Liquid lenses have been utilized in various applications due to their low size, weight, power, and cost. They have potential for use in space applications such as focus compensation, optical communications, and imaging systems. However, liquid lenses have not yet been evaluated for us in space environment. This work focuses on characterizing operational differences of commercially available liquid lenses from Corning Varioptic and Optotune between Earth gravity, microgravity, and hypergravity environments. Results show a linear drift in tip/tilt of 0.79 mrad and 4.13 mrad going from 1 g to 0 g for the Corning Varioptic A-39N0 lens and Optotune EL-16-40-TC-VIS lenses respectively, with lower optical aberrations in microgravity. This work is part of a wider space environment study showing that Corning Varioptic and Optotune’s commercial liquid lenses withstand thermal vacuum, typical low Earth orbit ionizing radiation exposure, and effectively handle high-intensity laser power in a vacuum without significant damage.

Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment.