Plasma sprayed titanium coatings with/without a shroud

Abstract: Titanium coatings were deposited by plasma spraying with and without a shroud. The titanium coatings were then assessed by scanning electron microscopy. A comparison in microstructure between titanium coatings with and without the shroud was carried out. The results showed that the shroud played an important role in protecting the titanium particles from oxidation. The presence of the shroud led to a reduction in coating porosity. The reduction in air entrainment with t he shroud resulted in better heating of the particles, and an enhanced microstructure with lower porosity in the shrouded titanium coatings were observed compared to the air plasma sprayed counterpart

A Comparison of Relict and Active Terrestrial Patterned Ground as an Analog for Mars

Patterned ground is a ubiquitous landform in periglacial regions of Earth and is also present across the mid to high latitudes of Mars. The association of terrestrial patterned ground to the presence of subsurface water ice in the form of permafrost that develops a seasonal ‘wet’ active layer during the summer thaw prompted further investigation of patterned ground on Mars. The Phoenix spacecraft was sent to the surface of the north polar plains of Mars to investigate an area of patterned ground where water ice was predicted to occur. The confirmation of subsurface water ice at the Phoenix landing site confirmed the hypothesis that water ice and patterned ground on Mars are intricately linked, however outstanding questions remain regarding the mode of formation of martian patterned ground. Dry modification via sublimation and thermal-driven processes are possible under present-day climate conditions, however warmer climate conditions are predicted to have occurred during past periods of high obliquity and could have supported periglacial freeze-thaw modification of patterned ground on Mars. Understanding the extent to which liquid water may have been available in the recent geological history of Mars is important to constraining past habitability as well as identifying resources for future long-duration human exploration. It is suggested that if patterned ground on Mars experienced periglacial modification in the past, then it currently exists in a relict form. This research examines the morphometry and surface roughness parameters of active and relict terrestrial patterned ground sites in conjunction with evaluations of martian patterned ground to identify parameters that may assist with ongoing efforts to determine the age and modes of historical modification of patterned ground on Mars

A Vision-Based Automatic Safe landing-Site Detection System

An automatic safe landing-site detection system is proposed for aircraft emergency landing, based on visible information acquired by aircraft-mounted cameras. Emergency landing is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing-site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing-site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on the external environmental factors that can impair human vision, and on the pilot\u27s flight experience that can vary significantly among pilots. Therefore, we propose a robust, reliable and efficient detection system that is expected to alleviate the negative impact of these factors. In this study, we focus on the detection mechanism of the proposed system and assume that the image enhancement for increased visibility and image stitching for a larger field-of-view have already been performed on terrain images acquired by aircraft-mounted cameras. Specifically, we first propose a hierarchical elastic horizon detection algorithm to identify ground in rile image. Then the terrain image is divided into non-overlapping blocks which are clustered according to a roughness measure. Adjacent smooth blocks are merged to form potential landing-sites whose dimensions are measured with principal component analysis and geometric transformations. If the dimensions of a candidate region exceed the minimum requirement for safe landing, the potential landing-site is considered a safe candidate and highlighted on the human machine interface. At the end, the pilot makes the final decision by confirming one of the candidates, also considering other factors such as wind speed and wind direction, etc

Land-Surface Parameters for Spatial Predictive Mapping and Modeling

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.Comment: 31 pages, 47 figure

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Utilization of Lidar Intensity Data and Passive Visible Imagery for Geological Mapping of Planetary Surfaces

While lidar has been historically used for generating digital terrain maps and as a navigation tool, recent research demonstrates that lidar has many potential scientific applications, including high resolution analysis of geological outcrops. Case studies were completed at the Tunnunik impact structure, Victoria Island, Arctic Canada, and the Nickel Rim South mine, Sudbury, Canada, to assess the fidelity of characterizing and differentiating mineralogical and lithological units remotely by integrating passive visible imagery with lidar intensity data. Unsupervised classification via k-means clustering was performed on the fused datasets, with results indicating that lithologies can indeed be successfully differentiated with minor a priori knowledge of the setting. Semi-quantitative analysis through XRD of Tunnunik samples demonstrates that distance-corrected intensity is linked in a linear relationship with both dolomite and clay content. The simultaneous acquisition of both geospatial and scientific data greatly increases the applications and value of using lidar, especially for mining, geological mapping in remote environments, and for future planetary missions

Reflectance spectroscopy (0.3–2.5 µm) at various scales for bulk-rock identification

We discussed the possibilities and open questions concerning laboratory refl ectance spectroscopy, spectroscopic measurements in the fi eld, hyperspectral image data from spacecraft, and integration of multiscale data. Open questions included: (1) bulk-rock spectral complexity, which provides the geologic basis for every spectroscopic analysis; (2) criteria for laboratory and fi eld spectra classifi cation, as a tool for (3) end-member selection for image data classifi cation; (4) peculiar spectral characteristics of Mount Etna basalts; and (5) effects of remote-sensing data quality. The last three items emerged during a multiscale survey on the Mount Etna volcano. Our laboratory spectroscopic analyses, supported by specifi c petrographic analyses, showed the relationship between absorption-band frequency and spectrally active functional groups and the unexpected effects of bulk-rock composition on this relationship. We studied the muscovite Al-OH band in quartzite and micaschist and Fe 2+ band in pyroxene-bearing cumulates. Laboratory refl ectance spectra of rocks were classifi ed using the concept of spectrofacies. In the case of metamorphic rocks, the result was a tree-structure of rock spectral classes mainly based on the predominant vibrational processes. In the case of basalts, characterized by an overall similarity in their composition, the classes were determined on the basis of overall shape of the spectral curve and on electronic process intensities. Here, we report fi rst results of multiscale data integration for the Mount Etna volcano. Etna rocks consist of basalts, with very low albedo and variable degrees of alteration, and recent lava fl ows are characterized by overall low refl ectance in both ASTER (advanced spaceborne thermal emission and refl ection radiometer) and Hyperion color-composite images. We carried out Spectral Angle Mapper (SAM) classifi cation of Hyperion images, where individual fi eld spectra represented suitable end members for classifi cation of recent lava and pyroclastic deposits. We used fi eld spectra linear combinations to classify mixed pixels and to approximate the classifi cation of altered and oxidized effusive products. Only two laboratory spectral classes coincided with fi eld spectra classes; laboratory spectra were mainly used for spectral features attribution. The overall spectral shape of some of these spectra is still under study. Noise level in Hyperion data precluded the identifi cation of subtle diagnostic iron absorption bands

Regional-scale controls on rockfall occurrence

Rockfalls exert a first-order control on the rate of rock wall retreat on mountain slopes and on coastal rock cliffs. Their occurrence is conditioned by a combination of intrinsic (resisting) and extrinsic (driving) processes, yet determining the exact effects of these processes on rockfall activity and the resulting cliff erosion remains difficult. Although rockfall activity has been monitored extensively in a variety of settings, high-resolution observations of rockfall occurrence on a regional scale are scarce. This is partly owing to difficulties in adequately quantifying the full range of possible rockfall volumes with sufficient accuracy and completeness, and at a scale that exceeds the influence of localised controls on rockfalls. This lack of insight restricts our ability to abstract patterns, to identify long-term changes in behaviour, and to assess how rock slopes respond to changes in both structural and environmental conditions, without resorting to a space for-time substitution. This thesis develops a workflow, from novel data collection to analysis, which is tailored to monitoring rockfall activity and the resulting cliff retreat continuously (in space), in 3D, and over large spatial scales $(> 10^4 m)$. The approach is tested by analysing rockfall activity and the resulting erosion recorded along 20.5 km of near-vertical coastal cliffs, in what is considered as the first multi-temporal detection of rockfalls at a regional-scale and in full 3D. The resulting data are then used to derive a quantitative appraisal of along-coast variations in the geometric properties of exposed discontinuity surfaces, to assess the extent to which these drive patterns in the size and shape of the rockfalls observed. High-resolution field monitoring is then undertaken along a subsection of the coastline $(> 10^2 m)$, where cliff lithology and structure are approximately uniform, in order to quantify spatial variations in wave loading characteristics and to relate these to local morphological conditions, which can act as a proxy for wave loading characteristics. The resulting rockfall inventory is analysed to identify the characteristics of rock slope change that only become apparent when assessed at this scale, placing bounds on data previously collected more locally $(< 10^2 m)$. The data show that spatial consistencies in the distribution of rockfall shape and volume through time approximately follow the geological setting of the coastline, but that variations in the strength of these consistencies are likely to be conditioned by differences in local processes and morphological controls between sites. These results are used to examine the relationships between key metrics of erosion, structural, and morphological controls, which ultimately permits the identification of areas where patterns of erosion are dominated by either intrinsic or extrinsic processes, or a mixture of both. Uniquely, the methodologies and data presented here mark a step-change in our ability to understand the competing effects of different processes in determining the magnitude and frequency of rockfall activity, and the resulting cliff erosion. The findings of this research hold considerable implications for our understanding of rockfalls, and for monitoring, modelling, and managing actively failing rock slopes