esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review Methodology
\begin{table}
\begin{tabular}{l l l l} \hline
**Author** & **Approach** & **Aim** & **Performance** & **Year** \\ \hline \multirow{8}{*}{[54]} & \multirow{8}{*}{Proposed explainable} & \multicolumn{3}{c}{The experimental findings} \\  & & & demonstrated the \\  & & & proposed framework’s \\ \cline{1-1}  & & & DL-based IDS’ & strong performance with a \\ \cline{1-1}  & & transparency and & 99.15\% accuracy and a \\ \cline{1-1}  & SHAP mechanism & resilience in & 98.83\% F1 score, \\ \cline{1-1}  & & IoT networks & highlighting its capacity to \\ \cline{1-1}  & & & defend IoV networks from \\ \hline \multirow{8}{*}{[92]} & Extensive review of & \multicolumn{3}{c}{To provide} \\  & & \multicolumn{3}{c}{state-of-the-art} \\ \cline{1-1}  & & \multicolumn{3}{c}{evaluation metrics and} \\ \cl \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **XAI Framework** & **Specific Usage** & **Detail** & **Year** \\ \hline
[54] & SHAP mechanism & \
\begin{tabular}{c} To increase the DL-based \\ IDS’ transparency and \\ resilience in IoT networks \\ \end{tabular} &
\begin{tabular}{c} Provided global and local \\ explanations to XAI \\ models using SHAP plots \\ \end{tabular} & 2022 \\ \hline
[102] & Novel VisExp & \
\begin{tabular}{c} Enhance the \\ trustworthiness of the \\ XAI-powered IV-IDS \\ \end{tabular} &
\begin{tabular}{c} Based on SHAP, compared \\ the proposed \\ knowledge-based VisExp \\ with a rule-based \\ explanation \\ \end{tabular} & 2022 \\ \hline
[103] & Integration of \
\begin{tabular}{c} Approach is to translate the \\ ANFIS output to linguistic \\ value easily understood by \\ humans to enhance the \\ understanding of \\ UAV behavior \\ \end{tabular} &
\begin{tabular}{c} Proposed approach shows \\ the potential application of \\ ANFIS for the \\ development of XAI \\ \end{tabular} & 2019 \\ \hline
[104] & RuleFit, SHapley, and SHAP & \
\begin{tabular}{c} Multiple XAI frameworks \\ built and integrated to \\ ML/DL-based IDS \\ \end{tabular} &
\begin{tabular}{c} Efficient, transparent and \\ trustworthy IDS for \\ IoT applications \\ \end{tabular} & 2022 \\ \hline
[72] & SHAP & \
\begin{tabular}{c} A unified framework for \\ prediction analysis \\ \end{tabular} &
\begin{tabular}{c} Conceptual identification \\ of a new class of additive \\ feature importance \\ measures that possesses \\ desirable features \\ \end{tabular} & 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected Publications Showing Usage of XAI Frameworks.
## 6 Conclusions  
This paper proposes a hybrid information integrity-monitoring architecture (HIIMA) to protect the embedded system's key data from being tampered with or their control flow hijacked when executing the program. This hardware-based security architecture introduces a data integrity (DI) protection mechanism and a control flow integrity (CFI) protection mechanism. When the program is offline, the binary code of the program is divided into basic blocks (BBs), and the start address of each BB, the static digest generated by the LHash, and the CFI label generated by the label allocation algorithm are extracted, and the final composition is the hybrid integrity reference information. To monitor the indirect jump instructions, this paper maps the legal target address into bitmaps. The reference information and bitmaps will be stored in the designated on-chip memory through a secure path when the program is loaded. When the program is running, the HIIMA will check the DI and CFI of the currently executing BB based on the address signal and the instruction signal. By building a hardware test platform to evaluate the security, resource overhead, and performance overhead of the proposed method, the results show that the HIIMA can effectively resist attacks on the data and control flow with a low performance overhead and low resource overhead.  
Conceptualization, Q.H.; methodology, Q.H.; software, Q.H. and Z.Z.; validation, Q.H., J.W. and D.X.; formal analysis, Z.Z. and J.M.; investigation, Q.H. and D.X.; resources, X.W.; data curation, J.L. and J.Z.; writing--original draft preparation, Q.H.; writing--review and editing, Q.H.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grants No. 60973106 and No. 81571142), the Key Project of the National Natural Science Foundation of China (Grant No. 61232009), and the National 863 Project of China under Grant No. 2011 AA010404.  
Not applicable.  
**Acknowledgments:** We sincerely appreciate the Shaoxing Yangyu AI Chip CO., LTD, Zhejiang 312035, China, for providing financial support and technical resources. The method proposed in this paper may be applied to the smart chip and its equipment of this company in the future.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 2 System Model
### Divergence Angle  
Diffraction-limited optics provide the highest laser beam narrowness, with a beamwidth of:  
\[\Theta=\frac{\lambda*2.24}{D} \tag{3}\]  
where \(\lambda\) is the wavelength of laser transmission, and \(D\) is the diameter of the optical aperture of the transmitter. Considering the laser beam width that was used in [19], with a 1550 nm wavelength and the same diameter optical receiver (11 cm) as in [5], the smallest divergence angle that can work for the proposed design is 6.944 \(\times\) 10\({}^{-5}\) radians. By using Equation (1), the radius at the distance of 1500 m will be 5.5 cm for the angle 6.944 \(\times\) 10  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wavelength & 1550 nm \\ Train speed & 270 m/s-750 m/s \\ Source diameter & 5 cm \\ Receiver diameter & 11 cm \\ Transmitter optical power & variable up to 1 W/cm\({}^{2}\) \\ Transmitter efficiency & 0.9 \\ Receiver efficiency & 0.9 \\ Beam divergence & variable \\ Receiver sensitivity, \(P_{\mathrm{rs}}\) & –36 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation parameters.  
Figure 2: Top geometrical view for the proposed model (ideal case).  
so this radius can cover all the transceiver lens area and acquires a high density of the beam for accurate communication.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Optimization of RIS Phase Shift Matrix
#### 4.2.3 RIS Phase Shift Matrix for Joint eMBB and URLLC \(\Phi_{e,u}^{*}\)  
The RIS phase shift matrix \(\Phi_{u}^{*}\) improves URLLC performance, which can severely impact eMBB performance. Therefore, a uniform RIS phase shift matrix is needed to enhance URLLC loads' performance and reduce eMBB traffic performance degradation. Similar to problem \(P3\), the \(\Phi_{e,u}^{*}\) optimization problem can be expressed as follows:  
\[P4:\max\limits_{\Phi_{e,u}}\min\limits_{1\leq x\leq E+U}\left|C\right|^{2} \tag{48}\]\[\begin{array}{l}s.t.if:x\in E\\ C_{e}^{u}=G_{RIS,x}^{e}\Phi_{e,u}H_{RIS,gNB}+D_{x,gNB}^{e},\end{array} \tag{49}\]  
\[\begin{array}{l}if:x\in\{E+1,\cdots,E+U\}\\ C_{u}=G_{RIS,x}^{u}\Phi_{e,u}H_{RIS,gNB}+D_{x,gNB}^{u},\\ \left|\Phi_{n}\right|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n\end{array} \tag{51}\]  
where constraint (49) denotes the representation of the channel gain when \(x\in E\), i.e., \(x\) is an eMBB service. Similarly, constraint (50) indicates the representation of the channel gain when \(x\in\{E+1,\cdots,E+U\}\), i.e., \(x\) is a URLLC service. Problem \(P4\) is solved in a similar way to problem \(P3\).
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## Appendix A  
Integral \(I_{1}\) in Equation (33) can be solved by using the alternative integral expression of the \(Q\)-function as:  
\[Q(x)=\frac{1}{\pi}\int_{0}^{\frac{\pi}{2}}e^{-\frac{x^{2}}{sin^{2}\theta}}d\theta. \tag{1}\]  
Therefore, integral \(I_{1}\) can be rewritten as follows:  
\[I_{1}=\frac{2}{\overline{\Psi}\pi}\int_{0}^{\infty}\int_{0}^{\frac{\pi}{2}}e^ {-\frac{\overline{\Psi}}{sin^{2}\theta}}e^{-\frac{\overline{\Psi}}{\overline {\Psi}}}d\theta d\Psi, \tag{2}\]  
\[I_{1}=\frac{2}{\overline{\Psi}\pi}\int_{0}^{\infty}\int_{0}^{\frac{\pi}{2}}e^ {-(\frac{1}{sin^{2}\theta}+\frac{1}{\overline{\Psi}})\overline{\Psi}}  
\[I_{1}=\frac{2}{\pi}\int_{0}^{\frac{\pi}{2}}\frac{tan^{2}(\theta)}{tan^{2}(\theta)+ \frac{\overline{\Psi}}{cos^{2}(\theta)}}d\theta, \tag{10}\]  
letting \(u=tan(\theta)=\frac{\dot{sin}(\theta)}{cos(\theta)}\) and using some mathematical manipulations, the integral \(I_{1}\) can be expressed as follows:  
\[I_{1}=\frac{2}{\pi}\int_{0}^{\frac{\pi}{2}}\frac{u^{2}}{\left(\left[1+ \overline{\Psi}\right]\right)u^{2}+\overline{\Psi})(u^{2}+1)}du. \tag{11}\]  
Using partial fraction, the integral \(I_{1}\) can be rewritten as follows:  
\[I_{1}=\frac{2}{\pi}\int_{0}^{\frac{\pi}{2}}\frac{-\overline{\Psi}}{\left( \left[1+\overline{\Psi}\right]\right)u^{2}+\overline{\Psi})}du+\frac{2}{\pi} \int_{0}^{\frac{\pi}{2}}\frac{1}{(u^{2}+1)}du. \tag{12}\]  
Using formulas in [29], integral \(I_{1}\) can be given as the formula in Equation (34).  
Appendix B Integral \(I_{3}\) in Equation (40) can be rewritten by using the \(Q\)-function alternative as follows:  
\[I_{3}=\frac{2m^{m}}{\pi\overline{\Psi}^{m}\ \Gamma(m)}\int_{0}^{\infty}\int_{ 0}^{\frac{\pi}{2}}\Psi^{m-1}\ e^{-(\frac{1}{sin^{2}\theta}+\frac{\overline{ \Psi}}{\overline{\Psi}})\Psi}d\theta d\Psi. \tag{13}\]  
But by letting \(C\) as follows:  
\[C=\int_{0}^{\infty}\Psi^{m-1}\ e^{-(\frac{1}{sin^{2}\theta}+\frac{m}{ \overline{\Psi}})\Psi}d\Psi. \tag{14}\]  
By using formulas in [29], the integral \(C\) can be expressed as follows:  
\[C=\frac{\Gamma(m)}{\left(\frac{1}{sin^{2}\theta}+\frac{m}{\overline{\Psi}} \right)^{m}}=\frac{\Gamma(m)}{\frac{\overline{\Psi}}{\overline{\Psi}}\left(1 +\frac{1}{sin^{2}\theta}\right)^{m}}. \tag{15}\]  
Substituting Equation (15) into Equation (13), integral \(I_{3}\) can be expressed as follows:  
\[I_{3}=\frac{2}{\pi}\int_{0}^{\frac{\pi}{2}}\left(1+\frac{\overline{\Psi}}{ msin^{2}\theta}\right)^{-m}d\theta. \tag{16}\]  
The integral \(I_{3}\) in Equation (16) is definite integral whose closed-form solution can be found in [28; 34] as Equation (41).
## 4 Results
### Vulnerability  
As shown in Equation (11), we adopted the AHP model to calculate the vulnerability of the study area. Moreover, the weight of each vulnerability factor was obtained by quantitatively analyzing the relationships among various vulnerability factors through the judgment matrix (Table 6). In the consistency test of this judgment matrix, the values of the CI, RI, and CR are 0.024, 1.120, and 0.022, respectively, indicating that the calculation process satisfies the requirements of the AHP model. Based on the natural breakpoint method, the generated vulnerability map of the study area was divided into five grades: very low (0-0.06), low (0.06-0.17), moderate (0.17-0.38), high (0.38-0.69), and very high (0.69-1).  
\[V=0.378\times VF_{1}+0.271\times VF_{2}+0.185\times VF_{3}+0.120\times VF_{4}+0.046\times VF_{5} \tag{11}\]  
where \(V\) is the vulnerability value of each slope unit, and \(VF_{1}\) to \(VF_{5}\) are the values of the corresponding vulnerability factors shown in Table 4.  
Figure 9 shows that the zones with high and very high vulnerability are mainly located in the northern part of the study area, including Kashi City and its surrounding towns of Shufu County and Shule County. The zones with high and very high vulnerability account for 0.55% and 1.42% of the study area, respectively. The moderate-vulnerability zones are mainly distributed in the surrounding towns of Kashi City, the county seat of Taskorgan County, and Aoyitake Town, accounting for 5.27% of the whole area. The coverage of very low- and low-vulnerability zones is significantly larger, accounting for 60.37% and 32.39% of the study area, respectively. In addition, the vulnerability is relatively high near the mining sites and roads.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vulnerability** & \(VF_{1}\) & \(VF_{2}\) & \(VF_{3}\) & \(VF_{4}\) & \(VF_{5}\) & **Weight** \\ \hline
**\(VF_{1}\)** & \(1\) & & & & & 0.378 \\
**\(VF_{2}\)** & \(1/2\) & \(1\) & & & & 0.271 \\
**\(VF_{3}\)** & \(1/3\) & \(1/2\) & \(1\) & & & 0.185 \\
**\(VF_{4}\)** & \(1/4\) & \(1/3\) & \(1/2\) & \(1\) & & 0.120 \\
**\(VF_{5}\)** & \(1/6\) & \(1/5\) & \(1/4\) & \(1/3\) & \(1\) & 0.046 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The judgment matrix and weight values of the vulnerability factors.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Invalid or Fraudulent Data Management
#### 3.5.2 Special Cases: Blockchains in Fraud Recognition in VANETs  
The organization of data and information flow in vehicular ad hoc networks (VANETs) is one of the key applications of blockchain technology in the field of intelligent transportation systems (ITSs). Providing organization is crucial in all domains, but it becomes particularly essential in vehicular networks due to the increasing complexity. This is because any disruption in the information flow can significantly impact the network's functionality and, by extension, the safety and efficiency of the transport system. The numerous mobile components and various entities involved make VANETs susceptible to and provide opportunities for fraudulent activities.  
By introducing enhanced data accessibility and improved network reliability, blockchains offer a secure and safe framework to address such issues, and, in some instances, they prevent fraud from occurring. Manipulation of the blockchain is challenging because a record can only be validated and modified through a consensus in the blockchain network. This decentralized and secure nature of blockchain technology provides a robust solution against potential threats and fraud in VANETs (see Figure 10).  
The process a piece of data goes through in a blockchain-based VANET involves the following: First, a vehicle generates data, which is then stored in the vehicle's local blockchain ledger. The validity of the data is then checked. If the data are valid, they are shared across the VANET, where the network peers validate the data. If the network peers accept the data, they are added to the global blockchain. If the network peers do not accept the data or if the data were initially found to be invalid, they are discarded.
## 4 Methodology
### Extending to a Simheuristic Approach  
Deterministic travel times are widely assumed in transportation problems. However, when dealing with real-life problems, which are often fraught with uncertainty, travel times are usually stochastic in nature. As introduced in Section 3, the deterministic travel time employed to traverse edge \((i,j)\), \(t_{ij}\), can be seen as the travel time required under ideal traffic conditions. In the proposed extension, the stochastic time to traverse \((i,j)\) is computed as \(T_{ij}=t_{ij}+D_{ij}\), where \(D_{ij}\) follows a \(logNormal(\mu,\sigma)\) probability distribution, and represents a random delay resulting from uncertain conditions. Since the \(logNormal\) probability distribution can only take positive values, it follows that \(T_{ij}>t_{ij}\), \(\forall(i,j)\) in the set of connecting edges. Algorithm 3 describes our simheuristic approach (Sim\({}_{\text{BRLH}}\)). Initially, a solution is generated by our BRLH in line 1 (Algorithm 2), by employing the greedy approach (i.e., \(\beta\approx 1\)). A short simulation is then performed on this initial solution (line 2), in order to estimate its average stochastic cost. This initial solution is set as the best-found stochastic solution cost (line 4). While the termination criterion is not met (line 6), different solutions are generated by BRLH (line 7). The deterministic cost of the initial solution is considered for guiding the search. Therefore, a solution is accepted for being submitted to the simulation module (line 10) only if its deterministic cost is smaller than the best-found deterministic solution cost plus \(m\%\) of its value (line 9). This solution filtering approach reduces the amount of time spent on testing unpromising solutions in computationally expensive simulations. Moreover, by allowing the acceptance of moderately worse solutions, controlled by the parameter \(m\), a betterexploration of the solution space can be achieved [63]. At this stage, \(q_{short}\) Monte Carlo simulation runs are used to test the accepted solution. Each simulation run replaces the deterministic travel times of a solution with randomly sampled ones-according to the assumed probability distribution. From this complete simulation process, the average stochastic cost of each solution is computed. Every time a new best stochastic cost is found (line 15), this solution is introduced into a pool of 'elite' solutions \(E\) (line 17). This process is repeated while the termination criterion is not met. On this reduced set of solutions, \(q_{long}\) Monte Carlo simulation runs are performed (line 23) in order to generate more accurate results for solutions in stochastic environments. During the simulation process we also obtain an estimate of the reliability rate of a solution [64]. This estimate is computed as the rate at which all routes show completion times lower than the maximum allowed travel time. At the end, the set of elite solutions is sorted in descending order of their expected cost (line 25), and the best-found stochastic solution is provided to the manager.
## 3 Data and Methods
### Data Collection and Processing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Relative Weight (P\({}_{i}\))**} & \multicolumn{10}{c}{**Normalization Factor (C)**} \\ \cline{3-11}  & & **100** & **90** & **80** & **70** & **60** & **50** & **40** & **30** & **20** & **10** & **0** \\ \hline T/\({}^{\circ}\)C & 1 & 21/16 & 22/15 & 24/14 & 26/12 & 28/10 & 30/5 & 32/0 & 36/-2 & 40/-4 & 45/-6 & 45/-6 \\ pH & & 7 & 7\(\sim\)8 & 7\(\sim\)85 & 7\(\sim\)9 & 6.5-7 & 6\(\sim\)9.5 & 5\(\sim\)10 & 4\(\sim\)11 & 3\(\sim\)12 & 2\(\sim\)13 & 1\(\sim\)14 \\ TUR/NTU & 2 & 5 & 10 & 15 & 20 & 25 & 30 & 40 & 60 & 80 & 5100 & 100 \\ DO/(mg/L) & 4 & \(\geqslant\)7.5 & 7 & 6.5 & 6 & 5 & 4 & 3.5 & 3 & 2 & \(\geqslant\)1 & 1 \\ TN/(mg/L) & 2 & 0.1 & 0.2 & 0.35 & 0.5 & 0.75 & 1 & 1.25 & 1.5 & 1.75 & \(\leqslant\)2 & 2 \\ NO\({}_{2}\)-N/(mg/L) & 3 & 0.01 & 0.05 & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 & 0.75 & 1 & \(\leqslant\)125 & 1.25 \\ NO\({}_{2}\)-N(mg/L) & 2 & 0.005 & 0.01 & 0.03 & 0.05 & 0.1 & 0.15 & 0.2 & 0.25 & 0.5 & \(\leqslant\)1 & 1 \\ NO\({}_{3}\)-N(mg/L) & & 0.5 & 2 & 4 & 6 & 8 & 10 & 15 & 20 & 50 & \(\leqslant\)100 & 100 \\ TP/(mg/L) & 1 & 0.01 & 0.02 & 0.05 & 0.1 & 0.15 & 0.2 & 0.25 & 0.3 & 0.35 & \(\leqslant\)0.4 & 100 \\ CO\({}_{\text{Mn}}\)/(mg/L) & 3 & 1 & 2 & 3 & 4 & 6 & 8 & 10 & 12 & 14 & \(\leqslant\)15 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WQI calculation table. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Comprehensive Water Quality Index Interval**} & \multicolumn{2}{c}{**Water Quality Conditions**} \\ \hline  & 91–100 & Excellent \\  & 71–90 & Good \\  & 51–70 & Medium \\  & 26–50 & Average \\  & Poor \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chaohu WQI. The water quality variable monitoring data collected in this case study were daily water quality from 2020 to 2022, totaling 40,400 items, and were divided into two datasets [27]: 75 percent of the data will be used for the training process of the model and 25 percent for the testing process. This classification method, which is widely used in such data-driven modeling as time-series prediction, was able to achieve relatively good Experimental results [5,27].
## 2 Related Work
### Routing Protocols through a Backbone  
Routing through a backbone is restricted in a set of particular nodes to reduce the communication overhead for route discovery and the number of active nodes for WSNs. A typical approach for backbone formation is to partition the network into clusters consisted of cluster heads and ordinary nodes. The cluster heads are then linked to form the connected backbone. Several approaches have been presented to select the cluster heads and construct the clusters. The optimal selection of cluster heads is an NPhard problem [22], thus many heuristic methods have been designed for approximate solutions. The clustering phase of heuristics can be done in both centralized and distributed ways. In our work, we are interested in the network clustering of large-scale distributed systems. The challenge is to partition the network in an efficient and distributed manner, i.e., to design an effective distributed clustering protocol.  
Such protocols include (i) highest connectivity cluster algorithm and (ii) highest-ID or lowest-ID cluster algorithm. The highest connectivity cluster algorithm of MMM [23] creates the clusters to consider the node's connectivity. Nodes with the highest connectivity are elected as cluster heads. In the case of a tie, the node with the lowest ID prevails. Other nodes will be members of the nearest cluster head. However, this approach can result in a high turnover of cluster heads as the network topology changes, which is undesirable due to the high overhead associated with cluster heads changeover. The lowest-ID cluster algorithm of MMM creates the clusters using the unique IDs of the nodes. The lowest-ID node among neighbors is elected as the cluster head. Other nodes could be organized using the same process of the highest connectivity cluster algorithm of MMM.  
HCDD applies the backbone construction algorithm for wireless ad hoc networks, such as Max-Min \(d\)-cluster, to WSNs. It forms \(d\)-hop clusters using the node ID. The \(d\)-hop cluster method is efficient for large-scale networks because it generates an appropriate number of cluster heads and the sensed data is transmitted in a multi-hop manner. The Max-Min \(d\)-cluster balances the load of the cluster heads by allocating a similar number of nodes in each cluster. However, there are many characteristics in WSNs, such as limited node resources, battery dependency and data fusion. Thus, this method cannot be applied to a realistic sensor environment. The Max-Min \(d\)-cluster algorithm forms clusters using a randomly allocating node ID. It has a tendency to re-elect existing cluster heads when it reconstructs the backbone. In spite of having a heavier load than that of the ordinary node, the cluster head will consume energy quickly if it has low energy. As a result, the network is easily disconnected or must form a new backbone. The Max-Min \(d\)-cluster algorithm may be suitable for ad hoc networks in which nodes have high energy and higher mobility. However, when we apply it to WSNs, it needs to form a backbone considering appropriate factors, rather than considering the fixed node ID.
# Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras
## 4 Results Evaluation
### Data Synchronization and Communication  
In the integrated center for autonomous driving or the road traffic control center, several traffic surveillance cameras are installed and operated in the management area to monitor road conditions. The process of analyzing multiple traffic surveillance camera information with a shooting speed of 30 frames per second (FPS) in real time and delivering it to multiple users requires a large amount of resources in terms of hardware and software, and specifically, it can be said that fast data loading and provision are necessary conditions. To this end, Kafka specialization for distributed processing environments was adopted and used for loading object information and delivering visualization information in this study. Kafka is a distributed data-streaming platform that can store and process data in real time. In other words, it is an open-source-based solution that processes data streams from multiple sources and delivers them to multiple users.