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We present here the information on the design and performance of the recently commissioned 2.5-meter telescope at the PRL Mount Abu Observatory, located at Gurushikhar, Mount Abu, India. The telescope has been successfully installed at the site, and the Site Acceptance Test (SAT) was completed in October 2022. It is a highly advanced telescope in India, featuring the Ritchey-Chr$\acute{e}$tien optical configuration with primary mirror active optics, tip-tilt on side-port, and wave front correction sensors. Along with the telescope, its two first light instruments {namely Faint Object Camera (FOC) and PARAS-2} were also integrated and attached with it in the June 2022. {FOC is a} camera that uses a 4096 X 4112 pixels detector SDSS type filters with enhanced transmission and known as u', g', r', i', z'. It has a limiting magnitude of 21 mag in 10 minutes exposure in the r'-band. The other first light instrument PARAS-2 is a state-of-the-art high-resolution fiber-fed spectrograph operates in 380-690 nm wave-band, aimed to unveil the super-Earth like worlds. The spectrograph works at a resolution of $\sim$107,000, making it the highest-resolution spectrograph in Asia to date, which is under {ultra}-stable temperature and pressure environment, at 22.5 $\pm$ 0.001 $^{\circ}$C and 0.005 $\pm$ 0.0005 mbar, respectively. Initial calibration tests of the spectrograph using a Uranium Argon Hollow Cathode Lamp (UAr HCL) have yielded intrinsic instrumental RV stability down to 30 cm s$^{-1}$., Comment: Accepted for publication in the Bulletin of Liege Royal Society of Sciences, Volume 93, 2024; 19 pages, 9 figures

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for the same person from time to time. As the choice of words and syntax vary while preparing a textual description, it is challenging for the system to reliably produce a consistently desirable output from different forms of language input. The prior works of scene generation have been mostly confined to rigorous sentence structures of text input which restrict the freedom of users to write description. In our work, we study a new pipeline that aims to generate static as well as animated 3D scenes from different types of free-form textual scene description without any major restriction. In particular, to keep our study practical and tractable, we focus on a small subspace of all possible 3D scenes, containing various combinations of cube, cylinder and sphere. We design a two-stage pipeline. In the first stage, we encode the free-form text using an encoder-decoder neural architecture. In the second stage, we generate a 3D scene based on the generated encoding. Our neural architecture exploits state-of-the-art language model as encoder to leverage rich contextual encoding and a new multi-head decoder to predict multiple features of an object in the scene simultaneously. For our experiments, we generate a large synthetic data-set which contains 13,00,000 and 14,00,000 samples of unique static and animated scene descriptions, respectively. We achieve 98.427% accuracy on test data set in detecting the 3D objects features successfully. Our work shows a proof of concept of one approach towards solving the problem, and we believe with enough training data, the same pipeline can be expanded to handle even broader set of 3D scene generation problems.

The increasing popularity of smart meters provides energy consumers in households with unprecedented opportunities for understanding and modifying their energy use. However, while a variety of solutions, both commercial and academic,have been proposed, research on effective visual analysis tools is still needed to achieve widespread adoption of smart meters. In this paper we explore an interface that seeks to balance the tradeoff between complexity and usability. We worked with real household data and in close collaboration with consumer experts of a large local utility company. Based on their continued feedback we designed EnergyScout - a dashboard with a versatile set of highly interactive visual tools with which consumers can understand the energy consumption of their household devices, discover the impact of their usage patterns, compare them with usage patterns of the past, and see via what-if analysis what effects a modification of these patterns may have, also in the context of modulated incentivized pricing, social and personal events, outside temperature, and weather. All of these are events which could explain certain usage patterns and help motivate a modification of behavior. We tested EnergyScout with various groups of people, households, and energy bill responsibilities in order to gauge the merits of this system.