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Observations of plasma tails of comets are reviewed. The basic morphology of plasma tails is examined. It is shown that two features, viz., the ray-folding phenomenon and disconnection events, are essential to a general understanding of plasma tail phenomena. A qualitative model of disconnection events is proposed that is based on the cyclic occurrence of magnetic reversals in the solar wind.
The recognition of some Martian landforms as volcanoes is based on their morphology and geologic setting. Other structures, however, may exhibit classic identifying features to a varying or a less degree; these may be only considered provisionally as having a volcanic origin. Regional geologic mapping of the western hemisphere of Mars from Viking images has revealed many more probable volcanoes and volcanotectonic features than were recognized on Mariner 9 pictures. These abundant volcanoes have been assigned to several distinct provinces on the basis of their areal distribution. Although the Olympus-Tharsis region remains as the principle center of volcanism on Mars, four other important provinces are now also recognized: the lowland plains, Tempe Terra plateau, southern highlands (in the Phaethontis and Thaumasia quadrangles), and a probable ignimbrite province, situated along the highland-lowland boundary in Amazonis Planitia. Volcanoes in any one province vary in morphlogy, size, and age, but volcanoes in each province tend to have common characteristics that distinguish that particular group.
Details of the spatial structure of the narrow-line region (NLR) in NGC 4151 are presented, and the structure of the NLR is compared to that of the radio and X-ray emission. Results for the morphology and excitation of the NLR, the luminosity of the extranuclear features, and the kinematics are presented, citing published research. These results are used to test several models: the X-ray gas in pressure equilibrium with the adjacent NLR, the X-ray gas identified as shock-heated NLR clouds flowing supersonically through the ambient galactic interstellar medium, X-rays arising in SNR and massive X-ray binaries produced in bursts of star formation stimulated by nuclear outflow, and X-ray and line emission regions ionized indirectly by the radio jet.
The macrostructure of perpendicular shocks in the supercritical regime is investigated theoretically. The coupling between the shock precursor (foot region) associated with reflected ions and the magnetic ramp associated with electron ohmic heating is analyzed using a model in which the ions are treated as a multifluid and the electrons as a massless fluid. The results are found to be in good agreement with a number of features exhibited by recent numerical simulations of perpendicular shocks, including the morphology of the reflected-gyrating ion stream, the presence of potential and magnetic field overshoots, and the Mach number dependence of the shock structure.
The morphology of channels, valleys, chaotic and fretted terrains and many smaller features on Mars is consistent with the hypothesis that localized deterioration of thick layers of ice-rich permafrost was a dominant geologic process on the Martian surface. Such ground ice deterioration gave rise to large-scale mass movement, including sliding, slumping and sediment gravity flowage, perhaps also catastropic floods. In contrast to Earth, such mass movement processes on Mars lack effective competition from erosion by surface runoff. Therefore, Martian features due to mass movement grew to reach immense size without being greatly modified by secondary erosional processes. The Viking Mission to Mars in 1976 provided adequate measurements of the relevant physical parameters to constrain models for Martian permafrost.
A large scale 13CO map (containing 33,000 spectra) of the giant molecular cloud located in the southern part of Orion is presented which contains the Orion Nebula, NGC1977, and the LI641 dark cloud complex. The overall structure of the cloud is filamentary, with individual features having a length up to 40 times their width. This morphology may result from the effects of star formation in the region or embedded magnetic fields in the cloud. We suggest a simple picture for the evolution of the Orion-A cloud and the formation of the major filament. A rotating proto-cloud (counter rotating with respect to the galaxy) contians a b-field aligned with the galaxtic plane. The northern protion of this cloud collapsed first, perhaps triggered by the pressure of the Ori I OB association. The magnetic field combined with the anisotropic pressure produced by the OB-association breaks the symmetry of the pancake instability, a filament rather than a disc is produced. The growth of instabilities in the filament formed sub-condensations which are recent sites of star formation.
High resolution X-ray images of three early type galaxies observed with the ROSAT HRI are presented. Data for NGC 1553 and NGC 5846 indicate that the emission is highly irregular, with interesting features on scales from a few arcsec to a few arcmin. The gas temperatures also vary both with the galactocentric radius and in correspondence to regions of higher emission and denser material. Strikingly similar features are observed in the X-ray and H(alpha) morphologies of NGC 1553 and NGC 5846, while smoother, more regular isophotes are observed in NGC 4649 at both wavelengths. A connection between these two kinds of emission therefore seems likely. In the light of our observations we discuss possible scenarios that can and account for the connection between X-ray and H(alpha) emissions.
We produced a geologic/geomorphologic map of the Chaac-Camaxtli region of Io's leading anti-Jovian hemisphere using regional resolution (186 m/pixel) Galileo imaging data collected during orbit I27 (February 2000) integrated with lower resolution (1.4 km/pixel) color data, along with other Galileo imaging and spectral data. This is the first regional map of Io made from Galileo data. Nine color and geomorphologic units have been mapped, and the close proximity of dark and various colored bright materials suggests that there is an intimate interaction between (presumably) silicate magmas and sulfur-bearing volatile materials that produced a variety of explosive and effusive deposits in the recent geologic past. This region of Io is dominated by 11 volcanic centers, most of which are paterae that are analogous in morphology to terrestrial calderas but larger in size. Mapping of structural features indicates that most of the active regions occur in topographic lows, and less active or inactive paterae are associated with topographic highs. This indicates that crustal thickness variations influence magma access to the surface. Surface changes in this region since the Voyager flybys (1979) are relatively minor (additional bright and dark flows, color changes), although several active vents have migrated within paterae. This observation, along with the identification of the relatively regular spacing of paterae (approx. 100- 150 km) along a line, may indicate there are multiple interlacing fractures in the crust that serve as magma conduits from the interior. This connection between volcanism and tectonism may have implications for tidal heating mechanisms and their effect on Io's lithosphere. Some inactive patera floors may be evolving into bright plains material, which, if composed of silicates, might explain the strength of Io's crust to support steep patera walls and tall mountains.
We produced a geologic/geomorphologic map of the Chaac-Camaxtli region of Io's leading anti-Jovian hemisphere using regional resolution ( 186 m/pixel) Galileo imaging data collected during orbit I27 (February 2000) integrated with lower resolution (1.4 km/pixel) color data, along with other Galileo imaging and spectral data. This is the first regional map of Io made from Galileo data. Nine color and geomorphologic units have been mapped, and the close proximity of dark and various colored bright materials suggests that there is an intimate interaction between (presumably) silicate magmas and sulfur-bearing volatile materials that produced a variety of explosive and effusive deposits in the recent geologic past. This region of Io is dominated by 11 volcanic centers, most of which are paterae that are analogous in morphology to terrestrial calderas but larger in size. Mapping of structural features indicates that most of the active regions occur in topographic lows, and less active or inactive paterae are associated with topographic highs. This indicates that crustal thickness variations influence magma access to the surface. Surface changes in this region since the Voyager flybys are relatively minor (additional bright and dark flows, color changes), although several active vents have migrated within paterae. This observation, along with the identification of the relatively regular spacing of paterae (approx. 100 - 150 km) along a line, may indicate there are multiple interlacing fractures in the crust that serve as magma conduits from the interior. This connection between volcanism and tectonism may have implications for tidal heating mechanisms and their effect on Io's lithosphere. Some inactive patera floors may be evolving into bright plains material, which, if composed of silicates, might explain the strength of Io's crust to support steep patera walls and tall mountains.
We present Chandra X-ray Observatory, Robert C. Byrd Green Bank Radio Telescope (GBT), and Rossi X-ray Timing Explorer (RXTE) observations directed toward the radio supernova remnant (SNR) G76.9+1.0. The Chandra investigation reveals a hard, unresolved X-ray source coincident with the midpoint of the double-lobed radio morphology and surrounded by faint, compact X-ray nebulosity. These features suggest that an energetic neutron star is powering a pulsar wind nebula (PWN) seen in synchrotron emission. Indeed, the spatial relationship of the X-ray and radio emissions is remarkably similar to the extended emission around the Vela pulsar. A follow-up pulsation search with the GBT uncovered a highly-dispersed (DM = 427 +/- 1 pc/cu cm) and highly-scattered pulsar with a period of 24 ms. Its subsequently measured spin-down rate implies a characteristic age T(sub c) = 8.9 kyr, making PSR J2022+3842 the most rapidly rotating young radio pulsar known. With a spin-down luminosity E = 1.2 x 10(exp 38) erg/s, it is the second-most energetic Galactic pulsar known, after the Crab pulsar. The 24-ms pulsations have also been detected in the RXTE observation; the combined Chandra and RXTE spectral fit suggests that the Chandra point-source emission is virtually 100% pulsed. The 2-16 keV spectrum of the narrow (0.06 cycles FWHM) pulse is well-fitted by an absorbed power-law model with column density N(sub H) = (1.7 +/- 0.5) x 10(exp 22)/sq cm and photon index Gamma = 1.0 +/- 0.2, strongly suggestive of magnetospheric emission. For an assumed distance of 10 kpc, the 2-10 keV luminosity of L(sub X) = 6.9 x 10(exp 33) erg/s suggests one of the lowest known X-ray conversion efficiencies L(sub X)/ E = 5.8 x 10(exp -5), similar to that of the Vela pulsar. Finally, the PWN around PSR J2022+3842 revealed by Chandra is also underluminous, with F(sub PWN)/ F(sub PSR) < or approx.1 in the 2-10 keV band, a further surprise given the pulsar's high spin-down luminosity.
Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.
In evolved and dusty circumstellar discs, two planets with masses comparable to Jupiter and Saturn that migrate outwards while maintaining an orbital resonance can produce distinctive features in the dust distribution. Dust accumulates at the outer edge of the common gas gap, which behaves as a dust trap, where the local dust concentration is significantly enhanced by the planets’ outward motion. Concurrently, an expanding cavity forms in the dust distribution inside the planets’ orbits, because dust does not filter through the common gaseous gap and grain depletion in the region continues via inward drifting. There is no cavity in the gas distribution because gas can filter through the gap, although ongoing gas accretion on the planets can reduce the gas density in the inner disc. Such behaviour was demonstrated by means of simulations neglecting the effects of dust diffusion due to turbulence and of dust backreaction on the gas. Both effects may alter the formation of the dust peak at the gap outer edge and of the inner dust cavity, by letting grains filter through the dust trap. We performed high-resolution hydrodynamical simulations of the coupled evolution of gas and dust species, the latter treated as pressureless fluids, in the presence of two giant planets. We show that diffusion and backreaction can change some morphological aspects of the dust distribution but do not alter some main features, such as the outer peak and the expanding inner cavity. Furthermore, these findings are confirmed for different parametrizations of gas viscosity.
Three 21-day tests of the effects of chronic centrifugation were carried out on populations of Arabidopsis thaliana. In addition to 1 g the resultant g-forces tested were: 2,4,6,8,16, and 20 g. Observed end points included gross morphological characters such as size of plant organs and, at the other extreme, features of sub-cellular structure and ultrastructure. Plants were grown on banks of clinostats. The acceleration vector was directed either parallel with the plants' axes or transverse to the axes. Plant responses to chronic axial acceleration and to transverse acceleration with clinostated plants were determined. From the data obtained it was possible in some cases: (1) to determine the g-functions of specific plant developmental characters; (2) to extrapolate those functions to the hypothetical value at zero g in order to predict (tentatively) the morphology of a plant grown in space, (3) to describe morphological effects of clinostat rotation, (4) to determine which of those effects was influenced by the prevailing g-force, and (5) to put to direct test the assumption that clinostat rotation nullifies or compensates for the influence of gravity.
The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.
It is shown that many characteristics of the Great Red Spot (GRS) and numerous other features that have been observed on Jupiter can be explained by solitary waves on a horizontally sheared zonal flow in a rotating, stratified atmosphere. Streamline patterns for waves corresponding to combined depression-elevation solitary waves (D-E solitrons) show a strong resemblence to the flow around the GRS. The morphology and flow pattern of the South Tropical Disturbance indicate that it was a D solitron. Numerous spot-like features situated in regions between cloud bands where horizontal shear forces might be expected have the morphology of E solitrons. Restrictions placed on the atmospheric parameters by the model are consistent with available models and observations.
More than 200 channels and valleys have been identified on the Magellan images of Venus. These are classified, on the basis of morphology. as simple channels (including sinuous rilles, simple channels with flow margins. and canali), complex channels (with or without flow margins), compound channels, and valley networks (including rectangular, labyrinthic, and pitted or irregular networks). Sinuous rilles closely resemble their lunar counterparts. Canali are exceptional for their remarkably constant width along very extenuated flow paths, exceeding 500 km. One of the compound channels, the outflow complex of Kallistos Vallis. extends over 1200 km and is up to 30 km wide. Venusian channels are globally distributed, but each class has a preferential topographic association. The canali are developed on the volcanic plains. while sinuous rilles occur at higher elevations, associated with volcanic complexes and coronae. Both canali and sinuous rilles have been deformed by post-emplacement tectonism. Highly fluid lavas, erupted at sustained, high discharges seem best to explain many of the channel features, particularly for the canali and the compound channels. Explanation of the canali morphologies may also involve unusual low-viscosity lavas, perhaps of exotic composition.
Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.
In this contribution, we present a systematic study on the microstructure of 1.3 GHz 9-cell TESLA type SRF cavity, processed with 2/0 Nitrogen-doping surface treatment, to explain the premature quench phenomena commonly observed in N-doping treated cavities. The microstructure characterization was carried out using Secondary electron images, advanced metallurgical techniques such as EBSD in parallel with chemical information obtained from spectroscopic techniques. The most remarkable difference is observed in the ends-cavities (1 and 9), which showed roughening of the surface, revealing a series of morphologies associated with Nb cubic phase. The cell-to-cell analysis also showed standard features such as pits with different geometry and distribution, located in grains and grain boundaries. The defects found in this system suggest that the standard electropolishing chemical etching was insufficient to eliminate history defects produced during the manufacture of the cavity, without discarding the role of the impurities, N and O, that could have induced the growth of these morphologies.