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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental Fuel Consumption Results from a Heterogeneous Four-Truck Platoon

Platooning has the potential to reduce greenhouse gas missions of heavy-duty vehicles. Prior platooning studies have chiefly focused on the fuel economy characteristics and three-truck platoons, and most have investigated aerodynamically homogeneous platoons with trucks of the same trim. For real world application and accurate return on investment for potential adopters, non-uniform platoons and the impacts of grade and disturbances on a platoon’s fuel economy must also be characterized. This study investigates the fuel economy of a heterogeneous four-truck platoon on a closed test track. Tests were run for one hour at a speed of 45 mph. The trucks used for this study are two 2015 Peterbilt 579’s with a Cummins ISX15 and a Paccar MX-13, and two 2009 Freightliner M915A5’s, one armored and the other unarmored. Many analysis methodologies were leveraged to describe and compare the fuel data, including lap-wise and track-segment analysis. The methodology for dividing the data into laps is described in detail. The influence of other factors beyond the aerodynamics of platooning is discussed. CAN fuel rate analysis showed excellent agreement with previous experimental trends for two and three-truck platoons. In general, the indicated fuel economy benefits in this study were 5-11% for following vehicles and 0-4% for the lead vehicle in platoon relative to their baseline fuel consumption. On a cumulative basis, all platoons saved fuel, ranging from 6% to 8% versus the sum of the standalone trucks’ fuel consumption. The practical implications of the fuel economy results are discussed, as well as avenues for future research. Introduction and Motivation platooning is controlled coordination of two or more vehicles in a convoy, sometimes called CACC (Coordinated or Cooperative Adaptive Cruise Control). The distance between vehicles in a platoon can be controlled tightly at no additional fatigue to the driver. Platooning vehicles can also respond quickly to braking events of the leader, much more quickly than the typical human driver’s reaction time of 1-1.5 s. Therefore, vehicles in a platoon can follow each other much more closely than would usually be considered a safe following distance under human operation. It is implied that under very close following conditions, platooning technology must be extremely robust before it is safe for wide-scale implementation. Platooning is under investigation as a fuel-saving technology. Close-following significantly reduces aerodynamic drag for both leading and trailing vehicles. According to the NRC in 2010, aerodynamic drag represents roughly half of a Class-8 truck’s on-highway fuel usage, meaning a 20% reduction in drag roughly equals a 10% reduction in fuel usage, if all other sources of energy loss remain equal (i.e. accessory, rolling resistance, drivetrain, braking). It is by aerodynamics that platooning saves fuel. At risk of oversimplifying the aerodynamics, following (or trailing) vehicles experience reduced wind velocity due to shielding, and the leading vehicles experience an increased aft pressure, especially at distances closer than 75’ (23 m).

greenhouse gas emissions, truck platoon, heavy-dut↗
NASA NTRS2013

Memory-Efficient Onboard Rock Segmentation

Rockster-MER is an autonomous perception capability that was uploaded to the Mars Exploration Rover Opportunity in December 2009. This software provides the vision front end for a larger software system known as AEGIS (Autonomous Exploration for Gathering Increased Science), which was recently named 2011 NASA Software of the Year. As the first step in AEGIS, Rockster-MER analyzes an image captured by the rover, and detects and automatically identifies the boundary contours of rocks and regions of outcrop present in the scene. This initial segmentation step reduces the data volume from millions of pixels into hundreds (or fewer) of rock contours. Subsequent stages of AEGIS then prioritize the best rocks according to scientist- defined preferences and take high-resolution, follow-up observations. Rockster-MER has performed robustly from the outset on the Mars surface under challenging conditions. Rockster-MER is a specially adapted, embedded version of the original Rockster algorithm ("Rock Segmentation Through Edge Regrouping," (NPO- 44417) Software Tech Briefs, September 2008, p. 25). Although the new version performs the same basic task as the original code, the software has been (1) significantly upgraded to overcome the severe onboard re source limitations (CPU, memory, power, time) and (2) "bulletproofed" through code reviews and extensive testing and profiling to avoid the occurrence of faults. Because of the limited computational power of the RAD6000 flight processor on Opportunity (roughly two orders of magnitude slower than a modern workstation), the algorithm was heavily tuned to improve its speed. Several functional elements of the original algorithm were removed as a result of an extensive cost/benefit analysis conducted on a large set of archived rover images. The algorithm was also required to operate below a stringent 4MB high-water memory ceiling; hence, numerous tricks and strategies were introduced to reduce the memory footprint. Local filtering operations were re-coded to operate on horizontal data stripes across the image. Data types were reduced to smaller sizes where possible. Binary- valued intermediate results were squeezed into a more compact, one-bit-per-pixel representation through bit packing and bit manipulation macros. An estimated 16-fold reduction in memory footprint relative to the original Rockster algorithm was achieved. The resulting memory footprint is less than four times the base image size. Also, memory allocation calls were modified to draw from a static pool and consolidated to reduce memory management overhead and fragmentation. Rockster-MER has now been run onboard Opportunity numerous times as part of AEGIS with exceptional performance. Sample results are available on the AEGIS website at http://aegis.jpl.nasa.gov.

Burl, Michael C.↗
NASA NTRSDate not supplied

Exploring Environmental and Aerosol Impacts on Maritime Tropical Convection using Airborne Radiometer, Radar, Lidar, and Dropsondes

The field deployment phase of NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) took place around the Philippines during August–October 2019, with the primary goal of jointly investigating aerosols and tropical meteorology at the micro-β to meso-β scales. A suite of instruments was deployed on NASA’s P-3B Orion aircraft to accomplish this mission, including: the Advanced Microwave Precipitation Radiometer (AMPR), Airborne Precipitation and cloud Radar 3rd Generation (APR-3), High Spectral Resolution Lidar 2 (HSRL2), and Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes. To examine potential aerosol influences on maritime tropical convection, P-3 flight segments throughout CAMP2Ex were binned into similar environmental groups using “low,” “medium,” and “high” values of nine AVAPS-derived parameters with known physical connections to convective frequency and/or intensity. Aerosol concentrations in each flight segment were evaluated using three HSRL2 variables at 355 and 532 nm: aerosol backscatter, aerosol extinction, and aerosol optical thickness. A set of radiometer- and radar-derived variables directly related to convective frequency and/or intensity was used to characterize convection, which included: AMPR-derived integrated cloud liquid water; polarization-corrected temperatures at 10.7, 19.35, 37.1, and 85.5 GHz; peak equivalent radar reflectivity factor (ZH); peak height of 30-dBZ ZH; and the number of radar data columns with composite ZH > 30 dBZ. The ZH analyses were performed using both Ku- and Ka-band APR-3 data. For each flight segment, correlation coefficients were calculated between each convective parameter and aerosol concentrations within the “low,” “medium,” and “high” groups for each environmental variable. The environmental stratification thresholds were then varied in a series of sensitivity tests. Several noteworthy correlations were observed between the convective and aerosol parameters within the environmental groups, especially when stratifying the environments based on their 850–500-hPa temperature lapse rate, 700–500-hPa temperature lapse rate, and K-Index. The convective parameters were often correlated most strongly with 355-nm extinction, 532-nm extinction, and 532-nm backscatter, while the presence of precipitation-sized liquid and ice hydrometeors contributed to some unexpected negative correlations. In general, as environmental conditions became more favorable for convection, a stronger correlation was observed between the convective parameters and aerosol concentrations. However, a “Goldilocks” zone of medium aerosol concentration was correlated most strongly with the convective parameters in many cases. These results stress the importance of considering environmental and aerosol conditions together when evaluating their impacts on convection. This presentation will provide a detailed discussion of these results and their implications, a summary of the limitations associated with such an analysis, and suggestions for future work.

Corey G. Amiot↗
NASA NTRS1983

Edge discrimination as applied to Thematic Mapper data

An evaluation of suitable edge discrimination techniques and their application to image segmentation is reported. From an analysis of Thematic Mapper Simulator data, it is concluded that segmentation by automated edge discrimination is a valuable technique which can be used in the development of per-field classifiers. A Laplacian convolution operator appears to be the most cost-effective high-pass filter. Spatial frequency domain filtering is more versatile in its ability to enhance different edge types. A simple global gray value threshold can produce good edge discrimination from an enhanced image which may be improved by using a local thresholding technique. A gap-fill postprocessing technique is necessary for useful segmentation. Gradient and other directionally dependent techniques are unsuitable for segmentation.

Hall, J. R.↗
NASA NTRS2018

Local Versus Remote Contributions of Soil Moisture to Near-Surface Temperature Variability

Soil moisture variations have a straightforward impact on overlying air temperatures, wetter soils can induce higher evaporative cooling of the soil and thus, locally, cooler temperatures overall. Not known, however, is the degree to which soil moisture variations can affect remote air temperatures through their impact on the atmospheric circulation. In this talk we describe a two-pronged analysis that addresses this question. In the first segment, an extensive ensemble of NASA/GSFC GEOS-5 atmospheric model simulations is analyzed statistically to isolate and quantify the contributions of various soil moisture states, both local and remote, to the variability of air temperature at a given local site. In the second segment, the relevance of the derived statistical relationships is evaluated by applying them to observations-based data. Results from the second segment suggest that the GEOS-5-based relationships do, at least to first order, hold in nature and thus may provide some skill to forecasts of air temperature at subseasonal time scales, at least in certain regions.

Koster, R.↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
NASA NTRS2021

On the Cycling of 231 Pa and 230 Th in Benthic Nepheloid Layers

The naturally-occurring radionuclides protactinium-231 ( 231 Pa) and thorium-230 ( 230 Th) are produced at approximately uniform rates in the ocean and thought to be removed from the water column through a reversible exchange with settling particles. Recent measurements along the U.S. GEOTRACES North Atlantic transect (GA03) revealed two features which are at odds with current understanding about 231 Pa and 230 Th cycling in the ocean: (i) a sharp decrease in dissolved 231 Pa ( 231 Pa d ) and 230 Th ( 230 Th d ) activities with depth below 2000-4000 m and (ii) very high particulate 231 Pa ( 231 Pa p ) and 230 Th ( 230 Th p ) activities near the bottom, at a number of stations between the New England continental shelf and Bermuda. Concomitant measurements of light attenuation from beam transmissometry showed that both features occur in benthic nepheloid layers (BNLs), which suggests that these features may stem, at least partly, from the presence of resuspended sediment in the deep water column. Here we explore the behaviour of 231 Pa and 230 Th in BNLs by using (i) radionuclide, optical, and hydrographic data from the western segment of GA03 (west of Bermuda) and (ii) a simplified model of particle and radionuclide cycling that includes a lateral particle source. First, the BNLs observed at GA03 stations are characterized from measurements of the beam attenuation coefficient converted to particle concentrations. At all stations, particle concentrations below the clear water minimum were the highest in the bottom mixed layer, whose thickness ranged from 95 to 320 m, and decreased generally with height above the bottom. The thickness of strong BNLs varied from 482 to 1358 m and the vertical integral of particle concentration in excess to that at the clear water minimum varied from 1 x 10 4 to 2 x 10 6 mg m −2 , among different stations. Second, the particle-radionuclide model is fitted to data from stations GT11-04 (New England continental rise) and GT11-08 (Hatteras abyssal plain), where samples for radionuclide analyses were collected in the BNL. The model can reproduce simultaneously the increase of particle concentration with depth, the low 231 Pa d and 230 Th d in the BNLs, and the high 231 Pa p and 230 Th p near the bottom. According to the model, at heights less than about 300 m above the seafloor, the dissolved phase was set primarily by a balance between adsorption and desorption, with vertical turbulent mixing playing a secondary role, whilst the particulate phase behaved largely as a non-reactive constituent supplied laterally and transported vertically by particle settling and turbulent mixing. Sensitivity tests with the model suggest that lateral particle sources near continental slopes and similar reliefs can produce significant biases both in the 230 Th normalization method and in the interpretation of sediment 231 Pa/ 230 Th records. Our findings yield insights into the influence of sediment resuspension and transport on 231 Pa and 230 Th in the deep ocean and highlight the need for considering these processes in paleoceanographic applications.

Nepheloid layer↗
NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗
DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗
NASA NTRS1998

Spatial Distribution of Ionospheric Plasma and Field Structures in the High-Latitude F Region

Ion density and velocity measurements from the Dynamics Explorer 2 (DE 2) spacecraft are used to obtain the average magnetic local time versus invariant latitude distribution of irregularities in the high-latitude F region ionosphere. To study the small-scale structure and its relationship to background conditions in the ionosphere, we have formed a reduced database using 2-s (approx. = 16 km) segments of the ion density and velocity data. The background gradients associated with each 2-s segment and the spectral characteristics, such as power at 6 Hz (approx. = 1.3 km) and spectral index, are among the reduced parameters used in this study. The relationship between the observed plasma structure and its motion is complex and dependent on the externally applied fields as well as locally generated plasma structure. The evolution of plasma structures also depends critically on the conductivity of the underlying ionosphere. Observations indicate an enhancement of irregularity amplitudes in two spatially isolated regions in both the ion density and the velocity. Convective properties seem to play a more important role in winter hemisphere where smaller-scale structures are maintained outside the source regions. (Delta)V irregularity amplitudes are enhanced in the cusp and the polar cap during northward interplanetary magnetic field regardless of season. The power in (Delta)V is usually higher than that associated with local polarization electric fields, suggesting that the observed structure in (Delta)N/N is strongly influenced by (Delta)V structure applied to large density gradients.

Kivanc, O.↗
NASA NTRS1974

Jet transport noise - A comparison of predicted and measured noise for ILS and two-segment approaches

Centerline noise measured during standard ILS and two-segment approaches in DC-8-61 aircraft were compared with noise predicted for these procedures using an existing noise prediction technique. Measured data is considered to be in good agreement with predicted data. Ninety EPNdB sideline locations were calculated from flight data obtained during two-segment approaches and were compared with predicted 90 EPNdB contours that were computed using three different models for excess ground attenuation and a contour with no correction for ground attenuation. The contour not corrected for ground attenuation was in better agreement with the measured data.

White, K. C.↗
DOE OSTI2020

Electronic Scanning Strategies in Adaptive Electrical Capacitance Volume Tomography: Tradeoffs and Prospects

Electrical Capacitance Volume Tomography (ECVT) has been applied for imaging of multiphase flows found in industrial applications. The ill-posed nature of the image reconstruction problem in ECVT and the consequent low resolution can be alleviated by employing electronic scanning enabled by electrode segmentation and reconfiguration during data acquisition. Here we study electronic scanning strategies that mimic physical rotation and shifting of the sensor along its symmetry axis. First, we study the feasibility of electronic scanning by analyzing the capacitance transducer circuit in SPICE. Then, we simulate electronic scanning using the finite element method for different electrode shapes, for which we compare the image reconstruction results and acquisition time. We find a noticeable improvement in image resolution for the scanning cases over conventional ECVT. Lastly, among the scanning cases, we emphasize a particular electrode shape that provides the best image resolution along with the minimal amount of acquisition time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

TICC Clustering Library v.1.0

SAND2024-01234O TICC is a clustering algorithm that labels a sequence of data points according to numerical properties. This library is a Python implementation of the algorithm described in "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data" (Hallac et al. 2017). It includes documentation, performance improvements, examples, and test coverage. This library allows users to automatically segment a series of multivariate data points according to their covariance—that is, the way the values at each data point are changing in relation to one another. This is useful for identifying periods in which a system is behaving. For example, if a sensor is measuring a car's velocity, steering wheel angle, braking and acceleration, TICC can determine when the car was stopped, beginning/exiting a turn, slowing or accelerating at an intersection, or driving on straight or curved roads. TICC can be applied to measure multiple quantities at known times. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Dalbey, Keith↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗
NASA NTRS1975

Combustor exhaust-emissions and blowout-limits with diesel number 2 and jet A fuels utilizing air-atomizing and pressure atomizing nozzles

Experimental tests with diesel number 2 and Jet A fuels were conducted in a combustor segment to obtain comparative data on exhaust emissions and blowout limits. An air-atomizing nozzle was used to inject the fuels. Tests were also made with diesel number 2 fuel using a pressure-atomizing nozzle to determine the effectiveness of the air-atomizing nozzle in reducing exhaust emissions. Test conditions included fuel-air ratios of 0.008 to 0.018, inlet-air total pressures and temperatures of 41 to 203 newtons per square centimeter and 477 to 811 K, respectively, and a reference velocity of 21.3 meters per second. Smoke number and unburned hydrocarbons were twice as high with diesel number 2 as with Jet A fuel. This was attributed to diesel number 2 having a higher concentration of aromatics and lower volatility than Jet A fuel. Oxides of nitrogen, carbon monoxide, and blowout limits were approximately the same for the two fuels. The air-atomizing nozzle, as compared with the pressure-atomizing nozzle, reduced oxides-of-nitrogen by 20 percent, smoke number by 30 percent, carbon monoxide by 70 percent, and unburned hydrocarbons by 50 percent when used with diesel number 2 fuel.

Ingebo, R. D.↗
NASA NTRS1976

Experiment to Evaluate the Feasibility of Utilizing Skylab-EREP Remote Sensing Data for Tectonic Analysis Through a Study of the Big Horn Mountain Region, Wyoming, South Dakota and Wyoming

The author has identified the following significant results. S190B imagery was the best single product from which fairly detailed structural and some lithologic mapping could be accomplished in the Big Horn basin, the Owl Creek Mountains, and the northern Big Horn Mountains. The Nye-Bowler lineament could not be extended east of its presently mapped location although a linear (fault or monocline) was noted that may be part of the lineament, but north of postulated extensions. Much more structure was discernible in the Big Horn basin than could be seen on LANDSAT-1 imagery; RB-57 color IR photography, in turn, revealed additional folds and faults. A number of linears, several of which could be identified as faults and one a monocline, cut obliquely the east-west trending Owl Creek uplift. The heavy forest cover of the Black Hills makes direct lithologic delineation impossible. However, drainage and linear overlays revealed differences in pattern between the areas of exposed Precambrian crystalline core and the flanking Paleozoic rocks. S192 data, even precision corrected segments, were not of much use.

Hoppin, R. A.↗
NASA NTRS1976

System for analysis of LANDSAT agricultural data: Automatic computer-assisted proportion estimation of local areas

The author has identified the following significant results. A conceptual man machine system framework was created for a large scale agricultural remote sensing system. The system is based on and can grow out of the local recognition mode of LACIE, through a gradual transition wherein computer support functions supplement and replace AI functions. Local proportion estimation functions are broken into two broad classes: (1) organization of the data within the sample segment; and (2) identification of the fields or groups of fields in the sample segment.

Nalepka, R. F.↗
NASA NTRS1979

LACIE registration processing

The basic requirements for the LACIE processing system are to extract specified test sites (sample segments) from LANDSAT MSS data, and to apply geometric corrections and perform correlations to ensure registration between successive data acquisitions to within 1 pixel (root mean square). The general flow within the LACIE processing system is described with emphasis on (1) determination of line and pixel location of a search area within an MSS frame; (2) determination of the geometric correction coefficient and the application of geometric corrections; (3) edge detection; and (4) correlation by coincidence of edges.

Grebowsky, G. J.↗