Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000495
Mário Nascimento Carvalho Filho, M. Campista
Communications over long distances and strong resilience to interference are vital aspects of LoRa. LoRa adjusts the modulation to allow higher data transmission rates, depending on the reception sensitivity threshold and the communication distance. The spreading factor and the transmission power, in turn, are directly related to energy consumption, influencing network performance. This paper proposes the use of supervised learning techniques to conFigure the spreading factor and the transmission power simultaneously. This approach differs from the literature as it configures two parameters instead of just one, the spreading factor. Different learning techniques are evaluated through simulations using a LoRa network. Our experiments compare the performance of our proposal with the traditional LoRaWAN and the state-of-the-art on intelligent configuration using only the spreading factor. The obtained results show that our proposal successfully reduces the energy consumption without affecting the packet delivery ratio.
{"title":"Intelligent Configuration of PHY-Layer Parameters to Reduce Energy Consumption in LoRa","authors":"Mário Nascimento Carvalho Filho, M. Campista","doi":"10.1109/LATINCOM56090.2022.10000495","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000495","url":null,"abstract":"Communications over long distances and strong resilience to interference are vital aspects of LoRa. LoRa adjusts the modulation to allow higher data transmission rates, depending on the reception sensitivity threshold and the communication distance. The spreading factor and the transmission power, in turn, are directly related to energy consumption, influencing network performance. This paper proposes the use of supervised learning techniques to conFigure the spreading factor and the transmission power simultaneously. This approach differs from the literature as it configures two parameters instead of just one, the spreading factor. Different learning techniques are evaluated through simulations using a LoRa network. Our experiments compare the performance of our proposal with the traditional LoRaWAN and the state-of-the-art on intelligent configuration using only the spreading factor. The obtained results show that our proposal successfully reduces the energy consumption without affecting the packet delivery ratio.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116096581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000559
Giovanni Aparecido Da Silva Oliveira, P. Lima, Fabio Kon, R. Terada, D. Batista, Roberto Hirata, Mosab Hamdan
Over the last three decades, cyberattacks have become a threat to national security. These attacks can compromise Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks and affect society. In this paper, we explore Artificial Intelligence (AI) techniques with Machine and Deep Learning models to improve the performance of an anomaly-based Intrusion Detection System (IDS). We use the ensemble classifier method to find the best combination between multiple models of prediction algorithms and to stack the output of these individual models to obtain the final prediction of a new and unique model with better precision. Although, there are many ensemble approaches, finding a suitable ensemble configuration for a given dataset is still challenging. We designed an Artificial Neural Network (ANN) with the Adam optimizer to update all model weights based on training data and achieve the best performance. The result shows that it is possible to use a stacked ensemble classifier to achieve good evaluation metrics. For instance, the average accuracy achieved by one of the proposed models was 99.7%. This result was better than the results obtained by any other individual classifier. All the developed code is publicly available to ensure reproducibility.
{"title":"A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks","authors":"Giovanni Aparecido Da Silva Oliveira, P. Lima, Fabio Kon, R. Terada, D. Batista, Roberto Hirata, Mosab Hamdan","doi":"10.1109/LATINCOM56090.2022.10000559","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000559","url":null,"abstract":"Over the last three decades, cyberattacks have become a threat to national security. These attacks can compromise Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks and affect society. In this paper, we explore Artificial Intelligence (AI) techniques with Machine and Deep Learning models to improve the performance of an anomaly-based Intrusion Detection System (IDS). We use the ensemble classifier method to find the best combination between multiple models of prediction algorithms and to stack the output of these individual models to obtain the final prediction of a new and unique model with better precision. Although, there are many ensemble approaches, finding a suitable ensemble configuration for a given dataset is still challenging. We designed an Artificial Neural Network (ANN) with the Adam optimizer to update all model weights based on training data and achieve the best performance. The result shows that it is possible to use a stacked ensemble classifier to achieve good evaluation metrics. For instance, the average accuracy achieved by one of the proposed models was 99.7%. This result was better than the results obtained by any other individual classifier. All the developed code is publicly available to ensure reproducibility.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114310938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000584
Fatemeh Banaeizadeh, M. Barbeau, Joaquín García, Venkata Srinivas Kothapalli, E. Kranakis
Ground users suffer from severe uplink interference originating from high altitude Unmanned Aerial Vehicle (UAV) line-of-sight channels. Using multi-armed bandit, we propose a method aiming to find the best resource block and transmit power level for a UAV dynamically paired with a ground user using Non-Orthogonal Multiple Access (NOMA). It is done according to the UAV’s location. It results in mitigating the UAV-uplink interference on its co-channel ground user and maximizing the sum of their data rate in the shared resource block. Performance is evaluated via simulating three exploration-exploitation strategies, namely, epsilon-greedy, upper confidence bound and Thompson sampling.
{"title":"Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA","authors":"Fatemeh Banaeizadeh, M. Barbeau, Joaquín García, Venkata Srinivas Kothapalli, E. Kranakis","doi":"10.1109/LATINCOM56090.2022.10000584","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000584","url":null,"abstract":"Ground users suffer from severe uplink interference originating from high altitude Unmanned Aerial Vehicle (UAV) line-of-sight channels. Using multi-armed bandit, we propose a method aiming to find the best resource block and transmit power level for a UAV dynamically paired with a ground user using Non-Orthogonal Multiple Access (NOMA). It is done according to the UAV’s location. It results in mitigating the UAV-uplink interference on its co-channel ground user and maximizing the sum of their data rate in the shared resource block. Performance is evaluated via simulating three exploration-exploitation strategies, namely, epsilon-greedy, upper confidence bound and Thompson sampling.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000522
S. Trindade, N. Fonseca
The dynamic spectrum allocation in Elastic Optical Networks with Space-Division Multiplexing can cause high spectrum fragmentation, reducing the probability for future connection establishments. This paper proposes a multipath routing algorithm to improve the spectrum allocation in EON-SDM networks using Multi-Core Fibers. The multipath algorithm parallelizes the search for frequency slots to be allocated to a lightpath and avoids the allocation of bands that can increase the fragmentation of the spectrum. Results show that our algorithm can effectively reduce the blocking probability of requests for connection establishment.
{"title":"Split-Demand and Multipath Routing in Space-Division Multiplexing Optical Networks","authors":"S. Trindade, N. Fonseca","doi":"10.1109/LATINCOM56090.2022.10000522","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000522","url":null,"abstract":"The dynamic spectrum allocation in Elastic Optical Networks with Space-Division Multiplexing can cause high spectrum fragmentation, reducing the probability for future connection establishments. This paper proposes a multipath routing algorithm to improve the spectrum allocation in EON-SDM networks using Multi-Core Fibers. The multipath algorithm parallelizes the search for frequency slots to be allocated to a lightpath and avoids the allocation of bands that can increase the fragmentation of the spectrum. Results show that our algorithm can effectively reduce the blocking probability of requests for connection establishment.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125651199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000530
H. Waldman, R. C. Bortoletto, Vinicius F. de Souza, R. C. Almeida
This paper presents simulations of a novel algorithm for spectrum assignment on an elastic link that achieves reductions of nearly 50% of the losses incurred under the first-fit algorithm with the same random sequence of requests. Instead of trying to reduce the fragmentation itself, the new algorithm aims to optimize the matching between each request and the spectral void chosen for its assignment. For this purpose, it prioritizes functional voids over dysfunctional ones, which are deferred in order to maximize their chance of recovering functionality through coalescence with neighbouring voids brought about by connection terminations. The simulations were performed for a 2-class traffic with requests for bitrates of 400 Gb/s and 1 Tb/s, with uniform and non-uniform traffic profiles, and with slot numbers optimized for short (400 km), intermediate (1600 km) and long (8000 km) distances.
{"title":"A Proactive Algorithm for the Mitigation of Fragmentation Losses in Elastic Links","authors":"H. Waldman, R. C. Bortoletto, Vinicius F. de Souza, R. C. Almeida","doi":"10.1109/LATINCOM56090.2022.10000530","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000530","url":null,"abstract":"This paper presents simulations of a novel algorithm for spectrum assignment on an elastic link that achieves reductions of nearly 50% of the losses incurred under the first-fit algorithm with the same random sequence of requests. Instead of trying to reduce the fragmentation itself, the new algorithm aims to optimize the matching between each request and the spectral void chosen for its assignment. For this purpose, it prioritizes functional voids over dysfunctional ones, which are deferred in order to maximize their chance of recovering functionality through coalescence with neighbouring voids brought about by connection terminations. The simulations were performed for a 2-class traffic with requests for bitrates of 400 Gb/s and 1 Tb/s, with uniform and non-uniform traffic profiles, and with slot numbers optimized for short (400 km), intermediate (1600 km) and long (8000 km) distances.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115786449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000531
Marcelo Gonzalez, Felipe Condon, P. Morales, N. Jara
Deep Reinforcement Learning (DRL) has proven a considerable potential for enabling non-trivial solutions to resource allocation problems in optical networks. However, applying plain DRL does not ensure better performance than currently known best heuristics solutions. DRL demands a parameter tuning process to improve its performance. One tuning possibility is the reward function design. The reward function allows feedback to the agents on whether the actions sent to the environment were successful or not. A transparent reward function returns whether the action succeeds or not, but an elaborate reward function may allow inducing the desired behaviour to improve DRL performance. Our work designs reward functions in multi-band elastic optical networks (MB-EON) to improve the overall network blocking probability. A test environment was set up to analyze the performance of four reward functions for inducing a lower blocking probability. The proposed reward functions use band usage, link compactness, spectrum availability and link fragmentation as feedback information to the agents. Analysis was carried out using the DQN agent in the NSFNet network topology. Results show that reward function design improves the blocking probability. The best-performing one uses the band availability criteria, decreasing the blocking probability, as an average, by 22% compared to the baseline reward function, with a peak of 63,67% of improvement for a 1000 Erlang traffic load scenario.
{"title":"Improving Multi-Band Elastic Optical Networks Performance using Behavior Induction on Deep Reinforcement Learning","authors":"Marcelo Gonzalez, Felipe Condon, P. Morales, N. Jara","doi":"10.1109/LATINCOM56090.2022.10000531","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000531","url":null,"abstract":"Deep Reinforcement Learning (DRL) has proven a considerable potential for enabling non-trivial solutions to resource allocation problems in optical networks. However, applying plain DRL does not ensure better performance than currently known best heuristics solutions. DRL demands a parameter tuning process to improve its performance. One tuning possibility is the reward function design. The reward function allows feedback to the agents on whether the actions sent to the environment were successful or not. A transparent reward function returns whether the action succeeds or not, but an elaborate reward function may allow inducing the desired behaviour to improve DRL performance. Our work designs reward functions in multi-band elastic optical networks (MB-EON) to improve the overall network blocking probability. A test environment was set up to analyze the performance of four reward functions for inducing a lower blocking probability. The proposed reward functions use band usage, link compactness, spectrum availability and link fragmentation as feedback information to the agents. Analysis was carried out using the DQN agent in the NSFNet network topology. Results show that reward function design improves the blocking probability. The best-performing one uses the band availability criteria, decreasing the blocking probability, as an average, by 22% compared to the baseline reward function, with a peak of 63,67% of improvement for a 1000 Erlang traffic load scenario.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"2673 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132786524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000523
Y. Nuñez, LISANDRO LOVISOLO, L. Mello, Carlos Orihuela
Millimeter-wave communication systems design require accurate path-loss prediction, critical to determining coverage area and system capacity. In this work, four machine learning algorithms are proposed for path-loss prediction in an indoor environment for 5G millimeter-wave frequencies, from 26.5 to 40 GHz. They are artificial neural network, support vector regression, random forest, and gradient tree boosting. We compare their performances, including extensions of the empirical path-loss models alpha-beta-gamma and close-in frequency-dependent exponent incorporating the number of crossed walls. The results show that the ML techniques improve the prediction accuracy of empirical models.
{"title":"Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques","authors":"Y. Nuñez, LISANDRO LOVISOLO, L. Mello, Carlos Orihuela","doi":"10.1109/LATINCOM56090.2022.10000523","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000523","url":null,"abstract":"Millimeter-wave communication systems design require accurate path-loss prediction, critical to determining coverage area and system capacity. In this work, four machine learning algorithms are proposed for path-loss prediction in an indoor environment for 5G millimeter-wave frequencies, from 26.5 to 40 GHz. They are artificial neural network, support vector regression, random forest, and gradient tree boosting. We compare their performances, including extensions of the empirical path-loss models alpha-beta-gamma and close-in frequency-dependent exponent incorporating the number of crossed walls. The results show that the ML techniques improve the prediction accuracy of empirical models.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133243694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000470
P. Belzarena
This work presents PyWiCh, an open source wireless channel simulator which can be used both as a stand-alone software through its graphical interface or integrated into other applications. Wireless channel simulators are an essential tool for the research and development of next-generation networks based on massive MIMO and millimeter waves. PyWiCh is the first simulator developed in Python that implements the 3GPP simulation model for 5G networks. PyWiCh also proposes solutions to two current research problems in wireless channel simulation: spatial consistency and scatters movement. Through its use, adoption, and enhancement, this project intends to build a community that continues work and research on this topic so as to improve the simulator and develop new features and new models which can be integrated into PyWiCh.
{"title":"PyWiCh: Python Wireless Channel Simulator","authors":"P. Belzarena","doi":"10.1109/LATINCOM56090.2022.10000470","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000470","url":null,"abstract":"This work presents PyWiCh, an open source wireless channel simulator which can be used both as a stand-alone software through its graphical interface or integrated into other applications. Wireless channel simulators are an essential tool for the research and development of next-generation networks based on massive MIMO and millimeter waves. PyWiCh is the first simulator developed in Python that implements the 3GPP simulation model for 5G networks. PyWiCh also proposes solutions to two current research problems in wireless channel simulation: spatial consistency and scatters movement. Through its use, adoption, and enhancement, this project intends to build a community that continues work and research on this topic so as to improve the simulator and develop new features and new models which can be integrated into PyWiCh.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/latincom56090.2022.10000516
{"title":"LATINCOM 2022 Message from the General Chairs","authors":"","doi":"10.1109/latincom56090.2022.10000516","DOIUrl":"https://doi.org/10.1109/latincom56090.2022.10000516","url":null,"abstract":"","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114482749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000520
N. C. Matson, D. Rajan, J. Camp
Inspired by its success in other fields, there have been many recent developments in the use of machine learning and neural networks to enable multiuser communication and to design efficient channel codes along with practical decoders. However, there has been little attempt to combine the results of these efforts. In this paper, for the first time, we present a neural network autoencoder architecture to jointly address both problems. The resulting codes designed by our simple and easy-to-train neural network can have arbitrary rates, are comparable to existing state-of-the-art neural network designed codes, and are directly applicable in a multiuser context. We analyze these single-user codes and characterize the design parameters which affect their performance. We then show that these same single-user codes can be used to operate close the maximum sum rate of a K-user Gaussian multiple access channel (MAC) under various SNR scenarios, without the need for retraining or learning a joint code. This improved performance is achieved by introducing a new iterative successive interference cancellation method (SIC) that outperforms traditional onion-peeling.
{"title":"Design and Analysis of Neural-Network-based, Single-User Codes for Multiuser Channels","authors":"N. C. Matson, D. Rajan, J. Camp","doi":"10.1109/LATINCOM56090.2022.10000520","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000520","url":null,"abstract":"Inspired by its success in other fields, there have been many recent developments in the use of machine learning and neural networks to enable multiuser communication and to design efficient channel codes along with practical decoders. However, there has been little attempt to combine the results of these efforts. In this paper, for the first time, we present a neural network autoencoder architecture to jointly address both problems. The resulting codes designed by our simple and easy-to-train neural network can have arbitrary rates, are comparable to existing state-of-the-art neural network designed codes, and are directly applicable in a multiuser context. We analyze these single-user codes and characterize the design parameters which affect their performance. We then show that these same single-user codes can be used to operate close the maximum sum rate of a K-user Gaussian multiple access channel (MAC) under various SNR scenarios, without the need for retraining or learning a joint code. This improved performance is achieved by introducing a new iterative successive interference cancellation method (SIC) that outperforms traditional onion-peeling.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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