小波分析理论在GPS技术中的应用

在GPS数据处理中 ,存在着误差影响、影响波的干扰、周跳和数据量大等问题。误差影响和影响波的干扰实质是在接收卫星信号时受到其它因素的影响 ;周跳是由于卫星信号的失锁而造成信号的不连续 ;数据量大是因为GPS观测需要采样间隔小又连续观测所致。由于小波理论具有时频分析、波形分解、特征提取和快速小波变换等特性 ,应用小波变换和波形分解可以解决误差影响和影响波的干扰的问题 ;应用特征提取可以解决周跳检测问题 ;应用快速小波变换可进行数据压缩     

LAMOST/GALAXY计划:利用LAMOST研究银河系

本文讨论了利用LAMOST作银河系研究的科学意义及可能性；并提出具体实施LAMOST/GAIAXY计划的初步方案。     

我国近年来天文的新发现

这里叙述的新发现仅指某一特殊类型的发现（如新星，超新星和小行星等）及某一特殊类型天体的认证（如X射线源证认为类星体等），而不包括某一类天体的某种天象及未知性质源的列表（如深度成像观测得到的前人没有纪录过的一般天体，如恒星，但一些特殊天体亦列入），由于收集不全面和所谓“发现”定义的困难，向有发向而未列入者先歉意。从80年代后期开始，我国的观测设备有了很大垢发展，所以有一批重的的天文发现，为天文提供了很好的机会，它包括下面几方面，层系层次天体：（1）用X射线源和射电源证认了一批包括类星体，BL Lac天体，赛弗特天体在内的活动星系核；（2）用IRAS源证认了一批W－R星系；（3）BATC巡天在一个星系周围发现了一个“环”。恒星层次天体：（1）河外星系中发现了一批超新星的一个新星，（2）发现了和用ROSAT源证认了一批激变变星，（4）BATC和超新星巡天发现了一批变星，（4）发现了若干新的δSct变星，（5）发现了一批Herbig-Haro天体，（6）发现了一批水脉泽。行星层次天体：（1）发现了大批小行星，特别是有些是近地不行星，（20发现若干彗星。     

Analysis of the 2003 Varunawat Landslide,Uttarkashi, India using Earth Observation data

Mass movements such as landslides in mountainous terrains are natural degradation processes and one of the most important landscape-building factors. Varunawat Parbat overlooking Uttarkashi town witnessed a series of landslides on 23 September 2003 and the debris slides and rock falls continued for 2 weeks. This landslide complex was triggered due to the incessant rainfall prior to the event, and its occurrence led to the blockage of the pilgrim route to Gangotri (source of the Ganges river) and evacuation of thousands of people to safer places. Though there was no loss of lives due to timely evacuation, heavy losses to the property were reported. High-resolution stereoscopic earth observation data were acquired after the incidence to study the landslide in detail with emphasis on the cause of the landslide and mode of failure. Areas along the road and below the Varunawat foothill region are mapped for landslide risk. It was found that the foothill region of the Varunawat Parbat was highly disturbed by man-made activities and houses are dangerously located below steep slopes. The potential zones for landslides along with the existing active and old landslides are mapped. These areas are critical and their treatment with priority is required in order to minimise further landslide occurrences.     

嵌入式Linux技术在海洋自动观测系统中的应用研究

随着Linux技术逐步走向成熟，嵌入式Linux技术在海洋自动观测系统中的应用将全面提高海洋观测系统整体性能，降低系统开发成本，进一步推进我国海洋自动观测系统产权自主化的建设。文章就如何研制适用于海洋自动观测系统的嵌入式Linux系统进行了论述。     

关于修改港工钢筋混凝土规范小偏心受压构件正截面强度计算的建议

本文提出在小偏心受压构件正截面强度计算中，取消弯曲抗压强度ｆｃｍ（Ｒｗ），采用轴心抗压强度ｆｃ，这与构件的实际破坏形态相符。对小偏心受压构件正截面强度计算提出了设计建议。特别是对工程上大量应用的矩形截面对称配筋小偏压构件提示了简捷的设计方法，与以往的近似计算方法相比具有物理概念明确，精度高，方法简捷的特点。     

GPS测高技术在无验潮水深测量中的应用

应用双频GPS动态后处理高精度定位技术，建立了一套完整的GPS无验潮海洋深度测量作业模式，通过海上试验与传统作业模式作了数值分析比较，结果表明，该作业模式不仅无需验潮，而且能够有效消除传统作业模式中船只动态吃水和涌浪等因素对测量成果的影响，显著提高水深测量成果的精度。     

SSA1—1型声学水位在长江口潮位站的应用

本文介绍了长江口区水文泥沙概况，举例说明了ＳＳＡ１－１型声水位计在崇头潮位站比测试验和投产应用情况，结果表明该水位计是收集长江口区潮位资料较理想的仪器。     

Difference characteristics of sea surface temperature observed by GLI and AMSR aboard ADEOS-II

This study compares infrared and microwave measurements of sea surface temperature (SST) obtained by a single satellite. The simultaneous observation from the Global Imager (GLI: infrared) and the Advanced Microwave Scanning Radiometer (AMSR: microwave) aboard the Advanced Earth Observing Satellite-II (ADEOS-II) provided an opportunity for the intercomparison. The GLI-and AMSR-derived SSTs from April to October 2003 are analyzed with other ancillary data including surface wind speed and water vapor retrieved by AMSR and SeaWinds on ADEOS-II. We found no measurable bias (defined as GLI minus AMSR), while the standard deviation of difference is less than 1°C. In low water vapor conditions, the GLI SST has a positive bias less than 0.2°C, and in high water vapor conditions, it has a negative (positive) bias during the daytime (nighttime). The low spatial resolution of AMSR is another factor underlying the geographical distribution of the differences. The cloud detection problem in the GLI algorithm also affects the difference. The large differences in high-latitude region during the nighttime might be due to the GLI cloud-detection algorithm. AMSR SST has a negative bias during the daytime with low wind speed (less than 7 ms⁻¹), which might be related to the correction for surface wind effects in the AMSR SST algorithm.     

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.