Based on the Chinese historical sunspots drawings, a data set consisting of the scanned images and all their digitized parameters from 1925 to 2015 have been constructed. In this paper, we briefly describe the developmental history of sunspots drawings in China. This paper describes the preliminary processing processes that strat from the initial data (inputing to the scanning equipment) to the parameters extraction, and finally summarizes the general features of this dataset. It is the first systematic project in Chinese solar-physics community that the historical observation of sunspots drawings were digitized. Our data set fills in an almost ninety years historical gap, which span 60 degrees from east to west and 50 degrees from north to south and have no continuous and detailed digital sunspot observation information. As a complementary to other sunspots observation in the world, our dataset provided abundant information to the long term solar cycles solar activity research.

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Quasi-biennial Oscillations (QBOs) of the Sun have a significant meaning as a benchmark of solar cycle, not only for understanding the dynamo action but also in terms of space weather prediction. In this paper, the hand-drawn sunspot images recorded from the Purple Mountain Observatory are used to investigate the solar QBOs and the Gnevyshev gap of the sunspot relative numbers (Rs) and group sunspot numbers (Rg) during the period 1954–2011. The main results are as follows: (1) both the Rs and Rg exhibit similar periods including the 22-year magnetic cycle, the 11-year Schwabe cycle, and the QBOs modes; (2) the reconstructed QBOs of both data sets exhibit coherent behavior and tend to have a high amplitude during the maximum phase of each solar cycle; (3) the Gnevyshev gap is produced by the superposition of the QBOs and the 11-year Schwabe cycle, and the Rs is better to study the variation of the Gnevyshev gap rather than the Rg.

The detection and parameterization of molecular clumps are the first step in studying them. We propose a method based on the Local Density Clustering algorithm while physical parameters of those clumps are measured using the Multiple Gaussian Model algorithm. One advantage of applying the Local Density Clustering to the clump detection and segmentation, is the high accuracy under different signal-to-noise levels. The Multiple Gaussian Model is able to deal with overlapping clumps whose parameters can reliably be derived. Using simulation and synthetic data, we have verified that the proposed algorithm could accurately characterize the morphology and flux of molecular clumps. The total flux recovery rate in 13CO (J = 1−0) line of M16 is measured as 90.2%. The detection rate and the completeness limit are 81.7% and 20 K km s−1 in 13CO (J = 1−0) line of M16, respectively.

A regenerative Er-doped fiber amplifier system for a high-repetition-rate optical pulse train is investigated for the first time. A signal pulse train with a wavelength tuning range of 18 nm is produced by a passive mode-locked fiber laser based on a nonlinear polarization rotation technique. In order to realize the amplification, an optical delay-line is used to achieve time match between the pulses' interval and the period of pulse running through the regenerative amplifier. The 16 dB gain is obtained for an input pulse train with a launching power of -30.4 dBm, a center wavelength of 1563.4 nm and a repetition rate of 15.3 MHz. The output properties of signal pulses with different center wavelengths are also discussed. The pulse amplification is found to be different from the regenerative amplification system for CW signals.