Geophysical parameters from the analysis of laser ranging to starlette

Starlette Satellite Laser Ranging (SLR) data were used, along with several other satellite data sets, for the solution of a preliminary gravity field model for TOPEX, PTGF1. A further improvement in the earth gravity model was accomplished using data collected by 12 satellites to solve another preliminary gravity model for TOPEX, designated PTGF2. The solution for the Earth Rotation Parameter (ERP) was derived from the analysis of SLR data to Starlette during the MERIT Campaign. Starlette orbits in 1976 and 1983 were analyzed for the mapping of the tidal response of the earth. Publications and conference presentations pertinent to research are listed

Altimeter measurements for the determination of the Earth's gravity field

Progress in the following areas is described: refining altimeter and altimeter crossover measurement models for precise orbit determination and for the solution of the earth's gravity field; performing experiments using altimeter data for the improvement of precise satellite ephemerides; and analyzing an optimal relative data weighting algorithm to combine various data types in the solution of the gravity field

Geophysical parameters from the analysis of laser ranging to Starlette

The results of geodynamic research from the analysis of satellite laser ranging data to Starlette are summarized. The time period of the investigation was from 15 Mar. 1986 to 31 Dec. 1991. As a result of the Starlette research, a comprehensive 16-year Starlette data set spanning the time period from 17 Mar. 1975 through 31 Dec. 1990, was produced. This data set represents the longest geophysical time series from any geodetic satellite and is invaluable for research in long-term geodynamics. A low degree and order ocean tide solution determined from Starlette has good overall agreement with other satellite and oceanographic tide solutions. The observed lunar deceleration is -24.7 +/- 0.6 arcsecond/century(exp 2), which agrees well with other studies. The estimated value of J2 is (-2.5 +/- 0.3) x 10(exp -11) yr(exp -1), assuming there are no variations in higher degree zonals and that the 18.6-year tide is fixed at an equilibrium value. The yearly fluctuations in the values for S(sub a) and S(sub sa) tides determined by the 16-year Starlette data are found to be associated with changes in the Earth's second degree zonal harmonic caused primarily by meteorological excitation. The mean values for the amplitude of S(sub a) and S(sub sa) variations in J2 are 32.3 x 10(exp -11) and 19.5 x 10(exp -11), respectively; while the rms about the mean values are 4.1 x 10(exp -11) and 6.3(10)(exp -11), respectively. The annual delta(J2) is in good agreement with the value obtained from the combined effects of air mass redistribution without the oceanic inverted-barometer effects and hydrological change. The annual delta(J3) values have much larger disagreements. Approximately 90 percent of the observed annual variation from Starlette is attributed to the meteorological mass redistribution occurring near the Earth's surface

Altimeter measurements for the determination of the Earth's gravity field

The ability of satellite-borne radar altimeter data to measure the global ocean surface with high precision and dense spatial coverage provides a unique tool for the mapping of the Earth's gravity field and its geoid. The altimeter crossover measurements, created by differencing direct altimeter measurements at the subsatellite points where the orbit ground tracks intersect, have the distinct advantage of eliminating geoid error and other nontemporal or long period oceanographic features. In the 1990's, the joint U.S./French TOPEX/POSEIDON mission and the European Space Agency's ERS-1 mission will carry radar altimeter instruments capable of global ocean mapping with high precision. This investigation aims at the development and application of dynamically consistent direct altimeter and altimeter crossover measurement models to the simultaneous mapping of the Earth's gravity field and its geoid, the ocean tides and the quasi-stationary component of the dynamic sea surface topography. Altimeter data collected by SEASAT, GEOS-3, and GEOSAT are used for the investigation

Gravity field determination and error assessment techniques

Linear estimation theory, along with a new technique to compute relative data weights, was applied to the determination of the Earth's geopotential field and other geophysical model parameters using a combination of satellite ground-based tracking data, satellite altimetry data, and the surface gravimetry data. The relative data weights for the inhomogeneous data sets are estimated simultaneously with the gravity field and other geophysical and orbit parameters in a least squares approach to produce the University of Texas gravity field models. New techniques to perform calibration of the formal covariance matrix for the geopotential solution were developed to obtain a reliable gravity field error estimate. Different techniques, which include orbit residual analysis, surface gravity anomaly residual analysis, subset gravity solution comparisons and consider covariance analysis, were applied to investigate the reliability of the calibration

An improved model for the Earth's gravity field

An improved model for the Earth's gravity field, TEG-1, was determined using data sets from fourteen satellites, spanning the inclination ranges from 15 to 115 deg, and global surface gravity anomaly data. The satellite measurements include laser ranging data, Doppler range-rate data, and satellite-to-ocean radar altimeter data measurements, which include the direct height measurement and the differenced measurements at ground track crossings (crossover measurements). Also determined was another gravity field model, TEG-1S, which included all the data sets in TEG-1 with the exception of direct altimeter data. The effort has included an intense scrutiny of the gravity field solution methodology. The estimated parameters included geopotential coefficients complete to degree and order 50 with selected higher order coefficients, ocean and solid Earth tide parameters, Doppler tracking station coordinates and the quasi-stationary sea surface topography. Extensive error analysis and calibration of the formal covariance matrix indicate that the gravity field model is a significant improvement over previous models and can be used for general applications in geodesy

The Effect of Small Molecule 390 on CXCR4 Receptors

CXCR4 is the chemokine receptor which aids in chemotaxis of stem cells, such as those in the bone marrow or the brain. SDF-1 is the natural ligand for the CXCR4 receptor. Similarities between novel molecule 390 synthesized by the Miller Lab and SDF-1 make this novel small molecule a possible agonist of the CXCR4 receptor. To determine whether 390 is an agonist to the CXCR4 receptor, we transfected cells with CXCR4 and exposed them to no agonist [vehicle control], SDF-1, or varying concentrations of our agonist drug. Next, we took calcium images using the dye fura-2, which indicates changes in calcium concentration in the cell due to CXCR4 activation. There was an increase in calcium in the cells when 390 was added, indicating that the receptor was being activated. When compared to the natural ligand, SDF-1, the levels were not as high, but they were higher than with the vehicle control. In the internalization assay, both SDF-1 and 390 internalized the receptor. The results suggest 390 is an agonist or partial agonist of the CXCR4 receptor. 390 may be the first small molecule agonist of CXCR4 receptors ever identified and may have a number of uses in medicine

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos

Human-Object Interaction (HOI) recognition in videos is important for analyzing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when multiple people and objects are involved in HOIs. Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN). The geometric-level graph models the interdependency between geometric features of humans and objects, while the fusion-level graph further fuses them with visual features of humans and objects. To demonstrate the novelty and effectiveness of our method in challenging scenarios, we propose a new multi-person HOI dataset (MPHOI-72). Extensive experiments on MPHOI-72 (multi-person HOI), CAD-120 (single-human HOI) and Bimanual Actions (two-hand HOI) datasets demonstrate our superior performance compared to state-of-the-arts.Comment: Accepted by ECCV 202