Results for “Policy Making” — Page 18

NASA NTRS2019

Astrobiology as a NASA Grand Challenge

"Are we alone" is a question whose ambition can only be met with a NASA-led global collaboration. In this white paper, we describe how this makes "The Search for Life Beyond Earth" a new Grand Challenge for NASA. As described in the White House Office of Science and Technology Policy and the White House National Economic Council, Grand Challenges are "ambitious but achievable goals that harness science, technology, and innovation to solve important national or global problems and that have the potential to capture the public's imagination." NASA had identified an "Asteroid Grand Challenge" centered on the Asteroid Retrieval Mission, which was closed out in June, 2017. Here, we explain how NASA's next Grand Challenge could be focused on "The Search for Life Beyond Earth," with a flagship-scale mission in Astrophysics as its centerpiece.

Domagal-Goldman, Shawn↗

NASA NTRSDate not supplied

National Aeronautics and Space Administration (NASA) Agency Report WGISS-51

The Committee on Earth Observation Satellites (CEOS) strives to enhance international coordination and data exchange and to optimize societal benefit. CEOS contributes to NASA’s core mission and is critical to NASA’s Earth Science program and to the future of Earth observations community as a whole because it advances mission planning, interagency coordination and technical implementation. Within NASA, the CEOS Working Group on Information System and Services (WGISS) is a forum for the Earth Sciences Data Systems (ESDS) Program to collaborate with other international and domestic agencies in the development of Earth observation data systems and services. NASA leads the development and demonstration of multiple prototypes supporting CEOS and Group on Earth Observations (GEO) requirements. NASA’s participation in WGISS influences NASA’s Earth Observing System Data and Information System’s (EOSDIS) ability to make high-quality data products available to the broad science community both nationally and internationally. Combined with NASA’s free and open data policy, EOSDIS’s involvement in WGISS is essential to widespread use of research satellite measurements. This presentation focuses on an overview and recent status of NASA’s EOSDIS.

Andrew Mitchell↗

NASA NTRSDate not supplied

NASA Agency Report WGISS 52

The Committee on Earth Observation Satellites (CEOS) strives to enhance international coordination and data exchange and to optimize societal benefit. CEOS contributes to NASA’s core mission and is critical to NASA’s Earth Science program and to the future of Earth observations community as a whole because it advances mission planning, interagency coordination and technical implementation. Within NASA, the CEOS Working Group on Information System and Services (WGISS) is a forum for the Earth Sciences Data Systems (ESDS) Program to collaborate with other international and domestic agencies in the development of Earth observation data systems and services. NASA leads the development and demonstration of multiple prototypes supporting CEOS and Group on Earth Observations (GEO) requirements. NASA’s participation in WGISS influences NASA’s Earth Observing System Data and Information System’s (EOSDIS) ability to make high-quality data products available to the broad science community both nationally and internationally. Combined with NASA’s free and open data policy, EOSDIS’s involvement in WGISS is essential to widespread use of research satellite measurements. This presentation focuses on an overview and recent status of NASA’s EOSDIS.

Andrew Mitchell↗

NASA NTRSDate not supplied

CEOS Enabling Open Science via Science Data Systems

The Committee on Earth Observation Satellites (CEOS) strives to enhance international coordination and data exchange and to optimize societal benefit. CEOS contributes to NASA’s core mission and is critical to NASA’s Earth Science program and to the future of Earth observations community as a whole because it advances mission planning, interagency coordination and technical implementation. Within NASA, the CEOS Working Group on Information System and Services (WGISS) is a forum for the Earth Sciences Data Systems (ESDS) Program to collaborate with other international and domestic agencies in the development of Earth observation data systems and services. NASA leads the development and demonstration of multiple prototypes supporting CEOS and Group on Earth Observations (GEO) requirements. NASA’s participation in WGISS influences NASA’s Earth Observing System Data and Information System’s (EOSDIS) ability to make high-quality data products available to the broad science community both nationally and internationally. Combined with NASA’s free and open data policy, EOSDIS’s involvement in WGISS is essential to widespread use of research satellite measurements. This presentation focuses on an overview and recent status of NASA’s EOSDIS.

Diane Davies↗

NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗

NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗

NASA NTRS2022

Unlocking the Spacecraft and Human Habitat Microbiome to Enable the Next Generation of Space Exploration

Planetary protection is the discipline that prevents harmful contamination of the solar system during exploration activities. The current international guidelines and NASA policy addressing biological contamination on spacecraft surfaces contains prescriptive guidelines of spore requirements (e.g., 300 spores/m2, 5×105 spores per spacecraft) applicable to spacecraft bound for Mars. To verify these requirements spacecraft engineers sample spacecraft surfaces throughout the assembly, test and launch operations phase of the mission using damp water cotton swabs and polyester wipes. After sampling, the potential biological contamination is enumerated using a series of traditional microbiology techniques to include sonication, heat shocking at 80°C for 15min to select for spores, and growth on tryptic soy agar at 32°C for 72 hours. To enable crewed missions to Mars and robotic exploration of the Ocean Worlds a risk informed decision making / performance-based approach to assess biological contamination offers a promising solution in the trade space. Recognizing the need for a performance-based approach, NASA’s new Planetary Protection policies now incorporate the agility for missions to be able to leverage a performance or prescriptive approach. One of top contenders in the option space is a coupled quantitative, descriptive and functional based approach to be able to assess the quantity, types and capabilities of the biological contamination present on spacecraft surfaces. A tailored, mission by mission assurance case could then be formulated by building an argument around the target body, projected capabilities surrounding the types of organisms their potential for survival and proliferation, and ability to be transported on the target body to contaminate an area of biological interest. A performance-based requirement would then be used to demonstrate the mission’s compliance in protecting the planetary environment safety objectives. This symposium talk will showcase the background and need case for NASA to develop such a capability as well as provide an update on the efforts underway in developing a transparent and responsible performance-based approach to biological contamination assessments on spacecraft surfaces.

Habitat Microbiome↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

NASA NTRS2009

Current Status of the International Lunar Network (ILN) Anchor Nodes Mission

NASA's Science Mission Directorate s (SMD) International Lunar Network Anchor Nodes Mission continues its concept development and is scheduled to complete the first formal milestone gate of a Mission Concept Review (MCR) in Autumn 2009. The mission will establish two-four nodes of the International Lunar Network (ILN), a network of lunar geophysical stations envisioned to be emplaced by the many nations collaborating on this joint endeavor. This mission will operate over six years or more and make significant progress in satisfying many of the National Research Council s lunar science objectives, while strategically contributing to the U.S. Vision for Space Exploration Policy's objective for a robust robotic lunar program. This paper will provide a status report on the ILN Anchor Nodes mission and overview of the concept to date, which is being implemented jointly by NASA's Marshall Space Flight Center and The Johns Hopkins University Applied Physics Laboratory.

Cohen, Barbara A.↗

NASA NTRSDate not supplied

Identifying Human Factors Research for Unmanned Aircraft Systems and Advanced Air Mobility

This paper identifies some of the key human factors (HF) challenges when integrating Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) into the civil airspace. Unique HF considerations—those which are derived from the key differentiating aspects of UAS/AAM compared to conventional aviation—are the primary basis for identifying HF research opportunities. By identifying what makes UAS and AAM fundamentally different from conventional aviation, from a human integration perspective, HF research can be targeted to effectively inform best practices, standards, policy, guidance, and regulations associated with aircraft and air traffic systems and operations. HF research areas are discussed within the following topic areas: Sustained low-altitude operations; loss of natural sensing; novel aircraft; novel operations; link management and lost link; link performance; distributed pilot teams; and increased automation. The identified research descriptions are intended to serve as illustrative examples of what research is fundamental, and why. They are not intended to prescribe, prioritize or exclude research.

AAM↗

NASA NTRSDate not supplied

Identifying Human Factors Research for Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM)

This paper identifies some of the key human factors (HF) challenges when integrating Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) into the civil airspace. Unique HF considerations—those which are derived from the key differentiating aspects of UAS/AAM compared to conventional aviation—are the primary basis for identifying HF research opportunities. By identifying what makes UAS and AAM fundamentally different from conventional aviation, from a human integration perspective, HF research can be targeted to effectively inform best practices, standards, policy, guidance, and regulations associated with aircraft and air traffic systems and operations. HF research areas are discussed within the following topic areas: Sustained low-altitude operations; loss of natural sensing; novel aircraft; novel operations; link management and lost link; link performance; distributed pilot teams; and increased automation. The identified research descriptions are intended to serve as illustrative examples of what research is fundamental, and why. They are not intended to prescribe, prioritize or exclude research.

AAM↗

NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end ]user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users f need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decisionmakers feasible.

Al-Hamdan, Mohammad↗

NASA NTRS1992

Earth science information: Planning for the integration and use of global change information

The Consortium for International Earth Science Information Network (CIESIN) was founded in 1989 as a non-profit corporation dedicated to facilitating access to, use and understanding of global change information worldwide. The Consortium was created to cooperate and coordinate with organizations and researchers throughout the global change community to further access the most advanced technology, the latest scientific research, and the best information available for critical environmental decision making. CIESIN study efforts are guided by Congressional mandates to 'convene key present and potential users to assess the need for investment in integration of earth science information,' to 'outline the desirable pattern of interaction with the scientific and policy community,' and to 'develop recommendations and draft plans to achieve the appropriate level of effort in the use of earth science data for research and public policy purposes.' In addition, CIESIN is tasked by NASA to develop a data center that would extend the benefits of Earth Observing System (EOS) to the users of global change information related to human dimensions issues. For FY 1991, CIESIN focused on two main objectives. The first addressed the identification of information needs of global change research and non-research user groups worldwide. The second focused on an evaluation of the most efficient mechanisms for making this information available in usable forms.

Lousma, Jack R.↗

DOE OSTI2026

Hanford Reach Fall Chinook Salmon Redd Monitoring Report for Calendar Year 2025

The U.S. Department of Energy, Hanford Field Office (HFO) conducts ecological monitoring on the Hanford Site to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing HFO activities. Ecological monitoring data provide baseline information about the plants, animals, and habitats under HFO stewardship at the Hanford Site required for decision making. Fall Chinook salmon redds have been monitored at the Hanford Site annually since 1948, including aerial counts, to provide an index of relative abundance among spawning areas and years

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Linking NASA Environmental Data with a National Public Health Cohort Study and a CDC On-Line System to Enhance Public Health Decision Making

The overall goal of this study is to address issues of environmental health and enhance public health decision making by utilizing NASA remotely-sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county, state or regional level as per users need and downloaded in tabular, graphical, and map formats. The linkage of these data provides a useful addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in this online system. It also substantially expands public access to NASA data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗

NASA NTRS1992

Software-Engineering Process Simulation (SEPS) model

The Software Engineering Process Simulation (SEPS) model is described which was developed at JPL. SEPS is a dynamic simulation model of the software project development process. It uses the feedback principles of system dynamics to simulate the dynamic interactions among various software life cycle development activities and management decision making processes. The model is designed to be a planning tool to examine tradeoffs of cost, schedule, and functionality, and to test the implications of different managerial policies on a project's outcome. Furthermore, SEPS will enable software managers to gain a better understanding of the dynamics of software project development and perform postmodern assessments.

Lin, C. Y.↗

NASA NTRS2022

SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.

Moritz A. Graule↗

DOE OSTI2025

Benefits of aggressively co-undergrounding electric and broadband lines outweigh costs

Not Available

24 POWER TRANSMISSION AND DISTRIBUTION↗