空间科学任务协同设计论证平台

空间科学任务协同设计论证存在设计方案耦合强,数据一致性差,数据变更难,数据与流程脱节等问题.为了解决上述问题,建立了典型空间科学任务的多层数字化模型;采用图形化方式对论证流程进行建模,并建立流程与数据的映射关系;通过共享数据池的方式为多岗位用户提供多方案数据协同机制;采用消息总线对数据变更进行及时提醒;利用方案依赖关系矩阵来判别方案耦合关系,最终自动合并任务总体设计方案.作为一个分布式平台,空间科学任务协同设计平台采用Eclipse RCP和Spring技术架构,整合了Hibernate、工作流Activiti5等中间件,提供统一门户,支持多岗位、多任务、多方案、多版本的管理能力,提供论证流程监控、数据协同交互等功能.结合某空间科学任务论证验证了该平台的有效性.      

基于眼动数据的网络搜索行为预测方法

预测用户的网络搜索行为对改进搜索引擎和提升用户体验十分重要.现有大多数方法是基于用户的交互数据,如查询、点击和鼠标移动等.提出一种基于眼动数据的用户网络搜索行为预测方法.通过眼动实验,采集用户在网络搜索任务时的眼睛运动数据,将这些数据转化成两种数据格式:直方图和序列.直方图数据描述用户注意力的分布情况,序列数据呈现用户的扫视路径.使用4种学习算法对用户决策或用户意图进行预测,同时研究两种数据格式的性能.结果显示,两种数据格式均适合于预测用户决策,而序列数据更适合于预测用户意图.该结果表明,利用眼动数据能够有效预测网络搜索行为.      

一种改进的多点融合非均匀校正技术

针对空间宽幅相机在进行非均匀性校正过程中相机镜头与积分球输出口的相对位置发生变换后非均匀性校正精度下降的原因进行了分析,提出了一种改进的融合多点位置的非均匀校正新方法,即为了消除积分球的角均匀性和面均匀性的不足,采用获取多个位置的大量定标图像,融合求取平均值的方法得到校正系数来削弱这种影响,并进行试验验证分析。结果表明,多点融合的校正方法在积分球输出口光强不均匀、又无法固定相机与积分球相对位置的前提下,减少了积分球光强的不均匀性对系数可靠性的影响,相对辐射定标的非均匀校正精度得到大幅提高。     

Influence of lunar topography on simulated surface temperature

The surface temperature of the Moon is one of the essential parameters for the lunar exploration, especially to evaluate the Moon thermophysical features. The distribution of the temperature is heavily influenced by the Moon topography, which, however, is rarely studied in the state-of-art surface temperature models. Therefore, this paper takes the Moon topography into account to improve the surface temperature model, Racca model. The main parameters, such as slopes along the longitude and latitude directions, are estimated with the topography data from Chang’E-1 satellite and the Horn algorithm. Then the effective solar illumination model is then constructed with the slopes and the relative position to the subsolar point. Finally, the temperature distribution over the Moon surface is obtained with the effective illumination model and the improved Racca model. The results indicate that the distribution of the temperature is very sensitive to the fluctuation of the Moon surface. The change of the surface temperature is up to 150 K in some places compared to the result without considering the topography. In addition, the variation of the surface temperature increases with the distance from the subsolar point and the elevation, along both latitude and longitude directions. Furthermore, the simulated surface temperature coincides well with the brightness temperature in 37 GHz observed by the microwave sounder onboard Chang’E-2 satellite. The corresponded emissivity map not only eliminates the influence of the topography, but also hints the inherent properties of the lunar regolith just below the surface. Last but not the least, the distribution of the permanently shadowed regions (PSRs) in the lunar pole area is also evaluated with the simulated surface temperature result.     

Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990–2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation.     

一种基于Kalman滤波的卫星遥测数据判读系统

分析了卫星遥测数据的变化规律,提出了一种基于卡尔曼(Kalman)滤波的遥测数据判读系统框架,并定义了系统中各模块的功能及接口关系,应用卫星实际遥测数据对判读系统的数据预测效果进行了验证。结果表明,此系统对数据异常反应迅速,能够很好地满足卫星测试的实时性要求。     

Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.     

Spectroscopic data and Stark broadening of Cu I and Ag I spectral lines: Selection and analysis

Optical emission and linear laser absorption spectroscopy techniques were used in investigation of plasma with copper and silver admixture. The method of selection of spectral lines and spectroscopic data with the aim of diagnostics of multicomponent air plasma with two metal vapors admixture was developed. Energy level populations behavior on the Boltzmann plot were used for Cu I and Ag I spectroscopic data selection. In this way the selection of spectroscopic data for some of Cu I and Ag I lines was realized. Stark broadening parameters of Cu I and Ag I were examined. Experimentally obtained temperature and electron density radial distributions were used in the calculation of plasma composition in the assumption of local thermodynamic equilibrium. Linear laser absorption spectroscopy was used to examine the state of plasma.     

Comprehensive data reduction package for the Immersion GRating INfrared Spectrograph: IGRINS

We present a Python-based data reduction pipeline package (PLP) for the Immersion GRating INfrared Spectrograph (IGRINS), an instrument that covers the complete H- and K-bands in one exposure with a spectral resolving power of 40,000. The reduction steps carried out by the PLP include flat-fielding, background removal, order extraction, distortion correction, wavelength calibration, and telluric correction using spectra of A type standard stars. As the spectrograph has no moving parts, the PLP automatically reduces the data using predefined functions for the processes of order extraction, distortion correction, and wavelength calibration. Before the telluric correction of the target spectra, the intrinsic hydrogen absorption features of the standard A star are removed with a Gaussian fitting algorithm. The final result is the flux of the target as a function of wavelength. Users can customize the predefined functions for the extraction of the spectrum from the echellogram and adjust the parameters for the fitting functions for the spectra of celestial objects, using “fine-tuning” options, as necessary. Presently, the PLP produces the best results for point-source targets.     

The effect of spatial truncation error on variance of gravity anomalies derived from inversion of satellite orbital and gradiometric data

The spatial truncation error (STE) is a significant systematic error in the integral inversion of satellite gradiometric and orbital data to gravity anomalies at sea level. In order to reduce the effect of STE, a larger area than the desired one is considered in the inversion process, but the anomalies located in its central part are selected as the final results. The STE influences the variance of the results as well because the residual vector, which is contaminated with STE, is used for its estimation. The situation is even more complicated in variance component estimation because of its iterative nature. In this paper, we present a strategy to reduce the effect of STE on the a posteriori   variance factor and the variance components for inversion of satellite orbital and gradiometric data to gravity anomalies at sea level. The idea is to define two windowing matrices for reducing this error from the estimated residuals and anomalies. Our simulation studies over Fennoscandia show that the differences between the 0.5°×0.5°$0.5°\times 0.5°$ gravity anomalies obtained from orbital data and an existing gravity model have standard deviation (STD) and root mean squared error (RMSE) of 10.9 and 12.1 mGal, respectively, and those obtained from gradiometric data have 7.9 and 10.1 in the same units. In the case that they are combined using windowed variance components the STD and RMSE become 6.1 and 8.4 mGal. Also, the mean value of the estimated RMSE after using the windowed variances is in agreement with the RMSE of the differences between the estimated anomalies and those obtained from the gravity model.