An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years,the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper,which is based on the Hyperspectral Features(HFs)of hypersonic targets covered with plasma sheath during high-speed flight.First,a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs.Then,the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar.Finally,an unsupervised classification method based on the Density Peak Clustering(DPC)for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy.The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set.The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy(OA),Average Accuracy(AA)and Kappa coefficient.

高吸附电化学纤维传感器用于实时、准确检测颅内一氧化氮

电化学一氧化氮传感器能够实时监测颅内一氧化氮浓度,对于了解大脑中一氧化氮的功能至关重要.然而,在大脑中使用的传统刚性传感电极面临着灵敏度低和植入后神经炎症引起一氧化氮浓度异常的问题.在这里,我们报道了一种结合物理和化学吸附能力、具有高灵敏度和准确性的电化学一氧化氮传感器.其对一氧化氮的物理和化学吸附能力分别来自于电极的高比表面积和丰富的羧基官能团.此外,柔软的电极可以与脑组织的力学性能相匹配,实现了一个高度适应的电极/组织界面.由此设计的颅内一氧化氮传感器表现出迄今为止所报道文献中最高的灵敏度,为3245 pA nmol^(-1)L,检测限为0.1 nmol L^(-1).电极在植入后未观察到显著的炎症反应以及过量的一氧化氮表达,提高了检测的准确性.该传感器成功捕捉了大脑中的一氧化氮波动,并实现了对多个脑区的同时检测,促进了对大脑中一氧化氮生理病理作用的研究.

Nonstationary channel model of reentry plasma sheath for spacecraft: Overview, parameter estimation, and perspective

The Tracking,Telemetering,and Command(TT&C)communication of spacecraft faces severe limitations during reentry.The Electromagnetic(EM)propagation channel is influenced by changes in the flight state and fluid dynamics.In addition to the high-speed spatial channel,the Plasma Sheath Channel(PSC),which covers the surface of spacecraft,exhibits unique nonstationary and highly dynamic time-varying characteristics.The absence of a channel model for the reentry process of spacecraft negatively impacts the quality evaluation and system design of TT&C communication.This paper summarizes the methods for nonstationary PSC modeling,involving calculation methods for attenuation and phase shift in the channel,as well as a multistate nonstationary channel statistical model based on a hidden Markov process.Furthermore,the correlation between amplitude variation and phase variation with electron density is analyzed.The paper also provides statistical characteristics of the channel and channel parameters at typical frequencies in the S/C/X/Ka bands.These findings serve as a reference for the design and evaluation of TT&C communication systems for reentry spacecraft.Finally,the paper discusses the prospects of various aspects related to the channel characteristics and modeling of spacecraft PSCs in the shortwave and terahertz/laser bands,as well as the potential challenges in cross-domain spacecraft channel sensing and prediction.