We conducted a search for technosignatures in 2018 and 2019 April with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set, as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ∼26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.

This paper proposes an on-orbit servicing logistics optimization framework that is capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-theart on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-Integer Linear Programming is leveraged to find the optimal operations of the servicers over a given period, while the Rolling Horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high-and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures. Nomenclature 𝐵 𝑣𝑠𝑡 = Servicer dispatch variables 𝒞 = Index set of Customer Nodes

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This work proposes an adaptation of the Facility Location Problem for the optimal placement of on-orbit servicing depots for satellite constellations in high-altitude orbit. The highaltitude regime, such as Medium Earth Orbit (MEO), is a unique dynamical environment

This paper proposes an optimization problem formulation to tackle the challenges of cislunar Space Domain Awareness (SDA) through multi-spacecraft monitoring. Due to the large volume of interest as well as the richness of the dynamical environment, traditional design approaches for Earth-based architectures are known to have challenges in meeting design requirements for the cislunar SDA; thus, there is a growing need to have a multi-spacecraft system in cislunar orbits for SDA. The design of multi-spacecraft-based cislunar SDA architecture results in a complex multi-objective optimization problem, where parameters such as number of spacecraft, observability, and orbit stability must be taken into account simultaneously. Through the use of a multi-objective hidden genes genetic algorithm, this study explores the entirety of the design space associated with the cislunar SDA problem. A demonstration case study shows that our approach can provide architectures optimized for both cost and effectiveness.