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Data delivery delay and cross-layer packet size analysis for reliable transmission of Licklider transmission protocol in space networks

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Science China(Information Sciences) 2024年第9期67卷 229-244页

作者： Guannan YANG Ruhai WANG Kanglian ZHAO Wenfeng LI Dong YAN School of Information engineering Nanjing University of Finance and Economics Drayer department of Electrical and computer engineering Lamar University School of Electronic Science and engineering Nanjing University Beijing Aircraft Overall Design department

Delay/disruption tolerant networking(DTN) is proposed as a networking architecture to overcome challenging space communication characteristics for reliable data transmission service in presence of long propagation delays and/or lengthy link disruptions. Bundle protocol(BP) and Licklider Transmission Protocol(LTP) are the main key technologies for DTN. LTP red transmission offers a reliable transmission mechanism for space networks. One of the key metrics used to measure the performance of LTP in space applications is the end-to-end data delivery delay, which is influenced by factors such as the quality of spatial channels and the size of cross-layer packets. In this paper, an end-to-end reliable data delivery delay model of LTP red transmission is proposed using a roulette wheel algorithm, and the roulette wheel algorithm is more in line with the typical random characteristics in space networks. The proposed models are validated through real data transmission experiments on a semi-physical testing platform. Furthermore, the impact of cross-layer packet size on the performance of LTP reliable transmission is analyzed, with a focus on bundle size, block size, and segment size. The analysis and study results presented in this paper offer valuable contributions towards enhancing the reliability of LTP transmission in space communication scenarios.

关键词： Licklider transmission protocol DTN bundle protocol cross-layer packets size space communication

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali department of computer Applications Karpagam College of EngineeringCoimbatore641032India department of computer Science and engineering Karpagam College of EngineeringCoimbatore641032India department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India department of computer Science and engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 675-694页

作者： Abdelfatah Ali Hossam H.H.Mousa Mostafa F.Shaaban Maher A.Azzouz Ahmed S.A.Awad the department of Electrical engineering American University of Sharjah26666 SharjahUnited Arab Emirates the department of Electrical engineering South Valley University83523 QenaEgypt the department of Electrical and computer engineering University of WindsorWindsorON N9B 3P4 Canada Electrical engineering department Qatar UniversityDoha 2713Qatar department of Electrical and computer engineering Sultan Qaboos UniversityMuscat123Oman

Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)***,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)*** electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in ***,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV *** examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power *** can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC *** address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV ***,power-train converters have been modified for high-efficiency and reliability in EV *** paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.

关键词： Charging station charging topology electric vehicle power electronic converter vehicle-to-grid

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Multi-wavelength nanowire micro-LEDs for future high speed optical communication

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Opto-Electronic Advances 2024年第3期7卷 1-3页

作者： Ayush Pandey Zetian Mi department of Electrical engineering and computer Science University of MichiganAnn ArborMI 48109USA

The future of optoelectronics is directed towards small-area light sources,foremost being ***,their use has been inhibited so far primarily due to fabrication and integration challenges,which impair efficiency and ***,bottom-up nanostructures grown using selective area epitaxy have garnered attention as a solution to the aforementioned *** Fu *** used this technique to demonstrate uniform p-i-n core-shell InGaAs/InP nanowire array light emitting *** devices are capable of voltage and geometry-controlled multi-wavelength and high-speed *** publication accentuates the wide capabilities of bottom-up nanostructures to resolve the difficulties of nanoscale optoelectronics.

关键词： communication high operations

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Maximizing aged photovoltaic array power:a computer modelling study

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Clean Energy 2024年第1期8卷 171-187页

作者： Fadi N.Sibai Electrical&computer engineering department Gulf University for Science and TechnologyMishrefKuwait

Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch

Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

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Big Data Mining and Analytics 2024年第1期7卷 205-216页

作者： Aafaq Mohi ud din Shaima Qureshi department of computer Science and engineering National Institute of Technology SrinagarSrinagar 190006India

Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.

关键词： Graph Neural Networks(GNNs) learning on graphs over-smoothing over-squashing isotropic-GNNs anisotropic-GNNs

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

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computers, Materials & Continua 2024年第7期80卷 819-841页

作者： Saad Abdalla Agaili Mohamed Sefer Kurnaz department of Electrical and computer engineering Altinbas UniversityIstanbul34000Turkey

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity ***,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork *** present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service *** compile a broad dataset of labeled VPN traffic flows from various apps and usage ***,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous *** effectively process and categorize encrypted packets,the neural network model has input,hidden,and output *** use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral *** also use cutting-edge optimizationmethods to optimize network characteristics and *** suggested ANN-based categorization method is extensively tested and *** show the model effectively classifies VPN traffic *** also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%*** study improves VPN security and protects against new *** VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security *** study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

关键词： VPN network traffic flow ANN classification intrusion detection data exfiltration encrypted traffic feature extraction network security

An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of computer Science Shaanxi Normal University Faculty of computer Science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences department of computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique

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Clean Energy 2024年第1期8卷 23-35页

作者： Afshin Balal Michael Giesselmann department of Electrical and computer engineering Texas Tech UniversityLubbockTX 79409USA

Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading ***,before installing solar arrays,it is necessary to simulate and determine the possible power *** power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic ***,maximum power point tracking is not a suitable solution for mismatches and partial shading *** overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this *** solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per *** this paper,distributed maximum power point tracking per module is implemented,which has the highest *** technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 ***,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that ***,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this ***,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor *** results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each ***,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous *** example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.

关键词： electric vehicles(EVs) distributed maximum power point tracking solar farm DC fast-charging station step-up DC-DC converter System Advisor Model economic analysis