Cloud cover is critical for astronomical sites because it can be used to assess the observability of the local sky and further the fractional photometric time. For cloud monitoring in site-testing campaigns with all-sky cameras, previous studies have mainly focused on moonless images, while the automatic processing methods for moonlight images are explored quite few. This paper proposes an automatic estimation method for cloud cover, which takes all cases of nighttime gray-scale all-sky images into account. For moonless images, the efficient Otsu algorithm is directly used to detect clouds. For moonlight images, they are transformed into cloud feature image using a colorization procedure, and then the Otsu algorithm is used to distinguish cloud pixels from sky pixels on the cloud feature image. The reliability of this method was evaluated on manually labeled images. The results show that the cloud cover error of this method is less than 9% in all scenarios. The fractional photometric time derived from this method is basically consistent with the published result of the Lenghu site.

Modeling the brightness of satellites in large Low-Earth Orbit (LEO) constellations can not only assist the astronomical community in assessing the impact of reflected light from satellites, optimizing observing schedules and guiding data processing, but also motivate satellite operators to improve their satellite designs, thus facilitating cooperation and consensus among different stakeholders. This work presents a photometric model of the Starlink satellites based on the Bidirectional Reflectance Distribution Function (BRDF) using millions of photometric observations. To enhance model accuracy and computational efficiency, data filtering and reduction are employed, and chassis blocking on the solar array and the earthshine effect are taken into account. The assumptions of the model are also validated by showing that the satellite attitude is as expected, the solar array is nearly perpendicular to the chassis, and both the solar array pseudo-specular reflection and the chassis earthshine should be included in the model. The reflectance characteristics of the satellites and the apparent magnitude distributions over station are finally discussed based on the photometric predictions from the model. In addition to assessing the light pollution and guiding the development of response measures, accurate photometric models of satellites can also play an important role in areas such as space situational awareness.

Geostationary orbit (GEO) satellites have extensive application in fields like communications and navigation due to their stationary properties. Using additional methods to identify and characterise GEO satellites is crucial for enhancing cataloguing capabilities and verifying their operation status. Reflectance spectroscopy is a promising technique for characterising satellites, but a deeper understanding of the primary factors influencing satellite reflectance spectra is required. So we carried out slitless spectroscopic observations on six GEO satellites using the 80 cm high-precision telescope of the Purple Mountain Observatory located in Yaoan, Yunnan. Reflectance spectra and by-product light curves were then obtained after necessary spectral extraction and correction procedures. Our experiment and observation lasted over ten months, and this paper utilizes a subset of data with high Signal-to-Noise Ratio(SNR). Based on the reflectance spectral data obtained, we discovered that: The dominant factor impacting the reflectance spectra is the Multi-layer insulation(MLI) on the surface of three-axis stabilized GEO satellites. Reflectance spectral trends of GEO satellites do not change significantly over the course of a night with changes in the sun-satellite-observatory phase angle. The normalised reflectance spectral trends remain unchanged despite variations in the solar declination. The aforementioned findings establish a strong basis for utilising reflectance spectra to recognise and classify non-cooperative GEO satellites.

Previous research has come to some findings on the attributes and characteristics of people who have psychopathic traits. There is a vacuum in these investigations, however, because no studies have looked at the Intelligence Quotient (IQ) of people who exhibit psychopathic features. Based on past research, the present study will look at the IQ of people who have psychopathic symptoms to determine whether there are any significant variations. This work outlined a research proposal, and the purpose of the research is to conduct proposed research on whether intelligence can distinguish successful and unsuccessful psychopathy, above and beyond other relevant personality traits.