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The purpose of a telemetry system is to reliably and transparently convey measurement information from a remotely located data generating source to users located in space or on Earth. Typically, data generators are scientific sensors, science housekeeping sensors, engineering sensors and other subsystems on-board a spacecraft.
The next era in human space exploration is rapidly approaching. The use of health countermeasures and biomonitoring systems for space missions are required to counteract space health hazards and to support life to thrive in deep space (e.g., humans, animals, plants, crops; entire ecosystems within spacecrafts/habitats/spacesuits). The development of these mission components will be highly dependent on our understanding of basic biological and health responses to myriad space hazards (ionizing radiation, altered gravitational fields, altered day-night cycles, confined isolation, hostile-closed environments, distance-duration from Earth, planetary dust-regolith, and extreme temperatures/atmospheres). The fast-growing array of space biological and mission telemetry data, which in the past was simply archived after minimal analysis, holds great potential once applied to these mission challenges if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its multi-hierarchical, multi-modal, and heterogenous nature (molecular, cellular, tissue, organ, whole organism, behavior, ecosystem, microbiome; tabular, omics, imaging, video, biospecimen, environmental physical-chemical telemetry). This session focuses on current approaches in this domain such as: making space biological data FAIR (findable, accessible, interoperable, reusable), effective data ingestion/dissemination, observational versus experimental data, Open Science collaborations, data analysis techniques, AI/ML/knowledge graph/modeling methods, and data integration/discovery tools.
The Space Transportation System's (STS) Solid Rocket Boosters (SRBs) experience a severe environment during their brief flight. During the last few years several SRB's have sustained noticeable structural damage. The environmental characteristics (vibration, structure, and thermal) encountered by the SRB's during ascent, descent and water impact are in most cases unknown. A developmental flight instrumentation (DFI) system collected data from the SRB's first four flights in the early 1980's, and after the first three flights during the shuttle return-to-flight phase after the Challenger accident. However, the DFI data collected are of low fidelity and do not correlate well with the observed structural damage. The DFI system was evaluated for reuse, but the cost to fly it was prohibitive. The STS is presently scheduled to fly until 2030. To support the STS schedule the avionics on SRB's will be upgraded. The environments on the different sections of the SRB will need to be defined to properly qualify the avionics for multiple flights. The DFI data previously gathered does not provide enough information to properly qualify the avionics. Marshall Space Flight Centers (MSFC) SRB Project Office requested the Science and Engineering Directorate to develop a self contained data acquisition system that could collect data from any area of the booster. This paper summarizes the effort to develop, test, qualify and fly an Enhanced Data Acquisition System (EDAS) using state-of-the-art commercial off the shelf (COTS) equipment. The first flight of this system occurred on shuttle mission STS-91 in June 1998.
Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.
This is a quick reference guide to the NASA Master Directory (MD), which is a free, online, multidisciplinary directory of space and Earth science data sets (NASA and non-NASA data) that are of potential interest to the NASA-sponsored research community. The MD contains high-level descriptions of data sets, other data systems and archives, and campaigns and projects. It provides mechanisms for searching for data sets by important criteria such as geophysical parameters, time, and spatial coverage, and provides information on ordering the data. It also provides automatic connections to a number of data systems such as the NASA Climate Data System, the Planetary Data System, the NASA Ocean Data System, the Pilot Land Data System, and others. The MD includes general information about many data systems, data centers, and coordinated data analysis projects, It represents the first major step in the Catalog Interoperability project, whose objective is to enable researchers to quickly and efficiently identify, obtain information about, and get access to space and Earth science data. The guide describes how to access, use, and exit the MD and lists its features.
Future unmanned space missions will continue to seek a full understanding of magnetic fields throughout the solar system. Severely constrained data rates during certain portions of these missions could limit the possible science return. This publication investigates the application of universal noiseless coding techniques to more efficiently represent magnetometer data without any loss in data integrity. Performance results indicated that compression factors of 2:1 to 6:1 can be expected. Feasibility for general deep space application was demonstrated by implementing a microprocessor breadboard coder/decoder using the Intel 8086 processor. The Comet Rendezvous Asteroid Flyby mission will incorporate these techniques in a buffer feedback, rate-controlled configuration. The characteristics of this system are discussed.
In response to the need for improved observations of environmental factors to better understand the links between human health and the environment, NASA has established a new program to significantly improve the utilization of NASA's diverse array of data, information, and observations of the Earth for health applications. This initiative, lead by Goddard Space Flight Center (GSFC) has the following goals: (1) To encourage interdisciplinary research on the relationships between environmental parameters (e.g., rainfall, vegetation) and health, (2) Develop practical early warning systems, (3) Create a unique system for the exchange of Earth science and health data, (4) Provide an investigator field support system for customers and partners, (5) Facilitate a system for observation, identification, and surveillance of parameters relevant to environment and health issues. The NASA Environment and Health Program is conducting several interdisciplinary projects to examine applications of remote sensing data and information to a variety of health issues, including studies on malaria, Rift Valley Fever, St. Louis Encephalitis, Dengue Fever, Ebola, African Dust and health, meningitis, asthma, and filariasis. In addition, the NASA program is creating a user-friendly data system to help provide the public health community with easy and timely access to space-based environmental data for epidemiological studies. This NASA data system is being designed to bring land, atmosphere, water and ocean satellite data/products to users not familiar with satellite data/products, but who are knowledgeable in the Geographic Information Systems (GIS) environment. This paper discusses the most recent results of the interdisciplinary environment-health research projects and provides an analysis of the usefulness of the satellite data to epidemiological studies. In addition, there will be a summary of presently-available NASA Earth science data and a description of how it may be obtained.
Satellite observations provide information about the Earth that can be critical to building situational awareness and filling in data gaps during disaster response. The National Aeronautics and Space Administration (NASA) Earth Science Division’s Disasters Program aims to advance Earth science data and information to support management decisions that prevent or mitigate the impacts of disasters. In support of this goal, NASA’s Disaster Response Coordination System (DRCS) manages a One-NASA approach to coordinate and mobilize the Agency’s assets and expertise to provide geospatial information during disasters. The purpose of the DRCS is to advance the utility of Earth observation information for supporting disaster response decision support, build skilled and effective response communities through improved coordination, engagement, and learning, and reduce impact to lives and livelihoods by empowering communities to respond to disasters more effectively. The DRCS employs a user-centered, activation framework that begins with direct requests from responders and ends with after-action assessments that feed lessons learned and process improvements. This poster will introduce the DRCS model and approach to expanding the use of Earth observations and geospatial information to support disaster response, share use cases for recent event activations, and highlight initial lessons learned.
NASA s Earth Observing System Data and Information System (EOSDIS) acquires, archives, and manages data from all of NASA s Earth science satellites, for the benefit of the Space Agency and for the benefit of others, including local governments, first responders, the commercial remote sensing industry, teachers, museums, and the general public. EOSDIS is currently handling an extraordinary amount of NASA scientific data. To give an idea of the volume of information it receives, NASA s Terra Earth-observing satellite, just one of many NASA satellites sending down data, sends it hundreds of gigabytes a day, almost as much data as the Hubble Space Telescope acquires in an entire year, or about equal to the amount of information that could be found in hundreds of pickup trucks filled with books. To make EOSDIS data completely accessible to the Earth science community, NASA teamed up with private industry in 2000 to develop an Earth science "marketplace" registry that lets public users quickly drill down to the exact information they need. It also enables them to publish their research and resources alongside of NASA s research and resources. This registry is known as the Earth Observing System ClearingHOuse, or ECHO. The charter for this project focused on having an infrastructure completely independent from EOSDIS that would allow for more contributors and open up additional data access options. Accordingly, it is only fitting that the term ECHO is more than just an acronym; it represents the functionality of the system in that it can echo out and create interoperability among other systems, all while maturing with time as industry technologies and standards change and improve.
Measurement of the microgravity acceleration environment on the International Space Station will be accomplished by two accelerometer systems. The Microgravity Acceleration Measurement System will record the quasi-steady microgravity environment, including the influences of aerodynamic drag, vehicle rotation, and venting effects. Measurement of the vibratory/transient regime comprised of vehicle, crew, and equipment disturbances will be accomplished by the Space Acceleration Measurement System-II. Due to the dynamic nature of the microgravity environment and its potential to influence sensitive experiments, Principal Investigators require distribution of microgravity acceleration in a timely and straightforward fashion. In addition to this timely distribution of the data, long term access to International Space Station microgravity environment acceleration data is required. The NASA Glenn Research Center's Principal Investigator Microgravity Services project will provide the means for real-time and post experiment distribution of microgravity acceleration data to microgravity science Principal Investigators. Real-time distribution of microgravity environment acceleration data will be accomplished via the World Wide Web. Data packets from the Microgravity Acceleration Measurement System and the Space Acceleration Measurement System-II will be routed from onboard the International Space Station to the NASA Glenn Research Center's Telescience Support Center. Principal Investigator Microgravity Services' ground support equipment located at the Telescience Support Center will be capable of generating a standard suite of acceleration data displays, including various time domain and frequency domain options. These data displays will be updated in real-time and will periodically update images available via the Principal Investigator Microgravity Services web page.
Accomplishments and research objectives are described in the following areas: (1) space sciences; (2) space and terrestrial applications; (3) flight projects and mission definition studies; (4) space tracking and data systems; and (5) space technology. Data analysis efforts, instrument development, and measurement projects are among the aspects considered.
The Voyager radio science experiments at Neptune required the creation of a ground data system array that includes a Deep Space Network complex, the Parkes Radio Observatory, and the Usuda deep space tracking station. The performance requirements were based on experience with the previous Voyager encounters, as well as the scientific goals at Neptune. The requirements were stricter than those of the Uranus encounter because of the need to avoid the phase-stability problems experienced during that encounter and because the spacecraft flyby was faster and closer to the planet than previous encounters. The primary requirement on the instrument was to recover the phase and amplitude of the S- and X-band (2.3 and 8.4 GHz) signals under the dynamic conditions encountered during the occultations. The primary receiver type for the measurements was open loop with high phase-noise and frequency stability performance. The receiver filter bandwidth was predetermined based on the spacecraft's trajectory and frequency uncertainties.
This report is the final product of a 90-day study performed for the Exploration Systems Mission Directorate. The study was to assemble lessons NASA has learned from previous programs that could help the Exploration Systems Mission Directorate pursue the Exploration vision. It focuses on those lessons that should have the greatest significance to the Directorate during the formulation of program and mission plans. The study team reviewed a large number of lessons learned reports and data bases, including the Columbia Accident Investigation Board and Rogers Commission reports on the Shuttle accidents, accident reports from robotic space flight systems, and a number of management reviews by the Defense Sciences Board, Government Accountability Office, and others. The consistency of the lessons, findings, and recommendations validate the adequacy of the data set. In addition to reviewing existing databases, a series of workshops was held at each of the NASA centers and headquarters that included senior managers from the current workforce as well as retirees. The full text of the workshop reports is included in Appendix A. A lessons learned website was opened up to permit current and retired NASA personnel and on-site contractors to input additional lessons as they arise. These new lessons, when of appropriate quality and relevance, will be brought to the attention of managers. The report consists of four parts: Part 1 provides a small set of lessons, called the Executive Lessons Learned, that represent critical lessons that the Exploration Systems Mission Directorate should act on immediately. This set of Executive Lessons and their supporting rationale have been reviewed at length and fully endorsed by a team of distinguished NASA alumni; Part 2 contains a larger set of lessons, called the Selected Lessons Learned, which have been chosen from the lessons database and center workshop reports on the basis of their specific significance and relevance to the near-term work of the Exploration Directorate. These lessons frequently support the Executive lessons but are more general in nature; Part 3 consists of the reports of the center workshops that were conducted as part of this activity. These reports are included in their entirety (approximately 200 pages) in Appendix G and have significance for specific managers; Part 4 consists of the remainder of the lessons that have been selected by this effort and assembled into a database for the use of the Explorations Directorate. The database is archived and hosted in the Lessons Learned Knowledge Network, which provides a flexible search capability using a wide variety of search terms. Finally, a spreadsheet lists databases searched and a bibliography identifies reports that have been reviewed as sources of lessons for this task. NASA has been presented with many learning opportunities. We have conducted numerous programs, some extremely successful and others total failures. Most have been documented with a formal lessons learned activity, but we have not always incorporated these learning opportunities into our normal modes of business. For example, the Robbins Report of 2001 clearly indicates that many project failures of the past two decades were the result of violating well documented best practices, often in direct violation of management instructions and directives. An overarching lesson emerges: that disciplined execution in accordance with proven best practices is the greatest single contributor to a successful program. The Lessons Learned task team offers a sincere hope that the lessons presented herein will be helpful to the Exploration Systems Directorate in charting and executing their course. The success of the Directorate and of NASA in general depends on our collective ability to move forward without having to relearn the lessons of those who have gone before.
The NASA Master Directory (MD) is a free, online, multidisciplinary directory of space and Earth science data sets (NASA and non-NASA data) that are of potential interest to the NASA-sponsored research community. The MD contains high-level descriptions of data sets, other data systems and archives, and campaigns and projects. It provides mechanisms for searching for data sets by important criteria such as geophysical parameters, time, and spatial coverage. The MD also provides information on ordering the data. In order to simplify the process of finding more detailed information or accessing online data, the MD provides automatic connections to a number of data systems such as the NASA Climate Data System, the Planetary Data System, the NASA Ocean Data System, the Pilot Land Data System, and others. The MD also provides general information about many data systems, data centers, and coordinated data analysis projects. It represents the first major step in the Catalog Interoperability project, whose objective is to enable researchers to quickly and efficiently identity, obtain information about, and get access to space and Earth science data.
The data system onboard the United States Operating Segment (USOS) of the ISS is currently used to capture, route, record and downlink high-rate science data from experiments inside the US Lab. Once NASDA's Japanese Experiment Module (JEM) and ESA's Attached Pressurized Module (APM) are launched - in the 2004 to 2005 timeframe - data from these facilities will also be routed to the ground using the USOS data system. A critical component of the USOS data system is the High Rate Frame Multiplexer (HRFM). The HRFM combines multiple data/video inputs and combines them into one data stream. This Ku-band data stream is then routed through the Tracking and Data Relay Satellite (TDRS) system to the ground. The Data Management Coordinator (DMC) - located at the Marshall Space Flight Center's Payload Operations Center (POC) - is responsible for commanding and controlling the HRFM. The HRFM can multiplex a maximum of eight digital data sources and four digital video sources. Thus far, this limitation has not been constraining to operations. However, once the JEM and APM are integrated, the HRFM limitations will become a major constraint to science operations onboard. The purpose of this paper is to characterize the limitations of the HRFM and to explain how these limitations can be successfully managed. With this information, Payload Developers and International Partners will be able to more effectively utilize the data systems onboard the ISS. Ultimately, more science data can be captured and downlinked to Flight Controllers and Scientists on the ground.
For many Earth and Space Science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a generalized Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C. area are presented.
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
Potential space station system options were examined for a permanent, manned, orbital space facility and to provide data to NASA program planners and decision makers for their use in future program planning. There were ten space station system objectives identified. These were categorized into five major objectives and five supporting objectives. The major objectives were to support the development of: (1) satellite power systems, (2) nuclear energy plants in space, (3) space processing, (4) earth services, and (5) space cosmological research and development. The five supporting objectives, to define space facilities which would be basic building blocks for future systems, were: (1) a multidiscipline science laboratory, (2) an orbital depot to maintain, fuel, and service orbital transfer vehicles, (3) cluster support systems to provide power and data processing for multiple orbital elements, (4) a sensor development facility, and (5) the facilities necessary to enhance man's living and working in space.