Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Generative deep learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
NASA NTRS2012

Launch Pad Closeout Operations for the Mars Science Laboratory's Heat Rejection System

The Mars Science Laboratory (MSL) rover was launched on an Atlas V on November 26, 2011. Preparations were carried out prior to launch in order to closeout the spacecraft's complex heat rejection system (HRS), which consists of two mechanically pumped CFC-11 fluid loops. The first HRS loop, onboard the Curiosity rover, was fully integrated, filled with CFC-11, and successfully operated prior to launch pad operations; however, the second thermal loop, called the cruise HRS loop, required final mechanical and thermal integration activities to occur while on the launch pad in order to accommodate the last minute installation of the rover's Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) power source. In order to prevent overheating of propellant tanks and critical avionics equipment buried deep within the spacecraft's aeroshell, the MMRTG needed to be pre-cooled using a separate non-flight mechanically pumped fluid loop prior to and during the final closeout and subsequent startup of the flight loop. This paper outlines the various steps that took place to safely install the MMRTG while carefully transitioning from the pre-cooling operation to the final startup and operation of the flight cruise HRS loop. Temperature data of the launch pad thermal transition from the ground support loop activity to the final flight loop operation is presented. Some background development of the ground support loop and lessons learned are also discussed. This successful launch pad integration activity required a close-knit coordination between NASA KSC, JPL, the Department of Energy, Idaho National Labs, Pratt and Whitney Rocketdyne Inc., Teledyne Technologies Inc., ULA, and Advanced Thermal Sciences Corp.

thermal↗
NASA NTRS2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

SST↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRSDate not supplied

Autoencoders for Denoising Atmospheric Profiles from ICESat-2

Abstract: The 2nd generation Ice, Cloud, and land Elevation Satellite (ICESat-2) is an altimetry mission designed primarily for measuring ice sheet elevation and sea ice thickness, provides atmospheric profiles of clouds and aerosols at 532 nm using a photo counting detection approach. While highly sensitive for the detection of tenuous aerosol and cloud features, during the day signal-to-noise-ratio (SNR) photon counting detectors are adversely impacted by solar contributions to the total signal. Averaging the data to coarser horizontal resolutions has been the standard way to increase SNR and thus allow clouds and aerosols to be more easily detectable. Recent work has demonstrated success in boosting SNR without decreasing resolution using advanced filtering techniques [Yorks et al., 2021], however, rapid advancements in Deep Learning based image denoising algorithms can further improve the SNR. Here, we present results using a state-of-the-art Deep Learning autoencoder applied to noisy ICESat-2 data to improve daytime SNR and discuss implications for atmospheric feature detection, classification, and optical property retrievals.

denoising↗
NASA NTRSDate not supplied

Machine-Learning for Safety Critical Airborne Applications Part II: Case Study

The exceptional progress in the field of Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology in recent years provides historic opportunities for the aviation industry. Current certification standards for avionics were developed prior to the ML renaissance and have several fundamental incompatibilities with the ML technology. WG-114 is working hard to release a new standard as soon as possible but for now there is no recognized means of compliance for ML based systems even of low criticality. In this talk, we present the custom ML workflow that can be used comply with all objectives of the current certification standards for a low-criticality (DAL D and C) ML-based system. To illustrate the practical application of the custom ML workflow we present a case study of a system based on a Deep Neural Network (DNN) intended to detect and identify airport runway signs. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D and DAL C systems.

Johann Schumann↗
NASA NTRSDate not supplied

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗
NASA NTRS2006

The International Space Station: Operations and Assembly - Learning From Experiences - Past, Present, and Future

As the Space Shuttle continues flight, construction and assembly of the International Space Station (ISS) carries on as the United States and our International Partners resume the building, and continue to carry on the daily operations, of this impressive and historical Earth-orbiting research facility. In his January 14, 2004, speech announcing a new vision for America s space program, President Bush ratified the United States commitment to completing construction of the ISS by 2010. Since the launch and joining of the first two elements in 1998, the ISS and the partnership have experienced and overcome many challenges to assembly and operations, along with accomplishing many impressive achievements and historical firsts. These experiences and achievements over time have shaped our strategy, planning, and expectations. The continual operation and assembly of ISS leads to new knowledge about the design, development and operation of systems and hardware that will be utilized in the development of new deep-space vehicles needed to fulfill the Vision for Exploration and to generate the data and information that will enable our programs to return to the Moon and continue on to Mars. This paper will provide an overview of the complexity of the ISS Program, including a historical review of the major assembly events and operational milestones of the program, along with the upcoming assembly plans and scheduled missions of the space shuttle flights and ISS Assembly sequence.

Fuller, Sean↗
NASA NTRS2017

Enabling Future Science and Human Exploration with NASA's Next Generation Near Earth and Deep Space Communications and Navigation Architecture

The National Aeronautics and Space Administration (NASA) is studying alternatives for the United States space communications architecture through the 2040 timeframe. This architecture provides communication and navigation services to both human exploration and science missions throughout the solar system. Several of NASA's key space assets are approaching their end of design life and major systems are in need of replacement. The changes envisioned in the relay satellite architecture and capabilities around both Earth and Mars are significant undertakings and occur only once or twice each generation, and therefore is referred to as NASA's next generation space communications architecture. NASA's next generation architecture will benefit from technology and services developed over recent years. These innovations will provide missions with new operations concepts, increased performance, and new business and operating models. Advancements in optical communications will enable high-speed data channels and the use of new and more complex science instruments. Modern multiple beam/multiple access technologies such as those employed on commercial high throughput satellites will enable enhanced capabilities for on-demand service, and with new protocols will help provide Internet-like connectivity for cooperative spacecraft to improve data return and coordinate joint mission objectives. On-board processing with autonomous and cognitive networking will play larger roles to help manage system complexity. Spacecraft and ground systems will coordinate among themselves to establish communications, negotiate link connectivity, and learn to share spectrum to optimize resource allocation. Spacecraft will autonomously navigate, plan trajectories, and handle off-nominal events. NASA intends to leverage the ever-expanding capabilities of the satellite communications industry and foster its continued growth. NASA's technology development will complement and extend commercial capabilities to meet unique space environment requirements and to provide capabilities that are beyond the commercial marketplace. The progress of the communications industry, including the emerging global space internet segment and its planned constellations of 100's of satellites offer additional opportunities for new capability and mission concepts. The opportunities and challenges of a future space architecture require an optimal solution encompassing a global perspective. The concepts and technologies intentionally define an architecture that applies not only to NASA, but to other U.S. government agencies, international space and government agencies, and domestic and international industries to advance the openness, interoperability, and affordability of space communications. Cooperation among the worlds space agencies, their capabilities, standards, operations, and interoperability are key to advancing humankinds understand of the universe and extending human presence into the solar system.

deep space↗
NASA NTRS2021

Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRS2017

Enabling Future Science and Human Exploration with NASA's Next Generation near Earth and Deep Space Communications and Navigation Architecture

The National Aeronautics and Space Administration (NASA) is studying alternatives for the United States space communications architecture through the 2040 timeframe. This architecture provides communication and navigation services to both human exploration and science missions throughout the solar system. Several of NASA's key space assets are approaching their end of design life and major systems are in need of replacement. The changes envisioned in the relay satellite architecture and capabilities around both Earth and Mars are significant undertakings and occur only once or twice each generation, and therefore is referred to as NASA's next generation space communications architecture. NASA's next generation architecture will benefit from technology and services developed over recent years. These innovations will provide missions with new operations concepts, increased performance, and new business and operating models. Advancements in optical communications will enable high-speed data channels and the use of new and more complex science instruments. Modern multiple beam/multiple access technologies such as those employed on commercial high throughput satellites will enable enhanced capabilities for on-demand service, and with new protocols will help provide Internet-like connectivity for cooperative spacecraft to improve data return and coordinate joint mission objectives. On-board processing with autonomous and cognitive networking will play larger roles to help manage system complexity. Spacecraft and ground systems will coordinate among themselves to establish communications, negotiate link connectivity, and learn to share spectrum to optimize resource allocation. Spacecraft will autonomously navigate, plan trajectories, and handle off-nominal events. NASA intends to leverage the ever-expanding capabilities of the satellite communications industry and foster its continued growth. NASA's technology development will complement and extend commercial capabilities to meet unique space environment requirements and to provide capabilities that are beyond the commercial marketplace. The progress of the communications industry, including the emerging global space internet segment and its planned constellations of 100's of satellites offer additional opportunities for new capability and mission concepts. The opportunities and challenges of a future space architecture require an optimal solution encompassing a global perspective. The concepts and technologies intentionally define an architecture that applies not only to NASA, but to other U.S. government agencies, international space and government agencies, and domestic and international industries to advance the openness, interoperability, and affordability of space communications. Cooperation among the worlds space agencies, their capabilities, standards, operations, and interoperability are key to advancing humankind's understand of the universe and extending human presence into the solar system.

Communications↗
NASA NTRS2022

Flight Rule Design, Implementation, Verification, and Validation for the Psyche Mission

NASA Jet Propulsion Lab (JPL)’s upcoming mission Psyche will begin its journey to the asteroid (16) Psyche in late 2022 in an effort to better understand its origins and, in turn, better understand our own. Operating the spacecraft safely will require the dedicated efforts of a small team that understands the spacecraft’s operational constraints, as well as a set of powerful spacecraft models designed to catch command errors that can pose risks to mission success. One of the responsibilities of the operations team is to ensure adherence to a set of Flight Rules written by spacecraft and instrument experts that are designed to mitigate these risks. Psyche’s innovations in Flight Rule design principles and advancements in the tools and processes used to implement and check Flight Rules are discussed. A comparison of Psyche’s approach to Flight Rules to other JPL missions will provide lessons learned for future missions that must perform constraint checking during operations. Flight Rule development faces several major challenges. First, flight rule developers must work with Subject Matter Experts (SME) to write the rules in a way that captures the intent of the constraint in a straightforward, enforceable manner. Second, software implementers must correctly interpret flight rules into code so that it meets the original intent of the SME. Finally, a means must be provided for SMEs to validate flight rule implementations without requiring them to understand the underlying software. Innovative processes intended to efficiently close the loop between stakeholders and software developers are described, such as the use of test-driven development to provide stakeholders with easy-to-review implementations. New guidelines for flight rule writing, designed to address these challenges, are described for future missions to adopt and build upon. Psyche Mission System has a variety of new and heritage tools that improve in the Flight Rule validation and checking process. Psyche developed a powerful, new tool called RandSEQ and made significant improvements to Octopusjam, two valuable tools that aid the development of Flight Rule unit tests. Advancements in the models and processes for performing sequence validation with SEQuence GENerator (SEQGEN), the primary, high-heritage tool used for automated flight rule checks on Psyche, are described. The development of new software and the advancements to existing software put Psyche at the forefront of Flight Rule technology. All missions must perform detailed constraint checking, so a comparison of Psyche’s approach to some of these items to the approaches taken by other missions such as Dawn, M2020, and Europa Clipper is done, specifically to examine SME-developer communication, tools used, and development process. Lessons learned from this comparison will be provided.

Weise, Tim↗
NASA NTRS2004

Assessment of High-Voltage Photovoltaic Technologies for the Design of a Direct Drive Hall Effect Thruster Solar Array

A three-year program to develop a Direct Drive Hall-Effect Thruster system (D2HET) begun in 2001 as part of the NASA Advanced Cross-Enterprise Technology Development initiative. The system, which is expected to reduce significantly the power processing, complexity, weight, and cost over conventional low-voltage systems, will employ solar arrays that operate at voltages higher than (or equal to) 300 V. The lessons learned from the development of the technology also promise to become a stepping-stone for the production of the next generation of power systems employing high voltage solar arrays. This paper summarizes the results from experiments conducted mainly at the NASA Marshal Space Flight Center with two main solar array technologies. The experiments focused on electron collection and arcing studies, when the solar cells operated at high voltages. The tests utilized small coupons representative of each solar array technology. A hollow cathode was used to emulate parts of the induced environment on the solar arrays, mostly the low-energy charge-exchange plasma (1012-1013 m-3 and 0.5-1 eV). Results and conclusions from modeling of electron collection are also summarized. The observations from the total effort are used to propose a preliminary, new solar array design for 2 kW and 30-40 kW class, deep space missions that may employ a single or a cluster of Hall- Effect thrusters.

Mikellides, I. G.↗
NASA NTRS2011

The Habitat Demonstration Unit Project: A Modular Instrumentation System for a Deep Space Habitat

NASA is focused on developing human exploration capabilities in low Earth orbit (LEO), expanding to near Earth asteroids (NEA), and finally to Mars. Habitation is a crucial aspect of human exploration, and a current focus of NASA activities. The Habitation Demonstration Unit (HDU) is a project focused on developing an autonomous habitation system that enables human exploration of space by providing engineers and scientists with a test bed to develop, integrate, test, and evaluate habitation systems. A critical feature of the HDU is the instrumentation system, which monitors key subsystems within the habitat. The following paper will discuss the HDU instrumentation system performance and lessons learned during the 2010 Desert Research and Technology Studies (D-RaTS). In addition, this paper will discuss the evolution of the instrumentation system to support the 2011 Deep Space Habitat configuration, the challenges, and the lessons learned of implementing this configuration. In 2010, the HDU was implemented as a pressurized excursion module (PEM) and was tested at NASA s D-RaTS in Arizona [1]. For this initial configuration, the instrumentation system design used features that were successful in previous habitat instrumentation projects, while also considering challenges, and implementing lessons learned [2]. The main feature of the PEM instrumentation system was the use of a standards-based wireless sensor node (WSN), implementing an IEEE 802.15.4 protocol. Many of the instruments were connected to several WSNs, which wirelessly transmitted data to the command and data handling system via a mesh network. The PEM instrumentation system monitored the HDU during field tests at D-RaTS, and the WSN data was later analyzed to understand the performance of this system. In addition, several lessons learned were gained from the field test experience, which fed into the instrumentation design of the next generation of the HDU.

Rojdev, Kristina↗
NASA NTRSDate not supplied

The Gateway as a Building Block for Space Exploration and Development

The Gateway, which will be a small, human-tended space station in orbit around the Moon, is an example of the building block approach to space exploration and development. The National Aeronautics and Space Administration (NASA) leads the Gateway Program and serves as the integrator of the spaceflight capabilities and contributions of U.S. commercial partners and international partners. Gateway is a cornerstone of deep space human exploration, one critical element of the strategies and architectures that will enable human exploration of the solar system. This paper will outline: 1) The concept of operations and describe how Gateway is a multipurpose space station, supporting lunar surface missions, cis-lunar research, and as a proving ground for human missions to Mars; 2) Gateway’s international partnerships as an expansion of the partnerships of the “second generation of space programmes,” namely the International Space Station (ISS); and 3) Future commercial, scientific, and technology demonstration opportunities that this infrastructure building block provides. Lastly, the strategies and architectures for future space exploration and development must include thoughtful approaches to the management and business operations of government space programs. This includes planning for limited outyear growth in budgets, partnership and acquisition strategies, pursuing public-private partnerships and the use of firm-fixed price contracts, enabling vendor-to-vendor relationships, the use of multilateral interoperability standards, and incorporating key lessons learned from prior governmental Programs. This paper will discuss the strategies and approaches undertaken when establishing the Gateway Program at the NASA Johnson Space Center in 2019 and the current operational status of this new, lean, and efficient Program.

Gateway↗
NASA NTRSDate not supplied

The Gateway as a Building Block for Space Exploration and Development

The Gateway, which will be a small, human-tended space station in orbit around the Moon, is an example of the building block approach to space exploration and development. The National Aeronautics and Space Administration (NASA) leads the Gateway Program and serves as the integrator of the spaceflight capabilities and contributions of U.S. commercial partners and international partners. Gateway is a cornerstone of deep space human exploration, one critical element of the strategies and architectures that will enable human exploration of the solar system. This paper will outline: 1) The concept of operations and describe how Gateway is a multi-purpose space station, supporting lunar surface missions, cis-lunar research, and as a proving ground for human missions to Mars; 2) Gateway’s international partnerships as an expansion of the partnerships of the “second generation of space programmes,” namely the International Space Station (ISS); and 3) Future commercial, scientific, and technology demonstration opportunities that this infrastructure building block provides. Lastly, the strategies and architectures for future space exploration and development must include thoughtful approaches to the management and business operations of government space programs. This includes planning for limited outyear growth in budgets, partnership and acquisition strategies, pursuing public-private partnerships and the use of firm-fixed price contracts, enabling vendor-to-vendor relationships, the use of multilateral interoperability standards, and incorporating key lessons learned from prior governmental Programs. This paper will discuss the strategies and approaches undertaken when establishing the Gateway Program at the NASA Johnson Space Center in 2019 and the current operational status of this new, lean, and efficient Program.

Gateway↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS1996

Experiences in Interagency and International Interfaces for Mission Support

The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.

Dell, G. T.↗