IEEE Open Journal of the Communications Society最新文献

{"title":"Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems","authors":"Van Nhan Vo;Nguyen Quoc Long;Viet-Hung Dang;Tu Dac Ho;Hung Tran;Symeon Chatzinotas;Dinh-Hieu Tran;Surasak Sanguanpong;Chakchai So-In","doi":"10.1109/OJCOMS.2025.3565764","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3565764","url":null,"abstract":"This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4140-4155"},"PeriodicalIF":6.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073252","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":"Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability","authors":"Yasir Ibraheem Mohammed;Rosilah Hassan;Mohammad Kamrul Hasan;Shayla Islam;Huda Saleh Abbas;Muhammad Asghar Khan;Muhammad Attique Khan","doi":"10.1109/OJCOMS.2025.3565471","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3565471","url":null,"abstract":"Uncrewed Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges; this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4295-4310"},"PeriodicalIF":6.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125455","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":"DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs","authors":"Bahareh Jafari;Mazen Hasna;Hossein Pishro-Nik;Nizar Zorba;Tamer Khattab;Hamid Saeedi","doi":"10.1109/OJCOMS.2025.3564837","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564837","url":null,"abstract":"Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations, and we propose specific UAV path planning. A point is said to be in a coverage hole if the coverage probability for that point is below a certain threshold, e.g., 90%. This definition is more suitable for applications such as surveillance or sensor networks. In this paper, we target applications such as wireless communications for which QoS requirement allow only for short time disconnections, i.e., minimal outage time. As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. We show that for a typical point on the cell, there is a trade-off between minimizing the maximum outage time length and consumed mechanical energy. Our observations indicate that such a trade-off is more pronounced for spiral trajectories compared to oval trajectories, but both of them are useful depending on the QoS and energy constraints imposed by the system.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4194-4205"},"PeriodicalIF":6.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072920","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":"Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models","authors":"Ruhul Amin Khalil;Junaid Bahadar Khan;Asiya Jehangir;Nasir Saeed","doi":"10.1109/OJCOMS.2025.3564497","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564497","url":null,"abstract":"In UAV-based localization systems utilizing Time of Arrival (ToA) measurements, Non-Line-of-Sight (NLoS) conditions present a persistent challenge by introducing significant errors that degrade localization accuracy. Traditional techniques rely heavily on prior knowledge of NLoS error statistics or measurement noise characteristics. These dependencies make such methods computationally intensive and less adaptable to dynamic or large-scale scenarios. This paper presents a low-complexity localization algorithm that overcomes these limitations by eliminating the need for prior NLoS error statistics or path status information. The proposed approach dynamically identifies and excludes ToA measurements affected by severe NLoS errors while refining localization accuracy through iterative updates. A two-stage Robust Regression Algorithm (RRA) is employed, combined with an adaptive UAV selection strategy, ensuring both computational efficiency and precise positioning. Theoretical convergence analysis verifies the algorithm’s robustness in selecting reliable UAVs and estimating the accurate position of the target. Simulation results show the algorithm’s superior performance compared to state-of-the-art methods, achieving higher accuracy and efficiency even under severe NLoS conditions. The proposed method’s adaptability, scalability, and robustness make it a valuable solution for accurate localization in complex and dynamic environments, including 5G ultra-dense networks and UAV-based deployments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4051-4062"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073253","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":"Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI","authors":"Hongsen Peng;Meixia Tao","doi":"10.1109/OJCOMS.2025.3564296","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564296","url":null,"abstract":"This paper investigates the cross-layer design for point-to-point ultra-reliable low latency communication (URLLC) over parallel fading sub-channels by jointly considering channel estimation and adaptive data transmission. The model includes a stochastic traffic arrival process and the transmissions are done in the finite blocklength (FBL) regime with imperfect channel state information (CSI). Specifically, we formulate a two-timescale total average power minimization problem under reliability, latency, and peak power constraints. In the large timescale, the pilot length and pilot power are optimized while in the small timescale, the data transmit power and decoding error probability are optimized according to the estimated channel coefficients and queueing information. As a starting step in our small timescale solution, we train a deep reinforcement learning (DRL) agent employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to allocate the data transmit power on each sub-channel and determine the decoding error probability to satisfy the URLLC constraints in an ideal environment with perfect instantaneous CSI. Then we utilize a water-filling framework to accommodate the trained TD3 network for the environment with imperfect CSI. Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. Simulation results are provided to reveal the impact of the reliability, latency and the number of sub-channels. Furthermore, the trained network is demonstrated to be robust towards different traffic arrival models, as well as variations of the average arrival rate.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4126-4139"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073250","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":"Probabilistic-Aware Satellite Constellation Scheduling for Integrated TN-NTN Operations","authors":"Arnau Singla;Isabel Gallardo-Duval;Anna Calveras;Juan A. Fraire;Joan A. Ruiz-De-Azua","doi":"10.1109/OJCOMS.2025.3564411","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564411","url":null,"abstract":"The integration of satellite-based Non-Terrestrial Networks (NTN) with terrestrial communication infrastructures introduces significant challenges, especially in coping with the unpredictable nature of traffic generated by end users. Traditional scheduling approaches in satellite systems often assume deterministic traffic models, limiting their effectiveness in dynamic and data-driven scenarios. This paper presents a framework for integrated NTN operations that incorporates stochastic traffic modeling into satellite scheduling, enabling a more flexible and realistic approach to resource management in NTNs. By leveraging statistical traffic models based on the central limit theorem, the proposed method accounts for traffic uncertainty and embeds it directly into the scheduling process. A key concept introduced is schedule certainty, which quantifies the reliability of a schedule under uncertain input conditions and serves as both a decision variable and an optimization parameter. This novel approach is exemplified through the Constellation Management System framework, extended with a data generation uncertainty model to showcase its practical implementation and benefits. Results demonstrate that probability-aware scheduling achieves a tightly controlled certainty level aligned with operator-defined thresholds, providing higher certainty levels for equivalent performance metrics. This allows satellite operators to dynamically adjust service coverage and system efficiency, accounting for varying levels of traffic uncertainty. This work highlights the importance of probability-aware scheduling in enabling more robust and efficient operation of future satellite-terrestrial hybrid networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3950-3963"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908361","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 Analysis of 5G Positioning Procedures on Resource-Constrained Devices","authors":"Nico Kalis;Christian Haubelt;Frank Golatowski","doi":"10.1109/OJCOMS.2025.3564538","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564538","url":null,"abstract":"The fast positioning of targets in indoor environments poses a significant challenge on resource-constrained devices. Therefore, this article provides novel insights into fast positioning based on the fifth generation (5G) of mobile communication. Initially, a detailed introduction to 5G positioning focusing on a network-based approach that enables Enhanced Cell ID (E-CID) Location Information Transfer is given. To highlight this positioning approach, this article proposes an implementation of corresponding positioning procedures that are based on the New Radio Positioning Protocol A (NRPPa). Additionally, the performance of these positioning procedures is also investigated in terms of their time behavior using statistical methods. More specifically, based on a 5G positioning system, which is executed on two Raspberry Pi 5 single board computers, the Round Trip Time as well as the Periodical Time Interval metrics are examined depending on different parameters, such as the task priority, the thread pool size and the number of parallel executed positioning procedures. The results show that a resource-constrained Raspberry Pi 5 in combination with the proposed implementation is capable to handle the maximum of 256 parallel positioning procedures. In this case, however, the Round Trip Time increases by a factor of up to 829 compared to sequential processing. Furthermore, it is generally possible to transmit measurement data periodically, if the smallest Periodical Time Interval of 120 milliseconds is selected according to the 5G standard. In contrast to the Round Trip Time, the deviation of the Periodical Time Interval from the expected value can be kept largely constant independently of the degree of parallelism, if the thread pool size is decreased.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4206-4222"},"PeriodicalIF":6.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090694","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":"Maximizing Spectrum Efficiency of Data-Carrying Reference Signals via Bayesian Optimization","authors":"Taiki Kato;Hiroki Iimori;Chandan Pradhan;Szabolcs Malomsoky;Naoki Ishikawa","doi":"10.1109/OJCOMS.2025.3562774","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562774","url":null,"abstract":"Data-carrying reference signals are a type of reference signal (RS) constructed on the Grassmann manifold, which allows for simultaneous data transmission and channel estimation to achieve boosted spectral efficiency at high signal-to-noise ratios (SNRs). However, they do not improve spectral efficiency at low to middle SNRs compared with conventional RSs. To address this problem, we propose a numerical optimization-based Grassmann constellation design on the Grassmann manifold that accounts for both data transmission and channel estimation. In our numerical optimization, we derive an upper bound on the normalized mean squared error (NMSE) of estimated channel matrices and a lower bound on the noncoherent average mutual information (AMI), and these bounds are optimized simultaneously by using a Bayesian optimization technique. The proposed objective function outperforms conventional design metrics in obtaining Pareto-optimal constellations for NMSE and AMI. The constellation obtained by our method achieves an NMSE comparable to conventional non-data-carrying RSs while enabling data transmission, resulting in superior AMI performance and improved spectral efficiency even at middle SNRs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3892-3903"},"PeriodicalIF":6.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918735","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":"Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning","authors":"Ramzi Al-Sharawi;Abdelfatah Ali;Mostafa Shaaban;Nasser Qaddoumi;Mohamed S. Abdalzaher","doi":"10.1109/OJCOMS.2025.3564069","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564069","url":null,"abstract":"Smart grid cybersecurity is a critical research challenge due to society’s dependence on reliable electricity. Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. The proposed approach optimizes PMU allocation to maximize topological observability and minimize system cost while improving the FDIA detection performance using machine learning. Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. To demonstrate the proposed approach’s efficacy, it is tested on the IEEE 38-bus distribution system. To verify the strength of the developed KNN classifier, we examined it using seven different metrics: accuracy, brier loss, F1-score, elapsed time, learning curve, receiver operating characteristic curve (ROC) curve, and confusion matrix. The simulation results show that the KNN model achieved superior attack classification performance with a top accuracy of 99.99% and a minimal Brier loss of <inline-formula> <tex-math>$9.9478 times 10^{-4}$ </tex-math></inline-formula> on the ±0.2% PMU observation tolerance dataset. These results highlight the success of our framework in concurrently optimizing attack detection performance, topological observability, and system cost.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4036-4050"},"PeriodicalIF":6.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073251","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":"Explainable Reinforcement and Causal Learning for Improving Trust to 6G Stakeholders","authors":"Miguel Arana-Catania;Amir Sonee;Abdul-Manan Khan;Kavan Fatehi;Yun Tang;Bailu Jin;Anna Soligo;David Boyle;Radu Calinescu;Poonam Yadav;Hamed Ahmadi;Antonios Tsourdos;Weisi Guo;Alessandra Russo","doi":"10.1109/OJCOMS.2025.3563415","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3563415","url":null,"abstract":"Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent’s behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4101-4125"},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072942","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}