esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Indoor Experiment and Establishment of Finite Element Model
### Finite Element Model  
#### 2.2.1 Model Parameter Settings  
The components of CRTSIII SBT structure system mainly include steel rails, slabs, SCC layers, base plates and offline supporting foundations. The fatigue-loaded model of the CRTSIII SBT structure system in the three tunnels was built by the finite element calculation software ANSYS. The slab gap was set between two adjacent slabs, with a width of 0.07 m. For mitigating the effect of the boundary, the track structure at the middle position was monitored. The basic parameters of each structure are shown in Table 2.  
To realize the mechanical properties of different track structures, the rail was simulated by beam188 element, the track plate, SCC and base plate were simulated by solid element solid65, and the offline foundation was simulated by spring element combin14. The track slab and the SCC layer were consolidated, and the contact relationship between the SCC and the base slab was simulated by target170 element.  
Figure 7 shows the lateral and longitudinal diagrams of the structure [41]. Table 3 shows the simulation element adopted by each structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Rail** & **Slab** & **Self-Compacting Layer** & **Base** & **Offline Basis** \\ \hline Elastic Modulus (MPa) & 210,000 & 36,000 & 34,000 & 32,000 & - \\ Poisson’s ratio & 0.3 & 0.2 & 0.2 & 0.2 & - \\ Stiffness (MPa/m) & - & - & - & - & 1200 \\ Density (kg/m\({}^{3}\)) & 7800 & 2500 & 2500 & 2500 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main structure parameters.  
Figure 7: The CRTSIII SBT structure system. (a) Lateral structure; (b) longitudinal structure.
## 4 Application
### Annotations and Instantiation
For anthropometric adaption, one Design Parameter (Crank Length) has to be related to the corresponding structural element, which can be found in the expressions of the CAD model (Intermediate State in Figure 10). To complete the Design Process, all requirements have to be related to Design Parameters. Therefore, the second Transition for Design Task 2 is processed analogously to Design Task 1, and the Design Process reaches the Objective State, which is illustrated in the expression tab of the CAD model (Figure 10). All expressions at one point of the chronological axis form a State. To distinguish single states, annotations are filtered by Design Tasks. As the annotation process is completed, the user has to confirm the MC Process. The semantically enriched CAD model is instantiated as assertional knowledge in the SeEDMC. First, the wrapper loops the model tree and stores structural and semantic data in tables that are used to load the data into the SeEDMC. The semantic data and the relevant structural data are provided in Tables A1-A7 of Appendix C. As an example, the code for the extrusion feature which is used as the annotation anchor in Figure 10 is provided in Appendix A.3. Second, the Protege-Plugin Cellife ([[https://github.com/protegeproject/cellfile-plugin](https://github.com/protegeproject/cellfile-plugin)]([https://github.com/protegeproject/cellfile-plugin](https://github.com/protegeproject/cellfile-plugin)), accessed on 24 September 2021) is used to write transformation rules in Manchester Syntax (see Transformation Rule in Figure 11) to transfer table data to individuals. With those rules, objects from a tables can be instantiated and linked via the ontology.The example in Figure 11 illustrates the instantiation of a Design Task. Through the inference rules of the ontology, individual Design Task 1 is related to the individual FitCrank, which is a Solution Principle and contains input and output information of the underlying python script (see Property assertions in Figure 11). Additionally, the respective Design Parameter SeED_CrankLength is related to Design Task 1. The individual contains the metrics of the terminal state of the design parameter, which are related via the data properties hasValue and hasUnit. A summary of the used transformation rules is provided in Appendix B.  
Figure 10: Illustration of the GUI used to annotate the Rationale of Expressions and the Intermediate State and the Objective State in the Expression tab of the CAD model to demonstrate how States can be distinguished by utilizing Design Tasks as filters.
## 4 Discussion
### RQ3: How Should Psychometrics Assess Emotional Experience?
#### 4.5.3 Experimental Sample  
The sample size can be highly dependent on the study needs and considerations. It plays an important role for the performance and the validation of the study. Analyzing the research of [59], which had the least subjects (10 participants), the accuracy reported by the classifier algorithms was similar or higher than studies with a similar objective but with a sample size of at least 30 subjects (300% larger) [26; 27]. No study performed a calculation for the size of the sample needed. This issue was previously addressed in [91], where the importance of a high power to reduce the probability of biased results is highlighted. Nevertheless, it is discussed that the authors alone are not to blame but that the journals should take into consideration the validity of the study groups before selection. Better metrics should be given to peer reviewers to exclude papers that do not present a valid criterion for sample size selection, as not doing so allows the publication of studies that are less probable to be replicated and more probable to be subjected to personal interests. More effort should be stressed in the research community to validate the sample size, an important aspect to increase the rigor of the study. Financially and ethically speaking, it is necessary (1) to know the number of participants needed to reach a previously fixed statistical power (generally 80%), or (2) to evaluate the estimated statistical power when, for logistic or financial reasons, the maximum sample size is known in advance. R software (R Foundation for Statistical Computing, Vienna, Austria) and G*Power [92] are valuable tools for a priori power and sample size estimation. The latter proposes a Graphical User Interface where the researcher can adjust the analysis to the statistical test needed, entering the specific test and its family (e.g., F-tests, \(t\)-tests). Sample size a priori estimation is calculated based on required power level, significance level and [PERSON]'s f effect size index. Besides, several packages of R software (e.g.,'stats' or 'pwr') include functions (i.e., 'power.t.test(0) of'stats' package) for a priori power and sample size estimations. Also, the \"superpower\" R package is a useful tool for simulation-based power analysis [93]. For instance, in the case of means comparisons with ANOVA, 'power.anova.test()' computes sample size (n) or statistical power according to the following input parameters: estimated standard deviation (sd) of each group, estimated mean difference (\(\Delta\)) and required significance level.
###### Abstract  
The LNG cargo containment system used in membrane-type LNG cargo tanks must have sufficient dynamic strength to withstand the impact of sloshing loads. However, performing direct dynamic nonlinear transient finite element assessments against design sloshing impact loads with different design specifications can be complicated and time-consuming. To address this, it is effective to use linear superposition methods, such as the triangular impulse response function (TIRF) method, to conduct dynamic transient FE assessments of LNG cargo containment systems. However, as LNG cargo containment systems have a high level of nonlinearities in terms of geometry, material, and boundary effects, it is necessary to evaluate the applicability of the TIRF method in advance. This study investigates the dynamic responses of an LNG cargo containment system using the TIRF method and compares the ultimate value of the structural responses and impulses with that obtained using direct dynamic nonlinear transient assessments. Based on a comparison of a series of FE analyses, the study proposes a design for the partial safety factors for calculating the ultimate bending and shear capacities of an LNG cargo containment system, taking into consideration the dynamic impact of sloshing loads using the TIRF method. Finally, the ultimate shear and bending capacities are calculated using the proposed method and compared with those obtained through direct dynamic nonlinear transient assessments. The results show that the proposed method provides conservative estimates against direct nonlinear finite element simulations, with a difference of around 10% for the mean minus two standard deviations. This approach can be practically applied for early basic design purposes in the shipbuilding industry.  
Keywords:triangular impulse response function (TIRF); LNG cargo containment system; sloshing; nonlinear FE simulations; ultimate shear and bending strength +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Search Strategy  
The Patient, Problem or Population-Intervention or Exposure-Comparison, Control or Comparator-Outcome[s] (PICO) design was explicitly used to state the question. The search strategy was used in the databases mentioned above. Where possible, the search was limited to scientific studies/journals and language (see exclusion criteria number 6). No lower date limit was applied for publication. The following search terms were used (see Table 1):  
_(Preschool OR kindergarten OR \"early childhood\") AND (\"microelectromechanical system \"\"OR MEMS OR gyroscope OR magnetometer OR accelerometer OR \"heart rate\") AND (\"PA\" OR sedentary)_  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Inclusion** & **Exclusion** & **Search Coherence** \\ \hline Population & Preschool children & Children beyond preschool age (e.g., primary or secondary education). & Preschool OR kindergarten OR “early childhood” \\ \hline \multirow{3}{*}{\begin{tabular}{c} Intervention or \\ Exposure \\ \end{tabular} } & \multirow{3}{*}{Measuring PA through big data technologies.} & \multirow{3}{*}{Measuring PA with questionnaires.} & \"microelectromechanical system \"\", MEMS, gyroscope, magnetometer, accelerometer \({}^{\star}\), wearable \({}^{\star}\), pedometer, ”heart rate” \\ \hline \multirow{3}{*}{\begin{tabular}{c} Comparison \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Comparing PA in weekdays \\ vs. weekend days. \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} No comparison was made between \\ weekdays and weekend days. \\ \end{tabular} } \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Outcome[s] \\ \end{tabular} } & \multirow{3}{*}{PA-related outcomes.} & \multirow{3}{*}{\begin{tabular}{c} Non-PA variables or studies lacking \\ outcome data (e.g., protocols). \\ \end{tabular} } & \"PA\", sedentary \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Other criteria \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} Original, peer-reviewed \\ full-text articles published in \\ English or Spanish. \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} Articles in languages other than \\ English or Spanish, or not \\ peer-reviewed full-text originals. \\ \end{tabular} } \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table}
Table 1: Eligibility criteria for the inclusion and exclusion of studies.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Recurrent Neural Networks (RNNs)  
Recurrent neural networks (RNNs) are deep learning architectures specifically designed to handle sequential data. Unlike traditional neural network models, RNNs possess the ability to maintain the continuous transmission of information across time steps, thereby enabling the retention of historical input data. Within its network structure, the RNN employs a specific connection mechanism that ensures that the output at each time step is influenced not only by the current input but also by the previous state. This characteristic significantly enhances the accuracy and robustness of the model in time series predictions. The basic RNN model can be considered a time series prediction tool with memory capabilities. In the field of traffic data analysis, where the data inherently contain complex spatiotemporal dependencies, RNNs and their various variants have been widely applied for traffic flow prediction [22].  
RNNs do not rigidly memorize fixed-length sequences; instead, they store information from previous time steps through their hidden states. A typical RNN structure is cyclical and consists of an input layer, an output layer, and a neural network unit. In this architecture, the RNN's neural network unit not only establishes connections with the input and output layers but also possesses an internal loop that facilitates the flow of information across different time steps in the network. Although the computational complexity of recurrent neural networks (RNNs) is predominantly determined by the sequence length, the overall cost is not excessively high. However, the sequential dependency--where the computation at each time step relies on the preceding state--significantly impedes parallelization. Moreover, due to the vanishing gradient problem, RNNs face substantial challenges when applied to long sequences, thereby limiting their scalability in large-scale sequence tasks. An example is shown in Figure 5, where \(x_{i}\) represents the input at the i-th  
Figure 4: AE model diagram.  
time step, and \(y_{i}\) corresponds to the output generated by \(x_{i}\). The computation formula for the RNN model is as follows:  
\[y_{i}=g(Vh_{i}), \tag{6}\]  
\[h_{i}=f(Ux_{i}+Wh_{i-1}). \tag{7}\]
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 5 Conclusions  
Temperature estimation is used in a variety of medical, industrial, and military applications. In particular, body temperature measurement has emerged as a very important issue due to a fever-producing disease such as COVID-19. However, it is difficult to accurately estimate temperature due to variables such as atmospheric transmittance. In this study, we propose a method of estimating the temperature for each distance using LiDAR, which can additionally acquire distance information in an infrared camera. The proposed method constructs a digital count-radiation mapping function that reflects the effect of variables by distance, and then, when a digital count value comes in at a specific distance, the accurate temperature is estimated through the mapping function at a specific distance. In addition, the problem of not being able to build a mapping function for all distances in advance is solved through digital count interpolation. The proposed method shows that it is possible to accurately estimate temperature by distance at various temperatures, and also shows the possibility of automating body temperature measurement through Infrared-LiDAR fusion in the future as an experiment on humans. However, changes in atmospheric temperature and humidity conditions are mentioned as limitations. Therefore, it is necessary to study a robust temperature estimation method that reflects air temperature and humidity in addition to distance information.  
Conceptualization, J.K.; methodology, J.K. and S.K.; Software, J.K.; validation, J.K. and S.K.; formal analysis, J.K. and S.K.; data curation, J.K. and S.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K. and S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NRF (NRF-2018R1D1A3B07049069), KIAT (P0008473), and 2021 Yeungnam University Research Grants.  
Not applicable.  
The datasets generated during the current study are available from the corresponding author on reasonable request.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A3B07049069). This work was supported by the 2021 Yeungnam University Research Grants. This paper was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0008473, HRD Program for Industrial Innovation).  
The authors declare no conflict of interest.
## 2 Overview of Machine Learning Principles
### Evaluation and Validation Strategies
#### 2.2.2 Evaluation Metrics for Regression Tasks  
The general aim of regression studies in student performance prediction is to predict the raw results of the students (i.e., Quiz: 8, Final: 67, Total: 87, CGPA: 2.89). Since the samples are not assigned to a specific class in regression problems, the evaluation of the models is based on real-valued data, usually considering the difference between the predicted and observed data. The most commonly used metrics are mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (\(R^{2}\) score).  
MSE considers outliers between the predicted and observed data more than other metrics by squaring the difference. However, tolerable errors may cause underestimation of the error [46]. The formula of MSE is shown in Equation (6).  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\bar{y}_{i})^{2} \tag{6}\]  
where \(N\) represents the samples in the dataset, and \(y_{i}\) and \(\bar{y}_{i}\) denote the actual and predicted values, respectively.  
MAE measures the magnitude of the errors between predicted and observed data. The direction of the error is not considered as in MSE. More consistent results might be obtained using MAE [29]. The formula of MAE is shown in Equation (7).  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{7}\]  
The \(R^{2}\) score is the scaled correlation level of predicted and observed data. This allows researchers to generally obtain and analyze the evaluation results more robustly [46]. The formula of the \(R^{2}\) score is shown in Equation (8).  
\[R^{2}\;\textit{score}=1-\frac{\sum(y_{i}-\hat{y}_{i})}{\sum(y_{i}-\hat{y}_{i})} \tag{8}\]  
where \(\hat{y}_{i}\) denotes the mean value of the samples in the dataset.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 7 Performance Evaluation
### Authentication Cost  
Figure 8 shows the total time cost of the handover authentications for n MTCDs within a group. The comparison of four handover authentication schemes is as follows. The NASS scheme needs a far higher handover authentication cost compared to the BSPGH scheme, due to the involvement of complete RSA signature verification and modular exponentiation for computation and a higher communication cost during the authentication process; thus, it increases the overall latency. The PPHAP scheme has a lower handover authentication time cost, while the BSPGH scheme provides enhanced security features in mitigating DDoS attacks. The 5G-AKA scheme has a lower latency but suffers from serious security vulnerabilities. Therefore, although the BSPGH protocol inevitably introduces a minor computational overhead, it remains the most effective protocol in balancing the security functionality and system performance.  
Figure 8: Comparison of the authentication cost.  
Figure 7: Comparison of communication cost.  
We assess the robustness of the protocol for estimating the robustness of handover authentication. In the system, handover authentication may be forced to stop and restart when facing unknown attacks. We assume that unknown attacks can occur at each step of the handover authentication, and the probability of unknown attacks is uniform [1]. The average time of successful handover authentication is calculated as follows:  
\[T=\frac{T_{success}+T_{failed}}{N_{success}}=\frac{\sum_{i=1}^{n}\frac{1}{n} \times t_{fail}\times p+t_{success}\times(1-p)}{1-p} \tag{38}\]  
where \(T\), \(T_{success}\), and \(T_{failed}\) are the average time taken for a successful handover authentication, the total time of successful handover authentications, and the total time for failed handover authentications, respectively, \(N_{success}\) is the number of successful handover authentications, \(p\) is the percentage of unknown attacks, \(n\) is the number of steps in the protocol, \(t_{\text{fail}}\) represents the total time cost before the attack happens in the \(i\)-th step, and \(t_{\text{success}}\) represents the time elapsed for one successful handover authentication before an attack occurs.  
The simulation results of comparison with a group size of 30 MTCDs are shown as in Figure 9, when the percentage of unknown attacks increases. It is evident that the BSPGH protocol has a lower total time cost compared to NASS, indicating a better performance. However, it has a higher time cost compared to the solutions of the 5G standard and the PPHAP scheme. This is because that the 5G standard and the PPHAP scheme use a symmetric cryptographic system and have suffered security vulnerabilities. As shown in Figure 6, our proposed protocol can resist most major malicious attacks, albeit with a slightly higher total time cost. Overall, the proposed BSPGH scheme demonstrates a better performance in terms of security functionality and system efficiency.
# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 2 Related Work  
### YCbCr Color Space  
YCbCr is a primary color space that is commonly used in the continuous image processing of films or digital photography systems. Among them, the Y component refers to the image brightness component, Cb and Cr part refers to the image color saturation attribute. The RGB components and YCbCr components can be converted to each other, and their conversion relationships are as follows:  
RGB to YCbCr formula:  
\[\left\{\begin{aligned} &\text{Y}=0.257*\text{R}+0.504*\text{G}+0.098* \text{B}+16\\ &\text{Cb}=-0.148*\text{R}-0.291*\text{G}+0.439*\text{B}+128\\ &\text{Cr}=0.439*\text{R}-0.368*\text{G}-0.071*\text{B}+128\\ \end{aligned}\right. \tag{1}\]YbCr to RGB formula:  
\[\left\{\begin{array}{l}\mathrm{R}=1.164*(\mathrm{Y}-16)+1.596*(\mathrm{Cr}-128) \\ \mathrm{G}=1.164*(\mathrm{Y}-16)-0.392*(\mathrm{Cb}-128)-0.813*(\mathrm{Cr}-128)\\ \mathrm{B}=1.164*(\mathrm{Y}-16)+2.017*(\mathrm{Cb}-128)\end{array}\right. \tag{2}\]  
Compared with the RGB space, the three-color components of YbCr have a stronger correlation with brightness, which changes the color proportion when enhancing the image, leading to image distortion. The brightness component and color component of the YbCr color space are separated from each other. In this paper, the Y component is extracted to construct a lighting network, which can avoid the influence of image distortion.
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 4 Proposed Intelligent Routing Method
### Action Space  
Intuitively, the selection of actions in the action space corresponds to the choice of the next hop node. Unlike Q-learning, which updates the Q value of each neighboring node online, DRL-FIR belongs to the type of offline learning mode. The size of the action space thus should be set in advance to be equal to the number of one-hop nodes in the state space. Mathematically, we have \(a\in\left\{node_{c1},node_{c2},\cdots,node_{c|\mathbf{N}_{c}|}\right\}\), where \(node_{c*}\) means that the next routing node is node \(*\) for the current node \(c\).