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NASA NTRS2009

Geolab 2010: Desert Rats Field Demonstration

In 2010, Desert Research and Technology Studies (Desert RATS), NASA's annual field exercise designed to test spacesuit and rover technologies, will include a first generation lunar habitat facility, the Habitat Demonstration Unit (HDU). The habitat will participate in joint operations in northern Arizona with the Lunar Electric Rover (LER) and will be used as a multi-use laboratory and working space. A Geology Laboratory or GeoLab is included in the HDU design. Historically, science participation in Desert RATS exercises has supported the technology demonstrations with geological traverse activities that are consistent with preliminary concepts for lunar surface science Extravehicular Activities (EVAs). Next year s HDU demonstration is a starting point to guide the development of requirements for the Lunar Surface Systems Program and test initial operational concepts for an early lunar excursion habitat that would follow geological traverses along with the LER. For the GeoLab, these objectives are specifically applied to support future geological surface science activities. The goal of our GeoLab is to enhance geological science returns with the infrastructure that supports preliminary examination, early analytical characterization of key samples, and high-grading lunar samples for return to Earth [1, 2] . Figure 1: Inside view schematic of the GeoLab a 1/8 section of the HDU, including a glovebox for handling and examining geological samples. Other outfitting facilities are not depicted in this figure. GeoLab Description: The centerpiece of the GeoLab is a glovebox, allowing for samples to be brought into the habitat in a protected environment for preliminary examination (see Fig. 1). The glovebox will be attached to the habitat bulkhead and contain three sample pass-through antechambers that would allow direct transfer of samples from outside the HDU to inside the glovebox. We will evaluate the need for redundant chambers, and other uses for the glovebox antechambers, such as a staging area for additional tools or samples. The sides of the glovebox are designed with instrument ports and additional smaller ports for cable pass-through, imagery feeds and environmental monitoring. This first glovebox version will be equipped with basic tools for manipulating, viewing, and early analysis of samples. The GeoLab was also designed for testing additional analytical instruments in a field setting.

Evans, Cindy A.↗
NASA NTRS2005

Giovanni: A System for Rapid Access, Visualization and Analysis of Earth Science Data Online

Collecting data and understanding data structures traditionally are the first steps that a user must take, before the core investigation can begin. This is a time-consuming and challenging task, especially when science objectives require users to deal with large multi-sensor data that are usually in different formats and internal structures. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has created the GES DISC Interactive Online Visualization and ANalysis Infrastructure, Giovanni, to enable Web-based visualization and analysis of satellite remotely sensed meteorological, oceanographic, and hydrologic data sets, without users having to download data. The current operational Giovanni interfaces provide the capability to process a number of important satellite measurements, such as (1) ozone and other trace gases from TOMS, OMI, HALOE, and MLS; (2) air temperature, water vapor, and geopotential height from AIRS; (3) aerosols from MODIS TerrdAqua, and GOCART model; (4) precipitation from TRMM and ground measurements; (5) chlorophyll and other ocean color products from SeaWiFS and MODIS Aqua; and (6) sea surface temperature from MODIS Aqua. Depending on the input data structure, the system provides simple statistical analysis and creates time-averaged area plot, area-averaged time series, animations, Hovmoller latitude vs. time and longitude vs. time plots, as well as vertical profiles. The inter-comparison interfaces allow a user to compare observations from different instruments, to conduct anomaly analysis, and to study basic relationships between physical parameters. Giovanni handles data with different temporal and spatial resolutions and, thus, enables both regional and global long-term climate research and short-term special events investigation, as well as data validations and assessments. Because of its simplicity of usage, Giovanni is powerful and versatile, able to assist a wide range of users, from the discipline scientists conducting preliminary research in various fields, to students in the classroom learning about weather, climate, and other natural phenomena. Giovanni can be accessed from: http://disc. esfc.nasa.gov/techlab/giovanni/index.shtml

Shen, S.↗
NASA NTRSDate not supplied

Near-term Persistent Platform Orbital Testbed: Three Candidate Architecture Options

On-orbit Servicing, Assembly, and Manufacturing (OSAM) will revolutionize the space industry by transforming the concept of operations of space systems and enabling new, radically different system implementations. These new implementations will benefit from a novel persistent asset design paradigm which focuses on evolvable designs that are tailored to the operational environment, not the launch environment. In addition, the ability to launch sub-systems independently enable future persistent assets to economically expand in capability and size, achieving cost effective and productive operations lasting for decades like terrestrial observatories. With few exceptions (International Space Station, Hubble Space Telescope, Mission Extension Vehicle customers), current space systems are not visited once they are operational. Leveraging emerging low cost commercial launch provides the ability to repeatedly and routinely revisit space systems. Thus, revolutionary new approaches for space system design are possible, creating completely new opportunities for small businesses and accelerating the growth of already established space industries. To usher in the revolutionary new operational paradigm, two things are needed. First, to build confidence in the technology and new paradigm, there must be a leading example, a bellwether persistent asset, that demonstrates the reliability and maturity of the new persistent asset paradigm (where repeated visits are common). Second, in order to rapidly advance and validate OSAM capabilities, an efficient means is required to conduct tests in the space environment. A persistent platform testbed satisfies both these needs. The space environment exhibits a plethora of characteristics that are difficult and costly to accurately simulate for a full system in a terrestrial laboratory, such as near zero gravity, a wide range of ionizing radiation types, atomic oxygen, and micro-meteoroids and space debris traveling at high velocity. In addition, since persistent assets range in mass from a few grams to several metric tons, it is difficult to accurately simulate interactions between these systems and visiting vehicles (that also exhibit a wide range of varying masses and capabilities). These interactions include the transmission of forces and/or exchanging mass (in the form of instruments, fuel, robotic assets, etc.). Thus, a rapid, versatile and cost efficient in-space testing capability that includes a persistent test platform and a surrounding in-space test zone is needed to mature technologies through experimentation. The testbed can provide common services, such as: power, thermal control, vibration isolation, data transmission between experiments and terrestrial experimenters, station-keeping, pointing, and robotic agents that can be leveraged by customer experiments. The onboard robotic agents can be used to provide payload handling services, such as: assembly, change out or upgrade, relocation, connecting/disconnecting utilities, inspection, repair or servicing, etc. Since the persistent platform cost will be amortized over many hosted payloads, its services can eventually be offered at a price much lower than if one were to design a unique and dedicated spacecraft and mission for those few experiments. The key to achieving an effective testbed is providing efficient cost effective access and infrastructure to a variety of commercial, academic and government customers coupled with extensibility, in the capability of an individual persistent platform test bed or replication of the test bed in a different operational regime. Three potential options for implementing a test bed were developed and evaluated in this study.

Persistent Platform↗
NASA NTRSDate not supplied

Near-term Persistent Platform Orbital Testbed: Three Candidate Architecture Options

On-orbit Servicing, Assembly, and Manufacturing (OSAM) will revolutionize the space industry by transforming the concept of operations of space systems and enabling new, radically different system implementations. These new implementations will benefit from a novel persistent asset design paradigm which focuses on evolvable designs that are tailored to the operational environment, not the launch environment. In addition, the ability to launch sub-systems independently enable future persistent assets to economically expand in capability and size, achieving cost effective and productive operations lasting for decades like terrestrial observatories. With few exceptions (International Space Station, Hubble Space Telescope, Mission Extension Vehicle customers), current space systems are not visited once they are operational. Leveraging emerging low cost commercial launch provides the ability to repeatedly and routinely revisit space systems. Thus, revolutionary new approaches for space system design are possible, creating completely new opportunities for small businesses and accelerating the growth of already established space industries. To usher in the revolutionary new operational paradigm, two things are needed. First, to build confidence in the technology and new paradigm, there must be a leading example, a bellwether persistent asset, that demonstrates the reliability and maturity of the new persistent asset paradigm (where repeated visits are common). Second, in order to rapidly advance and validate OSAM capabilities, an efficient means is required to conduct tests in the space environment. A persistent platform testbed satisfies both these needs. The space environment exhibits a plethora of characteristics that are difficult and costly to accurately simulate for a full system in a terrestrial laboratory, such as near zero gravity, a wide range of ionizing radiation types, atomic oxygen, and micro-meteoroids and space debris traveling at high velocity. In addition, since persistent assets range in mass from a few grams to several metric tons, it is difficult to accurately simulate interactions between these systems and visiting vehicles (that also exhibit a wide range of varying masses and capabilities). These interactions include the transmission of forces and/or exchanging mass (in the form of instruments, fuel, robotic assets, etc.). Thus, a rapid, versatile and cost efficient in-space testing capability that includes a persistent test platform and a surrounding in-space test zone is needed to mature technologies through experimentation. The testbed can provide common services, such as: power, thermal control, vibration isolation, data transmission between experiments and terrestrial experimenters, station-keeping, pointing, and robotic agents that can be leveraged by customer experiments. The onboard robotic agents can be used to provide payload handling services, such as: assembly, change out or upgrade, relocation, connecting/disconnecting utilities, inspection, repair or servicing, etc. Since the persistent platform cost will be amortized over many hosted payloads, its services can eventually be offered at a price much lower than if one were to design a unique and dedicated spacecraft and mission for those few experiments. The key to achieving an effective testbed is providing efficient cost effective access and infrastructure to a variety of commercial, academic and government customers coupled with extensibility, in the capability of an individual persistent platform test bed or replication of the test bed in a different operational regime. Three potential options for implementing a test bed were developed and evaluated in this study.

Persistent Platform↗
NASA NTRS2012

From EXOSAT to the High Energy Astrophysics Science Archive (HEASARC): X-ray Astronomy Comes of Age

In May 1983 the European Space Agency launched EXOSAT, its first X-ray astronomy observatory. Even though it lasted only 3 short years, this mission brought not only new capabilities that resulted in unexpected discoveries, but also a pioneering approach to operations and archiving that changed X-ray astronomy from observations led by small instrument teams, to an observatory approach open to the entire community through a guest observer program. The community use of the observatory was supported by a small dedicated team of scientists, the precursor to the data center activities created to support e.g. Chandra and XMM-Newton. The new science capabilities of EX OS AT included a 90 hr highly eccentric high earth orbit that allow unprecedented continuous coverage of sources as well as direct communication with the satellite that allowed real time decisions to respond to unexpected events through targets of opportunity. The advantages of this orbit demonstrated by EXOSAT resulted in Chandra and XMM-Newton selecting similar orbits. The three instruments on board the EXOSAT observatory were complementary, designed to give complete coverage over a wide energy band pass of 0.05-50 keY. An onboard processor could be programmed to give multiple data modes that could be optimized in response to science discoveries: These new capabilities resulted in many new discoveries including the first comprehensive study of AGN variability, new orbital periods in X-ray binaries and cataclysmic variables, new black holes, quasi-periodic oscillations from neutron stars and black holes and broad band X-ray spectroscopy. The EXOSAT team generated a well-organized database accessible worldwide over the nascent internet, allowing remote selection of data products, making samples and undertaking surveys from the data. The HEASARC was established by NASA at Goddard Space Flight Center in 1990 as the repository of NASA X-ray and Gamma-ray data. The proven EXOSAT database system became the core of the HEASARC infrastructure. The HEASARC pioneered many concepts now taken for granted including standardized formats using FITS files, restoring data from earlier missions, multi-mission analysis tools and a searchable archive over the world wide web.

White, Nicholas E.↗
NASA NTRS2006

NASA's Robotic Lunar Exploration Program (RLEP) 2 Mission

Before returning humans to the Moon for mankind's seventh lunar landing, NASA will embark upon a series of robotic missions to prepare the way for further exploration. These missions, part of the Robotic Lunar Exploration Program (RLEP), are designed to acquire decisive knowledge about the moon as well as to develop infrastructure needed to sustain human exploration in the lunar environment. Here we focus on the second mission in the RLEP program, RLEP-2, the first dedicated to landing in the south polar region of the moon. Managed by NASA's Marshall Space Flight Center in Huntsville, Alabama, along with the Applied Physics Laboratory and NASA Goddard Space Flight Center, RLEP-2 will build upon knowledge gained from the Chandraayan-1 and Lunar Robotic Orbiter orbital missions, to help further the prospects for sustainable human exploration on the moon. This mission will characterize the lighting environment in the polar region, critically important to understanding the amount of power available and to the thermal design of hardware, as well as explore the nature and distribution of volatiles that may be present in permanently shadowed regions of polar craters. We shall review the current status of the mission, articulate the results of onoging trade studies in power, surface mobility, launch vehicles, measurements and instrumentation, and navigation/communication, as well as discuss the primary mission objectives in detail.

Horack, John M.↗
NASA NTRSDate not supplied

Experimental Setup for Mechanically Testing Subscale Triangular, Rollable, and Collapsible Deployable Composite Booms

High-strain composite deployable space structures are used for space infrastructure and science applications such as solar array supports, antennae, camera masts, and lightweight structures supporting solar sailing propulsion elements. Triangular, Rollable, and Collapsible (TRAC) deployable composite booms are one example of a high-strain composite deployable structure and were studied using novel experimental test and characterization methods developed under the Gravity Offloading and Analysis of Long Imperfection-sensitive Elements (GOALIE) project. In the present work, a subscale 7-m-long TRAC boom was suspended vertically to orient gravity along the length of the boom. By orienting vertically, highly nonlinear and unstable behavior often encountered during horizontally oriented gravity offload testing of similar structures was reduced. Pretest analytical predictions of TRAC booms indicated three unique failure modes, loads, and locations for three unique loading cases of in-plane bending, out-of-plane bending, and axial compression. To investigate the predicted behavior, an experimental test was set up to impart mechanical loads to a subscale TRAC boom. The experimental setup, loading cases, and instrumentation used to characterize the mechanical response of a subscale TRAC boom are described in this paper.

Experimental testing↗
NASA NTRS2010

Geolab in NASA's First Generation Pressurized Excursion Module: Operational Concepts

We are building a prototype laboratory for preliminary examination of geological samples to be integrated into a first generation Habitat Demonstration Unit-1/Pressurized Excursion Module (HDU1-PEM) in 2010. The laboratory GeoLab will be equipped with a glovebox for handling samples, and a suite of instruments for collecting preliminary data to help characterize those samples. The GeoLab and the HDU1-PEM will be tested for the first time as part of the 2010 Desert Research and Technology Studies (DRATS), NASAs annual field exercise designed to test analog mission technologies. The HDU1-PEM and GeoLab will participate in joint operations in northern Arizona with two Lunar Electric Rovers (LER) and the DRATS science team. Historically, science participation in DRATS exercises has supported the technology demonstrations with geological traverse activities that are consistent with preliminary concepts for lunar surface science Extravehicular Activities (EVAs). Next years HDU1-PEM demonstration is a starting point to guide the development of requirements for the Lunar Surface Systems Program and test initial operational concepts for an early lunar excursion habitat that would follow geological traverses along with the LER. For the GeoLab, these objectives are specifically applied to enable future geological surface science activities. The goal of our GeoLab is to enhance geological science returns with the infrastructure that supports preliminary examination, early analytical characterization of key samples, insight into special considerations for curation, and data for prioritization of lunar samples for return to Earth.

Evans, C. A.↗
NASA NTRS2012

Bridging Informatics and Earth Science: a Look at Gregory Leptoukh's Contributions

With the tragic passing this year of Gregory Leptoukh, the Earth and Space Sciences community lost a tireless participant in--and advocate for--science informatics. Throughout his career at NASA, Dr. Leptoukh established a theme of bridging the gulf between the informatics and science communities. Nowhere is this more evident than his leadership in the development of Giovanni (GES DISC Interactive Online Visualization ANd aNalysis Infrastructure). Giovanni is an online tool that serves to hide the often-complex technical details of data format and structure, making science data easier to explore and use by Earth scientists. To date Giovanni has been acknowledged as a contributor in 500-odd scientific articles. In recent years, Leptoukh concentrated his efforts on multi-sensor data inter-comparison, merging and fusion. This work exposed several challenges at the intersection of data and science. One of these was the ease with which a naive user might generate spurious comparisons, a potential hazard that was the genesis of the Multi-sensor Data Synergy Advisor (MDSA). The MDSA uses semantic ontologies and inference rules to organize knowledge about dataset quality and other salient characteristics in order to advise users on potential caveats for comparing or merging two datasets. Recently, Leptoukh also led the development of AeroStat, an online Giovanni instance to investigate aerosols via statistics from station and satellite comparisons and merged maps of data from more than one instrument. Aerostat offers a neural net based bias adjustment to harmonize the data by removing systematic offsets between datasets before merging. These examples exhibit Leptoukh's talent for adopting advanced computer technologies in the service of making science data more accessible to researchers. In this, he set an example that is at once both vital and challenging for the ESSI community to emulate.

Source record↗
NASA NTRS2015

Intelligent Observation Strategies for Geosynchronous Remote Sensing for Natural Hazards

Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked thing with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a users smart phone as any other smart appliance.

0↗
NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗
NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗
NASA NTRSDate not supplied

Experimental Setup for Mechanically Testing Subscale Triangular, Rollable, and Collapsible Deployable Composite Booms

High-strain composite deployable space structures are used for space infrastructure and science applications such as solar array supports, antennae, camera masts, and lightweight structures supporting solar sailing propulsion ele-ments. Triangular, Rollable, and Collapsible (TRAC) deployable composite booms are one example of a high-strain composite deployable structure and were studied using novel experimental test and characterization methods developed under the Gravity Offloading and Analysis of Long Imperfection-sensitive Ele-ments (GOALIE) project. In the present work, a subscale 7-m-long TRAC boom was suspended vertically to orient gravity along the length of the boom. By ori-enting vertically, highly nonlinear and unstable behavior often encountered dur-ing horizontally oriented gravity offload testing of similar structures was reduced. Pretest analytical predictions of TRAC booms indicated three unique failure modes, loads, and locations for three unique loading cases of in-plane bending, out-of-plane bending, and axial compression. To investigate the predicted behav-ior, an experimental test was set up to impart mechanical loads to a subscale TRAC boom. The experimental setup, loading cases, and instrumentation used to characterize the mechanical response of a subscale TRAC boom are described in this paper.

High-strain composites↗
NASA NTRS2012

Metrology of Large Parts

As discussed in the first chapter of this book, there are many different methods to measure a part using optical technology. Chapter 2 discussed the use of machine vision to measure macroscopic features such as length and position, which was extended to the use of interferometry as a linear measurement tool in chapter 3, and laser or other trackers to find the relation of key points on large parts in chapter 4. This chapter looks at measuring large parts to optical tolerances in the sub-micron range using interferometry, ranging, and optical tools discussed in the previous chapters. The purpose of this chapter is not to discuss specific metrology tools (such as interferometers or gauges), but to describe a systems engineering approach to testing large parts. Issues such as material warpage and temperature drifts that may be insignificant when measuring a part to micron levels under a microscope, as will be discussed in later chapters, can prove to be very important when making the same measurement over a larger part. In this chapter, we will define a set of guiding principles for successfully overcoming these challenges and illustrate the application of these principles with real world examples. While these examples are drawn from specific large optical testing applications, they inform the problems associated with testing any large part to optical tolerances. Manufacturing today relies on micrometer level part performance. Fields such as energy and transportation are demanding higher tolerances to provide increased efficiencies and fuel savings. By looking at how the optics industry approaches sub-micrometer metrology, one can gain a better understanding of the metrology challenges for any larger part specified to micrometer tolerances. Testing large parts, whether optical components or precision structures, to optical tolerances is just like testing small parts, only harder. Identical with what one does for small parts, a metrologist tests large parts and optics in particular to quantify their mechanical properties (such as dimensions, mass, etc); their optical prescription or design (i.e. radius of curvature, conic constant, vertex location, size); and their full part shape. And, just as with small parts, a metrologist accomplishes these tests using distance measuring instruments such as tape measures, inside micrometers, coordinate measuring machines, distance measuring interferometers; angle measuring instruments such as theodolites, autocollimators; and surface measuring instruments including interferometers, stylus profilers, interference microscopes, photogrammetric cameras, or other tools. However, while the methodology may be similar, it is more difficult to test a large object for the simple reason that most metrologists do not have the necessary intuition. The skills used to test small parts or optics in a laboratory do not extrapolate to testing large parts in an industrial setting any more than a backyard gardener might successfully operate a farm. But first, what is a large part? A simple definition might be the part's size or diameter. For optics and diffuse surface parts alike, the driving constraint is ability to illuminate the part's surface. For reflective convex mirrors, large is typically anything greater than 1 meter. But, for refractive optics, flats or convex mirrors, large is typically greater than 0.5 meter. While a size definition is simple, it may be less than universal. A more nuanced definition might be that a large part is any component which cannot be easily tested in a standard laboratory environment, on a standard vibration isolated table using standard laboratory infrastructure. A micro-switch or a precision lens might be easily measured to nanometer levels under a microscope in a lab, but a power turbine spline or a larger telescope mirror will not fit under that microscope and may not even fit on the table.

Stahl, H. Philip↗
NASA NTRS2011

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Cloud Computing↗
NASA NTRS2019

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.

Earth Science missions; Advanced information Syste↗
NASA NTRS2019

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Simone Lolli↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗