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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

HAT m:N Cognitive Task Analysis (CTA)

This Cognitive Task Analysis (CTA) study was designed to understand the capability of the m:N Tactical Operator (TO) interfaces developed by the Human-Autonomy Teaming Laboratory at NASA Ames to support operators responsible for simplified pilot operations of 100 independently operated small UAS (sUAS) in a constrained geographic area. The m:N sUAS TO interface includes a central Tactical Situation Display (TSD) digital map with moving icons reflecting the sUAS location and planned flight route. The interface also has two side panels. The left panel includes a tabular list of UAS assets and mission tasking, a list of recently viewed assets, and a list of events and alerts. The right panel includes a tabular list of UAS assets and their associated telemetry, text-based chat communication window, and a tabbed checklist window. This CTA was adapted from the incident-based applied cognitive task analysis (Militello & Hutton, 1998) and included demographics questions, scenario-based simulations, a task diagram and knowledge audit methods. In addition to examining the support provided by this m:N sUAS TO interface, this CTA study, conducted with aviation subject matter experts in analogous roles to the future tactical operator, was designed to illuminate and project likely cognitive requirements of the tactical operator. Interviewees participated in two scenario-based simulations using the m:N sUAS TO interfaces. In the first simulation, the interviewees supervised 12 sUAS operating in downtown San Diego, California transiting to and from a central sUAS Hive, restaurant locations, and customer drop off locations. Interviewees were asked to react to a UAS Volume Reservation (UVR) event with a two-phase impact on food delivery operations. In the second scenario, the interviewees supervised 100 sUAS operating in the same airspace and with the same mission. Interviewees used the interfaces to recognize and react to two sUAS air vehicle problems. After each scenario, we asked the interviewees a semi-structured list of questions to elicit their reflections about using the interfaces. Interviewees were confident in their ability to respond to two off-nominal situations in each simulation. Interviewees felt that, given high levels of automation on the sUAS, they would be able to manage the events without requiring additional support or handing off the sUAS to a colleague or supervisor in both the n=12 or n=100 sUAS settings. In the n=12 sUAS condition, interviewees used the center map to understand the asset location and progress along with mission tasking. An additional display window, Asset Telemetry, helped interviewees understand battery state and sUAS altitude. When the number of sUAS increased, interviewees altered their behavior. Rather than maintaining awareness of individual assets, interviewees appeared to become more reactive, managing exceptions. Interviewees reported that they spent less time looking at the nominal aircraft, and focused their attention primarily on the off-nominal aircraft. In addition, in the second simulation with n=100 sUAS, interviewees reported that they relied more on the side panels (Mission Timeline and Asset Telemetry) to gather information.

human-autonomy teaming↗
NASA NTRS2019

JPSS-2 VIIRS version 2 at-launch relative spectral response characterization

The JPSS-2 VIIRS sensor has completed its pre-launch test program and is now awaiting launch in the 2022 timeframe. The VIIRS spectral characterization, in the form of band averaged and supporting detector level relative spectral response (RSR) for each VIIRS band, was completed in 2019 and is based upon independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements, including first time measurements of the VIIRS SWIR bands by a laser system. The measurements and subsequent analysis effort by subject matter experts of the VIIRS DAWG has led to the July 2019 VIIRS Version 2 RSR release, the official at-launch RSR characterization for the JPSS-2 VIIRS mission. Version 2 replaces and improves upon the August 2018 Version 1 release by incorporating the GLAMR measurements into the analysis to produce an updated “fused” RSR for reflective solar bands (M1- M10, I1-I3, DNBLGS, DNBMGS) and by applying a CO2 absorption correction to the SpMA measurements for thermal band M13. For all other bands (M11, M12, M14-M16, I4, I5), the Version 1 characterization, based entirely upon the SpMA measurements, is carried forward into the Version 2 release. An assessment on compliance with spectral performance metrics finds that VIIRS is compliant on nearly all metrics, with a few minor exceptions. The version 2 RSR release includes band average (over all detectors and subsamples) RSR plus supporting RSR for each detector and subsample, and is available under EAR99 restrictions to the science community at a restricted access NASA eRoom site.

JPSS-2↗
NASA NTRSDate not supplied

Spacecraft Line-Of-Sight Jitter Mitigation and Management Lessons Learned and Engineering Best Practices

Predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter caused by micro-vibrations due to onboard internal disturbance sources is a formidable multidisciplinary engineering task. It is especially challenging for those missions hosting high-performance, vibration-sensitive optical sensor payloads with stringent pointing stability requirements. Both NASA and ESA are planning technically aggressive spaceflight missions that include ultra-high-performance optical payloads with delicate, highly vibration-sensitive scientific and observational instruments. The GN&C community of practice will need to leverage and build upon their collective experiences and lessons learned to better address future micro-vibration challenges. To identify lessons learned and best engineering practices the NASA Engineering & Safety Center (NESC) sponsored a two-day Spacecraft LoS Jitter Workshop in late 2019. The workshop’s goal was to provide a multidisciplinary forum to elicit deeper understanding of the issues related to solving the spacecraft LoS jitter/micro-vibration problem. A primary objective was to identify and share best practices, rules of thumb, and options for jitter-related activities. Representatives from NASA, JPL, ESA, along with NASA’s industrial partners, independent consultant subject matter experts, and members of academia participated in the workshop. This paper will describe the motivation for the NASA Spacecraft LoS Jitter Workshop and summarize the identified findings, observations, and recommendations.

Lessons Learned;↗
NASA NTRS2021

Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization

This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.

strategic planning↗
NASA NTRS2021

EnDEVR: An Environment for Data Engineering in VR

Organizations within the public and private sectors are looking to improve their data science operations for research, development, and operational purposes. As this interest in data science grows, so too does interest in tools and programs that facilitating such operations. This paper presents EnDEVR, the Environment for Data Engineering in Virtual Reality, a user-extensible system that allows for the execution of custom data science functions. We describe the user-centered design process for the EnDEVR system, based on the needs of subject matter experts within the aerospace and aviation industries. Fundamental to this design is a method for users to integrate their existing analysis code (that may leverage third-party vendors) as usable objects in the virtual environment; this allows organizations to operate without the price tag and lifetime obligations associated with commercial data science toolkits, a concept known as “vendor lock-in”. We then present our system design with iterative implementations, and conclude with lessons learned. The presented work may provide insight into future user-centered design processes for VR applications, particularly for digital transformation stakeholders to circumvent vendor lock-in.

Human-centered computing↗
NASA NTRS2021

NASA / NIST / FAA Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis for Aerospace Applications

This report documents the goals, organization and outcomes of a Technical Interchange Meeting (TIM) on Computational Materials Approaches for Qualification by Analysis, co-organized by NASA, NIST and the FAA. The TIM was held at NASA Langley Research Center on January 15-16, 2020. Approximately 60 subject matter experts (SMEs) representing 8 aerospace manufacturers, 7 government organizations and 2 universities participated. Expertise of the SMEs spanned the Technology Readiness Level (TRL) scale from the low-to-mid TRL focus of government laboratories and universities to the high TRL perspective of the regulatory organizations and aerospace manufacturers. During this TIM, the future needs of the government regulators and manufacturers motivated the overall discussion and framed the input given by the participants. Hence, the key objectives of the TIM were to understand existing gaps in model-based, e.g., computational materials, processing and performance predictions for aerospace materials and components and forecast how they can be matured to support material, process and part-level qualification and certification (Q&C). The TIM focused on process-intensive metallic materials technologies, including, but not limited to, additive manufacturing. Participation was roughly evenly divided among the processing and performance tracks, suggesting that both topic areas are generally perceived as being both valuable and requiring additional investment. The output of this TIM may be used by both participating and other organizations, in part, as guidance for future national efforts on maturing computational materials capabilities for use in the Q&C of advanced metallic material systems in aerospace applications.

Edward H Glaessgen↗
NASA NTRS2020

Behavior modeling for cybersecurity

In this paper, we elaborate on the key actors within the context of the cyber world at the Jet Propulsion Laboratory and use Bayesian Belief Networks to represent the causal and probabilistic relationships between the various elements that affect an actor and the likelihood of exploits and adverse consequences that can occur due to an exploit. We assess the effectiveness of each of the mitigative methods and the sensitivity of the system to each of the aggravating as well as mitigative factors. We use a combination of objective incident data and Subject Matter Expert knowledge as input to these models.

King, James↗
NASA NTRS2021

Reliability and Safety Assessment of Urban Air Mobility Concept Vehicles

The following work was commissioned by the National Aeronautics and Space Administration (NASA) to guide industry and future regulation related to urban air mobility (UAM). Prior studies compared the relative safety of NASA concept vehicles, Figure ES1, designed for UAM and provided recommendations for industry research, aircraft architectural improvements, and regulatory updates. After the prior study completed, the European Aviation Safety Agency (EASA) released regulatory guidance in the form of a special condition (SC), SC-VTOL-01, for multirotors with distributed propulsion and flight controls (DPFC). The objective of the current work was to develop DPFC architectures that will comply with SC-VTOL-01. Vehicle designs, DPFC architectures, and stability & control (S&C) models were developed to find limitations and trends to guide industry. To guide this task, NASA developed quad, hex, and an octorotor to better define vehicle attributes and trade space. Aircraft for study included electric, hybrid-electric, and turboshaft powerplants and collective and RPM control schemes. Assessments are in terms of the safety level achieved, and/or aircraft component/features needed to meet SC-VTOL-01. The most challenging criteria being the catastrophic failure rate, ≤10-9 catastrophic failures per flight hour, and that no single failures may result in a catastrophic event. A disciplined process was followed, similar to that in Aerospace Recommended Practice (ARP) 4761. A preliminary system safety assessment (PSSA) leveraged prior work as a basis of creating failure rate budgets for system design teams. System designs were updated and iterated upon, working with reliability and safety subject matter experts to develop SC-VTOL-01 compliant designs. Design changes were reflected in updated PSSAs for initial verification of compliance. The DPFC architecture was broken into four system design teams, the (1) flight control system (FCS), (2) drive and power system, (3) thermal management system (TMS), and (4) electrical power and distribution system. The FCS including elements necessary to control the aircraft, drive and power including elements necessary to generate and transmit shaft power, the TMS including elements necessary to maintain temperature limits in all operating environments, and electrical pow-er and distribution including equipment necessary to store and transmit electrical energy. Results found that all aircraft evaluated may have paths to comply with SC-VTOL-01, Figure ES2. However, S&C models showed large power transients that must be addressed and PSSA results show that future work is needed in single load path structures, high voltage power storage and distribution, and in motor/rotor overspeed protection.

Boeing↗
NASA NTRS2021

Computing Risk of Pyrotechnic Devices Using Lot Acceptance Testing

This paper presents an application of statistical engineering to solve the problem of determining the effects of reducing test sample sizes and environment levels in lot acceptance testing (LAT) of ordnance devices used by NASA and DoD space systems. Unlike environmental testing of dedicated qualification hardware, ordnance devices have a test-like-you-fly exception and use lot acceptance sampling or LAT. LAT is performed by randomly selecting a predefined number of devices from an individual lot and exposing them to a more severe dynamic shock and vibration testing environments than flight (sometimes as high as 6 decibels over flight environments) and then performing a destructive test on the device. Recent trends have been suggested to reduce LAT costs by reducing environment test levels or the number of devices per LAT, or both. A method is given here to compute flight risk, so the proposed methods can be compared by subject matter experts to the baseline LAT methods. In addition, this method can be used to determine the best methods to include in future LAT standards for pyrotechnic devices.

Computing Risk↗
NASA NTRS2021

Exploration Mission Tasks: A Technical Manual

This technical manual is an abridgement of the Generalizable Skills and Knowledge for Exploration Missions (NASA/CR-2018-22045) report (Stuster et al., 2019), describing research conducted under Cooperative Agreement 80NSSC18K0042 for the Human Factors and Behavioral Performance Element, Human Research Program, located at the National Aeronautics and Space Administration’s (NASA) Johnson Space Center. The research identified tasks that will be conducted by human crew during an expedition to Mars, and the abilities, skills, and knowledge that will be required of crew members. The 3-year study uses research methods that were developed to analyze the work performed by a variety of civilian and military occupational specialties and is consistent with Human Factors methods. The work began by developing a comprehensive inventory of 1,125 tasks that are likely to be performed during the 12 phases of the first human expeditions to Mars, from launch to landing 30 months later. Sixty subject matter experts (SMEs) rated expedition tasks in terms of (likely) frequency of performance, difficulty to learn, and importance to mission success; a fourth metric (criticality), was derived by summing the mean ratings of the three dimensions. Seventy-two SMEs placed the physical, cognitive, and social abilities necessary to perform the tasks in order of importance for specialist domains identified by the task analysis. The research team then identified: 1) Abilities, skills, and knowledge that can be retained and generalized across tasks and 2) Implications for crew size and composition. Study results also led to recommendations concerning equipment, habitats, and procedures for exploration-class space missions. Note: The full-mission task inventory was developed during a comprehensive review of documentation and concepts of operations. It was understood by the study team that the tasks were based on currently available information, and that the tools, equipment, propulsion methods, and/or other aspects of actual human expeditions to Mars might be different from those described here, as a consequence of technological development and evolving Mars Design Reference Missions. The purpose and scope of this technical manual is to present the core, actionable information that resulted from this research. The abridged format is intended to address the needs of development and research teams to quickly access, discern, and use the information in the course of their exploration-related work.

Brandin Munson↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRS2021

Spacecraft Line-of-Sight Jitter Management and Mitigation Lessons Learned and Engineering Best Practices

Predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter caused by micro-vibrations due to on-board internal disturbance sources is a formidable multidisciplinary engineering task. It is especially challenging for those missions hosting high-performance (e.g., nano-radian/milli-arcsecond class), vibration-sensitive optical sensor payloads with stringent pointing stability requirements. The Nation Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) are planning technically aggressive spaceflight missions that include ultra-high-performance optical payloads with delicate, highly vibration-sensitive scientific and observational instruments. The guidance, navigation, and control community of practice will need to leverage collective experiences and document their best practices and lessons learned to address future micro-vibration challenges. To identify lessons learned and best practices the NASA Engineering & Safety Center sponsored a 2-day Spacecraft LoS Jitter Workshop in late 2019. The workshop’s goal was to provide a multidisciplinary forum to elicit deeper understanding of the issues related to addressing the spacecraft LoS jitter/micro-vibration problem. The primary objective was to identify, document, and share lessons learned, best practices, and preferred options for jitter-related analysis and test activities. Representatives from NASA, ESA, along with NASA’s industrial partners, independent consultant subject matter experts, and members of academia participated in the workshop. This paper describes the motivation for the workshop and summarize the identified findings and recommendations.

Lessons Learned↗
NASA NTRSDate not supplied

Demonstration of Airline-Based Airborne Reroute Operations using Trajectory Option Sets and Third-Party Tools

A concept that enables advanced airborne reroute operations is proposed, built on NextGen capabilities called Airborne Reroute (ABRR) and Trajectory Option Set (TOS). The concept introduces new third-party services / tools for the airline dispatchers to generate airline-preferred trajectories in response to convective weather events. The airborne TOS routes are sent to the traffic flow managers, who can evaluate the feasibility of the trajectory options with the help of their own third-party services / tools which have been built for this concept. A virtual demonstration study was conducted to elicit feedback from air traffic and airline subject-matter-experts. The feedback was generally positive, both in the benefits of the concept and the feasibility / need for the new tools to enable the concept but their feedback was mixed on the feasibility of the new tools to be third-party services instead of being integrated into their core tools. Nevertheless, this concept explores and demonstrates an evolutionary pathway toward a service-oriented future that shifts the responsibilities and the capabilities of air traffic operations from the air traffic service providers to the airline industry and third-party vendors.

airborne reroutes↗
NASA NTRSDate not supplied

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗
NASA NTRS2021

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗
NASA NTRSDate not supplied

Application of Analytical Hierarchy Process for Narrowing Down Nep Candidate Reactor Designs

NASA is currently studying the possibility of human exploration of Mars in the late 2030s timeframe using a hybrid nuclear electric propulsion (NEP)/chemical propulsion system. Present analyses indicate such a mission could require a reactor power system in the range of 2 to 6 MWe operating for approximately 3 years. At present, the Technology Readiness Levels (TRL) of key NEP technologies are still low, implying that significant technology development activities must be undertaken prior to making informed design selections for an integrated propulsion system. NASA’s Space Nuclear Propulsion (SNP) project has recently undertaken an in-depth effort to identify NEP candidate technologies and determine the required developmental work to mature these technologies to the point where they could realistically support the design and assembly of a full NEP system. A version of the Analytical Hierarchy Process is being employed to narrow the technology candidates for SNP investment planning. This multi-attribute decision-making process relies on quantified technical inputs and the judgement of Subject Matter Experts (SMEs) to evaluate technology options against key technical and programmatic Figures-of-Merit (FOM) at the subsystem and optimized system level. The process and an example evaluation of nuclear fuel/moderator combinations in a sodium heat pipe honeycomb-geometry reactor concept are described. The evaluation and analysis methodology described in this paper can be employed for pairwise comparisons between reactor concepts and technology choices as new data become available.

Dasari V Rao↗
NASA NTRSDate not supplied

An Automated Approach to Labelling Datasets in Earth Science Publications

NASA Data Active Archive Centers, orDAACs, ingest, store, and distribute dataacquired from satellites, ground systems as well asreanalysis models. Many authors use this datain their research. However, most of the datasets usedin Earth Science Publications are not citedcorrectly or not cited at all. Thus, there is no directlink between the datasets used and thescientific publications which reference them. Thisleads to issues with reproducibility of theresults, attribution of the research results, anddiscovery of new datasets. This project began byexploring various methods of automatically labellingGoddard Earth Sciences Data andInformation Services Center (GES DISC) datasets usingSupervised Machine Learning and EarthData Search Common Metadata Repository (CMR) queries.The ultimate goal was to create alibrary of citations that utilized automated citationlabeling to directly link the researchpublications to the data they use. Supervised MachineLearning approaches struggled due to thelimited amount of labelled training data to learnfrom. Increasing the volume of training data isdifficult as it requires subject matter experts todevote time to manually reviewing journalarticles and determining the datasets used. The CMRqueries were inconsistent because theunderlying metadata is continuously being updated.Thus, it is hard to generalize theeffectiveness of the CMR results as they are dependenton the internal state of CMR. Theseapproaches helped inform the decision to transitionthe project into using a Knowledge Graph.Another key aspect of this project focused on theautomated extraction of features (platform,instrument, variables, etc) and explicit citationsfrom within Earth Science Publications. Theseautomated extractions were used to classify researchpapers based on their platform/instrumentcouples. This information was input into the CitationManagement System for GES DISC. Theseplatform/instrument couples also provide an additionalfacet that can be searched on the GESDISC website.

Edward Jahoda↗