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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Halococcus salifodinae sp. nov., an Archaeal Isolate from an Austrian Salt Mine

A novel extremely halophilic archaeon (archaebacterium) was isolated from rock salt obtained from an Austrian salt mine. The deposition of the salt is thought to have occurred during the Permian period (225 x 106 to 280 x 10(exp 6) years ago). This organism grew over a pH range of 6.8 to 9.5. Electron microscopy revealed cocci in tetrads or larger clusters. The partial 16S rRNA sequences, polar lipid composition, and menaquinone content suggested that this organism was related to members of the genus Halococcus, while the whole-cell protein patterns, the presence of several unknown lipids, and the presence of pink pigmentation indicated that it was different from previously described coccoid halophiles. We propose that this isolate should be recognized as a new species and should be named Halococcus salifodinae. The type strain is Bl(sub p) (= ATCC 51437 = DSM 8989). A chemotaxonomically similar microorganism was isolated from a British salt mine.

Denner, Ewald B. M.↗
NASA NTRS1984

Pattern recognition for Space Applications Center director's discretionary fund

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

Singley, M. E.↗
NASA NTRS1992

A fuzzy clustering algorithm to detect planar and quadric shapes

In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications.

Krishnapuram, Raghu↗
NASA NTRS2022

Aerosols Optical and Radiative Properties in Indonesia Based on AERONET Version 3

Heterogeneity in seasonal aerosol types due to different sources, local characteristics, and meteorology lead to different optical and radiative properties from one place to another. This study analyzes the long-term averages of aerosol optical and radiative properties obtained by the Aerosol Robotic Network (AERONET) version 3 at four sites in Indonesia representing fire prone areas (Palangka Raya, Pontianak, Jambi: 2012–2020), and urban-industrial (Bandung: 2009–2018). We examined the seasonal variation of aerosol optical depth (AOD), Angstrom Exponent (AE), Precipitable Water Vapor (PWV), and Single Scattering Albedo (SSA) combined with meteorological parameters at these Indonesian sites. Multiple clustering techniques from different relationships among various aerosol optical and radiative parameters was employed to further study the aerosol type in respect to different seasonality. The results exhibited a distinct seasonal pattern at all sites except Bandung, whereas AOD increased owing to forest and peat fire outbreaks during the dry season. The trend in AOD annual depicts an increase in Bandung as emission from urban/industrial increases episodically, while it depicts a decrease at the three biomass burning sites. As AOD increased, Palangka Raya, Pontianak, and Jambi exhibited wider ranges of observed AE (1.2–1.8) and high FMF (>0.8) in the dry burning period (June to October). On the other hand, Bandung has more aerosol distribution in the range of FMF values 0.6–1 and AE 1–1.6, compared with other sites, suggesting contribution of complex aerosol emission from different sources and likely due to the formation of secondary aerosol production from trace gases. Palangka Raya, Pontianak, and Jambi exhibited non-absorbing characteristics, as indicated by high SSA (>0.95), high FMF, and high EAE values (1.2–1.7) with no discrepancy in seasonal patterns, implying that biomass burning aerosols in Indonesia tend to cool the atmosphere with strong scattering rather than absorbing. At Bandung, aerosols mixed with slightly absorbing and non-absorbing aerosols owing to urban and industrial emissions. This study lays a foundation for further research on the contribution of aerosol radiative forcing to climate within the complex aerosol and cloud environments in Indonesia.

Aerosol optical depth↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS1979

Shallow convection on day 261 of GATE - Mesoscale arcs

Cloudy convection in the moist layer of a cloud cluster growing in the GATE ship array is examined. Analyses suggest that the moist layer was dominated by features of horizontal dimension roughly 40 km and lifetime roughly 2 h, with arc patterns triggered by dense downdraft air accompanying rainfall, and composed of many small cumulus clouds. Aircraft recorded data on thermodynamic quantities and winds, indicating that the arcs persisted as mesoscale circulations driven by the release of latent heat in the clouds, rather than being driven by the original density current at the surface. It is also suggested that the mesoscale cloud patterns of the moist layer play a primary role in heat transfer upward within this layer, and contribute to the forcing of showering midtropospheric clouds.

Warner, C.↗
NASA NTRS1988

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Gettig, Gary A.↗
NASA NTRS2017

Large-Scale Circulation and Climate Variability

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Perlwitz, J.↗
NASA NTRS2001

Extrapolation of the dna fragment-size distribution after high-dose irradiation to predict effects at low doses

The patterns of DSBs induced in the genome are different for sparsely and densely ionizing radiations: In the former case, the patterns are well described by a random-breakage model; in the latter, a more sophisticated tool is needed. We used a Monte Carlo algorithm with a random-walk geometry of chromatin, and a track structure defined by the radial distribution of energy deposition from an incident ion, to fit the PFGE data for fragment-size distribution after high-dose irradiation. These fits determined the unknown parameters of the model, enabling the extrapolation of data for high-dose irradiation to the low doses that are relevant for NASA space radiation research. The randomly-located-clusters formalism was used to speed the simulations. It was shown that only one adjustable parameter, Q, the track efficiency parameter, was necessary to predict DNA fragment sizes for wide ranges of doses. This parameter was determined for a variety of radiations and LETs and was used to predict the DSB patterns at the HPRT locus of the human X chromosome after low-dose irradiation. It was found that high-LET radiation would be more likely than low-LET radiation to induce additional DSBs within the HPRT gene if this gene already contained one DSB.

NASA Discipline Radiation Health↗
NASA NTRS1995

Effect of Stacking Faults on the X-Ray Diffraction Profiles of Beta-SiC Powders

X-ray diffraction patterns or beta-SiC (3C or the cubic polytype or sic) powders often exhibit an additional peak at d = 0.266 nm, high background intensity around the (111) peak, and relative intensities for peaks which differ from those predicted from the crystal structure. Computer simulations were used to show that all these features are due to stacking faults in the powders and not due to the presence of other polytypes in the powders. Such simulations allow diffraction patterns to be generated for different types, frequencies, and spatial distribution or faults. Comparison of the simulation results to the XRD data indicates that the B-SiC particles consist either of heavily faulted clusters distributed irregularly between regions that have only occasional faults or twins, or the powders consist of two types of particles with different populations of faults: those with a high density of faults and those with only twins or occasional faults. Additional information is necessary to determine which description is correct. However, the simulation results can be used to rule out certain fault configurations.

Pujar, Vijay V.↗
NASA NTRS2011

The Solar Wind as a Magnetofluid Turbulence Laboratory

The solar wind is the Sun's exosphere. As the solar atmosphere expands into interplanetary space, it is accelerated and heated. Data from spacecraft located throughout the heliosphere have revealed that this exosphere has velocities of several hundred kilometers/sec, densities at Earth orbit of about 5 particles/cu cm, and an entrained magnetic field that at Earth orbit that is about 5 10-5 Gauss. A fascinating feature of the solar wind is that the magnetic field fluctuates in a way that is highly reminiscent of "Alfven waves, i.e., the fluctuating magnetic fields are more-or-less aligned with fluctuations in the velocity of the plasma and, with proper normalization, have approximately equal magnitudes. The imperfect (observed) alignment leads to a variety of complex interactions. In many respects, the flow patterns appear to be an example of fully developed magneto fluid turbulence. Recently, the dissipation range of this turbulence has been studied using search coil magnetometer data from the STAFF instrument on the four Cluster spacecraft. I will attempt to give an overview of selected properties of this large-scale and small-scale turbulence.

Goldstein, Melvyn L.↗
NASA NTRSDate not supplied

Trace Elemental Abundances in Calcium-Aluminum-Rich Inclusions in CV Chondrites

Introduction: Calcium-aluminum-rich inclusions (CAIs), are the first formed solids that define the age of the Solar System [1,2]. CAIs are thought to have condensed from nebular gas [3,4] within the first <1 Ma of Solar System formation [5,6]. CAIs have experienced numerous early Solar System processes including condensation, evaporation, melting, recrystallization, and aqueous alteration [e.g., 7]. The chemical, mineralogical, and textural diversity among CAIs results from a range of chemical and physical processes recorded during nebular and parent body epoch. This study aims to explore the mineralogical, textural, and chemical compositions of CAIs including the trace elemental abundances in CAI phases to determine the early Solar System processes recorded in them. Samples and Analytical Methods: We analyzed one CAI each from CV3 chondrites Northwest Africa (NWA) 5508 designated as ‘Saguaro’, and Northwest Africa (NWA) 12772 designated as ‘Hoopoe’. Back-scatter electron (BSE) images were collected using a Phenom XL scanning electron microscope (SEM) at the Lunar and Planetary Institute (LPI) and the JEOL JXA-8530F electron probe microanalyzer (EPMA) at Johnson Space Center (JSC)-NASA. Additionally, energy dispersive X-ray spectrometry (EDS) elemental maps of select areas for these samples were collected using a 15.0kV beam energy and a 40µA emission current. Using the EPMA, wavelength-dispersive X-ray spectroscopy (WDS) quantitative data were collected. In-situ trace element measurements for both CAIs were determined at JSC-NASA using a Photon Machines 193nm laser ablation system and a Thermo-Scientific Element-XR inductively coupled plasma mass spectrometer (ICP-MS). Analyses consisted of 30s ablations at 10Hz, spot sizes of 20-25µm, and a fluence of 6.0 J/cm2 for anorthite and melilite, and a 3.5 J/cm2 fluence for all other phases. NIST612 was used to correct for instrument drift, while BHVO-2g was used as a primary calibration standard. BCR-2g and in-house mineral standards were regularly measured as unknowns to ensure accuracy. Results: Saguaro is a coarse-grained CAI, ~11 x 6 mm in dimensions. Saguaro contains spinel, Al-rich pyroxene, anorthite, Mg-rich melilite, and minor perovskite in its interior and is therefore classified as a Type B CAI. Individual melilite grains shows normal compositional zoning with an Ak content ranging from ~24 to 54 with no apparent trend from the core to the edge of the CAI. The spinel appears euhedral and occurs both as clusters and as spinel palisades [8]. Two rim sequences surround most of the sample: the inner rim being a Wark-Lovering (WL) rim (~10-35 µm) containing pyroxene, spinel, and melilite (or anorthite), and the outer rim is a finer-grained, thicker (~100 µm), accretionary rim (Fig. 1). The mineral phases in Saguaro record an overall flat REE pattern with an average negative Eu anomaly in pyroxene, and an average positive Eu anomaly in anorthite and melilite respectively. Anorthite, melilite, and pyroxene have a minor depletion in Tm (Fig. 2). The Hoopoe CAI is a compact, coarse-grained ~6 × 4 mm in size. The major mineralogy includes hibonite, spinel, melilite, anorthite, and perovskite. Therefore, it is classified as a compact transitional type A and B. (?)zoning was observed in some hibonites. Individual melilite grains show both reverse and normal zoning, where the Ak content ranges from ~6- to 28. Melilite shows two distinct textures. One texture consisted of smooth melilite that appeared homogenous, while the second appeared to consist of many fine fractures. The spinel also often appears clustered. The WL-rim sequence surrounding Hoopoe is ~25 µm thick and composed of spinel, perovskite, hibonite, and melilite/anorthite. It is then partially surrounded by an outer accretionary rim (~75µm). Like before, refractory metal nuggets appeared concentrated near the WL rims. Other metal assemblages rich in Fe and Ni were also observed. All major mineral phases in Hoopoe display relatively flat REE patterns, except for varying Eu and Tm between phases (Fig. 2). There is a prominent negative Eu anomaly in perovskite and an average positive Eu anomaly in spinel, anorthite, and hibonite respectively (Fig. 2, 3). The mixed phases along the rim of the CAI also display a negative Eu anomaly, and all phases the CAI were depleted in Pb. Discussion: The CV3 CAIs analyzed in this study were classified based on their mineralogy and textures into Type A versus Type B CAIs [10]. Hibonite appears to be pseudomorphically replacing the spinel, (i.e., is hibonite in composition, but appears in the shape of spinel). Spinel palisades. The presence of spinel palisades present in Saguaro are consistent with the melting and recrystallization experienced by this CAI. Trace elemental analyses. Saguaro and Hoopoe display similar trace element patterns to each other, with both appearing generally flat, with anomalies in Eu, and Tm. Melilite and anorthite display positive Eu anomalies in both CAIs, in addition to the hibonite in Hoopoe (Fig. 2). The phases that are depleted in Eu are pyroxene and perovskite in both Saguaro and Hoopoe, respectively (Fig. 3). Given that Eu is volatile in reducing environments [11], this could possibly indicate reducing conditions at the time anorthite and melilite crystallized, with the gas they formed from containing Eu. As these CAIs continued to form, this gas as a result would become depleted in Eu. This also could be supported by the propensity of anorthite and melilite to take up Eu from its surroundings and incorporate it into their structure [12]. In addition, analyzing the assemblage of the phases in the Saguaro, melilite and anorthite (Eu enriched) often surround the pyroxene (Eu depleted) as they are crystallized. This intergrowth of phases and the proximity of the phases would support that the Eu is being incorporated into some phases, preventing it from incorporating into other. Trace elemental analyses of the CAI rims will be evaluated in more detail, as they are complicated by the transient signal being composed of a mixture of mineral phases. Broadly, however, the patterns in the rims of both CAIs are comparable to each other, and for Hoopoe, to the mixed phase patterns in the core (Fig. 3). Other studies have found that CAI rims can be depleted in Ce and Yb [13], however we did not observe these anomalies in the two CAIs discussed here. Given their similarity, the trace elemental analyses of the mixed interior (i.e. core) and rim phases could be interpreted as forming from similar, if not the same, reservoirs. The REE abundance between the rim and core of Hoopoe are also similar, indicating they may have formed from a gas of the same or similar composition. Acknowledgments: We thank the ASU Center for Meteorite Studies for loaning the samples used in this work and Tabb Prissel for his assistance with the analysis. Mouti Al-Hashimi thanks Sam Crossley and Cyrena Goodrich for their help with the LPI SEM training. This work was supported by the LPI Summer Intern Program in Planetary Science and the LPI Cooperative Agreement. References: [1] Connelly J.N. (2012) Science, 338, 651-655. [2] MacPherson G. J. (2014) Treatise on Geochem., 2, 139-179. [3] Grossman L. (1972) GCA, 36, 597-619. [4] Ebel, D.S. (2006) Meteorites and the Early Solar System II (D. S. Lauretta & H. Y. McSween, Eds.) 253-277. [5] MacPherson G. J. (2012) Earth Planet. Sci. Lett., 331-332, 43-54. [6] MacPherson G.J. (2017) GCA, 201, 65-82. [7] Krot A.N. (1995) Meteoritics & Planet. Sci., 30, 748-775. [8] Wark and Lovering (1982) GCA, 46, 2595-2607. [9] Palme H. and Jones A. (2003) Treatise on Geochemistry (H. D. Holland and K. K. Turekian Eds.), 1, 41-61. [10] Grossman L. (1980) Ann. Rev. Earth Planet. Sci., 8, 559-608. [11] Floss C. et al. (1996) GCA, 60, 1975-1997. [12] Mason B. and Martin P. M. (1974) Earth Planet. Sci. Lett., 22, 141-144. [13] Wark B. and Boynton W. V. (2001) Meteoritics & Planet. Sci., 36, 1135-1166.

X Mouti↗
NASA NTRS1977

BLOB: An unsupervised clustering approach to spatial preprocessing of MSS imagery

A basic concept of Multispectral Scanner data processing was developed for use in agricultural inventories; namely, to introduce spatial coordinates of each pixel into the vector description of the pixel and to use this information along with the spectral channel values in a conventional unsupervised clustering of the scene. The result is to isolate spectrally homogeneous field-like patches (called blobs). The spectral mean vector of a blob can be regarded as a defined feature and used in a conventional pattern recognition procedure. The benefits of use are: ease in locating training units in imagery; data compression of from 10 to 30 depending on the application; reduction of scanner noise and consequently potential improvements in classification/proportion estimation performances.

Kauth, R. J.↗
NASA NTRS1977

Blob - An unsupervised clustering approach to spatial preprocessing of MSS imagery

A basic concept of MSS data processing has been developed for use in agricultural inventories; namely, to introduce spatial coordinates of each pixel into the vector description of the pixel and to use this information along with the spectral channel values in a conventional unsupervised clustering of the scene. The result is to isolate spectrally homogeneous field-like patches (called 'blobs'). The spectral mean vector of a blob can be regarded as a defined feature and used in a conventional pattern recognition procedure. The benefits of use are: ease in locating training units in imagery; data compression of from 10 to 30 depending on the application; reduction of scanner noise and consequently potential improvements in classification/proportion estimation performances.

Kauth, R. J.↗
NASA NTRS1985

Feed System Design Considerations for Large Space Antenna Systems. Part 2: Single Aperture with Overlapping Feeds

Contiguous multiple beams are used to make use of the set of frequency bands at different beams at different geographical locations and achieve the most efficient use of precious frequency allocation. This is feasible only if the interference among cochannel beams is below some acceptable level, by carrier/interference (C/I) ratio. Individual beam patterns of very low sidelobes, which in turn necessitates narrow feed are required. A physically realizable solution to this problem entails either more than one reflector, more complicated optics, or breaking of each feed into a cluster of smaller elements, some of which would then be shared by adjacent beams. The latter, however, requires a complicated beamforming network (BFN) for the proper feeding of the elements. The feedpacking problem is not unique to reflector antennas and exists in lens type antennas as well. The BFN and associated problems are present in the phased array antennas and on a much larger scale. Poor scan capabilities are associated with reflector systems. The scan properties of offset fed reflector systems can be improved by choosing a very large focal length to parent reflector diameter ((F/Dp) ratio, which requires a longer boom to support the feed. In the case of reflectors with cluster feed arrangements, the scan capability for smaller F/Dp ratios is improved by proper adjustment of cluster element excitations. Such a system seems to be appropriate for up to 10 beamwidths scan. For a larger number of beams, systems with wider scan capabilities, such as phased arrays or phased array/reflector combinations, become more appealing.

Jamnejad, V.↗
NASA NTRS2002

CNO abundances in the quintuplet cluster M supergiant 5-7

We present and analyze infrared spectra of the supergiant VR 5-7, in the Quintuplet cluster 30 pc from the Galactic center. Within the uncertainties, the [C/H],[N/H], and [O/H] abundances in this star are equal of Ori, a star which exhibits mixing of CNO processed elements, but distinct from the abundance patterns in IRS 7.

stars Galactic Center↗
NASA NTRS2013

Current Structure and Nonideal Behavior at Magnetic Null Points in the Turbulent Magnetosheath

The Poincaré index indicates that the Cluster spacecraft tetrahedron entraps a number of 3-D magnetic nulls during an encounter with the turbulent magnetosheath. Previous researchers have found evidence for reconnection at one of the many filamentary current layers observed by Cluster in this region. We find that many of the entrained nulls are also associated with strong currents. We dissect the current structure of a pair of spiral nulls that may be topologically connected. At both nulls, we find a strong current along the spine, accompanied by a somewhat more modest current perpendicular to the spine that tilts the fan toward the axis of the spine. The current along the fan is comparable to the that along the spine. At least one of the nulls manifests a rotational flow pattern in the fan plane that is consistent with torsional spine reconnection as predicted by theory. These results emphasize the importance of examining the magnetic topology in interpreting the nature of currents and reconnection in 3-D turbulence.

cluster↗