PaddleSat Optical Charging Station in Space

This work investigates the feasibility and design trade-offs for a companion spacecraft, or PaddleSat, to charge a host spacecraft by wirelessly transmitting power using a directional laser system. The primary goal of the PaddleSat is to supplement power on a host spacecraft to reduce the requirements for onboard power systems of the host spacecraft or extend mission lifetimes. System performance estimates, link budget calculations, optical transmission hardware and link analysis, design tradeoffs between beam divergence, optical efficiency, and relative orbital control requirements are examined.

Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music

Music source separation demixes a piece of music into its individual sound sources (vocals, percussion, melodic instruments, etc.), a task with no simple mathematical solution. It requires deep learning methods involving training on large datasets of isolated music stems. The most commonly available datasets are made from commercial Western music, limiting the models' applications to non-Western genres like Carnatic music. Carnatic music is a live tradition, with the available multi-track recordings containing overlapping sounds and bleeds between the sources. This poses a challenge to commercially available source separation models like Spleeter and Hybrid Demucs. In this work, we introduce 'Sanidha', the first open-source novel dataset for Carnatic music, offering studio-quality, multi-track recordings with minimal to no overlap or bleed. Along with the audio files, we provide high-definition videos of the artists' performances. Additionally, we fine-tuned Spleeter, one of the most commonly used source separation models, on our dataset and observed improved SDR performance compared to fine-tuning on a pre-existing Carnatic multi-track dataset. The outputs of the fine-tuned model with 'Sanidha' are evaluated through a listening study.