Reduce the application of phosphorus fertilizer in peanut fields and improve its efficiency by using iron modified biochar to adsorb phosphorus recovery products.

Introduction: To address the scarcity of agricultural phosphorus (P) fertilizers and reduce phosphorus accumulation in wastewater, this study employed iron-modified biochar (Fe-B) to adsorb phosphorus from water. The phosphorus-loaded iron-modified biochar (Fe-BP) was subsequently applied to peanut fields. Batch experiments were conducted to determine the optimal adsorption parameters and mechanism of Fe-B for phosphate ions (PO ).

Foreign Object Debris Automatic Target Detection for Millimeter-Wave Surveillance Radar.

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system's detection method has a short effective range, and the detectable objects' radar cross-section intensity is no less than -20 dBsm.

FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar.

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability.

Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks.

Tomographic SAR (TomoSAR) is a remote sensing technique that extends the conventional two-dimensional (2-D) synthetic aperture radar (SAR) imaging principle to three-dimensional (3-D) imaging. It produces 3-D point clouds with unavoidable noise that seriously deteriorates the quality of 3-D imaging and the reconstruction of buildings over urban areas. However, existing methods for TomoSAR point cloud processing notably rely on data segmentation, which influences the processing efficiency and denoising performance to a large extent.

A Novel Scheme for MIMO-SAR Systems Using Rotational Orbital Angular Momentum.

The vortex electromagnetic (EM) wave with orbital angular momentum (OAM) brings a new degree of freedom for synthetic aperture radar (SAR) imaging, although to date, its application to multi-input multi-output (MIMO) SAR has not yet been widely reported. In this paper, an orbital angular momentum (OAM)-based MIMO-SAR system is proposed. The rotational Doppler Effect (RDE) of vortex EM waves offers a novel scheme for an OAM-based MIMO-SAR system.