Stable Atmospheric Boundary-Layer Experiment in Spain (SABLES 98): A Report

This paper describes the Stable AtmosphericBoundary Layer Experiment in Spain (SABLES 98),which took place over the northern Spanish plateaucomprising relatively flat grassland,in September 1998. The main objectives of the campaign were to study the properties of themid-latitude stable boundary layer (SBL).Instrumentation deployed on two meteorologicalmasts (of heights 10 m and 100 m)included five sonic anemometers, 15 thermocouples,five cup anemometers and three propeller anemometers,humidity sensors and radiometers.A Sensitron mini-sodar and a tetheredballoon were also operated continuously. Atriangular array of cup anemometers wasinstalled to allow the detection ofwave events. Two nocturnal periods analysedon 14–15 and 20–21 September are used toillustrate the wide-ranging characteristics of the SBL.     

A New Approach to ID-TIMS U–Pb Dating of Cassiterite by the Example of the Pitkäranta Tin Deposit

Doklady Earth Sciences - A new technique for the complete dissolution of cassiterite, which makes it possible to determine the age of this mineral by U–Pb isotope dilution (ID-TIMS), has been...     

Prediction of Uplift Pile Displacement Based on Cone Penetration Tests (CPT)

Accurate prediction of uplift pile displacement is necessary to ensure appropriate structural and serviceability performance of civil projects. On the other hand, in recent years, machine-learning models have been applied to many geotechnical-engineering problems, with some degrees of success. The scope of this research includes three main stages: (1) the compilation of load–displacement data sets, obtained from the published literature, (2) analysis of machine learning models that predict the uplift pile displacement based on the cone penetration test data, and the relative importance of input parameters that have been evaluated using senility analysis by the artificial neural network, In addition, this paper also examines the different selection of input parameters and internal network parameters to obtain the optimum model, (3) A parametric study has also been performed for the input parameters to study the consistency of the suggested model. The statistical parameters and parametric study obtained in this research show the superiority of the current model. It is demonstrated that machine learning models such as ANN and GP models outperform the traditional methods, and provide accurate uplift pile displacement predictions.     

Calibration measurements on the DEPFET Detectors for the MIXS instrument on BepiColombo

The Mercury Imaging X-ray Spectrometer (MIXS) will be launched on board of the 5th ESA cornerstone mission BepiColombo. The two channel instrument MIXS is dedicated to the exploration of the elemental composition of the mercurian surface by imaging x-ray spectroscopy of the elemental fluorescence lines. One of the main scientific goals of MIXS is to provide spatially resolved elemental abundance maps of key rock-forming elements. MIXS will be the successor of the XRS instrument, which is currently orbiting Mercury on board of NASAs satellite MESSENGER. MIXS will provide unprecedented spectral and spatial resolution due to its innovative detector and optics concepts. The MIXS target energy band ranges from 0.5 to 7 keV and allows to directly access the Fe-L line at 0.7 keV, which was not accessible to previous missions. In addition, the high spectroscopic resolution of FWHM ≤ 200 eV at the reference energy of 1 keV after one year in Mercury orbit, allows to separate the x-ray fluorescence emission lines of important elements like Mg (1.25 keV) and Al (1.49 keV) without the need for any filter. The detectors for the energy and spatially resolved detection of x-rays for both channels are identical DEPFET (DEpleted P-channel FET) active pixel detectors. We report on the calibration of the MIXS flight and flight spare detector modules at the PTB (Physikalisch-Technische Bundesanstalt) beamlines at the BESSY II synchrotron radiation facility. Each detector was calibrated at least at 10 discrete energies in the energy range from 0.5 to 10 keV. The excellent spectroscopic performance of all three detector modules was verified.     

Geometric Aspects and Testing of the Galactic Center Distance Determination from Spiral Arm Segments

We consider the problem of determining the geometric parameters of a Galactic spiral arm from its segment by including the distance to the spiral pole, i.e., the distance to the Galactic center (R₀). The question about the number of points belonging to one turn of a logarithmic spiral and defining this spiral as a geometric figure has been investigated numerically and analytically by assuming the direction to the spiral pole (to the Galactic center) to be known. Based on the results obtained, in an effort to test the new approach, we have constructed a simplified method of solving the problem that consists in finding the median of the values for each parameter from all possible triplets of objects in the spiral arm segment satisfying the condition for the angular distance between objects. Applying the method to the data on the spatial distribution of masers in the Perseus and Scutum arms (the catalogue by Reid et al. (2014)) has led to an estimate of R₀ = 8.8 ± 0.5 kpc. The parameters of five spiral arm segments have been determined from masers of the same catalogue. We have confirmed the difference between the spiral arms in pitch angle. The pitch angles of the arms revealed by masers are shown to generally correlate with R₀ in the sense that an increase in R₀ leads to a growth in the absolute values of the pitch angles.     

Method of density moment sums for photometric calibration of photographic plates

A new method — the method of moment density sums — is suggested for determining the characteristic curve of a plate as well as the magnitude zero point, using PDS microdensitometer measurements of stars of known brightness. It may be used for surface photometry of plates without sensitometer spots.     

Radiation hydrodynamic simulation of a photoionised plasma experiment at the Z facility

New, high spectral resolution X-ray observations from astrophysical photoionised plasmas have been recorded in recent years by the Chandra and XMM-Newton orbiting telescopes. These observations provide a wealth of detailed information and have motivated new efforts at developing a detailed understanding of the atomic kinetics and radiation physics of photoionised plasmas. The Z facility at Sandia National Laboratories is a powerful source of X-rays that enables us to produce and study photoionised plasmas in the laboratory under well characterised conditions. We discuss a series of radiation-hydrodynamic simulations to help understand the X-ray environment, plasma hydrodynamics and atomic kinetics in experiments where a collapsing wire array at Z is used as an ionising source of radiation to create a photoionised plasma. The numerical simulations are used to investigate the role that the key experimental parameters have on the photoionised plasma characteristics.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.