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Detailed observations of geosynchronous satellites from earth are very limited. To better inspect these high altitude satellites, the use of small, refuelable satellites is proposed. The small satellites are stationed on a carrier platform in an orbit near the population of geosynchronous satellites. A carrier platform equipped with deployable, refuelable SmallSats is a viable option to inspect geosynchronous satellites. The propellant requirement to transfer to a targeted geosynchronous satellite, perform a proximity inspection mission, and transfer back to the carrier platform in a nearby orbit is determined. Convex optimization and traditional optimization techniques are explored, determining minimum propellant trajectories. Propellant is measured by the total required change in velocity, delta-v. The trajectories were modeled in a relative reference frame using the Clohessy-Wiltshire equations. Mass estimations for the carrier platform and the SmallSat were determined by using the rocket equation. The mass estimates were compared to the mass of a single, non-refuelable satellite performing the same geosynchronous satellite inspection missions. From the minimum delta-v trajectories and the mass analysis, it is determined that using refuelable SmallSats and a carrier platform in a nearby orbit can be more effcient than using a single non-refuelable satellite to perform multiple geosynchronous satellite inspections.

Tropical cyclones are among the most devastating natural disasters for human beings and the natural and manmade assets near to Atlantic basin. Estimating the current and future intensity of these powerful storms is crucial to protect life and property. Many methods and models exist for predicting the evolution of Atlantic basin cyclones, including numerical weather prediction models that simulate the dynamics of the atmosphere which require accurate measurements of the current state of the atmosphere (NHC, 2019). Often these models fail to capture dangerous aspects of storm evolution, such as rapid intensification (RI), in which a storm undergoes a steep increase in intensity over a short time. To improve prediction of these events, scientists have turned to statistical models to predict current and future intensity using readily collected satellite image data (Pradhan, 2018). However, even the current-intensity prediction models have shown limited success in generalizing to unseen data, a result we confirm in this study. Therefore, building models for the estimating the current and future intensity of hurricanes is valuable and challenging. In this study we focus on to estimating cyclone intensity using Geostationary Operational Environmental Satellite images. These images represent five spectral bands covering the visible and infrared spectrum. We have built and compared various types of deep neural models, including convolutional networks based on long short term memory models and convolutional regression models that have been trained to predict the intensity, as measured by maximum sustained wind speed. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection