esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods  
This study employed machine learning techniques to explore the phenomenon of phubbing, setting it apart from traditional research methods. By analysing large datasets, machine learning methods can help us to understand how phubbing behaviour is associated with different variables and how this behaviour may develop in the future. There are 5.35 billion internet users worldwide, and 96.5% of this number use mobile phones to access the internet. Most of the total internet traffic is also carried out by smartphones [11]. In this direction, it would be meaningful to use the correlation between internet usage and mobile phones to reveal the future status of the phishing concept, which is a phenomenon caused by excessive phone usage.  
This study aimed to apply machine learning-based analyses, offering a fresh perspective on academic research regarding phubbing and facilitating a more comprehensive examination of the phenomenon.  
Besides the four models used in this study (ANN, RF, SVR, and time series), comparisons with more advanced time series forecasting methods, such as LSTM and XGBoost, which are frequently suggested in the literature, have not been made. This shortcoming is recognised as a limitation regarding a more comprehensive evaluation of the results. Future studies can improve the accuracy and reliability of time series analyses by including these methods in model comparisons.
## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.6 Transportation Engineering
Even though assessing the distribution of travel times across lanes and other vehicles, in addition to their predicted values, is crucial for high-level traffic control and management of urban roadways with unique lane-to-lane circumstances, it has received relatively little attention. In [65], the authors present a novel approach for estimating the lane-based distribution of trip times for various vehicles by comparing low-resolution video pictures received from conventional traffic surveillance cameras. The system utilized deep learning neural architectures in conjunction with bipartite graph matching. They used a case study of a crowded metropolitan street in Hong Kong. According to the findings, the suggested technique effectively calculates the travel time distribution along linked lanes according to the vehicle type.
## 2 Materials and Methods
### Modelling Framework
#### 2.3.1 Light Use Efficiency Model  
The most widely used model for estimating GPP from satellite data is the light use efficiency model developed by [PERSON] (1977), Equation (1). Clara West is located in the temperate climatic zone, and for this study, it is assumed that the scalar effect of environmental drivers, such as air temperature (\(\rm T_{a}\)) and vapor pressure deficit (VPD), is negligible:  
\[\rm GPP_{Modelled}=\varepsilon\times f_{PAR}\times PAR \tag{1}\]  
where \(\rm GPP_{Modelled}\) is LUE-modelled GPP; \(\varepsilon\) is the radiation use conversion efficiency of the vegetation (g C MJ\({}^{-1}\)), also known as light use efficiency (LUE) parameter; PAR is the photosynthetically active radiation incident at the canopy (MJ m\({}^{-2}\)); and \(\rm f_{PAR}\) is the fraction of incident PAR absorbed by the canopy (MJ m\({}^{-2}\)). The \(\rm f_{PAR}\) variable is usually modelled as a function of a satellite-derived indices and can be expressed as in Equation (2):  
\[\rm f_{PAR}=APAR/PAR\approx indices. \tag{2}\]  
where APAR is absorbed photosynthetically active radiation (MJ m\({}^{-2}\) d\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formula** & **Description** \\ \hline
**Normalized Difference VI** & \(\rm NDVI=(\frac{NIR-Red}{NIR+Red})\) & \(\rm 1\geq NDVI>0.1\) describes the presence and condition (health) of vegetation, (dimensionless index). \\ \hline
**Enhanced VI (EVI)**[48; 49] & \(\rm EVI=\frac{2.5*(NIR-Red)}{((NIR+6.0*Red-7.5*Blue)+1.0}\) & noise caused by canopy \\  & & background and atmospheric effects, and it describes the health of vegetation. \\ \hline
**Land Surface Water Index** & \(\rm LSWI=(\frac{NIR-SWIR}{NIR+SWIR})\) & \(\rm LSWI>0.5\); detects open water and highlights the wet-vegetation communities. \\ \hline
**Normalized Difference** & \(\rm NDWI2\) is used mainly for the detection and delineation of water bodies from the soil and vegetation. \\ \hline
**(Modified Chlorophyll Absorption in Reflectance Index)** & \(\rm MCARI=(\rm REF1-\frac{\rm{R}EP1}{Red})*\left(\frac{REP1}{Red}\right)\) & \(\rm MCARI\) depicts the leaf chlorophyll concentrations and responds to LAI-chlorophyll interactions. \\ \hline
**Red Edge Position (REP2)**[53] & \(\rm REP2=740\) nm & \(\rm REP2\) (red edge position-2) depicts the chlorophyll concentrations and highlights the green vegetation present in the area. \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of vegetation and water indices used as a proxy for \(\rm f_{PAR}\) to model GPP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Frame** & \(\varepsilon\) **(g C MJ\({}^{-1}\))** & **Source** \\ \hline Growing season & 0.61 & [20] \\ \hline Annual & 1.21 & [21] \\ \hline Growing season & 0.83 & [22] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Literature-based LUE parameter values for peatlands.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 6 Conclusions  
In this paper, we propose a hybrid RSU method, a BS/UE-type RSU-based mutual cooperation scheme, to minimize the shaded areas under the 5G-NR V2X urban environments and improve communication reliability, to maximize traffic safety and road efficiency. To verify the performance of the proposed hybrid RSU method, we implement SLS according to the 3 GPP technical documents for V2X. We analyze its performance using BS- and UE-type RSUs, respectively, within hybrid RSU scenarios. The proposed approach satisfies the necessary resource allocation requirements in the 5G-V2X environment, while minimizing outage issues caused by high vehicular mobility. It ensures reliability for all V2I/N links and maximizes the total ergodic throughput of the links. In addition, we evaluate performances when applying a resource cooperative management scheme, CRE, and 3D beamforming technologies, along with parameter analysis by each RSU type, to improve V2I/N link reliability. We check the outage probability, interference, and throughput for performance evaluation, to verify the reduced shaded areas and improve communication reliability. Simulation results show that a trade-off relationship occurs between interference and throughput when using independent RSUs. Applying the proposed hybrid RSU method results in a lower interference and higher throughput, thereby improving the reliability of V2X communication links. When dynamic ICIC, CS-CoMP, and 3D beamforming technologies are applied to BS-type RSUs to maximize performance, as the effective resource allocation of cell edge users is ensured through the relaxation of CCI between cells and the combination of horizontal and vertical beam patterns. We apply CRE to UE-type RSUs, and the average throughput improves by solving the problem of reception interference of UE-type RSUs due to the presence of high-power BS-type RSUs. As a result, we validate the significant performance improvement in throughput of the proposed hybrid RSU method and the proposed techniques in this paper, contributing to the overall reliability improvement of V2X communication links.  
Conceptualization, S.A. and K.C.; methodology, S.A. and K.C.; software, S.A. and K.C.; validation, S.A. and K.C.; writing--original draft preparation, S.A. and K.C.; writing--review and editing, S.A. and K.C.; visualization, S.A. and K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00521, Development of 5G-NR-V2X communication technology to support over Lv.4 autonomous driving service).  
Not applicable.  
The authors declare no conflict of interest.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 3 Properties of Linear Photoacoustic
### Laser Repetition Rate  
Choosing an arbitrary laser repetition rate creates a very broadband signal. By carefully choosing the value of repetition rate a narrowband signal generation is possible [66]. In order to do that, an optimum value of \(\delta\) is required to maximizes \(P(R,\omega)\) because \(\delta\) is directly related to the laser repetition rate. To better understand the effect of \(\delta\) on the acoustic power spectrum we can rewrite Equation (10) as:  
\[P(R,\omega)=A.F(x).G(x) \tag{12}\]  
Figure 6: General geometry for linear optoacoustic mechanism (Redrawn from [66]).  
where,  
\[F(x)=\left(\frac{x^{2}}{x^{2}+1}\right)e^{-\left(\frac{Sx}{2}\right)^{2}} \tag{13}\]  
and  
\[G(x)=\frac{1}{N}\left|\frac{\sin\left(\frac{Ngx}{2}\right)}{\sin\left(\frac{Sx} {2}\right)}\right| \tag{14}\]  
Figure 7 shows the effect of \(\delta\) on \(F\) and \(G\). Since power spectral response \(P(R,\omega)\) is the product of \(F\) and \(G\), \(P\) will be maximum when both \(F\) and \(G\) are high. If one of them is low the products become low. In Figure 7a, when the value of \(\delta=6\), we observe that multiple peak values of \(G\) overlap with a high value of \(F\). However, in Figure 7b, when \(\delta=2.4\) only one peak value of \(G\) overlaps with a high value of \(F\), which indicates that having \(\delta=2.4\) creates a narrowband signal.
# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 1 Introduction
In this paper, experiments are carried out on the University of California at Riverside (UCR) collection [15]. The experimental results show that the proposed method can achieve satisfactory results. Our research in this paper significantly differs from previous work in the following aspects:  
* The framework imitates the mechanism of the human brain, in that the human brain's cognition is a combination of the human body's multiple senses. Thus, we use a DNN to learn features from different aspects to enhance the feature space. Then, based on the fusion of features, we carry out the TSC task and gain promising results. * The framework uses well-trained LSTM-A and CNN-A to extract sequence features and visualization features, as well as combining an attention mechanism to extract the key features that contribute to classification. In particular, an innovational category trend attention (CT-Attention) is gained from data belonging to the same category in an innovative way. * The framework transforms TS data into Area Graphs. Compared with the existing visualization methods (such as RP), this conversion method is simpler and avoids the loss of some information in complex conversions as well as additional computational cost. The rest of this paper is organized as follows: In Section 2, we present the related work. Section 3 describes the proposed method in detail, including the structure of the network and how to calculate the attention to find out the contributing region in the raw data for the specific labels. Section 4 presentsthe evaluation results. Finally, we present conclusions, discussions, and suggestions for future research in Section 5.
## 5 Consideration of the Measurement Device  
It is important to consider a mechanical connection to rotate the tire with a constant slip ratio. As a simple mechanism, it can be realized by arranging sprockets or a toothed pulley with different numbers of teeth on the two tires shown in Figure 14, and connecting them with a chain or a timing belt. In Figure 14, tire 1 is the main tire (driving tire) and tire 2 is the measurement tire. What may be a problem here is that the main tire may be greatly affected by the measurement tire and may not be able to rotate sufficiently. Since these tires are attached to a trailer or a vehicle, they have the same speed, but it is necessary to calculate the slip ratio of each tire in this case. Here, the same tires are used (assumption \(R_{I}=R_{2}\)), the sprocket radii (\(r_{I}\), \(r_{2}\)) are changed, and the slip ratios of each tire are calculated as \(s_{1}\) and \(s_{2}\) from the vehicle speed \(v\) and the respective speeds \(\omega_{1}\) and \(\omega_{2}\). Here, it is assumed that the rolling resistance is small, and that the \(\mu\)-\(s\) characteristics of each tire are the same. In this case, the slip ratio of the measured tire to the main tire is represented below as Equation (8) using the mechanical connection condition:  
\[s_{2}=1-\frac{r_{1}}{r_{2}}(1-s_{1}) \tag{8}\]  
In addition, the following Equation (9) can be obtained from the relationship between the torque balance of individual tires and the tension of the chain:  
\[F_{B1}=-\frac{r_{1}}{r_{2}}F_{B2}=-\frac{r_{1}}{r_{2}}\Big{[}a\sin\Big{\{}b\ \tan^{-1}(c\ s_{2})\Big{\}}\Big{]}\ N_{2} \tag{9}\]  
Tire 1 becomes a drive tire, and the slip ratio can be kept small due to the sprocket ratio. Therefore, under the condition (\(s_{1}<<1\)), the longitudinal force of tire 1 can be approximated as follows:  
\[F_{B1}\cong a\ b\ c\ s_{1}N_{1} \tag{10}\]  
Transforming Equation (10) above, the slip ratio of the main tire is described as follows as Equation (11):  
\[s_{1}=-\frac{1}{b\ c}\ \frac{r_{1}}{r_{2}}\ \frac{N_{2}}{N_{1}}\sin\Big{\{}b\ \tan^{-1}(c\ s_{2})\Big{\}} \tag{11}\]  
Since the measurement tires investigated in this study have the configuration shown in Figure 15, it is necessary to extend these equations. It is assumed that each load \(N_{s}\) of the sensing tire is the same:  
\[s_{1}=-\frac{r_{1}}{b\ c}\ \frac{N_{s}}{N_{1}}\ \Bigg{[}\frac{\sin\{b\ \tan^{-1}(c\ s_{2})\}}{r_{2}}+ \frac{\sin\{b\ \tan^{-1}(c\ s_{3})\}}{r_{3}}+\frac{\sin\{b\ \tan^{-1}(c\ s_{4})\}}{r_{4}}\Bigg{]} \tag{12}\]  
Figure 14: Mechanical properties between two tires.  
As an example, Figure 16 shows the results of a calculation with tire loads of 500 N, \(b=1.7\), and \(c=12\). In addition, since braking force is always applied to these measured tires, it is necessary to calculate the traction force \(FT\), but when calculating the longitudinal forces of four tires from Equation (12), it is approximately 1.2 kN. From this value, it is considered that the traction force is not a value that hinders normal running, and that this system functions sufficiently.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 5 Results
### Outdoor Experiment  
The results are presented in Figure 11. This figure displays a snapshot taken for three concentration densities: low (a), medium (b) and high (c). The white rectangle encloses the detected beacon signal. The graphs shown on the picture's right side represent the red, green and blue pixel values from a one-pixel column located within this rectangle. Furthermore, Figure 11d represents the reference case where the signal is extracted without being affected by the microalgae channel. The signal recovered corresponds to the sequentially pulsed LEDs: white cold, white warm, dark guard (no pulse), green, red and blue.  
It can be observed that this species produces significant attenuation in the blue and green portions of the spectrum (attaining the Bayer filter's spectrum response of the camera). Despite that the green, blue and red pulses cannot be distinguished in Figure 11c (due to low optical transmitting power), it is still possible to differentiate the white cold and white warm spectral signatures. Therefore, those LEDs are viable for communication purposes.  
Figure 11: Experimental transmission of a well-known beacon signal.  
Finally, analysing the reference signal (Figure 11d), it is observed that the sunlight increases the light power received at the container's surface. It sets an offset value and consequently reduces the dynamic range available for communications. However, as the concentration increases, this offset value decreases because of the attenuation of the microalgae. This implies that the external light that enters through the container's sides is attenuated, just like the signal of interest. Therefore, in high biomass concentration conditions, increasing the photobioreactor's light emission power will expand the available dynamic range. Pixel values obtained from dark pulses will tend gradually to zero, while the signal power is increased on purpose.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 4 The PRIMUS Architecture
### PRIMUS Architecture
#### 4.2.2 Network Slice Management Module  
It comprises a fundamental component of network slicing, being responsible for managing and orchestrating network slices. It also allows, configures, and determines slice policies, such as restricted access by a given user, and configures the network resources used by it. Hence, the module has visibility and control of end-to-end slices and the performance of each slice. Thus, it controls the overall functioning of the slices. Moreover, it controls the slice life cycle, i.e., it controls the preparation, configuration, supervision, and deactivation phases, as illustrated in Figure 4.  
The preparation phase consists of creating and verifying the network slice, since it prepares the necessary network environment to encompass the network slice created, such as virtual network functions. In addition, it verifies if there are running slices of the same type (e.g., eMBB and URLLC). Furthermore, it marks each slice with the Slice Differentiator \(SD\) to add extra information, if necessary. The configuration phase organizes and configures all shared and dedicated network resources. Then, it activates the slice. In the run phase, PRIMUS manages the network traffic transmission and observes the communication services and resources. If any application requirements are compromised, changes may be made, such as the virtual route used, slice topology or capacity, associating or disassociating virtual network functions. These changes are intended to improve slice performance and do not harm the application and the end-user. Finally, the deactivation phase makes available used network resources. Then, the created slice no longer exists [5].
## 3 Introduction  
The mapping of hydrothermal alteration zones associated with mineralized systems is of great importance in mineral exploration, especially in the early stages of metal de posit exploration [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. However, the hydrothermal alteration minerals formed can vary significantly, depending on the chemical composition of the primary rock and hydrothermal fluids, the nature of the host, temperature and pressure conditions, and the tectonic setting [12].  
Many methods have been proposed to map the spatial distribution of hydrothermal alteration zones. The method of processing multispectral satellite images, especially those of the ASTER sensor, is qualified among the most used approaches [4; 6; 7; 8; 10; 11; 13; 14; 15; 16; 17; 18]. Apart from remote sensing data, processing of geophysical data contributes assuredly to minerals exploration through their different geophysical components (gravimetric, magnetometric, electrical, electromagnetic and natural gamma spectrometric methods) [19; 20; 21; 22; 23; 24]. The natural gamma-ray spectrometric method has been widely and successfully used in mineral exploration and identification of alteration zones [20; 21; 25; 26; 27; 28; 29; 30]. However, few studies have been tested the application of this method in conjunction with the open accessible free satellite images.  
In this regard, we used satellite data from the ASTER sensor, airborne natural gamma-ray spectrometry and surface geological data to identify potential mineral explo-ration sites in the area of Kelaat M'Gouna/Morocco. This area is located in the Jbel Saghoz massif, one of the most important massifs of Morocco's Anti-Atlassic chain, which is recognized for its composition in mineral raw material (Figure 1). This area has recently been the subject of mining exploration activity that has led to discovering three gold deposits: Ismlal, Talat-n-Tabarought and Tawrift-n-Cwahl. These deposits are controlled by complex hydrothermal processes such as silicification, chloritization, hematization and sericitization [31; 32]. These processes occurred under specific geodynamic conditions that characterized the geological history of the Anti-Atlas.  
The aim of this study is to conduct a geological mapping of mineral composition in Kelaat M'Gouna area by combining geophysical and remote sensing data. This study was conducted with main objective of (i) accurately mapping hydrothermal alteration zones; (ii) developing a mineral prospectivity maps by combining thematic layers using fuzzy logic modelling; (iii) relating mineral formation processes to hydrothermal events; and (iv) clarifying the characteristics of the mineral formations in the different tectonic units of the study area.
## 3 Theoretical Basis of Thermal Stress Processes  
### Fundamental Heat Transfer Equations  
The temperature change of a homogeneous isotropic body as a function of time is described by the well-known [PERSON]'s heat equation:  
\[\rho c\frac{dT}{d\tau}=k\left(\frac{\partial^{2}T}{\partial x^{2}}+\frac{ \partial^{2}T}{\partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\right)+Q, \tag{2}\]  
where \(\rho\) is the density of the material [kg/m\({}^{3}\)], \(c\) is the specific heat of concrete [J/(kg K)], \(T\) is the temperature [\({}^{\circ}\)C], \(k\) is the thermal conductivity coefficient [W/(m K)] and \(Q\) is the internal heat generation per unit volume [W/m\({}^{3}\)].  
In practical problem solving, the demanded solution is \(T\)(\(x\),\(y\),\(z\),\(\tau\)) of Equation (2), which satisfies the appropriate initial and boundary conditions.  
Initial conditions imply that the distribution of temperature in a cross-section at the initial time (\(\tau=0\)) is known and described by the temperature field \(T\)(\(x\),\(y\),\(z\),0), while boundary conditions define the exchanged heat flux between the cast blocks and the environment:  
\[-k\left(\frac{\partial T}{\partial n}\right)=q_{c}+q_{r}, \tag{3}\]  
where \(\frac{\partial T}{\partial n}\) represents the temperature gradient at the blocks' surfaces [\({}^{\circ}\)C/m], \(q_{c}\) is the convective heat flux [W/m\({}^{2}\)] and \(q_{r}\) is the radiative heat flux [W/m\({}^{2}\)].  
The heat exchanged between blocks and the environment through convection, namely, between the blocks' surfaces and the air, is described by [PERSON]'s law of cooling:  
\[q_{c}=h_{c}(T-T_{a}) \tag{4}\]  
where \(T\) represents the temperature of the block's surface [\({}^{\circ}\)C], \(T_{a}\) is the ambient temperature [\({}^{\circ}\)C] and \(h_{c}\) is the convective heat transfer coefficient [W/m\({}^{2}\) K].  
The heat exchanged between the blocks' surfaces through radiation is described by the linearized Stefan-Boltzmann radiation law:  
\[q_{r}=h_{r}(T-T_{a}),\]where \(h_{r}\) is the linearized radiation heat transfer coefficient [W/m\({}^{2}\) K], defined by the equation [18]:  
\[h_{r}=\varepsilon\sigma\Big{(}T^{2}+T_{a}^{2}\Big{)}(T+T_{a}), \tag{6}\]  
where \(\varepsilon\) is the block's surface emissivity and \(\sigma\) is the Stefan-Boltzmann constant (5.669\(\cdot\)10\({}^{-8}\) W/m\({}^{2}\)).  
The boundary condition (3), having in mind expressions (4) and (5), can be described in the following manner:  
\[-k\bigg{(}\frac{\partial T}{\partial n}\bigg{)}=h(T-T_{a}), \tag{7}\]  
where \(h\) represents the combined (\(h_{c}+h_{r}\)) convective heat transfer coefficient [W/m\({}^{2}\) K].  
Figure 2 shows the examined heat transfer processes in the concrete gravity dam (CGD) represented in this paper.