Gamma-ray bursts (GRBs) are the most luminous explosions in the Universe. They are produced during the collapse of massive stellar-sized objects, which create a black hole and eject material at ultra-relativistic speeds. They are unique tools to study the evolution of our Universe, as they are the only objects that, thanks to their extraordinary luminosity, can be observed during the complete history of star formation, from the era of reionisation to our days.One of the main tools to obtain information from GRBs and their environment is optical and near-infrared spectroscopy. After 17 years of studies spectroscopic data for around 300 events that have been collected. However, spectra were obtained by many groups, at different observatories, and using instruments of very different types, making data difficult to access, process and compare.Here we present GRBspec: A collaborative database that includes processed GRB spectra from multiple observatories and makes them available to the community. The website provides access to the datasets, allowing queries based not only on the observation characteristics but also on the properties of the GRB that was observed. Furthermore, the website provides visualisation and analysis tools, that allow the user to asses the quality of the data before downloading and even make data analysis online.
All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.
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