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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Earth Observation Data Provenance for Future Climate Research - Requirements and Challenges

Observations and measurements of the Earth’s environment have been collected from space since the 1960’s. Flight Projects, airborne and field campaigns have developed and operated multi-year missions with global observing instruments, and Principle Investigator Science Teams have developed algorithms, calibrated, and derived a wide variety of Earth system environmental parameters. These data are archived and distributed for research purposes by NASA’s ESDIS Project and Distributed Active Archive Centers (DAACs). They are expected to be an important basis for Earth science and climate change research extending well beyond their observation times, the Principle Investigators/Science Teams research projects and life of the Flight Projects.

John Moses↗
NASA NTRS2015

An Overview of the Challenges With and Proposed Solutions for the Ingest and Distribution Processes for Airborne Data Management

The current data management practices for NASA airborne field projects have successfully served science team data needs over the past 30 years to achieve project science objectives, however, users have discovered a number of issues in terms of data reporting and format. The ICARTT format, a NASA standard since 2010, is currently the most popular among the airborne measurement community. Although easy for humans to use, the format standard is not sufficiently rigorous to be machine-readable. This makes data use and management tedious and resource intensive, and also create problems in Distributed Active Archive Center (DAAC) data ingest procedures and distribution. Further, most DAACs use metadata models that concentrate on satellite data observations, making them less prepared to deal with airborne data.

Beach, Aubrey↗
DOE OSTI2022

Considerations for Developing a Regulatory Roadmap for Distributed Energy Resource (DER) Integration in Colombia

The USAID-NREL Partnership in Colombia, in conjunction with SURE and USEA, selected four action plan teams from the Young Professionals Leadership Program to receive tailored NREL technical assistance to support action plan implementation. The four plans selected represent various aspects associated with planning for the efficient integration of DERs, including electric mobility, residential and commercial energy applications, distributed generation modeling, and regulatory considerations. In response to national grid modernization, decentralization, digitalization, and electrification trends, CREG is looking to update Colombia's electric distribution code, with an emphasis on the integration of electric vehicles and charging stations, but also energy storage systems, non-conventional sources of renewable energy, and other distributed energy resources (DERs). CREG aims to establish the general steps that must be carried out to update the distribution code for the integration of DERs, with a view to the design and development of new markets. The objective of the TA provided to CREG was to support them in identifying and outlining the general processes and approaches to consider when updating Colombia's distribution code for the integration of electric vehicles, distributed renewable energy systems, and energy storage systems, with a view to the design and development of new markets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Autonomous mobile robot teams

This paper describes autonomous mobile robot teams performing tasks in unstructured environments. The behavior and the intelligence of the group is distributed, and the system does not include a central command base or leader. The novel concept of the Tropism-Based Cognitive Architecture is introduced, which is used by the robots in order to produce behavior transforming their sensory information to proper action. The results of a number of simulation experiments are presented. These experiments include worlds where the robot teams must locate, decompose, and gather objects, and defend themselves against hostile predators, while navigating around stationary and mobile obstacles.

Agah, Arvin↗
NASA NTRS1999

TRMM Realtime Data Production, Transfer, and Future Plans

The Tropical Rainfall Measuring Mission, a joint US NASA and Japan NASDA mission, was launched on November 27, 1997 from Tanegashima Island, Japan on a NASDA provided H-II launch vehicle. The TRMM objectives are: to obtain and study multi-year science data sets of tropical and sub- tropical rainfall measurement; to understand how interactions between the sea, air, and land masses produce changes in global rainfall and climate; to help improve modeling of tropical rainfall processes; to test, evaluate, and improve the performance of satellite rainfall estimates measurements and techniques. The routine production and quick distribution of standard products provides an important underpinning to these objectives. The determination of standard TRMM algorithms and the requirements for routine production and distribution fall within the authority of the joint US-Japan Science Team. In April of 1998, the Joint TRMM Science team requested that TRMM data be made available in near real-time to approved international agencies and modelers. This request was an addition to the routine production of the standard TRMM data products. The science team requested that near-realtime products be as small as possible to facilitate their transfer via networks. Indeed, it was determined that only network transfer would be available. This presentation provides an overview of the type of products available from the realtime system and how the TRMM system was developed at the lowest cost in the shortest amount of time. It then describes the real-time data flow. The presentation also summarizes how the science quality of real-time data is maintained. It describes how interested groups can get access to this data and the importance of networks for the future of real-time data expansion. The presentation concludes real-time possibilities and improvements possible as part of the proposed Global Precipitation Mission. High speed network access availability is an important catalyst to real-time data flow during the proposed mission.

Stocker, Erich Franz↗
NASA NTRSDate not supplied

Studying the 2019 Australian Bushfires Disaster using NASA Data: A Data-Driven Storytelling Approach

The 2019-2020 Australian fire season was particularly devastating, with millions of acres of land burned and impacts affecting Australian ecology, local populations and air quality, and the atmosphere. Australia saw unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. The fires gave rise to a host of atmospheric phenomenon, including smoke transport and lofting generated by storm-induced by fires, known as pyrocumulonimbus, reaching the stratosphere. NASA’s satellites not only tracked the event in real time, but also gathered data to further inform forecasting and response methods in the future. To better assist the public in understanding the lead up, impacts, and aftermath effects of these fires, the Science Outreach Team at NASA Langley Research Center’s Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) used Esri’s storymap tool to guide users through understanding relevant phenomenon, contributing factors, the effects this event has had on global atmospheric composition, and the science behind researching the tie between disasters and public health. The storymap uses data from the ASDC-supported NASA missions Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), the Stratospheric Aerosol and Gas Experiment (SAGE III), and Multi-angle Imaging SpectroRadiometer (MISR). By using data-driven storytelling to communicate impacts of a large fire event, we hope to provide an accessible, engaging science outreach tool format.

Sanjana Paul↗
NASA NTRSDate not supplied

Atmospheric Data-Driven Visualization of Air Quality Variation in Megacities

The growth and spread of human settlements and increasing urban density are important processes in global change. Urbanization has accelerated with population growth, and more densely populated urban areas have major effects on the local and regional environments. Air quality in megacities has been a concern for decades. Data for anthropogenic emissions, pollutants, and particulate matter can inform research on the interactions between urban landscapes and the atmosphere, assist policy makers in developing sustainable, healthy environments, and inform the general public. To better assist researchers, students, the public, and policymakers in understanding the annual and seasonal variation of aerosol and gas intensity in megacities, the Science Outreach Team at NASA Langley Research Center’s Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) demonstrate data products at the ASDC that can be used to visualize these parameters. The presentation uses data from the ASDC-supported NASA missions Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol and Infrared Pathfinder Satellite Observation (CALIPSO), Tropospheric Emission Spectrometer (TES), and Multi-angle Imaging Spectro Radiometer (MISR)

Air Quality↗
NASA NTRSDate not supplied

pyQuARC: Open Source Library for Earth Observation Metadata Quality Assessment

Metadata quality is essential to effective data discovery and has become increasingly vital as more Earth Science data sets become available. The Common Metadata Repository (CMR) hosts metadata describing NASA’s Earth Observation data products, which are archived across 12 Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, conducts metadata quality assessments to ensure that these data products are discoverable, accessible, and usable. To achieve these goals, the ARC team has developed a metadata quality assessment framework to evaluate metadata completeness, correctness, and consistency. ARC uses a combination of manual and automated methods to assess these three components and identify areas of improvement; the team then collaborates with the DAACs to resolve any findings. To streamline this process, ARC is currently developing a host of scripts, known as pyQuARC, to automate metadata quality assessments as much as possible. pyQuARC is an open source library for Earth Observation Metadata Quality Assessment, and the tool utilizes ARC’s metadata quality assessment framework to make basic validation checks, pinpoint inconsistencies between dataset-level (i.e. collection) and file-level (i.e. granule) metadata, and identify opportunities for more descriptive and robust information. Since pyQuARC is also customizable, other users can make modifications as needed, and future metadata standards can also be implemented. Once pyQuARC is fully developed, it will support multiple schema types to serve the broader EOSDIS metadata community. This presentation will provide an overview of pyQuARC and its process of development while showcasing the tool’s valuable features and uses.

Jenny Wood↗
NASA NTRSDate not supplied

pyQuARC: Preparing for Full Release

Metadata holds the contextual information about data and is the underlying structure for many data search portals. High quality metadata optimizes search results, allowing users to quickly retrieve the data they need. With the abundant volume and diversity of Earth observation datasets, data discovery and metadata quality are critical for end users. The Common Metadata Repository (CMR), for example, currently hosts metadata for over 9,000 Earth observation data products archived across 12 NASA Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, assesses the completeness, correctness, and consistency of these metadata records to ensure they are accessible, usable, and discoverable. In 2021, ARC began developing pyQuARC, an open source library for Earth Observation Metadata Quality Assessment to automate this effort. The tool uses ARC’s existing metadata quality framework to provide prioritized recommendations for metadata improvement. During initial testing, pyQuARC automatically identified 58% of metadata findings when compared with a sample of manually reviewed records. Using the results from initial testing, this presentation will focus on recent advancements and improvements of the tool as the ARC team prepares for pyQuARC’s full release. It will also demonstrate pyQuARC's enrichment value, not only for the ARC team, but the broader EOSDIS metadata community as well.

Essence Raphael↗
DOE OSTI2022

ECAR-5367 Rev 0 Hydraulic Assessment of the Primary Coolant Test System Design

A MARVEL thermal-hydraulic analysis team has developed the computational fluid dynamicsmodel of the Primary Coolant Test (PCAT) system design based on the CAD drawing providedfrom INL PCAT design team. This ECAR addresses the hydraulic system characteristics(pressure loss and flow distribution) in the various sub-components of the PCAT design. Thisreport provides potential design optimization direction on the PCAT test. Most importantly, thisreport evaluates the feasibility of the natural circulation at the targeted heat balance between thecore and heat sink design.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS2015

SPHERES: From Ground Development to Operations on ISS

SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites) is an internal International Space Station (ISS) Facility that supports multiple investigations for the development of multi-spacecraft and robotic control algorithms. The SPHERES Facility on ISS is managed and operated by the SPHERES National Lab Facility at NASA Ames Research Center (ARC) at Moffett Field California. The SPHERES Facility on ISS consists of three self-contained eight-inch diameter free-floating satellites which perform the various flight algorithms and serve as a platform to support the integration of experimental hardware. To help make science a reality on the ISS, the SPHERES ARC team supports a Guest Scientist Program (GSP). This program allows anyone with new science the possibility to interface with the SPHERES team and hardware. In addition to highlighting the available SPHERES hardware on ISS and on the ground, this presentation will also highlight ground support, facilities, and resources available to guest researchers. Investigations on the ISS evolve through four main phases: Strategic, Tactical, Operations, and Post Operations. The Strategic Phase encompasses early planning beginning with initial contact by the Principle Investigator (PI) and the SPHERES program who may work with the PI to assess what assistance the PI may need. Once the basic parameters are understood, the investigation moves to the Tactical Phase which involves more detailed planning, development, and testing. Depending on the nature of the investigation, the tactical phase may be split into the Lab Tactical Phase or the ISS Tactical Phase due to the difference in requirements for the two destinations. The Operations Phase is when the actual science is performed; this can be either in the lab, or on the ISS. The Post Operations Phase encompasses data analysis and distribution, and generation of summary status and reports. The SPHERES Operations and Engineering teams at ARC is composed of experts who can guide the Payload Developer (PD) and Principle Investigator (PI) in reaching critical milestones to make their science a reality using the SPHERES platform. From performing integrated safety and verification assessments, to assisting in developing crew procedures and operations products, to organizing, planning, and executing all test sessions, to helping manage data products, the SPHERES team at ARC is available to support microgravity research with the SPEHRES Guest Scientist Program.

ISS Research↗
NASA NTRS1998

Flying Cassini with Virtual Operations Teams

The Cassini Program's challenge is to fly a large, complex mission with a reduced operations budget. A consequence of the reduced budget is elimination of the large, centrally located group traditionally used for uplink operations. Instead, responsibility for completing parts of the uplink function is distributed throughout the Program. A critical strategy employed to handle this challenge is the use of Virtual Uplink Operations Teams. A Virtual Team is comprised of a group of people with the necessary mix of engineering and science expertise who come together for the purpose of building a specific uplink product. These people are drawn from throughout the Cassini Program and participate across a large geographical area (from Germany to the West coast of the USA), covering ten time zones. The participants will often split their time between participating in the Virtual Team and accomplishing their core responsibilities, requiring significant planning and time management. When the particular uplink product task is complete, the Virtual Team disbands and the members turn back to their home organization element for future work assignments. This time-sharing of employees is used on Cassini to build mission planning products, via the Mission Planning Virtual Team, and sequencing products and monitoring of the sequence execution, via the Sequence Virtual Team. This challenging, multitasking approach allows efficient use of personnel in a resource constrained environment.

Dodd, Suzanne↗
DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Exploration of Potential Application of Distributed Ledger Technology for Managing Transactions Under Joint Technology Development and Transfer Agreements

This document summarizes the findings from the team’s evaluation of applying DLT to transactions taking place inside and outside of a joint technology development and transfer agreement. As part of this examination, PNNL focused on addressing whether DLT could be used to promote civil nuclear cooperation without raising proliferation concerns.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

SMC: SCENIC Model Control

NASAs Space Communications and Navigation (SCaN) program manages three active networks: the Near Earth Network, the Space Network, and the Deep Space Network. These networks simultaneously support NASA missions and provide communications services to customers worldwide. To efficiently manage these resources and their capabilities, a team of student interns at the NASA Glenn Research Center is developing a distributed system to model the SCaN networks. Once complete, the system shall provide a platform that enables users to perform capacity modeling of current and prospective missions with finer-grained control of information between several simulation and modeling tools. This will enable the SCaN program to access a holistic view of its networks and simulate the effects of modifications in order to provide NASA with decisional information. The development of this capacity modeling system is managed by NASAs Strategic Center for Education, Networking, Integration, and Communication (SCENIC). Three primary third-party software tools offer their unique abilities in different stages of the simulation process. MagicDraw provides UMLSysML modeling, AGIs Systems Tool Kit simulates the physical transmission parameters and de-conflicts scheduled communication, and Riverbed Modeler (formerly OPNET) simulates communication protocols and packet-based networking. SCENIC developers are building custom software extensions to integrate these components in an end-to-end space communications modeling platform. A central control module acts as the hub for report-based messaging between client wrappers. Backend databases provide information related to mission parameters and ground station configurations, while the end user defines scenario-specific attributes for the model. The eight SCENIC interns are working under the direction of their mentors to complete an initial version of this capacity modeling system during the summer of 2015. The intern team is composed of four students in Computer Science, two in Computer Engineering, one in Electrical Engineering, and one studying Space Systems Engineering.

Simulation↗
NASA NTRS2005

Astrobiology at Arizona State University: An Overview of Accomplishments

During our five years as an NAI charter member, Arizona State University sponsored a broadly-based program of research and training in Astrobiology to address the origin, evolution and distribution of life in the Solar System. With such a large, diverse and active team, it is not possible in a reasonable space, to cover all details of progress made over the entire five years. The following paragraphs provide an overview update of the specific research areas pursued by the Arizona State University (ASU) Astrobiology team at the end of Year 5 and at the end of the 4 month and subsequent no cost month extensions. for a more detailed review, the reader is referred to the individual annual reports (and Executive Summaries) submitted to the NAI at the end of each of our five years of membership. Appended in electronic form is our complete publication record for all five years, plus a tabulation of undergraduates, graduate students and post-docs supported by our program during this time. The overarching theme of ASU s Astrobiology program was "Exploring the Living Universe: Studies of the Origin, Evolution and Distribution of Life in the Solar System". The NAi-funded research effort was organized under three basic sub- themes: 1. Origins of the Basic Building Blocks of Life. 2. Early Biosphere Evolution. and 3. Exploring for Life in the Solar System. These sub-theme areas were in turn, subdivided into Co-lead research modules. In the paragraphs that follow, accomplishments for individual research modules are briefly outlined, and the key participants presented in tabular form. As noted, publications for each module are appended in hard copy and digital formats, under the name(s) of lead co-Is.

Farmer, Jack↗
NASA NTRSDate not supplied

Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science

Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.

Distributed Systems↗