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The Four Bed Carbon Dioxide Scrubber technology demonstration is presently operating onboard the International Space Station after being launched in August of 2021and activated in October. This work describes the ground and flight performance of the 4BCO2 and the methods used to determine performance on the ISS where limited sensor data is available.
In 2016, a routine repair operation at the Willwood Dam released tons of built-up sediment into the Shoshone River, polluting the river and negatively impacting the ecosystem. This release greatly affected the communities that rely on the river for farming, recreation, and tourism. In partnership with the Wyoming Department of Environmental Quality (WYDEQ), Shoshone River Partners, and the United States Geological Survey (USGS) Wyoming–Montana Water Science Center, this project utilized satellite imagery and precipitation data to examine turbidity patterns in the Shoshone River between the Buffalo Bill Dam and the Willwood Dam. We used PlanetScope satellite images to assess changes in surface reflectance of the river in response to precipitation events and Global Precipitation Measurement (GPM) Integrated Multi-Spectral Retrieval (IMERG) precipitation data to estimate the lag time between rainfall events and increased turbidity. The National Land Cover Dataset (2019) was used to identify the main land cover types within each sub-basin. The end products included a turbidity analysis, land cover analysis, and precipitation analysis that provided the partners with a better understanding of sediment dynamics in the river. The results demonstrated the feasibility of using PlanetScope data to examine turbidity spatially along small rivers. Sediment plumes from tributaries were visually identified for multiple high turbidity events, and we calibrated an equation that translated reflectance to turbidity, accurately representing plume extent. Inconsistent spectral quality of PlanetScope data, however, limited our ability to assess the relative sediment contribution of the tributaries.
Early atmospheres on rocky planets where life may develop form through outgassing of their original starting blocks, likely a mixture of chondritic material (carbonaceous chondrites (CC), ordinary chondrites (OC), and enstatite chondrites (EC)). However, there is limited experimental data to inform models connecting a planet’s bulk composition to its early atmospheric properties and thus its possibility for life. Thompson et al. (2021) took a major step for-ward in exploring this knowledge gap by measuring outgassing of 3 volatile-rich CCs, providing important experimental constraints on the initial chemical com-position of early rocky planet atmospheres. These data provided novel insights into the gas chemistry released into evolving atmospheres early in a rocky planet’s history and differed from those currently assumed by many theoretical models of rocky planet atmosphere formation. In this study, we focused on the outgassing and condensation reactions of a primitive EC3, which has a lower intrinsic oxygen fugacity ( ƒ O 2 ) and lower volatile content than the CCC meteorites investigated by. We selected the EC to explore differences between EC and CC in low-pressure, high-temperature outgassing and condensation including S, Cl, Na, and C species.
One of the most practical, and sought after, applications of quantum mechanics in the field of information science is the use of entanglement distribution to communicate quantum information effectively. Similar to the continued improvements of functional quantum computers over the past decade, advances in demonstrations of entanglement distribution over long distances may enable new applications in aeronautics and space communications. The existing NASA Quantum Communications Analysis Suite (NQCAS) software models such applications, but limited experimental data exists to verify the model’s theoretical results. There is, however, a large body of experimental data in the relevant literature for entanglement-based quantum key distribution (QKD). This paper details a Monte Carlo based QKD model that uses NQCAS input parameters to generate an estimated QKD link budget for verification of NQCAS. The model generates link budget statistics like key rates, error rates, and S values that can then be compared to the experimental values in the literature. Preliminary comparisons show many similarities between the simulated and experimental data, supporting the model’s validity. A verified NQCAS model will inform experimental work conducted in Glenn Research Center’s (GRC) NASA Quantum Metrology Laboratory (NQML), supporting the United States Quantum Initiative and potential NASA missions.
Total organic carbon (TOC) is the amount of carbon associated with organic compounds in solution and is often used as a non-specific indicator for water quality or cleanness. Currently, the Total Organic Carbon Analyzer (TOCA) is being used onboard the International Space Station (ISS) for water recovery system process control. An exploration-class TOCA is now being developed to close the technology gaps between the State-of-the-Art ISS TOCA and the emerging requirements of future exploration missions. One of the technical gaps for an exploration TOC analyzer is the development of suitable methods for on-orbit calibration. While the ISS TOCA uses pre-packaged ground-supplied TOC calibration standards, there is limited stability data. Other options include preparing calibration standards in space, which can be challenging due to the lack of low TOC reagent water. This paper explores the practice of the standard addition method and passive dosing approaches as potential solutions to overcome this problem.
In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.
Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.
During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.
NASA’s plans for landing human-scale payloads on Mars in the next decade require that retrorockets be used to decelerate the atmospheric entry vehicle continuously from supersonic conditions through soft touchdown. Conventional Mars entry vehicle architectures that include a single parachute for supersonic-to-subsonic descent are not scalable to the sizes needed to land humans on Mars (~20 metric tons). The major aerosciences risks are the uncertainties in predicting aerodynamic stability and performance during powered free-flight and landing. These risks are influenced partially by current limitations in relevant data and testing methods. Consequently, trajectory simulations currently depend on unvalidated powered descent and landing aerodynamics models. NASA engineers have identified gaps in testing methods that, if addressed, would improve the ability to validate these models. There are gaps in capabilities to test multi-engine hot-gas retropropulsion systems in US wind tunnels. This is partially due to the successful use of parachutes as decelerators for human spaceflight at Earth and for the entire Mars lander program to date. Retropulsion test data historically and to this day are limited to using high pressure air jets, at comparatively low temperatures,as engine plume simulants on subscale wind tunnel models. Additionally, the ability to directly measure aerodynamic interference force and moments is limited by existing flow-through balance capabilities. This paper briefly covers historical and recent test data, and identifies new ground test techniques as a means to provide more relevant test data for powered flight and landing aerodynamic model validation.These techniques include using heated inert gases as a substitute for combustion products, additively manufactured 6-component flow-through force and moment balances, and off-body quantitative diagnostic measurements.
A limitation has been identified in the existing test standards used for making controlled, two-body abrasion scratch measurements based solely on the width of the resultant score on the surface of the material. A new, more robust method is proposed for analyzing a surface scratch that takes into account the full three-dimensional profile of the displaced material. To accomplish this, a set of four volume- displacement metrics was systematically defined by normalizing the overall surface profile to denote statistically the area of relevance, termed the Zone of Interaction. From this baseline, depth of the trough and height of the plowed material are factored into the overall deformation assessment. Proof-of-concept data were collected and analyzed to demonstrate the performance of this proposed methodology. This technique takes advantage of advanced imaging capabilities that allow resolution of the scratched surface to be quantified in greater detail than was previously achievable. When reviewing existing data analysis techniques for conducting two-body abrasive scratch tests, it was found that the ASTM International Standard G 171 specified a generic metric based only on visually determined scratch width as a way to compare abraded materials. A limitation to this method was identified in that the scratch width is based on optical surface measurements, manually defined by approximating the boundaries, but does not consider the three-dimensional volume of material that was displaced. With large, potentially irregular deformations occurring on softer materials, it becomes unclear where to systematically determine the scratch width. Specifically, surface scratches on different samples may look the same from a top view, resulting in an identical scratch width measurement, but may vary in actual penetration depth and/or plowing deformation. Therefore, two different scratch profiles would be measured as having identical abrasion properties, although they differ significantly.
A short report discusses a methodology for designing Ka-band Deep-Space-to- Earth radio-communication links. This methodology is oriented toward minimizing the effects of weather on the Ka-band telecommunication link by maximizing the expected data return subject to minimum link availability and a limited number of data rates. This methodology differs from the current standard practices in which a link is designed according to a margin policy for a given link availability at 10 elevation. In this methodology, one chooses a data-rate profile that will maximize the average data return over a pass while satisfying a minimum-availability requirement for the pass, subject to mission operational limitations expressed in terms of the number of data rates used during the pass. The methodology is implemented in an intelligent search algorithm that first finds the allowable data-rate profiles from the mission constraints, spacecraft-to-Earth distance, spacecraft EIRP (effective isotropic radiated power), and the applicable zenith atmospheric noise temperature distribution, and then selects the best data rate in terms of maximum average data return from the set of allowable data-rate profiles.
The program, LACVIN, calculates vegetative indexes numbers on limited area coverage/high resolution picture transmission data for selected IJ grid sections. The IJ grid sections were previously extracted from the full resolution data tapes and stored on disk files.
Two studies are presented, and in the first study, Surlyn 8920 (an ionic and amorphous low density polyethylene made by Dupont) was evaluated as a possible replacement of Plexyglass G as PHE visor material. Four formulations of the polymer were made by adding different amounts of UV stabilizer, energy quencher, and antioxident in a Brabender Plasticorder. The formulated polymers were molded in the form of sheets in a compression molder. Cut samples from the molded sheets were exposed in a weatherometer and tested on Instron Tensile Tester for strength and elongation. Specially molded samples of the formulated polymers were subjected to Charpy Impact Tests. In the second study, preliminary evaluations of adhesives for improvement of bonding between Teflon and stainless steel (SS) were performed. Kapton, a high temperature polyimide made by Dupont, and a rubber based adhesive made by Potter Paint Co., were evaluated against industrial quality epoxy, the current material used to bond Teflon and SS. The degreased surfaces of the SS discs were etched mechanically, with a few of these etched chemically. The surfaces of the SS discs were etched mechanically, with a few of these etched chemically. Bonding strengths were evaluated using lap shear tests on the Instron Tensile Tester for the samples bonded by Kapton and industrial quality epoxy. Bond strengths were also evaluated using a pull test on the Instron for the samples bonded by Potter adhesive (CWL-152) and industrial quality epoxy. Based on limited lap shear data, Kapton gave bond strength favorable compared to that of industrial epoxy. Based on limited pull test data, Kapton bonded and CWL-152 bonded samples showed poor strength compared to epoxy bonded sample.
To explore high priority landing sites and to prepare for eventual human exploration, future Mars missions will involve rovers capable of traversing tens of kilometers. However, the current process by which scientists interact with a rover does not scale to such distances. Specifically, numerous command cycles are required to complete even simple tasks, such as, pointing the spectrometer at a variety of nearby rocks. In addition, the time required by scientists to interpret image data before new commands can be given and the limited amount of data that can be downlinked during a given command cycle constrain rover mobility and achievement of science goals. Experience with rover tests on Earth supports these concerns. As a result, traverses to science sites as identified in orbital images would require numerous science command cycles over a period of many weeks, months or even years, perhaps exceeding rover design life and other constraints. Autonomous onboard science analysis can address these problems in two ways. First, it will allow the rover to transmit only "interesting" images, defined as those likely to have higher science content. Second, the rover will be able to anticipate future commands. For example, a rover might autonomously acquire and return spectra of "interesting" rocks along with a high resolution image of those rocks in addition to returning the context images in which they were detected. Such approaches, coupled with appropriate navigational software, help to address both the data volume and command cycle bottlenecks that limit both rover mobility and science yield. We are developing fast, autonomous algorithms to enable such intelligent on-board decision making by spacecraft. Autonomous algorithms developed to date have the ability to identify rocks and layers in a scene, locate the horizon, and compress multi-spectral image data. Output from these algorithms could be used to autonomously obtain rock spectra, determine which images should be transmitted to the ground, or to aid in image compression. We will discuss these and other algorithms and demonstrate their performance during a recent rover field test.
To explore high priority landing sites and to prepare for eventual human exploration, future Mars missions will involve rovers capable of traversing tens of kilometers. However, the current process by which scientists interact with a rover does not scale to such distances. Specifically, numerous command cycles are required to complete even simple tasks, such as, pointing the spectrometer at a variety of nearby rocks. In addition, the time required by scientists to interpret image data before new commands can be given and the limited amount of data that can be downlinked during a given command cycle constrain rover mobility and achievement of science goals. Experience with rover tests on Earth supports these concerns. As a result, traverses to science sites as identified in orbital images would require numerous science command cycles over a period of many weeks, months or even years, perhaps exceeding rover design life and other constraints. Autonomous onboard science analysis can address these problems in two ways. First, it will allow the rover to preferentially transmit "interesting" images, defined as those likely to have higher science content. Second, the rover will be able to anticipate future commands. For example, a rover might autonomously acquire and return spectra of "interesting" rocks along with a high-resolution image of those rocks in addition to returning the context images in which they were detected. Such approaches, coupled with appropriate navigational software, help to address both the data volume and command cycle bottlenecks that limit both rover mobility and science yield. We are developing fast, autonomous algorithms to enable such intelligent on-board decision making by spacecraft. Autonomous algorithms developed to date have the ability to identify rocks and layers in a scene, locate the horizon, and compress multi-spectral image data. We are currently investigating the possibility of reconstructing a 3D surface from a sequence of images acquired by a robotic arm camera. This would then allow the return of a single completely in focus image constructed only from those portions of individual images that lie within the camera's depth of field. Output from these algorithms could be used to autonomously obtain rock spectra, determine which images should be transmitted to the ground, or to aid in image compression. We will discuss these algorithms and their performance during a recent rover field test.
A limitation has been identified in the existing test standards used for making controlled, two-body abrasion scratch measurements based solely on the width of the resultant score on the surface of the material. A new, more robust method is proposed for analyzing a surface scratch that takes into account the full three-dimensional profile of the displaced material. To accomplish this, a set of four volume displacement metrics are systematically defined by normalizing the overall surface profile to statistically denote the area of relevance, termed the Zone of Interaction (ZOI). From this baseline, depth of the trough and height of the ploughed material are factored into the overall deformation assessment. Proof of concept data were collected and analyzed to demonstrate the performance of this proposed methodology. This technique takes advantage of advanced imaging capabilities that now allow resolution of the scratched surface to be quantified in greater detail than was previously achievable. A quantified understanding of fundamental particle-material interaction is critical to anticipating how well components can withstand prolonged use in highly abrasive environments, specifically for our intended applications on the surface of the Moon and other planets or asteroids, as well as in similarly demanding, harsh terrestrial settings
The limited space flight data suggest that exposure to microgravity decreases muscle strength in humans and muscle mass in lower mammals. Several earth-based models have been used to address the effect of unloading on the human neuromuscular system due to the limited access of biological research to long-term space flight. Bedrest eliminates body weight bearing of both lower limbs. Unilateral lower limb suspension (ULLS), where all ambulatory activity is performed on crutches with an elevated sole on the shoe of one foot, has recently been used to unload one lower limb. The results from studies using these two models support their efficacy. The decrease in strength of m. quadriceps femoris, for example, after four to six weeks of bedrest, ULLS or space flight is 20 to 25 percent. The results from the earth-based studies show that this response can be attributed in part to a decrease in the cross-sectional area of the KE which reflects muscle fiber atrophy. The results from the ground based studies also support the limited flight data and show that reductions in strength are larger in lower than upper limbs and in extensor than flexor muscle groups. They also raise issue with the generally held concept that postural muscle is most affected by unweighting. Slow-twitch fibers in lower limb muscles of mixed fiber type composition and muscle composed mainly of slow-twitch fibers do not preferentially atrophy after bedrest or ULLS. Taken together, the data suggest that unloading causes remarkable adaptations in the neuromuscular system of humans. It should be appreciated, however, that this area of research is in its infancy.
For high photon-efficiency deep space or low power optical communications links, such as the Orion Artemis-2 Optical Communications System (O2O) project, the received optical signal is attenuated to the extent that single- photon detectors are required. For direct-detection receivers operating at 1.55 µm wavelength, single-photon detectors including Geiger-mode InGaAs avalanche photon diodes (APDs), and in particular superconducting nanowire single-photon detectors (SNSPDs) offer the highest sensitivity and fastest detection speeds. However, these photon detectors exhibit a recovery time between registered input pulses, effectively reducing the detection efficiency over the recovery interval, resulting in missed photon detections, reduced count rate, and ultimately limiting the achievable data rate. A method to overcome this limitation is to divide the received optical signal into multiple detectors in parallel. Here we analyze this approach for a receiver designed to receive a high photon efficiency serially concatenated pulse position modulation (SCPPM) input waveform. From measured count rate and efficiency data using commercial SNSPDs, we apply a model from which we determine the effective detection efficiency, or blocking loss, for different input signal rates. We analyze the scalability of adding detectors in parallel for different modulation orders and background levels to achieve desired data rates. Finally we show tradeoffs between the number of detectors and the required received optical power, useful for real link design considerations.