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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Discovery and Vetting of Exoplanets. I. Benchmarking K2 Vetting Tools

We have adapted the algorithmic tools developed during the Kepler mission to vet the quality of transit-like signals for use on the K2 mission data. Using the four sets of publicly available light curves at MAST, we produced a uniformly vetted catalog of 772 transiting planet candidates from K2 as listed at the NASA Exoplanet Archive in the K2 Table of Candidates. Our analysis marks 676 of these as planet candidates and 96 as false positives. All confirmed planets pass our vetting tests. Sixty of our false positives are new identifications, effectively doubling the overall number of astrophysical signals mimicking planetary transits in K2 data. Most of the targets listed as false positives in our catalog show either prominent secondary eclipses, transit depths suggesting a stellar companion instead of a planet, or significant photocenter shifts during transit. We packaged our tools into the open-source, automated vetting pipeline Discovery and Vetting of Exoplanets (DAVE), designed to streamline follow-up efforts by reducing the time and resources wasted observing targets that are likely false positives. DAVE will also be a valuable tool for analyzing planet candidates from NASA's TESS mission, where several guest-investigator programs will provide independent light-curve sets—and likely many more from the community. We are currently testing DAVE on recently released TESS planet candidates and will present our results in a follow-up paper.

Kostov, Veselin B.↗
NASA NTRS2019

How Certain Physical Considerations Impact Aerostructural Wing Optimization

Wing design optimization has been studied extensively and is of continued interest as optimization tools are developed and become more accessible. In each of these studies, certain assumptions and simplifications are made to make the design problem tractable. However, it is difficult to find systematic studies in which several considerations are added or removed one at a time to study how much impact they have. In this work, we examine how certain physical considerations (viscous drag, wave drag, thrust loads, and inertial relief from structural, fuel, and engine masses), impact the aerostructural optimization results for three distinct aircraft wings. The goal is to help develop a rough idea of how important these physical considerations are. We do this using gradient-based optimization and a multidisciplinary design optimization framework, OpenMDAO. We use the open-source tool OpenAeroStruct that couples a vortex lattice method to a finite element method. We establish a baseline aerostructural design optimization problem then perform a series of optimizations, each with one physical consideration removed from the baseline case. We find that depending on the size of the aircraft and flight conditions, the importance of some of these physical considerations varies considerably whereas the importance of others do not. Specifically, the optimal designs change radically without proper viscous and wave drag considerations and smaller aircraft with more distributed propulsion are more affected by the inclusion of engine loads.

Aeropropulsion↗
NASA NTRS2016

Managing and Servicing Physical Oceanographic Data at a NASA Distributed Active Archive Center

The NASA Earth Science Data Information Systems Project funds and operates 12 Distributed Active Archive Center(s) (DAAC) throughout the United States. Of these 12 centers, the Physical Oceanography DAAC (PO.DAAC) is committed to providing long term archival, distribution and stewardship for NASA physical oceanographic data, primarily derived from space-born satellite systems, but also including a growing set of recent and future in situ observations from the SPURS-1 and SPURS-2 campaigns. Notable NASA missions supported include: Seasat, TOPEX/Poseidon, NSCAT, QuikSCAT, ISS-RapidScat, Jason-1, Jason-2/OSTM, GRACE, Aquarius, GHRSST, and MODIS. The following interagency and international missions are also supported by PO.DAAC: AVHRR, Coriolis, DMSP, MetOp-A, MetOp-B, Oceansat-2. The PO.DAAC currently holds 525 datasets in public distribution, spanning the following observational parameters: sea surface temperature, sea surface salinity, ocean color, ocean surface currents, ocean surface wind speed, ocean surface wind direction, sea surface height, significant wave height, ocean water mass/thickness, and sea ice age. A hundred of these datasets are available in near-real-time. Datasets are distributed through a variety of open-source access protocols including FTP, OPeNDAP, and THREDDS. FTP will soon be phased out in favor of a recently introduced HTTPS PO.DAAC Drive interface that supports WebDAV and interoperable machine-to-machine communication. OPeNDAP supports remote data/metadata query, subset, and download. THREDDS provides the features of OPeNDAP with the additional feature of temporal aggregation. PO.DAAC also offers proprietary tools and services to further enhance the data discovery, visualization and analysis experience, including but not limited to: State of the Ocean, Web Services (data/metadata discovery and extraction), HiTIDE Level-2 subsetter, Live Access Server (LAS), Webification (w10nsci), and Rich Site Summary (RSS) Datacasting. To assist with provenance of datasets, PO.DAAC has implemented DOIs for the data it distributes so that they can be properly cited. There is a user forum and helpdesk that contains data recipes and via which users can get guidance. In summary, this presentation aims to provide a general overview of PO.DAAC’s web portal and data holdings along with a set of illustrative examples leading prospective data users into the practical utility of its tools and services.

Moroni, David F.↗
NASA NTRS2019

HDF5 Roadmap 2019-2020

In this talk we will give an overview of the new features of the upcoming HDF5 release 1.12.0, and outline the HDF5 roadmap for the next year. We will demonstrate new open source file drivers to access HDF5 files via Amazon Simple Storage Service (Amazon S3) and on Hadoop Distributed File system (HDFS). We will use this presentation to get feedback on the HDF5 roadmap from the ESDIS users and application developers.

Cloud↗
NASA NTRS2019

Astrobee Robot Software: Enabling Mobile Autonomy on the ISS

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This slides presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot.The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). We present the software approach, infrastructure required, and main software components.

Fluckiger, Lorenzo↗
NASA NTRS2019

Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour.

Allan, Mark↗
NASA NTRS2019

Air Traffic Management Blockchain Infrastructure for Security, Authentication, and Privacy

Current radar-based air traffic service providers may preserve privacy for military and corporate operations by procedurally preventing public release of selected flight plans, position, and state data. The FAA mandate for national adoption of Automatic Dependent Surveillance Broadcast (ADS-B) in 2020 does not include provisions for maintaining these same aircraft-privacy options, nor does it address the potential for spoofing, denial of service, and other well-documented risk factors. This paper presents an engineering prototype that embodies a design and method that may be applied to mitigate these ADS-B security issues. The design innovation is the use of an open source permissioned blockchain framework to enable aircraft privacy and anonymity while providing a secure and efficient method for communication with Air Traffic Services, Operations Support, or other authorized entities. This framework features certificate authority, smart contract support, and higher-bandwidth communication channels for private information that may be used for secure communication between any specific aircraft and any particular authorized member, sharing data in accordance with the terms specified in the form of smart contracts. The prototype demonstrates how this method can be economically and rapidly deployed in a scalable modular environment.

ADS-B cybersecurity↗
NASA NTRS2019

Preparing General Mission Analysis Tool for Operational Maneuver Planning of the Advanced Composition Explorer Mission

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

mission design↗
NASA NTRS2019

Data Preservation: Final Step in the Life Cycle of a Mission

Many NASA Earth Observing System (EOS) have either already reached the end of their active life or are nearing it. Preservmissionsation of data products is a fairly well defined task for the NASA EOS Data Centers or DAACs.The Goddard Earth Sciences Data and Information Services Center (GES-DISC) has implemented a repository system, which is capable of long-term archive of documentation artifacts and other associated digital content. The existing GES-DISC Repository System is based on Fedora Commons, an open-source repository management software, for cost savings and flexibility.The first mission to utilize the GES-DISC Repository System was the High Resolution Dynamics Limb Sounder (HIRDLS) on the Aura spacecraft. Since then, the GES DISC has gathered documentation from the UARS and TOMS into the Repository. The Microwave Limb Sounder (MLS) team has begun delivering some early pre-launch documents to the GES-DISC Repository System as well. Other missions in planning or progress include AIRS, OMI, SORCE, SNPP Sounder, and TRMM.

Johnson, J.↗
NASA NTRS2018

Southern California Megacity CO2, CH4, and CO Flux Estimates Using Ground- and Space-Based Remote Sensing and a Lagrangian Model

We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

Total Carbon Column Observing Network (TCCON)↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
NASA NTRS2016

RACE: Building Airspace Simulations Faster and Better with Actors

Large, distributed aerospace simulations traditionally have been the domain of customized, closed designs, using statically compiled code based on specialized messaging systems such as DDS and HLA. While this can be suitable for one-off systems or specialized in-house product lines, it increases development costs and lowers extensibility. We propose to use contemporary internet software technology to solve this problem.Our Runtime for Airspace Concept Evaluation (RACE) architecture was born out of the need to rapidly develop and evaluate what-if scenarios that involve the whole National Airspace System (NAS), live NAS data feeds such as the FAA's System Wide Information Management (SWIM) servers, and existing flight simulators. It had to run on off-the-shelf hardware, be open sourced, and support visualization components up to multiple synchronized large screen geo viewers used in situation rooms. Most of all, it had to be extensible - being a viable platform for the development of future simulation components.

Mehlitz, Peter↗
NASA NTRS2019

Formalizing and Analyzing Requirements with FRET

Formal verification and simulation are powerful tools to validate requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically written in ambiguous natural language. There is a gap between such requirements and formal notations that can be used by verification tools, and lack of support for proper association of requirements with software artifacts for verification. We propose to write requirements in an intuitive, structured natural language with formal semantics, and to support formalization and model/code verification as a smooth, well-integrated process. To this end, we have developed an end-to-end, open source requirements analysis framework that checks Simulink models against requirements written structured natural language.

Mavridou, Anastasia↗
NASA NTRS2019

Tuning the Performance of a Computational Persistent Homology Package

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performanceof Eirene, a computational package for persistent homology. Eirene is a 5000-line open-source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time-consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved.

Persistent Homology↗
NASA NTRS2019

NASA EOSDIS Data Usage Metrics- Insight and Assessment

NASA's Earth Science Data and Information System (ESDIS) Project collects Earth science data usage metrics on a daily basis through the ESDIS Metrics System (EMS). This includes metrics on distribution of data products, users, data volumes, and number of files, which are key parameters in evaluating system-level performance of any of the Distributed Active Archive Centers (DAACs) encompassed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS). EOSDIS data usage metrics illustrate the benefits of making NASA data openly available to the public and show a rapid growth in data distribution to a worldwide user community. In fact, each year since 2014 the EOSDIS has distributed over one billion data files of products from EOS satellite, airborne, and in situ observations. An assessment of the long-term trends of data usage metrics and user characterization provides insights into data usability.This study will focus on describing the EMS as a metrics collection tool and will provide a comprehensive analysis of EOSDIS data usage metrics over the last 10 years. This study will also characterize the product distribution metrics by various tools and services, such as Giovanni, the Open-source Project for a Network Data Access Protocol (OPeNDAP), and subsets, to address how these tools/services have extended the usage of data in the EOSDIS collection. Data usage patterns based on discipline and study area will further assist in understanding how EOSDIS data user needs have evolved over time. Results from this study will provide useful information for the DAACs that can help them improve the functionality of their tools and services as well as more efficiently allocate the resources necessary for enhanced access and availability of their data products. Knowledge of these metrics may also benefit user discovery of data in the EOSDIS collection, promote research collaboration, and stimulate new ideas from work and research conducted using specific datasets and data collections.

Kafle, Durga N.↗
NASA NTRS2019

Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods

Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself. Transfer-learning applied to NLP has greatly improved performance and reduced training data requirements. In this talk, we’ll compare machine-generated metadata to human-generated metadata and discuss characteristics of metadata and data archives that affect suitability for machine-learning reuse of metadata. Where as human-generated metadata is often populated once, populated from the perspective of data supplier, populated by many individuals with different words for the same thing, and limited in length, machine-generated metadata can be updated any number of times, generated from the perspective of any user, constrained to a standardized set of terms that can be evolved over time, and be any length required. Machine-learning generated metadata offers benefits but also additional needs in terms of version control, process transparency, human-computer interaction, and IT requirements. As a successful example, we’ll discuss how a dataset of abstracts and associated human-tagged keywords from a standardized list of several thousand keywords were used to create a machine-learning model that predicted keyword metadata for open-source code projects on code.nasa.gov. We’ll also discuss a less successful example from data.nasa.gov to show how data archive architecture and characteristics of initial metadata can be strong controls on how easy it is to leverage programmatic methods to reuse metadata to create additional metadata.

Gosses, Justin↗
NASA NTRS2019

Assessing the Use of SAR/Optical Data Fusion and TensorFlow for Improved Mangrove Mapping

Mangrove forests are found in intertidal zones of tropical regions around the world and provide important ecological and economic benefits – they are considered carbon sequesters, habitats for flora and fauna, and natural barriers to hurricanes and tsunamis. Wood from mangrove forests are used as fuel and building materials in surrounding coastal communities, therefore promoting local livelihoods. Despite the importance of these ecosystems, mangrove forests have historically been degraded in natural processes such as severe weather, and anthropogenic factors like conversion to agriculture and aquaculture. This study assesses change in mangrove forests in Nigeria and Mozambique from 2015 to 2018 using SAR and optical data fusion. Due to frequent cloud cover over the study area, SAR and optical data is fused to obtain gap-free imagery without clouds. Landsat-8 OLI and Sentinel-1 imagery is fused with TensorFlow, an open source platform used in developing machine learning models. The resulting images are classified to discriminate mangrove forest cover from other land cover types, and change is estimated using image differencing. Understanding the rates and magnitude of mangrove change across space and time can aid in identifying priority areas for forest regeneration, and can help construct sustainable management practices for the future.

Strattman, Katherine↗
NASA NTRS2019

Using Big Data Technologies with Earth Science Data in HDF5: HDF5 Scalable Solutions

HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.

Knox, Larry↗