To achieve better observation for sea surface,a new generation of wide-swath interferometric altimeter satellites is proposed.Before satellite launch,it is particularly important to study the data processing methods a...To achieve better observation for sea surface,a new generation of wide-swath interferometric altimeter satellites is proposed.Before satellite launch,it is particularly important to study the data processing methods and carry out the detailed error analysis of ocean satellites,because it is directly related to the ultimate ability of satellites to capture ocean information.For this purpose,ocean eddies are considered a specific case of ocean signals,and it can cause significant changes in sea surface elevation.It is suitable for theoretical simulation of the sea surface and systematic simulation of the altimeter.We analyzed the impacts of random error and baseline error on the sea surface and ocean signals and proposed a combined strategy of low-pass filtering,empirical orthogonal function(EOF)decomposition,and linear fitting to remove the errors.Through this strategy,sea surface anomalies caused by errors were considerably improved,and the capability of satellite for capturing ocean information was enhanced.Notably,we found that the baseline error in sea surface height data was likely to cause inaccuracy in eddy boundary detection,as well as false eddy detection.These abnormalities could be prevented for"clean"sea surface height after the errors removal.展开更多
Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately descr...Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.展开更多
基金Supported by the National Key R&D Program of China(No.2016YFC1401008)the Key R&D Program of Shandong Province,China(No.2019GHY112055)+6 种基金the National Natural Science Foundation of China(Nos.U2006211,42090044,41606200,41776183,41906157)the Major Scientifi c and Technological Innovation Projects in Shandong Province(No.2019JZZY010102)the Strategic Priority Research Program of the Chinese Academy of Sciences(Nos.XDA19060101,XDB42000000)the Key Project of Center for Ocean Mega-Science,Chinese Academy of Sciences(No.COMS2019R02)the CAS(Chinese Academy of Sciences)100-Talent Program(No.Y9KY04101L)the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2018SDKJ0102-2)the Fundamental Research Funds for the Central Universities(Hohai University)(No.2018B41814)。
文摘To achieve better observation for sea surface,a new generation of wide-swath interferometric altimeter satellites is proposed.Before satellite launch,it is particularly important to study the data processing methods and carry out the detailed error analysis of ocean satellites,because it is directly related to the ultimate ability of satellites to capture ocean information.For this purpose,ocean eddies are considered a specific case of ocean signals,and it can cause significant changes in sea surface elevation.It is suitable for theoretical simulation of the sea surface and systematic simulation of the altimeter.We analyzed the impacts of random error and baseline error on the sea surface and ocean signals and proposed a combined strategy of low-pass filtering,empirical orthogonal function(EOF)decomposition,and linear fitting to remove the errors.Through this strategy,sea surface anomalies caused by errors were considerably improved,and the capability of satellite for capturing ocean information was enhanced.Notably,we found that the baseline error in sea surface height data was likely to cause inaccuracy in eddy boundary detection,as well as false eddy detection.These abnormalities could be prevented for"clean"sea surface height after the errors removal.
基金supported by the National Natural Science Foundation of China(Grant Nos.52225502,52305200)New Cornerstone Science Foundation through the XPLORER PRIZE,National Key Research and Development Program of China(2024YFB3410201,2018YFB0704300)+1 种基金Scientific and Technological Project of Yunnan Precious Metals Laboratory(YPML-20240502088)Key Research and Development Program of Yunnan Province(202203ZA080001).
文摘Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.
