arXiv subjects

Satyam Mishra

Publications and source records attributed to Satyam Mishra.

3 recordsLinked to original sources

arXiv2026

Modernising Observatory Control: Integrating Legacy DRAMA with Next-Generation Instrumentation

The DRAMA framework was originally developed at the Anglo-Australian Observatory (AAO) in the early 1990s, with its first release in 1992. Designed in C and C++, DRAMA provides a distributed, message-based control architecture that has powered major instruments such as the 2dF fibre positioner, fibre-fed spectrographs like AAOmega and HERMES, and the IRIS2 infrared imager on the Anglo-Australian Telescope, and the TAIPAN instrument on the UK Schmidt Telescope. Despite its age, DRAMA remains a robust and well-structured system whose real-time behaviour and modular task oriented design continue to be valuable for astronomical instrumentation. As new instruments are being developed at the AAO, we are extending and adapting the DRAMA environment to meet the current software engineering and deployment requirements. Our recent improvements include containerising DRAMA using Docker and a modern Python interface that enables integration without modifying the underlying codebase. This approach preserves the reliability of the original framework, and allows new instruments to adopt DRAMA within more flexible and maintainable ecosystems. At present, DRAMA continues to play an active role in our development efforts: it is being used for the newly built KSPEC spectrograph on the KMTNet telescope, and also forms the control architecture for the high-resolution spectrograph being developed for the Devasthal Optical Telescope.

astro-ph.IM↗

arXiv2025

SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library

We introduce SafeRL-Lite, an open-source Python library for building reinforcement learning (RL) agents that are both constrained and explainable. Existing RL toolkits often lack native mechanisms for enforcing hard safety constraints or producing human-interpretable rationales for decisions. SafeRL-Lite provides modular wrappers around standard Gym environments and deep Q-learning agents to enable: (i) safety-aware training via constraint enforcement, and (ii) real-time post-hoc explanation via SHAP values and saliency maps. The library is lightweight, extensible, and installable via pip, and includes built-in metrics for constraint violations. We demonstrate its effectiveness on constrained variants of CartPole and provide visualizations that reveal both policy logic and safety adherence. The full codebase is available at: https://github.com/satyamcser/saferl-lite.

cs.LG↗

arXiv2020

A Comprehensive Survey on Real-Time Voltage Stability Assessment for Power Systems

Accurate real-time assessment of power systems voltage stability has been an active area of research in the past few decades. In the past decade, after the development of phasor measurement units (PMU), a lot of discussions has been going on phasor measurement techniques for real-time voltage stability. The fundamental idea behind these methods is to find the Thevenin equivalents of the system, and then determine the voltage stability margin based on the equivalent circuits. Some approaches also include the use of Artificial Neural Networks (ANN), for online monitoring of voltage stability margins. These methods are really fast as compared to the other methods. It has been shown that if we can obtain the phase angles and voltage magnitude in real-time from the phasor measurement units (PMU), then the voltage stability margins can be obtained in real-time and we can initiate voltage stability control methods. We are going to discuss Thevenin's equivalent methods and Artificial Intelligence methods in detail in this paper. We will also introduce the traditional methods which were earlier used for power systems stability assessment such as Time Domain methods, Static Methods, and Sensitivity methods. We are going to finally compare these methods and try to give general guidance on choosing a power stability method.

eess.SY↗