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NASA NTRS2016

Monitoring O3 and Aerosols with the NASA LaRC Mobile Ozone Lidar System

The NASA's Langley Mobile Ozone Lidar (LMOL) system routinely measures tropospheric ozone and aerosol profiles, and is part of the Tropospheric Lidar Network (TOLNet). Recent upgrades to the system include a new pump laser that has tripled the transmission output power extending measurements up to 8 km in altitude during the day. In addition, software and algorithm developments have improved data output quality and enabled a real-time ozone display capability. In 2016, a number of ozone features were captured by LMOL, including the dynamics of an early-season ozone exceedance that impacted the Hampton Roads region. In this presentation, we will review current LMOL capabilities, recent air quality events observed by the system, and show a comparison of aerosol retrieval through the UV channel and the green line channel.

Ganoe, Rene↗
NASA NTRS2020

UAS Service Supplier Network Performance

This report summarizes the performance of Unmanned Aircraft System (UAS) Service Suppliers (USS) in the Technical Capability Level 4 (TCL4) flight test performed by NASA and its partners in support of the UAS Traffic Management (UTM) concept. TCL4 is the final in a series of TCL demonstrations of a traffic management system for small UAS (sUAS). All demonstrations have been executed in collaboration with industry partners. The [FAA 2018] UTM Concept of Operations document describes UTM as:...the manner in which the FAA will support operations for predominantly sUAS operating in low altitude airspace. UTM utilizes industry’s ability to supply services under FAA’s regulatory authority where these services do not currently exist. It is a community-based traffic management system, where the Operators are responsible for the coordination, execution, and management of operations, with rules of the road established by FAA. UTM is designed to support the demand and expectations for a broad spectrum of operations with ever-increasing complexity and risk. UTM should be considered a collection of services rather than a monolithic application. The following section describes the architecture at a high level. For further insight, the FAA’s concept document [FAA 2018] or NASA’s earlier concept publication [NASA 2016] should be consulted.

Rios, Joseph L.↗
NASA NTRS2013

Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

aerosol optical depth↗
NASA NTRS2004

Standard Observing Bands: Is Now the Time to Replace S/X with X/Ka?

In this paper we will argue that the VLBI community should be developing a road map to transition from S/X to simultaneous X and Ka-band (32 GHz) observations. There are both negative and positive reasons for planning such a transition. On the negative side, we will outline concerns that S-band observations may be headed toward obsolescence. On the positive side, we will refer to evidence that X/Ka has potential for providing a more stable reference frame than S/X. We will propose timetables for a transition to X/Ka observing starting from the current status of X/Ka and plans that are now taking shape. First X/Ka fringes were obtained in 2001 with the Deep Space Network. Future plans will be discussed including a proposed X/Ka-band upgrade to the VLBA. Lastly, we will consider the need for a period of overlap between S/X and X/Ka so that the long and rich history of astrometric and geodetic VLBI is not compromised.

Jacobs, C. S.↗
NASA NTRS2023

Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation

The construction of solar energy facilities can have positive or negative impacts on biodiversity depending on siting and associated land use transitions. We identified drivers of solar siting and quantified patterns of buildout in states surrounding the Chesapeake Bay watershed – a biodiversity hotspot with numerous ecosystem services. Using a convolutional neural network, we mapped the footprints of ground-mounted solar arrays present in satellite imagery annually from 2017 to 2021 in Delaware, Maryland, Pennsylvania, New York, Virginia, and West Virginia. As of 2021, we identified 958 solar arrays covering 52.3 km2 built primarily on previously cultivated land, while avoiding natural landcover. We fit a binomial-Weibull model to these solar timeseries data in a hierarchical, Bayesian framework to quantify the relationship between geospatial covariates and rate of solar development. Solar array construction rate increased in cultivated areas, areas of lower agricultural suitability, lower slope, lower forest cover, lower biodiversity protection, and greater distances from roads. We also estimated changes in the rate of solar construction over time and found differences among states: acceleration in Virginia and deceleration in New York. We used parameter estimates to map the relative likelihood of future solar development across the study area. This methodology can be used to anticipate where solar is likely to be built in different landscapes and how these patterns align with conservation goals. Around the Chesapeake Bay watershed, the selection of lower quality agricultural areas for solar energy minimizes removal of important habitat and provides opportunities for native plant and pollinator restoration.

Artificial Intelligence↗
NASA NTRS2017

Smashing the Stovepipe: Leveraging the GMSEC Open Architecture and Advanced IT Automation to Rapidly Prototype, Develop and Deploy Next-Generation Multi-Mission Ground Systems

Satellite/Payload Ground Systems - Typically highly-customized to a specific mission's use cases - Utilize hundreds (or thousands!) of specialized point-to-point interfaces for data flows / file transfers Documentation and tracking of these complex interfaces requires extensive time to develop and extremely high staffing costs Implementation and testing of these interfaces are even more cost-prohibitive, and documentation often lags behind implementation resulting in inconsistencies down the road With expanding threat vectors, IT Security, Information Assurance and Operational Security have become key Ground System architecture drivers New Federal security-related directives are generated on a daily basis, imposing new requirements on current / existing ground systems - These mandated activities and data calls typically carry little or no additional funding for implementation As a result, Ground System Sustaining Engineering groups and Information Technology staff continually struggle to keep up with the rolling tide of security Advancing security concerns and shrinking budgets are pushing these large stove-piped ground systems to begin sharing resources - I.e. Operational / SysAdmin staff, IT security baselines, architecture decisions or even networks / hosting infrastructure Refactoring these existing ground systems into multi-mission assets proves extremely challenging due to what is typically very tight coupling between legacy components As a result, many "Multi-Mission" ops. environments end up simply sharing compute resources and networks due to the difficulty of refactoring into true multi-mission systems Utilizing continuous integration / rapid system deployment technologies in conjunction with an open architecture messaging approach allows System Engineers and Architects to worry less about the low-level details of interfaces between components and configuration of systems GMSEC messaging is inherently designed to support multi-mission requirements, and allows components to aggregate data across multiple homogeneous or heterogeneous satellites or payloads - The highly-successful Goddard Science and Planetary Operations Control Center (SPOCC) utilizes GMSEC as the hub for it's automation and situational awareness capability Shifts focus towards getting GS to a final configuration-managed baseline, as well as multi-mission / big-picture capabilities that help increase situational awareness, promote cross-mission sharing and establish enhanced fleet management capabilities across all levels of the enterprise.

GMSEC↗
NASA NTRS2012

Contrasting Patterns of Damage and Recovery in Logged Amazon Forests From Small Footprint LiDAR Data

Tropical forests ecosystems respond dynamically to climate variability and disturbances on time scales of minutes to millennia. To date, our knowledge of disturbance and recovery processes in tropical forests is derived almost exclusively from networks of forest inventory plots. These plots typically sample small areas (less than or equal to 1 ha) in conservation units that are protected from logging and fire. Amazon forests with frequent disturbances from human activity remain under-studied. Ongoing negotiations on REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus enhancing forest carbon stocks) have placed additional emphasis on identifying degraded forests and quantifying changing carbon stocks in both degraded and intact tropical forests. We evaluated patterns of forest disturbance and recovery at four -1000 ha sites in the Brazilian Amazon using small footprint LiDAR data and coincident field measurements. Large area coverage with airborne LiDAR data in 2011-2012 included logged and unmanaged areas in Cotriguacu (Mato Grosso), Fiona do Jamari (Rondonia), and Floresta Estadual do Antimary (Acre), and unmanaged forest within Reserva Ducke (Amazonas). Logging infrastructure (skid trails, log decks, and roads) was identified using LiDAR returns from understory vegetation and validated based on field data. At each logged site, canopy gaps from logging activity and LiDAR metrics of canopy heights were used to quantify differences in forest structure between logged and unlogged areas. Contrasting patterns of harvesting operations and canopy damages at the three logged sites reflect different levels of pre-harvest planning (i.e., informal logging compared to state or national logging concessions), harvest intensity, and site conditions. Finally, we used multi-temporal LiDAR data from two sites, Reserva Ducke (2009, 2012) and Antimary (2010, 2011), to evaluate gap phase dynamics in unmanaged forest areas. The rates and patterns of canopy gap formation at these sites illustrate potential issues for separating logging damages from natural forest disturbances over longer time scales. Multi-temporal airborne LiDAR data and coincident field measurements provide complementary perspectives on disturbance and recovery processes in intact and degraded Amazon forests. Compared to forest inventory plots, the large size of each individual site permitted analyses of landscape-scale processes that would require extremely high investments to study using traditional forest inventory methods.

Morton, D. C.↗
NASA NTRS2004

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

Laurance, William F.↗
NASA NTRS2017

Participatory Technology Assessment for Mars Mission Planning: Public Values and Rationales

Public support and interest are needed to design an ambitious human spaceflight program. However, it is difficult to understand what the public values and would support. And it is even more challenging and rare to consider public views prior to actually developing a mission. Participatory technology assessment (pTA) is a method that aims to understand public preferences and values in order to inform upstream government decision-making. We assess a recently completed experiment in pTA, the "Informing NASA's Asteroid Initiative" project. Through a cooperative agreement with NASA, the Expert and Citizen Assessment of Science and Technology (ECAST) network conducted a pTA-based forum on NASA's Asteroid Initiative and the Journey to Mars. ECAST organized two citizen forums in Phoenix, Arizona and Boston, Massachusetts in November 2014, with a total of 183 citizens selected so as to minimize self-selection biases. This paper focuses on the "Journey to Mars" session, which had the primary goal of soliciting citizen perceptions about different Mars exploration scenarios and mission planning approaches. Citizens were given background information about three potential Mars exploration scenarios that NASA could carry out: 1) Crewed orbital mission to direct robots on the surface of Mars; 2) Short exploratory crewed mission to the surface of Mars; and 3) establishing a permanent settlement. Citizens then engaged in structured facilitated discussions about their preferences among the scenarios and NASA's mission planning approach. Using a grounded theory coding approach, we analyzed participants' written rationales and dialogue about Mars exploration. In general, participants did not show a strong preference for any particular mission profile, but there was a slight preference for the crewed orbital robotics scenario. Participants who supported this approach saw it as the quickest, safest, and least costly road to a successful mission. However, many participants were interested in seeing "boots on the ground," as they believed this would propel scientific advancement, increase excitement about space exploration, and make humans a "two-planet species."

Bertrand, Pierre↗
NASA NTRS2016

Meteorite Falls Observed in U.S. Weather Radar Data in 2015 and 2016 (To Date)

To date, over twenty meteorite falls have been located in the weather radar imagery of the National Oceanic and Atmospheric Administration (NOAA)'s NEXRAD radar network. We present here the most prominent events recorded since the last Meteoritical Society meeting, covering most of 2015 and early 2016. Meteorite Falls: The following events produced evidence of falling meteorites in radar imagery and resulted in meteorites recovered at the fall site. Creston, CA (24 Oct 2015 0531 UTC): This event generated 218 eyewitness reports submitted to the American Meteor Society (AMS) and is recorded as event #2635 for 2015 on the AMS website. Witnesses reported a bright fireball with fragmentation terminating near the city of Creston, CA, north of Los Angeles. Sonic booms and electrophonic noise were reported in the vicinity of the event. Weather radar imagery records signatures consistent with falling meteorites in data from the KMUX, KVTX, KHNX and KVBX. The Meteoritical Society records the Creston fall as an L6 meteorite with a total recovered mass of 688g. Osceola, FL (24 Jan 2016 1527 UTC): This daytime fireball generated 134 eyewitness reports on AMS report number 266 for 2016, with one credible sonic boom report. The fireball traveled roughly NE to SW with a terminus location north of Lake City, FL in sparsely populated, forested countryside. Radar imagery shows distinct and prominent evidence of a significant meteorite fall with radar signatures seen in data from the KJAX and KVAX radars. Searchers at the fall site found that recoveries were restricted to road sites by the difficult terrain, and yet several meteorites were recovered. Evidence indicates that this was a relatively large meteorite fall where most of the meteorites are unrecoverable due to terrain. Osceola is an L6 meteorite with 991 g total mass recovered to date. Mount Blanco, TX (18 Feb 2016 0343 UTC): This event produced only 39 eyewitness reports and is recorded as AMS event #635 for 2016. No reports of sonic booms or electrophonic noise are recorded in the AMS eyewitness reports, but videos of the event show a relatively long-lasting fireball with fragmentation. Evidence of falling meteorites is seen in radar imagery from the KAMA and KLBB radars defining a roughly WNW to ESE trend with the dominant wind direction. This event featured favorable search ground composed mostly of farmland and ranchland and was extensively searched. Rather surprisingly, only a single L5 chondrite of 36.2g has been recovered to date.

Fries, Marc↗
NASA NTRS2020

Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product

We estimated the distribution of aerosol optical thickness (AOT) with a spatial resolution of 1 km over the Moscow megacity using the MAIAC aerosol product based on MODIS satellite data (Lyapustin et al., 2018) for the warm period of the year (May–September, 2001–2017). AERONET (Aerosol Robotic Network)-based validation of satellite estimates near the city centre at Moscow_MSU_MO and over the Moscow suburbs at Zvenigorod revealed that MAIAC AOT at 0.47 μm is in agreement with AERONET AOT though underestimated by 0.05–0.1 for AOT < 1 and overestimated for smoke conditions with AOT > 1. The MAIAC AOT biases were almost the same for the Moscow_MSU_MO and Zvenigorod AERONET sites, which indicated that the MAIAC algorithm effectively removed the effect of the bright urban surface in the city centre. For the ground-based measurements, the difference between annual median AOT at Moscow_MO_MSU and Zvenigorod (∆AOT) varied within -0:002 to +0:03, with statistically significant positive bias for most years, and an average ∆AOT was about 0.02. According to the MAIAC dataset, the ∆AOT varied within ±0:01 and was not statistically significant. The ∆AOT started decreasing recently due to intensive urban development of the territory around Zvenigorod and the decrease in pollutant emissions in Moscow, which is mainly caused by the environmental regulations. According to the MAIAC dataset, the most pronounced spatial AOT differences over the territory of Moscow were observed at the 5% quantile level, where they reached 0.05–0.06 over several locations and could be attributed to the stationary sources of aerosol pollution, for example, large areas of construction sites, aerosol pollution from roads and highways, or agriculture activities. The differences between the maximum and the mean AOT for different quantiles, except the 95% quantile, within the Moscow region, were about 0.02–0.04, which could be attributed to the local aerosol sources. The application of the MAIAC algorithm over the whole Moscow region has revealed a decreasing AOT trend over the centre of Moscow and an increasing trend over the “New” Moscow territory which experienced an intensive build-up and agricultural development.

urban aerosol pollution↗
NASA NTRS1995

Stratospheric Ozone Intercomparison Campaign (STOIC) 1989: Overview

The NASA Upper Atmosphere Research Program organized a Stratospheric Ozone Intercomparison Campaign (STOIC) held in July-August 1989 at the Table Mountain Facility (TMF) of the Jet Propulsion Laboratory (JPL). The primary instruments participating in this campaign were several that had been developed by NASA for the Network for the Detection of Stratospheric Change: the JPL ozone lidar at TMF, the Goddard Space Flight Center trailer-mounted ozone lidar which was moved to TMF for this comparison, and the Millitech/LaRC microwave radiometer. To assess the performance of these new instruments, a validation/intercomparison campaign was undertaken using established techniques: balloon ozonesondes launched by personnel from the Wallops Flight Facility and from NOAA Geophysical Monitoring for Climate Change (GMCC) (now Climate Monitoring and Diagnostics Laboratory), a NOAA GMCC Dobson spectrophotometer, and a Brewer spectrometer from the Atmospheric Environment Service of Canada, both being used for column as well as Umkehr profile retrievals. All of these instruments were located at TMF and measurements were made as close together in time as possible to minimize atmospheric variability as a factor in the comparisons. Daytime rocket measurements of ozone were made by Wallops Flight Facility personnel using ROCOZ-A instruments launched from San Nicholas Island. The entire campaign was conducted as a blind intercomparison, with the investigators not seeing each others data until all data had been submitted to a referee and archived at the end of the 2-week period (July 20 to August 2, 1989). Satellite data were also obtained from the Stratospheric Aerosol and Gas Experiment (SAGE 2) aboard the Earth Radiation Budget Satellite and the Total Ozone Mapping Spectrometer (TOMS) aboard Nimbus 7. An examination of the data has found excellent agreement among the techniques, especially in the 20- to 40-km range. As expected, there was little atmospheric variability during the intercomparison, allowing for detailed statistical comparisons at a high level of precision. This overview paper summarizes the campaign and provides a 'road map' to subsequent papers in this issue by the individual instrument teams which will present more detailed analysis of the data and conclusions.

Margitan, J. J.↗
NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as various means to download and access the data including programmatically through the GeneLab Open API (GLOpenAPI). The open access of datasets in NASA’s OSDR provides a unique opportunity for the scientific community, as well as citizen scientists and students, to continue using OSDR resources to further unlock profound insights into the consequences of space travel on the human body. Through implementation of security measures to protect sensitive human data, the OSDR seeks to strengthen the science exchange between the Biological and Physical Sciences Program and the Human Research Program, per recommendation 4-1 of the 2023-2032 Decadal Survey, and encourage further sharing and dissemination of astronaut data to provide the scientific community with the resources needed to lay the groundwork for developing targeted mitigation strategies to help withstand the rigors of long-duration spaceflight.

Amanda Marie Saravia-butler↗
NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as instructions for how to download and access the data. The I4 datasets described here re present the first ever comprehensive collection of commercial astronaut data.

Amanda M Saravia-Butler↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗