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NASA NTRSDate not supplied

Human Systems Integration Relationships Between Deep Space and Deepwater Exploration Challenges

NASA pursuit of human missions Beyond Earth Orbit (BEO) to the moon and eventually Mars will pose many new challenges. These missions are characterized by increased risks to human performance due to stress, fatigue, radiation exposure, and isolation. Fundamental changes in spaceflight operations will be required as well. A very critical part of future missions will be assisting the crew with problem-solving and contingency resolution using next-generation smart spacecraft systems. Human Systems Integration (HSI) must be included as part of the systems engineering process. Gateway1 will serve as part of the proving ground for maturing technology such as autonomous systems and space operations procedures to help meet moon mission goals and prepare humans for missions to Mars. For deep space missions, risk factors affecting humans such as mission duration, distance, and physiological/psychological effects, must be considered. A human is not a simple component but rather a system in a systems-of-systems application-a system influenced by cognitive and biological factors that are difficult to model/analyze. System complexity will demand an integrated approach that requires multiple viewpoints, including the understanding of the astronauts' capabilities and limitations to enhance safety, performance, and satisfaction on the interaction between all system users in a variety of environments to achieve acceptable mission goals and performance. HSI will play a vital role in the development of these future missions. This paper provides a discussion of HSI used at NASA and the critical role human factors engineering will play in the HSI process for future missions along with the challenges. Industries such as oil and gas (O&G) could benefit from the content of this paper to help improve the safety and operations of deepwater/oil and gas exploration activities. NASA engineering and human systems subject-matter expert personnel reviewed challenges reported in space-related environments for long space duration missions and identified similarities with the challenges encountered in deepwater exploration. The researchers noted the space industry's current solutions and proposed a system development view to solve future deepwater exploration challenges using HSI principles. This paper discusses the results of the review.

George Salazar↗
NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗
NASA NTRS1994

An Algorithm to Generate Deep-Layer Temperatures from Microwave Satellite Observations for the Purpose of Monitoring Climate Change

An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate. The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: (1) the sum of the coefficients must be one, (2) the noise of the product is minimized, and (3) the shape of the approximated averaging kernel is well-behaved. Note that a trade-off between constraints 2 and 3 is unavoidable. The algorithm can also be used to combine measurements from a future sensor (i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)) to yield the same averaging kernel as that based on an earlier sensor (i.e., the 4-channel Microwave Sounding Unit (MSU)). This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.

Goldberg, Mitchell D.↗
NASA NTRS1980

Spaceborne imaging radar - Monitoring of ocean waves

A well-organized, very low energy ocean swell system off the East Coast of the United States was tracked with the Seasat synthetic aperture radar from deep water, across the continental shelf, and into shallow water. The results indicate that spaceborne imaging radar may be used to accurately measure ocean wavelength and direction, even in coastal areas and in the presence of a mixed ocean.

Beal, R. C.↗
NASA NTRS1992

DSN acquisition of Magellan high-rate telemetry data

The Magellan Project levied the stringent requirement of a 98 percent high-rate telemetry data capture rate on the Deep Space Network (DSN) during the Magellan Prime Mapping Mission. To meet this requirement, the DSN undertook extensive development of the DSN Telemetry System, as well as extensive DSN operation planning and test and training. In actuality, the DSN substantially exceeded the requirement by achieving a Prime Mapping Mission high-rate telemetry data capture rate of 99.14 percent. This article details the DSN telemetry system development, and DSN operations planning and test and training. In addition, the actual high-rate telemetry data outages are comprehensively presented and analyzed.

Berman, A. L.↗
NASA NTRS1992

Doppler tracking of planetary spacecraft

This article concerns the measurement of Doppler shift on microwave links that connect planetary spacecraft with the Deep Space Network. Such measurements are made by tracking the Doppler effect with phase-locked loop receivers. A description of equipment and techniques as well as a summary of the appropriate mathematical models are given. The two-way Doppler shift is measured by transmitting a highly-stable microwave (uplink) carrier from a ground station, having the spacecraft coherently transpond this carrier, and using a phase-locked loop receiver at the ground station to track the returned (downlink) carrier. The largest sources of measurement error are usually plasma noise and thermal noise. The plasma noise, which may originate in the ionosphere or the solar corona, is discussed; and a technique to partially calibrate its effect, involving the use of two simultaneous downlink carriers that are coherently related, is described. Range measurements employing Doppler rate-aiding are also described.

Kinman, Peter W.↗
NASA NTRS1994

Development of a prototype real-time automated filter for operational deep space navigation

Operational deep space navigation has been in the past, and is currently, performed using systems whose architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational data flow has been developed. This system can allow for more rapid response as well as much reduced staffing to support mission navigation operations.

Masters, W. C.↗
NASA NTRS2002

A Giant Warm Baryonic Halo for the Coma Cluster

Several deep PSPC observations of the Coma cluster unveil a very large-scale halo of soft X-ray emission, substantially in excess of the well know radiation from the hot intra-cluster medium. The excess emission, previously reported in the central cluster regions through lower-sensitivity EUVE and ROSAT data, is now evident out to a radius of 2.5 Mpc, demonstrating that the soft excess radiation from clusters is a phenomenon of cosmological significance. The spectrum at these large radii cannot be modeled non-thermally, but is consistent with the original scenario of thermal emission at warm temperatures. The mass of this plasma is at least on par with that of the hot X-ray emitting plasma, and significantly more massive if the plasma resides in low-density filamentary structures. Thus the data lend vital support to current theories of cosmic evolution, which predict greater than 50 percent by mass of today's baryons reside in warm-hot filaments converging at clusters of galaxies.

Bonamente, Max↗
NASA NTRS2003

A Massive Warm Baryonic Halo in the Coma Cluster

Several deep PSPC observations of the Coma Cluster reveal a very large scale halo of soft X-ray emission, substantially in excess of the well-known radiation from the hot intracluster medium. The excess emission, previously reported in the central region of the cluster using lower sensitivity Extreme Ultraviolet Explorer (EUVE) and ROSAT data, is now evident out to a radius of 2.6 Mpc, demonstrating that the soft excess radiation from clusters is a phenomenon of cosmological significance. The X-ray spectrum at these large radii cannot be modeled nonthermally but is consistent with the original scenario of thermal emission from warm gas at approx. 10(exp 6) K. The mass of the warm gas is on par with that of the hot X-ray-emitting plasma and significantly more massive if the warm gas resides in low-density filamentary structures. Thus, the data lend vital support to current theories of cosmic evolution, which predict that at low redshift approx. 30%-40% of the baryons reside in warm filaments converging at clusters of galaxies.

Bonamente, Massimiliano↗
NASA NTRS2005

Pico Reentry Probes: Affordable Options for Reentry Measurements and Testing

It is generally very costly to perform in-space and atmospheric entry experiments. This paper presents a new platform - the Pico Reentry Probe (PREP) - that we believe will make targeted flight-tests and planetary atmospheric probe science missions considerably more affordable. Small, lightweight, self-contained, it is designed as a "launch and forget" system, suitable for experiments that require no ongoing communication with the ground. It contains a data recorder, battery, transmitter, and user-customized instrumentation. Data recorded during reentry or space operations is returned at end-of-mission via transmission to Iridium satellites (in the case of earth-based operations) or a similar orbiting communication system for planetary missions. This paper discusses possible applications of this concept for Earth and Martian atmospheric entry science. Two well-known heritage aerodynamic shapes are considered as candidates for PREP: the shape developed for the Planetary Atmospheric Experiment Test (PAET) and that for the Deep Space II Mars Probe.

Ailor, William H.↗
NASA NTRS2009

Memory Technologies and Data Recorder Design

Missions, both near Earth and deep space, are under consideration that will require data recorder capacities of such magnitude as to be unthinkable just a few years ago. Concepts requiring well over 16,000 GB of storage are being studied. To achieve this capacity via "normal means" was considered incredible as recently as 2004. This paper is presented in two parts. Part I describes the analysis of data recorder capacities for missions as far back as 35 years and provides a projection of data capacities required 20 years from now based upon missions either nearing launch, or in the planning stage. The paper presents a similar projection of memory device capacities as baselined in the ITRS - the International Technology Roadmap for Semiconductors. Using known Total Ionizing Dose tolerance going back as far as a decade, a projection of total dose tolerance is made for two prime technologies out to the year 2028.

Strauss, Karl F↗
NASA NTRS2020

Cascading toward a Kilometer‐scale GCM: Impacts of a Scale‐aware Convection Parameterization in the Goddard Earth Observing System GCM.

The NASA Goddard Earth Observing System global model is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a non-hydrostatic dynamical core and includes a scale-aware, deep convection parameterization (DPCP). 40-daysimulations at six resolutions (100km to 3km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out; one with no DPCP, and one with its scale-awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale-aware deep convection parameterization even at 3kmscale, as the run relying solely on explicit grid-scale production of rainfall performs more poorly at this resolution.

Saulo Freitas↗
NASA NTRS2020

Disruptive Technologies and Their Putative Impacts Upon Society and Aerospace- Entering The Virtual Age

Developments in technology over the recent decades have been extraordinary. They include the IT, bio, nano, and now quantum and energetics technology arenas and their many combinatorial interactions and impacts. In the main, these are at the frontiers of the small and in a combinational, synergistic feeding frenzy with each other. They fall under the broad category of Disruptive Technologies and have greatly altered society. The outlook for the runout of these and other technology developments augers mid-term to later alterations in components of the human existence theorem, including the requirement to work for our living and our physiological makeup and longevity (Ref 1). The IT revolution began in the 1950s with the development of solid-state electronics. The biologics revolution began later in the 1960s and 1970s with DNA and genomics, and the nano revolution in the 1990s with self-forming nano systems and carbon nanotubes. Quantum technology is now developing rapidly, aided by enabling nano systems, and the energetics revolution is providing ever more efficient and less expensive renewable energy sources. The IT revolution has produced improvements of an astounding eleven orders of magnitude in computing speed since the late 1950s. As we shift from silicon to biological, optical, nano, molecular, and atomic computing, improvements of some 4 orders of magnitude are evidently possible from either optical or DNA computing [Refs 2and 3], then there are combinatorials. Then there is quantum computing, under development worldwide for an increasing number of applications and proffering phenomenal capabilities. The current fastest computers are considerably beyond human brain speed. Machine intelligence is developing well after decades of inadequate machine capability, now no longer the case, and a detour into expert systems. Researchers in machine intelligence are now pursuing deep learning approaches using neural nets, which are proving to be extremely useful. Some believe the frontier of potential human-level machine intelligence may be found in biomimetics and brain-emulation approaches. There is even a possibility of “emergence”—i.e., when the machine intelligence is complex enough that it “wakes up,” as when human intelligence emerged via evolution during the million-plus years of the hunter-gatherer epoch [ Ref 4]. In fact, some posit that human intelligence can be improved upon and is only a cul-de-sac of what is conceivable. The IT revolution has produced massive changes in human society and economics—from the Internet, enabling the rapid expansion of knowledgeability (and even what is knowable), to an increasingly pervasive trend of “tele-everything.” The extraordinary compilation, storage, and availability of truly massive amounts of information could, when combined with AI and under the mantra of “big data,” greatly improve many of our technical and commercial processes and their content including elucidating new heuristic governing laws.

Dennis M. Bushnell↗
NASA NTRS2021

Advancing Space Life Science Research Using Drosophila Melanogaster

As we embark on exploring deep space, it is imperative to unravel the impacts of such unique and dynamic environments on biological systems. Multiple studies using D. melanogaster, a well-established spaceflight model, have paved the way towards our understanding of spaceflight-associated health risks. Further, Drosophila quantitative genomic experiments along with improved automated hardware systems will enable efficient and effective identification of the underlying genetic and molecular determinants capable of physiological adaptation to endure harsh space environments.

Quantitative genomics↗
NASA NTRSDate not supplied

On the Hunt for Detectable Biosignatures in Jezero Crater: What to Look for and Where

Introduction: The Perseverance rover, which is currently exploring Jezero crater on Mars, is equipped with seven instruments that allow for observation of textures, minerals, color, structure, and chemistry of rocks and sediments in order to search for signs of ancient life, understand the geologic history of the crater, and identify candidates for sample return (Farley et al. 2020). The first of these aims includes the direct detection of potential biosignatures, including textures, organic molecules, minerals, and elemental chemistries that are of biogenic origin (Mustard et al. 2013). The presence of these biosignatures will be constrained by the habitability of the local region, the preservation potential of the host rocks, and the sensitivity of the instrument suite, and their biogenicity will be investigated after return to Earth as part of the Mars Sample Return campaign. Here, we examine key targets on the three planned campaigns, potential biosignatures that may be present, and the capabilities of key rover instruments. High Potential Biosignature Sites in Jezero: To date, measurements have been made on multiple sites that both contain minerals known in terrestrial settings to preserve biosignatures and likely were habitable settings (Williford et al. 2021). Two examples include the fine-grained rocks at the base of the delta fan and the NW inner margin of the crater. Fine-grained Rocks at Base of Delta Fan. Fine-grained, clay-bearing rocks may have been deposited as muddy lake sediments that could have hosted life and preserved biosignatures settling out of the water column. The report of organic molecules by the Curiosity rover in the Sheepbed mudstone and Murray formation has highlighted this site in particular. Potential biosignatures in mudstones, especially those rich in silica (McMahon et al. 2018), microbialites and complex organics. NW Inner Margin of Crater. This unit, located along the inner margin of the crater, contains strong carbonate signatures and may have been the littoral zone of a lake (Horgan et al. 2020). The potential biosignatures here include microfossils, microbialites, biominerals, and complex organics. Detectability of Biosignatures by the Mars 2020 Instrument Suite: The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument comprises a Deep UV spectrometer, context imager, and color camera to generate spatially resolved chemical maps (Bhartia et al. 2020). It is sensitive to trace organics as well as a range of minerals and can detect native fluorescence from aromatic organics; the WATSON and ACI cameras can be used to observe morphologies such as stromatolitic laminations or filaments ranging from the tens of micron to millimeter scale. SHERLOC can detect organics that may be present in either of the high potential biosignature targets, as well as detect carbonates in the latter. However, the presence of high amounts of iron, such as in iron-rich clays, would cause attenuation of spectral response through UV absorption. The Planetary Instrument for X-ray Lithochemistry (PIXL) comprises an X-ray fluorescence spectrometer and camera that can scan rock surfaces to generate elemental maps (Allwood et al. 2020). PIXL can detect chemical biosignatures such as spatial variations of elemental abundances that may have resulted from biological activity. PIXL would be particularly useful in detecting fine textures and elemental chemistries in either high potential biosignature target, but cannot directly detect minerals such as carbonate. The SuperCam instrument performs three types of spectroscopy, color imaging, and acoustic recording to remotely examine elemental composition, minerals, organics, and textures (Maurice et al. 2021). Using laser induced breakdown spectroscopy and time resolved luminescence spectroscopy, SuperCam can detect major elemental building blocks of organics (i.e., C, H, N, O, P, S) and conjugated organic structures, respectively, which may be found in either site. While in other contexts, luminescence is a useful tool for biosignature identification, luminescence generated by the 532 nm laser may obscure the Raman signal. Conclusion: The three instruments discussed can be used collaboratively to establish the presence of potential biosignatures in samples. These high-priority samples may then be returned to Earth for detailed laboratory analysis.

S. Sharma↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS2011

On-Line Tool for the Assessment of Radiation in Space - Deep Space Mission Enhancements

The On-Line Tool for the Assessment of Radiation in Space (OLTARIS, https://oltaris.nasa.gov) is a web-based set of tools and models that allows engineers and scientists to assess the effects of space radiation on spacecraft, habitats, rovers, and spacesuits. The site is intended to be a design tool for those studying the effects of space radiation for current and future missions as well as a research tool for those developing advanced material and shielding concepts. The tools and models are built around the HZETRN radiation transport code and are primarily focused on human- and electronic-related responses. The focus of this paper is to highlight new capabilities that have been added to support deep space (outside Low Earth Orbit) missions. Specifically, the electron, proton, and heavy ion design environments for the Europa mission have been incorporated along with an efficient coupled electron-photon transport capability to enable the analysis of complicated geometries and slabs exposed to these environments. In addition, a neutron albedo lunar surface environment was also added, that will be of value for the analysis of surface habitats. These updates will be discussed in terms of their implementation and on how OLTARIS can be used by instrument vendors, mission designers, and researchers to analyze their specific requirements.12

Sandridge, Chris a.↗
NASA NTRS2024

VIIRS Version 2 Deep Blue Aerosol Products

NASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA-20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine-mode aerosol optical models to better represent anthropogenic aerosols over land. Cross-calibration gain factors are derived for the NOAA-20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA-20 VIIRS are comparable, indicating that cross-calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Deep Blue↗