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NASA NTRS2016

Physics of the Cosmos: Program Annual Technology Report

From ancient times, humans have looked up at the night sky and wondered: Are we alone? How did the universe come to be? How does the universe work? PCOS focuses on that last question. Scientists investigating this broad theme use the universe itself as their laboratory, investigating its fundamental laws and properties. They test Einstein's General Theory of Relativity to see if our current understanding of space-time is borne out by observations. They examine the behavior of the most extreme environments - supermassive black holes, active galactic nuclei, and others - and the farthest reaches of the universe, to expand our understanding. With instruments sensitive across the spectrum, from radio, through infrared (IR), visible light, ultraviolet (UV), to X rays and gamma rays, as well as gravitational waves (GWs), they peer across billions of light-years, observing echoes of events that occurred instants after the Big Bang. The Laser Interferometer Gravitational-Wave Observatory (LIGO) recently recorded the first direct measurement of long-theorized GWs. Another surprising recent discovery is that the universe is expanding at an ever-accelerating rate, the first hint of so-called "dark energy," estimated to account for 75% of mass-energy in the universe. Dark matter, so called because we can only observe its effects on regular matter, accounts for another 20%, leaving only 5% for regular matter and energy. Scientists now also search for special polarization in the cosmic microwave background to support the notion that in the split-second after the Big Bang, the universe inflated faster than the speed of light! The most exciting aspect of this grand enterprise today is that we can finally develop the tools needed for such discoveries.

COR↗
NASA NTRS2016

Multifunctional 2D- Materials: Selenides and Halides

Material is the key component and controls the performance of the detectors, devices and sensors. The materials design, processing, growth and fabrication of bulk and nanocrystals and fabrication into devices and sensors involve multidisciplinary team of experts. This places a large burden on the cost of the novel materials development. Due to this reason there is a big thrust for the prediction of multifunctionality of materials before design and development. Up to some extent design can achieve certain properties. In multinary materials processing is also a big factor. In this presentation, examples of two classes of industrially important materials will be described.

Singh, N. B.↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
NASA NTRS2016

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Ambur, Manjula↗
NASA NTRS2017

NASA GES DISC New Data Service and Data Management for the Air Quality Community

President Obama's Big Data Research and Development Initiative seeks to improve our ability to acquire knowledge and discover insights into large and complex collections of digital data. The Big Earth Data Initiative (BEDI) Invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data.

air pollution↗
NASA NTRS2017

Managing Capital Investments and Resources for Large, Complex Satellite Development Efforts

Once the schedule was baselined and the primary, secondary, and tertiary critical paths (at least) were established: Identified what entity control which portion of the schedule reserve Dollarized the time associated with the schedule reserve and segregated it off from other budget reserves authorization to spend only came when authorization to utilize the time was given Created a process to manage the control of these resources This process was above and beyond the typical schedule controls used to monitor and maintain the day-to-day schedules Webb telescope will be the premier space observatory for astronomers worldwide,extending the tantalizing discoveries of the Hubble Space telescope• An international collaboration among NASA, the European Space Agency, and theCanadian Space Agency• The largest telescope ever placed in space, Webb will be ~100 times more powerful thanHubble• It is so big it has to fold origami-style to fit in the rocket and will unfold like a transformeronce in space• The 5-layer sunshield protects the telescope from the Sun, Earth, and Moon’s infraredradiation. It’s like having sun protection of SPF 1 million• Unprecedented infrared sensitivity will peer back in time over 13.5 billion years to see thefirst galaxies born after the Big Bang• Hubble orbits ~350 miles above the Earth; Webb will orbit the sun ~1 million miles fromEarth• Launch from French Guiana in 2018

Ryan, Richard M.↗
NASA NTRS2017

Program Annual Technology Report: Physics of the Cosmos Program Office

From ancient times, humans have looked up at the night sky and wondered: Are we alone? How did the universe come to be? How does the universe work? PCOS focuses on that last question. Scientists investigating this broad theme use the universe as their laboratory, investigating its fundamental laws and properties. They test Einstein’s General Theory of Relativity to see if our current understanding of space-time is borne out by observations. They examine the behavior of the most extreme environments – supermassive black holes, active galactic nuclei, and others – and the farthest reaches of the universe, to expand our understanding. With instruments sensitive across the spectrum, from radio, through infrared (IR), visible light, ultraviolet (UV), to X rays and gamma rays, as well as gravitational waves (GWs), they peer across billions of light-years, observing echoes of events that occurred instants after the Big Bang. Last year, the LISA Pathfinder (LPF) mission exceeded expectations in proving the maturity of technologies needed for the Laser Interferometer Space Antenna (LISA) mission, and the Laser Interferometer Gravitational-Wave Observatory (LIGO) recorded the first direct measurements of long-theorized GWs. Another surprising recent discovery is that the universe is expanding at an ever-accelerating rate, the first hint of so-called “dark energy,” estimated to account for 75% of mass-energy in the universe. Dark matter, so called because we can only observe its effects on regular matter, is thought to account for another20%, leaving only 5% for regular matter and energy. Scientists now also search for special polarization in the cosmic microwave background to support the notion that in the split-second after the Big Bang, the universe inflated faster than the speed of light! The most exciting aspect of this grand enterprise today is the extraordinary rate at which we can harness technologies to enable these key discoveries.

COR↗
NASA NTRS2018

Challenges Beyond the Standard Model: An Examination and Evaluation of Alternatives with Respect to Unsolved Problems in Energetics and Signaling

The standard model of particle physics is regarded as one of the greatest achievements of modern physics. It has successfully predicted the existence of numerous hadrons; e.g. the Higgs Boson, the W and Z Bosons, B mesons, etc. Moreover it accurately models many phenomena such as Beta and Radioactive decay, pair production, hadron mass splitting, and others. In spite of this, the standard model has many shortfalls. For example it fails to account for or explain the presence of dark matter and dark energy. Many alternative models of the substructure of matter etc. have been proposed. Some of these are examined. In addition the strongly related standard model of cosmology, that is the big bang theory, accounts for all observed celestial phenomena. What it cannot do is satisfactorily justify all astrophysical events. As noted, dark matter and dark energy are not well understood yet they are core elements of the big bang model. Many elements of this standard model of cosmology are challenged regularly, yet somehow the community always fits the problems with the model into another mysterious element. This model and others are examined for consistency and reconciliation. Evidence of particles that move faster than the speed of light has long been sought, but remains elusive. Some of the known space-like observations of physics are examined. Potential consequences of such particles are likewise investigated. Some conclusions are drawn with respect to the framework of tachyons in a rational but non-rigorous sense. Finally, some of my ideas are presented, concerning all of the above material. The framework of theories of everything is examined, considering what such a theory requires for validity. Sociological notes on the structure of research in physics are given.

Coxe, Alexander M.↗
NASA NTRS2019

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO Seasonal Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.

Hackert, E.↗
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗
NASA NTRS2019

SERVIR: Leveraging the Expertise of a Space Agency and a Development Agency to Increase Impact of Earth Observation in the Developing World

SERVIR is a joint initiative of the National Aeronautics and Space Administration (NASA) and the U.S. Agency for International Development (USAID), in collaboration with leading technical organizations around the world-- called SERVIR hubs--that serve and empower developing countries to use satellite data addressing critical challenges in food security and agriculture; water and water-related disasters; land cover, land use and ecosystems; and weather and climate. Over the past fourteen years, the program has worked with stakeholders in 50 countries across the world, partnered with 390 institutions, and generated and shared over 70 products from 27 satellites and sensors. In that process, around 7,400 specialists have been trained in the application of Earth observation data and technology. In its lifetime, SERVIR has been agile and innovative in shifting from what was essentially an incubator for testing and deploying Earth observation science and technology to making co-development the hallmark of its work, exemplified by both South-South and North-South scientific collaborations. SERVIR’s approach has embodied the concept that to solve really big problems, big, creative solutions are needed. SERVIR represents the world working together to address environmental challenges using spaced-based and geospatial technologies. Aligning with the very meaning of SERVIR, i.e. “to serve,” the program continues to be demand-driven in developing and deploying services (versus one-off products) which address development challenges using geospatial tools and Earth observation science. In 2016, as part of SERVIR’s evolution, USAID and NASA released the ‘SERVIR Service Planning Toolkit,’ a guidance document which provides a framework for how geospatial services can be used to tackle development challenges in a sustained manner. Since then, the Service Planning Toolkit’s systematic approach has begun to catch on in other Earth observation efforts. To improve access and use, SERVIR launched a Service Catalogue in February 2019, a searchable collection of demand-driven geospatial services that use Earth observations to support decision making. SERVIR implementing hub partners –include SERVIR-West Africa at the Agrometeorology, Hydrology and Meteorology (AGRHYMET) Regional Center, in Niamey, Niger; SERVIR-Eastern & Southern Africa at the Regional Centre for Mapping of Resources for Development in Nairobi, Kenya; SERVIR-Hindu Kush Himalaya at the International Centre for Integrated Mountain Development in Kathmandu, Nepal; SERVIR-Mekong at the Asian Disaster Preparedness Center in Bangkok, Thailand; and SERVIR’ -Amazonia, at the International Center for Tropical Agriculture (CIAT) in Cali, Colombia.

Searby, Nancy D.↗
NASA NTRS2019

A Web of Data Analytics Services

Cloud Computing has become the ubiquitous approach to our Big Data challenge. However, one will quickly discover that moving (a.k.a. forklifting) existing on-premise data analytics solutions to the Cloud doesn’t always translate to costing saving and performance boost. The Cloud’s elasticity, its availability, and its wide selection of computing options and selections of costing models making Cloud an attractive environment to tackle our Big Data challenge. The fact is Cloud, on its own, is not the silver bullet to our daunting challenge need for analyze and derive scientific inferences through vast collections of multi-sensor measurements. We would like to have all scientific data in one easy to access environment, but getting the world of scientific data in one analytic system is immensely difficult to achieve. This paper describes the data analytics web architecture NASA is developing by infusing instances of Integrated Data Analytics systems next to the data. The goal is to minimize unnecessary data movement through collection of data access and analytics webservices for researchers to interact with and analyze measurements without have to download data to their local computer. These services are RESTful and provisioned by the data centers with the help from subject matter and science experts. These services encapsulate the physical computing infrastructure, which could local computing cluster, on-premise or public Cloud environment.

Huang, Thomas↗
NASA NTRS2019

An Integrated Data Analytics Platform

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in-situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data distributed through data centers such as the PO.DAAC, COAPS, NCAR, and many others to conduct their research. In typical investigations, scientists would engage in: search for data, evaluate the relevance of that data, download it, and then apply algorithms to identify trends. Such workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. NASA’s Surface Water and Ocean Topography (SWOT) mission is expected to produce massive amount of observational data during its 3-year nominal mission. Collections like SWOT challenges all existing Earth Science data archival, distribution and analysis paradigms. In this paper, we will discuss how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Yang, Chaowei↗
NASA NTRSDate not supplied

Managing Risk for the James Webb Space Telescope Deployment Mechanisms: Enabling First Light

The $10 billion James Webb Space Telescope (JWST) is perhaps the most ambitious astronomy mission in NASA history and is the largest and most powerful space science telescope optimized for infrared detection. It is operating in an L2 Orbit about a million miles away from the earth. This will complement and extend the discoveries of the Hubble Space Telescope. It was so big that it had to fold origami – style to fit in the rocket and open in space.Webb’s 5-layer tennis court sized sunshield assembly(SSA) protects the telescope from the Sun’s thermal radiation enabling instruments to operate about 230degree C below 0. It relayed the first amazing picture of galaxies to the world on 12 July 2022. Webb’s unprecedented infrared sensitivity will peer back in time over 13.5 billion years to see the first galaxies born after the Big Bang.

JWST↗
NASA NTRSDate not supplied

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗
NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗
NASA NTRS2021

NOAA-17 Break-up Engineering Investigation Board Final Report

The NOAA-17 break-up was found to be a single, localized debris event producing about100 trackable pieces; there is likely no catastrophic damage to the whole spacecraft. NOAA-17 debris is very much in family with NOAA-16 debris, and DMSP F11 and F13 are very much in family with each other and share similarities with NOAA-16 and 17; it is likely all four (“The Big Four”) share the same debris source. DMSP F13 break-up occurred simultaneous with a known battery overcharge and therefore battery rupture is most likely intermediate cause of the Big Four break-ups; this is a low confidence assessment since other debris sources cannot be definitively ruled out. No root cause was found as the NOAA-17 batteries were all confirmed to have been disconnected from the charge path as intended. Possible conditions for reconnection are all unlikely including short circuits and commanding from a “bad actor.” All related spacecraft pose a risk of similar break-ups for decades to come and are a threat to the critical 800-850 km polar orbit regime; even appropriately decommissioned spacecraft appear to be at risk. Recommendations include an update to the decommissioning procedure and consideration of further investigations and active debris removal, consistent with national policy.

Maggie Atkinson↗