}\) & \\ \hline Radon & 100 Bq/m\({}^{3}\) & \\ \hline Lead & 0.5 \(\mu\)g/m\({}^{3}\) & Annual \\ \hline \hline \end{tabular}
\end{table}
Table 1: Latest recommended guideline values for each pollutant from WHO [2].
## 3 Methods
### Vision Transformer (ViT)  
The Vision Transformer (ViT) introduced by [PERSON] et al. [2] is a groundbreaking model in the field of computer vision that was designed to leverage the power of Transformer architectures originally developed for natural language processing tasks. ViTs are particularly effective in handling image classification and other vision-related tasks by processing images as sequences of patches, similar to how Transformers process sequences of words.  
The architecture of a Vision Transformer, as illustrated in Figure 3, consists of several key components:  
* Input image: the process begins with an input image, which is divided into fixed-size patches.
* Patch + position embedding: each patch is then embedded into a vector space, and positional embeddings are added to retain spatial information.
* Linear projection of flattened patches: the embedded patches are flattened and linearly projected to form a sequence of tokens.
* Transformer Encoder: The core of the ViT architecture is the Transformer Encoder, which processes the sequence of tokens through multiple layers. Each layer includes the following:
* Normalization: normalization layers ensure stable training by standardizing the inputs.
* Multi-head attention: this mechanism allows the model to focus on different parts of the input sequence simultaneously, capturing complex relationships between patches.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Params (\(\times\)10\({}^{6}\))** & **Layers** & **Input Size** \\ \hline AlexNet & 57.0 & 8 & 227 \(\times\) 227 \\ \hline VGG16 & 138.4 & 16 & 224 \(\times\) 224 \\ \hline GoogleNet & 5.61 & 22 & 224 \(\times\) 224 \\ \hline SqueezeNet & 1.2 & 18 & 227 \(\times\) 227 \\ \hline ResNet50 & 23.5 & 50 & 224 \(\times\) 224 \\ \hline DenseNet201 & 18.1 & 201 & 224 \(\times\) 224 \\ \hline ShuffleNetV1 & 1.26 & 50 & 224 \(\times\) 224 \\ \hline DarkNet53 & 41.6 & 53 & 256 \(\times\) 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNN model summary: total parameters (in millions), layers, and input image sizes.  
MLP (Multi-Layer Perceptron): a feed-forward neural network that further processes the attended features.
* MLP head: the output from the Transformer Encoder is passed through an MLP head, which produces the final classification or regression output.
* Output: the final output layer provides the predictions for the given task, such as class labels for image classification.  
The Vision Transformer has demonstrated remarkable performance in various computer vision tasks, showcasing the versatility and effectiveness of Transformer architectures beyond their original application in natural language processing. An overview of key Vision Transformer (ViT) models, emphasizing their unique features, architectures, and parameter counts in the field of medical images can be found in [PERSON] et al. [20].  
After the first version of the ViT transformer by [PERSON] et al. [2], other versions have been proposed in the literature, some of which were used in this work and, for this reason, are briefly described in the following sections.
# Optimization of Metal-Ceramic Functionally Graded Plates Using the Simulated Annealing Algorithm
## 2 Constitutive Relations of the Functionally Graded Material (FGM)  
The properties of the FGM material are assumed to vary continuously, through the thickness, from a full metal at the bottom of the plate to a full ceramic at the top of the plate, as represented in Figure 1. The volume fractions of ceramic and metal constituents, \(V_{c}\) and \(V_{m}\), are obtained by [41]:  
\[V_{c}=\left(\frac{1}{2}+\frac{z}{h_{tot}}\right)^{p},\quad V_{m}=1-V_{c} \tag{1}\]  
The material properties, elastic moduli E and G, Poisson coefficient \(\
u\), density \(\rho\), coefficient of thermal expansion \(\alpha\), coefficient of thermal conductivity \(\kappa\), vary according to Equation (2). The properties can be expressed as a non-linear function of temperature by the expression [37, 38, 41]:  
\[P_{f}=P_{0}\left(P_{-1}T^{-1}+1+P_{1}T+P_{2}T^{2}+P_{3}T^{3}\right) \tag{2}\]  
where \(P_{0},\ P_{-1},\ P_{1},\ P_{2}\) and \(P_{3}\) are the coefficients of the temperature (in Kelvin) which are given in Table 1, for the relevant materials used in Section 5, and \(P_{f}\) is the corresponding  
Figure 1: Geometry definition of the functionally graded metal–ceramic plate with metal and ceramic skins.  
material property at the given temperature \(T\). The material properties in FGM plates vary across the thickness of the plate and the temperature variation occurs only in the thickness direction being constant in the x-y plane [38].  
The relation between stresses and strains is represented by:  
\[\boldsymbol{\sigma}=\mathbf{Q}\ \Big{\{}\boldsymbol{\varepsilon}-\boldsymbol{ \varepsilon}^{th}\Big{\}} \tag{3}\]  
where \(\boldsymbol{\sigma}\) is the elastic stress vector, \(\boldsymbol{\varepsilon}\) is the elastic strain vector and \(\boldsymbol{\varepsilon}^{th}\) is the thermal vector of strains caused by the temperature change \(\Delta T(z)=T(z)-T_{0}\). \(T_{0}\) represents the stress-free reference temperature and \(\mathbf{Q}\) is the elastic constitutive matrix [41].
## Materials and Experiments
### Fabrication Process  
The graphite preforms were prepared by sintering mixture powder with a HK S500 selective laser sintering machine (provided by Wuhan Huake 3D Technology Co., Ltd., Wuhan, China), and the process parameters were as follows: fill power 17 W, fill speed 2500 mm/s, fill spacing 0.1 mm, layer thickness 0.1 mm, preheating temperature 40 \({}^{\circ}\)C, laser spot diameter 0.4 mm, adopting contour scanning mode. The post-treatment of the graphite preforms included four processes: vacuum pressure infiltration of phenolic resin solution, carbonization, vacuum pressure infiltration of silica sol solution, and high-temperature sintering. The fabrication process of 3D natural flake graphite/ceramic composite parts is shown in Figure 1. The vacuum pressure infiltration was to put the graphite preforms into the vacuum infiltration machine, vacuum to below 100 Pa, add the phenolic resin solution with the viscosity of 1000 mPa.s (the mass fraction is 30 wt %, the density is 1.06 g.cm\({}^{-3}\)), soak for 15 min, take out to clean the surface, and then dry in the oven at 60 \({}^{\circ}\)C for curing. These solidified parts were carbonized in NT-ZKTH-50-1500 vacuum carbonization furnace (Made in Changsha Nuotian Electronic Technology Co., Ltd.) by heating from room temperature to 200 \({}^{\circ}\)C at 60 \({}^{\circ}\)C/h, then to 600 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/h, and then to 800 \({}^{\circ}\)C at 60 \({}^{\circ}\)C/h and holding for 1 h and finally cooling to room temperature. The carbonized parts were followed by vacuum pressure infiltration in silica sol solution (the mass fraction is 30 wt %, the density 1.20 g.cm\({}^{-3}\), and the average particle size of SiO\({}_{2}\) 8-15 nm) with a vacuum degree of less than 100 Pa for 10 to 20 min and drying. Finally, high-temperature sintering was carried out under the protection of nitrogen. The temperature was increased from room temperature to 100 \({}^{\circ}\)C at 60 \({}^{\circ}\)C/h, then to 1100 \({}^{\circ}\)C at 240 \({}^{\circ}\)C/h, then to 1500 \({}^{\circ}\)C at 120 \({}^{\circ}\)C/h and kept for 3 h, and then cooled to room temperature to attain the final sintered parts.
## 3 Illustrative Examples
### Mathematical Examples  
**Example 1**.: _A highly nonlinear function is given as [26]_  
\[G_{1}=X_{1}^{4}+2X_{2}^{4}-20 \tag{24}\]  
_where \(X_{1}\) and \(X_{2}\) are normal random variables with \(\mu_{1}=\mu_{2}=10\) and \(\sigma_{1}=\sigma_{2}=5\)._  
The number of iterations, \(G\)-evaluations, \(\
abla G\)-evaluations, and reliability index for all algorithms are listed in Table 1. Figure 4 shows the iterative processes of reliability index. According to Table 1, the STM, RHL-RF, FSL, and proposed AFSL algorithms converge to stable solutions, but the HL-RF and HLRF-BFGS algorithms fail to converge. The HL-RF algorithm produces the periodic solutions of [0.9267, 0.9863]. The results calculated by MCS with \(10^{6}\) samples are \(P_{f}=1.87\times 10^{-3}\) and \(\beta=2.8998\). The AFSL algorithm converges to the stable result as \(\beta=2.3842\), which is more accurate than the other algorithms. In addition, according to the number of iterations, \(G\)-evaluations and \(\
abla G\)-evaluations, they have shown that the proposed algorithm is more efficient than the other algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Iterations** & **\(\mathbf{G}\)-Evaluations** & \(\
abla\mathbf{G}\)**-Evaluations** & \(\beta\) & \(\mathbf{P}_{f}\) \\ \hline HL-RF & – & – & – & – & – \\ STM & 154 & 154 & 154 & 2.3654 & \(9.01\times 10^{-3}\) \\ HLRF-BFGS & – & – & – & – & – \\ RHL-RF & 181 & 361 & 181 & 2.3654 & \(9.01\times 10^{-3}\) \\ FSL & 108 & 108 & 108 & 2.3655 & \(9.00\times 10^{-3}\) \\ Proposed AFSL & 26 & 47 & 26 & 2.3842 & \(8.56\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different algorithms for Example 1.  
Figure 4: Iterative processes of reliability index for Example 1.  
**Example 2**.: _A convex performance function is given by [29]_  
\[G_{2}=\frac{1}{P}\ln[\exp[P(1+X_{1}-X_{2})]+\exp[P(5-5X_{1}-X_{2})]] \tag{25}\]  
_where \(P=1\), both \(X_{1}\) and \(X_{2}\) are standard normal variables. Its limit state surface is shown in Figure 5._  
The iterative processes of reliability index for all compared algorithms are given in Table 2. The iterative processes of reliability index are shown in Figure 6. The failure probability computed by MCS with \(10^{6}\) samples is \(3.43\times 10^{-3}\) (\(\beta=2.7035\)). From Table 2 and Figure 6, the HL-RF algorithm fails to converge, for its iterative process falls into a periodic loop. The STM, HLRF-BFGS, RHL-RF, FSL, and the proposed AFSL algorithms converge to stable results, i.e., \(\beta=2.2995\), \(P_{f}=1.07\times 10^{-2}\) and \(X^{*}=(0.8641,\ 2.1310)\). The number of iterations is 119, 16, 106 43, and 17, respectively. The proposed A
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Curve Correction Model
#### 2.5.2 Examples of the Curve-Correction Calculations
\[f=f_{1}f_{2} \tag{20}\]  
where, \(f_{1}\) is the coving factor, or in another way, geometrical curve correction factor corresponding to the ratio of the projected area by surface area, \(f_{2}\) is the irradiance ratio due to the local cosine loss and self-shading loss, in other words, optical curve correction factor. The parameter \(f_{1}\) may represent the overall shape of the curved surface.