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NASA NTRS2008

Dynamic Method for Identifying Collected Sample Mass

G-Sample is designed for sample collection missions to identify the presence and quantity of sample material gathered by spacecraft equipped with end effectors. The software method uses a maximum-likelihood estimator to identify the collected sample's mass based on onboard force-sensor measurements, thruster firings, and a dynamics model of the spacecraft. This makes sample mass identification a computation rather than a process requiring additional hardware. Simulation examples of G-Sample are provided for spacecraft model configurations with a sample collection device mounted on the end of an extended boom. In the absence of thrust knowledge errors, the results indicate that G-Sample can identify the amount of collected sample mass to within 10 grams (with 95-percent confidence) by using a force sensor with a noise and quantization floor of 50 micrometers. These results hold even in the presence of realistic parametric uncertainty in actual spacecraft inertia, center-of-mass offset, and first flexibility modes. Thrust profile knowledge is shown to be a dominant sensitivity for G-Sample, entering in a nearly one-to-one relationship with the final mass estimation error. This means thrust profiles should be well characterized with onboard accelerometers prior to sample collection. An overall sample-mass estimation error budget has been developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Carson, John↗
NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗
NASA NTRS2000

North Atlantic Aerosol Single Scattering Albedos: TARFOX and ACE-2 Results and Their Relation to Radiative Effects Derived from Satellite Optical Depths

Bergstrom and Russell estimated direct solar radiative flux changes caused by atmospheric aerosols over the mid-latitude North Atlantic Ocean under cloud-free and cloudy conditions. They excluded African dust aerosols, primarily by restricting calculations to latitudes 25-60 N. As inputs they used midvisible aerosol optical depth (AOD) maps derived from AVHRR satellite measurements and aerosol intensive properties determined primarily in the 1996 IGAC Troposheric Aerosol Radiative Forcing Observational Experiment (TARFOX). Those aerosol intensive properties, which included optical depth wavelength dependence and spectra of single scattering albedo (SSA) and scattering asymmetry parameter, were also checked against initial properties from the 1997 North Atlantic Aerosol Characterization Experiment (ACE 2). Bergstrom and Russell investigated the sensitivity of their derived flux changes to assumed input parameters, including midvisible AOD, SSA, and scattering asymmetry parameter. Although the sensitivity of net flux change at the tropopause to SSA was moderate over the ocean (e.g., a SSA uncertainty of 0.07 produced a flux-change uncertainty of 21%), the sensitivity over common land surfaces can be much larger. Also, flux changes within and below the aerosol layer, which affect atmospheric stability, heating rates, and cloud formation and persistence, are quite sensitive to aerosol SSA. Therefore, this paper focuses on the question: "What have we learned from TARFOX and ACE 2 regarding aerosol single scattering albedo?" Three techniques were used in TARFOX to determine midvisible SSA. One of these derived SSA as a best-fit parameter in comparing radiative flux changes measured by airborne pyranometer to those computed from aerosol properties. Another technique combined airborne measurements of aerosol scattering and absorption by nephelometer and absorption photometer. A third technique obtained SSA from best-fit complex refractive indices derived by comparing vertical profiles of lidar backscatter, sunphotometer extinction, and relative size distribution. In ACE 2 midvisible SSA was determined both as a best-fit parameter in comparing measured and calculated flux changes at the surface and by combining nephelometer and absorption photometer measurements. The nephelometer/absorption-photometer results were obtained on the ACE 2 ship (10 m asl), at the Sagres, Portugal site at 50 m asl, and also on the Pelican aircraft. This paper presents and compares the TARFOX and ACE 2 SSA results from the above techniques for different situations (e.g., marine vs continental flows, "clean" vs polluted conditions). It also discusses the strengths and limitations of the techniques, including whether they describe the aerosol in its ambient state or as perturbed by sampling processes; whether they describe the aerosol at the surface, as a function of altitude, or integrated over a column; the ease of acquiring representative data sets; results obtained in tests of consistency with radiative flux changes, and the likelihood of various artifacts and errors.

Russell, P. B.↗
NASA NTRS2012

Fischer-Tropsch Cobalt Catalyst Improvements with the Presence of TiO2, La2O3, and ZrO2 on an Alumina Support

The objective of this study was to evaluate the effect of titanium oxide, lanthanum oxide, and zirconium oxide on alumina supported cobalt catalysts. The hypothesis was that the presence of lanthanum oxide, titanium oxide, and zirconium oxide would reduce the interaction between cobalt and the alumina support. This was of interest because an optimized weakened interaction could lead to the most advantageous cobalt dispersion, particle size, and reducibility. The presence of these oxides on the support were investigated using a wide range of characterization techniques such as SEM, nitrogen adsorption, x-ray diffraction (XRD), temperature programmed reduction (TPR), temperature programmed reduction after reduction (TPR-AR), and hydrogen chemisorptions/pulse reoxidation. Results indicated that both La2O3 and TiO2 doped supports facilitated the reduction of cobalt oxide species in reference to pure alumina supported cobalt catalysts, however further investigation is needed to determine the effect of ZrO2 on the reduction profile. Results showed an increased corrected cluster size for all three doped supported catalysts in comparison to their reference catalysts. The increase in reduction and an increase in the cluster size led to the conclusion that the support-metal interaction weakened by the addition of TiO2 and La2O3. It is also likely that the interaction decreased upon presence of ZrO2 on the alumina, but further research is necessary. Preliminary results have indicated that the alumina-supported catalysts with titanium oxide and lanthanum oxide present are of interest because of the weakened cobalt support interaction. These catalysts showed an increased extent of reduction, therefore more metallic cobalt is present on the support. However, whether or not there is more cobalt available to participate in the Fischer-Tropsch synthesis reaction (cobalt surface atoms) depends also on the cluster size. On one hand, increasing cluster size alone tends to decrease the active site density; on the other hand, by increasing the size of the cobalt clusters, there is less likelihood of forming oxidized cobalt complexes (cobalt aluminate) during Fischer-Tropsch synthesis. Thus, from the standpoint of stability, improving the extent of reduction while increasing the particle size slightly may be beneficial for maintaining the sites, even if there is a slight decrease in overall initial active site density.

Klettlinger, Jennifer Lindsey Suder↗
NASA NTRS2009

Motion Cueing Algorithm Modification for Improved Turbulence Simulation

Atmospheric turbulence cueing produced by flight simulator motion systems has been less than satisfactory because the turbulence profiles have been attenuated by the motion cueing algorithms. Cardullo and Ellor initially addressed this problem by directly porting the turbulence model output to the motion system. Reid and Robinson addressed the problem by employing a parallel aircraft model, which is only stimulated by the turbulence inputs and adding a filter specially designed to pass the higher turbulence frequencies. There have been advances in motion cueing algorithm development at the Man-Machine Systems Laboratory, at SUNY Binghamton. In particular, the system used to generate turbulence cues has been studied. The Reid approach, implemented by Telban and Cardullo, was employed to augment the optimal motion cueing algorithm installed at the NASA LaRC Simulation Laboratory, driving the Visual Motion Simulator. In this implementation, the output of the primary flight channel was added to the output of the turbulence channel and then sent through a non-linear cueing filter. The cueing filter is an adaptive filter; therefore, it is not desirable for the output of the turbulence channel to be augmented by this type of filter. The likelihood of the signal becoming divergent was also an issue in this design. After testing on-site it became apparent that the architecture of the turbulence algorithm was generating unacceptable cues. As mentioned above, this cueing algorithm comprised a filter that was designed to operate at low bandwidth. Therefore, the turbulence was also filtered, augmenting the cues generated by the model. If any filtering is to be done to the turbulence, it will utilize a filter with a much higher bandwidth, above the frequencies produced by the aircraft response to turbulence. The authors have developed an implementation wherein only the signal from the primary flight channel passes through the nonlinear cueing filter. This paper discusses three new algorithms. Testing shows that the new methods provide the pilot with a more realistic sensation of turbulence; the cues are not attenuated by algorithm. Results of offline testing show the credibility of the models. Offline test verification was based primarily on the evaluation of the power spectral density of the outputs and the time response.

Ercole, Anthony V.↗
NASA NTRSDate not supplied

Validation of Multisystem Countermeasures Protocol for Spaceflight during Antarctica Winter-over at Palmer Station (Palmer Countermeasures)

Stressors associated with spaceflight induce persistent immune compromise in astronauts which increase subclinical latent virus reactivation. In select crews, adverse clinical events have been documented. Antarctica winter-over (AWO) mission most closely reproduces these mission stressors: prolonged deployment, extreme environment, circadian misalignment, isolation, station lifestyle, and personal risk. The US maintains three primary stations in Antarctica: South Pole Station, McMurdo, and Palmer. Previous studies suggest that stations located near the interior of Antarctica (South Pole, McMurdo) have confounding effects on the immune system due to persistent hypobaric hypoxia. We hypothesized that winter-over at a coastal station (Palmer) would be more akin to spaceflight due to its normoxic but still extreme environment. Therefore, AWO at Palmer Station was selected, and validated in a pilot study [2], as the platform for testing and validating the effectiveness of an immune-restorative countermeasure protocol designed for deep space missions. Specifics include diet modifications, nutritional supplementation (vitamin D, probiotic, etc.), prescribed aerobic and resistive exercise, and a protocol of stress relieving virtual reality exercises. A multitude of biological sample types, including blood, saliva, and hair will be collected in tandem with the countermeasures in order to examine the combined effectiveness of the countermeasures. Samples and logs from subjects will be transported from Palmer Station to Johnson Space Center for further processing and distribution to co-investigators at the end of each winter-over. Extracted samples will be analyzed by appropriate testing platforms (Multiplex, qPCR, ELISA, etc.) to monitor alterations in leukocyte distribution, T cell and NK function, cytokine profiles, reactivation of latent herpesviruses, and nutritional factors. The data collected will be compared to a control year in which no countermeasures were deployed to evaluate the overall effectiveness of the analog and to validate the candidate immune countermeasure strategy. AWO 2023 concluded with the 4th in-mission timepoint conducted in September 2023. Samples for 16 subjects, including blood, saliva, hair, surveys, and PCR data, were all successfully returned from Antarctica to NASA/JSC mid-November 2023. Samples have since been distributed to co-investigators for further processing and analysis. With the completion of the first countermeasure year, preliminary data on the effectiveness of the deep-space protocol is being evaluated, however, no conclusions can be drawn yet until the completion of the second AWO countermeasure year, AWO 2024. AWO 2024 commenced in late-March 2024, with 13 subjects consenting and performing their baseline data collections (BDCs). Unique to the 2024 deployment, NSF lifted certain COVID restrictions and rallied the crewmembers in Punta Arenas, Chile. All NSF activities were transferred to this location and NASA was allowed, for the first time, to perform consent briefings, baseline samplings and training in person. This augment greatly increased the likelihood of success for the overwinter activities.

Cody L Gutierrez↗
NASA NTRSDate not supplied

Validation of Multisystem Countermeasures Protocol for Spaceflight during Antarctica Winter-over at Palmer Station (Palmer Countermeasures)

Stressors associated with spaceflight induce persistent immune compromise in astronauts which increase subclinical latent virus reactivation. In select crews, adverse clinical events have been documented. Antarctica winter-over (AWO) mission most closely reproduces these mission stressors: prolonged deployment, extreme environment, circadian misalignment, isolation, station lifestyle, and personal risk. The US maintains three primary stations in Antarctica: South Pole Station, McMurdo, and Palmer. Previous studies suggest that stations located near the interior of Antarctica (South Pole, McMurdo) have confounding effects on the immune system due to persistent hypobaric hypoxia. We hypothesized that winter-over at a coastal station (Palmer) would be more akin to spaceflight due to its normoxic but still extreme environment. Therefore, AWO at Palmer Station was selected, and validated in a pilot study, as the platform for testing and validating the effectiveness of an immune-restorative countermeasure protocol designed for deep space missions. Specifics include diet modifications, nutritional supplementation (vitamin D, probiotic, etc.), prescribed aerobic and resistive exercise, and a protocol of stress relieving virtual reality exercises. A multitude of biological sample types, including blood, saliva, and hair will be collected in tandem with the countermeasures in order to examine the combined effectiveness of the countermeasures. Samples and logs from subjects will be transported from Palmer Station to Johnson Space Center for further processing and distribution to co-investigators at the end of each winter-over. Extracted samples will be analyzed by appropriate testing platforms (Multiplex, qPCR, ELISA, etc.) to monitor alterations in leukocyte distribution, T cell and NK function, cytokine profiles, reactivation of latent herpesviruses, and nutritional factors. The data collected will be compared to a control year in which no countermeasures were deployed to evaluate the overall effectiveness of the analog and to validate the candidate immune countermeasure strategy. AWO 2023 concluded with the 4th in-mission timepoint conducted in September 2023. Samples for 16 subjects, including blood, saliva, hair, surveys, and PCR data, were all successfully returned from Antarctica to NASA/JSC mid-November 2023. Samples have since been distributed to co-investigators for further processing and analysis. With the completion of the first countermeasure year, preliminary data on the effectiveness of the deep-space protocol is being evaluated, however, no conclusions can be drawn yet until the completion of the second AWO countermeasure year, AWO 2024. AWO 2024 commenced in late-March 2024, with 13 subjects consenting and performing their baseline data collections (BDCs). Unique to the 2024 deployment, NSF lifted certain COVID restrictions and rallied the crewmembers in Punta Arenas, Chile. All NSF activities were transferred to this location and NASA was allowed, for the first time, to perform consent briefings, baseline samplings and training in person. This augment greatly increased the likelihood of success for the overwinter activities.

Cody L Gutierrez↗
NASA NTRS2005

Raman Lidar Measurements during the International HZO Project: Instrumentation and Analysis Techniques, Popular Summary - 1

The amount of water vapor in the atmosphere helps to determine the likelihood that severe storms may develop. The concentration of water vapor, though, is highly variable in space and time. And yet small changes in water vapor concentration over a short period of time or over a short spatial distance can determine whether a storm may or may not develop. Therefore, in order to improve the ability to forecast severe weather such as thunderstorms it is important to measure water vapor in the atmosphere with high spatial and temporal resolution. One of the most attractive research tools for measuring water vapor in the atmosphere with high spatial and temporal resolution is a Raman lidar. A Raman lidar consists of a laser transmitter, a telescope receiver and optics and electronics for processing opticand electronic signals. A laser pulse is emitted into the atmosphere and it interacts with molecules in the atmosphere causing them to become excited and to emit, through the Raman process, photons of different wavelength than emitted by the laser. The molecule that emitted these emitted. This is the way that a Raman lidar identifies water vapor molecules in the atmosphere. can be identified based on the wavelength of the photons One of the great challenges in Raman lidar measurements has been to make useful daytime measurements of the water vapor profile under bright daytime conditions. In this first of two papers, we describe the instrumentation and analysis of the first documented Raman lidar that is able to measure water vapor in the daytime with sufficient quality to permit the study of developing storm systems.

Whiteman, D. N.↗
NASA NTRS1993

Enhanced decoding for the Galileo S-band mission

A coding system under consideration for the Galileo S-band low-gain antenna mission is a concatenated system using a variable redundancy Reed-Solomon outer code and a (14,1/4) convolutional inner code. The 8-bit Reed-Solomon symbols are interleaved to depth 8, and the eight 255-symbol codewords in each interleaved block have redundancies 64, 20, 20, 20, 64, 20, 20, and 20, respectively (or equivalently, the codewords have 191, 235, 235, 235, 191, 235, 235, and 235 8-bit information symbols, respectively). This concatenated code is to be decoded by an enhanced decoder that utilizes a maximum likelihood (Viterbi) convolutional decoder; a Reed Solomon decoder capable of processing erasures; an algorithm for declaring erasures in undecoded codewords based on known erroneous symbols in neighboring decodable words; a second Viterbi decoding operation (redecoding) constrained to follow only paths consistent with the known symbols from previously decodable Reed-Solomon codewords; and a second Reed-Solomon decoding operation using the output from the Viterbi redecoder and additional erasure declarations to the extent possible. It is estimated that this code and decoder can achieve a decoded bit error rate of 1 x 10(exp 7) at a concatenated code signal-to-noise ratio of 0.76 dB. By comparison, a threshold of 1.17 dB is required for a baseline coding system consisting of the same (14,1/4) convolutional code, a (255,223) Reed-Solomon code with constant redundancy 32 also interleaved to depth 8, a one-pass Viterbi decoder, and a Reed Solomon decoder incapable of declaring or utilizing erasures. The relative gain of the enhanced system is thus 0.41 dB. It is predicted from analysis based on an assumption of infinite interleaving that the coding gain could be further improved by approximately 0.2 dB if four stages of Viterbi decoding and four levels of Reed-Solomon redundancy are permitted. Confirmation of this effect and specification of the optimum four-level redundancy profile for depth-8 interleaving is currently being done.

Dolinar, S.↗
NASA NTRS1994

Long river profiles, tectonism, and eustasy: A guide to interpreting fluvial terraces

Along three rivers at the Mendocino triple junction, northern California, strath, cut, and fill terraces have formed in response to tectonic and eustatic processes. Detailed surveying and radiometric dating at multiple sites indicate that lower reaches of the rivers are dominated by the effects of oscillating sea level, primarily aggradation and formation of fill terraces during sea level high stands, alternating with deep incision during low stands. A eustasy-driven depositional wedge extends tens of kilometers upstream on all rivers (tapering to zero thickness). This distance is greater than expected from studies of the effects of check dams on much smaller streams elsewhere, due in part to the large size of these rivers. However, the change in gradient is nearly identical to other base level rise studies: the depositional gradient is about half that of the original channel. Middle to upper reaches of each river are dominated by the effects of long-term uplift, primarily lateral and vertical erosion and formation of steep, unpaired strath terraces exposed only upstream of the depositional wedge. Vertical incision at a rate similar to that of uplift has occurred even during the present sea level high stand along rivers with highest uplift rates. Strath terraces have steeper gradients than the modern channel bed and do not merge with marine terraces at the river mouth; consequently, they cannot be used to determine altitudes of sea level high stands. Strath formation is a continuous process of response to long-term uplift, and its occurrence varies spatially along a river depending on stream power, and hence position, upstream. Strath terraces are found only along certain parts of a coastal stream: upstream of the aggradational effects of oscillating sea level, and far enough downstream that stream power is in excess of that needed to transport the prevailing sediment load. For a given size river, the greater the uplift rate, the greater the rate of vertical incision and, consequently, the less the likelihood of strath terrace formation and preservation.

Merritts, Dorothy J.↗
NASA NTRSDate not supplied

Adding GPU Support to the Markov Chain Monte Carlo Code Catmip

In geophysics, we are confronted with many under-determined inverse problems. For example, all of our observations of earthquakes are made at the Earth’s surface. So, when we try to infer how slip during an earthquake evolves in space and time, we find that there are many potential slip histories that are consistent with our limited observations and our understanding of earthquake physics. One way to approach these problems is with Bayesian analysis which allows us to infer the ensemble of all potential slip models that satisfy the observations and our prior knowledge of earthquake physics. In Bayesian analysis, our prior knowledge is known as the prior probability density function or prior PDF, the fit to the data is known as the data likelihood, and the target PDF that satisfies both the prior PDF and data likelihood is known as the posterior PDF. However, simulating the posterior PDF typically requires using Markov Chain Monte Carlo (MCMC) to draw tens of billions of random realizations of earthquake slip models, which may not be computationally feasible. To make this and similar geophysical inversions computationally tractable, we developed the Cascading Adaptive Transitional Metropolis In Parallel (CATMIP) algorithm. CATMIP is an efficient parallel Markov Chain Monte Carlo (MCMC) sampler that is used for model fitting and uncertainty quantification in geophysics. Example use cases are earthquake rupture modeling, determining mineral composition on Mars, reconstructing the history of ocean salinity, and historical earthquake relocation. CATMIP employs many parallel instances of the Metropolis algorithm for sampling in a transitioning framework. Transitioning is a process in which a set of random samples at equilibrium with a known probability density function (PDF) are used as seeds for the Markov chains to sample successive target PDFs that incrementally move the distribution from the starting seeds to the final desired PDF that describes the relative plausibility of potential values for the model parameters. The algorithm is implemented as a Master-Worker model employing MPI for communication. The worker processes are loosely coupled with global parameters periodically optimized by the master process. This provides a very high amount of parallelism with little communication between updates. During the presentation we will discuss the history of the algorithm and elaborate the earthquake rupture modeling use case for the CATMIP package. Our first step toward GPU optimization was to optimize the code for the CPU. CPU profiling revealed that most of the compute time is spent in calls to level 2 BLAS routines and calls to GSL random number generators. We revised the algorithm to employ level 3 BLAS routines instead. In our presentation we will describe how this was accomplished. Adding GPU support to CATMIP consisted mostly of replacing the calls to GSL with calls to GPU vendor-provided library routines. A small number of loops were directly implemented in CUDA. In the presentation will provide implementation details. Finally, we will discuss methods for profiling and opportunities for further optimizing GPU execution. By creating a code with the flexibility to run on either a CPU or GPU architecture, CATMIP can be used on systems ranging from large CPU-based HPC environments to single servers with GPU acceleration and everything in between.

HECC↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
NASA NTRS2019

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

NASA’s Traffic Aware Planner (TAP) software is a research-prototype decision support tool that provides pilots with time- and fuel-saving route recommendations that optimize their current trajectory. The software runs on a first-of-a-kind system architecture onboard three aircraft in revenue service conducting operational evaluations with a major domestic airline. Therefore, significant NASA-internal testing is required prior to releasing the software to the partner airline. This paper describes a flight test plan that exercises the functionality of the TAP software in a representative operational environment, describes the system architecture developed and implemented for the NASA Langley HU-25A Guardian aircraft to support the test objectives, presents outcomes of the flight test campaign, and discusses use cases that demonstrate the value of flight testing for this activity.Research into flight path optimization of transport aircraft conducted by the National Aeronautics and SpaceAdministration (NASA) has produced an operational concept known as Traffic Aware Strategic Aircrew Requests(TASAR) [1, 2]. This near-term concept [3] provides the aircrew with a flight deck decision support tool known asthe Traffic Aware Planner (TAP). The TAP software leverages a growing number of information sources on the flightdeck to make time- and fuel-saving route optimization recommendations to the aircrew while en route. The aircrewcan then use the suggestions provided by the tool to make route change requests with a greater likelihood of acceptanceby air traffic control (ATC). Since TASAR is a concept intended for the current operational environment, it isintentionally designed to have no safety-critical impact or require any changes to current Federal AviationAdministration (FAA) rules and procedures [4, 5].The research prototype TAP system [6–8], explained further in Section III.C, continually incorporates up-to-dateaircraft state data from onboard avionics, as well as the latest position of surrounding traffic, the most recent windforecast, and the most recent convective weather forecast, in order to calculate candidate trajectory modifications thatimprove upon the current active route. These trajectories account for user-selectable objective functions [3] of reducedfuel burn, reduced flight time, or an airline-derived combination of factors known as trip cost. Previous analyses andsimulations have estimated substantial savings for airlines employing this technique within the U.S. National AirspaceSystem (NAS) [9–11]. Operational evaluations with Alaska Airlines seek to validate these projected benefits usingmeasured data while simultaneously providing benefits to the airline [12, 13].The TAP software has undergone a number of human-in-the-loop simulations [14] and flight test activities[15–17] in order to validate the operational concept, evaluate human factors considerations (e.g., workload, usability,distraction, etc.), and to assess the ability of the software to function in a representative operational environment (e.g.,connected to live avionics data, using in-flight internet connectivity, etc.). However, these simulations and flight testcampaigns did not account for the hardware architecture implemented on the three aircraft for Alaska Airlines’operational evaluations of the TAP software. Therefore, a need was identified to thoroughly test the functionality ofthe software in a similar hardware architecture to that of the partner airline’s aircraft. Information regarding testapparatus and environments used to evaluate TAP prior to testing on the HU-25A can be found in reference [18].A campaign of flight trials on a NASA aircraft, the HU-25A Guardian, was conducted to ensure that the researchprototype TAP system functions well in a configuration similar to the Alaska Airlines aircraft prior to deployment.This airborne, networked environment enables an assessment of the operational factors unique to the flight environment. Additionally, this activity evaluated the effectiveness and benefit of new TAP functionality andoperation in a relevant flight environment while allowing the rapid prototyping of new concepts and features.This paper is organized as follows: Section II discusses the details of the flight test plan, flight profiles, and theduties of personnel involved with conducting flight operations. Section III describes the test platform, avionicsequipage, and system architecture. Section IV presents a discussion of results, and Section V contains concludingremarks.

Underwood, Matthew C.↗
NASA NTRSDate not supplied

Setting the Bar for the Replacement of the Probability of Collision Metric

To date, satellite conjunction assessment (CA) risk analysis has largely embraced the probability of collision (Pc) as the omnibus metric to evaluate collision likelihood, and its use in such assessments has mostly been straightforward: at the point at which a conjunction mitigation decision is required, the calculated Pc is compared to a threshold; and if the calculated Pc exceeds that threshold, then a mitigation action is warranted. With only minor variation, this approach is employed by major CA risk assessment centers (e.g., NASA, EUSST, CNES, JAXA) and is advanced as the preferred method in the published CA best practices handbooks. Despite this near unanimity of operational practice, there is a major strain of secondary literature critical of the Pc and willing to propose alternatives. Alfano (2005) pointed out the ability of the Pc to underrepresent the risk in certain situations and counselled a maximum Pc construct. Carpenter (2017, 2019) reiterated this criticism and proposed using instead a confidence interval on the miss distance. Balch et al. (2019) identified what they argued was a defect in the entire Bayesian Pc construct and believed that the use of a more conservative methodology based on covariance ellipsoid overlap was necessary. Delande (2022) introduced the framework of collision “plausibility” to the risk assessment process and sketched out how this might be used operationally. Elkantassi (2022) published a full development of the miss distance confidence interval approach and applied it to several worked examples. While these different approaches to collision risk assessment do differ in their details, they all converge on two central points: first, the Pc’s failure to give an adequate expression of the risk in dilution region situations is a fatal flaw; and second, a conjunction should be presumed risky and in need of mitigation until the evidence of the situation can establish otherwise. These criticisms, if correct, would counsel a number of modifications to current CA operational practice; as such, they force a re-examination of fundamental aspects of the CA problem, including the following: 1. Is the CA risk assessment a probability problem, a statistics problem, or something else? 2. If it is a statistics problem, does it lend itself naturally to a hypothesis test construction? 3. If it can be construed as a hypothesis test, what form should the null hypothesis take, to wit: what constraints exist on the choice of the null hypothesis, what selections are in best alignment with all of the attendant parameters of the problem, and what is implied philosophically by different choices? 4. What are the implications of using the different proposed risk assessment parameters for CA? This question should be answered both in determining how frequently the dilution region situation cited by the critics of the Pc actually appears in an operationally significant manner and the missed detection and false alarm rates of all of the proposed risk assessment metrics, compared both to the Pc and to each other. This paper explores and offers preliminary answers to the above questions, presenting a researched treatment of the philosophical nature of the CA problem and the null hypothesis choice that achieves the greatest consistency with all of the different aspects of operational CA conduct. It then profiles all of the different proposed risk assessment metrics enumerated in the earlier paragraph against an extremely large database of conjunction events at both the 550km and 700km altitudes. The combination of the philosophical exploration of the CA problem and the results of the profiling activity articulates what a risk assessment metric will need to demonstrate, in terms of both innate construction and performance, in order to be a true competitor to the Pc.

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