Gravity-assist trajectories to Venus, Mars, and the ice giants: Mission design with human and robotic applications

Gravity-assist trajectories to Uranus and Neptune are found (with the allowance of impulsive maneuvers using chemical propulsion) for launch dates ranging from 2024 to 2038 for Uranus and 2020 to 2070 for Neptune. Solutions are found using a patched conic model with analytical ephemeris via the Satellite Tour Design Program (STOUR), originally developed at the Jet Propulsion Laboratory (JPL). Delivered payload mass is computed for all solutions for select launch vehicles, and attractive solutions are identified as those that deliver a specified amount of payload mass into orbit at the target body in minimum time. The best cases for each launch year are cataloged for orbiter missions to Uranus and Neptune. Solutions with sufficient delivered payload for a multi-planet mission (e.g. sending a probe to Saturn on the way to delivering an orbiter at Uranus) become available when the Space Launch System (SLS) launch vehicle is employed. A set of possible approach trajectories are modeled at the target planet to assess what (if any) adjustments are needed for ring avoidance, and to determine the probe entry conditions. Mars free-return trajectories are found with an emphasis on short flight times for application to near-term human flyby missions (similar to that of Inspiration Mars). Venus free-returns are also investigated and proposed as an alternative to a human Mars flyby mission. Attractive Earth-Mars free-return opportunities are identified that use an intermediate Venus flyby. One such opportunity, in 2021, has been adopted by the Inspiration Mars Foundation as a backup to the currently considered 2018 Mars free-return opportunity. Methods to establish spacecraft into Earth-Mars cycler trajectories are also investigated to reduce the propellant cost required to inject a 95-metric ton spacecraft into a cycler orbit. The establishment trajectories considered use either a V-infinity leveraging maneuver or low thrust. The V-infinity leveraging establishment trajectories are validated using patched conics via the STOUR program. Establishment trajectories that use low-thrust were investigated with particular focus on validating the patched-conic based solutions at instances where Earth encounter times are not negligible

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare

Staff perceptions of PRN medication in a residential care setting

Purpose- The aim was to explore the perceptions of staff towards psychotropic Pro Re Nata (PRN) medication in a residential care setting. Design- Three male and seven female participants were interviewed using a semi-structured interview. Transcripts were analysed using Thematic Analysis. Findings- Four themes pertaining to PRN medication emerged from the data; behaviour change, calming effect, importance of timing and perceived uniqueness. Research Limitations/Implications- The participant group was not homogenous and findings may have been different in a more qualified cohort. This care setting may not be representative of other environments where PRN medication is administered. The findings do however highlight some of the challenges facing the administration of PRN medication in mental health and care settings. Practical Implications- The awareness of these themes are significant for improving staff knowledge, training practices and policies towards the use and administration of psychotropic PRN medication. Originality: This the first study to engage in a thematic analysis of staff views towards the administration of PRN medication

Utilizing Guided Simulation in Conjunction with Digital Learning Tools in Air Traffic Control Training to Enhance Learning at the Collegiate Level

Students in an air traffic control program are required to learn and apply advance knowledge and skills in a limited time frame. All students learn at different rates as well as through different learning styles. Swivl is a video capture tool designed to enhance student learning by allowing students to refer back to their individual classroom lab training session videos via an online portal. Swivl is being utilized in two ATC lab courses. During this research, two technological shortcomings were discovered: (1) Swivl lacks the ability to capture audio from the COA’s existing communication software and (2) Swivl cannot focus on the radar display. As a result, the videos have lacked visual clarity when reviewing the session. Consequently Swivl has been shown to be an ineffective digital learning tool for this situation. Swivl, used in conjunction with a simulated ATC tower, has proven to be effective in enhancing overall learning due to the visual nature of the tower learning environment. The nature of the tower simulator allows for better visual acuity and effective communication exchange within the Swivl videos. Once these two issues are resolved, Swivl will have the potential to be an effective tool in ATC training, and may enhance learning by allowing students to sharpen those skills necessary for advancement in the field of air traffic control

Automated Sensitivity Analysis of Interplanetary Trajectories for Optimal Mission Design

This work describes a suite of Python tools known as the Python EMTG Automated Trade Study Application (PEATSA). PEATSA was written to automate the operation of trajectory optimization software, simplify the process of performing sensitivity analysis, and was ultimately found to out-perform a human trajectory designer in unexpected ways. These benefits will be discussed and demonstrated on sample mission designs

Gravity-Assist Trajectories to the Ice Giants: An Automated Method to Catalog Mass- Or Time-Optimal Solutions

This work presents an automated method of calculating mass (or time) optimal gravity-assist trajectories without a priori knowledge of the flyby-body combination. Since gravity assists are particularly crucial for reaching the outer Solar System, we use the Ice Giants, Uranus and Neptune, as example destinations for this work. Catalogs are also provided that list the most attractive trajectories found over launch dates ranging from 2024 to 2038. The tool developed to implement this method, called the Python EMTG Automated Trade Study Application (PEATSA), iteratively runs the Evolutionary Mission Trajectory Generator (EMTG), a NASA Goddard Space Flight Center in-house trajectory optimization tool. EMTG finds gravity-assist trajectories with impulsive maneuvers using a multiple-shooting structure along with stochastic methods (such as monotonic basin hopping) and may be run with or without an initial guess provided. PEATSA runs instances of EMTG in parallel over a grid of launch dates. After each set of runs completes, the best results within a neighborhood of launch dates are used to seed all other cases in that neighborhood-allowing the solutions across the range of launch dates to improve over each iteration. The results here are compared against trajectories found using a grid-search technique, and PEATSA is found to outperform the grid-search results for most launch years considered

Utilizing Guided Simulation in Conjunction with Digital Learning Tools in Air Traffic Control Training

For students in an air traffic control discipline, simulated training time is limited to in - class time and is divided among the entire class. Students are required to advance and obtain knowledge, skills, and abilities in the lab because there is almost no way to practice at home. All students learn at different rates as well as through different learning styles. Swivl is a digital learning/capture tool designed to enhance student learning by allowing students to refer back to the individual classroom lab training session via an online portal. Swivl is currently being used in two ATC Lab courses. There are two technological deficiencies that have arisen: (1) Swivl (in the Terminal Radar Approach Control and En - Route Radar environments) lacks the ability to record what is being said over the frequencies. (2) Swivl does not have the ability to focus on the radar scope targets. As a result, the students’ captures have a deficiency in visual clarity. However, the nature of the tower simulator does allow for better visual acuity and effective communication exchange within the Swivl capture

Global, Multi-Objective Trajectory Optimization With Parametric Spreading

Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented

Integrating Unmanned Aircraft Operations into the National Airspace System

Commercial unmanned aircraft systems (UAS) are expected to dominate the National Airspace System (NAS) in the years to come. One particular barrier preventing integration of UAS into the NAS is the lack of standardized procedures for separating aircraft and communicating with ATC. In preparation for adopting unmanned flight operations into a complex control system, it is important to identify solutions to effectively control UAS in the NAS. The Joint UAS and ATC Team (JUAT) group has designed several simulated ATC scenarios in order to determine effective solutions for integration. Through the use of digitized radar display overlays that replicate the military grid reference system (MGRS) in conjunction with traditional airspace sectors/boundaries the JUAT is able to simulate UAS operations on a basic level