esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, critical temperature and frequency characteristics of the panel in the thermal environment using NSGT and 2D-GDQM were investigated. The equations of our mathematical model for the panel were formulated with [PERSON]'s principle. The novelty of the current study was in considering the effects of the thermal environment, various boundary conditions, and size effects on the frequency and critical temperature of the GPLRC panel. For more verification, a finite element model was presented using the finite element package to simulate the response of the current structure. The results created from a finite element simulation illustrates a close agreement with the numerical method results. Based on the results of this work can claim the following:  
* The effects of the \(R_{1}/a\) parameter on the frequency response of the GPLRC panel was dependent on the values of the \(l\)/\(h\) parameter.
* The dynamic stability of the system, unlike static stability, could be affected by the nonlocal parameter indirectly.
* At the high value of \(g_{GPL}\) the effect of the \(\mu/h\) on the dimensionless frequency of the composite panel was much more remarkable in comparison with the low value of it.
* The effect of the \(g_{GPL}\) parameter on the critical temperature of the structures was hardly dependent on the values of the \(R_{1}/a\) parameter.  
Figure 15: The effects of \(l\)/\(h\) and \(R_{1}/a\) parameters on the critical temperature of the GPLRC panel subjected to CCCC boundary conditions (b/a = 4, h = a/10, R2 = 5b, N\({}_{\rm L}\) = 10, \(\mu\ =\ h/10\), \(g_{GPL}\) = 1%, and GPL-O).  
**Author Contributions:** Conceptualization A.A., K.H.S. and M.S.; formal analysis K.H.S. and M.H.; writing --original, A.A., M.S. and M.S.H.A.-F.; writing--review M.S.H.A.-F. and G.C.; visualization M.S.H.A.-F. and G.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (51805475, 51675148, 51275141, 51305113) and Islamic Azad University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification
## 3 Experimental Details
### Evaluation Metrics  
In the training stage of the model, the performance evaluation criteria used were the precision of accident report classification, recall rate, F1-score and average weighted F1-score. Precision is the ultimate criterium of the predicted result. It can be calculated by using Equation (7) and is obtained by dividing the true result by the sum of the true and false positive values.  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{7}\]  
Recall is a measure of how well each unique label fits into the predicted results. It can be seen, from Equation (8), that the recall rate is the sum of the real result divided by the real value and the false negative value.  
\[\text{Recall }=\frac{TP}{TP+FN} \tag{8}\]  
The F1-score is the harmonic mean of precision and recall rate, in which the F1-score reaches the best value when 1 and the worst value when 0. Formula for obtaining F1-score is shown in Equation (9).  
\[\text{F1-score }=\frac{2(\text{Precision }*\text{Recall})}{(\text{ Precision }+\text{Recall})} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Cause** & **Labeled Number** \\ \hline
1 & Traffic & 63 \\
2 & Collapse of object & 212 \\
3 & Falls & 236 \\
4 & Caught in/between objects & 68 \\
5 & Struck by moving objects & 134 \\
6 & Others & 43 \\
7 & Exposure to chemical substance & 29 \\
8 & Fires and explosion & 47 \\
9 & Electrocution & 108 \\
10 & Struck by falling object & 43 \\
11 & Exposure to extreme temperatures & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Labeled cause distribution of dataset provided by [PERSON] and [PERSON] [9].  
When unbalanced classes appear in the dataset, an average weighted F1-score is required. Count the number of cases and the total number of case classes that involve support for a particular tag. The average weighted F1-score can be computed by  
\[\text{avg F1}_{\text{weighted}}=\sum_{i=1}^{N}\left(\frac{S_{i}}{T}*\text{F1}_{i }\right), \tag{10}\]  
where \(S_{i}\) is the number of cases supported by label \(i\) and \(T\) is the total number of the dataset.  
When constructing the model, in order to balance the precision and recall of the model, the training objective of the model is selected to maximize the average weighted F1 score.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Experiment of Carrierless PPM Demodulation Using a PLL Method  
For demodulation using PLL, a PPM carrier frequency of 12 kHz is utilized with a lock range of 9.6 kHz to 14.1 kHz. The circuit is portrayed in Figure 19. The input information signals are sine and triangle waves of the same frequency as those used for integrator demodulation. These signals are first modulated into PPM and then demodulated using the proposed methods. The demodulation results are shown in Figure 20.  
Figure 19: Carrierless demodulating circuit using the PLL technique.  
Figure 18: Demodulation results using an integrator circuit: (**a**) Output of the integrator demodulation with a 180 Hz sine wave and triangle wave input from conventional PPM signal; (**b**) Output of the integrator demodulation with a 180 Hz sine wave and triangle wave input from proposed PPM signal. (CH1) Information signal (upper line); (CH2) Demodulated output signal using the integrator circuit (lower line).
## 4 Results
### Algorithms Selection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mathbf{S}_{i}^{-}\) & \(\mathbf{S}_{i}^{+}\) & \(\mathbf{B}_{i}\) & **Rank** \\ \hline K-means & 0.24 & 0.16 & 0.60 & 14 \\ Modified K-means\#1 & 0.31 & 0.09 & 0.78 & 8 \\ Modified K-means\#2 & 0.21 & 0.19 & 0.53 & 23 \\ WFA K-means & 0.21 & 0.19 & 0.53 & 22 \\ IWFA K-means & 0.32 & 0.08 & 0.81 & 7 \\ Hopfield K-means & 0.21 & 0.19 & 0.53 & 24 \\ minCentropy & 0.33 & 0.07 & 0.82 & 6 \\ K-means\_A & 0.29 & 0.12 & 0.71 & 9 \\ K-means\_B & 0.21 & 0.19 & 0.53 & 21 \\ K-medoids & 0.34 & 0.06 & 0.86 & 2 \\ SL & 0.36 & 0.04 & 0.91 & 1 \\ CL & 0.26 & 0.14 & 0.64 & 11 \\ UPGMA & 0.33 & 0.07 & 0.82 & 3 \\ WPGMA & 0.26 & 0.14 & 0.64 & 10 \\ WPGMC & 0.26 & 0.14 & 0.64 & 12 \\ UPGMC & 0.33 & 0.07 & 0.82 & 5 \\ MVM & 0.33 & 0.07 & 0.82 & 4 \\ FCM & 0.21 & 0.19 & 0.52 & 25 \\ IFCM & 0.19 & 0.22 & 0.46 & 26 \\ SOM & 0.15 & 0.25 & 0.38 & 28 \\ AVQ & 0.16 & 0.24 & 0.39 & 27 \\ Hopfield & 0.24 & 0.16 & 0.59 & 15 \\ FDL & 0.09 & 0.31 & 0.23 & 29 \\ CLA & 0.23 & 0.17 & 0.58 & 24 \\ IRC & 0.25 & 0.15 & 0.62 & 13 \\ BCEC1 & 0.23 & 0.17 & 0.58 & 20 \\ BCEC2 & 0.23 & 0.17 & 0.58 & 19 \\ CSC & 0.23 & 0.17 & 0.58 & 18 \\ SVC & 0.23 & 0.17 & 0.58 & 16 \\ ISODATA & 0.01 & 0.39 & 0.02 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Algorithms ranking. clusters as an input parameter rather than defining the number of clusters through other parameters, e.g., distance threshold. According to Table 8, partitional algorithms lead to lowest errors than the rest. However, Modified K-means#1 and IWFA K-means#2 are complex in terms of required execution time. Overall, hierarchical clustering has 3 main advantages: Minimum input parameter requirements, speed and software availability.
## 2 Materials and Methods
### Network Analysis
#### 2.3.1 Setting Travel Time  
As mentioned, the 2 SFCA method is used to measure the spatial accessibility of the MOH healthcare centers in Jeddah based on a travel time (i.e., drive-time threshold in this study). Therefore, some procedures should firstly be performed within the road network database to set the travel time that is also considered as an essential step to create the OD cost matrix later. According to the available data, four types of roads were used for this study, which are expressways, main, secondary, and local. The Ministry of Municipal and Rural Affairs (MOMRA) in Saudi Arabia issued the Engineering Design Manual for Roads in 2019. In this manual, speed limits are identified according to types of roads, where the desired speed of expressways is 100 km/h, the desired speed of main roads is 80 km/h, the desired speed of secondary roads is 60 km/h, while the desired speed of local roads is 50 km/h. To create a network capable of calculating the travel time, three main fields were added to the road network database within the GIS environment. First, the field of road length was added to calculate the length of each road (line segment) in meters. Assigning the length value for each road was performed by using the \"Calculate Geometry Tool\". Second, the field of speed limit was also added to assign a speed limit of travel for each road type in kilometer/hour. Third, the field of travel time was also added to assign the travel time value in minutes it would take to travel within each road type based on its speed limit. Assigning the travel time value for each road type was performed by using the \"Field Calculator Tool\". After calculating the travel time for each road type (line segment), some procedures were performed to identify potential problems of the road network shapefile, before producing a network dataset under ArcCatalog module. The problem of duplicated segments resulting from the existence of multiple names and features was solved by applying the \"Must Not Overlap\" rule within the ArcMap, where the road network does not consider travel or turn restrictions. Furthermore, the (Integrate) tool was used to ensure that all segments were connected, where many segments had no connectivity; thus, the Network Analyst Tools could not be perfectly run.
# Comparison of Tutorial Methods in Virtual Reality Games for a Better User Experience
## 4 Usability Evaluation
### Evaluation Factors  
The VREQ was developed based on a literature review focusing on keywords such as VR games, presence, learning, and enjoyment (Table 1). It comprised four major categories: presence, control learnability, intrinsic motivation, and perceived learning effectiveness. The items judged to be suitable for this study were selected based on previous studies. For presence, the items outlined by [41], consisting of spatial presence, experienced realism, and immersion, were used. For control learnability, the items outlined by [20], consisting of mental load, mental effort, and learnability, were employed. Intrinsic motivation was reconstructed based on items from [42; 43; 44] and consisted of enjoyment, continual intention to use, and player experience.  
For perceived learning effectiveness, the items provided by [45] were used, and sub-items did not exist. Table 1 lists the items and sample questions from the VREQ. They rated the VREQ on a scale from 0 (totally disagree) to 10 (totally agree).  
For the SSQ, the items from the [34] study were employed, and the score was calculated using the SSQ calculation method. They rated the SSQ on a scale from 0 (none) to 3 (severe).  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Factor**} & \multicolumn{1}{c}{**Item**} & **Reference** \\ \hline \multirow{8}{*}{Presence} & \multicolumn{2}{c}{Spatial Presence} & I felt present in the virtual space. & [41] \\ \cline{2-4}  & \multirow{4}{*}{Experienced Realism} & How much of your experience in the virtual environment seemed consistent with your real-world experience? & [41] \\ \cline{2-4}  & & Involvement & I was completely captivated by the virtual world. & [41] \\ \cline{2-4}  & \multirow{4}{*}{Mapping} & The method of interacting (playing) in a game environment was the same as the method used in real life. & [41] \\ \cline{2-4}  & & Mental Load & The game’s controls were difficult to learn for me. & [46; 47] \\ \cline{2-4}  & \multirow{4}{*}{Mental Effort} & I needed to put lots of mental effort into learning the game’s controls. & [46; 47] \\ \cline{2-4}  & & Learnability & When I wanted to do something in the game, it was easy to remember the corresponding control. & [48] \\ \hline \multirow{4}{*}{Intrinsic Motivation} & Enjoyment & I was very interested in VR games. & [42] \\ \cline{2-4}  & \multirow{2}{*}{Continual Intention to Use} & I wanted to play VR games longer without stopping. & [43] \\ \cline{2-4}  & & Player Experience & The realism of VR helps enhance my understanding. & [7] \\ \hline \multicolumn{2}{c}{Perceived Learning Effectiveness} & I learned a lot of factual information from the VR game. & [45] \\ \hline \end{tabular}
\end{table}
Table 1: Virtual reality engagement questionnaire (VREQ) factors and items.  
Figure 9: Usability testing procedure: Group 1 experimented in the order of no-tutorial, instruction-screen tutorial, and context-sensitive tutorial, while Group 2 conducted it in the order of no-tutorial, context-sensitive tutorial, and instruction-screen tutorial.
## 2 Classical Theory of Beams (CBT)  
### Kinematics  
The displacement field of the classical beam theory (CBT) is constructed assuming that transverse lines perpendicular to the beam axis (\(x\)) remain: (1) straight, (2) inextensible, and (3) perpendicular to the tangents of the deflected \(x\)-axis. These assumptions, known as the Euler-Bernoulli hypothesis, result in the following displacement field:  
\[\mathbf{u}(x,z)=[u(x)+z\theta_{x}(x)]\mathbf{\hat{e}}_{x}+w(x)\mathbf{\hat{e}} _{z},\ \ \theta_{x}\equiv-\frac{dw}{dx}\, \tag{9}\]  
where \((\mathbf{\hat{e}}_{x},\mathbf{\hat{e}}_{z})\) are the unit basis vectors along the \((x,y)\) coordinates and \((u,w)\) denote the axial and transverse displacements, respectively, of a point on the midplane of the beam.  
Based on the displacement field in Equation (9), the only nonzero strain in the present case is (see [PERSON] [35]) \(\varepsilon_{xx}\):  
\[\varepsilon_{xx}(x,z) =\frac{du}{dx}+\frac{1}{2}\bigg{(}\frac{dw}{dx}\bigg{)}^{2}+z \bigg{(}-\frac{d^{2}w}{dx^{2}}\bigg{)}\equiv\varepsilon_{xx}^{(0)}+z\ \varepsilon_{xx}^{(0)}, \tag{10}\] \[\varepsilon_{xx}^{(0)}(x) =\frac{du}{dx}+\frac{1}{2}\bigg{(}\frac{dw}{dx}\bigg{)}^{2},\ \ \ \varepsilon_{xx}^{(1)}(x)=-\frac{d^{2}w}{dx^{2}}. \tag{11}\]  
The only nonzero components of the rotation and curvature are  
\[\omega_{y}=\frac{1}{2}\bigg{(}\frac{\partial u_{1}}{\partial z}-\frac{ \partial u_{3}}{\partial x}\bigg{)}=-\frac{dw}{dx}\,\ \ \chi_{xy}=\frac{1}{2}\frac{\partial\omega_{y}}{\partial x}=-\frac{1}{2}\frac{d^ {2}w}{dx^{2}} \tag{12}\]
###### Abstract  
This study examines the hazards faced by food manufacturing workers during their daily work hours and compares the exposure time between pain complainants and non-complainants. Also, this study identifies key factors influencing musculoskeletal pain. This study selected 552 food manufacturing workers and compared the exposure time to hazards between groups using \(t\)-tests. Additionally, this study conducted a logistic regression analysis to identify factors contributing to musculoskeletal pain, considering worker-related factors (gender, age, work experience, working hours per week, occupation, and industry type) and hazard exposure levels (physical, biochemical, and ergonomic hazards) as independent variables. The results of the exposure time analysis for hazards indicated that workers were most exposed to standing or repetitive movements, followed by awkward postures, handling heavy loads, and vibration. The mean comparison test of hazard exposure time between groups revealed that workers with musculoskeletal pain exhibited the greatest difference in low-temperature exposure time compared to workers without pain, followed by awkward postures and handling heavy loads. Logistic regression analysis showed that musculoskeletal pain increases with years of work experience and exposure time to handling heavy loads. In addition, females were more likely to complain of pain in the upper and lower limbs than males, and older workers were more likely to complain of back pain. Meanwhile, vibration exposure time increased the likelihood of upper limb pain, and low-temperature exposure time increased the likelihood of lower limb pain. The analysis of factors affecting musculoskeletal pain among food manufacturing workers showed that improvements are needed to focus on a universal design that considers female and older workers. Furthermore, this study's results can help prioritize improvements and provide baseline data for developing tailored strategies and preventive measures.  
food industry; exposure to hazards; musculoskeletal pain; universal design +
Footnote †: journal: Applied Sciences
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 4 Security Analysis and Performance Evaluation
### Informal Cryptanalysis
#### 4.1.1 User Anonymity  
In our scheme, \(U_{i}\) sends \(PID_{i}=h(T_{U}\parallel n_{U})\) to \(S_{j}\) instead of \(ID_{i}\) in clear text. Moreover, \(PID_{i}\) is updated after finalizing the user authentication sub-phase with \(T_{U}^{new}\) and \(n_{U}^{new}\), keeping the identity of each user anonym. In consequence, when the user sends the authentication request message to \(S_{j}\), the \(PID_{i}\) will be different. Furthermore, an attacker cannot recover \(ID_{i}\) from \(PID_{i}\), \(D_{1}\), or \(D_{2}\) without security parameters. Therefore, the scheme provides user anonymity.
###### Abstract  
The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios extremely difficult. Since the late notification of vehicles or incidents can lead to the loss of human lives, this paper focuses on improving urban vehicle-to-vehicle (V2V) communications at intersections by using a transmission scheme able of adapting to the surrounding environment. Therefore, we proposed a neuroevolution of augmenting topologies-based adaptive beamforming scheme to control the radiation pattern of an antenna array and thus mitigate the effects generated by shadowing in urban V2V communication at intersection scenarios. This work considered the IEEE 802.11p standard for the physical layer of the vehicular communication link. The results show that our proposal outperformed the isotropic antenna in terms of the communication range and response time, as well as other traditional machine learning approaches, such as genetic algorithms and mutation strategy-based particle swarm optimization.  
Keywords:antenna array; genetic algorithm (GA); intelligent transport systems (ITS); neuroevolution of augmenting topologies (NEAT); vehicle-to-vehicle (V2V) communications +
Footnote †: journal: Sensors
## 2 Methodology  
### Overview  
This study proposes a research framework for extracting social perception information and conducting disaster damage situational awareness using multimodal social media data. Unlike most approaches that rely on a single data source, this method leverages multimodal data from Sina Weibo to effectively extract disaster damage information, making it widely applicable for disaster monitoring. The primary objective of the research is to extract disaster damage locations and information from multimodal social media data and to achieve disaster situational awareness by analyzing their spatiotemporal characteristics.  
Figure 2 illustrates the research framework, which consists of three main steps:  
* Step 1: Data Collection and Preprocessing--Collect multimodal data from Sina Weibo related to Typhoon Haikui in Fuzhou and filter images related to disaster damage using the MobileNetV3 binary classifier.
* Step 2: Multimodal Social Sensing Information Extraction--Extract disaster-related information by segmenting and classifying images with DeepLabV3+ and analyzing text with the ERNIE model to identify geographic locations and disaster categories.
* Step 3: Spatiotemporal Heterogeneity of Disaster and Driver Analysis--Visualize the spatiotemporal distribution of disaster information, conduct disaster hotspot analysis, and apply the Geodetector to explore spatial heterogeneity and driving factors of disaster hotspots.  
Figure 1: Regional and local overview of the study area.  
Figure 2: The structure of the proposed framework.  
In this study, experiments were conducted using an NVIDIA GeForce RTX 3050 Laptop GPU. The relevant image classification and disaster damage image segmentation tests were performed in a deep learning environment running on the PyTorch 1.7.1 framework.
## 3 Research Methodology
### Classification Algorithms
#### 3.5.2 Random Forest  
According to the scientific literature, one of the most powerful methods for developing ML models is Random Forest, which involves constructing several DTs and estimating the mean prediction and classification of individual trees. Random Forest is a machine learning technique that enhances performance by combining multiple DTs during training. It addresses one of the most common issues in DTs, overfitting, which occurs when multiple models are used to analyze the data. Training each tree on a randomly selected subset of the data allows for more robust models [28; 29; 30; 31; 32; 33].  
A Random Forest is a combination of DTs. Although it does not have a single formula, it relies on the predictions of various DTs to arrive at its results.  
For classification, the following is calculated:  
\[\hat{y}=majority\ vote(T_{1}(x),T_{2}(x),\ldots,\ T_{n}(x)) \tag{5}\]  
where \(T_{i}(x)\) is the prediction from the \(i\)th tree, and \(\hat{y}\) is the final prediction (the class with the most votes).  
For regression, the following is calculated:  
\[\hat{y}=\frac{1}{n}\sum_{i=1}^{n}\ T_{i}(x) \tag{6}\]  
where \(T_{i}(x)\) is the predicted value from the \(i\)th tree, and \(\hat{y}\) is the average of all predicted values.  
Random Forest trees are constructed on a randomly selected subset of the training data. The features are also randomly chosen at each split.  
One of the main advantages of the Random Forest is its ability to handle large-scale datasets. It can analyze various features of the data, and its feature importance scores are useful in determining the impact of each one on a prediction's outcome. However, it is not as interpretable as a single DT. In addition, the number of trees in the cluster makes it harder to perform well [28; 29; 30; 31; 32; 33].
## 5 Discussion
### Novelty, Performances and Opportunities
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Site 1**} & \multicolumn{4}{c}{**Site 2**} \\ \cline{2-9}  & \
\begin{tabular}{c} **RMSE** \\ **(mm/8 days)** \\ \end{tabular} & **a** & **b** & **R\({}^{2}\)** &
\begin{tabular}{c} **RMSE** \\ **(mm/8 days)** \\ \end{tabular} & **a** & **b** & **R\({}^{2}\)** \\ \hline Downscaled Landsat ET (30 m) & 11.8 & 0.37 & 2.90 & 0.76 & 12.5 & 0.57 & 1.89 & 0.77 \\ MODIS ET (1 km) & 14.2 & 0.38 & 1.48 & 0.60 & 18.3 & 0.33 & 3.34 & 0.83 \\ Modeled ET (1 km) & 14.6 & 0.26 & 3.01 & 0.52 & 19.5 & 0.30 & 3.50 & 0.73 \\ Aggregated Landsat ET (1 km) & 14.7 & 0.37 & 2.02 & 0.60 & 18.5 & 0.31 & 4.04 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Linear regression results between satellite-based ET and _in situ_ ET at Sites 1 and 2. The downscaling method is easy to implement; it does not require computation of Landsat daily ET, hence does not need parameter tuning for ET estimation models such as SEB, and the method is not impacted by the accuracy of meteorological parameters such as solar radiation and wind speed. All indicators used in the machine learning models can be easily derived based on Landsat 8 OLI and TIRS data. Because the satellite-based indicators are spatially explicit, the method can be applied over large areas or basins for ET estimation. It is also expected that this approach can also be applied to the downscaling of daily MODIS ET or ET from other coarse-resolution satellites such as MERIS.