Design of a high update-rate star sensor for arcsec-level attitude determination from balloon-borne X/γ astronomy platforms

This paper describes the design of a star sensor based upon a high dynamic range CCD in order to reach an arcsec-level attitude determination in balloon-borne missions. A custom star identification software was developed and laboratory-tested on a prototype assembled using commercial components. A set of numerical simulations have been carried out to study the dependence on the pointing precision of the centroid position accuracy, the number of detected stars and the effect of the image focusing. Moreover, the role of the electronic noise and the discrete pixel structure on the light signals is identified by the analysis of numerical simulations. Laboratory tests confirm that the arcsec pointing accuracy with a 1 Hz update rate can be achieved with our combination of custom-developed software and selected hardware components.     

Is Precipitation the Dominant Controlling Factor of High Inorganic Nitrogen Content in the Changjiang River and Its Mouth？

The main reasons for the high content of inorganic N and its increase by several times in the Changjiang River and its mouth during the last 40 years were analysed in this work. The inorganic N in precipitation in the Changjiang River catchment mainly comes from gaseous loss of fertilizer N, N resulting from the increases of population and livestock, and from high temperature combustions of fossil fuels. N from precipitation is the first N source in the Changjiang River water and the only direct cause of high content of inorganic N in the Changjiang River and its mouth. The lost N in gaseous form and from agriculture non-point sources fertilizer comprised about 60% of annual consumption of fertilizer N in the Changjiang River catchment and were key factors controlling the high content of inorganic N in the Changjiang River mouth. The fate of the N in precipitation and other N sources in the Changjiang River catchment are also discussed in this paper.     

Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1