esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The effects of temperature and time of isothermal holding in the bainitic range on the retained austenite stability were investigated in four medium-Mn steels with and without Nb microaddition. The following conclusions are drawn:  
* The optimal time and temperature of isothermal holding for all the steels were 400 \({}^{\circ}\)C and 300 s, respectively. Under these conditions, 16-18% of \(\gamma\) phase was obtained for the 3 Mn type steels, whereas 10-13% of retained austenite was detected in 5 Mn type steels. The amount of retained austenite was similar for the steels with and without Nb microaddition;
* as the concentration of Mn increases, the amount of retained austenite decreases due to the smaller C enrichment of this phase. For 3 Mn type steels, the carbon content in retained austenite was 0.9-1.4%, and it was up to 1.3% for 5 Mn type steels. The carbon content in retained austenite was slightly lower in the steels with Nb microaddition;
* at 350 \({}^{\circ}\)C, the highest C enrichment of the retained austenite was obtained. Increasing the bainitic transformation temperature to 450 \({}^{\circ}\)C and extending the time over 300 s initiated the austenite decomposition and reduced the C content in this phase;
* in steels containing 3% Mn, isothermally held at 400 \({}^{\circ}\)C, mostly blocky-type retained austenite occurred, whereas at 350 \({}^{\circ}\)C the RA was located as layers between bainitic ferrite laths. As the temperature rose, the thickness of the \(\gamma\) phase layers increased and the blocky grains partially underwent martensitic transformation, forming M-A islands.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments and wrote the paper; [PERSON] reviewed and supervised the work; [PERSON] and [PERSON] visualized the results.  
**Funding:** This research was funded by the rector grant in the area of scientific research and development works. Silesian University of Technology, grant number 10/010/RGJ17/0143.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise
### Autoregressive Moving Average Model  
The combination of AR(\(p\)) and MA(\(q\)) processes can be formulated as  
\[X_{t}=\mu-\sum_{k=1}^{p}h_{k}X_{t-k}+\sum_{\ell=0}^{q}g_{\ell}E_{t-\ell}.\]  
The combination inherits most of its properties from these simpler processes. Neither the autocorrelation nor the partial autocorrelation function vanishes after a threshold lag. The autocorrelation function exhibits damped exponential and/or oscillatory behaviour for lags \(h>q-p\), while \(\widehat{\rho_{p}}(h)\) is dominated by exponential and periodic terms for lag values \(h>p-q\). The variance of the sample autocorrelation function can exceed the Box-Jenkins error (see Figure 3). As a result, it is often difficult to determine the model orders; model verification and iterative parameter estimation are needed.  
For normally and independently distributed \(E_{t}\) with zero mean and constant variance, and for stationary \(\{X_{t}\}\), [PERSON] [46] provided an efficient algorithm for calculating the exact likelihood function of an ARMA(\(p\), \(q\)) model. This algorithm requires \(O(p^{2}+q^{2}+n(p+3q))\) operations for \(n\) observations, which--in practice--can be reduced by considering the observations in a finite window.
# Temperature Effects on the Natural Frequencies of Composite Girders
## 2 Temperature Effects on a Composite Bridge
### Thermal Properties and Elastic Moduli  
Elastic moduli and some thermal properties are affected by temperature, depending on the material. Therefore, these quantities are typically expressed as functions of temperature [18; 19; 20]. The elastic modulus of concrete tends to decrease with increasing temperature and can be calculated using Equation (1) proposed in Model Code 2010 [21]. In contrast to the elastic modulus, the specific heat and thermal conductivity are less affected by concrete temperature [22; 23; 24; 25]. The specific heat of concrete remains relatively constant up to 400 \({}^{\circ}\)C [22], and the thermal conductivity is not affected by temperature unless it exceeds 100 \({}^{\circ}\)C [23; 24; 26].  
\[Eci(T)=Eci(1.06-0.003T) \tag{1}\]  
where  
\(Eci(T)\) is the modulus of elasticity of concrete at temperature \(T\);  
\(Eci\) is the elastic modulus of concrete at 20 \({}^{\circ}\)C (MPa);  
\(T\) is the temperature of concrete (\({}^{\circ}\)C).  
The modulus of elasticity of steel decreases with temperature and can be evaluated using Equation (2) proposed by [PERSON] and [PERSON] [27].  
\[Ec(T)=Ec_{20^{\circ}\mathrm{C}}[1-\frac{(T-22)}{978}] \tag{2}\]  
where  
\(E_{c}(T)\) is the modulus of elasticity of steel at temperature \(T\),  
\(Ec_{20^{\circ}\mathrm{C}}\) is the elastic modulus of steel at 20 \({}^{\circ}\mathrm{C}\) (MPa), and  
\(T\) is the temperature of steel (\({}^{\circ}\mathrm{C}\)).  
The specific heat (\(Ca\)) and the thermal conductivity (\(\lambda a\)) of steel are affected by temperature. The thermal properties of steel deteriorate with increasing temperature [25; 28; 29]. These properties can be evaluated using Equations (3) and (4) proposed in Eurocode (EN 1993-1-2) [30].  
For 20 \({}^{\circ}\mathrm{C}\leq\theta_{a}<600\)\({}^{\circ}\mathrm{C}\),  
\[Ca=425+0.733\theta_{a}-1.69\times 10^{-3}\theta_{a}{}^{2}+2.22\times 10^{-6} \theta_{a}{}^{3}\mathrm{J/kgk} \tag{3}\]  
and for 20 \({}^{\circ}\mathrm{C}\leq\theta_{a}<800\)\({}^{\circ}\mathrm{C}\),  
\[\lambda a=54-3.33\times 10^{-2}\theta_{a}\ \mathrm{W/mk}; \tag{4}\]  
where \(\theta_{a}\) is the temperature of steel (\({}^{\circ}\mathrm{C}\)).
## 5 Results
### Performance of MCDO NNs
Pink and deep pink lines show MCDO forecasts; blue and navy lines show SARIMA forecasts. Green line denotes actual data. while producing narrower WIF (8.95 vs. 10.07) without compromising CIF, which remained at 100% for both models, as depicted in Figure 11.  
For Model 4 (phase shift with trend), the MCDO model outperformed SARIMA(1,1,2) (0,0)\({}_{12}\), which had an AIC of 786.576. MCDO achieved a lower RMSE (2.009 vs. 12.56) and MAPE (3.12% vs. 21.78%). It also produced a narrower WIF (18.56 vs. 36.88) and a higher CIF (100% vs. 91.67%), as illustrated in Figure 12.  
For Model 5 (nonlinear modulation with trend), the MCDO model demonstrated notable advantages over SARIMA(1,1,2)(0,0,1)\({}_{12}\), which had an AIC of 857.898. While both models achieved a 100% CIF, MCDO yielded a significantly lower RMSE (1.79 vs. 3.68) and MAPE (2.90% vs. 6.13%), indicating superior forecasting accuracy. Additionally, MCDO produced a much narrower WIF (28.85 vs. 48.86), further enhancing the precision of its predictions, as shown in Figure 8:0  
For Model 6 (noise variability with trend), the SARIMA model selected was ARIMA(2,1,3)(2,0,1)\({}_{12}\), which achieved the lowest AIC of 987.396. The MCDO model outperformed SARIMA, achieving a lower RMSE (2.51 vs. 2.74) and MAPE (4.05% vs. 4.56%). Additionally, MCDO produced a significantly narrower WIF (13.90 vs. 31.23) while maintaining a comparable CIF, as shown in Figure 8:1  
While MCDO consistently produced narrower prediction intervals than SARIMA, its coverage probabilities remained comparable or higher. This combination indicates that MCDO's intervals are not only sharper but also more reliable. In practical terms, such intervals provide more actionable forecasts, offering users greater confidence in the expected range of future values. This is particularly useful in real-world scenarios where planning or decision-making depends not just on point predictions, but also on understanding the range of likely outcomes
## 5 Prediction of Photovoltaic Output Power Based on MHPSO-LSTM
### Economic and Environment Influence  
Table 5 demonstrate the economic and emissions influence under different calculation model of shipborne PV system. In our cases, generating side including diesel generator and solar PV power generation system, case 1 is the shipborne PV system with traditional model, case 2 representative the PV system under considerate the effect of ship's sway.  
This paper considers the hourly fuel consumption \(F\) of diesel generators, defined as  
\[F=\eta\cdot P_{d} \tag{31}\]  
where the \(\eta=0.264\) (L/h) is diesel generator fuel consumption coefficient, \(P_{d}\) is the power generation of the diesel per hour. The hourly fuel cost defined as  
\[\text{Cost}=price_{fuel}\cdot F \tag{32}\]  
The \(price_{fuel}\) is international oil price in July 2020, defined as 0.71$/L. In the simulation process, the estimated annual energy production of diesel of ship power stations under different model is 3,975,644 kWh and (3,969,636-4,001,909) kWh respectively, the capacity of diesel in PV power generation system is 2 MW. According to the CO\({}_{2}\) emission coefficient, set as 0.92 (kg CO\({}_{2}\)/kg) in this paper, we also could estimate the amount of dioxide emissions.  
Due to the swing of the PV panel, the fuel cost and CO\({}_{2}\) emissions of the auxiliary diesel also not a fixed value, but fluctuates within a range, such calculation results could provide a more reliable reference for formulating the energy management strategy of the power grid or the configuration method of energy storage. It is worth noting that the upper limit value of dioxide emissions in case 2  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **BP NN** & **Kalman** & **LSTM** & **PSO-LSTM** & **MHPSO-LSTM** \\ \hline \multirow{2}{*}{Level 3} & MAPE\% & 16.58 & 15.65 & 12.63 & 12.84 & 6.87 \\  & RMSE & 0.2015 & 0.1921 & 0.1816 & 0.1643 & 0.0982 \\ \hline \multirow{2}{*}{Level 5} & MAPE\% & 14.02 & 13.42 & 11.027 & 7.06 & 4.71 \\  & RMSE & 0.0311 & 0.0384 & 0.0353 & 0.0366 & 0.0125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The MAPE and RMSE values of the 5 methods for forecasting the energy production.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Case 1** & **Case 2** \\ \hline Fuel cost (\$) & 6,252,155 & (6,238,000; 6,301,000) \\ Dioxide Emissions(kg) & 1,658,220 & (1,654,368; 1,670,640) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The cost of marine power grid under two models.  
Figure 20: Power fluctuation forecasting under level-3 sea state.  
is 12,420 kg higher than case 1, studies related to optimize scheduling that consider environmental protection should take this neglected numerical difference as an important operational constrains. Thus, the proposed calculation model can be used in calculate the power output of shipborne PV, which could provide more reliable operation constraint condition for grid optimization.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 2 RFPA Fundamentals  
The efficiency of an RFPA, as its central performance metric, quantifies how effectively the amplifier converts the input power into useful output power while minimizing losses, and is defined as the ratio of net output power \(P_{out}\) and the total supplied power \(P_{dc}\). The basic block diagram of an amplifier is shown in Figure 1a, wherein the circular pink dots represent matching network topology.  
Here, \(P_{in}\) is the input RF power, \(P_{out}\) is the output RF power delivered to load \(Z_{L}\), \(P_{DC}\) is the DC power supplied, \(P_{diss}\) is the dissipated power in the device, and \(Z_{L}\) is the ideal load that needs to be connected to realize maximum efficiency. A detailed description of it is presented by [PERSON] in [15]. The fundamental relation between these quantities arises due to conservation laws: \(P_{in}+P_{DC}=P_{out}+P_{diss}\). In this context, it is also useful to consider the gain \(G\), the efficiency \(\eta\), and the power added efficiency \(\eta_{add}\), which are given by Equations (1)-(3), respectively [16].  
\[G=\frac{P_{out}}{P_{in}} \tag{1}\]  
\[\eta=\frac{P_{out}}{P_{DC}} \tag{2}\]  
\[\eta_{add}=\frac{P_{out}-P_{in}}{P_{DC}}=\eta\bigg{(}1-1\Big{/}_{\text{G}}\bigg{)} \tag{3}\]  
The typical performance of an RFPA is characterized as a function of the input drive, with an example being shown in Figure 1b. In practical RF power amplifiers, the relation between the input power and output power is non-linear after a certain threshold. As the input power increases, the output power eventually reaches a point where it saturates instead of increasing further. This point is known as the compression point, \(P_{in,\,1 dB}\), as shown in Figure 1b. At this point, the input power level drops by 1 dB, and the corresponding efficiency is usually measured at this point.
## 3 Results  
### Training of Convolutional Neural Networks  
For the CNN-ROI proposal the dataset was split in 70% training (7867 samples) and 30% (3372 samples) testing sets. Table 2 shows the evaluation results when testing ROI images as inputs to the CNN-model. As shown, the accuracy obtained was \(98.42\pm 0.173\%\) (mean \(\pm\) standard deviation), representing that 98% of the time the estimation is the same as the targets.  
For the CNN-whole-image proposal, we tested three different approaches for detecting the two phases of breathing, depending on the input images to the CNN: (i) the CNN-model with original video (OV) without processing, (ii) the CNN-model with the reconstructed magnified components video (MCV), and (iii) the CNN-model with the magnified video (MV). In the three cases, we split the data into a 70% training set (34,660) and a 30% (14,854) testing set. Table 3 summarizes the performance of the CNN models using the testing set, where the subject and his/her pose are listed in the first two columns. As shown, the least accurate CNN-model corresponds to the one using the original video without any processing (83.33 \(\pm\) 4.92% for OV approach) while the other two approaches look similar in response (97.30 \(\pm\) 1.32% for MCV approach and 97.66 \(\pm\) 1.26% for MV approach).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject** & **Accuracy (\%)** \\ \hline
2 & 98.25 \\
3 & 98.35 \\
5 & 98.30 \\
7 & 98.56 \\
8 & 98.65 \\ \hline
**Mean** & 98.42 \\
**SD** & \(\pm\)0.173 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance results for the CNN-ROI proposal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Pose** & **OV Video (\%)** & **MCV (\%)** & **MV (\%)** \\ \hline
1 & LU & 81.36 & 94.13 & 93.54 \\
1 & LF & 80.79 & 97.86 & 98.41 \\ \hline
2 & LD & 76.77 & 97.92 & 98.88 \\ \hline
3 & LD & 84.25 & 97.48 & 97.86 \\
3 & S & 92.87 & 98.01 & 98.40 \\ \hline
4 & LU & 88.46 & 98.05 & 98.57 \\
4 & LF & 79.75 & 97.27 & 97.43 \\
4 & LD & 76.44 & 98.11 & 98.46 \\
4 & S & 85.24 & 97.71 & 98.15 \\ \hline
5 & LU & 87.49 & 96.94 & 97.16 \\
5 & LD & 78.42 & 98.35 & 97.97 \\
5 & S & 76.57 & 97.54 & 97.54 \\ \hline
6 & LU & 79.43 & 93.16 & 94.09 \\
6 & LD & 80.09 & 95.10 & 97.03 \\ \hline
7 & LU & 92.33 & 98.12 & 98.57 \\
7 & LF & 87.53 & 98.63 & 98.21 \\
7 & LD & 85.37 & 97.23 & 98.34 \\
7 & S & 93.41 & 98.30 & 99.34 \\ \hline
8 & LU & 84.56 & 96.04 & 97.85 \\
8 & LF & 80.33 & 97.76 & 98.09 \\
8 & LD & 84.06 & 98.27 & 98.33 \\
8 & S & 81.34 & 97.32 & 97.65 \\ \hline
9 & LF & 80.40 & 97.03 & 97.40 \\
9 & LD & 80.20 & 98.28 & 98.50 \\
9 & S & 87.33 & 97.65 & 97.77 \\ \hline
10 & LF & 81.86 & 97.62 & 97.62 \\ \hline
**Mean** & 83.33 & 97.30 & 97.66 \\
**SD** & \(\pm\)4.92 & \(\pm\)1.32 & \(\pm\)1.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy results of the CNN, in testing, using different approaches.
###### Abstract  
The aim of this study was to compare the efficacy of early treatment of Class III malocclusions with rapid maxillary expansion (RME) and facial mask (FM) versus the removable mandibular retractor (RMR) re-evaluated at a postpubertal observation on lateral cephalograms. All prepubertal patients with Class III malocclusion treated consecutively from 1986 to 2013 by means of RME/FM or RMR were analyzed. Twenty-nine patients treated with RME/FM therapy and 23 patients treated with RMR were selected. Lateral cephalograms were available at 3 time points, before treatment (T1), at the end of active treatment (T2), and at a postpubertal observation (T3). Statistical comparisons were performed with independent sample \(t\) tests or Mann-Whitney tests. During the T1-T3 interval, a significantly greater maxillary protraction (SNA +1.5 mm, \(p\) = 0.031) and significantly greater improvements in ANB and Wits appraisal (+1.9 degrees, \(p\) = 0.002, and +2.2 mm, \(p\) = 0.012, respectively) were recorded in the RME/FM group. No statistically significant changes could be found in vertical skeletal measurements. In the dentoalveolar region, the RME/FM group showed a significantly greater correction of the molar relationship (\(-1.5\) mm, \(p\) = 0.021). Early treatment of Class III malocclusion with RME/FM protocol in comparison with RMR protocol showed a greater maxillary advancement and greater improvements in sagittal skeletal Class III relationships.  
Class III malocclusion; lateral cephalogram; cephalometrics 2021 11 8393 10.3390/app1188393 10.3390/app1188393 10.3390/app1188393 10.3390/app1188393 10.3390/app1188393 10.3390/app1188393 10.3390/app1188393 10.390/app1188393 10.3390/app1188393 10.  
a follow-up period of 14.5 months. In the literature no data are available regarding the outcomes of RMR therapy re-evaluated at a postpubertal observation. This information is essential for the evaluation of orthopedic treatment of patients with a Class III malocclusion, as a significant tendency to restore Class III dentoskeletal relationships after maxillary protraction has been demonstrated, especially after the pubertal growth peak [4]. Second, pubertal growth tends to last longer in Class III subjects compared with Class I subjects [16].  
On the other end, postpubertal assessments of the treatment effects produced by different orthopedic and orthodontic approaches are available. [PERSON] et al. [7] reported that chin-cup therapy can induce favorable short-term changes with controversy about the long-term stability. As for RME/FM, [PERSON] et al. [4] showed that the posttreatment changes in most variables reflected significant relapse, including backward retrusion of the maxilla. Moreover, no previous study has compared the dento-skeletal effects produced by the RMR versus RME/FM.  
The aim of this study, therefore, was to compare postpubertal treatment effects produced by early treatment of Class III malocclusion with RME/FM versus RMR evaluated on lateral cephalograms.
## 2 Theoretical Formulations
### Displacement Function and Unified Solution
\begin{array}{l}T=\omega^{2}\frac{B}{2}\int_{0}^{L}\left(\left[\frac{1}{t_ {1}}+I_{1}^{\mathrm{c}}+I_{1}^{\mathrm{b}}\right)\Psi^{2}(x)+2\left(I_{2}^{ \mathrm{c}}-I_{1}^{\mathrm{c}}+I_{2}^{\mathrm{b}}\right)\right.\\ \left.\cdot\Psi(x)\left(\frac{\partial\Phi(x)}{\partial x}\right)+\left(I_{1} ^{\mathrm{t}}+I_{1}^{\mathrm{b}}\right)\left(\frac{\partial\Phi(x)}{\partial x }\right)^{2}+\right.\\ \left.\left(I_{4}^{\mathrm{t}}+I_{2}^{\mathrm{c}}+I_{4}^{\mathrm{b}}\right) \Phi^{2}(x)\right]dx\end{array}  \tag{14}\]  
Here  
\[ \begin{array}{l}I_{1}^{\mathrm{t}}=\left(c^{2}/4\right)\int_{-h/2}^{-c/2} \rho_{f}dz,\;I_{2}^{\mathrm{t}}=(c/2)\int_{-h/2}^{-c/2}\rho_{f}dz,\;I_{3}^{ \mathrm{t}}=\int_{-h/2}^{-c/2}\rho_{f}z^{2}dz,\;I_{4}^{\mathrm{t}}=\int_{-h/2} ^{-c/2}\rho_{f}dz,\\ I_{1}^{\mathrm{c}}=\int_{-c/2}^{c/2}\rho_{c}z^{2}dz,\;I_{2}^{\mathrm{c}}=\int_{ -c/2}^{c/2}\rho_{c}dz,\;I_{1}^{\mathrm{b}}=\left(c^{2}/4\right)\int_{c/2}^{h /2}\rho_{f}dz,\;I_{2}^{\mathrm{b}}=\left(c/2\right)\int_{c/2}^{h/2}\rho_{f}dz,\\ I_{3}^{\mathrm{b}}=\int_{c/2}^{h/2}\rho_{f}z^{2}dz,\;I_{4}^{\mathrm{b}}=\int_{ c/2}^{h/2}\rho_{f}dz\end{array} \]  
and \(\omega\) is the angular frequency of the lattice sandwich beam. Based on the above derivation, the kinetic and potential energy will be shown as the function of unknown coefficients \(A_{m}\), \(B_{m}\), \(A_{n}^{a}\) and \(B_{n}^{a}\). Thus, the Lagrange energy function \(L\) of the structure will be expressed as follows:  
\[L=\left(U_{B}+U_{S}\right)-T \tag{15}\]  
The partial derivatives of the Lagrange energy function for all unknown coefficients \(A_{m}\), \(B_{m}\), \(A_{n}^{a}\) and \(B_{n}^{a}\) are all calculated by using the standard solution process of the Rayleigh-Ritz method. The specific calculation formula is shown as:  
\[\frac{\partial L}{\partial A_{r}}=0,\,\frac{\partial L}{\partial A_{s}^{a}}=0,\,\frac{\partial L}{\partial B_{r}}=0,\,\frac{\partial L}{\partial B_{s}^{a }}=0 \tag{16}\]  
where \(m,r=0\,\sim\infty,\,\mathrm{n},\,\mathrm{s}=1,\,2\). Substituting Equations (10)-(14) and Equation (13) into Equations (16) and (17), the following can be obtained:  
\[\left(\mathbf{K}-\omega^{2}\mathbf{M}\right)\mathbf{A}=0 \tag{17}\]  
where \(\mathbf{M}\) and \(\mathbf{K}\) are called the mass and rigidity matrices of the structure, respectively; \(\mathbf{A}\) denotes the unknown coefficient vector. According to Equation (17), the eigenvalue equation of lattice sandwich beams can be obtained. Thus, the vibration frequencies and modes of lattice sandwich beams under arbitrary boundary conditions can be calculated by the standard eigenvalue solution method. Equation (17) is described in detail in Appendix A.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 2 Literature Review  
### The Definition of Smart Highway  
The ongoing wave of the digital revolution has led to the rapid evolution of smart highways, emerging as a new type of digital infrastructure in the field of intelligent transportation. It has become a global hotspot, with countries striving to accelerate its deployment. In the United States, research on smart highways is primarily directed towards traffic safety, emphasizing the development of vehicular networks and autonomous driving applications that utilize highways as platforms [7]. This approach enriches application scenarios for the intelligent upgrading of highways [8]. In Europe, the focus is on proactive traffic management, creating digital traffic corridors, strengthening digital connectivity and information sharing among road users, infrastructures, and traffic management systems [9]. Japan is predominantly exploring the domain of user interaction and information sharing with intelligent expressways, delineating a development direction based on vehicle-to-road communication [10]. South Korea combines emerging technologies such as IT and communication with vehicles and road infrastructure to develop safe and comfortable smart highways [11].  
Technologically, smart highways integrate information technology into the construction of regular highways, imparting intelligent features to improve service levels, reduce environmental pollution, and enhance road safety [12]. It represents a significant early form of implementation for smart transportation [13]. Smart highways are expected to embody principles such as efficiency, safety, intelligence, green technology, economy, and convenience [14]. This study summarizes the components of the smart highway system, as depicted in Figure 1.