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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Chemical method of measuring volume of urine samples using lithium chloride dilution technique, does not interfere with analysis, is faster, and more accurate than standard volumetric of specific gravity/weight techniques. Adaptation of procedure to urinalysis could prove generally practical for hospital mineral balance and catechoamine determinations.
Portable unit takes eight 24-hour samples. Volatile organic compounds in air collected for analysis by portable, self-contained sampling apparatus. Sampled air drawn through sorbent material, commercial porous polymer of 2, 3-diphenyl-p-phenylene oxide. High-boiling-point organic compounds adsorbed onto polymer, while low-boiling-point organics pass through and returned to atmosphere. Sampler includes eight sample tubes filled with polymeric sorbent. Organic compounds in atmosphere absorbed when air pumped through sorbent. Designed for checking air in spacecraft, sampler adaptable to other applications as leak detection, gas-mixture analysis, and ambient-air monitoring.
An adaptive array is used to receive a desired signal in the presence of weak interference signals which need to be suppressed. A modified sample matrix inversion (SMI) algorithm controls the array weights. The modification leads to increased interference suppression by subtracting a fraction of the noise power from the diagonal elements of the covariance matrix. The modified algorithm maximizes an intuitive power ratio criterion. The expected values and variances of the array weights, output powers, and power ratios as functions of the fraction and the number of snapshots are found and compared to computer simulation and real experimental array performance. Reduced-rank covariance approximations and errors in the estimated covariance are also described.
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
Prolonged exposure to spaceflight conditions results in a battery of physiological changes, some of which contribute to sensorimotor and neurovestibular deficits. Upon return to Earth, functional performance changes are tested using the Functional Task Test (FTT), which includes an obstacle course to observe post‐flight balance and postural stability, specifically during turning. Aims: To quantify changes in movement strategies during turning events by observing the latency between head‐andtrunk coordinated movement. Hypothesis: It is hypothesized that subjects experiencing neurovestibular adaptations will exhibit head‐to‐trunk locking ('en bloc' movement) during turning, exhibited by a decrease in latency between head and trunk movement. Sample: FTT data samples were collected from Shuttle and ISS missions. Samples were analyzed three times pre exposure, immediately post‐exposure (0 or 1 day post) and 2‐to‐3 times during recovery from the microgravity environment. Methods: Two 3D inertial measurements units (XSens MTx) were attached to subjects, one on the head and one on the upper back. This study focused primarily on the yaw movements about the subject's center of rotation. Time differences (latency) between head and trunk movement were calculated at two points: the first turn (Fturn) to enter the obstacle course (approximately 90⁰ turn) and averaged across a slalom obstacle portion, consisting of three turns (approximately three 90⁰ turns). Results: Preliminary analysis of the data shows a trend toward decreasing head‐to‐trunk movement latency during post‐flight ambulation, after reintroduction to Earth gravity in Shuttle and ISS astronauts. Conclusion: It is clear that changes in movement strategies are adopted during exposure to the microgravity environment and upon reintroduction to a gravity environment. Some subjects exhibit symptoms of neurovestibular neuropathy ('en bloc movement') that may impact their ability to perform post‐flight functional tasks.
An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
Reusable holders have been devised for evaluating high-temperature, plasma-resistant re-entry materials, especially fabrics. Typical material samples tested support thermal-protection-system damage repair requiring evaluation prior to re-entry into terrestrial atmosphere. These tests allow evaluation of each material to withstand the most severe predicted re-entry conditions.
Topics covered include: Measurements of Ultra-Stable Oscillator (USO) Allan Deviations in Space; Gaseous Nitrogen Orifice Mass Flow Calculator; Validation of Proposed Metrics for Two-Body Abrasion Scratch Test Analysis Standards; Rover Low Gain Antenna Qualification for Deep Space Thermal Environments; Automated, Ultra-Sterile Solid Sample Handling and Analysis on a Chip; Measuring and Estimating Normalized Contrast in Infrared Flash Thermography; Spectrally and Radiometrically Stable, Wideband, Onboard Calibration Source; High-Reliability Waveguide Vacuum/Pressure Window; Methods of Fabricating Scintillators With Radioisotopes for Beta Battery Applications; Magnetic Shield for Adiabatic Demagnetization Refrigerators (ADR); CMOS-Compatible SOI MESFETS for Radiation-Hardened DC-to-DC Converters; Silicon Heat Pipe Array; Adaptive Phase Delay Generator; High-Temperature, Lightweight, Self-Healing Ceramic Composites for Aircraft Engine Applications; Treatment to Control Adhesion of Silicone-Based Elastomers; High-Temperature Adhesives for Thermally Stable Aero-Assist Technologies; Rockballer Sample Acquisition Tool; Rock Gripper for Sampling, Mobility, Anchoring, and Manipulation; Advanced Magnetic Materials Methods and Numerical Models for Fluidization in Microgravity and Hypogravity; Data Transfer for Multiple Sensor Networks Over a Broad Temperature Range; Using Combustion Synthesis to Reinforce Berms and Other Regolith Structures; Visible-Infrared Hyperspectral Image Projector; Three-Axis Attitude Estimation With a High-Bandwidth Angular Rate Sensor Change_Detection.m; AGATE: Adversarial Game Analysis for Tactical Evaluation; Ionospheric Simulation System for Satellite Observations and Global Assimilative; Modeling Experiments (ISOGAME); An Extensible, User- Modifiable Framework for Planning Activities; Mission Operations Center (MOC) - Precipitation Processing System (PPS) Interface Software System (MPISS); Automated 3D Damaged Cavity Model Builder for Lower Surface Acreage Tile on Orbiter; Mixed Linear/Square-Root Encoded Single-Slope Ramp Provides Low-Noise ADC with High Linearity for Focal Plane Arrays; RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers; Powered Descent Guidance with General Thrust-Pointing Constraints; X-Ray Detection and Processing Models for Spacecraft Navigation and Timing; and Extreme Ionizing-Radiation-Resistant Bacterium
This experiment was designed to measure the immune response in acutely stressed and chronically centrifuged hyper-G-adapted male rats. Rats were exposed to 2.1 and 3.1 G. Acutely stressed animals were injected with sheep red blood cells (SRBC) on the day of inital exposure to hyper-G, and were chronically centrifuged for 10 to 15 days after immunization. Hyper-G-adapted rats were chronically centrifuged for 28 days prior to antigen injection and for 21 days after injection. Booster injections were given and serum samples taken at intervals from 3 to 9 days after the initial and booster injections. Antigen dose, injected ip, ranged between 1.35 x 10 to the 6th and 1.38 x 10 to the 9th SRBC/100 g. body weight. Pair-fed and ad libitum fed noncentrifuged controls were used. No significant differences in anti-SRBC antibody titers were found between centrifuged and control animals, although there were some changes in WBC counts and a significant increase in adrenal-gland size in acutely stressed animals.
With the emergence of lead-salt diode laser technology, a broad and important IR spectral region, roughly 3-30 microns, became accessible to tunable laser spectroscopy. More recent advances in fabrication techniques have provided experimenters with relatively high power quasi-single-mode high-temperature lasers that are readily adapted to compact automated instruments for field experiments. An especially attractive capability of diode lasers is the ease of using them for simultaneous multiconstituent sampling in the atmosphere. This paper presents a summary description of field instruments for atmospheric research which employ diode lasers and second-harmonic detection. Representative results obtained with some of these instruments are presented.
A universal noiseless coding structure was developed that provides efficient performance over an extremely broad range of source entropy. This is accomplished by adaptively selecting the best of several easily implemented variable length coding algorithms. Custom VLSI coder and decoder modules capable of processing over 20 million samples per second are currently under development. The first of the code options used in this module development is shown to be equivalent to a class of Huffman code under the Humblet condition, other options are shown to be equivalent to the Huffman codes of a modified Laplacian symbol set, at specified symbol entropy values. Simulation results are obtained on actual aerial imagery, and they confirm the optimality of the scheme. On sources having Gaussian or Poisson distributions, coder performance is also projected through analysis and simulation.
The development and design of a system of containers for the efficient integration of Space Station payloads is described called the Space Station Payload Adaptation System (SSPAS). The SSPAS was developed to address the incorporation of multiple payloads, the use of a small payload carrier, large numbers of samples, and on-orbit servicing. SSPAS subsystems such as the Spacelab rack are modular and designed for integration into the 'Quick Is Beautiful' (QIB) facility. The QIB is designed to provide access to space for small- and medium-sized microgravity research projects and proof-of-concept investigations. The power-distribution and heat-rejection potential of the QIB are described, and an improved experiment-apparatus container is proposed. The SSPAS rack-mounting and container concepts are concluded to make up an efficent system that can effectively exploit the R&D potential of the Space Station.
Claypack is a cost-effective portable system developed by Barringer Research Ltd. for rapid on-site analysis of clay minerals. It is an adaptation of a hand-held rationing radiometer. By measuring the intensity of reflected radiation, the device discriminates among different minerals present in a sample. It simultaneously analyzes radiation intensities in two separate bands of the spectrum, and calculates the ratio of one to the other. The "reflectance ratio" is computer processed and displayed in digital form.
The structure of an optimal adaptive array receiver for ground-based optical communications is described and its performance investigated. Kolmogorov phase screen simulations are used to model the sample functions of the focal-plane signal distribution due to turbulence and to generate realistic spatial distributions of the received optical field. This novel array detector concept reduces interference from background radiation by effectively assigning higher confidence levels at each instant of time to those detector elements that contain significant signal energy and suppressing those that do not. A simpler suboptimum structure that replaces the continuous weighting function of the optimal receiver by a hard decision on the selection of the signal detector elements also is described and evaluated. Approximations and bounds to the error probability are derived and compared with the exact calculations and receiver simulation results. It is shown that, for photon-counting receivers observing Poisson-distributed signals, performance improvements of approximately 5 dB can be obtained over conventional single-detector photon-counting receivers, when operating in high background environments.
Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.
Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.
Continued space bioscience research onboard the International Space Station (ISS) and future long-duration flight missions to the Moon or Mars will require the ability to conduct on-orbit molecular analysis of biological samples independently from Earth. In the last year two new molecular analytic technologies have been installed and the technologies demonstrated onboard the ISS: The Sample Prep Module (SPM) WetLab-2 (WL2) qRT-PCR toolbox and the Oxford Nanopore MinIon Biomolecule Sequencer. Here we describe protocol development and integration into existing ISS technology for end-to-end on-orbit biological sample processing and molecular analysis with real time results generated utilizing only field offline analytic software. For this experiment we isolated primary cells from bone marrow flushes of wild type B6129SF2 mice (Jackson Labs) long bones. The cell isolate was then processed using the SPM to produce total 147nanograms of RNA. The total RNA was purified to only messenger RNA (mRNA) and transferred to Smartcycler Thermocycle ISS kit consumable tube using Eppendorf gel loading pipette tips for further processing. Complementary first strand cDNA was synthesized using OLIGO dT priming followed by addition of SuperScript II Reverse Transcriptase and thermal cycling as per manufacturers instruction. All thermal cycling was conducted using the ISS WetLab-2 Cephid Smarcycler real time thermal cycler. Our protocol takes advantage of mRNAs native poly(A) tail, synthesized in vivo to protect the mRNA from degradation by endonucleases, to eliminate end-prep for adapter ligation. The adapted library is purified using MyOne C1 Streptavidin beads before elution in buffer. The pre-sequencing library is diluted in the loading buffer and injected into the MinIon sample port, drawn into the nanopore window by capillary action, and sequenced using the MinKnown software with local basecalling. The sequencing read produced 34.5 million events and local basecalling produced 117,301 successful reads. NCBI Blast of the data for the mouse genome resulted in 2,462 successful nucleotide collection matches (gene sequences) exceeding 70 homology. These results demonstrate the viability of this novel flight ready end-to-end sample analytic methodology and provide a real time homolog for flight experimentation utilizing supply kits and technologies that have already been demonstrated on ISS.