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NASA NTRS2019

IKOS: Sound Static Program Analysis

IKOS (Inference Kernel for Open Static Analyzers) is a static analyzer for C/C++ based on the theory of Abstract Interpretation. It can detect or prove the absence of runtime errors (e.g, buffer overflows, integer overflows, null pointer dereferences, etc.) in the source code. IKOS uses Abstract Interpretation techniques to compute an over-approximation of all the reachable states of the program, thus it cannot miss a bug. In this talk, I will give an overview of the tool, then show how to apply it to a large software. I will present ikos-view, a web interface to examine the analysis results. I will discuss about methods to improve the analysis, such as adding code annotations, modeling library functions, and avoiding specific code patterns.

Arthaud, Maxime↗
NASA NTRS2019

Using Science Operations Tools and Methods from NASA for Remote Presence Ocean Exploration on Earth: E/V Nautilus Cruise NA108

NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.

Lees, David S.↗
NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗
NASA NTRSDate not supplied

Lunar Mega Project: Processes, Work Flow and Terminology of the Terrestrial Construction Industry versus the Space Industry

Recent developments around the world show an increased interest and international activity toward developing a lunar surface human and robotic presence with a long term, sustainable vision. In Europe, the construction of a "Moon Village" has been proposed, and China has stated its intention to build a research station with a crew at the lunar South Pole. Russia stated it will land cosmonauts on the Moon in the 2030's. India has sent an orbiter, a robotic lander, and rover to the Moon. The USA intends to land the first woman and a man on the Moon by 2024 to initiate a sustained human lunar presence.A lunar research station with associated commercial activities will require infrastructure to become a permanent capability. Landing and launch pads, propellant storage and distribution farms, spacecraft access and handling structures, cranes, blast protection berms or walls, roads, graded areas, dust stabilized areas, foundations, parking lots, radiation shelters, micro-meteorite protection hangars, habitats and other human shelters, greenhouse farms, utility trenches, power plants, industrial water and oxygen extraction plants, communications antenna towers, thermal protection, mining zones, crater access and sub-surface access will be required to create a safe and sustainable lunar operations capability. This infrastructure will require significant construction activities in an extreme environment, with risky and expensive operations. On Earth, there are similar large and expensive projects (>$1 Billion) in difficult locations that attract a lot of public attention. These are known as "Mega Projects" and examples include bridges, tunnels, highways, railways, airports, seaports, power plants, dams, wastewater projects, Special Economic Zones (SEZ), oil and natural gas extraction projects, public buildings, information technology systems, aerospace projects, and weapons systems. Large consortiums consisting of public and private entities typically implement these infrastructure Mega-Projects.This paper will compare the typical design process, project management process, and work flow in the terrestrial construction industry to the space industry in order to create a better understanding between the terrestrial construction industry and the space industry, to further enable collaboration on a lunar Mega Project to build infrastructure on the Moon. In addition, a glossary of respective industry terminology with annotated linkages and definitions has been compiled. This information will enable contracts and business practices to be formulated for generating requests for proposals (RFP) by governments to consortiums consisting of construction and space industry companies that will bid on Lunar Infrastructure Projects that could lead to contracts to build the permanent capabilities.

Robert P Mueller↗
NASA NTRS2020

X-HAB 2020: AR Field Treks Summary and Conclusions

As part of the FY20 X-Hab Challenge, BLiSS sought to create an Augmented Reality (AR) toolkit to help with analog field trek operations under the supervision of the Solar System Exploration Research Virtual Institute (SSERVI). These treks are operational and technical demonstrations at space-like destinations on Earth to test current extra-vehicular activity (EVA) techniques. While BLiSS as an organization has experience studying operational tasks such as this, it has never developed AR software at this scale. For that reason, another team at the University was brought on to work in parallel. The Collaborative Lab for Advancing Work in Space (CLAWS) is a veteran group of the NASA Spacesuit User Interface Technologies for Students (SUITS) challenge in which Hololens displays for astronauts are created within a year. The operational and technological pairing was ideally suited for tackling this problem. The team divided its responsibilities so that BLiSS would handle the research required to shape the project. As this deliverable had an end user, it was decided that interviewing these field geologists and operations specialists would provide the best insight. These interviews paired with literature review would reveal niche applications for AR that remained within feasible bounds. These science-driven EVAs in unknown terrain require more flexible tools than the current generation of EVA assistants. Rather than focus on sequential instructions, there instead needs to be a broad toolkit that's only called upon in specific instances. This AR Toolkit for Lunar Astronauts and Scientists (ATLAS) became the development goal of the project: create a non-intrusive assembly of tools that could be accessed in AR on the field. The current ATLAS design makes use of a geospatially and temporally annotated eld note system called GeoNotes. This allows for data to be collected and coordinated in a way that's synchronized across time, space, and different users. A Mission Control Center (MCC) and Mobile Support Equipment (MSE) were all needed to transport the AR headset into the field with the user. A network infrastructure was designed and set up within the University to enable this functionality. The software is based on a Protocol-Module structure that allows for modular development of each capability. A Protocol Manager coordinates different protocols that make use of modules. Each module tackles a different individual task while the protocol puts each one to use. The protocol manager coordinates when these are called to be used. This software is hosted on a head-mounted display (HMD) with the MCC acting as support from afar. While the software would be unit-tested at each level and each hardware component verified, a final demonstration would serve to prove the system's capabilities: an analog field trek. The team would prepare to support a user in a remote location from the MCC back at the University. A local area near campus would be tested before going out to do sample field geology further away. This unfortunately became impossible with the arrival of COVID-19. Access to all of the facilities to complete the project as planned were shut down. Our team was scattered across the globe and forced to complete the rest virtually. Adjustments were made to produce a small virtual concept in Adobe XD in the meantime. Even digital surveys were created based on the NASA task-load index (TLX) originally intended for testing actual users. The goal shifted towards completing software and getting feedback on the user interfaces (UI) and user experiences (UX). This team has reformed in response to COVID and its focus has shifted to what can be done remotely. There is still an intention to finish the original deliverable described in this report. The work has been expanded beyond the original X-Hab challenge and has instead become its own research e ort to be continued afterwards. This report collects the processes and knowledge gained from a year of studying and working at this problem with two teams. It should preserve it for the time until the world returns to normal and work can resume. CLAWS will be taking over full responsibility from that point forward, eventually surpassing the original needs of the project. While this document captures the work done towards an eventual end, the CLAWS team has written their own proposal alongside it. It outlines a new future for ATLAS beyond X-Hab, BLiSS, and hopefully beyond COVID-19. This project began as a vague goal hoping to place a new technology into the unique setting of exploration science. The project has since comfortably taken root and will hopefully bloom over the next year.

Alex Sena↗
NASA NTRS2021

An Ontology-Based Virtual Orrery

Tutorials in this technical memorandum explain how to use the Protégé ontology editor to import data from Excel and export data from Protégé in the JavaScript Object Notation Linked Data (JSON-LD) format then how to transform the JSON-LD file into variables for web apps. These tutorials apply JavaScript and R programming languages and 3D graphics libraries. Ontologies enable standardization, data sharing, and semantic interoperability. An ontology models a domain of knowledge as taxonomies of annotated classes, data properties, relational object properties, and inference rules. This technical memorandum includes a data dictionary for a subset of the Space Situational Awareness Ontology (SSAO).

Ontologies↗
NASA NTRS2018

Online Self-Supervised Long-Range Scene Segmentation for MAVs

Recently, there have been numerous advances in the development of payload and power constrained lightweight Micro Aerial Vehicles (MAVs). As these robots aspire for high-speed autonomous flights in complex dynamic environments, robust scene understanding at long-range becomes critical. The problem is heavily characterized by either the limitations imposed by sensor capabilities for geometry-based methods, or the need for large-amounts of manually annotated training data required by data-driven methods. This motivates the need to build systems that have the capability to alleviate these problems by exploiting the complimentary strengths of both geometry and data-driven methods. In this paper, we take a step in this direction and propose a generic framework for adaptive scene segmentation using self-supervised online learning. We present this in the context of vision-based autonomous MAV flight, and demonstrate the efficacy of our proposed system through extensive experiments on benchmark datasets and real world field tests.

Matthies, Larry↗
NASA NTRSDate not supplied

Celestial Mapping System for Lunar Surface Mapping and Analytics

Celestial Mapping System (CMS) is a software platform to generate virtual 3D globe for celestial bodies within our solar system. Various layers are built on top of the virtual globe to provide visualization of high resolution imagery, enable precise measurements, build analytical capabilities and broad range of functionalities to assist planetary scientists and mission planners. CMS is built using OpenJDK 11 and will run on a wide variety of platforms such as Linux, Windows, OSX, etc. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The present focus of CMS is on developing lunar mapping tool kits to provide features such as - 3D first person view with zoom and navigational capabilities, realistic terrain visualization based on LRO data, measurement tools, Apollo landing site annotations, stereoscopic view, elevation profiles, line of sight analysis and many more. The application is developed to provide situational and domain awareness on Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on Lunar surface. CMS utilizes NASA WorldWind Java library and OpenGL to achieve high-performance rendering of data and measurements, and also adheres to OGC standards. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. Nomenclature is pulled from USGS Moon IAU2000 database, and lunar parameters are based of the standardized IAU2000 Moon ellipsoid. GDAL (Geospatial Data Abstraction Library) was used to modify and test the accuracy of datasets before integrating into the application. Our high-resolution global elevation model was compared with the LRO LOLA DEM elevation values and tested to ensure accuracy. Celestial Mapping System has several potential use cases for NASA including subsurface lava tubes visualization and analysis, soil analysis, resource visualization and representation on 3D globe.

GIS system↗
NASA NTRSDate not supplied

NASA biological and physical sciences databases: who’s the FAIRest of them all?

Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.

database↗
NASA NTRSDate not supplied

Identification of Health Events in Astronaut Missions Using Longitudinal Molecular Signature Detection

Individualized health monitoring can now incorporate a precision medicine approach, profiling multiple molecular and physiological measures of health (generalized omics) longitudinally to enable the timely diagnosis and treatment of disease. Such measurements can include blood chemistries, gene expression data, metabolite measurements, and digital device data. We will present our work on extending such an approach to monitoring individual astronaut health for deep space missions. We have developed and implemented novel algorithms to monitor and detect physiolgical state departures from individualized healthy astronaut baselines , utilizing and biologically annotating generalized omics. Our new methods can detect baseline deviations across omics corresponding to potentially adverse medical events. Events pointing to changes in individual health are then compared across individuals to identify common responses and detect changes affecting multiple crewmembers. We show the utility of our methods in detecting temporal health changes across subjects using retrospective Earth and astronaut mission data (metabolite and immune marker data across multiple missions), in order for this technique t o be applicable for future missions.

G I Mias↗
NASA NTRS2021

Machine Vision based Sample-Tube Localization for Mars Sample Return

A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detecting and localizing sample tubes deposited on the Martian surface. Towards this end, we study two machine-vision based approaches: First, a geometrydriven approach based on template matching that uses hardcoded filters and a 3D shape model of the tube; and second, a data-driven approach based on convolutional neural networks (CNNs) and learned features. Furthermore, we present a large benchmark dataset of sample-tube images, collected in representative outdoor environments and annotated with ground truth segmentation masks and locations. The dataset was acquired systematically across different terrain, illumination conditions and dust-coverage; and benchmarking was performed to study the feasibility of each approach, their relative strengths and weaknesses, and robustness in the presence of adverse environmental conditions.

Detry, R.↗
NASA NTRS2022

Environmental Justice Needs Assessment for Disasters: Assessing the Landscape and Capacity of Organizations & Communities Working Towards Environmental Justice with Potential to Use NASA Earth Observations to Support Equitable Disaster Management and Risk Reduction

Natural disasters pose an increasing risk to communities worldwide. Marginalized populations, in particular, experience compounding vulnerabilities that contribute to unequal burdens of natural hazards as a result of systemic inequality stemming from historical disenfranchisement, disinvestment, and discriminatory policies such as racial redlining. This project connected with community organizations working at the intersection of environmental justice (EJ) and natural disaster management throughout the United States, to assess how NASA DEVELOP can leverage geospatial science to advance EJ efforts. Our team conducted a landscape analysis, which included a literature review, annotated bibliography, and identification of organizations working in EJ and disasters. We engaged EJ organizations in discussions to understand their current resources, challenges, and geospatial needs to inform how DEVELOP and NASA Applied Sciences can support their EJ and disaster work. Findings were compiled in a synthesis report and visualized in an ArcGIS StoryMap to showcase the work of EJ organizations, provide geospatial resources for them to explore, and provide examples of how remote sensing can be utilized in EJ and disasters work. The knowledge gained and end products created support the integration of EJ in future DEVELOP projects, and the expanded use of Earth observations by communities in support of a more just tomorrow.

Julianne Liu↗
NASA NTRS2021

AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars

Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1

Ono, Hiro↗
NASA NTRS2020

Shedding Light on Microbial Dark Matter with A Universal Language of Life

The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

A Hoarfrost↗
NASA NTRS2019

Local frustration around enzyme active sites

Conflicting biological goals often meet in the specification of protein sequences for structure and function. Overall, strong energetic conflicts are minimized in folded native states according to the principle of minimal frustration, so that a sequence can spontaneously fold, but local violations of this principle open up the possibility to encode the complex energy landscapes that are required for active biological functions. We survey the local energetic frustration patterns of all protein enzymes with known structures and experimentally annotated catalytic residues. In agreement with previous hypotheses, the catalytic sites themselves are often highly frustrated regardless of the protein oligomeric state, overall topology, and enzymatic class. At the same time a secondary shell of more weakly frustrated interactions surrounds the catalytic site itself. We evaluate the conservation of these energetic signatures in various family members of major enzyme classes, showing that local frustration is evolutionarily more conserved than the primary structure itself.

Maria I. Freiberger↗
NASA NTRS2021

Decoding the effects of synonymous variants

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.

Zishuo Zeng↗
NASA NTRS2022

Visualizing Multi-process CPU Utilization using CUSP

The CPU Utilization Statistics Plotter (CUSP) tool automates the interpretation of detailed CPU Utilization trace data and statistics. It puts you on the cusp of understanding how CPU resources are split among the many parallel components of a software system.CUSP combines time-sampled CPU utilization numbers and Event Log annotations to generate human-readable plots and tables. It automatically splits up large CPU usage log files around interesting events, determines and highlights just the tasks of primary relevance by evaluating their changing contribution to each plot's total CPU usage, automatically eliminates irrelevant tasks, provides context by labeling plots with names and durations of all active commands, and uses consistent color-coding to enable quick visual comparison across multiple plots.CUSP has been used to process CPU Utilization trace logs on the Mars Science Laboratory and the Mars 2020 Rover missions during flight software development and Flight Operations on the Martian surface since December 2013.

Maimone, Mark W↗