Optimizing the end-to-end transmission scheme for hybrid satellite and multihop networks

Zong, Liang; Wang, Han; Wei, Du; Zhao, Chenglin; Luo, Gaofeng

doi:10.1007/s00521-021-06156-7

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Define the difference between samples x i and x j on the kth attribute as shown in Equations ( 1) and (2).…”

Section: Packet Loss Differentiation Feature Selection Methodsmentioning

confidence: 99%

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A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

Wei,

Guo,

Yang

et al. 2023

Entropy

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In light of the high bit error rate in satellite network links, the traditional transmission control protocol (TCP) fails to distinguish between congestion and wireless losses, and existing loss differentiation methods lack heterogeneous ensemble learning models, especially feature selection for loss differentiation, individual classifier selection methods, effective ensemble strategies, etc. A loss differentiation method based on heterogeneous ensemble learning (LDM-HEL) for low-Earth-orbit (LEO) satellite networks is proposed. This method utilizes the Relief and mutual information algorithms for selecting loss differentiation features and employs the least-squares support vector machine, decision tree, logistic regression, and K-nearest neighbor as individual learners. An ensemble strategy is designed using the stochastic gradient descent method to optimize the weights of individual learners. Simulation results demonstrate that the proposed LDM-HEL achieves higher accuracy rate, recall rate, and F1-score in the simulation scenario, and significantly improves throughput performance when applied to TCP. Compared with the integrated model LDM-satellite, the above indexes can be improved by 4.37%, 4.55%, 4.87%, and 9.28%, respectively.

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“…Define the difference between samples x i and x j on the kth attribute as shown in Equations ( 1) and (2).…”

Section: Packet Loss Differentiation Feature Selection Methodsmentioning

confidence: 99%

“…In light of these issues, extensive research has been conducted by numerous scholars [2,3]. From a measurement acquisition perspective, packet loss differentiation can be categorized into explicit and implicit methods.…”

Section: Introductionmentioning

confidence: 99%

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

Wei,

Guo,

Yang

et al. 2023

Entropy

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“…The process achieves a "negative emissions technology" (NET). As of the year 2022, DAC is loss making process since the cost of using DAC to sequester carbon dioxide is far much more than the carbon price (Zong et al, 2023).…”

Section: Direct Air Capturementioning

confidence: 99%

Carbon Capture and Sequestration in The Energy Transition

Barasa,

Olanrewaju

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Carbon capture and sequestration (CCS) as ais a viable mitigation strategy for reducing greenhouse gas (GHG) emissions in fossil-fuel power plants. Although CCS technology can mitigate the anthropogenic GHG emissions, the technologies are associated with additional water requirements for chemical and physical processes to capture and separate CO2 and other environmental impacts like introduction of parasitic loads imposed by carbon capture on power plants hence reduced efficiency and more cooling requirements and groundwater contamination due to CO2 leakage during geologic sequestration. The energy transition requires the decarbonizing of the power, transport, and industry sectors. Carbon capture and storage (CCS) and carbon capture and utilization (CCU) technologies will play a major role in this energy transition by cutting down emissions from new fossil fuel power plants. Main challenges facing the CCS and CCU include low oil price to make CO2-enhanced oil recovery profitable, lack of financial incentives for CO 2 geological storage, limited acceptance, lack facilitating government policy and CCS regulations, and high capital investment.

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A Study of Transmission Overhead of a Hybrid Bundle Retransmission Approach for Deep-Space Communications

Zhou

Wang²,

Yang³

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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Optimizing the end-to-end transmission scheme for hybrid satellite and multihop networks

Cited by 3 publications

References 28 publications

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

Carbon Capture and Sequestration in The Energy Transition

A Study of Transmission Overhead of a Hybrid Bundle Retransmission Approach for Deep-Space Communications

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