IEEE Open Journal of Vehicular Technology最新文献

{"title":"Explainable Federated Framework for Enhanced Security and Privacy in Connected Vehicles Against Advanced Persistent Threats","authors":"Sudhina Kumar G K;Krishna Prakasha K;Balachandra Muniyal;Muttukrishnan Rajarajan","doi":"10.1109/OJVT.2025.3576366","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3576366","url":null,"abstract":"The increasing adoption of autonomous and intelligent vehicles within ground transportation systems faces new security challenges. This shift from human-controlled operations opens up a broader attack surface for malicious players. As the interconnected Internet of Things (IoT) become ubiquitous in vehicles, they continuously generate and exchange a large amount of data. This tendency creates vulnerabilities that attackers can exploit using sophisticated techniques, such as Advanced Persistent Threats (APT). Detecting APTs in IoT-enabled vehicular environments is crucial. These APTs demand advanced detection mechanisms. The critical need for vehicular data privacy restricts traditional centralized Machine Learning (ML) approaches. Furthermore, the absence of publicly available APT datasets in the vehicular domain complicates model development and validation, creating a significant gap in cybersecurity capabilities for this evolving vehicular domain. This research proposes a novel Federated Deep Neural Network (FDNN) framework with a privacy-preserving technique to address these concerns. This study presents the key challenges in the APT detection phase and outlines the novel contributions to the body of knowledge. The research questions guiding the investigation are addressed and discussed. The features of the UNSW-NB15, Edge-IIoTset, and CSE-CIC-IDS2018 datasets are aligned with different stages of APT attacks. Using these datasets, the developed framework is analyzed and evaluated. For the mentioned datasets, the framework without privacy-preserving technique shows high APT detection accuracies of 97.32%, 96.81% and 98.06%, respectively. However, with the privacy-preserving technique, the framework shows 95.62%, 96.11% and 95.63% accuracies, respectively. All results with other evaluation metrics, such as Precision, False positive rate, F1 score etc., are tabulated. The developed framework is subjected to “Shapley Additive explanations (SHAP),” analysis to filter the considerably influential features in APT detection. This research establishes the efficacy of a novel framework for detecting APTs in distributed vehicular environments. The framework achieves superior performance by minimizing the number of data and reducing the number of features, which is demonstrated through rigorous experimentation on multiple benchmark datasets. The potential of the developed framework to detect the APTs in the cross-domain is discussed in future works.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1438-1463"},"PeriodicalIF":5.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wavelet Transform Aided Single-Carrier FDMA With Index Modulation","authors":"Zhou Lu;Mohammed El-Hajjar;Lie-Liang Yang","doi":"10.1109/OJVT.2025.3576062","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3576062","url":null,"abstract":"Single-carrier frequency-division multiple access (SC-FDMA) is a well-known multiuser transmission method for uplink communications owing to its low peak-to-average power ratio (PAPR) characteristics. Simultaneously, index modulation (IM) has been widely studied owing to its flexibility for spectral-efficiency versus energy-efficiency trade-off. However, applying conventional IM schemes with SC-FDMA may affect the desirable characteristics of SC-FDMA signals, resulting in the increase of PAPR, for example. On the other side, Wavelet Transform (WT) has been shown to provide an improved performance over the fast Fourier transform (FFT)-based SC-FDMA, owing to WT's local focusing capability in both time and frequency domains. In this paper, we propose three IM schemes, namely Symbol Position Index Modulation (SPIM), Spreading Matrix Index Modulation (SMIM) and Joint Matrix-Symbol Index Modulation (JMSIM) schemes, which perform IM at the symbol vector level, spreading matrix level, or a combination of both. These IM schemes are implemented with the WT-based SC-FDMA for data transmission. We consider two spreading matrix design schemes, namely random dispersion matrix design and Gram-Schmidt (GS) orthogonalization matrix design. Correspondingly, we propose different detection schemes, including Maximum Likelihood Detection (MLD), Simplified Maximum Likelihood Detection (SMLD), and the Two Stage Index-QAM Detection (TSD). The performance of the proposed schemes is evaluated by simulations. Our studies and results show that all the three schemes can effectively reduce the PAPR encountered by the conventional IM-assisted SC-FDMA signals. Moreover, the method of GS matrices can provide a gain upto 20 dB compared with the method of random dispersion matrices. Furthermore, the GS-based system can employ the proposed low-complexity TSD, allowing to achieve a similar bit error rate (BER) performance as MLD, while requiring significantly low complexity.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1524-1538"},"PeriodicalIF":5.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation-Based Depth Correction Methods for Near Field iToF LiDAR in Motion State","authors":"Mena Nagiub;Thorsten Beuth;Ganesh Sistu;Heinrich Gotzig;Ciarán Eising","doi":"10.1109/OJVT.2025.3565811","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3565811","url":null,"abstract":"This paper presents two approaches to enhance depth correction of the indirect time-of-flight (iToF) LiDAR sensors during a motion state, addressing the challenges of depth ambiguity and motion blur noise. iToF sensors are a key component in modern automotive applications, providing dense depth information for short-range vision applications for autonomous driving and Advanced Driver-Assistance Systems (ADAS). However, the periodic nature of iToF signals leads to depth ambiguity, making it challenging to measure distances accurately, especially in complex environments. Moreover, iToF sensors suffer from motion blur noise when the vehicle is in motion, compromising the accuracy of depth measurements. The proposed methods, which rely on depth correction indirectly through predicting depth bins using segmentation techniques, offer a promising alternative to direct depth regression. By focusing on segmentation-driven prediction, these new methods open up possibilities for more robust and precise depth correction in LiDAR sensor technology, potentially revolutionizing various applications that rely on accurate depth sensing. The results demonstrate the superiority of segmentation methods for depth frames based on 4 DCS samples, highlighting the potential impact and significance of this research in the field and the potential revolutionizing effect of these solutions on various applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1262-1279"},"PeriodicalIF":5.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining Driving Range Prediction of Electric Vehicles Based on Personalized Driving Behavior in Complex Traffic Scenarios","authors":"Yu Jiang;Jianhua Guo;Dong Xie;Zhuoran Hou;Jintao Deng","doi":"10.1109/OJVT.2025.3565498","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3565498","url":null,"abstract":"With the development of electric vehicles (EVs), accurate prediction of the remaining driving range (RDR) is a crucial issue. However, predicting RDR for EVs still poses significant challenges, particularly under complex traffic scenarios and personalized driving behaviors, where existing methodologies struggle to meet adaptability requirements. This study proposes a novel RDR prediction method for EVs that incorporates personalized driving behavior and emphasizes the influence of future route information on prediction performance. Real-world driving data, encompassing driving patterns, traffic conditions, and road characteristics, serve as the foundation for constructing a precise driving behavior model with Hidden Markov Model (HMM) and Fuzzy Markov Model (FMM). Furthermore, an energy consumption model that integrates the physical model with machine learning techniques is constructed to accurately predict energy consumption rates under future driving cycles. By coupling this consumption rate with battery load in an equivalent circuit model (ECM), the RDR is accurately predicted. To validate the performance of the proposed method, it is compared with baseline models under various driving scenarios. Experimental results demonstrate the accuracy and adaptability of the model, with a relative error within 10% in practical driving validation. This methodology offers insights into energy efficiency optimization and intelligent decision-making for EVs in complex traffic environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1333-1347"},"PeriodicalIF":5.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Frozen Foundation Models and Multimodal Fusion for BEV Segmentation and Occupancy Prediction","authors":"Seamie Hayes;Ganesh Sistu;Ciarán Eising","doi":"10.1109/OJVT.2025.3563677","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3563677","url":null,"abstract":"In Bird's Eye View perception, significant emphasis is placed on deploying well-performing, convoluted model architectures and leveraging as many sensor modalities as possible to reach maximal performance. This paper investigates whether foundation models and multi-sensor deployments are essential for enhancing BEV perception. We examine the relative importance of advanced feature extraction versus the number of sensor modalities and assess whether foundation models can address feature extraction limitations and reduce the need for extensive training data. Specifically, incorporating the self-supervised DINOv2 for feature extraction and Metric3Dv2 for depth estimation into the Lift-Splat-Shoot framework results in a 7.4 IoU point increase in vehicle segmentation, representing a relative improvement of 22.4%, while requiring only half the training data and iterations compared to the original model. Furthermore, using Metric3Dv2’s depth maps as a pseudo-LiDAR point cloud within the Simple-BEV model improves IoU by 2.9 points, marking a 6.1% relative increase compared to the Camera-only setup. Finally, we extend the famous Gaussian Splatting BEV perception models, GaussianFormer and GaussianOcc, through multimodal deployment. The addition of LiDAR information in GaussianFormer results in a 9.4-point increase in mIoU, a 48.7% improvement over the Camera-only model, nearing state-of-the-art multimodal performance even with limited LiDAR scans. In the self-supervised GaussianOcc model, incorporating LiDAR leads to a 0.36-point increase in mIoU, representing a 3.6% improvement over the Camera-only model. This limited gain can be attributed to the absence of LiDAR encoding and the self-supervised nature of the model. Overall, our findings highlight the critical role of foundation models and multi-sensor integration in advancing BEV perception. By leveraging sophisticated foundation models and multi-sensor deployment, we can further model performance and reduce data requirements, addressing key challenges in BEV perception.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1241-1261"},"PeriodicalIF":5.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Planning of Electrified Road Structures Using Queuing Models","authors":"Eiman ElGhanam;Mohamed S. Hassan;Ahmed M. Benaya;Ahmed Osman","doi":"10.1109/OJVT.2025.3563109","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3563109","url":null,"abstract":"Dynamic wireless charging (DWC) of electric vehicles (EVs) is an attractive solution to the EV driving range limitations and the associated range anxiety problem. In DWC, charging lanes are deployed along city roads to wirelessly supply the needed charging power to EVs during their motion. However, due to the high construction costs of electrified road structures (ERS) with wireless charging lanes and the likely increase in the energy demand by EV owners, an optimal deployment plan is essential to maximize the net returns to the infrastructure owners and ensure maximal demand coverage. Therefore, to formulate a reliable optimization framework, accurate modeling of the charging lane operation is needed at each potential lane location. In this work, the traffic behavior at different locations is modeled analytically using queuing theory. This accurately represents the desired flow of vehicles on the charging lanes and provides a reliable estimate of the EV charging demand, particularly due to the lack of EV traffic flow datasets with the currently low but expanding penetration of EVs. A multi-objective optimization framework is then developed based on the established traffic model to determine the most optimal locations for the deployment of DWC lanes within a smart city infrastructure. The model is tested on 24 candidate roads selected from the United Arab Emirates map and the corresponding optimal locations are determined by solving the optimization problem on GAMS/CONOPT solver. Sensitivity analysis is also conducted to validate the results of the proposed model.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1222-1240"},"PeriodicalIF":5.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971955","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Efficient Route and Velocity Planning for Electric Vehicles: A Hierarchical Eco-Driving Framework Integrating Traffic and Road Information","authors":"Dong Xie;Jianhua Guo;Yu Jiang;Zhuoran Hou;Jintao Deng","doi":"10.1109/OJVT.2025.3562317","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3562317","url":null,"abstract":"The growing demand for decarbonization, coupled with the development of intelligent transportation systems (ITS), has driven the emergence of eco-driving technologies for electric vehicles (EVs). However, existing eco-driving technologies rarely integrate path and velocity planning while neglecting macro traffic flow and environmental impacts, resulting in less practical and less precise planning outcomes. Therefore, this study proposes a hierarchical eco-driving model that establishes a high-dimensional system incorporating macro traffic flow, micro vehicle model, and road environments. First, a traffic network model is constructed based on the real road topology. Next, a high-precision vehicle energy consumption model and a database of typical driving cycles are established to calculate the edge costs of the road network. Then, an energy-efficient route is efficiently planned using the proposed multi-heuristic A* algorithm. Finally, based on the route information from the upper level, along with traffic, kinematic, and road information, a convex optimization algorithm is employed to achieve accurate and efficient velocity planning. Experimental results demonstrate that the proposed method computes in less than 2 s for most scenarios and can effectively save energy and time by over 10%. The proposed framework offers a new solution for eco-driving and has significant practical implications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1317-1332"},"PeriodicalIF":5.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collision Avoidance Strategies for Uncrewed Aircraft Systems in Structured Airspace Using a Roundabout Intersection","authors":"Skyler Hawkins;Jaya Sravani Mandapaka;Logan McCorkendale;Zachary McCorkendale;Kamesh Namuduri;Shane Nicoll","doi":"10.1109/OJVT.2025.3562581","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3562581","url":null,"abstract":"The increasing size of the Uncrewed Aircraft System (UAS) ecosystem necessitates effective infrastructure and Collision Avoidance (CA) systems to facilitate high-density UAS traffic in urban environments. Unfortunately, current-generation Air Traffic Management (ATM) and CA systems used for crewed aircraft cannot be used with UAS due to scalability issues and operational constraints. This paper introduces a novel UAS intersection called the Roundabout, specifically designed for facilitating UAS traffic in structured airspace. This paper also proposes the methodology for a CA system based on Vehicle-to-Vehicle (V2V) communications, specifically UAS-to-UAS (U2U) communications, for Tactical Deconfliction (TD) between UAS in real-time. Simulation results demonstrate the system's efficacy in handling the deconfliction process between two quadrotor UAS and can be expected to generalize to deconfliction scenarios involving UAS of all types, given that the proper control systems and trajectory generation methods are available. Overall, these findings highlight the Roundabout's potential for enhancing UAS operations in the National Airspace System (NAS).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1193-1208"},"PeriodicalIF":5.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analyses of MRT/MRC in NOMA Full-Duplex Relay Networks With Residual Hardware Impairments","authors":"Mesut Toka;Eray Güven;Güneş Karabulut Kurt;Oğuz Kucur","doi":"10.1109/OJVT.2025.3560515","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3560515","url":null,"abstract":"This paper analyzes the performance of maximum-ratio transmission (MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal multiple access (NOMA) full-duplex (FD) relay networks in the presence of residual hardware impairments (RHIs). The effects of channel estimation errors (CEEs) and imperfect successive interference cancellation are also considered to deal with a more realistic scenario. In the network, the base station and multiple users utilize MRT and MRC, respectively, while a dedicated relay operates in amplify-and-forward mode. Exact outage probability (OP) expression is derived for Nakagami-<inline-formula><tex-math>$m$</tex-math></inline-formula> fading channels. Furthermore, tight lower bound and asymptotic expressions are also derived to provide further insights in terms of diversity order and array gain. The investigated network has been compared to half-duplex (HD)-NOMA and FD-orthogonal multiple access counterparts. The analytical results validated by simulations and test-bed implementations (by using software defined radios) demonstrate the importance of loop-interference cancellation process in the FD relay for the investigated system to perform better than HD-NOMA counterpart. Also, a performance trade-off between the MRT and MRC schemes is observed under CEE effects among users. Furthermore, it is shown that RHIs have a significant effect on the performance of users with lower power coefficients, however it does not change the diversity order. RHIs and CEEs have the most and least deterioration effects on the system performance, respectively.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1178-1192"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Effective Tree-Structured AI Model for Reducing Overhead of Life Cycle Management in Wireless Communication","authors":"Yingshuang Bai;Zhaohui Huang;Chen Sun;Yujie Zhang;Tao Cui;Samuel Atungsiri","doi":"10.1109/OJVT.2025.3560189","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3560189","url":null,"abstract":"Artificial intelligence (AI) has been widely applied across various industries, including wireless communication. AI has been a topic of extensive discussion within the 3rd Generation Partnership Project (3GPP), particularly in the context of the physical layer. It involves applications such as beam management, positioning accuracy enhancement, and channel state information (CSI) feedback improvement. Evaluation results from various companies indicate significant gains in beam management and positioning through AI integration. While AI can replace traditional mechanisms to enhance performance, it also introduces new overheads. For example, the introduction of AI models necessitates addressing model life cycle management (LCM) issues, such as model identification, activation/deactivation, monitoring, updating/finetuning, selection, switching. These operations result in a significant amount of overhead. In this paper, we present the progress of model LCM in 3GPP, and also propose a tree-structured model to reduce the overhead associated with LCM operations. This model can be used in scenarios where multiple models serve the same functionality by merging similar structures, thereby saving storage space, and making the processes of model switching, expansion, and deletion more effective. We also conduct simulations to demonstrate that our approach maintains stable AI model performance while simplifying the model structure.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1100-1107"},"PeriodicalIF":5.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}