Search NASA⌕ Search

SEARCH · Search NASA

Results for “PDS”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Inflight Radiometric Calibration of New Horizons' Multispectral Visible Imaging Camera (MVIC)

We discuss two semi-independent calibration techniques used to determine the inflight radiometric calibration for the New Horizons Multi-spectral Visible Imaging Camera (MVIC). The first calibration technique compares the measured number of counts (DN) observed from a number of well calibrated stars to those predicted using the component-level calibration. The ratio of these values provides a multiplicative factor that allows a conversation between the preflight calibration to the more accurate inflight one, for each detector. The second calibration technique is a channel-wise relative radiometric calibration for MVIC's blue, near-infrared and methane color channels using Hubble and New Horizons observations of Charon and scaling from the red channel stellar calibration. Both calibration techniques produce very similar results (better than 7% agreement), providing strong validation for the techniques used. Since the stellar calibration described here can be performed without a color target in the field of view and covers all of MVIC's detectors, this calibration was used to provide the radiometric keyword values delivered by the New Horizons project to the Planetary Data System (PDS). These keyword values allow each observation to be converted from counts to physical units; a description of how these keyword values were generated is included. Finally, mitigation techniques adopted for the gain drift observed in the near-infrared detector and one of the panchromatic framing cameras are also discussed.

Howett, C. J. A.↗
NASA NTRS2017

The JPL Neptune Radiation Model (NMOD)

The objective of this study is the development of a comprehensive radiation model of the Neptunian environment for JPL mission planning. The ultimate goal is to provide a description of the high-energy electron and proton environments and the magnetic field at Neptune that can be used for engineering design. The JPL Neptune Radiation Model (NMOD) models the high-energy electrons and protons between 0.025 MeV and 5 MeV based on the California Institute of Technology's Cosmic Ray Subsystem and the Applied Physics Laboratory's Low Energy Charged Particle Detector on Voyager 2. As in previous JPL radiation models, the form of the Neptunian model is based on magnetic field coordinates and requires a conversion from spacecraft coordinates to Neptunian-centered magnetic "B-L" coordinates. Two types of magnetic field models have been developed for Neptune: 1) simple "offset, tilted dipoles" (OTD), and 2) a complex, multi-pole expansion model ("O8"). A review of the existing data on Neptune and a search of the NASA Planetary Data System (PDS) were completed to obtain the most current descriptions of the Neptunian high-energy particle environment. These data were fit in terms of the O8 B-L coordinates to develop the electron and proton flux models. The flux predictions of the new model were used to estimate the total ionizing dose (TID) rate along the Neptunian equator, meridional flux contours for the electrons and protons, and for flux and dose comparisons with the other radiation belts in the Solar System.

Garrett, Henry↗
NASA NTRS2017

The JPL Neptune Radiation Model (NMOD)

The objective of this study is the development of a comprehensive radiation model of the Neptunian environment for JPL mission planning. The ultimate goal is to provide a description of the high-energy electron and proton environments and the magnetic field at Neptune that can be used for engineering design. The JPL Neptune Radiation Model (NMOD) models the high-energy electrons and protons between 0.025 MeV and 5 MeV based on the California Institute of Technology's Cosmic Ray Subsystem and the Applied Physics Laboratory's Low Energy Charged Particle Detector on Voyager 2. As in previous JPL radiation models, the form of the Neptunian model is based on magnetic field coordinates and requires a conversion from spacecraft coordinates to Neptunian-centered magnetic "B-L" coordinates. Two types of magnetic field models have been developed for Neptune: 1) simple "offset, tilted dipoles" (OTD), and 2) a complex, multi-pole expansion model ("O8"). A review of the existing data on Neptune and a search of the NASA Planetary Data System (PDS) were completed to obtain the most current descriptions of the Neptunian high-energy particle environment. These data were fit in terms of the O8 B-L coordinates to develop the electron and proton flux models. The flux predictions of the new model were used to estimate the total ionizing dose (TID) rate along the Neptunian equator, meridional flux contours for the electrons and protons, and for flux and dose comparisons with the other radiation belts in the Solar System.

magnetic field↗
NASA NTRS2015

Parallel Study of HEND, RAD, and DAN Instrument Response to Martian Radiation and Surface Conditions

Nuclear detection methods are being used to understand the radiation environment at Mars. JPL (Jet Propulsion Laboratory) assets on Mars include: Orbiter -2001 Mars Odyssey [High Energy Neutron Detector (HEND)]; Mars Science Laboratory Rover -Curiosity [(Radiation Assessment Detector (RAD); Dynamic Albedo Neutron (DAN))]. Spacecraft have instruments able to detect ionizing and non-ionizing radiation. Instrument response on orbit and on the surface of Mars to space weather and local conditions [is discussed] - Data available at NASA-PDS (Planetary Data System).

Mars↗
NASA NTRS2018

History and Status of ALSEP and the Apollo Lunar Data Project

A suite of automated scientific instruments (the Apollo Lunar Surface Experiment Package, or ALSEP) was installed at each of the landing sites of Apollo 12, 14, 15, 16, and 17 from 1969 to 1972. They operated from deployment until decommissioning on 30 September 1977. These data were continuously transmitted to Earth and saved on the Range Tapes, which were recorded at the Manned Space Flight Network stations. These data were also broken out by experiment and sent to the experiment Principal Investigators on what were called the P.I. Tapes. Starting in April 1973 the Range Tape data were stored in digital format on 7-track magnetic tapes, the ARCSAV Tapes. In February 1976, the handling of the Range Tapes was transferred to UT Galveston. They produced 9-track tapes referred to as the Work Tapes. Following the Apollo program the Range and ARCSAV tapes, which were never archived, were lost. The Work Tapes were archived at the National Space Science Data Center (NSSDC). Some investigators archived their individual experiment data with NSSDC as well, but much of the data had minimal documentation, were not in digital form, or were stored in difficult to translate formats. Data from many experiments were never delivered to the NSSDC. The Lunar Data Project was started to address the problem of both missing and not readily usable data. Our effort has resulted in recovery of some of the ARCSAV tapes, recovery and digitization of a large volume of Apollo scientific and technical documentation, and restoration of many ALSEP and other Apollo data collections. Restoration involves deciphering formats, assembling necessary ancillary data (metadata), and packaging data in digital format to be archived with the Planetary Data System (PDS). Recovery of the data from the ARCSAV tapes involved having the tapes read on special equipment and extracting the individual experiment data out of the integrated data stream. We will report on the history and status of the various recovery efforts.

Work Tapes↗
NASA NTRS2016

International Space Station (ISS) Payload Autonomous Operations Past, Present and Future

Draper Laboratorys Timeliner is a scripting and automation system that runs onboard computers in the International Space Station (ISS). Timeliner is fully integrated with ISS and can be used to automate ISS operations tasks. Some of the most challenging aspects of operating a payload in low earth orbit are communication delays, ground equipment failures, and human errors. How does a Payload Developer (PD) know their equipment is operating nominally and collecting science in the most efficient way possible or even powered at any given time? During a ground Loss of Signal (LOS) data outage, PDs have no insight into their experiments state, and benefit greatly from Timeliner scripts executing on ISS to perform telemetry monitoring and commanding operations. This paper will discuss current software designs, and new operational uses for Timeliner. Existing Timeliner capabilities discussed will include: autonomous EXPRESS Rack activation and deactivation; autonomous science data downlinks; Minus Eighty Degree Freezer (MELFI) Dewar autonomous safing; JAXA and ESA module autonomous payload facility safing; as well as many others. New operational concepts discussed will include allowing Timeliner on the payload computer to issue core commands (Thermal, Power, Fire Detection), creation of new ground tools that will monitor the current status of Payload Racks as well as all the messaging from autonomous scripts executing, decreasing approval time for Timeliner bundles, and opening up the Payload MDM Enhanced Processor Integrated Communications Card (EPIC) interface. The EPIC interface could provide a new crew interface for PL Timeliner execution. The new interface could operate on either a Payload Computer System (PCS) or a Station Support Computer (SSC) that is plugged into either the Payload LAN or the Operations LAN which will make communicating to the PL MDM more flexible and greatly increase band width for communication.

Space Mission Automation↗
NASA NTRS2018

Model-Driven Development For PDS4 Software And Services

Software and services that access Planetary Data System (PDS) PDS4 data products need to parse product labels to retrieve, interpret, and process the referenced digital objects. Under PDS4 a driving principle is that the product label provide all of the information necessary for these functions to be performed accurately. However, significantly more information is available in the PDS4 Information Model (IM)[1], the controlling document used to define, create, and syntactically and semantically verify the product labels. This additional information in the IM is made available for use, by both software and services, to configure, promote resiliency, and improve interoperability.

Padams, Jordan↗
NASA NTRS2015

The PDS4 Information Model Design Principles - How Well Did They Work?

The Planetary Data System (PDS) recently released Version 1.5 of the PDS4 Information Model, the primary component of the PDS4 Information Architecture. The Information Model is now stable and is in use by three active missions and several missions in various phases of development. The Information Model drives the PDS4 Information System using a multi-level governance structure that provides for common, discipline, and mission level management of the system’s information standards. For the development of the PDS4 Information Model several design principles were adopted. This paper will describe each design principle and then explain how well they worked, problems encountered during development and their solutions, and how well the results meet the requirements of the multi-discipline planetary science community.

Martin, Michael↗
NASA NTRS2021

Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed contentbased classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.

Mandrake, Lukas↗
NASA NTRSDate not supplied

Lunar Science Investigations and Exploration in Celestial Mapping System

Introduction: As NASA expands the mission portfolio on the lunar surface, there is a need for applications with a broad range of analytical and functional capabilities that can be simultaneously deployed onto multiple mobile and desktop platforms to perform in-situ operations and hence enabling extensive Lunar exploration. Celestial Mapping System (CMS) [1,2] is developed to address the need for tools for science investigations, mission planning, operations and support for planetary sciences. Built on top of NASA WorldWind libraries, CMS can be simultaneously deployed onto multiple platforms, has the flexibility to update to the latest imagery and terrain datasets as they are being acquired (in real time) before and/or during the exploration mission and has the potential to enable traverse path planning suited for rovers, EVA and surface mobility units. It can provide critical functionalities such as equipment planning and optimized placement on Lunar surface, line of sight analysis to inform the coverage area for various equipment, powerful measurement tools based on 3D terrain, 3D COLLADA models to represent rovers, humans and equipment, visualization of derived mapping products (e.g. resource maps), and a data engine for hosting new observations that are not available in other contemporary lunar data tools [1]. Visualization of PSRs: The current presentation focuses on the work performed by the authors, to consume a unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [3]. This tool was developed in direct support of NASA's VIPER and Artemis programs to enhance the extremely low-light images of the interior of PSRs and provide the first-time ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the region near Nobili crater near Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Line of Sight Analysis and Traverse Planning in PSRs: We have developed an in-built line of sight analysis (LOS) tool in CMS that analyzes the terrain profile and obstructions and provides the visibility of a given terrain for a remote observer. This tool was utilized to perform viewshed analysis to investigate the area inside the PSR, a remote observer such as a rover could see without actually crossing the region. Figure 1 shows the viewshed analysis on the PSR in Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. Figure 1: (left) PSR image on top of a high-resolution mosaic of sunlit images of a crater, in Nobile region (right) Viewshed Analysis of the same PSR with observer location shown by yellow pin. This analysis was extended further to set different heights for various observers and then perform the viewshed analysis. Combining the different visibility profiles can help designing improved traverses within the crater. Future plans: Eventually, HORUS datasets will be integrated into CMS as a layer in selected lunar polar regions. Hazard Maps will then be created based on terrain analysis in those regions. This integration will enable multiple scientific and exploration applications, such as designing traverses within PSRs, analyzing potential landing and science mission targets, investigating the meter-scale geomorphology of PSRs, including craters, boulder, surface roughness, mass wasting features and other indications of the presence of water-ice and other volatiles. Acknowledgments: CMS developers team including Kaitlyn J. Dickinson, Tyler A. Lucarz, Tyler W. Choi from USRA, NASA WorldWind Advisory team including Mark Peterson and Guillermo Miguel Del Castillo, HORUS team member V.T. Bickel, Robinson, M., LRO MOON LROC 2 EDR V1.0, LRO-L-LROC-2-EDR-V1.0, NASA Planetary Data System (PDS), 2009. https://doi.org/10.17189/1520643 References: [1] https://celestial.arc.nasa.gov [2] Agrawal et. al. “Celestial Mapping System for Lunar Surface Mapping and Analytics”, Lunar Surface Innovation Consortium, 2021 [3] Bickel V. et al. (2021) Nat Commun 12, 5607

Lunar Mapping↗
NASA NTRSDate not supplied

Investigating the Applications of In-Situ Resource Utilization for Future Crewed Mars Missions

Several advancements are necessary before humans can safely explore Mars. One emerging strategy is In-Situ Resource Utilization (ISRU): the process of extracting raw materials found onsite and converting them into usable products to ensure a constant flow of vital resources and greatly reduce overall mission cost. Scientists must confirm the presence and accessibility of these resources for future crewed missions. Our team investigated the feasibility of ISRU in NE Syrtis, a region of Mars with varied geology, mineralogy, and hydrological history. NE Syrtis has abundant subsurface water and a diverse suite of mineral deposits including olivine, phyllosilicates, sulfates, and carbonates–all of which can be leveraged for fuel, medical supplies, and/or construction materials. We conceptualized a joint rover and helicopter expedition to analyze and sample these minerals over a two-year primary mission using Java Mission-planning and Analysis for Remote Sensing (JMARS), Planetary Data Systems (PDS), and Computer-Aided Design (CAD). We plotted a strategic traverse to regions of interest identified from remote sensing data and planned operations at each target. Our mission aimed to prepare for human presence on Mars by demonstrating the utility and viability of ISRU-focused operations.

Zahra M. Ronizi↗
DOE OSTI2025

Safety Assurance of Software and Machine Learning Development for Nuclear Instrumentation and Controls

Digital instrumentation and control (DI&C) systems monitor and control parameters in nuclear power plants. Ensuring their safety is a critical part of ensuring overall plant safety. Nuclear power plant licensing generates thousands of safety documents that could be organized more effectively using a safety assurance case (SAC). We conducted a literature survey of SACs and created a SAC framework for DI&C software using Goal Structuring Notation (GSN). This framework focuses on four software development processes: management & assurance, pre-developed software (PDS) qualification, the Software Development Life Cycle (SDLC), and the Machine Learning Development Life Cycle (MLDLC). We organized our framework using a novel level structure that can be applied to other SACs to improve their clarity. Finally, we demonstrate how our framework can be incorporated as part of a SAC for a larger reactor system.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗