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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗
NASA NTRS2008

Global Characterization of Biomass-Burning Patterns using Satellite Measurements of Fire Radiative Energy

Remote sensing is the most practical means of measuring energy release from large open-air biomass burning. Satellite measurement of fire radiative energy (FRE) release rate or power (FRP) enables distinction between fires of different strengths. Based on a 1-km resolution fire data acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, instanteaneous FRP values ranged between 0.02 MW and 1866 MW, with global daily means ranging between 20 and 40 MW. Regionally, at the Aqua-MODIS afternoon overpass, the mean FRP values for Alaska, Western US, Western Australia, Quebec and the rest of Canada are significantly higher than these global means, with Quebec having the overall highest value of 85 MW. Analysis of regional mean FRP per unit area of land (FRP flux) shows that a peak fire season in certain regions, fires can be responsible for up to 0.2 W/m(sup 2) at peak time of day. Zambia has the highest regional monthly mean FRP flux of approximately 0.045 W/m(sup 2) at peak time of day and season, while the Middle East has the lowest value of approximately 0.0005 W/m(sup 2). A simple scheme based on FRP has been devised to classify fires into five categories, to facilitate fire rating by strength, similar to earthquakes and hurricanes. The scheme uses MODIS measurements of FRP at 1-km resolution as follows: catagory 1 (less than 100 MW), category 2 (100 to less than 500 MW), category 3 (500 to less than 1000 MW), category 4 (1000 to less than 1500 MW), catagory 5 (greater than or equal to 1500 MW). In most regions of the world, over 90% of fires fall into category 1, while only less than 1% fall into each of categories 3 to 5, although these proportions may differ significantly from day to day and by season. The frequency of occurence of the larger fires is region specific, and could not be explained by ecosystem type alone. Time-series analysis of the propertions of higher category fires based on MODIS measured FRP from 2002 to 2006 does not show any moticeable trend because of the short time period.

Ichoku, Charles↗
NASA NTRS1985

X ray microbursts and VLF chorus

On January 4, 1978, at 1140 UT, a SuperArcas sounding rocket was launched from Siple Station, Antarctica (L = 4.2, 76 deg S, 84 deg W), during a geomagnetically disturbed period (Kp = 6-) with intense X-ray and VLF chorus activity. The parachuted payload observed an intense microburst precipitation event of 10-minute duration. These data have been correlated with measurements of FLF chorus by receivers on the ground at both Siple and its magnetic conjugate point, Roberval, Quebec. Detailed one-to-one correspondence between the microbursts and the chorus was not a consistent feature of the data. Time series analysis fo the data did indicate a significant correlation between the Siple X-ray precipitation and the Roberval VLF waves with an arrival time delay of 0.1 + or 0.3.

Roeder, J. L.↗
NASA NTRS1976

Evidence for the regulation of solar wind heat flux at 1 AU

Observational evidence favoring the local regulation of solar-wind heat flux at 1 AU is reviewed, and four months of IMP 6 plasma and magnetic-field data are merged and analyzed in order to investigate what might be regulating the heat flux. A statistical analysis of the data shows that the solar-wind Alfven speed is probably regulating the heat flux locally at 1 AU and that the Alfven speed, the velocity difference between the peak of low-energy electrons and the bulk plasma velocity, and the solar-wind velocity component projected along the local spiral angle are statistically well correlated for Alfven speeds not exceeding about 70 km/s. A time-series analysis of the data indicates that only the Alfven speed and the velocity difference between the peak of low-energy electrons and the bulk plasma velocity are well correlated both qualitatively and quantitatively on a microscopic time scale. It is strongly suggested that, at times, the solar-wind heat flux is locally regulated by the magnitude of the Alfven speed at 1 AU. Uncertainties in the results are discussed.

Feldman, W. C.↗
NASA NTRS2004

The Latitude Dependence of the Effect of Pinatubo on Stratospheric Ozone

Statistical analysis of TOMS and SBLT total ozone data indicate that the eruption of Pinatubo in 1991 led to a significant decrease in ozone at northern midlatitudes with little or no effect at southern midlatitudes. We argue that this puzzling absence of a southern hemisphere effect may be an artifact of the statistical analysis. We have run a 3D CTM simulation of the past 30 years of stratospheric photochemistry with variable forcing due to chlorine/bromine compounds, solar ultraviolet radiation, and volcanic aerosols. This integration used winds from the FVGCM, which has similar interannual variability to the atmosphere. When this CTM output was examined with a standard time-series analysis, we found an effect of Pinatubo in the southern hemisphere, but not in the northern hemisphere. We then reran the CTM without volcanic aerosols. The subtraction of the two simulations indicated that, as expected, that Pinatubo affected both hemispheres in the model. This means that the northern hemisphere effect was in the model but did not show up in the statistical analysis. We also had an on-line parameterized chemical ozone tracer with seasonally repeating production and loss over the simulation. We used this as a dynamical surrogate to remove interannual variability from the original model output. The residual time series was then analyzed for the Pinatubo effect and we were able to find it in both hemispheres. We suggest that the combination of the two volcanoes, El Chichon and Pinatubo, with the solar cycle and interannual variability led to this problem of analysis in the northern hemisphere of our model. We furthermore suggest that a similar think may be occurring in the southern hemisphere of the data. An analysis of the atmosphere's southern hemisphere with a good dynamical surrogate may solve the mystery of the missing southern hemisphere effect of Pinatubo on ozone.

Stolarski, Richard S.↗
NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗
NASA NTRS2004

Interannual Variations In the Low-Degree Components of the Geopotential derived from SLR and the Connections With Geophysical/Climatic Processes

Recent Satellite Laser Ranging derived long wavelength gravity time series analysis has focused to a large extent on the effects of the recent large changes in the Earth s 52, and the potential causes. However, it is difficult to determine whether there are corresponding signals in the shorter wavelength zonals from the existing SLR-derived time variable gravity results, although it appears that geophysical fluid transport is being observed. For example, the recovered J3 time series shows remarkable agreement with NCEP-derived estimates of atmospheric gravity variations. Likewise, some of the non-zonal spherical harmonic coefficient series have significant interannual signal that appears to be related to mass transport. The non-zonal degree 2 terms show reasonable correlation with atmospheric signals, as well as climatic effects such as El Nino Southern Oscillation. While the formal uncertainty of these terms is significantly higher than that for J2, it is also clear that there is useful signal to be extracted. Consequently, the SLR time series is being reprocessed to improve the time variable gravity field recovery. We will present recent updates on the J2 evolution, as well as a look at other components of the interannual variations of the gravity field, complete through degree 4, and possible geophysical and climatic causes.

Chao, Benjamin F.↗
NASA NTRS2017

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Ulsig, Laura↗
NASA NTRS1990

Upper atmosphere dynamics

The spatial distribution of stratospheric ozone is useful in diagnosis of some features of the large scale atmospheric circulation, and the ozone may also interact with the atmospheric general circulation. Local maxima in the column ozone distribution are often associated with disturbances in the lower stratosphere and upper troposphere, which may herald cyclone development in the troposphere. One research objective is to explore these issues by means of time series analysis of a zonal index of total column ozone, to suggest the existence or nonexistence of relationships between column ozone and dynamical processes which are known to occur on various time scales. Another objective is to investigate the correlation between the ozone mixing ratio on the 350 K isentropic surface and the column integrated ozone, and to investigate the use of an easily derived parameter as a proxy for ozone mixing ratio, which is conserved in the stratosphere for time scales shorter than the photochemical time scale. The source of data for these studies is the Total Ozone Mapping Spectrometer (TOMS) data set.

Miller, Timothy L.↗
NASA NTRS2007

Application of the Hilbert-Huang Transform to the Search for Gravitational Waves

We present the application of a novel method of time-series analysis, the Hilbert-Huang Transform, to the search for gravitational waves. This algorithm is adaptive and does not impose a basis set on the data, and thus the time-frequency decomposition it provides is not limited by time-frequency uncertainty spreading. Because of its high time-frequency resolution it has important applications to both signal detection and instrumental characterization. Applications to the data analysis of the ground and space based gravitational wave detectors, LIGO and LISA, are described.

Camp, Jordan B.↗
NASA NTRS2016

Time Series Explorer: Bayesian Blocks with Generalized Profiles and in Higher Dimensions

The Time Series Explorer (TSE) is a project aimed at provided new and advanced time series analysis algorithms in two forms: a tool kit and an automated pipeline applying selected tools in machine learning settings. I will present a sketch of TSE with emphasis on time-domain modeling in general and recent improvements of the Bayesian Block (BB) algorithm in particular. This includes generalizing the shape of the elementary blocks from the current constant-rate model to general shapes, such as two-sided exponentials. Related topics will include extension of BB to higher dimensions and a novel way to detect and characterize short time-scale bursts in time-tagged event data. The Fermi Gamma Ray Space Telescope light curve for the Crab Nebula will be used as an example for all of the algorithms discussed. This work is in collaboration with Tom Loredo.

Bayesian Block (BB) algorithm↗
NASA NTRS2019

MARGInS Model-Based Analysis of Realizable Goals in Systems

The high complexity of modern aircraft and spacecraft requires elaborate Verification and Validation (V&V) approaches to make sure that such complex systems work properly and reliably. MARGInS is a framework for the analysis, understanding, and prediction of the behavior of a complex, hybrid system. MARGInS contains a set of machine learning and statistical algorithms for multivariate clustering, treatment learning, critical factor determination, time-series analysis, event prediction, and safety-boundary detection and characterization. The framework supports system testing and can be configured to find novel features in test suites, determine classes of behavior, propose new experiments that can efficiently explore and characterize the boundaries between classes of system behavior, and to create visualizations and reports.

He, Yuning↗
NASA NTRS1985

A perspective on the evidence for periodic cometary impacts on earth

Recent suggestions of periodic cometary showers coincident with mass extinctions have prompted a review of the terrestrial cratering record. From the point of view of completeness of search and crater retention, the record used to argue for statistical periodicities is incomplete and may not be representative. Estimates of crater ages are of variable accuracy and precision and, in some cases, different dating methods yield discordant results. It is possible to define through time-series analysis a number of periodicities of different magnitude (about 29, 21, 18.5, and 13.5 m.y.) and phase, depending on which craters are considered the most representative sample of the record. Arguing against periodic cometary showers is the observation that estimates of the average cratering rate in recent time and observations on Apollo bodies are equivalent. In addition, siderophile element data from impact melt rocks suggest a variety of projectile types were responsible for relatively large terrestrial craters. Given these observations, it is concluded that statements regarding periodic cometray showers are not categorical and require evidence beyond arguments based on time-series analyses of an incomplete cratering record.

Grieve, R. A. F.↗
NASA NTRS2019

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗
NASA NTRS2004

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Srivastava, Ashok↗
NASA NTRS2012

Using NASA's Giovanni System to Simulate Time-Series Stations in the Outflow Region of California's Eel River

Oceanographic time-series stations provide vital data for the monitoring of oceanic processes, particularly those associated with trends over time and interannual variability. There are likely numerous locations where the establishment of a time-series station would be desirable, but for reasons of funding or logistics, such establishment may not be feasible. An alternative to an operational time-series station is monitoring of sites via remote sensing. In this study, the NASA Giovanni data system is employed to simulate the establishment of two time-series stations near the outflow region of California s Eel River, which carries a high sediment load. Previous time-series analysis of this location (Acker et al. 2009) indicated that remotely-sensed chl a exhibits a statistically significant increasing trend during summer (low flow) months, but no apparent trend during winter (high flow) months. Examination of several newly-available ocean data parameters in Giovanni, including 8-day resolution data, demonstrates the differences in ocean parameter trends at the two locations compared to regionally-averaged time-series. The hypothesis that the increased summer chl a values are related to increasing SST is evaluated, and the signature of the Eel River plume is defined with ocean optical parameters.

Acker, James G.↗
NASA NTRS2005

Regression Analysis of Long-term Profile Ozone Data Set from BUV Instruments

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9,11, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUV instrument, which operated from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established from comparisons with SSBUV shuttle flight data. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. We use statistical time-series analysis of the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. The Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. We extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the statistics of the residuals from the fit for the Nimbus 4 period to those obtained from the 1978-2003 period over which the statistical model coefficients were estimated.

Frith, Stacey↗
NASA NTRS2017

Understanding the International Space Station Crew Perspective following Long-Duration Missions through Data Analytics & Visualization of Crew Feedback

The International Space Station (ISS) first became a home and research laboratory for NASA and International Partner crewmembers over 16 years ago. Each ISS mission lasts approximately 6 months and consists of three to six crewmembers. After returning to Earth, most crewmembers participate in an extensive series of 30+ debriefs intended to further understand life onboard ISS and allow crews to reflect on their experiences. Examples of debrief data collected include ISS crew feedback about sleep, dining, payload science, scheduling and time planning, health & safety, and maintenance. The Flight Crew Integration (FCI) Operational Habitability (OpsHab) team, based at Johnson Space Center (JSC), is a small group of Human Factors engineers and one stenographer that has worked collaboratively with the NASA Astronaut office and ISS Program to collect, maintain, disseminate and analyze this data. The database provides an exceptional and unique resource for understanding the "crew perspective" on long duration space missions. Data is formatted and categorized to allow for ease of search, reporting, and ultimately trending, in order to understand lessons learned, recurring issues and efficiencies gained over time. Recently, the FCI OpsHab team began collaborating with the NASA JSC Knowledge Management team to provide analytical analysis and visualization of these over 75,000 crew comments in order to better ascertain the crew's perspective on long duration spaceflight and gain insight on changes over time. In this initial phase of study, a text mining framework was used to cluster similar comments and develop measures of similarity useful for identifying relevant topics affecting crew health or performance, locating similar comments when a particular issue or item of operational interest is identified, and providing search capabilities to identify information pertinent to future spaceflight systems and processes for things like procedure development and training. In addition, the comments were scored for sentiment using a polarity scoring algorithm to identify both positive and negative comments for particular groups and clusters, allowing the team to make analytically informed decisions regarding future hardware and operating procedures. The use of polarity scoring with time series analysis was used to provide insight into how crew health and habitability is changing throughout various spaceflight increments or the station lifecycle as a whole. Finally, a visualization framework was developed to address the needs of the end users to search for and analyze comments by user, category or mission. This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of crew perspective and shortened the time for analysis allowing for more informed decisions and rapid development of improvements. These methods are significantly optimizing the way that this valuable data can be assessed and applied to current and future spaceflight design and development. This collaboration allows the FCI OpsHab team to effectively analyze and share data in a more automated and timely fashion. Trends are no longer derived manually and can be illustrated effectively and accurately with these evolving techniques to an ever growing group of human spaceflight end users.

Bryant, Cody↗