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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The gravitational lensing imprints of DES Y3 superstructures on the CMB: a matched filtering approach

Low-density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence |$\kappa$|⁠. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 (DES Y3) data set, covering approximately 4200 deg|$^2$| of the sky. We use two distinct void-finding algorithms: a 2D void-finder that operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the Marenostrum Institut de Ciències de l’Espai N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure |$A_\kappa$|⁠, the amplitude of the observed lensing signal relative to the simulation template, obtaining |$A_\kappa = 1.03 \pm 0.22$| (⁠|$4.6\sigma$| significance) for Voxel and |$A_\kappa = 1.02 \pm 0.17$| (⁠|$5.9\sigma$| significance) for 2D voids, both consistent with Lambda cold dark matter expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure |$A_\kappa = 0.87 \pm 0.15$| (⁠|$5.9\sigma$| significance). The leading source of noise in our measurements is Planck noise, implying that data from the Atacama Cosmology Telescope, South Pole Telescope and CMB-S4 will increase sensitivity and allow for more precise measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

The Evolution of Icelandic Volcano Emissions, as Observed From Space in the Era of NASA's Earth Observing System (EOS)

Volcanoes are natural phenomena that have global environmental impacts. Satellite remote sensing can help classify volcanic eruptions and track the dispersion of emissions. We assess multiple volcanic eruptions in Iceland (Eyjafjallajökull 2010, Grímsvötn 2011 and Holuhraun 2014-2015), using space-borne observations to infer information about the geological dynamics of each volcano and the properties and evolution of plume particles. We derive qualitative constraints on plume particle size, shape and light-absorption characteristics from Multi-angle Imaging SpectroRadiometer (MISR) space-borne imagery. With the MISR Research Algorithm (RA), we distinguish sulfate/water-dominated volcanic plumes from Holuhraun and ash-dominated plumes from Eyjafjallajökull and Grímsvötn, and even identify subtler changes in ash particle size and light-absorption within plumes. Additionally, plume heights are retrieved geometrically from MISR. These are combined with surface thermal anomalies from the MODerate resolution Imaging Spectroradiometer (MODIS) and SO2 concentrations derived from the Ozone Monitoring Instrument (OMI) to synthesize eruption remote-sensing chronologies. Signals related to differences in particle properties are identified and linked to evolving magma composition at Eyjafjallajökull. The results illustrate the potential to distinguish qualitative differences in eruptive magma composition based on particle light absorption and plume profile from remote-sensing. For the sulfate-rich Holuhraun plumes, the influence of aerosol hygroscopic growth during transport is inferred from such data. Three processes appear to dominate plume evolution in Iceland: downwind aerosol formation; particle hydration; and particle deposition. This work demonstrates enhanced MISR capabilities, and more generally, remote-sensing analysis that can be applied globally, especially where suborbital volcano observations are limited or entirely absent.

remote sensting↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
NASA NTRS2017

Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India

Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.

Data↗
NASA NTRS1983

Real-time MST radar signal processing using a microcomputer running under FORTH

Data on power, correlation time, and velocity were obtained at the Urbana radar using microcomputer and a single floppy disk drive. This system includes the following features: (1) measurement of the real and imaginary components of the received signal at 20 altitudes spaced by 1.5 km; (2) coherent integration of these components over a 1/8-s time period; (3) continuous real time display of the height profiles of the two coherently integrated components; (4) real time calculation of the 1 minute averages of the power and autocovariance function up to 6 lags; (5) output of these data to floppy disk once every 2 minutes; (6) display of the 1 minute power profiles while the data are stored to the disk; (7) visual prompting for the operator to change disks when required at the end of each hour of data; and (8) continuous audible indication of the status of the interrupt service routine. Accomplishments were enabled by two developments: the use of a new correlation algorithm and the use of the FORTH language to manage the various low level and high level procedures involved.

Bowhill, S. A.↗
DOE OSTI2020

Finding wombling boundaries in LHC data with Voronoi and Delaunay tessellations

We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Using Hardware-In-The-Loop Methodology to Develop Test Systems

Hardware in the Loop (HIL) testing methodologies have become widespread in industry. Typically, they focus on developing control algorithms for systems such as autonomous vehicles or aircraft. An oft overlooked aspect of product development is the design and fabrication of a test system for validating that the product meets requirements. Abstractly, a test system differs little from a control system—testers provide signals to the unit, monitor feedback, and base decisions on the results. While the time scales may differ, the functionalities are conceptually similar. Viewed in this light, it becomes natural to extend HIL approaches to tester development. By replacing a physical unit with a proxy model deployed to a real-time or pseudo real-time target, test systems can be developed in parallel with the design and fabrication of a first production unit. This saves considerable time in the life cycle from conceptual design to realized product. This manuscript demonstrates the process flow using a capacitive discharge unit as an exemplar.

42 ENGINEERING↗
NASA NTRS2004

Active Suppression of Instabilities in Engine Combustors

A method of feedback control has been proposed as a means of suppressing thermo-acoustic instabilities in a liquid- fueled combustor of a type used in an aircraft engine. The basic principle of the method is one of (1) sensing combustor pressure oscillations associated with instabilities and (2) modulating the rate of flow of fuel to the combustor with a control phase that is chosen adaptively so that the pressure oscillations caused by the modulation oppose the sensed pressure oscillations. The need for this method arises because of the planned introduction of advanced, lean-burning aircraft gas turbine engines, which promise to operate with higher efficiencies and to emit smaller quantities of nitrogen oxides, relative to those of present aircraft engines. Unfortunately, the advanced engines are more susceptible to thermoacoustic instabilities. These instabilities are hard to control because they include large dead-time phase shifts, wide-band noise characterized by amplitudes that are large relative to those of the instabilities, exponential growth of the instabilities, random net phase walks, and amplitude fluctuations. In this method (see figure), the output of a combustion-pressure sensor would be wide-band-pass filtered and then further processed to generate a control signal that would be applied to a fast-actuation valve to modulate the flow of fuel. Initially, the controller would rapidly take large phase steps in order to home in, within a fraction of a second, to a favorable phase region within which the instability would be reduced. Then the controller would restrict itself to operate within this phase region and would further restrict itself to operate within a region of stability, as long as the power in the instability signal was decreasing. In the phase-shifting scheme of this method, the phase of the control vector would be made to continuously bounce back and forth from one boundary of an effective stability region to the other. Computationally, this scheme would be implemented by the adaptive sliding phaser averaged control (ASPAC) algorithm, which requires very little detailed knowledge of the combustor dynamics. In the ASPAC algorithm, the power of the instability signal would be calculated from the wide-bandpass- filtered combustion-pressure signal and averaged over a period of time (typically of the order of a few hundredths of a second) corresponding to the controller updating cycle [not to be confused with the controller sampling cycle, which would be much shorter (typically of the order of 10(exp -4) second)].

Kopasakis, George↗
NASA NTRS2017

Surface-Height Determination of Crevassed Glaciers-Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration.The Advanced Topographic Laser Altimeter System aboard NASAs future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information.We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in auto-adaptive threshold determination. The auto-adaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Braer in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting lidar data over complex and simple surfaces.

Herzfeld, Ute C.↗
NASA NTRS2001

Molecular Simulations in Astrobiology

One of the main goals of astrobiology is to understand the origin of cellular life. In the absence of any record of the earliest ancestors of contemporary cells, protocells, the most direct way to test our understanding of their characteristics is to construct laboratory models of protocells. Such efforts, currently underway in the NASA Astrobiology Program, are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs of molecules that are capable of performing protocellular functions. Many of these functions, such as importing nutrients, capturing and storing energy, and responding to changes in the environment, are carried out by proteins bound to membranes. We use computer simulations to address the following, questions about these proteins: (1) How do small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes? (2) How do peptides aggregate to form membrane-spannin(y structures (e.g., channels)? (3) By what mechanisms do such aggregates perform their functions? The simulations are performed using the molecular dynamics (MD) method. In this method, Newton's equations of motion for each atom in the system are solved iteratively. At each time step, the forces exerted on each atom by the remaining atoms are evaluated by dividing them into two parts. Short-range forces are calculated directly in real space while long-range forces are evaluated in reciprocal space, usually using a particle-mesh algorithm which is of order O(NlnN). Currently, a time step of 2 femtoseconds is typically used, thereby making studies of problems occurring on multi-nanosecond time scales (10(exp 6) - 10(exp 8) time steps) accessible. To address a broader range of problems, simulations need to be extended by three orders of magnitude. Such an extension requires both algorithmic improvements and codes scalable to a large number of parallel processors. Work in this direction is in progress. Two specific series of simulations that demonstrate how peptides self-organize and function in membranes are discussed. In one series of simulations, it was shown that nonpolar peptides, disordered in water, translocate to the nonpolar interior of the membrane and, simultaneously, fold into two different helical structures, which remain in equilibrium. Once in the membrane, the peptides can readily change their orientation, especially in response to local electric fields. This structural and orientational flexibility of peptides with changing conditions may have provided a mechanism of transmitting signals between the environment and the interior of the protocell. In another series of simulations, the mechanism by which a simple protein channel efficiently mediates proton transport across membranes was investigated. This process is a key step in cellular bioenergetics. In the channel under study, proton transport is gated by four histidines that occlude the channel pore. The simulations demonstrate that protons move through the gate by a "shuttle" mechanism, wherein one histidine is protonated on the extracellular side and, subsequently, the proton bound on the opposite side is released.

Pohorille, Andrew↗
NASA NTRS1991

NASA SETI microwave observing project: Sky Survey element

The SETI Sky Survey Observing Program is one of two complimentary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the sky survey is to search the entire sky over the frequency range of 1.0 to 10.0 GHz for evidence of narrow band signals of extraterrestrial intelligent origin. Frequency resolutions of 30 Hz or narrower will be used across the entire band. Spectrum analyzers with upwards of ten million channels are required to keep the survey time approximately 6 years. Data rates in excess of 10 megabits per second will be generated in the data taking process. Sophisticated data processing techniques will be required to determine the ever changing receiver baselines, and to detect and archive potential SETI signals. Existing radio telescopes, including several of NASA's Deep Space Network (DSN) 34 meter antennas located at Goldstone, CA and Tidbinbilla, Australia will be used for the observations. The JPL has the primary responsibility to develop and carry out the sky survey. In order to lay the foundation for the full scale SETI Sky Survey, a prototype system is being developed at the JPL. The system will be installed at the new 34-m high efficiency antenna at the Deep Space Station (DSS) 13 research and development station, Goldstone, CA, where it will be used to initiate the observational phase of the NASA SETI Sky Survey. It is anticipated that the early observations will be useful to test signal detection algorithms, scan strategies, and radio frequency interference rejection schemes. The SETI specific elements of the prototype system are: (1) the Wide Band Spectrum Analyzer (WBSA); a 2-million channel fast Fourier transformation (FFT) spectrum analyzer which covers an instantaneous bandpass of 40 MHz; (2) the signal detection processor; and (3) the SETI Sky Survey Manager, a network-based C-language environment that provides observatory control, performs data acquisition and analysis algorithms. A high level description of the prototype hardware and software systems will be given and the current status of the system development will be reported.

Klein, M. J.↗
NASA NTRS2019

Applying the Dark Target Aerosol Algorithm with Advanced Himawari Imager Observations During the KORUS-AQ Field Campaign

For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The Korean–United States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during May–June 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here,we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD in-creases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70t hat may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40 degree glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, reevaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm.

Gupta, Pawan↗
NASA NTRS1989

Multinode reconfigurable pipeline computer

A multinode parallel-processing computer is made up of a plurality of innerconnected, large capacity nodes each including a reconfigurable pipeline of functional units such as Integer Arithmetic Logic Processors, Floating Point Arithmetic Processors, Special Purpose Processors, etc. The reconfigurable pipeline of each node is connected to a multiplane memory by a Memory-ALU switch NETwork (MASNET). The reconfigurable pipeline includes three (3) basic substructures formed from functional units which have been found to be sufficient to perform the bulk of all calculations. The MASNET controls the flow of signals from the memory planes to the reconfigurable pipeline and vice versa. the nodes are connectable together by an internode data router (hyperspace router) so as to form a hypercube configuration. The capability of the nodes to conditionally configure the pipeline at each tick of the clock, without requiring a pipeline flush, permits many powerful algorithms to be implemented directly.

Nosenchuck, Daniel M.↗
DOE OSTI2023

Recent Developments in Hydrogeologic Applications for Strain Tensor Analyses

Changes in fluid pressure deform porous media and this effect occurs in a variety of hydrogeologic processes, from the change in storage during pumping or injection to fluctuations in water levels caused by barometric pressure. We have developed instruments for measuring small strains in porous media, and we have used the resulting strain data to evaluate well testing, hydraulic fracturing, manual loading at the ground surface, and ambient hydrologic processes, like rainfall and evaporation. A particularly important application is the use of strain tensor data measured at shallow depths to analyze well tests or hydraulic fractures conducted at much greater depths. An early demonstration of this technique was conducted at the North Avant Field north of Tulsa, Oklahoma, where Pennsylvanian sandstone creates a confined aquifer and oil reservoir at a depth of 530m. We have showed that the strains caused by injecting into the aquifer could be measured at a depth of 30m and used to evaluate the properties of the aquifer. We recently expanded the array of strainmeters at the North Avant Field by deploying three more instruments at shallow depth (30m) along with a deep instrument at 520m depth in the winter, 2021. The deep instrument is deployed in shale caprock slightly above the aquifer. To our knowledge, the deep strainmeter at the North Avant Field is the deepest strainmeter ever deployed and this required refining methods originally developed for shallow deployments. The instrument was lowered to depth on oil field tubing and cemented in place using techniques and materials developed for use in oil wells. Optical fiber used to communicate with the instrument was cemented in the annulus on the outside of tubing. This is significant because the techniques we used could readily be extended to greater depth, suggesting that strainmeters can be deployed over a wide range of depths for monitoring critical subsurface processes. For example, it suggests that strainmeters could be deployed through the caprock to monitor for leaks from underlying CO2 storage reservoirs. The strainmeter array at the North Avant Field has been used to characterize deformation during a series of injection tests in the spring and summer, 2021. All the new strainmeters respond to pumping, and the strainmeters we installed earlier also responded. To our knowledge, this is the first application of well testing in a deep aquifer that was monitored by an array of strainmeters—our earlier work used strainmeters at a single location. We are currently analyzing the strain data using an analytical solution, a proxy-based Bayesian inversion algorithm, and other methods. Strainmeter data has also been used to characterize periodic pumping tests by us and Riley Blais. A periodically varying pumping rate causes both hydraulic head and strain signals that vary with the same period as the pumping. The peaks and troughs of the head in monitoring wells lag behind the peaks and troughs of the head in the pumping well, and this lag time increases with distance from the pumping well. The lag time of the pressure and the distance to the monitoring well can be used in a simple analysis to estimate the hydraulic diffusivity of the aquifer. The lag time determined from strain data can be used to estimate aquifer properties using the same analysis that works for the pressure only for strain data measured at particular locations. That is because the strain field in a confining unit advances upward, laterally and then downward even though the pressure in the underlying aquifer only advances laterally, according to our recent simulations. We have field data showing that a small periodic signal superimposed on an injection rate at the North Avant Field will create a periodic strain signal at shallow strainmeters. The field data and the recent simulations suggest that including a periodic component to injection or pumping and then monitoring the resulting strain signal could be a way to monitor the subsurface.

Murdoch, Larry↗