The mass/eccentricity limit in double star astronomy

A research that we conducted in 1963 on the evolution of the binaries based on the available orbital data to obtain a philosophical degree, led to the establishment of an interesting and new diagram between the logarithm of the total mass and a particular parameterX, bound to the areal constant. This appeared to have a real physical significance but the basic observational material was insufficiently extended to assure its undeniable existence. In 1981, a new research based on a more extended orbital material, has confirmed this diagram. Presently, another important increase in the orbital material and the availability of highly accurate trigonometric parallaxes produced by the Hipparcos satellite, gave us the opportunity to confirm once more the stability of this diagram. This last research is here described.     

2001-2002年上海天文台卫星激光测距观测报告

介绍了 2 0 0 1- 2 0 0 2年上海天文台卫星激光测距观测概况和亚厘米级精度的测距实验     

REVIEWS OF GEOGRAPHIC SOFTWARE

Reviews of geographic software in this article: DEMO-GRAPHICS: WORLD POPULATIONS AND PROJECTIONS. ESP GAUSS. CEMODEL S. Damus LIMDEP. William H. Greene MICROSTAT 4.1 OTIS PCIPS. (Personal Computer Image Processing System) . H.J. Meyers and R. Bernstein. REGRESSION ANALYSIS OF TIME SERIES (RATS) SPSS/PC+ URBAN DATA MANAGEMENT SOFTWARE (UDMS)     

Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.