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An amperometric solid electrolyte oxygen (O2) microsensor using a novel and robust structure has been developed with a detection range of 0.025 to 21 percent of O2 concentration. The microsensor has a simple structure with a sensing area of 1.10 0.99 mm(exp 2), and is operated by applying voltage across the electrodes and measuring the resulting current flow at a temperature of 600 C.
A spaceborne lidar mission could serve multiple scientific purposes including remote sensing of ecosystem structure, carbon storage, terrestrial topography and ice sheet monitoring. The measurement requirements of these different goals will require compromises in sensor design. Footprint diameters that would be larger than optimal for vegetation studies have been proposed. Some spaceborne lidar mission designs include the possibility that a lidar sensor would share a platform with another sensor, which might require off-nadir pointing at angles of up to 16 . To resolve multiple mission goals and sensor requirements, detailed knowledge of the sensitivity of sensor performance to these aspects of mission design is required. This research used a radiative transfer model to investigate the sensitivity of forest height estimates to footprint diameter, off-nadir pointing and their interaction over a range of forest canopy properties. An individual-based forest model was used to simulate stands of mixed conifer forest in the Tahoe National Forest (Northern California, USA) and stands of deciduous forests in the Bartlett Experimental Forest (New Hampshire, USA). Waveforms were simulated for stands generated by a forest succession model using footprint diameters of 20 m to 70 m. Off-nadir angles of 0 to 16 were considered for a 25 m diameter footprint diameter. Footprint diameters in the range of 25 m to 30 m were optimal for estimates of maximum forest height (R(sup 2) of 0.95 and RMSE of 3 m). As expected, the contribution of vegetation height to the vertical extent of the waveform decreased with larger footprints, while the contribution of terrain slope increased. Precision of estimates decreased with an increasing off-nadir pointing angle, but off-nadir pointing had less impact on height estimates in deciduous forests than in coniferous forests. When pointing off-nadir, the decrease in precision was dependent on local incidence angle (the angle between the off-nadir beam and a line normal to the terrain surface) which is dependent on the off-nadir pointing angle, terrain slope, and the difference between the laser pointing azimuth and terrain aspect; the effect was larger when the sensor was aligned with the terrain azimuth but when aspect and azimuth are opposed, there was virtually no effect on R2 or RMSE. A second effect of off-nadir pointing is that the laser beam will intersect individual crowns and the canopy as a whole from a different angle which had a distinct effect on the precision of lidar estimates of height, decreasing R2 and increasing RMSE, although the effect was most pronounced for coniferous crowns.
We are developing a system of modular, autonomous "tensegrity end-caps" to enable the rapid exploration of untethered tensegrity robot morphologies and functions. By adopting a self-contained modular approach, different end-caps with various capabilities (such as peak torques, or motor speeds), can be easily combined into new tensegrity robots composed of rods, cables, and actuators of different scale (such as in length, mass, peak loads, etc). As a first step in developing this concept, we are in the process of designing and testing the end-caps for SUPERball (Spherical Underactuated Planetary Exploration Robot), a project at the Dynamic Tensegrity Robotics Lab (DTRL) within NASA Ames's Intelligent Robotics Group. This work discusses the evolving design concepts and test results that have gone into the structural, mechanical, and sensing aspects of SUPERball. This representative tensegrity end-cap design supports robust and repeatable untethered mobility tests of the SUPERball, while providing high force, high displacement actuation, with a low-friction, compliant cabling system.
The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.
A structure is proposed for a geographically-oriented computer-based information system applicable to the analysis of remote sensing digital data. The structure, intended to answer a wide variety of user needs, would permit multiple views of the data, provide independent management of data security, quality and integrity, and rely on automatic data filing. Problems in geographically-oriented data systems, including those related to line encoding and cell encoding, are considered.
Electrical time domain reflectometry (ETDR) sensing technique can be best described as "closed-loop radar," where the information is derived from the reflections of a voltage pulse sent through a transmission medium. The ETDR sensing technique is a well-developed method and has been widely used to locate and evaluate discontinuities in long coaxial power transmission cables. The ETDR technique provides a true distributed sensing capability which can not only sense the distributed loading condition of the structure but also can pin-point the location of disturbance, such as the locations of stress concentration and structural damages. Proof-of-concept experiments have been conducted using photoelastic specimens with embedded commercial coaxial cables, i.e., RG85/U and RG174, to demonstrate the stress/strain sensing capability of ETDR sensors for structural health monitoring application. Although the test results showed that the ETDR sensor signals capture specimen deformation pattern both in bending and tension and indicate the location and type of crack damages of the photoelastic specimen; yet, the low signal-to-noise ratio of the sensor signal smears the details of the strain measurement that the ETDR signals can convey. A high-sensitivity ETDR coaxial strain sensor prototype newly developed at Clark Atlanta University will be presented. The construction of the prototype sensing cable as well as its electrical properties relevant to distributed strain sensing application will be shown in details. Test results of the sensitivity and tension responses of the ETDR signal of the prototype sensor will be presented and compared with those of commercial coaxial cables. Promising potentials of the ETDR distributed strain sensing method for shape measurement application of large aerospace structures will also be demonstrated using long slender beam with surface-bonded ETDR distributed strain sensor.
Brief descriptions are given of the techniques involved in determining the global structure of the mesosphere and stratosphere based on sounding rocket observations and satellite remotely sensed measurements.
Fiber-optic strain sensing (FOSS) provides an opportunity for additional awareness and can enable other mitigations such as flutter suppression or gust load alleviation but requires an accurate computational model as part of a design process that integrates structures and controls. A modeling approach for fiber-optic strain sensors has been developed using a shell element formulation in a detailed finite element model. The new FOSS modeling approach is designed to better scale to more complex realistic structures and fiber layouts than previous approaches. The modeling method was applied to the X-56A vehicle and results were compared to experimental static ground-test data. Additionally, the experimental sensor noise of these fiber optic sensors was assessed for the correlations in time and correlations along the fiber. The noise does show significant correlations both in time and along the fibers. These correlations of the noise should also be considered in modeling and assessing algorithms that will use FOSS.
Fiber-optic strain sensing (FOSS) provides an opportunity for additional awareness and can enable other mitigations such as flutter suppression or gust load alleviation but requires an accurate computational model as part of a design process that integrates structures and controls. A modeling approach for fiber-optic strain sensors has been developed using a shell element formulation in a detailed finite element model. The new FOSS modeling approach is designed to better scale to more complex realistic structures and fiber layouts than previous approaches. The modeling method was applied to the X-56A vehicle and results were compared to experimental static ground-test data. Additionally, the experimental sensor noise of these fiber optic sensors was assessed for the correlations in time and correlations along the fiber. The noise does show significant correlations both in time and along the fibers. These correlations of the noise should also be considered in modeling and assessing algorithms that will use FOSS.
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There are no author-identified significant results in this report.
The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.
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This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.
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Early in its evolution, the Moon underwent a magma ocean phase leading to its differentiation into a feldspathic crust, cumulate mantle, and iron core. However, far from the simplest view of a uniform plagioclase flotation crust, the present-day crust of the Moon varies greatly in thickness, composition, and physical properties. Recent significant improvements in both data and analysis techniques have yielded fundamental advances in our understanding of the structure and evolution of the lunar interior. The structure of the crust is revealed by gravity, topography, magnetics, seismic, radar, electromagnetic, and VNIR remote sensing data. The mantle structure of the Moon is revealed primarily by seismic and laser ranging data. Together, this data paints a picture of a Moon that is heterogeneous in all directions and across all scales, whose structure is a result of its unique formation, differentiation, and subsequent evolution. This brief review highlights a small number of recent advances in our understanding of lunar structure.
We will discuss a new data assimilation model of ionosphere, the Global Assimilative Ionosphere Model (GAIM), capable of integrating measurements from GPS and other sensors with a physics-based ionospheric model, to provide detailed global nowcasts of ionospheric structure, useful for science and applications.
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