Geosynchronous Orbit Synthetic Aperture Radar (GEO SAR) has recently received increasing attention due to its ability of performing staring observations of ground targets. However, GEO SAR staring observation has an ultra-long integration time that conventional frequency domain algorithms cannot handle because of the inaccurately assumed slant range model and existing azimuth aliasing. To overcome this problem, this paper proposes an improved chirp-scaling algorithm that uses a fifth-order slant range model where considering the impact of the "stop and go" assumption to overcome the inaccuracy of the conventional slant model and a two-step processing method to remove azimuth aliasing. Furthermore, the expression of two-dimensional spectrum is deduced based on a series of reversion methods, leading to an improved chirp-scaling algorithm including a high-order-phase coupling function compensation, range and azimuth compression. The important innovations of this algorithm are implementation of a fifth-order order slant range model and removal of azimuth aliasing for GEO SAR staring observations. A simulation of an L-band GEO SAR with 1800 s integration time is used to demonstrate the validity and accuracy of this algorithm.
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http://dx.doi.org/10.3390/s17051058 | DOI Listing |
Sci Rep
December 2024
Earth Observatory of Singapore, Nanyang Technological University, Singapore, 639798, Singapore.
Coastal populations are susceptible to relative sea-level (RSL) rise and accurate local projections are necessary for coastal adaptation. Local RSL rise may deviate from global mean sea-level rise because of processes such as geoid change, glacial isostatic adjustment (GIA), and vertical land motion (VLM). Amongst all factors, the VLM is often inadequately estimated.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, PR China. Electronic address:
The comprehensive effects of environmental dredging on heavy metals (HM) are still uncertain. This study comprehensively evaluates the long-term effects of dredging on the environmental risk and bioavailability of HM (Cu, Ni, Zn, Pb, Cd, Cr, and As) in Lake Taihu, China, by comparing simulated dredged treated (D) and undredged (UD) sediment cores under in-situ conditions for one year. Threshold effect level (TEL), geological accumulation index (I), potential ecological risk index (RI), and ratios of secondary phase and primary phase (RSP) methods were used to assess the environmental risk of sediment HM; and the diffusive gradient in thin-films (DGT) technique was applied to assess the bioavailability of sediment HM.
View Article and Find Full Text PDFAdv Mater
November 2024
Key Laboratory of Advanced Catalysis, Gansu province, State Key Laboratory of Applied Organic Chemistry, College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
The development of efficient and durable electrocatalysts for the acidic oxygen evolution reaction (OER) is essential for advancing renewable hydrogen energy technology. However, the slow deprotonation kinetics of oxo-intermediates, involving the four proton-coupled electron steps, hinder the acidic OER progress. Herein, a RuTiO solid solution electrocatalyst is investigated, which features bridged oxygen (O) sites that act as proton acceptors, accelerating the deprotonation of oxo-intermediates.
View Article and Find Full Text PDFFront Immunol
November 2024
State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
J Dent Res
December 2024
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, PR China.
Accurately delineating individual teeth in 3-dimensional tooth point clouds is an important orthodontic application. Learning-based segmentation methods rely on labeled datasets, which are typically limited in scale due to the labor-intensive process of annotating each tooth. In this article, we propose a self-supervised pretraining framework, named Geo-Net, to boost segmentation performance by leveraging large-scale unlabeled data.
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