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The depth dependence of fossil tracks in particularly favorable lunar rocks has been used to investigate the constancy of galactic VH nuclei. When dynamic lunar surface processes are taken into account, it is concluded that both the spectral shape and the absolute flux has not changed significantly over the past 50 million years.
Calculations are performed for the predicted ground states of TiH(4-phi), VH(5-delta), CrH(6-sigma-plus), MnH(7-sigma-plus), Fett(4,6-delta) and NiH(2-delta). For FeH both the 6-delta and 4-delta states are studied, since both are likely candidates for the ground state. The ground state symmetries are predicted based on a combination of atomic coupling arguments and coupling of 4s(2)3d(n) and 4s(1)3d(n+1) terms in the molecular system. Electron correlation is included by a CASSCF/CI (SD) treatment. The CASSCF includes near-degeneracy effects, while correlation of the 3d electrons in included at the CI level.
The "all-particle" cosmic ray energy spectrum appears to be exhibiting a significant change in the spectral index just above approximately 3000 TeV. This could indicate (1) a change in the propagation of the cosmic rays in the galactic medium, and/or (2) the upper limit of the supernova shock wave acceleration mechanism, and/or (3) a new source of high-energy cosmic rays. Air shower and JACEE data indicate the spectral change is associated with a composition change to a heavier element mixture whereas DICE does not indicate this. A detector concept will be presented that utilizes the energy dependence of the production of direct Coulomb electron-positron pairs by energetic heavy ions. Monte Carlo simulations of a direct electron pair detector consisting of Pb target foils interleaved with planes of 1-mm square scintillating optical fibers will be discussed. The goal is to design a large area, non-saturating instrument to measure the energy spectrum of the individual cosmic ray elements in the "VH-group" for energies greater than 10 TeV/nucleon.
Heavy and ultraheavy nuclei observations of cosmic rays using ionization chamber-Cerenkov counter system
X-ray diffraction (XRD) studies were performed on hydride phases formed by vanadium and its carbon substituted alloys. It was previously found that thermal cycling of VHx across the B-y mixed phase region changed the reversible hydrogen storage capacity and other properties.
The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While
OBJECTIVE: To determine whether measurements of center of gravity-center of pressure separation (CG-CP moment arm) during gait initiation can differentiate healthy from disabled subjects with sufficient specificity and sensitivity to be useful as a screening test for dynamic balance in elderly patients. SUBJECTS: Three groups of elderly subjects (age, 74.97+/-6.56 yrs): healthy elders (HE, n = 21), disabled elders (DE, n = 20), and elders with vestibular hypofunction (VH, n = 18). DESIGN: Cross-sectional, intact-groups research design. Peak CG-CP moment arm measures how far the subject will tolerate the whole-body CG to deviate from the ground reaction force's CP; it represents dynamic balance control. Screening test cutoff points at 16 to 18 cm peak CG-CP moment arm predicted group membership. RESULTS: The magnitude of peak CG-CP moment arm was significantly greater in HE than in DE and VH subjects (p<.01) and was not different between the DE and VH groups. The peak CG-CP moment arm occurred at the end of single stance phase in all groups. As a screening test, the peak moment arm has greater than 50% sensitivity and specificity to discriminate the HE group from the DE and VH groups with peak CG-CP moment arm cutoff points between 16 and 18 cm. CONCLUSIONS: Examining dynamic balance through the use of the CG-CP moment arm during single stance in gait initiation discriminates between nondisabled and disabled older persons and warrants further investigation as a potential tool to identify people with balance dysfunction.
This paper presents the techniques and the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation. Three channels of ratioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The technique assumes redundancy of the VH and HV polarization and only VH polarization is used. The ratioed data are linearly stretched to yield a digital number within a range of 0 to 255. Based on the separability measure for two-class delineation, it was found that (1) the ratioed data resulted in a better delineation of surface features with high like (HH or VV) polarization digital number, and (2) the use of ratioed data provided further information not available from the original three-polarization data. The results suggest an advantage in using the ratioed data and the original three-polarization data for surface feature delineation.
Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.
During three balloon flights of a 1 sq m sr ionization chamber/Cerenkov counter detector system, measurements were made of the atmospheric attenuation, flux, and charge composition of cosmic ray nuclei with 16 is less than or = Z is less than or = 30 and rigidity greater than 4.5 GV. The attenuation mean free path in air of VH (20 less than or = Z less than or = 30) nuclei is found to be 19.7 + or - 1.6 g/sq cm, a value somewhat greater than the best previous measurement. The attenuation mean free path of iron is found to be 15.6 + or - 2.2 g/sq cm, consistent with predictions of geometric cross-section formulae. An absolute flux of VH nuclei 10 to 20% higher than earlier experiments at similar geomagnetic cutoff and level of solar activity was measured. The relative abundances of even-charged nuclei are found to be in good agreement with results of other recent high resolution counter experiments. The observed cosmic ray chemical composition implies relative abundances at the cosmic ray source of Ca/Fe = 0.12 + or - 0.04 and S/Fe = 0.14 + or - 0.05.
Balloon-flight measurements were used to determine ratios of cosmic-ray L nuclei (charge Z ranging from 3 to 5) to M nuclei (Z ranging from 6 to 8) and of VH nuclei (Z from 20 to 27) to M nuclei using a magnetic spectrometer. The purpose of the measurements was to establish whether both ratios vary with rigidity as this would provide evidence for more than one basic acceleration mechanism. The results provide no indication that the VH spectrum is steeper than the M spectrum.
Radar engineers have used several polarization combinations (HH, HV, VV, and/or VH) in the design of radar imagers and scatterometers for remote sensing research and applications. Scientists have explored their use for vegetation identification, mapping, and canopy condition assessment. In some cases, one polarization combination or another has produced good results; however, the results have not been consistent. In this paper, the use of polarized radar measurements is considered for vegetation studies on a theoretical basis to define ways of isolating parameters related to canopy structure and composition in the presence of backscattering from the underlying surface. It is found that scientists should use all three polarization combinations (VV, HH, and VH or HV) and their ratios.
This study was conducted as a part of the research tasks under the Radar Land Cover Analysis Program. The Radar Land Cover Analysis objective is, through utilization of multisensor data, to gain a basic understanding of the measurements and data characteristics in the visible-IR-microwave regions of the electromagnetic spectrum associated with specific surface features and cover types. Since the results of analysis of data acquired by Shuttle Imaging Radar (SIR-A) and LANDSAT Thematic Mapper (TM) over the study area were reported (NSTL/ERL Report No. 228, December 1984), this study focused on the analysis and evaluation of the L-band multipolarization airborne SAR data acquired over a southeastern pine forest scene. The data acquisition mission was flown on September 8 and 9, 1983. The HH, HV polarizations and the VV, VH polarizations were used on the first and the second day, respectively. Due to instrumentation difficulties, the digital recorder recorded only the second day's data. Because of this, only the VV and VH polarization data were used in this analysis. However, the HH and HV polarization images were available for visual comparison. It appears that SAR digital numbers correlate with the index of green biomass.
This paper presents the results on cosmogenic Ne extracted from individual meteoritic grains by a laser extraction system which used, at different times, two CW lasers: an Ar-ion laser and an Nd:YAG laser, with 20 and 70 W of deliverable power, respectively. Chemical etching was used to select grains exposed to solar flare VH particles. Results show that most of the grains with solar flare VH tracks (but not those which did not exhibit such tracks) contain spallation-produed Ne in significant excess of that due to the nominal cosmic-ray exposure, providing evidence for extensive energetic particle exposure during the precompaction era.
The European Space Agency (ESA), our international partner in the ESA-NASA Earth Science & Observation Joint Program Planning Group (JPPG), is hosting the VH-RODA (Very High-resolution Radar and Optical Data Assessment Workshop) at ESA/ European Space Research Institute (ERSIN) in Frascati, Italy from November 7-10, 2022. The objective of the VH-RODA workshop is to provide an open forum (for the new space, commercial and institutional space sectors) for presenting and discussing about the current status and future developments related to Earth Observation (EO) data quality, calibration and validation of space-borne very high-resolution Synthetic Aperture Radar (SAR) and Optical sensors and data products, with a dedicated focus on commercial EO data providers and related Calibration/Validation activities, synergies between optical and SAR communities, presentation of standards and best practices for data quality. Additionally, the ESA-NASA Joint Program Planning Group (JPPG) Third Party Mission component will meet to further develop non-binding practices on evaluation, identify common guard rails for comparison of data, and coordinate future schedules to leverage shared knowledge. I serve as the Project Manager for the NASA’s Commercial Smallsat Data Acquisition (CSDA) Program and will participate in the VHRODA workshop on behalf of the program.
Topics discussed include diffuse gamma rays, gamma ray sources (in particular, the Crab Nebula and Crab Pulsar), X-ray sources, isotopic and nuclear composition, spectra of nuclei, searches for antinuclei, studies of VH and VVH nuclei, measurements of electrons and positrons, the spectra of high-energy electrons, cosmic gamma rays, cosmic ray sources, interplanetary radial gradients, the theory and observation of electron modulation, short-period cosmic ray intensity variations, atmospheric and coupling effects on neutron monitors and muon telescopes, secondary particles and photons in the atmosphere, harmonics of diurnal variations, geomagnetic effects and cutoffs, planetary and interplanetary effects, solar particle access observations, long-term cosmic ray intensity modulation by solar activity and interplanetary effects, Forbush decreases, modulation and anisotropy of low-energy particles, the theory and models of solar particle propagation, solar flare particle composition, and relativistic solar particles. Individual items are announced in this issue.
A detailed experimental and analytical evaluation was performed to define the response of TD nickel chromium alloy (20 percent chromium) and coated columbium (R512E on CB-752 and VH-109 on WC129Y) to shuttle orbiter reentry heating. Flight conditions important to the response of these thermal protection system (TPS) materials were calculated, and test conditions appropriate to simulation of these flight conditions in flowing air ground test facilities were defined. The response characteristics of these metallics were then evaluated for the flight and representative ground test conditions by analytical techniques employing appropriate thermochemical and thermal response computer codes and by experimental techniques employing an arc heater flowing air test facility and flat face stagnation point and wedge test models. These results were analyzed to define the ground test requirements to obtain valid TPS response characteristics for application to flight. For both material types in the range of conditions appropriate to the shuttle application, the surface thermochemical response resulted in a small rate of change of mass and a negligible energy contribution. The thermal response in terms of surface temperature was controlled by the net heat flux to the surface; this net flux was influenced significantly by the surface catalycity and surface emissivity. The surface catalycity must be accounted for in defining simulation test conditions so that proper heat flux levels to, and therefore surface temperatures of, the test samples are achieved.