esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Requirements Prediction in Product Customized Design  
Personalized requirements modeling is the foundation of product customized design. Missing requirements prediction is a potential problem in requirements modeling, gaining the ever-increasing attention of researchers. Many studies have been conducted on this topic, including predicting the implicit requirements and forecasting the trends of requirements.  
One research field is implicit requirements prediction. With the development of the Internet and Internet of Things (IoT), many researches extract implicit CN online or by a cloud-service platform combined with some new techniques in data mining. [PERSON] et al. [12] tried to extract and normalize the implicit requirements by a series of techniques including metaphor, clustering, mapping, and visualization; the requisite requirements collected and predicted for product customization are neglected. [PERSON] et al. [13] designed an automatic filtering model integrated with the Kano model to analyse online reviews, and the mined information is applied to improve product design strategies. [PERSON] et al. [14] built a consumer-centric relationship network by IoT technologies to predict the personalized requirements. [PERSON] et al. [15] analyzed the online reviews by association rule mining based on multi-objective particle swarm optimization for affective design. [PERSON] et al. [16] proposed a two-layer model for latent CN elicitation through use case reasoning, where SVM is used for sentiment analysis in the first layer, and case analogical reasoning is applied to identify implicit CN characteristics in the second layer. The other research field of requirements prediction concerns requirement status. [PERSON] et al. [17] integrated grey theory for fewer requirements data, the Kano model for requirements classification, and a Markov chain for local fluctuations to predict the dynamic requirements. [PERSON] et al. [18] estimated and transmitted weights in quality function deployment to the design attributes to deal with dynamics of CN. [PERSON] et al. [19] combined theKano model and online reviews to analyze dynamic requirements change in CN.  
Many research studies have made full use of online reviews or big data for prediction, which is inspirational. It is useful to link customer groups with clusters of requirements to predict the missing values. However, there exist some technical challenges in requirements prediction. First, most predicted results are indirect and need analysis and processing by professional managers. Then, most approaches focus mainly on the subjective requirements, which is incomplete in real-world requirements for product customization. Finally, CN is often regarded as an individual object, without considering the design activities afterwards. To bridge this gap, this paper presents an approach for predicting missing values of FR, considering the characteristics of different requirements and providing valuable predicted results.
## 2 Overview of the Research Site
### The Case Study Panel  
As seen in Figure 1, Longwall (LW) 7434, positioned adjacent to Goaf 7432 to the south, is bounded by the solid coal area to the west and north and the mining area downhill to the east. The mining depth for LW7434 ranges from 796 to 900 m. In terms of dimensions, LW7434 extends approximately 1237 m along the strike direction and 197 m in width along the inclination. This longwall extracts coal from the No. 7 seam, which has an average thickness of approximately 4.2 m and an inclination angle ranging from 18\({}^{\circ}\) to 25\({}^{\circ}\), with an average of around 23\({}^{\circ}\). Within LW7434, 27 faults have been exposed, among which four exhibit a displacement exceeding 3.5 m. Figure 2 depicts the general stratigraphic column.  
LW7434 is susceptible to rockburst risks due to factors such as its considerable burial depth (resulting in high static load stress levels), variations in coal seam thickness and inclination, fault structures, and fluctuations in the mining rate. From the start of mining in February 2018 to the end of December 2019, 28,565 seismic events were monitored within LW7434, with 363 of these events exhibiting seismic source energies exceeding 9 \(\times\) 10\({}^{3}\) J.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.2 Postequalization GK-DNN  
Since the conventional nonlinear postequalization methods based on the Volterra series suffer from a massive increase in computational complexity when dealing with high-order nonlinearity, researchers have turned to the ML for new inspirations. However, the time-consuming training progress of most ML-based postequalizers limits its actual application. To accelerate the training processing and greatly relieve the computational complexity of the equalizer at the receiver side, researchers have proposed the Gaussian kernel-aided deep neural network (GK-DNN) [54] in the field of VLC systems.  
Compared to the classical MLP, the major unique feature of GK-DNN is that the input data would go through a functional mapping that is based on Gaussian function, namely the Gaussian kernel, which maps the windowed input data to a nonlinear space to reduce the number of iterations and time consumption of the fitting progress. The researchers believe that the adjacent symbols' influence towards the central (or current) one is in accordance with Gaussian distribution, hence the mapping operation would accelerate the training processing. The expression of the Gaussian kernel is given in [54]. It should be noted that the scope-controlling parameter of the Gaussian kernel would greatly affect the equalization performance of GK-DNN. Generally, the larger the parameter is, the faster the training process would be. However, there is a trade-off between the training process acceleration and equalization performance. Therefore, the Gaussian kernel parameter selection is vital to obtain the best performance. Moreover, the selection of the number of hidden layer nodes is equivalently important, which directly decides the computational complexity of the equalizer. According to the experimental results in [54], the GK-DNN equalizer could efficiently realize the postequalization in the VLC system with the aid of Gaussian kernel, which reduces the iteration epochs of the neural network by 47.06%.
## 4 Results
### Empirical Analysis
The overall model reached a significant level (F = 19.669, \(p\) < 0.001), and the overall model explanatory power increased to 34.7%. In terms of the independent variables, the contractual agreement strategy has a positive and significant effect on corporate performance, which means that the contractual agreement strategy has a positive relationship with corporate performance. When the marginal benefit of the contractual agreement strategy is greater than the marginal cost, there is still a positive marginal return. That is, the higher the contractual agreement strategy, the better the performance of the company and the continued expansion; that is to say, in the process of overseas market expansion, the increase in the complexity of the overseas market does not affect the profitability of the company. Therefore, scholars believe that enterprises still maintain good performance after exceeding the optimal level of internationalization [42; 54; 55; 56]. Based on the above discussion, it was proved that hypothesis 1, 2, and 3 was established. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Enterprise**} & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{**Enterprise**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{**Enterprise**} & \multicolumn{1}{c}{**t**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**} \\ \hline
**Dependent variable** & & & & & & & \\ \hline Independent enterprise & \(-\)0.105 & \(-\)2.186 * & \(-\)0.059 & \(-\)1.335 & \(-\)0.047 & \(-\)1.152 \\ \hline Enterprise size & 0.216 & 4.199 *** & 0.191 & 3.772 *** & 0.165 & 3.544 *** \\ \hline Enterprise debt ratio & \(-\)0.311 & \(-\)6.077 *** & \(-\)0.283 & \(-\)5.661 *** & \(-\)0.355 & \(-\)7.251 *** \\ \hline
**Independent variable** & & & & & & \\ \hline Contractual agreement strategy & & & 0.411 & 2.544 * & 0.854 & 4.602 *** \\ \hline Foreign direct investment strategy & & & \(-\)0.322 & \(-\)1.981 * & \(-\)0.665 & \(-\)3.221 ** \\ \hline Manufacturing advantage & & & 0.295 & 5.917 *** & 0.311 & 6.551 *** \\ \hline After-sales service advantage & & & 0.227 & 4.941 *** & 0.188 & 4.215 *** \\ \hline
**Interfering variable** & & & & & & \\ \hline Industrial Environment & & & & & & \\ \hline
**Interaction** & & & & & & \\ \hline Contractual agreement Strategy \(\times\) industrial environment & & & & & \\ \hline F & 15.941 *** & 17.893 *** & & 19.669 *** \\ \hline R\({}^{2}\) & 0.113 & 0.250 & & 0.346 \\ \hline \(\Delta\)R\({}^{2}\) & 0.113 & 0.138 & & 0.095 \\ \hline \multicolumn{2}{c}{N = 286; * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001.} & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hierarchical regression analysis.
## 3 Problem Definition and Formulations
### Mathematical Formulation  
The mathematical formulation of the comprehensive CDP is presented as a MILP model, derived from the graphical formulation introduced in Section 3.2.
# Integrated Digital-Twin-Based Decision Support System for
## 6 Case Study Implementation
### Test Scenario  
To test the effectiveness of the proposed DT framework in module allocation planning for RMBs, a simulated logistics scenario was conducted based on the DT platform developed in previous chapters. This platform incorporates comprehensive BIM, GIS, and simulated IoT sensor data for all RMS projects. The BIM data include module geometries, while GIS data offer insights into geographic locations and accessibility, enhancing the logistics and planning processes.  
Table 1 details the specifics of a hypothetical RMS project named \"Demo new project\". This project is located at coordinates 36.4613928, 127.1220681, and is scheduled to be in use from 1 May 2024 to 4 December 2024. The project demands a single-floor structure with a two-sided corridor layout and specified quantities for various module types such as classrooms, teacher rooms, restrooms, machine rooms, lobbies, and halls.  
Figure 11: RMS project building information.  
Figure 10: RMS project mapping and clustering.  
The DT platform to visualize the spatial distribution of existing RMS projects relative to the new simulated project location is depicted on the map, where red spots indicate the locations of surrounding RMS projects. This geographic visualization serves as a critical input for the DT platform's task of identifying suitable modules for the new project. The system is tasked with selecting module units that are available and meet the project's requirements, prioritizing factors like shortest transport distance, module usage period compatibility, and current condition ratings.
## 2 Literature Review
### Intrusion Path-Based Analysis Methods  
Intrusion path-based vulnerability assessment methods for PPS currently represent the predominant research direction. These research methods originated from the EASI model developed by Sandia National laboratories (SNL), which evaluates protection vulnerability along a designated path under specified threat scenarios [4; 12]. Subsequently, SNL developed the SAVI, Very-Simplified EASI (VEASI), Multipath Very-simplified EASI (MP-VEASI), and Analytic System and Software for Evaluating Safeguards and Security (ASSESS) models as enhanced and simplified versions of the original EASI framework [12; 13; 14]. Other researchers have also conducted studies based on the EASI model, with [PERSON] et al. being the first to apply it to calculate vulnerability in nuclear reactor PPSs [15]. The team later developed a multi-path model for nuclear facility PPSs that identifies paths with the lowest interruption probabilities. [PERSON] et al. [16] addressed vulnerable path assessment in a PPS through a heuristic path-finding algorithm that analyzes adversary attack paths, using detection and interruption probabilities as heuristic information based on design-based threats. They subsequently established a virtual environment-based method for PPS design, analysis, and training exercises. [PERSON] et al. [17] proposed an adaptive probability mapping matrix search algorithm for PPS vulnerability analysis, which identifies critical adversary paths from a risk perspective. Later, they developed a heuristic backward path search algorithm incorporating the EASI model to evaluate the remaining delay time after critical detection points. [PERSON] et al. [18] optimized PPS communication probabilities based on intrusion modeling theory, considering the equivalence between communication delay time and timely response time, thereby improving the accuracy of interruption probability assessment. [PERSON] et al. [19; 20] designed a PPS for nuclear facilities that integrates multiple advanced technologies, evaluated its security using the EASI methodology, and subsequently collaborated with other researchers to assess a hypothetical nuclear research center using the SAVI method. [PERSON] et al. [21] developed an EASI-based three-dimensional method for identifying PPS vulnerability paths that incorporates both aerial and underwater threat response capabilities. [PERSON] et al. [11] enhanced the EASI method to include the evaluation of cyber-attack response capabilities in PPS.
## 8 Numerical Simulation and Experimental Testing
### Experimental Testing and Analysis  
#### 8.2.1 Establishment of Unmanned-Ship Experimental Environment  
In order to further verify the effectiveness of the multi-sensor data fusion positioning method of the unmanned ship for water quality testing based on the proposed TCPF, the ZigBee-GPS/BDS multi-sensor combined positioning system test platform as shown in Figures 1 and 2 was established with the three-body unmanned ship as the carrier, and the unmanned ship navigation positioning experiment was carried out in the river channel of Zhangjiagang Campus of Jiangsu University of Science and Technology. In order to facilitate the experimental analysis and calculation, the starting point coordinate was subtracted from the preprocessed unmanned ship coordinate data, and the relative coordinate was used for testing. After calculation, the starting point coordinate of the unmanned ship was set to (3.8941, 0).  
In order to evaluate the filtering accuracy of the unmanned-ship position information based on the proposed TCPF data fusion algorithm, first, the industrial computer equipped with the Linux system collected the position information of the unmanned ship using multiple sensors and outputted it from the serial port to the PC in the form of a log file through the wireless communication module. Then, the PC integrated the received position information of the unmanned ship into a data set [33]. After that, the designed TCPF data fusion algorithm was used to fuse and filter the position information data set of the unmanned ship, and finally realize the precise positioning of the unmanned ship. Figure 5 shows the experimental environment for the unmanned-ship positioning.  
In order to verify the validity and superiority of the ZigBee-GPS/BDS multi-sensor positioning system for unmanned-ship navigation in different environments, the test river was divided into two sections, A and B, and two groups of ZigBee reference nodes were set along the A/B river according to the ZigBee networking principle. Among them, section A was located on the west side of the school library, which has an open water surface and was easy for the unmanned ship to navigate. However, the B section of the river was located on the west side of the school gymnasium, with dense vegetation and narrow channels, which had a certain environmental disturbance effect on the signal transmission of the ZigBee-GPS/BDS sensors. Figure 6 shows the schematic diagram of the node layout and navigation trajectory on the A/B river.  
Figure 4: RMSE and Std tests of five algorithms against different models: (**a**) \(f_{1}\) model, \(Q_{1}\)\(\sim\)\(N\)(0,0.5), \(R_{1}\)\(\sim\)\(N\)(0,1); (**b**) \(f_{1}\) model, \(Q_{1}\)\(\sim\)\(N\)(0,0.5), \(R_{1}\)\(\sim\)\(N\)(0,1); (**c**) \(f_{2}\) model, \(Q_{2}\)\(\sim\)\(N\)(0,1), \(R_{3}\)\(\sim\)\(N\)(0,5); (**d**) \(f_{2}\) model, \(Q_{2}\)\(\sim\)\(N\)(0,1), \(R_{3}\)\(\sim\)\(N\)(0,5); (**e**) \(f_{3}\) model, \(Q_{3}\)\(\sim\)\(N\)(0,5), \(R_{5}\)\(\sim\)\(N\)(0,10); and (**f**) \(f_{3}\) model, \(Q_{3}\)\(\sim\)\(N\)(0,5), \(R_{5}\)\(\sim\)\(N\)(0,10).
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 1 Introduction
In order to prevent impersonation and to establish a firm trust, each fog-node or user in the system must be authenticated and identified uniquely. Further, to ensure privacy and security, there is a vital need to encrypt the data that are transmitted via open medium and then stored in the cloud server or fog-nodes. Nonetheless, the pre-sharing of session keys between the end devices and the fog-nodes is not realistic due to the mobile and dynamic nature of various fog-nodes and end devices. Thus, AKA (authenticated key agreement) protocol provides the best solution for authenticating nodes or users and generating session keys [14]. The main contributions of this article are as follows:  
* This paper presents a three-party identity-based authenticated key agreement scheme to secure the communication of data among the participants in an IoT based fog-driven healthcare system. The robust identity-based three-party AKA protocol is designed using hash, XOR and concatenation operations. * The proposed protocol ensures mutual authentication among users, fog-nodes and cloud service providers for the establishment of a shared session key. This shared session key is used by all the participants. * Our designed protocol guarantees that the identity of any user can not be exposed to any adversary except the cloud server. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Authors** & **Year** & **Technique** & **Demerits** \\ \hline [PERSON] and [PERSON] [5] & 2009 & ECC-based & \begin{tabular}{l} Lacks perfect forward secrecy and vulnerable \\ to impersonation attacks \\ \end{tabular} \\ \hline [PERSON] et al. [6] & 2012 & Identity-based & Prone to impersonation and DoS attacks \\ \hline [PERSON] and [PERSON] [7] & 2015 & Bilinear pairing & \begin{tabular}{l} High computation overhead and prone to impersonation attacks \\ \end{tabular} \\ \hline [PERSON] et al. [8] & 2018 & Hash-based & Prone to stolen smart-card and insider attacks \\ \hline [PERSON] et al. [9] & 2018 & Identity-based & Lacks user anonymity and privacy \\ \hline [PERSON] et al. [10] & 2019 & Identity-based & Vulnerable to impersonation attacks \\ \hline [PERSON] et al. [11] & 2020 & Bilinear pairing & \
\begin{tabular}{l} Vulnerable to impersonation and secret key \\ leakage attacks \\ \end{tabular} \\ \hline [PERSON] et al. [12] & 2020 & Bilinear pairing &
\begin{tabular}{l} Vulnerable to ephemeral secret leakage \\ (ESL) attack \\ \end{tabular} \\ \hline [PERSON] et al. [13] & 2020 & Bilinear pairing & Vulnerable to impersonation attacks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing studies. The rest of our article is formulated according to the following. The complexity assumptions are illustrated in Section 2, while introduced protocol is presented in Section 3. Section 4 describes the security analysis of our presented scheme. Performance comparison of the proposed protocol and related competing protocols is illustrated in Section 5. Lastly, we have concluded our work in Section 6.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 5 The Layered Architecture of the IoV and Emerging Technologies in Smart Cities
### AI Layer  
This layer is a key enabler of the intelligent decision-making process that constitutes the applications of IoV. This allows the AI Layer to reveal meaningful information and recommendations for the efficient and viable operation of the IoV network, further accelerating the operation of connected vehicles and devices. AI and big data technologies combined with 6G provide opportunities for smart cities equipped with the new generation of intelligent applications [71]. Research is being carried out on the fusion of ML, DL, and swarm intelligence techniques in conjunction with the 5G/6G-enabled IoV networks to enrich intelligent vehicles within a smart city architecture [72; 73].
## 4 Conclusions  
SSO-MIPs microspheres with a particle size of about 50-500 \(\upmu\)m were synthesized via emulsion polymerization with solanesol as a template molecule, methyl methacrylate as a functional monomer, ethylene glycol dimethacrylate as a cross-linking agent, and potassium persulfate as an initiator. The optimized process conditions were determined to be a molar ratio of the template molecules to the functional monomers to the cross-linking agents of 1:8:30 (mol:mol:mol), a synthesis temperature of 70 \({}^{\circ}\)C, and a dosage of the initiator of 10 mg.  
The SSO-MIPs were synthesized with the optimal synthesis process condition. Static adsorption experiments were conducted to study the kinetics and adsorption isotherms of the SSO-MIPs. The maximum adsorption capacity of the SSO-MIPs was found to reach 67.55 mg/g, and the imprinting factor reached 2.51. The adsorption of SSO-MIPs to solanesol reached the adsorption equilibrium at 3 h, and the adsorption amount of the SSO-MIPs did not increase when the concentration of solanesol in the adsorption solution was greater than 3 g/L.  
In this paper, emulsion polymerization was applied to the synthesis of solanesol molecularly imprinted polymers, and good adsorption effect were obtained, thereby providing the report of a reliable synthesis method. However, there have not been many reports on the synthesis of solanesol molecularly imprinted polymers via emulsion polymerization. Thus, in the future, more research should be that includes, but is not limited to, the following aspects: (1) the investigation of a synthesis method capable of producing a more uniform spherical solanesol molecularly imprinted polymer, (2) the investigation of the specific recognition ability of solanesol molecularly imprinted polymers, and (3) the study of the large-scale application of solanesol molecularly imprinted polymers.  
[PERSON]. ([PERSON]) and [PERSON]; designed the experiments and provided testing instruments. [PERSON] and [PERSON]; revised the format and language of the article and conducted data analysis. [PERSON]. ([PERSON]); guided the whole experiment process. [PERSON] and [PERSON]; reviewed the article and provided comments and suggestions. [PERSON] and [PERSON]; performed the experiments and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51503215) and the Young talent program of Chinese Academy of Agricultural Sciences.  
The authors declare no conflict of interest.  
Figure 14: Percentage of solanesol peak area in desorption solution.