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At least 91 records · Page 5

Community Input on the Need, Scope, and Development Roadmap of a Proposed National Virtual Biosecurity for Bioenergy Crops Center (NVBBCC)

Brookhaven National Laboratory (BNL) was awarded a pilot project in FY22 under the U.S. Department of Energy (DOE) Office of Science Biopreparedness Research Virtual Environment (BRaVE) initiative, to define research priorities, needs, and requirements for a national virtual center devoted to the biosecurity of bioenergy crops. The mission of the proposed center, referred to as the National Virtual Biosecurity for Bioenergy Crop Center (NVBBCC), would be to provide the scientific basis and tools to detect, characterize, model, and mitigate biothreats to bioenergy crops. This function will be essential to ensure the projected increased US reliance over the next few decades on key plant-based energy products, such as biojet fuel. The NVBBCC is envisioned as a distributed, virtual center with multiple national laboratories at its core to maximize the use of existing unique facilities and expertise across the DOE complex. A major goal of the pilot project was to develop a roadmap for establishing NVBBCC through a series of meetings to gather community input. A total of about 150 individuals, drawn from DOE laboratories, the USDA, academia, NIH, DHS and the private sector participated in six planning meetings held in FY23. Four of the meetings were focused on specific research topics (disease detection, dispersion and disease propagation, biomolecular characterization of plant-pathogen interaction, and mitigation strategies). These four meetings were followed by a meeting that focused on computational needs to support collaborative, data-intensive research within a distributed center as well as workforce development. A final meeting focused on establishing and maintaining preparedness within NVBBCC to respond to an emerging disease within bioenergy crops and how it would collaborate and coordinate with USDA and DHS.

09 BIOMASS FUELS↗

The Quest for Organic Carbon on Mars

We are entering an era of Mars exploration in which organic carbon detection, characterization, and structural identification will be key to addressing some of the outstanding science objectives of the Mars Exploration Program. Success of these missions will depend on technical, scientific, and strategic elements--all of which are strongly determined based on terrestrial experience and knowledge of organic matter formation, concentration, and preservation. Analog studies including Precambrian sediments, modern endolithic communities, and experiments help us fine-tune these approaches, but we also need to expect the unexpected. This presentation will provide perspective on the challenges of detecting organic carbon on Mars, how we may achieve such detections with the in situ instruments, such as the SAM (Science Analysis at Mars) instrument suite onboard Curiosity, the rover for the 2011 Mars Science Laboratory mission.

Eigenbrode, Jennifer↗

The NASA Applied Sciences Program: Volcanic Ash Observations and Applications

Since 2000, the NASA Applied Sciences Program has been actively transitioning observations and research to operations. Particular success has been achieved in developing applications for NASA Earth Observing Satellite (EOS) sensors, integrated observing systems, and operational models for volcanic ash detection, characterization, and transport. While other observations and applications remain to be developed for the current constellation of NASA EOS sensors and integrated with observing and forecast systems, future requirements and capabilities for volcanic ash observations and applications are also being developed. Many of these are based on technologies currently being tested on NASA aircraft, Unmanned Aerial Systems (UAS) and balloons. All of these efforts and the potential advances that will be realized by integrating them are shared in this presentation.

John J Murray↗

An Overview of SHERLOC Raman and Fluorescence Spectroscopy Results Obtained During Perseverance’s Green Zone Campaign at Jezero Crater, Mars

The Perseverance rover landed in Jezero crater, Mars, in February 2021. The field site was chosen because orbiter data provided evidence that the crater hosted an ancient (>2.7 Ga) fluvio-lacustrine environment. The Octavia E. Butler landing site is located ~1.9 km east of the erosional remnant of the Jezero river delta. Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is an arm-mounted instrument that combines fluorescence and Raman spectroscopy with microscopic imaging to analyze geological materials in order to better understand the history of the environments recorded in the rocks within Jezero crater and to search for potential biosignatures. SHERLOC has two microscopic cameras, the Autofocus and Context Imager (ACI) and the Wide-Angle Topographic Sensor for Operations and eNgineering, (WATSON). These subsystems obtain high spatial resolution (10.1–100 µm/pixel) images to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially-resolved correlation of trace organic materials. SHERLOC’s 248.6 nm deep UV laser generates a 100 µm-diameter spot. Photons generated by Raman scattering and fluorescence emission are collected and the spectra are downlinked to Earth for analysis. Knowledge of where the laser is pointed allows for mineral and compositional maps to be generated and overlain in ACI and WATSON images. Early spectroscopy observations focused on natural rock surfaces at targets named Nataani (sol 83), Bi_la_sana (sol 98) and Foux (sol 141). These surfaces included patches of aeolian dust that had settled upon the rocks. Perseverance’s abrasion tool is expected to become available in August 2021; it will grind to depths of 14 mm to remove dust and penetrate coatings and weathering rinds. This presentation will summarize major results from analysis of the rocks examined by SHERLOC during Perseverance’s first science and sample coring campaign (Green Zone Campaign).

Luther W Beegle↗

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗

Automated Guided-Wave Scanning Developed to Characterize Materials and Detect Defects

The Nondestructive Evaluation (NDE) Group of the Optical Instrumentation Technology Branch at the NASA Glenn Research Center has developed a scanning system that uses guided waves to characterize materials and detect defects. The technique uses two ultrasonic transducers to interrogate the condition of a material. The sending transducer introduces an ultrasonic pulse at a point on the surface of the specimen, and the receiving transducer detects the signal after it has passed through the material. The aim of the method is to correlate certain parameters in both the time and frequency domains of the detected waveform to characteristics of the material between the two transducers. The scanning system is shown. The waveform parameters of interest include the attenuation due to internal damping, waveform shape parameters, and frequency shifts due to material changes. For the most part, guided waves are used to gauge the damage state and defect growth of materials subjected to various mechanical or environmental loads. The technique has been applied to polymer matrix composites, ceramic matrix composites, and metal matrix composites as well as metallic alloys. Historically, guided wave analysis has been a point-by-point, manual technique with waveforms collected at discrete locations and postprocessed. Data collection and analysis of this type limits the amount of detail that can be obtained. Also, the manual movement of the sensors is prone to user error and is time consuming. The development of an automated guided-wave scanning system has allowed the method to be applied to a wide variety of materials in a consistent, repeatable manner. Experimental studies have been conducted to determine the repeatability of the system as well as compare the results obtained using more traditional NDE methods. The following screen capture shows guided-wave scan results for a ceramic matrix composite plate, including images for each of nine calculated parameters. The system can display up to 18 different wave parameters. Multiple scans of the test specimen demonstrated excellent repeatability in the measurement of all the guided-wave parameters, far exceeding the traditional point-by-point technique. In addition, the scan was able to detect a subsurface defect that was confirmed using flash thermography This technology is being further refined to provide a more robust and efficient software environment. Future hardware upgrades will allow for multiple receiving transducers and the ability to scan more complex surfaces. This work supports composite materials development and testing under the Ultra-Efficient Engine Technology (UEET) Project, but it also will be applied to other material systems under development for a wide range of applications.

Martin, Richard E.↗

Applicability of Electrical and Electroanalytical Techniques to Detect Water and Characterize the Geochemistry of Undisturbed Planetary Soils

The search for life is a primary goal of NASA s planetary exploration program. The search is, of necessity, tiered in both the detection approach (looking for evidence of microbial fossils or the presence of water in the geological history of a planetary body and/or looking for evidence of water, energy sources, precursors to life, signatures of life and/or life itself in the present day planetary environment) and in the survey method (scale, range, specificity) employed. Terrestrial investigations suggests that life as we know it requires water. Thus, the search for extant microbial life and habitats requires identifying water-bearing soils. Determining Reduction-Oxidation (REDOX) couples present in water, once it is found, provides information on soil geochemistry and identifies potential chemical energy sources for life. Mars offers a near-term target for conducting this search. The identification of gully formation [1], layered deposits [2] and elemental ratios of bromine and chlorine [3] present indirect evidence that water was abundant locally in the Martian past. Additionally, Viking images of polar ice and frost formation on the surface of Mars demonstrate that water can exist in at least some near-surface regions of present-day Mars. Atmospheric pressure data further suggest that liquid water may be stable for short periods of time in the mid-latitudes of the Martian surface. [4] Measurements of the global distribution of hydrogen in the Martian regolith offer tantalizing indirect evidence that water may at least exist in near-surface soils. [5] Evidently, any water to be found is likely to exist as soil mixtures at levels ranging between approx.0.5% and approx.5 %.

Seshadri, S.↗

Performance Characterization of Obstacle Detection Algorithms for Aircraft Navigation

The research reported here is a part of NASA's Synthetic Vision System (SVS) project for the development of a High Speed Civil Transport Aircraft (HSCT). One of the components of the SVS is a module for detection of potential obstacles in the aircraft's flight path by analyzing the images captured by an on-board camera in real-time. Design of such a module includes the selection and characterization of robust, reliable, and fast techniques and their implementation for execution in real-time. This report describes the results of our research in realizing such a design.

Kasturi, Rangachar↗

Rapid Detection and Quick Characterization of African Swine Fever Virus Using the VolTRAX Automated Library Preparation Platform

African swine fever virus (ASFV) is the causative agent of a severe and highly contagious viral disease affecting domestic and wild swine. The current ASFV pandemic strain has a high mortality rate, severely impacting pig production and, for countries suffering outbreaks, preventing the export of their pig products for international trade. Early detection and diagnosis of ASFV is necessary to control new outbreaks before the disease spreads rapidly. One of the rate-limiting steps to identify ASFV by next-generation sequencing platforms is library preparation. Here, we investigated the capability of the Oxford Nanopore Technologies’ VolTRAX platform for automated DNA library preparation with downstream sequencing on Nanopore sequencing platforms as a proof-of-concept study to rapidly identify the strain of ASFV. Within minutes, DNA libraries prepared using VolTRAX generated near-full genome sequences of ASFV. Thus, our data highlight the use of the VolTRAX as a platform for automated library preparation, coupled with sequencing on the MinION Mk1C for field sequencing or GridION within a laboratory setting. These results suggest a proof-of-concept study that VolTRAX is an effective tool for library preparation that can be used for the rapid and real-time detection of ASFV.

60 APPLIED LIFE SCIENCES↗

Instrument characterization for the detection of long-term changes in stratospheric ozone - An analysis of the SBUV/2 radiometer

The scientific objective of unambiguously detecting subtle global trends in upper stratospheric ozone requires that one maintains a thorough understanding of the satellite-based remote sensors intended for this task. The instrument now in use for long term ozone monitoring is the SBUV/2 being flown on NOAA operational satellites. A critical activity in the data interpretation involves separating small changes in measurement sensitivity from true atmospheric variability. By defining the specific issues that must be addressed and presenting results derived early in the mission of the first SBUV/2 flight model, this work serves as a guide to the instrument investigations that are essential in the attempt to detect long-term changes in the ozone layer.

Frederick, J. E.↗

Development of the Space Debris Sensor (SDS)

Debris Resistive/Acoustic Grid Orbital Navy-NASA Sensor (DRAGONS) is an impact sensor designed to detect and characterize collisions with small orbital debris: from 50 microns to greater than 1millimeter debris size detection; Characterizes debris size, speed, direction, and density. The Space Debris Sensor (SDS) is a flight demonstration of DRAGONS on the International Space Station: Approximately 1 square meter of detection area facing the ISS velocity vector; Minimum two year mission on Columbus External Payloads Facility (EPF); Minimal obstruction from ISS hardware; Development is nearing final checkout and integration with the ISS; Current launch schedule is SpaceX13, about September 2017, or SpaceX14, about Jan 2018.

Hamilton, Joe↗

Computational Reduction of Specimen Noise to Enable Improved Thermography Characterization of Flaws in Graphite Polymer Composites

Flaw detection and characterization with thermographic techniques in graphite polymer composites are often limited by localized variations in the thermographic response. Variations in properties such as acceptable porosity, fiber volume content and surface polymer thickness result in variations in the thermal response that in general cause significant variations in the initial thermal response. These result in a "noise" floor that increases the difficulty of detecting and characterizing deeper flaws. A method is presented for computationally removing a significant amount of the "noise" from near surface porosity by diffusing the early time response, then subtracting it from subsequent responses. Simulations of the thermal response of a composite are utilized in defining the limitations of the technique. This method for reducing the data is shown to give considerable improvement characterizing both the size and depth of damage. Examples are shown for data acquired on specimens with fabricated delaminations and impact damage.

Winfree, William P.↗

Characterizing Cool Giant Planets in Reflected Light

While the James Webb Space Telescope will detect and characterize extrasolar planets by transit and direct imaging, a new generation of telescopes will be required to detect and characterize extrasolar planets by reflected light imaging. NASA's WFIRST space telescope, now in development, will image dozens of cool giant planets at optical wavelengths and will obtain spectra for several of the best and brightest targets. This mission will pave the way for the detection and characterization of terrestrial planets by the planned LUVOIR or HabEx space telescopes. In my presentation I will discuss the challenges that arise in the interpretation of direct imaging data and present the results of our group's effort to develop methods for maximizing the science yield from these planned missions.

James Webb Space Telescope↗

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗

Process-Induced Carbon and Sub-Layer in SiC/BN/SiC Composites: Characterization and Consequences

Following our detection of films of elemental carbon in the Hi-Nicalon TM/BN/SiC composite and its deleterious effect on oxidative durability, we have examined other SiC/BN/SiC systems. The problem is pervasive, and significant residues of free carbon are confirmed in Sylramic /BN/SiC materials. Effective techniques for routine detection and characterization of adventitious carbon in SiC/BN/SiC composites are discussed.

Ogbuji, L. U. J. T↗

Optimized bandpasses for the Habitable Worlds Observatory's exoEarth survey

A primary scientific goal of the future Habitable Worlds Observatory will be the direct detection and characterization of Earth-like planets. Estimates of the exoplanet yields for this concept will help guide mission design through detailed trade studies. It is therefore critical that yield estimation codes optimally adapt observations to the mission’s performance parameters to ensure accurate trade studies. To aid in this, we implement wavelength optimization in yield calculations for the first time, allowing the yield code to determine the ideal detection and characterization bandpasses. We use this capability to confirm the observational wavelength assumptions made for the large UV/Optical/IR surveyor, design B (LUVOIR-B) study, namely that the optimum detection wavelength is 500 nm for the majority of targets and the optimum wavelength to detect water is near 1000 nm, given LUVOIR-B’s assumed instrument performance parameters. We show that including the wavelength-dependent albedo of an Earth twin as a prior provides no significant benefit to the yields of exoEarth candidates and caution against tuning observations to modern Earth twins. We also show that coronagraphs whose inner working angles are similar to step functions may benefit from wavelength optimization and demonstrate how wavelength-dependent instrument performance can impact the optimum wavelengths for detection and characterization. The optimization methods we implement automate wavelength selection and remove uncertainties regarding these choices, helping to adapt the observations to the instrument’s performance parameters.

telescopes↗