Results for “Federated Transfer Learning”

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

NASA NTRS2003

Supporting the Growing Needs of the GIS Industry

Visual Learning Systems, Inc. (VLS), of Missoula, Montana, has developed a commercial software application called Feature Analyst. Feature Analyst was conceived under a Small Business Innovation Research (SBIR) contract with NASA's Stennis Space Center, and through the Montana State University TechLink Center, an organization funded by NASA and the U.S. Department of Defense to link regional companies with Federal laboratories for joint research and technology transfer. The software provides a paradigm shift to automated feature extraction, as it utilizes spectral, spatial, temporal, and ancillary information to model the feature extraction process; presents the ability to remove clutter; incorporates advanced machine learning techniques to supply unparalleled levels of accuracy; and includes an exceedingly simple interface for feature extraction.

Source record↗

NASA NTRS1992

Technology transfer in the NASA Ames Advanced Life Support Division

This paper summarizes a representative set of technology transfer activities which are currently underway in the Advanced Life Support Division of the Ames Research Center. Five specific NASA-funded research or technology development projects are synopsized that are resulting in transfer of technology in one or more of four main 'arenas:' (1) intra-NASA, (2) intra-Federal, (3) NASA - aerospace industry, and (4) aerospace industry - broader economy. Each project is summarized as a case history, specific issues are identified, and recommendations are formulated based on the lessons learned as a result of each project.

Connell, Kathleen↗

NASA NTRS2012

Electronic Handbooks Simplify Process Management

Getting a multitude of people to work together to manage processes across many organizations for example, flight projects, research, technologies, or data centers and others is not an easy task. Just ask Dr. Barry E. Jacobs, a research computer scientist at Goddard Space Flight Center. He helped NASA develop a process management solution that provided documenting tools for process developers and participants to help them quickly learn, adapt, test, and teach their views. Some of these tools included editable files for subprocess descriptions, document descriptions, role guidelines, manager worksheets, and references. First utilized for NASA's Headquarters Directives Management process, the approach led to the invention of a concept called the Electronic Handbook (EHB). This EHB concept was successfully applied to NASA's Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, among other NASA programs. Several Federal agencies showed interest in the concept, so Jacobs and his team visited these agencies to show them how their specific processes could be managed by the methodology, as well as to create mockup versions of the EHBs.

Source record↗

NASA NTRS1996

NASA/DOD Aerospace Knowledge Diffusion Research Project: Survey of Reader Preferences Concerning the Format of NASA Langley-Authored Technical Reports - Paper 58

The U.S. government technical report is a primary means by which the results of federally funded research and development (R&D) are transferred to the U.S. aerospace industry. However, little is known about this information product in terms of its actual use, importance, and value in the transfer of federally funded R&D. Little is also known about the intermediary-based system that is used to transfer the results of federally funded R&D to the U.S. aerospace industry. To help establish a body of knowledge, the U.S. government technical report is being investigated as part of the NASA/DOD Aerospace Knowledge Diffusion Research Project. In this paper, we summarize the literature on the U.S. government technical report and present the results of a survey of U.S. aerospace engineers and scientists that solicited their opinions concerning the format of NASA Langley Research Center (LaRC)-authored technical reports. To learn more about the preferences of U.S. aerospace engineers and scientists concerning the format of NASA LaRC-authored technical reports, we surveyed 133 report producers (i.e., authors) and 137 report users in March-April 1996. Questions covered such topics as (1) the order in which report components are read, (2) components used to determine if a report would be read, (3) those components that could be deleted, (4) the placement of such components as the symbols list, (e) the de-sirability of a table of contents, (5) the format of reference citations, (6) column layout and right margin treatment, and (7) and person and voice. Mail (self-reported) surveys were used to collect the data. The response rates for report producers (i.e., authors) was 68% and for users was 62%.

Pinelli, Thomas E.↗

NASA NTRS1997

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 65: Survey of Reader Preferences Concerning the Format of NASA Langley-Authored Technical Reports

The U.S. government technical report is a primary means by which the results of federally funded research and development (R&D) are transferred to the U.S. aerospace industry. However, little is known about this information product in terms of its actual use, importance, and value in the transfer of federally funded R&D. Little is also known about the intermediary-based system that is used to transfer the results of federally funded R&D to the U.S. aerospace industry. To help establish a body of knowledge, the U.S. government technical report is being investigated as part of the NASA/DoD Aerospace Knowledge Diffusion Research Project. In this article, we summarize the literature on the U.S. government technical report and present the results of a survey of U.S. aerospace engineers and scientists that solicited their opinions concerning the format of NASA Langley Research Center (LaRC)-authored technical reports. To learn more about the preferences of U.S. aerospace engineers and scientists concerning the format of NASA LaRC-authored technical reports, we surveyed 133 report producers (i.e., authors) and 137 report users in March-April 1996. Questions covered such topics as: (a) the order in which report components are read; (b) components used to determine if a report would be read; (c) those components that could be deleted; (d) the placement of such components as the symbols list; (e) the desirability of a table of contents; (f) the format of reference citations; (g) column layout and right margin treatment; and (h) writing style in terms of person and voice. Mail (self-reported) surveys were used to collect the data. The response rates for report producers (i.e., authors) was 68% and for users was 62%.

Pinelli, Thomas E.↗

NASA NTRSDate not supplied

DEVELOP’s Approach to Experiential Learning

The NASA DEVELOP Program addresses environmental decision making needs and geoscience workforce development through 10-week feasibility studies that apply Earth observations to environmental issues at hand. The program builds capacity to use geospatial information in both its participants (students, recent graduates, early career professionals, and transitioning career professionals) and partner organizations (federal agencies, state & local governments, non-profits, and private industry). This is accomplished through a structured project execution model that provides opportunities for participants to have autonomy, learn “on the job,” and gain new skillsets for working with remote sensing data. A pipeline of leadership positions enhances opportunities for individuals engaged in the program to get hands-on experience conducting data analyses, communicating their work, leading technical projects, and building their knowledge bank of Earth-observing satellite capabilities. These skillsets and knowledge are then transferred to partners through the projects. This panel contribution will introduce the DEVELOP model, highlight experiences of participants, and share the program’s insights into good practices for effective experiential learning.

Capacity Building↗

NASA NTRS2003

Spinoff 2003: 100 Years of Powered Flight

Today, NASA continues to reach milestones in space exploration with the Hubble Telescope, Earth-observing systems, the Space Shuttle, the Stardust spacecraft, the Chandra X-Ray Observatory, the International Space Station, the Mars rovers, and experimental research aircraft these are only a few of the many initiatives that have grown out of NASA engineering know-how to drive the Agency s missions. The technical expertise gained from these programs has transferred into partnerships with academia, industry, and other Federal agencies, ensuring America stays capable and competitive. With Spinoff 2003, we once again highlight the many partnerships with U.S. companies that are fulfilling the 1958 Space Act stipulation that NASA s vast body of scientific and technical knowledge also benefit mankind. This year's issue showcases innovations such as the cochlear implant in health and medicine, a cockpit weather system in transportation, and a smoke mask benefiting public safety; many other products are featured in these disciplines, as well as in the additional fields of consumer/home/recreation, environment and resources management, computer technology, and industrial productivity/ manufactacturing technology. Also in this issue, we devote an entire section to NASA s history in the field of flight and showcase NASA s newest enterprise dedicated to education. The Education Enterprise will provide unique teaching and learning experiences for students and teachers at all levels in science, technology, engineering, and mathematics. The Agency also is committed, as never before, to engaging parents and families through NASA s educational resources, content, and opportunities. NASA s catalyst to intensify its focus on teaching and learning springs from our mission statement: to inspire the next generation of explorers as only NASA can.

Source record↗

NASA NTRS1995

Technology Transfer: Marketing Tomorrow's Technology

The globalization of the economy and the end of the Cold War have triggered many changes in the traditional practices of U.S. industry. To effectively apply the resources available to the United States, the federal government has firmly advocated a policy of technology transfer between private industry and government labs, in this case the National Aeronautics and Space Administration (NASA). NASA Administrator Daniel Goldin is a strong proponent of this policy and has organized technology transfer or commercialization programs at each of the NASA field centers. Here at Langley Research Center, the Technology Applications Group (TAG) is responsible for facilitating the transfer of Langley developed research and technology to U.S. industry. Entering the program, I had many objectives for my summer research with TAG. Certainly, I wanted to gain a more thorough understanding of the concept of technology transfer and Langley's implementation of a system to promote it to both the Langley community and the community at large. Also, I hoped to become more familiar with Langley's research capabilities and technology inventory available to the public. More specifically, I wanted to learn about the technology transfer process at Langley. Because my mentor is a member of Materials and Manufacturing marketing sector of the Technology Transfer Team, another overriding objective for my research was to take advantage of his work and experience in materials research to learn about the Advanced Materials Research agency wide and help market these developments to private industry. Through the various projects I have been assigned to work on in TAG, I have successfully satisfied the majority of these objectives. Work on the Problem Statement Process for TAG as well as the development of the Advanced Materials Research Brochure have provided me with the opportunity to learn about the technology transfer process from the outside looking in and the inside looking out. Because TAG covers all of the research efforts conducted at Langley, my studies with TAG were ab!e to provide me an excellent overview of Langley's contribution to the aeronautics industry.

Tcheng, Erene↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

NASA NTRS2019

WWAO-WSWC Workshop Report 2019 Final

EXECUTIVE SUMMARY The Western States Water Council (WSWC) and the NASA Western Water Applications Office (WWAO) hosted a joint workshop on technology transfer for water management in the Western U.S. The goals of the workshop were to understand how different agencies approach the technology transfer and research to operations (R2O) process, identify best practices, and discuss existing barriers to successful technology infusion into operational water resource management systems at the state and federal level. The workshop took place August 7-9, 2019 in Irvine, CA. Key outcomes of the meeting include the following:• U.S. Rep. Grace Napolitano provided opening remarks for the workshop, where she highlighted the critical value of water data and the importance of collaboration between state and federal agencies in working to advance the use of water data in water management, planning and policy. • A total of 33 participants (including remote participants) were part of the workshop. They included principal investigators and project teams supported by NASA (Cyanobacteria Assessment Network, Evapotranspiration for Western States, Evaporative Stress Index, the Airborne Snow Observatory, Satellite-based Snow Water Equivalent in the Sierra Nevadas, and Fallowed Area Mapping) as well as representatives from federal (USGS, NOAA, USBR, EPA) and state (CA, WY, OR, NE) agency partners. • One main outcome of the meeting was the consensus that successful transitions of new applications and new technologies into operations require careful planning, effective communication within and across institutions, resources and considerable time investments. In addition, there was broad agreement that significant lead time is often required to allow for identification of financial and technical resources to sustain operational use of new data, information and tools.• The meeting included remarks from U.S. Rep. Napolitano and discussions during presentations and breakout groups about key opportunities to develop best practices and streamline the technology transfer process. • For example, one key set of best practices that emerged revolved around the the importance of building trust and establishing clear lines of communication between the research and operational institutions. The conversations led to defining two key components of trust-building. The first aspect is purely technical. It requires effectively demonstrating that the proposed application meets the end user’s needs in terms of accuracy, format, resolution, latency, metadata and documentation. The second aspect of building trust involves developing sustained, productive and mutually-beneficial relationships with the partner operational agency. The best practices presented here span both the technical as well as the relational aspects of cultivating trust. • This workshop served as a first step in developing a broader community discussion around R2O in western water management. Many of the best practices and lessons learned described in this report represent starting places for action within the WWAO, WSWC and our colleagues’ institutions. • Effective implementation of the best practices that emerged from this workshop will require sustained investments of time, resources and transition planning. In recognition of this, the WSWC and the WWAO proposed continuation of discussions begun at the workshop through a series of semi-annual or annual workshops.

Wilardson, Tony↗

NASA NTRSDate not supplied

Feasibility Study to Interactive Workshop: Building End-user Capacity to Integrate Earth Observation Data into Federally Endangered Atlantic Salmon (Salmo salar) Habitat Monitoring in Main

Changes in temperature and precipitation patterns, along with alterations in land cover threaten ongoing conservation efforts for Federally Endangered Atlantic salmon (Salmo salar) in Maine. Earth observation data offers a unique perspective for habitat monitoring that can complement habitat restoration and conservation activity on the ground. As a dual capacity building program, the NASA DEVELOP National Program strives to build the capacity of program participants by leveraging Earth observation data to address environmental concerns across the globe, while also building capacity in partner organizations to integrate Earth observation data into their decision making practices. Between September 2021 and August 2022, three NASA DEVELOP teams demonstrated the feasibility of utilizing NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM) in conjunction with Sentinel-2 MultiSpectral Instrument (MSI) to assess temperature, precipitation, and land use land cover (LULC) over time throughout salmon habitat in Maine. While the first two teams completed projects that were categorized as NASA DEVELOP’s traditional feasibility projects, the third and final project team generated resources and planned an interactive workshop to transfer project methods to end-user organizations. Ultimately, the goal of this work was to not only inform the partner’s ongoing salmon population recovery and habitat restoration initiatives but provide tools that allow partner organizations to continue integrating Earth observation data into their work beyond their partnership with the program. This project serves as a case study within the NASA DEVELOP Program and provides lessons learned for moving beyond traditional feasibility studies to more interactive partner engagement and knowledge transfer practices.

Nicole Ramberg-Pihl↗

NASA NTRS2017

NASA's Bio-Inspired Acoustic Absorber Concept

Transportation noise pollutes our worlds cities, suburbs, parks, and wilderness areas. NASAs fundamental research in aviation acoustics is helping to find innovative solutions to this multifaceted problem. NASA is learning from nature to develop the next generation of quiet aircraft.The number of road vehicles and airplanes has roughly tripled since the 1960s. Transportation noise is audible in nearly all the counties across the US. Noise can damage your hearing, raise your heart rate and blood pressure, disrupt your sleep, and make communication difficult. Noise pollution threatens wildlife when it prevents animals from hearing prey, predators, and mates. Noise regulations help drive industry to develop quieter aircraft. Noise standards for aircraft have been developed by the International Civil Aviation Organization and adopted by the US Federal Aviation Administration. The US National Park Service is working with the Federal Aviation Administration to try to balance the demand for access to the parks and wilderness areas with preservation of the natural soundscape. NASA is helping by conceptualizing quieter, more efficient aircraft of the future and performing the fundamental research to make these concepts a reality someday. Recently, NASA has developed synthetic structures that can absorb sound well over a wide frequency range, and particularly below 1000 Hz, and which mimic the acoustic performance of bundles of natural reeds. We are adapting these structures to control noise on aircraft, and spacecraft. This technology might be used in many other industrial or architectural applications where acoustic absorbers have tight constraints on weight and thickness, and may be exposed to high temperatures or liquids. Information about this technology is being made available through reports and presentations available through the NASA Technical Report Server, http:ntrs.nasa.gov. Organizations who would like to collaborate with NASA or commercialize NASAs technology are encouraged to contact the NASA Glenn Technology Transfer Office, https:technology.grc.nasa.gov. The NASA Glenn Office of Education https:www.nasa.govcentersglenneducationindex.html and the NASA Glenn Virtual Interchange for Nature-Inspired Exploration https:www.grc.nasa.govvine are also helping to make research like this accessible to the public and students of all ages.

Acoustics↗