Results for “delay errors.” — Page 35

NASA NTRS1999

An Interactive Concatenated Turbo Coding System

This paper presents a concatenated turbo coding system in which a Reed-Solomon outer code is concatenated with a binary turbo inner code. In the proposed system, the outer code decoder and the inner turbo code decoder interact to achieve both good bit error and frame error performances. The outer code decoder helps the inner turbo code decoder to terminate its decoding iteration while the inner turbo code decoder provides soft-output information to the outer code decoder to carry out a reliability-based soft- decision decoding. In the case that the outer code decoding fails, the outer code decoder instructs the inner code decoder to continue its decoding iterations until the outer code decoding is successful or a preset maximum number of decoding iterations is reached. This interaction between outer and inner code decoders reduces decoding delay. Also presented in the paper are an effective criterion for stopping the iteration process of the inner code decoder and a new reliability-based decoding algorithm for nonbinary codes.

Liu, Ye↗

NASA NTRS2020

Characterization of How CO2 Level May Impact Crew Performance Related to the HSIA Risk

Safety and mission critical anomalies are inevitable on NASA exploration missions. Delays and interruptions in communication with Earth-experts drives the requirement that crew resolve these anomalies on their own. The Human Factors and Behavioral Performance (HFBP) element of the NASA Human Research Program (HRP) recently stood-up the Human-System Integration Architecture (HSIA) risk. The specific risk statement is: “Given decreasing real-time ground support for execution of complex operations during future exploration missions, there is a possibility of adverse performance outcomes including that crew are unable to adequately respond to unanticipated critical malfunctions or detect safety critical procedural errors.” Reliable, on-board capabilities will need to support the crew - not only to resolve anomalies, but also to promote situation awareness (SA) and to reduce workload. Unfortunately, the present level of technological advancement limits the range of achievable intelligent support (Wu & Vera, 2019). The Human Capabilities Assessment for Autonomous Missions (HCAAM) projects were devised to identify how technology may benefit crew situation awareness and enhance crew trust in the intelligent systems. A suite of time-critical and complex exploration tasks have been identified (Holden et al., 2019) that may advance human-integration Concept of Operations (ConOps) development. This ConOps (a future HFBP directed task) along with standards and guidelines (Holden et al., 2019) may be used as the foundation for human-system requirements for a self-reliant crew. Spaceflight CO2 levels are elevated relative to terrestrial levels. Above 2.5 mmHg there is an increase in the incidence of crew headaches (Law et al., 2014) and Mission Control reports of mood and performance changes in the crew. The concern is that elevated CO2 effects may exacerbate the HSIA risk. Unfortunately, effects of higher concentrations of CO2 on performance decrements are not well established. The main goal of the current report is to identify how elevated CO2 impacts the HSIA risk. The approach taken is to trace the relationship between cognitive and motor abilities required for anomaly response with critical mission tasks and the level of technological advancement to support anomaly resolution to identify how elevated CO2 may impact the HSIA risk. The conclusion is that elevated CO2 could exacerbate the HSIA risk. Several research gaps are identified.

cognition↗

NASA NTRS2000

Error Control Coding Techniques for Space and Satellite Communications

This paper presents a concatenated turbo coding system in which a Reed-Solomom outer code is concatenated with a binary turbo inner code. In the proposed system, the outer code decoder and the inner turbo code decoder interact to achieve both good bit error and frame error performances. The outer code decoder helps the inner turbo code decoder to terminate its decoding iteration while the inner turbo code decoder provides soft-output information to the outer code decoder to carry out a reliability-based soft-decision decoding. In the case that the outer code decoding fails, the outer code decoder instructs the inner code decoder to continue its decoding iterations until the outer code decoding is successful or a preset maximum number of decoding iterations is reached. This interaction between outer and inner code decoders reduces decoding delay. Also presented in the paper are an effective criterion for stopping the iteration process of the inner code decoder and a new reliability-based decoding algorithm for nonbinary codes.

Lin, Shu↗

NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗

NASA NTRS1988

Astrometric results of 1978-1985 Deep Space Network radio interferometry - The JPL 1987-1 extragalactic source catalog

An astrometric radio reference frame has been determined from intercontinental dual-frequency radio interferometric measurements. These measurements were carried out on a regular basis during 1978-1985 between NASA's Deep Space Network stations in California, Spain, and Australia. Analysis of 6800 pairs of delay and delay-rate observations made during 51 sessions produced estimates of 1300 parameters. The most significant of these are geophysical quantities and positions of extragalactic sources. The source catalog resulting from this analysis includes 106 sources fairly uniformly distributed over the celestial sphere, north of -45 deg declination. Almost all of the resulting source positions have formal uncertainties between 0.5 and 3 milliarcseconds (mas), with rms values of 2 mas in both angular coordinates. Internal consistency checks, as well as comparisons with independently determined source catalogs of comparable quality, indicate that relative source coordinates determined by VLBI contain systematic errors at the level of 1 to 2 mas.

Sovers, O. J.↗

NASA NTRS1988

A simulation study of data link message exchange - Flight deck perspective

Data link message exchange for Air Traffic Control (ATC) offers the potential of increasing flight safety and efficiency by reducing communication errors and enabling more information to be exchanged between aircraft and ground facilities. Digital communication also presents an opportunity to relieve the overloading of air traffic control radio frequencies, which hampers efficient message exchange during peak traffic periods in many busy terminal areas. A study has been completed at NASA Langley Research Center to compare the use of data link, for tactical ATC messages and ATIS reports, to conventional voice communication. The results indicate that flight crews prefer the data link message exchange for routine communication with air traffic control facilities. The data link communication process, however, entailed delays related to the physical characteristics of the link and the nature of the interface explored in this study. Consequently, the crews preferred voice radio when exchanging time critical messages.

Waller, Marvin C.↗

NASA NTRS2006

CFDP Performance over Weather-Dependent Ka-Band Channel

This study presents an analysis of the delay performance of the CCSDS File Delivery Protocol (CFDP) over weather-dependent Ka-band channel. The Ka-band channel condition is determined by the strength of the atmospheric noise temperature, which is weather dependent. Noise temperature data collected from the Deep Space Network (DSN) Madrid site is used to characterize the correlations between good and bad channel states in a two-state Markov model. Specifically, the probability distribution of file delivery latency using the CFDP deferred Negative Acknowledgement (NAK) mode is derived and quantified. Deep space communication scenarios with different file sizes and bit error rates (BERs) are studied and compared. Furthermore, we also examine the sensitivity of our analysis with respect to different data sampling methods. Our analysis shows that while the weather-dependent channel only results in fairly small increases in the average number of CFDP retransmissions required, the maximum number of transmissions required to complete 99 percentile, on the other hand, is significantly larger for the weather-dependent channel due to the significant correlation of poor weather states.

weather↗

NASA NTRS2006

CFDP Performance over Weather-dependent Ka-band Channel

This study presents an analysis of the delay performance of the CCSDS File Delivery Protocol (CFDP) over weather-dependent Ka-band channel. The Ka-band channel condition is determined by the strength of the atmospheric noise temperature, which is weather dependent. Noise temperature data collected from the Deep Space Network (DSN) Madrid site is used to characterize the correlations between good and bad channel states in a two-state Markov model. Specifically, the probability distribution of file delivery latency using the CFDP deferred Negative Acknowledgement (NAK) mode is derived and quantified. Deep space communication scenarios with different file sizes and bit error rates (BERs) are studied and compared. Furthermore, we also examine the sensitivity of our analysis with respect to different data sampling methods. Our analysis shows that while the weather-dependent channel only results in fairly small increases in the average number of CFDP retransmissions required, the maximum number of transmissions required to complete 99 percentile, on the other hand, is significantly larger for the weather-dependent channel due to the significant correlation of poor weather states.

deep space communications↗

NASA NTRS2020

Improved Orbit Determination of the Cygnss Satellites and its Application to Gnss-R Ocean Altimetry

The accuracy of spaceborne ocean surface altimetry depends on precise knowledge of the altimeter satellite orbit. Previous studies of the potential utility of CYGNSS GNSS Reflectometry (GNSS-R) measurements for ocean altimetry have identified its orbit error as a limiting factor. To address this, a recent firmware upgrade to the CYGNSS satellites have enabled the downlink of additional GPS raw measurements from its navigation receiver and increased numerical precision in open-loop GNSS-R tracking information. In this paper, we present improvements to the accuracy of both the orbit determination and the ocean surface height retrievals as a result of these recent upgrades. JPL’s GipsyX software is used to process 120 days of GPS navigation measurements for one of the 8 CYGNSS satellites. Incorporating high fidelity dynamic models and antenna group delay correction, daily orbit overlaps throughout the period show mean RMS differences of 3 cm in height, 7 cm in cross-track, and 10 cm in along-track. Applying these new orbits reduced the standard deviation of retrieved surface height anomalies. When spatially averaged, the sea surface height anomalies were reduced from 1.0 m to 0.8 m. Further improvements are expected using new ionospheric corrections and re-tracking methods currently in development.

Conrad, Alex↗

NASA NTRS2014

Micro-Spec: an Integrated, Direct-Detection Spectrometer for Far-Infrared and Submillimeter Astronomy

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements. Micro-Spec (u-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 micromillimeter wavelength range which will enable a wide range of flight missions that would otherwise be challenging due to the large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 square cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for Micro-Spec is presented, with particular attention given to its twodimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance. A point design with resolving power of 257, an RMS phase error less than 0.1 radians and four stigmatic points was developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

microwave kinetic inductance detectors (MKIDs)↗

NASA NTRS2014

Micro-spec: an Integrated Direct-detection Spectrometer for Far-infrared Space Telescopes

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements.Micro-Spec (μ-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 (micrometers) wavelength range which will enable a wide range of flight missions that would otherwise be challenging due tothe large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 sq cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for micro-Spec is presented, with particular attention given to its two-dimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance.Two point designs with resolving power of 260 and 520 and an RMS phase error less than approximately 0.004 radians were developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

Far-infrared↗

DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗

DOE OSTI2023

Electrodynamic Shaker Capability Estimation Through Experimental Dynamic Substructuring [Thesis]

Electrodynamic shaker systems are an essential tool in shock and vibration testing of dynamic environments. However, the specific performance capability of these systems is difficult to characterize. The dynamics of the shaker itself, the device under test and the specific test configuration used all couple to create a dynamic response unique to each test. Poorly predicted limitations in shaker capability affect the ability to achieve test specifications, delay testing schedules, and create difficulties for choosing test equipment. To predict shaker capability for a specific test configuration prior to setup, a lumped parameter model of the shaker system and a modal model of a device under test was developed. These models were then analytically coupled using LaGrange multiplier frequency based substructuring to estimate their coupled frequency response functions. The coupled frequency response functions were used to predict electrical inputs required to meet a given test specification. These input requirements were finally compared to a validation test using the specification and setup. Input requirements estimated using the substructuring estimated frequency response functions showed significant error. However, results using an ideal frequency response function showed very little error. These results indicate that with a better method of experimental dynamic substructuring employed it would be possible to accurately predict shaker capability for a given test configuration prior to setup.

42 ENGINEERING↗

NASA NTRS1999

An Airborne Scanning Laser Altimetry Survey of Long Valley, California

Between 28 September and 7 October 1995, we conducted an airborne laser altimetry experiment over the Long Valley caldera, California, in which each of two scanning laser altimeters (dubbed SLICER and RASCAL) were flown in a NASA T-39 jet aircraft. Operating concurrently were a Global Positioning System (GPS) guidance system and dual frequency receivers for precise navigation and post-flight calculation or the airplane trajectory relative to a ground station, and an inertial navigation system (INS) for attitude determination. Reduction of raw laser ranges requires merging the differential kinematic GPS aircraft trajectory and the INS data with the laser data, and determination of the atmospheric delay. Data geolocation consists of obtaining the centre location and the mean elevation within each footprint in a geodetic coordinate system. The elevation of Crowley Lake is recovered to an accuracy of approximately 3 cm or better from 3 km above ground level and crossover analysis indicates that the elevation estimates are consistent from pass to pass. We test our geolocation procedures by comparing laser-derived elevations with those determined in situ for recognizable ground features. A comparison of laser and GPS-derived positions shows that the horizontal accuracy is better than the diameter of the footprint and vertical accuracy is within the error inherent in the range measurement. A comparison of SLICER elevation data with digital elevation models (DEMs) of the region shows that the DEM data provides surface topography to within stated accuracy limits. Although research continues to utilize the full potential of laser altimetry data, our results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic changes.

Hofton, M. A.↗

DOE OSTI2022

Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances

We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] versus [Mg/H] with a 2-process model, which decomposes abundance patterns into a "prompt" component tracing core-collapse supernovae and a "delayed" component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals Δ[X/H] from this two-parameter fit. The rms residuals range from ~0.01–0.03 dex for the most precisely measured APOGEE abundances to ~0.1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co. Selecting stars poorly fit by the 2-process model reveals a rich variety of physical outliers and sometimes subtle measurement errors. Residual abundances allow for the comparison of populations controlled for differences in metallicity and [α/Fe]. Relative to the main disk (R = 3–13 kpc), we find nearly identical abundance patterns in the outer disk (R = 15–17 kpc), 0.05–0.2 dex depressions of multiple elements in LMC and Gaia Sausage/Enceladus stars, and wild deviations (0.4–1 dex) of multiple elements in ω Cen. The residual abundance analysis opens new opportunities for discovering chemically distinctive stars and stellar populations, for empirically constraining nucleosynthetic yields, and for testing chemical evolution models that include stochasticity in the production and redistribution of elements.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1999

Soil Surface Runoff Scheme for Improving Land-Hydrology and Surface Fluxes in Simple SiB (SSiB)

Evapotranspiration on land is hard to measure and difficult to simulate. On the scale of a GCM grid, there is large subgrid-scale variability of orography, soil moisture, and vegetation. Our hope is to be able to tune the biophysical constants of vegetation and soil parameters to get the most realistic space-averaged diurnal cycle of evaporation and its climatology. Field experiments such as First ISLSCP Field Experiment (FIFE), Boreal Ecosystem-Atmosphere Study (BOREAS), and LBA help a great deal in improving our evapotranspiration schemes. However, these improvements have to be matched with, and coupled to, consistent improvement in land-hydrology; otherwise, the runoff problems will intrinsically reflect on the soil moisture and evapotranspiration errors. Indeed, a realistic runoff simulation also ensures a reasonable evapotranspiration simulation provided the precipitation forcing is reliable. We have been working on all of the above problems to improve the simulated hydrologic cycle. Through our participation in the evaluation and intercomparison of land-models under the behest of Global Soil Wetness Project (GSWP), we identified a few problems with Simple SiB (SSIB; Xue et al., 1991) hydrology in regions of significant snowmelt. Sud and Mocko (1999) show that inclusion of a separate snowpack model, with its own energy budget and fluxes with the atmosphere aloft and soil beneath, helps to ameliorate some of the deficiencies of delayed snowmelt and excessive spring season runoff. Thus, much more realistic timing of melt water generation was simulated with the new snowpack model in the subsequent GSWP re-evaluations using 2 years of ISLSCP Initiative I forcing data for 1987 and 1988. However, we noted an overcorrection of the low meltwater infiltration of SSiB. While the improvement in snowmelt timing was found everywhere, the snowmelt infiltration has became excessive in some regions, e.g., Lena river basin. This leads to much reduced runoff in many basins as compared to observations. We believe this is a consequence of neglect of the influence of subgrid-scale variations in orography that affects the production of surface runoff.

Sud, Y. C.↗