Sinusoidal variables from the Tycho Epoch Photometry Annex

The Tycho Epoch Photometry Annex A, a data base of photometry of more than 34 000 bright stars, has been searched for periodic variable stars with approximately sinusoidal light curves. Advantage was taken of special properties of the observing programme (photometry in two wavebands, availability of repeated measurements) to use simple but efficient variable selection criteria. Details of 70 strong candidate variables are presented.     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.     

顾及有效角动量与IGS超快解数据的极移预报方法

极移高精度预报对卫星实时定轨、深空探测器导航等应用至关重要.本文提出了一种联合有效角动量(Effective Angular Momentum, EAM)与国际全球卫星导航系统服务组织(International GNSS Service, IGS)提供的超快解数据进行极移预报的方法.该方法基于IGS超快解数据得到的极移第1天预报值, 对引入EAM得到的极移预报结果进行校正, 获得联合预报值.首先, 基于LS(Least Squares)+AR(Auto-Regressive)模型实现了引入EAM的极移预报, 相对国际地球自转与参考系统服务组织(International Earth Rotation and Reference Systems Service, IERS)提供的公报A数据, 在超短期(第1~10天)预报跨度可以得到更高精度的极移预报结果, 其中大气和海洋角动量发挥了主要作用.其次, 鉴于IGS超快解数据精度高、更新快的特点, 以IGS超快解为基础数据, 基于LS+AR模型可以得到极移第1天预报值, 其精度显著优于IERS公报A的极移第1天预报值.最后, 利用第1天预报值对顾及有效角动量的预报结果进行校正获得了联合预报值, 进一步提高了超短期极移预报精度(尤其是第1~5天).2020年7月24日—2022年1月30日间的联合预报结果表明: 第1~20天的预报值总体优于IERS公报A的预报值.其中, 第1~10天的预报精度显著提升, 在预报第1天, X、Y方向预报值相对公报A预报值的精度提升分别可达39.5%~62.3%和24.5%~51.9%;在预报第10天, 相对公报A预报值的精度提升分别可达28.0%~28.9%和21.9%~23.4%.

     

A data‐based mechanistic modelling (DBM) approach to understanding dynamic sediment transmission through Wyresdale Park Reservoir,Lancashire, UK

This paper outlines the application of a new data‐based mechanistic (DBM) modelling methodology to the characterization of the sediment transmission dynamics in a small upland reservoir, Wyresdale Park, Lancashire. The DBM modelling strategy exploits advanced statistical procedures to infer the dynamic model structure and its associated parameters directly from the instrumented data, producing a parametrically efficient, continuous time, transfer function model which relates suspended sediment load at the reservoir inflow to the outflow at the event scale. The associated differential equation model parameters have physical attributes which can be interpreted in terms of sediment transmission processes and associated reservoir trap efficiency. Sedigraph analysis suggests that wind‐induced resuspension episodically supplies an additional load to the reservoir outlet. The stochastic nature of the DBM model makes it ideal for evaluating the effects of uncertainty through Monte Carlo simulations (MCS) for discharge and sediment transmission. Copyright © 2000 John Wiley & Sons, Ltd.     

大数据环境下数字填图数据集成服务技术

应用数字填图技术形成了大量地质填图图幅数据,这些数据空间结构化和非结构化特征并存,如何在网络环境下提供高效的数据服务是急需解决的一个难题。大数据技术的发展为数字填图、数据集成服务提供了一种新的途径。通过对数字填图数据特征的分析,在研究地质调查信息网格大数据处理框架的基础上,提出了结构化和非结构化数据相结合的有序化组织管理、发布与服务方法,并对关键技术进行了研究与试验,取得了良好的效果。     

基于ALOS数据的乌鲁木齐市绿地景观格局研究

以乌鲁木齐市2009年的ALOS卫星影像为研究数据,在地理信息系统(GIS)及遥感(RS)技术的支持下,提取绿地信息,建立乌鲁木齐市区绿地景观的空间数据库,应用景观生态学的理论和研究方法,选取景观分维数、破碎度、分离度等景观指数来定量分析乌鲁木齐市区的绿地景观格局。结果表明,整个研究区内的绿地类型以公园绿地和单位附属绿地为主,市区绿地景观多样性较低,各类型绿地景观所占比例不均匀,公共绿地数量少,趋于团聚分布,形成许多绿化服务盲区;各类型绿地景观分维数较高,形状较不规则;单位附属绿地和居住绿地的破碎度较高,因为它们受人类的影响较大、形状相对多样、不规则。     

基于VisIt的全球科学数据并行可视化——以大气温度场为例

针对当前大规模全球科学数据可视化中存在的单机可视化数据量有限、从底层开发并行可视化系统难度大等问题,该文基于分布式环境和VisIt,提出了一种简便、开放而又有效的大规模全球科学数据可视化方法。介绍了VisIt的体系结构及运行机制,给出了自定义数据的并行可视化方法;并基于NCEP数据集及全球空间格网,在小规模集群环境下实现了小粒度适应性球体退化八叉树格网(SDOG)下的全球大气温度场的并行可视化。VisIt的并行可视化性能测试结果表明:通过增加计算节点,VisIt能有效摆脱传统单机可视化对数据量的限制,可实现大规模全球科学数据的并行可视化。     

基于作息空间思想的人口统计数据空间化方法

探讨了基于作息空间思想的人口统计数据空间化方法。定义居住和工作的活动空间范围为作息空间,并结合土地利用类型数据和泰森多边形构建了作息空间,以作息空间人口密度作为人口空间分布的表达方式。对辽宁省2005年人口统计数据进行了实例分析,精度优于传统的面积权重内插法,结果的地图渲染能够表现受自然因素和社会经济因素影响的人口分布特征。该方法不涉及复杂的权重计算过程,也可以避免主观因素的干扰,简单易行,有较强的实用性。