高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIM...高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIMAC(HINOC Medium Access Control)拆帧端接收的数据流失序。针对该问题,文中提出了一种拆帧重排序方法。通过重排序队列缓存管理、入队逻辑地址计算、超时判断及清空以及出队判断等关键技术的设计和实现来解决多信道绑定机制引起的拆帧乱序问题,并对其关键功能点进行仿真验证和板级验证。实验结果表明,所提方法能够有效处理多信道绑定导致的乱序问题,并且能够确保系统在遇到错误情况时稳定运行,具有较强的鲁棒性,满足万兆同轴宽带接入HIMAC 3.0的功能和性能要求。展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
文摘高性能同轴电缆网络(High Performance Network Over Coax,HINOC)技术是一种光纤同轴混合接入技术,已发展至第3代。为了实现万兆以太网的接入速率,第3代HINOC引入了多信道绑定机制。但该机制在有效扩展HINOC网络信道带宽的同时易导致HIMAC(HINOC Medium Access Control)拆帧端接收的数据流失序。针对该问题,文中提出了一种拆帧重排序方法。通过重排序队列缓存管理、入队逻辑地址计算、超时判断及清空以及出队判断等关键技术的设计和实现来解决多信道绑定机制引起的拆帧乱序问题,并对其关键功能点进行仿真验证和板级验证。实验结果表明,所提方法能够有效处理多信道绑定导致的乱序问题,并且能够确保系统在遇到错误情况时稳定运行,具有较强的鲁棒性,满足万兆同轴宽带接入HIMAC 3.0的功能和性能要求。
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
