基于身份的空间网络安全研究

空间网络安全的研究是天地一体化网络研究的重要组成部分,日益受到研究人员的重视。本文根据空间网络面临的安全威胁,分析需要采用的安全机制,构建了空间网络的安全体系结构。深入分析基于身份的密码体制（IBC）,在空间网络中应用IBC提供安全服务框架,以此为基础,提出了一种密钥交换协议,并详细描述了安全通信的建立过程。分析表明,同传统的基于公钥基础设施（PKI）体制的安全解决方案相比,IBC具有较低的存储、通信和计算开销,是适合空间网络的有效安全方案。     

运载火箭测发控网络设计

运载火箭测试发射控制系统对数据处理能力、数据通信能力的要求越来越高。本文采用最新的网络通信技术,结合CZ-3A运载火箭测发控网络的实际设计需求和设计原则,概要介绍了运载火箭测发控网络的特点,着重从测发控网络的拓扑结构、双网卡捆绑技术、静态路由技术等五方面详细阐述了测发控网络的设计,最后通过测发控网络的功能测试验证了该设计的可靠性和先进性。     

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic con gestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas (PRDs).In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors (NSLF) are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in mainland China is collected as the origin-destination (OD) air port pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67％,while the total length of flight segments and air routes drop by 32.03％ and 5.82％ respectively.The NSLF decreases by 5.82％ with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22％.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.     

网络时代大学生思想教育面临的挑战与对策

互联网作为当今最具影响力的媒体之一,深刻地影响和改变着人们的工作、生活、学习和思雏方式。在网络时代的冲击和挑战下,大学生思想政治教育面临着很多新问题,要求思想政治教育工作者不断提出新的对策,进一步加强和改进大学生思想政治教育。     

微硅加速度传感器的动态特性补偿方法研究

提出了一种用以扩展加速度传感器使用频率范围的动态特性补偿方法,其基本原理是根据加速度传感器的输入输出传递函数,构造出一个基于硬件的补偿网络,这个补偿网络的传递函数的零极点分别与加速度传感器的传递函数的极零点相抵消,从而使由加速度传感器输出的信号经过补偿环节后其动态特性得到补偿,扩展了加速度传感器的使用频带,同时并不要求加速度传感器处于临界阻尼状态。     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

基于VxWorks的无人机模拟器设计与实现CSCD

无人机模拟器通过模拟与地面发控设备的电气接口及逻辑关系和无人机空中飞行全过程实现了对无人机的模拟,是一套用于飞行协同训练及地面发控设备检查的地面辅助设备。该设备采用基于CPCI总线结构、Vx Works操作系统的计算机,实现了串口通信、UDP网络通信、图形人机交互界面等功能。     

现代微波测量的发展动态

从微波测量仪器、测量技术及测量分析出发 ,简单地阐述了现代微波测量发展的趋势。供微波测量人员参考     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.