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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imagery

Stray light↗
NASA NTRS2013

Automated Generation of Fault Management Artifacts from a Simple System Model

Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.

Spinup and Orient↗
DOE OSTI2020

Probing for Artifacts: Detecting Imagenet Model Evasions

While deep learning models have made incredible progress across a variety of machine learning tasks, they remain vulnerable to adversarial examples crafted to fool otherwise trustworthy models. In this work we approach this problem through the lens of a detection framework. We propose a classification network that uses the hidden layer activations of a trained model as inputs to detect adversarial artifacts in an input. We train this classification network simultaneously against multiple adversarial algorithms to create a more robust detector and show higher detection rates than several alternatives. The novelty of our approach is in the scale and scope of probing Imagenet models for adversarial artifacts. In addition, we propose an improvement to feature squeezing, another common adversarial example detection method.

Rounds, Jeremiah↗
NASA NTRS2000

Qualitative and quantitative effects of harmonic echocardiographic imaging on endocardial edge definition and side-lobe artifacts

Harmonic imaging is a new ultrasonographic technique that is designed to improve image quality by exploiting the spontaneous generation of higher frequencies as ultrasound propagates through tissue. We studied 51 difficult-to-image patients with blinded side-by-side cineloop evaluation of endocardial border definition by harmonic versus fundamental imaging. In addition, quantitative intensities from cavity versus wall were compared for harmonic versus fundamental imaging. Harmonic imaging improved left ventricular endocardial border delineation over fundamental imaging (superior: harmonic = 71.1%, fundamental = 18.7%; similar: 10.2%; P <.001). Quantitative analysis of 100 wall/cavity combinations demonstrated brighter wall segments and more strikingly darker cavities during harmonic imaging (cavity intensity on a 0 to 255 scale: fundamental = 15.6 +/- 8.6; harmonic = 6.0 +/- 5.3; P <.0001), which led to enhanced contrast between the wall and cavity (1.89 versus 1.19, P <.0001). Harmonic imaging reduces side-lobe artifacts, resulting in a darker cavity and brighter walls, thereby improving image contrast and endocardial delineation.

NASA Discipline Cardiopulmonary↗
DOE OSTI2020

Origin and Suppression of Beam Damage-Induced Oxygen-K Edge Artifact from γ-Al 2 O 3 using Cryo-EELS

Gamma-alumina (γ-Al 2 O 3 ), like other low-Z oxides, is readily damaged when exposed to an electron beam. This typically results in the formation of a characteristic pre-edge peak in the oxygen-K edge of electron energy-loss spectra (EELS) acquired during or after the damage process. This artifact can mask the presence of intrinsic O-K edge fine structure that would reveal chemical properties of the material; therefore, its suppression is key. In this work, we systematically investigate the conditions that give rise to the damage-induced O-K pre-edge peak and show that it can be effectively suppressed by performing EELS experiments at cryogenic (cryo) temperatures. Prolonged exposure of γ-Al 2 O 3 to a focused electron beam results in a hole bored through the sample; this was used as a reproducible beam damage condition. O-K edge EELS spectra were collected from a single-crystal γ-Al 2 O 3 sample both during and after focused 2 electron beam hole drilling, and at room and cryo temperatures, using a monochromated scanning transmission electron microscope (STEM). Furthermore, the characteristic 531 eV pre-edge peak visible in the room temperature EELS spectra was completely suppressed in the cryo-EELS spectra, even in the presence of a visible drilled hole. We then correlated these experimental observations with multiple-scattering EELS simulations to determine the likely atomistic origin of the damage-induced O-K pre-edge peak. The findings indicate that the pre-edge peak is caused primarily by the presence of surface O dimer (O-O) bonds formed during beam damage, and that operating at cryo temperature suppresses the formation of surface O-O bonds, thus preventing formation of the O-K pre-edge peak. Additionally, Al-L 2,3 edge EELS spectra revealed Al loss primarily from tetrahedral sites during hole drilling.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Reduced Artifact Approach for Determining Diffusion Coefficients in Time-Resolved Microscopy

Ultrafast microscopy methods traditionally assume a Gaussian profile to extract excited state diffusivities from transport measurements. Although this fitting method recovers accurate diffusion coefficients when the point spread function is well-represented by a Gaussian, even minor spatial aberrations introduced by the imaging system cause significant errors in the determined value. To provide a more accurate measure of excited state transport in nano- and microscale materials systems, in this work an alternative analysis protocol is proposed that numerically convolves the Green’s function solution to the diffusion equation with the experimentally measured point spread function. In contrast to the Gaussian fitting approach, the numerical convolution is shown to be robust against artifacts caused by nonideal point spread functions. Furthermore, the numerical convolution approach is highly effective at resolving anisotropic diffusion in modeled data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Inversion of Many-Beam Bragg Intensities for Phasing by Iterated Projections: Removal of Multiple Scattering Artifacts from Diffraction Data

An iterated projection algorithm (N-Phaser) is developed that reconstructs a scattering potential from N-beam multiple Bragg scattered intensities. The method may be used to eliminate multiple scattering artifacts from electron diffraction data, solving the phase problem and increasing the thicknesses of samples used in materials science, solid-state chemistry, and small molecule crystallography. For high-energy transmission electron diffraction, we show that the algorithm recovers accurate complex structure factors from a wide range of thicknesses, orientations, and relativistic beam energies, and does not require known thickness or atomic-resolution data if sufficient multiple scattering occurs. Furthermore, extensions to Cryo-electron microscopy and Micro-electron diffraction are suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, sub­tomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗
DOE OSTI2024

Excitation-dependent features and artifacts in 2-D terahertz spectroscopy

Recently, two-dimensional terahertz spectroscopy (2DTS) has attracted increasing attention for studying complex solids. A number of recent studies have applied 2DTS either with long pulses or away from any material resonances, situations that yield unconventional 2DTS spectra that are often difficult to interpret. Here, we clarify the generic origins of observed spectral features by examining 2DTS spectra of ZnTe, a model system with a featureless optical susceptibility at low terahertz frequencies. These results also reveal possible artifacts that may arise from electro-optic sampling in collinear 2DTS experiments, including the observation of spurious rectified or second harmonic signals.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Substrate birefringence as a source of artifacts in spintronic THz emission spectra

Terahertz (THz) emission from spintronic THz emitters (STEs) has been extensively studied, both for its potential application in THz technologies and as a tool for probing spin dynamics, conductivity, and crystal anisotropy via time-domain THz spectroscopy. However, substrate birefringence can modify the emitted THz polarization, distorting the measured spectra and leading to potential misinterpretation of the results. While some aspects of substrate birefringence on THz emission have been studied in the time domain, its effects on THz spectra have not been discussed in detail. In the THz emission spectrum from STEs, we observe distinct spectral signatures that depend on sample orientation. To understand these signatures, we use STEs grown on c-cut sapphire and systematically vary the orientation of a (100) rutile TiO 2 window in both pump-through and THz-through geometries to identify the birefringence-induced effects. These findings demonstrate that substrate birefringence plays a critical role in the emitted THz spectrum and must be carefully considered in the analysis of time-domain THz spectroscopy data. We also show that artifacts from smaller birefringence can have a more misleading effect on the THz spectra.

Shrestha, Shreya [Univ. of Delaware, Newark, DE (U↗
DOE OSTI2025

INL CRMO XRF Report 2025-04: Provenance Determinations for 116 Obsidian Artifacts from Upper Snake U.S. Bureau of Reclamation Archaeological Collections

This report presents provenance determinations for 116 obsidian artifacts from Upper Snake U.S. Bureau of Reclamation collections. Provenance determinations were made using energy dispersive X-ray fluorescence (ED-XRF) spectrometry at the Idaho National Laboratory (INL) Cultural Resource Management Office (CRMO) under the direction of Dr. Kyle Freund.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

INL CRMO XRF Report 2025-06: Provenance Determinations for 246 Obsidian and Fine-grained Volcanic Artifacts from Folsom-era Sites in Eastern Idaho

This report presents provenance determinations for 246 obsidian and fine-grained volcanic artifacts from Folsom-era sites in eastern Idaho. Provenance determinations were made using energy dispersive X-ray fluorescence (ED-XRF) spectrometry at the Idaho National Laboratory (INL) Cultural Resource Management Office (CRMO) under the direction of Dr. Kyle Freund.

58 GEOSCIENCES↗
DOE OSTI2026

Cloverleaf Data Artifacts for ArtIMis LDRD

This report summarizes the use of the open-source CloverLeaf/CloverLeaf3D mini-apps to generate synthetic data sets to train foundation models for the ArtIMis LDRD DI. These data artifacts are intended to be used by LANL collaborators and shared externally with our university and institutional partners. Note that CloverLeaf/CloverLeaf3D is not a LANL simulation code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Protein carbamylation and proteomics: from artifacts to elucidation of biological functions

Lysine carbamylation is a non-enzymatic protein post-translational modification (PTM) that plays important roles in regulating enzymatic activity and the pathogenesis of diseases such as atherosclerosis, rheumatoid arthritis, and uremia. The progress of understanding the roles of carbamylation in biological systems has been delayed due to lack of systematic assays to study its functions. To aggravate this scenario, carbamylation is a major artifact in proteomics analysis given that urea, which is used during sample preparation, induces carbamylation. In addition, anti-acetyllysine antibodies co-purify carbamylated and acetylated peptides. In a recent paper, we leveraged co-purification with anti-acetyllysine antibodies to develop a method for analyzing carbamylated proteomes. In this perspective article, we discuss how this method may be applied to characterize the physiological functions of carbamylation in humans and other biological models, as well as the utility of establishing novel disease biomarkers.

59 BASIC BIOLOGICAL SCIENCES↗