This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two win...This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).展开更多
Based on rain gauge data during 2008-2021 from national meteorological observation stations,this study investigated the performance of the precipitation field from the 1-km-resolution version of the China Atmospheric ...Based on rain gauge data during 2008-2021 from national meteorological observation stations,this study investigated the performance of the precipitation field from the 1-km-resolution version of the China Atmospheric Realtime Analysis(CARAS)over Hubei from the perspective of climatology,multiple-time scale variations,as well as fusion accuracy and detection capability at multiple temporal scales.The results show that CARAS precipitation can reproduce the spatial distribution patterns of climatological seasonal precipitation and rainy days well over the whole of Hubei compared with observational(OBS)precipitation,albeit deviations exist between CARAS and OBS in terms of magnitude.Moreover,high correlation and consistency between CARAS and OBS can be found in multiple-time scale variations over Hubei,with correlation coefficients of interannual,seasonal,and diurnal variation generally exceeding 0.85,0.98,and 0.95,respectively.Furthermore,CARAS has a relatively higher fusion accuracy in summer and winter,and stronger/weaker detection capability in spring/winter at a daily scale.However,the detection capability of CARAS at an hourly scale is weaker than that at a daily scale.With different precipitation intensity levels considered,CARAS daily precipitation shows relatively higher fusion accuracy in estimating moderate and heavy rain,and better detection capability in capturing no rain events.The variations of accuracy metrics and detection metrics under different precipitation intensities at an hourly scale generally resemble those at a daily scale.However,CARAS precipitation at an hourly scale shows a relatively lower fusion accuracy and weaker detection capability compared with that at a daily scale.This paper provides an insight into the characteristics of systematic deviations in CARAS precipitation over Hubei,which will benefit relevant applications of CARAS in meteorological operations over Hubei and the improvement of CARAS in the future.展开更多
Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of th...Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of the oracle may suffer accuracy degradation when instantiating it through a side-channel trace distinguisher due to the environmental noise and the cross-device issue.While various deep learning-based approaches have been proposed to address the inaccuracy problem caused by the cross-device issue,they often suffer from complexity and limited interpretability.This work investigates realistic numerous side-channel attack(SCA)scenarios and focuses on the cross-device issue when implementing a reliable PC oracle in SCAs against Kyber.TtLR is proposed,it combines the ttest with a logistic regression model to implement a lightweight but effcient side-channel distinguisher against Kyber KEM.The proposed approach is validated through experiments on STM32F407G boards equipped with ARM Cortex-M4 microcontrollers,using the Kyber512 implementations from the pqm4 library.The results demonstrate that the proposed method achieves high PC oracle accuracy across different boards with low computational and memory overhead.This makes the proposed distinguisher practical for deployment on resource-constrained platforms such as the Raspberry Pi running a Linux system.展开更多
Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people i...Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.展开更多
基金National Key Research and Development Program of the Ministry of Science(2018YFB1502801)Hubei Provincial Natural Science Foundation(2022CFD017)Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)。
文摘This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).
基金Key Research Project of Hubei Provincial Tobacco Company(027Y2022-006)Hubei Provincial Natural Science Foundation and Meteorological Innovation and Development Joint Foundation of China(2023AFD104,2022CFD132)+4 种基金Open Project Fund of China Meteorological Administration Basin Heavy Rainfall Key Laboratory(2023BHR-Y03)Open Re-search Fund of China Meteorological Administration/Ministry of Rural Agriculture Tobacco Meteorological Service Center(KYZX2023-08)National Natural Science Foundation of China(42105039)Basic Research Fund of WHIHR(202314)Open Research Topics of Key Open Laboratory of Hydro-Meteorology,China Meteorological Administration(23SWQXM018)。
文摘Based on rain gauge data during 2008-2021 from national meteorological observation stations,this study investigated the performance of the precipitation field from the 1-km-resolution version of the China Atmospheric Realtime Analysis(CARAS)over Hubei from the perspective of climatology,multiple-time scale variations,as well as fusion accuracy and detection capability at multiple temporal scales.The results show that CARAS precipitation can reproduce the spatial distribution patterns of climatological seasonal precipitation and rainy days well over the whole of Hubei compared with observational(OBS)precipitation,albeit deviations exist between CARAS and OBS in terms of magnitude.Moreover,high correlation and consistency between CARAS and OBS can be found in multiple-time scale variations over Hubei,with correlation coefficients of interannual,seasonal,and diurnal variation generally exceeding 0.85,0.98,and 0.95,respectively.Furthermore,CARAS has a relatively higher fusion accuracy in summer and winter,and stronger/weaker detection capability in spring/winter at a daily scale.However,the detection capability of CARAS at an hourly scale is weaker than that at a daily scale.With different precipitation intensity levels considered,CARAS daily precipitation shows relatively higher fusion accuracy in estimating moderate and heavy rain,and better detection capability in capturing no rain events.The variations of accuracy metrics and detection metrics under different precipitation intensities at an hourly scale generally resemble those at a daily scale.However,CARAS precipitation at an hourly scale shows a relatively lower fusion accuracy and weaker detection capability compared with that at a daily scale.This paper provides an insight into the characteristics of systematic deviations in CARAS precipitation over Hubei,which will benefit relevant applications of CARAS in meteorological operations over Hubei and the improvement of CARAS in the future.
基金National Natural Science Foundation of China(62172374)。
文摘Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of the oracle may suffer accuracy degradation when instantiating it through a side-channel trace distinguisher due to the environmental noise and the cross-device issue.While various deep learning-based approaches have been proposed to address the inaccuracy problem caused by the cross-device issue,they often suffer from complexity and limited interpretability.This work investigates realistic numerous side-channel attack(SCA)scenarios and focuses on the cross-device issue when implementing a reliable PC oracle in SCAs against Kyber.TtLR is proposed,it combines the ttest with a logistic regression model to implement a lightweight but effcient side-channel distinguisher against Kyber KEM.The proposed approach is validated through experiments on STM32F407G boards equipped with ARM Cortex-M4 microcontrollers,using the Kyber512 implementations from the pqm4 library.The results demonstrate that the proposed method achieves high PC oracle accuracy across different boards with low computational and memory overhead.This makes the proposed distinguisher practical for deployment on resource-constrained platforms such as the Raspberry Pi running a Linux system.
基金Supported by the National Key Research and Development Program of China(Nos.2021YFC3101801,2023YFC3008200)the National Natural Science Foundation of China(Nos.42476219,41976200)+6 种基金the National Foreign Experts Program(No.S20240134)the Innovative Team Plan of the Department of Education of Guangdong Province(No.2023KCXTD015)the Tropical Ocean Environment in Western Coastal Waters Observation and Research Station of Guangdong Province(No.2024B1212040008)the Independent Research Project of the Southern Ocean Laboratory(No.SML2022SP301)the Shandong Innovation and Development Research Institute Think Tank Projectthe Guangdong Ocean University Scientific Research Program(No.060302032106)the Start-up Fund for Ph D Researchers(No.060302032104)。
文摘Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.
