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At least 253 records · Page 14
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NASA NTRS2002

Changes in Earth 360 380 nm Reflectivity: 1980 2000

The Total Ozone Mapping Spectrometer (TOMS) reflectivity time series 1980 to 1992 and 1997 to 2000 have been combined to estimate change that have occurred over a 21 year period. The relative calibration of the two TOMS (Nimbus-7, N7 and Earth-Probe, EP) has been adjusted using the measured minimum reflectivity over mid-latitude ocean and land locations (50o). The result is that the N7 reflectivity has been decreased by 0.02 to match the better-calibrated Earth-Probe/TOMS. Most of the local trend features seen in the N7 time series (1980 to 1992) have been continued in the combined time series, but the overall zonal average and global trends have changed. The correlation of cloud cover with solar activity (measured by the 10.7 cm solar radiation) that was present during the Nimbus-7 period (1980 to 1992) is no longer evident for the period 1980 to 2000. The UV reflectivity data are compared with changes in the Advanced Very High Resolution Radiometer (AVHRR) Outgoing Long-wavelength Radiation (OLR) and show an expected anti-correlation with cloud-cover changes over the same period for many, but not all, features. The key results include a continuing decrease in cloud cover over Europe and North America and an increase in reflectivity near climatology should be reduced by 0.02. The TOMS reflectivity time series 1980 to 1992 and 1997 to 2000 have been Antarctica. This also means that the previously published N7 UV surface reflectivity.

Herman, J. R.↗
NASA NTRS2002

Changes in Earth 360 to 380 nm Reflectivity: 1980 to 2000

The TOMS reflectivity time series 1980 to 1992 and 1997 to 2000 have been combined to estimate change that have occurred over a 21 year period. The relative calibration of the two TOMS (Nimbus-7, N7 and Earth-Probe, EP) has been adjusted using the measured minimum reflectivity over mid-latitude ocean and land locations (50 deg.). The result is that the N7 reflectivity has been decreased by 0.02 to match the better-calibrated Earth-Probe/TOMS. Most of the local trend features seen in the N7 time series (1980 to 1992) have been continued in the combined time series, but the overall zonal average and global trends have changed. The correlation of cloud cover with solar activity (measured by the 10.7 cm solar radiation) that was present during the Nimbus-7 period (1980 to 1992) is no longer evident for the period 1980 to 2000. The UV reflectivity data are compared with changes in the AVHRR outgoing long-wavelength radiation (OLR) and show an expected anti-correlation with cloud-cover changes over the same period for many, but not all, features. The key results include a continuing decrease in cloud cover over Europe and North America and an increase in reflectivity near Antarctica. This also means that the previously published N7 UV surface reflectivity climatology should be reduced by 0.02.

Herman, J. R.↗
NASA NTRS2007

Remote and Ground Truth Spectral Measurement Comparisons of FORMOSAT III

FORMOSAT III are a set of six research satellites from Taiwan that were launched in April 2006. The satellites are in 800 km, 71 degree inclination orbits and separated by 24 degrees in ascending node. Laboratory spectral measurements were taken of outer surface materials on FORMOSAT III. From those measurements, a computer model was built to predict the spectral reflectance accounting for both solar phase angle and orientation of the spacecraft relative to the observer. However, materials exposed to the space environment have exhibited spectral changes including a darkening and a "reddening" of the spectra. This "reddening" is characterized by an increase in slope of the reflectance as the wavelength increases. Therefore, the model of pre-flight materials was augmented to include the presumed causative agent: space weathering effects. Remote data were collected on two of the six FORMOSAT satellites using the 1.6 meter telescope at the AMOS (Air Force Maui Optical and Supercomputing) site with the Spica spectrometer. Due to the separation in ascending node, observations were acquired of whichever one of the six satellites was visible on that specific night. Three nights of data were collected using the red (6000 - 9500 angstroms) filter and five nights of data were collected using the blue (3200 - 6600 angstroms) filter. A comparison of the data showed a good match to the pre-flight models for the blue filter region. The absorption feature near 5500 angstroms due to the copper colored Kapton multi-layer insulation (MLI) was very apparent in the remote samples and a good fit to the data was seen in all satellites observed. The features in the red filter regime agreed with the pre-flight model up through 7000 angstroms where the reddening begins and the slope of the remote sample increases. A comparison of the satellites showed similar features in the red and blue filter regions, i.e. the satellite surfaces were aging at the same rate. A comparison of the pre-flight model to the first month of remote measurements showed the amount by which the satellite had reddened. The second month of data observed a satellite at a higher altitude and was therefore, not compared to the first month. A third month of data was collected but of satellites at the lower altitude regime and can only be compared to the first month. One cause of the reddening that was ruled out in early papers was a possible calibration issue.

Abercromby, Kira Jorgensen↗
NASA NTRS2017

Extension of HCDstruct for Transonic Aeroservoelastic Analysis of Unconventional Aircraft Concepts

A substantial effort has been made to implement an enhanced aerodynamic modeling capability in the Higher-fidelity Conceptual Design and structural optimization tool. This additional capability is needed for a rapid, physics-based method of modeling advanced aircraft concepts at risk of structural failure due to dynamic aeroelastic instabilities. To adequately predict these instabilities, in particular for transonic applications, a generalized aerodynamic matching algorithm was implemented to correct the doublet-lattice model available in Nastran using solution data from a priori computational fluid dynamics anal- ysis. This new capability is demonstrated for two tube-and-wing aircraft configurations, including a Boeing 737-200 for implementation validation and the NASA D8 as a first use case. Results validate the current implementation of the aerodynamic matching utility and demonstrate the importance of using such a method for aircraft configurations featuring fuselage-wing aerodynamic interaction.

Quinlan, Jesse R.↗
NASA NTRS2021

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scientific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns

ocean↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRS2016

Detached Eddy Simulation of Flap Side-Edge Flow

Detached Eddy Simulation (DES) of flap side-edge flow was performed with a wing and half-span flap configuration used in previous experimental and numerical studies. The focus of the study is the unsteady flow features responsible for the production of far-field noise. The simulation was performed at a Reynolds number (based on the main wing chord) of 3.7 million. Reynolds Averaged Navier-Stokes (RANS) simulations were performed as a precursor to the DES. The results of these precursor simulations match previous experimental and RANS results closely. Although the present DES simulations have not reached statistical stationary yet, some unsteady features of the developing flap side-edge flowfield are presented. In the final paper it is expected that statistically stationary results will be presented including comparisons of surface pressure spectra with experimental data.

Simulation↗
NASA NTRS2002

Near-Infrared Spectrophotometry of Phobos and Deimos

We have observed the leading and trailing hemispheres of Phobos from 1.65 to 3.5 micrometers and Deimos from 1.65 to 3.12 micrometers near opposition. We find the trailing hemisphere of Phobos to be brighter than its leading hemisphere by 0.24 plus or minus 0.06 magnitude at 1.65 micrometers and brighter than Deimos by 0.98 plus or minus 0.07 magnitude at 1.65 micrometers. We see no difference larger than observational uncertainties in spectral slope between the leading and trailing hemispheres when the spectra are normalized to 1.65 micrometers. We find no 3-micrometer absorption feature due to hydrated minerals on either hemisphere to a level of approximately 5-10% on Phobos and approximately 20% on Deimos. When the infrared data are joined to visible and nearby data obtained by previous workers, our data suggest the leading (Stickney-dominated) side of Phobos is best matched by T-class asteroids. The spectral slope of the trailing side of Phobos and leading side of Deimos are bracketed by the D-class asteroids. The best laboratory spectral matches to these parts of Phobos are mature lunar soils and heated carbonaceous chondrites. The lack of 3-micrometer absorption features on either side of Phobos argues against the presence of a large interior reservoir of water ice according to current models of Phobos' interior.

Rivkin, A. S.↗
NASA NTRS2002

Near-Infrared Spectrophotometry of Phobos and Deimos

We have observed the leading and trailing hemispheres of Phobos from 1.65 to 3.5 microns and Deimos from 1.65 to 3.12 microns near opposition. We find the trailing hemisphere of Phobos to be brighter than its leading hemisphere by 0.24 +/- 0.06 magnitude at 1.65 microns and brighter than Deimos by 0.98 +/- 0.07 magnitude at 1.65 microns. We see no difference larger than observational uncertainties in spectral slope between the leading and trailing hemispheres when the spectra are normalized to 1.65 microns. We find no 3-micron absorption feature due to hydrated minerals on either hemisphere to a level of approx. 5-10% on Phobos and approx. 20% on Deimos. When the infrared data are joined to visible and near-IR data obtained by previous workers, our data suggest the leading (Stickney-dominated) side of Phobos is best matched by T-class asteroids. The spectral slope of the trailing side of Phobos and leading side of Deimos are bracketed by the D-class asteroids. The best laboratory spectral matches to these parts of Phobos are mature lunar soils and heated carbonaceous chondrites. The lack of 3-micron absorption features on either side of Phobos argues against the presence of a large interior reservoir of water ice according to current models of Phobos' interior.

Rivkin, A. S.↗
NASA NTRS2002

Near-Infrared Spectrophotometry of Phobos and Deimos

We have observed the leading and trailing hemispheres of Phobos from 1.65 to 3.5 microns and Deimos from 1.65 to 3.12 microns near opposition. We find the trailing hemisphere of Phobos to be brighter than its leading hemisphere by 0.24 +/- 0.06 magnitude at 1.65 microns and brighter than Deimos by 0.98 +/- 0.07 magnitude at 1.65 microns. We see no difference larger than observational uncertainties in spectral slope between the leading and trailing hemispheres when the spectra are normalized to 1.65 microns. We find no 3-microns absorption feature due to hydrated minerals on either hemisphere to a level of approx. 5 - 10% on Phobos and approx. 20% on Deimos. When the infrared data are joined to visible and near-IR data obtained by previous workers, our data suggest the leading (Stickney-dominated) side of Phobos is best matched by T-class asteroids. The spectral slope of the trailing side of Phobos and leading side of Deimos are bracketed by the D-class asteroids. The best laboratory spectral matches to these parts of Phobos are mature lunar soils and heated carbonaceous chondrites. The lack of 3-microns absorption features on either side of Phobos argues against the presence of a large interior reservoir of water ice according to current models of Phobos' interior.

Rivkin, A. S.↗
NASA NTRS1990

Unidentified ions in comets

Optical spectra were taken of comets Halley, West, and Brorsen-Metcalf in the wavelength region 3200 to 6400 A. These spectra were offset approximately 10(exp -5) to 10(exp -6) km tailward from the nucleus so that the features detected were all ions with the exception of a very small residual C2 emission at 5165 A. The full labeled tail spectrum of comet Halley is given. While most of the features detected are attributable to CO(+), H2O(+), CO2(+), CH(+), and OH(+), there are three moderately strong bands in the spectra of comets Halley and Brorsen-Metcalf which remain unidentified. These features were not detected in comet West. All three spectra in the 4800 to 5400 A region are presented, Brorsen-Metcalf is the gassiest of the three. The central wavelengths of the unidentified features are 4930, 5300, and 6000 A. In an effort to identify the ions responsible for these features as well as to confirm previous identifications, laboratory spectra of ions were compared with the comet spectra. H2O(+) and CO(+) are known to have extensive emission in the 4800 to 6200 A region. Therefore, the possibility that these unidentified features are due to H2O(+) and CO(+) was investigated. A search for the following bands was conducted: H2O(+) (11-0), H2O(+) (12-0), H2O(+) (13-1), CO(+) (1-1), and CO(+) (0-0). CO(+) (1-1) and CO(+) (0-0) were previously identified and are present in all the spectra examined. The H2O(+) (11-0) band was identified. Comparison of the H2O(+) (11-0) synthetic spectrum with comet Brorsen-Metcalf data is presented. The relative fluxes of the blended H2O(+) lines in the synthetic spectrum match those of the cometary data. After eliminating H2O(+) and CO(+) as significant contributors to the stronger unidentified features, spectra of ions which are not yet identified in the optical region of comets were compared to the data. These ions are NH(+), CS(+), and C2(+). None of these ions appears to be a significant contributor to the optical spectra of these three comets. In conclusion, H2O(+) (11-0) was unabiguously identified in cometary optical spectra. No other molecular ion for which there are laboratory spectra can explain the unidentified features found in the ion tails of comets Halley and Brorsen-Metcalf.

Engel, Lisa↗
NASA NTRS1988

Modal analysis applied to circular, rectangular, and coaxial waveguides

Recent developments in the analysis of various waveguide components and feedhorns using Modal Analysis (Mode Matching Method) are summarized. A brief description of the theory is presented, and the important features of the method are pointed out. Specific examples in circular, rectangular, and coaxial waveguides are included, with comparisons between the theory and experimental measurements. Extensions to the methods are described.

Hoppe, D. J.↗
NASA NTRS1987

Image-analysis techniques for determination of morphology and kinematics in arctic sea ice

SAR data have been used to study sea ice with respect to its motion and formation/deformation. With the prospect of the Alaska SAR Facility development in the near future, there is a great need for robust and efficient sea-ice analysis techniques. This paper presents a sea-ice motion analysis technique that can be used for: (1) local motion analysis of a selected ice patch, and (2) a global ice motion over the entire image area. In order to meet the operational speed requirement (over fifty images per day) a sea-ice motion analysis technique has been developed which requires very little human interaction. The proposed technique uses a subset of easily distinguishable features to predict global motion characteristics. The developed technique is applied to two pairs of SEASAT SAR images, one pair with a minor motion of 'ice pack' and another with a larger and discontinuous motion of 'fast ice'. The new approach enables the development of a set of computer-aided tools for feature selection and registration and the implementation of an optimal search strategy for automatic template matching via a motion prediction model.

Lee, Meemong↗
NASA NTRS2008

Pattern-Recognition System for Approaching a Known Target

A closed-loop pattern-recognition system is designed to provide guidance for maneuvering a small exploratory robotic vehicle (rover) on Mars to return to a landed spacecraft to deliver soil and rock samples that the spacecraft would subsequently bring back to Earth. The system could be adapted to terrestrial use in guiding mobile robots to approach known structures that humans could not approach safely, for such purposes as reconnaissance in military or law-enforcement applications, terrestrial scientific exploration, and removal of explosive or other hazardous items. The system has been demonstrated in experiments in which the Field Integrated Design and Operations (FIDO) rover (a prototype Mars rover equipped with a video camera for guidance) is made to return to a mockup of Mars-lander spacecraft. The FIDO rover camera autonomously acquires an image of the lander from a distance of 125 m in an outdoor environment. Then under guidance by an algorithm that performs fusion of multiple line and texture features in digitized images acquired by the camera, the rover traverses the intervening terrain, using features derived from images of the lander truss structure. Then by use of precise pattern matching for determining the position and orientation of the rover relative to the lander, the rover aligns itself with the bottom of ramps extending from the lander, in preparation for climbing the ramps to deliver samples to the lander. The most innovative aspect of the system is a set of pattern-recognition algorithms that govern a three-phase visual-guidance sequence for approaching the lander. During the first phase, a multifeature fusion algorithm integrates the outputs of a horizontal-line-detection algorithm and a wavelet-transform-based visual-area-of-interest algorithm for detecting the lander from a significant distance. The horizontal-line-detection algorithm is used to determine candidate lander locations based on detection of a horizontal deck that is part of the lander.

Huntsberger, Terrance↗
NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗
NASA NTRS1988

A knowledge-based object recognition system for applications in the space station

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

Dhawan, Atam P.↗
NASA NTRS1988

Topological numbering of features on a mesh

Assume a nxn binary image is given containing horizontally convex features; i.e., for each feature, each of its row's pixels form an interval on that row. The problem of assigning topological numbers to such features is considered; i.e., assign a number to every feature f so that all features to the left of f have a smaller number assigned to them. This problem arises in solutions to the stereo matching problem. A parallel algorithm to solve the topological numbering problem in O(n) time on an nxn mesh of processors is presented. The key idea of the solution is to create a tree from which the topological numbers can be obtained even though the tree does not uniquely represent the to the left of relationship of the features.

Atallah, Mikhail J.↗