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.     

Non-stationarity and cross-correlation effects in the MHD solar activity

The analysis of turbulent processes in sunspots and pores which are self-organizing long-lived magnetic structures is a complicated and not yet solved problem. The present work focuses on studying such magneto-hydrodynamic (MHD) formations on the basis of flicker-noise spectroscopy using a new method of multi-parametric analysis. The non-stationarity and cross-correlation effects taking place in solar activity dynamics are considered. The calculated maximum values of non-stationarity factor may become precursors of significant restructuring in solar magnetic activity. The introduced cross-correlation functions enable us to judge synchronization effects between the signals of various solar activity indicators registered simultaneously.     

基于EVENT模型的单向循环航路侧向碰撞风险分析

我国空域骨干航路拥挤不堪,一些支线航路的利用率却很低,单向循环航路的出现很好地解决了这一问题,对其进行安全评估是划设航路最重要的工作之一。在总结EVENT理论和相关文献的基础上,对单向循环航路和混合航路侧向碰撞风险进行比较分析,推算出目标安全等级下单向循环航路容量,并对单向循环航路侧向碰撞风险进行预测。结果表明,单向循环航路侧向碰撞风险远低于混合航路侧向碰撞风险,且未来10年均保持在比较稳定的安全水平。     

强短波衰落事件的太阳活动区磁场位形先兆

本文利用北京天文台提供的高空间分辨率的活动区纵场磁图对1988年的18个活动区进行分析研究,以寻求强SWF耀斑的先兆.结果表明,一磁流在异极性磁流区浮现,同极性磁流合并、异极性磁流侵入和纵向磁场强度剧增4种磁位形演化与强SWF耀斑有密切关系。文中还用1988年9—10月份的25个活动区的磁图和耀斑资料佐证上面的结果。      

太阳射电精细结构事件的频率特性

本文分析了太阳射电短厘米、毫米波段准周期脉动现象(精细结构)的频率特性,得知该现象是由非热电子准周期注入的回旋同步辐射所致.      

直升机旋翼载荷中的非线性强迫振动方程迭代算法

介绍用迭代方法求解直升机桨叶气动载荷中的非线性强迫振动方程。计算时,先假定诱导下洗已确定,求出桨叶的初始载荷;再通过非线性强迫振动方程求出弯－弯－扭弹性变形。以某一直升机为例,求出了桨叶的各阶谐波载荷和弹性变形。计算结果与实验数据吻合。     

高速气流吹袭问题

在高速弹射时,防护气流吹袭对人体产生的损伤目前仍未有令人满意的方法。本文阐述了高速气流吹袭对人体的致伤机理并提供了头及四肢的防护约束方程组。     

交流电量参数采集器的设计

介绍以单片机8031为核心的三相电网电量参数采集器的软硬件设计方法。本采集器采用同步采样技术,对电压、电流直接进行同步交流采样,利用单片机运算处理能力,得出电网的电压、电流、有功、无功功率因素等电量参数。     

空间CCD图像传感器辐射损伤评估方法

CCD图像传感器作为高灵敏度光电集成部件,易受空间辐射的影响而发生性能退化和工作异常。开展其辐射效应评价研究并制定相应的防护对策,是保证其在空间可靠应用的前提。文章通过地面模拟试验和辐射屏蔽计算,对空间用CCD图像传感器电离辐射损伤与位移损伤效应开展评估方法研究,为CCD抗辐射加固设计与地面评估试验提供参考。该方法也可用于其他光电器件和材料辐射效应评价。     

激光辅助车削工艺参数对单晶硅表面质量的影响

为了提高单晶硅激光辅助车削加工表面质量,通过开展激光辅助和常规车削加工试验,结合表面粗糙度、表面形貌及拉曼光谱检测,研究激光辅助车削技术对加工质量的影响。基于正交试验方法,研究单晶硅激光辅助车削工艺参数对表面粗糙度的影响;通过方差分析和极差分析评估各因素对表面粗糙度的影响。研究结果表明：与常规车削相比,激光辅助车削可有效提高加工表面质量,降低材料表面的残余应力。主轴转速、进给速度、切削深度和脉冲占空比对表面粗糙度的贡献率分别为17.51%、44.48%、6.69%和14.70%。确定最佳加工参数组合如下：主轴速度为4 000 r/min,进给速度为2 mm/min,切削深度为5μm,脉冲占空比为30%,最终获得表面粗糙度Rq为2.4 nm的高质量表面。