相位检测误差对核磁共振陀螺输出频率的影响

针对核磁共振陀螺中采用相位检测方案时可能引起额外频率误差的问题,提出了通过控制电子顺磁共振失谐量及静磁场扰动来抑制额外频率误差的方案。基于Bloch方程,推导了惰性气体原子系综输出频率的表达式,并将相位检测的过程包含在内。建立了考虑相位检测误差的核磁共振陀螺频率误差方程,给出了相位检测引入的额外频率误差表达式并进行了数值仿真。仿真结果表明,通过设定合适的共振失谐量,其额外频率误差至少可以抑制1个数量级,而通过精确地抑制静磁场的一阶及二阶扰动,可以进一步抑制1～3个数量级,将额外频率误差降低到nHz量级。     

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.     

新型柔索式杆束结构压紧释放装置

针对空间机械臂、空间可展开天线等空间机构在收拢状态的杆束结构压紧与释放问题，提出一种基于柔性索捆绑压紧与热刀释放的新型压紧释放装置。建立了柔索式压紧释放装置锁紧状态的力学模型，分析了静态压紧和考虑惯性载荷情况下的绳索预紧力变化情况，得到了满足可靠压紧的绳索预紧力设计值。基于质量和刚度等效，设计了机械臂杆束结构力学等效件，并进行了正弦与随机振动力学试验。试验结果验证了所设计的柔索式杆束结构压紧释放装置的可行性，并可以推广到具有复杂包络的杆束结构压紧释放中。     

反推力装置运动学与动力学仿真

为了研究叶栅式反推力装置各部件在工作过程中的运动学与动力学特性,根据机构运动原理对反推力装置进行简化并建立了运动学与动力学数学模型。以滑动整流罩位移与阻流门所受气动负荷为输入进行运动学与动力学仿真,得到了反推力装置各部件的位移、速度及受力特性曲线,并对比分析了在不同尺寸参数下各部件特征点的运动轨迹和反推力装置负载力变化。结果表明:运动学及动力学仿真结果与工程实际相符；反推力装置机构参数选择不合理时,各设计点会发生干涉现象并导致机构无法运动;机构参数变化对负载力最大值影响尤为突出,在阻流门AC段长度值增大6%,阻流门CB段长度值减小9%的情况下,负载力正向最大值将增大19.53%,负向最大值增大12.67%。研究方法及研究结果可为反推力装置运动学及动力学分析,以及为反推力装置机构优化设计提供参考。      

航天器姿态预设性能控制方法综述

针对存在不确定惯量矩阵及其它未知不确定性(如执行器故障、航天器结构发生突变等)下的航天器姿态控制问题，综述了预设性能控制及其应用的研究进展。首先阐释了预设性能控制方法的基本内涵及其关键步骤；然后分析总结了现有航天器姿态控制以及预设性能控制研究的基本概况与发展趋势；最后面向未来复杂化、智能化的空间任务对航天器控制系统提出的新需求，提出航天器预设性能控制值得研究的问题和方向。     

Evaluation of SARAL/AltiKa performance using GNSS/IMU equipped buoy in Sajafi,Imam Hassan and Kangan Ports

Performance of SARAL/AltiKa mission has been evaluated within 2016 altimeter calibration/validation framework in Persian Gulf through three campaigns conducted in the offshore waters of Sajafi, Imam Hassan and Kangan Ports, while the altimeter overflew the passes 470, 111 and 25 on 13 Feb, 7 March and 17 June 2016, respectively. As the preparation, a lightweight buoy was equipped with a GNSS receiver/choke-ring antenna and a MEMS-based IMU to measure independent datasets in the field operations. To obtain accurate sea surface height (SSH) time series, the offset of the onboard antenna from the equilibrium sea level was predetermined through surveying operations as the buoy was deploying in the onshore waters of Kangan Port. Accordingly, the double-difference carrier phase observations have been processed via the Bernese GPS Software v. 5.0 so as to provide the GNSS-derived time series at the comparison points of the calibration campaigns, once the disturbing effects due to the platform tilt and heave have been eliminated. Owing to comparing of the SSH time series and the associating altimetry 1?Hz GDR-T datasets, the calibration/validation of the SARAL/AltiKa has been performed in the both cases of radiometer and ECMWF wet troposphere corrections so as to identify potential land contamination. An agreement of the present findings in comparison with those attained in other international calibrations sites confirms the promising feasibility of Persian Gulf as a new dedicated site for calibration/validation of ongoing and future altimetry missions.     

Assessment of between-floor structural and topological properties on cognitive map development in multilevel built environments

The present study investigated cognitive map development in multilevel built environments. Three experiments were conducted in complex virtual buildings to examine the effects of five between-floor structural factors that may impede the accuracy of humans’ ability to build multilevel cognitive maps. Results from Experiments 1 and 2 (of three experiments) revealed that difficulties in developing multilevel cognitive maps are not solely caused by the z-axis offset, as is suggested in the literature, but are due to the factorial combination of a between-floor overlap and a z-axis offset. Results from Experiment 2 showed that this process becomes substantially more difficult when the reference directions between different floors have an angular offset from each other. Finally, results from Experiment 3 demonstrated that confusing between-floor heading shifts in aligned buildings did not make it reliably harder to build multilevel cognitive maps. The implications of these findings are discussed in terms of theories of mental representations in multilayered three-dimensional spaces, as well as for architectural design.     

Simulating planetary wave propagation to the upper atmosphere during stratospheric warming events at different mountain wave scenarios

Parameterization schemes of atmospheric normal modes (NMs) and orographic gravity waves (OGWs) have been implemented into the mechanistic Middle and Upper Atmosphere Model (MUAM) simulating atmospheric general circulation. Based on the 12-members ensemble of runs with the MUAM, a composite of the stratospheric warming (SW) has been constructed using the UK Met Office data as the lower boundary conditions. The simulation results show that OGW amplitudes increase at altitudes above 30 km in the Northern Hemisphere after the SW event. At altitudes of about 50 km, OGWs have largest amplitudes over North American and European mountain systems before and during the composite SW, and over Himalayas after the SW. Simulations demonstrate substantial (up to 50–70%) variations of amplitudes of stationary planetary waves (PWs) during and after the SW in the mesosphere-lower thermosphere of the Northern Hemisphere. Westward travelling NMs have amplitude maxima not only in the Northern, but also in the Southern Hemisphere, where these modes have waveguides in the middle and upper atmosphere. Simulated variations of PW and NM amplitudes correspond to changes in the mean zonal wind, EP-fluxes and wave refractive index at different phases of the composite SW events. Inclusion of the parameterization of OGW effects leads to decreases in amplitudes (up to 15%) of almost all SPWs before and after the SW event and their increase (up to 40–60%) after the SW in the stratosphere and mesosphere at middle and high northern latitudes. It is suggested that observed changes in NM amplitudes in the Southern Hemisphere during SW could be caused by divergence of increased southward EP-flux. This EP-flux increases due to OGW drag before SW and extends into the Southern Hemisphere.     

SSPS-OMEGA超大型球形聚光器结构拓扑构型与设计

为同时兼顾千米量级尺度空间太阳能电站聚光系统的在轨发射安装难度、光学收集效率、稳定性及结构工艺等多方面的因素，提出采用基于球面正多面体的经纬线划分法模块化构建球形聚光器。该方法不仅可实现基础划分单元分布均匀、规格种类少，降低在轨构建与运载发射难度。而且只要合理控制弧高等分数和基础划分单元的口径就可以同时保证聚光系统的光学收集效率。理论与仿真结果显示，当弧高等分数为24时，该划分法共有25种基础单元件，整个球面被分成5762块。并且此时模块化构建的实际球形聚光器的光学收集效率与理想球面相比基本保持不变，解决了太阳能电站聚光系统设计中存在的关键问题。     

基于深度学习的毫米波和亚毫米波成像仪的图像增强技术

风云四号卫星毫米波和亚毫米波成像仪(MMSI)数据根据采样方式分为过采样和非过采样数据。由于采样方式的影响,非过采样数据在采样过程中会有一定的信息损失。为解决采用简单的线性插值方法做精细化处理时提升精度有限问题,采用基于深度学习的方法增强MMSI亮温图像,设计卷积神经网络重建风云四号卫星MMSI的亮温图像和风云三号卫星微波成像仪亮温图像。实验结果显示:相比传统的双三次插值方法,在风云三号卫星微波成像仪亮温图像样本上峰值信噪比提升了1.13dB,结构相似度提升了0.01。实验结果表明:对于非过采样亮温数据,采用基于深度学习的方法增强图像具有更高的精度,同时可在其他微波探测仪数据中使用,具有很强的普适性。