Results for “Mission Data System” — Page 29

NASA NTRS1975

Multi-kilowatt modularized spacecraft power processing system development

A review of existing information pertaining to spacecraft power processing systems and equipment was accomplished with a view towards applicability to the modularization of multi-kilowatt power processors. Power requirements for future spacecraft were determined from the NASA mission model-shuttle systems payload data study which provided the limits for modular power equipment capabilities. Three power processing systems were compared to evaluation criteria to select the system best suited for modularity. The shunt regulated direct energy transfer system was selected by this analysis for a conceptual design effort which produced equipment specifications, schematics, envelope drawings, and power module configurations.

Andrews, R. E.↗

NASA NTRS1987

A Computer System for Mission Managers

Mission Managers' Workstation (MMW) is personal-computer-based system providing data management and reporting functions to assist Space Shuttle mission managers. Allows to relate events and stored data in timely and organized fashion. Using MMW, standard reports formatted, generated, edited, and electronically communicated with minimum clerical help. Written in PASCAL, BASIC, and assembler.

Tolchin, Robert↗

NASA NTRS1993

GRYPHON: Air launched space booster

The project chosen for the winter semester Aero 483 class was the design of a next generation Air Launched Space Booster. Based on Orbital Sciences Corporation's Pegasus concept, the goal of Aero 483 was to design a 500,000 pound air launched space booster capable of delivering 17,000 pounds of payload to Low Earth Orbit and 8,000 pounds of payload to Geosynchronous Earth Orbit. The resulting launch vehicle was named the Gryphon. The class of forty senior aerospace engineering students was broken down into eight interdependent groups. Each group was assigned a subsystem or responsibility which then became their field of specialization. Spacecraft Integration was responsible for ensuring compatibility between subsystems. This group kept up to date on subsystem redesigns and informed those parties affected by the changes, monitored the vehicle's overall weight and dimensions, and calculated the mass properties of the booster. This group also performed the cost/profitability analysis of the Gryphon and obtained cost data for competing launch systems. The Mission Analysis Group was assigned the task of determining proper orbits, calculating the vehicle's flight trajectory for those orbits, and determining the aerodynamic characteristics of the vehicle. The Propulsion Group chose the engines that were best suited to the mission. This group also set the staging configurations for those engines and designed the tanks and fuel feed system. The commercial satellite market, dimensions and weights of typical satellites, and method of deploying satellites was determined by the Payloads Group. In addition, Payloads identified possible resupply packages for Space Station Freedom and identified those packages that were compatible with the Gryphon. The guidance, navigation, and control subsystems were designed by the Mission Control Group. This group identified required tracking hardware, communications hardware telemetry systems, and ground sites for the location of the Gryphon's mission control center. The Structures group was responsible for ensuring the structural integrity of the vehicle. Their designs included the payload shroud, payload support structure, exterior hull and engine support struts. The Gryphon's power requirements were determined by the Power/Thermal/Attitude Control Group. This group then selected suitable batteries and other components to meet these requirements. The group also designed heat shielding and cooling systems to ensure subsystem performance. In addition to these responsibilities this group designed the attitude control methods and RCS components for the vehicle. The Aircraft Integration Group was responsible for all aspects of the booster aircraft connection. This included the design of the connection structure and the drop mechanism. This group also designed the vehicle assembly facility and identified possible ground bases for the plane.

Source record↗

NASA NTRS2002

Experimental evaluation of a COTS system for space applications

The use of COTS-based systems in space missions for scientific data processing is very attractive, as their ratio of performance to power consumption of commercial components can be an order of magnitude greater than that of radiation hardened components, and the price differential is even higher.

fault injection on-board processing cluster commut↗

NASA NTRS2000

A distributed component framework for science data product interoperability

Correlation of science results from multi-disciplinary communities is a difficult task. Traditionally data from science missions is archived in proprietary data systems that are not interoperable. The Object Oriented Data Technology (OODT) task at the Jet Propulsion Laboratory is working on building a distributed product server as part of a distributed component framework to allow heterogeneous data systems to communicate and share scientific results.

XML distributed framework science interprobability↗

NASA NTRS2019

Big Data, Cloud, and Earth Science

Given the reality of a Big Data future, we need to reevaluate our ability to quickly process immense amounts of data while maintaining our responsibility of stewarding our archives. At National Aeronautics and Space Administration (NASA), our Science Missions Directorate and Earth Science Data Systems have been exploring the commercial cloud as a potential mechanism for data ingest, archive, and distribution since 2015. This talk will discuss our timeline and strategies being employed and will discuss the process of introducing commercial cloud entities into a largely on-premise hardware-reliant approach to ingest, archive, and distribution. We will address topics such as vendor lock-in, varying compute and costing strategies, and end-user adoption. Please join us for an open and authentic conversation about the opportunities we are embracing and the risks we are undertaking as a part of this evolution.

Informatics↗

NASA NTRS2019

EDOS Initiatives to Decrease Latency of NRT Data for LANCE

NASA's EOS Data and Operations System (EDOS) is the primary supplier of NRT (near real-time) data to the NASA NRT user community known as the Land, Atmosphere NRT Capability for EOS (LANCE). EDOS provides NRT data for various instruments on the EOS missions Terra, Aqua, Aura, as well as for the NOAA missions Suomi NPP and NOAA-20. This poster describes an overview of the EDOS multi-mission system with emphasis on the NRT products distributed for LANCE elements: AIRS, MISR, MLS, MODIS, MOPITT, OMPS, OMI and VIIRS. Remote EDOS high-rate data capture systems are deployed at NASA ground stations which provide data-driven capture of high-rate science for EOS missions. The remote EDOS components transfer the science data via high-rate WANs to the centralized EDOS Level-zero processing systems located at Goddard Space Flight Center. EDOS produces session-based data sets especially for LANCE NRT use from a single ground station contact; this data is sent to dual LANCE destinations as part of the standard redundancy requirement for LANCE elements. EDOS has implemented various latency improvements with the ultimate goal to have EDOS processing of NRT data keep up with the spacecraft data downlink. EDOS enhancements have included implementation of priority-based QoS, expanded network architecture to include open networks, and use of a delay-tolerant protocol. EDOS has streamlined its systems and infrastructure to minimize latency for NRT data delivery for LANCE. EDOS begins to transfer the NRT data to the LANCE elements within minutes of the end of the contact session with an average packet latency from instrument observation to Level 0 product delivery to each LANCE element of just over one hour.

Michael, Karen↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2004

CRYSTAL/FACE

Droplet Measurement Technologies (DMT), under funding from NASA, participated in the CRYSTAL/FACE field campaign in July, 2002 with measurements of cirrus cloud hydrometeors in the size range from 0.5 to 1600 microns. The measurements were made with the DMT Cloud, Aerosol and Precipitation Spectrometer (CAPS) that was flown on NASA's WB57F. With the exception of the first research flight when the data system failed two hours into the mission, the measurement system performed almost flawlessly during the thirteen flights. The measurements from the CAPS have been essential for interpretation of cirrus cloud properties and their impact on climate. The CAPS data set has been used extensively by the CRYSTAL/FACE investigators and as of the date of this report, have been included in five published research articles, 10 conference presentations and six other journal articles currently in preparation.

Baumgardner, Darrel↗

NASA NTRS2020

Real Time Optical Receiver Project

The goals for the Real Time Optical Receiver project are to Infuse Consultative Committee for Space Data Systems High Photon Efficiency standard into missions, such as Artemis and to provide a real time ground receiver solution (fiber device, detector, real time FPGA-based receiver) that is: (1) Scalable: data rate, atmosphere conditions, telescope aperture, etc., and (2) Uses COTS components when available and work with companies to move custom parts to COTS.

Katz, Evan J.↗

NASA NTRS1997

Bidirectional Reflectance Functions for Application to Earth Radiation Budget Studies

Reflected solar radiative fluxes emerging for the Earth's top of the atmosphere are inferred from satellite broadband radiance measurements by applying bidirectional reflectance functions (BDRFs) to account for the anisotropy of the radiation field. BDRF's are dependent upon the viewing geometry (i.e. solar zenith angle, view zenith angle, and relative azimuth angle), the amount and type of cloud cover, the condition of the intervening atmosphere, and the reflectance characteristics of the underlying surface. A set of operational Earth Radiation Budget Experiment (ERBE) BDRFs is available which was developed from the Nimbus 7 ERB (Earth Radiation Budget) scanner data for a three-angle grid system, An improved set of bidirectional reflectance is required for mission planning and data analysis of future earth radiation budget instruments, such as the Clouds and Earth's Radiant Energy System (CERES), and for the enhancement of existing radiation budget data products. This study presents an analytic expression for BDRFs formulated by applying a fit to the ERBE operational model tabulations. A set of model coefficients applicable to any viewing condition is computed for an overcast and a clear sky scene over four geographical surface types: ocean, land, snow, and desert, and partly cloudy scenes over ocean and land. The models are smooth in terms of the directional angles and adhere to the principle of reciprocity, i.e., they are invariant with respect to the interchange of the incoming and outgoing directional angles. The analytic BDRFs and the radiance standard deviations are compared with the operational ERBE models and validated with ERBE data. The clear ocean model is validated with Dlhopolsky's clear ocean model. Dlhopolsky developed a BDRF of higher angular resolution for clear sky ocean from ERBE radiances. Additionally, the effectiveness of the models accounting for anisotropy for various viewing directions is tested with the ERBE along tract data. An area viewed from nadir and from the side give two different radiance measurements but should yield the same flux when converted by the BDRF. The analytic BDRFs are in very good qualitative agreement with the ERBE models. The overcast scenes exhibit constant retrieved albedo over viewing zenith angles for solar zenith angles less than 60 degrees. The clear ocean model does not produce constant retrieved albedo over viewing zenith angles but gives an improvement over the ERBE operational clear sky ocean BDRF.

Manalo-Smith, N.↗

NASA NTRS2014

Handling Late Changes to Titan Science

The Cassini mission has been in orbit for eight years, returning a wealth of scientific data from Titan and the Saturnian system. The mission, a cooperative undertaking between NASA, ESA and ASI, is currently in its second extension of the prime mission. The Cassini Solstice Mission (CSM) extends the mission's lifetime until Saturn's northern summer solstice in 2017. The Titan Orbital Science Team (TOST) has the task of integrating the science observations for all 56 targeted Titan flybys in the CSM. In order to balance Titan science across the entire set of flybys during the CSM, to optimize and influence the Titan flyby altitudes, and to decrease the future workload, TOST went through a "jumpstart" process before the start of the CSM. The "jumpstart" produced Master Timelines for each flyby, identifying prime science observations and allocating control of the spacecraft attitude to specific instrument teams. Three years after completing this long-range plan, TOST now faces a new challenge: incorporating changes into the Titan Science Plan without undoing the balance achieved during the jumpstart.

Pitesky, Jo Eliza↗

NASA NTRS2014

Adapting a Large-Scale Multi-Mission Ground System for Low-Cost CubeSats

The majority of today's CubeSat fleet consists of Earth-orbiting missions that mostly use existing ground systems developed by universities because of availability, simplicity, and low-cost. The Interplanetary NanoSpacecraft Pathfinder In Relevant Environment (INSPIRE) mission is a revolutionary CubeSat mission that will launch a pair of CubeSats into deep space to study the feasibility of CubeSats beyond low-Earth orbit. This uncovers a new set of systems and software engineering challenges to the development of a robust and reliable ground system in a low-cost environment. In this paper, we discuss the approach to these challenges by using the Jet Propulsion Laboratory's (JPL) Advanced Multimission Operation System (AMMOS) Ground Data System (GDS) as well as the methodologies used to engineer the flight system to work with an existing ground system developed for large-scale missions. Specifically we will focus on the command and telemetry subsystem of AMMOS, the Multimission Data Processing and Control System. We conclude with a retrospective on the challenges encountered and a brief discussion on our efforts to provide AMMOS to support future deep space CubeSat missions.

Quach, William L.↗

NASA NTRS1967

Telemetry communications guidelines

Data transmission capabilities of spacecraft telemetry communications systems, and guidelines for planning 1975-1980 missions

SPACECRAFT COMMUNICATIONS SYSTEM↗

NASA NTRS1988

Wide band data collection system

The Incorporated Research Institutes for Seismology (IRIS) approached NASA Headquarters in 1986 about the need to collect data daily from seismic stations around the world as part of the Earth Observing System (EOS) mission. A typical IRIS Seismic Station generates 16 Megabytes of data per day when there is seismic activity. The Preliminary Design Parameters of the Wide Band Data Collection System are summarized.

Turkiewicz, J. M.↗

NASA NTRS2006

Advanced Technologies for Future Spacecraft Cockpits and Space-based Control Centers

The National Aeronautics and Space Administration (NASA) is embarking on a new era of Space Exploration, aimed at sending crewed spacecraft beyond Low Earth Orbit (LEO), in medium and long duration missions to the Lunar surface, Mars and beyond. The challenges of such missions are significant and will require new technologies and paradigms in vehicle design and mission operations. Current roles and responsibilities of spacecraft systems, crew and the flight control team, for example, may not be sustainable when real-time support is not assured due to distance-induced communication lags, radio blackouts, equipment failures, or other unexpected factors. Therefore, technologies and applications that enable greater Systems and Mission Management capabilities on-board the space-based system will be necessary to reduce the dependency on real-time critical Earth-based support. The focus of this paper is in such technologies that will be required to bring advance Systems and Mission Management capabilities to space-based environments where the crew will be required to manage both the systems performance and mission execution without dependence on the ground. We refer to this concept as autonomy. Environments that require high levels of autonomy include the cockpits of future spacecraft such as the Mars Exploration Vehicle, and space-based control centers such as a Lunar Base Command and Control Center. Furthermore, this paper will evaluate the requirements, available technology, and roadmap to enable full operational implementation of onboard System Health Management, Mission Planning/re-planning, Autonomous Task/Command Execution, and Human Computer Interface applications. The technology topics covered by the paper include enabling technology to perform Intelligent Caution and Warning, where the systems provides directly actionable data for human understanding and response to failures, task automation applications that automate nominal and Off-nominal task execution based on human input or integrated health state-derived conditions. Shifting from Systems to Mission Management functions, we discuss the role of automated planning applications (tactical planning) on-board, which receive data from the other cockpit automation systems and evaluate the mission plan against the dynamic systems and mission states and events, to provide the crew with capabilities that enable them to understand, change, and manage the timeline of their mission. Lastly, we discuss the role of advanced human interface technologies that organize and provide the system md mission information to the crew in ways that maximize their situational awareness and ability to provide oversight and control of aLl the automated data and functions.

Garcia-Galan, Carlos↗

NASA NTRS1986

Exploration of the solar system: Achievements and future plans in NASA's programme

The Voyager 2 encounter with Uranus, ground-based and spacecraft observations of Comet Halley, and other NASA solar system exploration is reviewed. The Challenger tragedy significantly delayed the next NASA planetary mission, Galileo, as well as the Ulysses and the Space Telescope missions, all of which will provide data vital to understanding of the solar system. However, the results anticipated from these missions, as well as those from the 1989 Voyager encounter with Neptune and from other approved and planned planetary missions promise that NASA's future role in solar system exploration will remain alive and vital.

Brunk, William E.↗