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NASA NTRS2011

Soft-Decision-Data Reshuffle to Mitigate Pulsed Radio Frequency Interference Impact on Low-Density-Parity-Check Code Performance

This presentation briefly discusses a research effort on mitigation techniques of pulsed radio frequency interference (RFI) on a Low-Density-Parity-Check (LDPC) code. This problem is of considerable interest in the context of providing reliable communications to the space vehicle which might suffer severe degradation due to pulsed RFI sources such as large radars. The LDPC code is one of modern forward-error-correction (FEC) codes which have the decoding performance to approach the Shannon Limit. The LDPC code studied here is the AR4JA (2048, 1024) code recommended by the Consultative Committee for Space Data Systems (CCSDS) and it has been chosen for some spacecraft design. Even though this code is designed as a powerful FEC code in the additive white Gaussian noise channel, simulation data and test results show that the performance of this LDPC decoder is severely degraded when exposed to the pulsed RFI specified in the spacecraft s transponder specifications. An analysis work (through modeling and simulation) has been conducted to evaluate the impact of the pulsed RFI and a few implemental techniques have been investigated to mitigate the pulsed RFI impact by reshuffling the soft-decision-data available at the input of the LDPC decoder. The simulation results show that the LDPC decoding performance of codeword error rate (CWER) under pulsed RFI can be improved up to four orders of magnitude through a simple soft-decision-data reshuffle scheme. This study reveals that an error floor of LDPC decoding performance appears around CWER=1E-4 when the proposed technique is applied to mitigate the pulsed RFI impact. The mechanism causing this error floor remains unknown, further investigation is necessary.

Ni, Jianjun David↗
NASA NTRS2021

Cryovolcanic flooding in Viking Terra on Pluto

A prominent fossa trough (Uncama Fossa) and adjacent 28-km diameter impact crater (Hardie) in Pluto's Viking Terra, as seen in the high-resolution images from the New Horizons spacecraft, show morphological evidence of in-filling with a material of uniform texture and red-brown color. A linear fissure parallel to the trough may be the source of a fountaining event yielding a cryoclastic deposit having the same composition and color properties as is found in the trough and crater. Spectral maps of this region with the New Horizons LEISA instrument reveal the spectral signature of H2O ice in these structures and in distributed patches in the adjacent terrain in Viking Terra. A detailed statistical analysis of the spectral maps shows that the colored H2O ice filling material also carries the 2.2-μm signature of an ammoniated component that may be an ammonia hydrate (NH3·nH2O) or an ammoniated salt. This paper advances the view that the crater and fossa trough have been flooded by a cryolava debouched from Pluto's interior along fault lines in the trough and in the floor of the impact crater. The now frozen cryolava consisted of liquid H2O infused with the red-brown pigment presumed to be a tholin, and one or more ammoniated compounds. Although the abundances of the pigment and ammoniated compounds entrained in, or possibly covering, the H2O ice are unknown, the strong spectral bands of the H2O ice are clearly visible. In consideration of the factors in Pluto's space environment that are known to destroy ammonia and ammonia-water mixtures, the age of the exposure is of order ≤109 years. Ammoniated salts may be more robust, and laboratory investigations of these compounds are needed.

Pluto↗
NASA NTRSDate not supplied

The BioMole Facility: Advancement of In Situ Microbiome Analysis for the International Space Station

Characterization of the International Space Station (ISS) microbiome has been enabled by sample return and Earth-based analysis. As human exploration pushes beyond low-Earth orbit, microbial-related crew health, planetary protection, and space research requires in situ capabilities. Steps toward reducing Earth-dependence for complex sample analysis began in 2016 with the amplification of DNA within the miniPCR thermal cycler and DNA sequencing with the MinION sequencer onboard the ISS; for both, samples were prepared on Earth. In 2017, these platforms synergistically enabled the in-situ identification of unknown bacteria collected and cultured from ISS surfaces, thereby shifting the paradigm that microbial cultures had to be returned to Earth. The following year, a culture-independent, swab-to-sequencer method further advanced spaceflight microbiology, demonstrating that culturing could be excluded and provided enhanced insight into the bacterial profile of ISS surfaces. Based on the success of these payloads in confirming the ability to meet crew health identification requirements and the benefits accompanying a culture-independent method, the BioMole Facility was established by the medical operations Crew Health Care Systems team. BioMole is the set of hardware, consumables, and procedures required to support sample preparation and nanopore sequencing onboard the ISS. BioMole goals include expanding sample sources, comparing data to previous methods, demonstrating onboard data analytics, and validating new hardware. To date, comparative surface analysis, molecular- and culture-based, has been completed. Additionally, the demonstration of a sample-to-answer process was achieved when BioMole data was processed onboard using the IBM Open Data and AI Edge software platform installed on the ISS-residing Spaceborne Computer-2. The taxonomic profiles generated from the edge analysis were as expected and paralleled that of the downlinked processed data. Future BioMole efforts involve microbial profiling of the ISS water system, ISS validation of the MinION Mk1C, and an expansion to a research facility available to investigators.

Sarah L. Castro-Wallace↗
NASA NTRS2013

Investigating the Origin of Chlorohydrocarbons Detected by the Sample Analysis at Mars (SAM) Instrument at Rocknest

The search for organic compounds on Mars, including molecules of either abiotic or biological origin is one of the key goals of the Mars Science Laboratory (MSL) mission. Previously the Viking and Phoenix Lander missions searched for organic compounds, but did not find any definitive evidence of martian organic material in the soils. The Viking pyrolysis gas chromatography mass spectrometry (GCMS) instruments did not detect any organic compounds of martian or exogenous origin above a level of a few parts-per-billion (ppb) in the near surface regolith at either landing site [1]. Viking did detect chloromethane and dichloromethane at pmol levels (up to 40 ppb) after heating the soil samples up to 500 C (Table 1), although it was originally argued that the chlorohydrocarbons were derived from cleaning solvents used on the instrument hardware, and not from the soil samples themselves [1]. More recently, it was suggested that the chlorohydrocarbons detected by Viking may have been formed by oxidation of indigenous organic matter during pyrolysis of the soil in the presence of perchlorates [2]. Although it is unknown if the Viking soils contained perchlorates, Phoenix did reveal relatively high concentrations (~0.6 wt%) of perchlorate salt in the icy regolith [3], therefore, it is possible that the chlorohydrocarbons detected by Viking were produced, at least partially, during the experiments [2,4]. The Sample Analysis at Mars (SAM) instrument suite on MSL analyzed the organic composition of the soil at Rocknest in Gale Crater using a combination of pyrolysis evolved gas analysis (EGA) and GCMS. One empty cup procedural blank followed by multiple EGA-GCMS analyses of the Rocknest soil were carried out. Here we will discuss the results from these SAM measurements at Rocknest and the steps taken to determine the source of the chlorohydrocarbons.

Glavin, D.↗
NASA NTRS2015

An Empirical State Error Covariance Matrix Orbit Determination Example

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance is suspect. In its most straight forward form, the technique only requires supplemental calculations to be added to existing batch estimation algorithms. In the current problem being studied a truth model making use of gravity with spherical, J2 and J4 terms plus a standard exponential type atmosphere with simple diurnal and random walk components is used. The ability of the empirical state error covariance matrix to account for errors is investigated under four scenarios during orbit estimation. These scenarios are: exact modeling under known measurement errors, exact modeling under corrupted measurement errors, inexact modeling under known measurement errors, and inexact modeling under corrupted measurement errors. For this problem a simple analog of a distributed space surveillance network is used. The sensors in this network make only range measurements and with simple normally distributed measurement errors. The sensors are assumed to have full horizon to horizon viewing at any azimuth. For definiteness, an orbit at the approximate altitude and inclination of the International Space Station is used for the study. The comparison analyses of the data involve only total vectors. No investigation of specific orbital elements is undertaken. The total vector analyses will look at the chisquare values of the error in the difference between the estimated state and the true modeled state using both the empirical and theoretical error covariance matrices for each of scenario.

Frisbee, Joseph H., Jr.↗
NASA NTRSDate not supplied

Small Particle Glovebox Experiments - Preliminary Results

A substantial portion of the asteroid samples returned by the HAYABUSA2 and OSIRIS-REx missions will consist of small (submm) components (due to the collection of those particles on the asteroid surface and due to the fragmenting of larger, friable material post-collection). In order to minimize the alteration/oxidation of asteroid regolith, the bulk collections will be stored and curated in nitrogen purged gloveboxes. The processing of small particles in an N2 glovebox will present challenges that are different from those experienced during lunar and meteorite sample processing. Particles in this size range are susceptible to unpredictable electrostatic charging that can result in sample loss during processing operations. Methods for the handling of sub-mm particles have been well developed for environments with ambient atmospheric conditions and relative humidity (RH) ranges between 40-70%. In such conditions, a number of factors can be successfully employed to minimize the effects of triboelectric charging, including the use of Po-210 sources that neutralize excess charge and the utilization of conductive manipulation tools and sample substrates. However, relative humidity levels above 40% also contribute significantly to dissipation of triboelectric effects. We had not previously investigated our charge mitigation methods in a completely dry, nitrogen-purged environment, or whether they would be sufficient in enabling the successful processing of sub-mm samples. Current glovebox configurations in use in our lunar and meteorite curation laboratories are optimized for the processing of macroscopic samples and tools; these glovebox designs are likely unsuitable for the processing of collections for which the bulk collection is comprised of sub-mm components. Small particle sample preparation requires the use of an optical magnification instrument – typically a stereo binocular microscope with at least 20x magnification. Current glovebox designs are not optimized for the utilization or integration of stereo microscopes; while many current cabinets include microscope viewports that enable the use of small, externally mounted stereo microscopes, the focusing methods (usually involving the use of a lab jack) lack the fidelity and precision required for small particle manipulation and imaging. Working distances of higher (>50x magnification) objective lenses may preclude the external use of a stereo microscope through a viewport; in order to successfully manipulate and image very small (< 20m) particles, stereo and digital microscope systems that are integrated within the glovebox should be investigated. Finally, ergonomic considerations for small particle work within a glovebox must be considered to minimize risk of injury to sample processors. In order to investigate some of the unknown parameters relating to small particle processing within an N2 glovebox, we conducted preliminary, qualitative experiments utilizing a small lunar cabinet that was originally used for film development.

C. J. Snead↗
NASA NTRS2011

An Empirical State Error Covariance Matrix for Batch State Estimation

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).

Frisbee, Joseph H., Jr.↗
NASA NTRS2008

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Aerosols and especially their effect on clouds are one of the key components of the climate system and the hydrological cycle [Ramanathan et al., 2001]. Yet, the aerosol effect on clouds remains largely unknown and the processes involved not well understood. A recent report published by the National Academy of Science states "The greatest uncertainty about the aerosol climate forcing - indeed, the largest of all the uncertainties about global climate forcing - is probably the indirect effect of aerosols on clouds [NRC, 2001]." The aerosol effect on clouds is often categorized into the traditional "first indirect (i.e., Twomey)" effect on the cloud droplet sizes for a constant liquid water path [Twomey, 1977] and the "semi-direct" effect on cloud coverage [e.g., Ackerman et al ., 2001]." Enhanced aerosol concentrations can also suppress warm rain processes by producing a narrow droplet spectrum that inhibits collision and coalescence processes [e.g., Squires and Twomey, 1961; Warner and Twomey, 1967; Warner, 1968; Rosenfeld, 19991. The aerosol effect on precipitation processes, also known as the second type of aerosol indirect effect [Albrecht, 1989], is even more complex, especially for mixed-phase convective clouds. Table 1 summarizes the key observational studies identifying the microphysical properties, cloud characteristics, thermodynamics and dynamics associated with cloud systems from high-aerosol continental environments. For example, atmospheric aerosol concentrations can influence cloud droplet size distributions, warm-rain process, cold-rain process, cloud-top height, the depth of the mixed phase region, and occurrence of lightning. In addition, high aerosol concentrations in urban environments could affect precipitation variability by providing an enhanced source of cloud condensation nuclei (CCN). Hypotheses have been developed to explain the effect of urban regions on convection and precipitation [van den Heever and Cotton, 2007 and Shepherd, 2005]. Please see Tao et al. (2007) for more detailed description on aerosol impact on precipitation. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

Tao, Wei-Kuo↗
NASA NTRS2003

The Galileo Probe: How it Has Changed Our Understanding of Jupiter

The Galileo Mission to Jupiter, which arrived in December of 1995, provided the first study by an orbiter, and the first in-situ sampling via an entry probe, of an outer planet atmosphere. The rationale for an entry probe is that, even from an orbiter, remote sensing of the jovian atmosphere could not adequately retrieve the information desired. This paper provides a current summary of the most significant aspects of the data returned from the Galileo entry probe. As a result of the probe measurements, there has been a reassessment of our understanding of outer planet formation and evolution of the solar system. The primary scientific objective of the Galileo probe was to determine the composition of the jovian atmosphere, which from remote sensing remained either very uncertain, or completely unknown, with respect to several key elements. The probe found that the global He mass fraction is. significantly above the value reported from the Voyager Jupiter flybys but is slightly below the protosolar value, implying that there has been some settling of He to the deep jovian interior. The probe He measurements have also led to a reevaluation of the Voyager He mass fraction for Saturn, which is now determined to be much closer to that of Jupiter. The elements C, N, S, Ar, Kr, Xe were all found to have global abundances approximately 3 times their respective solar abundances. This result has raised a number of fundamental issues with regard to properties of planetesimals and the solar nebula at the time of giant planet formation. Ne, on the other hand, was found to be highly depleted, probably as the result of it being carried along with helium as helium settles towards the deep interior. The global abundance of O was not obtained by the probe because of the influence of local processes at the probe entry site (PES), processes which depleted condensible species, in this case H2O, well below condensation levels. Other condensible species, namely NH3 and H2S, were similarly affected but attained their deep equilibrium mixing ratios before the maximum depth sampled by the probe. Processes that might be capable of producing such effects on the condensibles are still under investigation. Measured isotopic ratios of noble gases and other heavy elements are solar, and (D + (Sup 3)He)/H is the same to within measurement uncertainties as in the local interstellar medium. No thick clouds were detected, and in particular no significant water cloud, but the PES location clearly affected the probe measurements of clouds. In fact, the probe data must be understood in the context of the location of the PES, which was within what is termed a 5 micron hot spot, a local clearing in the clouds that is bright near the 5 microns spectral region. The thermal structure at the PES was determined from approximately 1000 km above the 1 bar pressure level (10(exp -9 bars)) to 132 km 1 bar (22bars). The probe showed the atmosphere to have a generally sub-adiabatic temperature gradient (static stability) of = 0.1 K/km to as deep as the probe made measurements. In the upper atmosphere the probe derived a maximum positive vertical temperature gradient of approximately 5 K/km, and maximum temperature of = 900 K. The energy sources producing the warm upper atmosphere have yet to be completely identified. At first glance, Doppler tracking of the probe indicates that the long observed cloud level zonal winds extend to levels at least as deep as the probe made measurements. Zonal wind increases from = 80 m/s at pressures less than a bar to about 180 m/s near 5 bars, and remains approximately constant with depth thereafter. However, there is a question as to whether the winds measured from probe tracking are representative of the general wind field, or are considerably influenced by localized winds associated with the PES.

Young, Richard E.↗