The clustering of Lyman-break galaxies

We calculate the statistical clustering of Lyman-break galaxies predicted in a selection of currently fashionable structure formation scenarios. These models are all based on the cold dark matter model, but vary in the amount of dark matter, the initial perturbation spectrum, the background cosmology and the presence or absence of a cosmological constant term. If Lyman-break galaxies form as a result of hierarchical merging, the amplitude of clustering depends quite sensitively on the minimum halo mass that can host such a galaxy. Interpretation of the recent observations by Giavalisco et al. would therefore be considerably clarified by a direct determination of the relevant halo properties. For a typical halo mass around 10¹¹  h ⁻¹ M⊙ the observations do not discriminate strongly between cosmological models, but if the appropriate mass is larger, say 10¹²  h ⁻¹ M⊙ (which seems likely on theoretical grounds), then the data strongly favour models with a low matter density.     

The bias field of dark matter haloes

Accepted 1998 January 26. Received 1998 January 26; in original form 1997 August 13This paper presents a stochastic approach to the clustering evolution of dark matter haloes in the Universe. Haloes, identified by a Press–Schechter-type algorithm in Lagrangian space, are described in terms of 'counting fields', acting as non-linear operators on the underlying Gaussian density fluctuations. By ensemble-averaging these counting fields, the standard Press–Schechter mass function as well as analytic expressions for the halo correlation function and corresponding bias factors of linear theory are obtained, extending the recent results by Mo & White. The non-linear evolution of our halo population is then followed by solving the continuity equation, under the sole hypothesis that haloes move by the action of gravity. This leads to an exact and general formula for the bias field of dark matter haloes, defined as the local ratio between their number density contrast and the mass density fluctuation. Besides being a function of position and 'observation' redshift, this random field depends upon the mass and formation epoch of the objects and is both non-linear and non-local. The latter features are expected to leave a detectable imprint on the spatial clustering of galaxies, as described, for instance, by statistics like the bispectrum and the skewness. Our algorithm may have several interesting applications, among which is the possibility of generating mock halo catalogues from low-resolution N -body simulations.     

Excursion set approach to the clustering of dark matter haloes in Lagrangian space

We present a stochastic approach to the spatial clustering of dark matter haloes in Lagrangian space. Our formalism is based on a local formulation of the 'excursion set' approach by Bond et al., which automatically accounts for the 'cloud-in-cloud' problem in the identification of bound systems. Our method allows us to calculate correlation functions of haloes in Lagrangian space using either a multidimensional Fokker–Planck equation with suitable boundary conditions, or an array of Langevin equations with spatially correlated random forces. We compare the results of our method with theoretical predictions for the halo autocorrelation function considered in the literature, and find good agreement with the results recently obtained within a treatment of halo clustering in terms of 'counting fields' by Catelan et al. Finally, the possible effect of spatial correlations on numerical simulations of halo merger trees is discussed.     

A statistical analysis of flank eruptions on Etna volcano

A singularly complete record exists for the eruptive activity of Etna volcano. The time series of occurrence of flank eruptions in the period 1600–1980, in which the record is presumably complete, is found to follow a stationary Poisson process. A revision of the available data shows that eruption durations are rather well correlated with the estimates of the volume of lava flows. This implies that the magnitude of an eruption can be defined directly by its duration. Extreme value statistics are then applied to the time series, using duration as a dependent variable. The probability of occurrence of a very long (300 days) eruption is greater than 50% only in time intervals of the order of 50 years. The correlation found between duration and total output also allows estimation of the probability of occurrence of a major event which exceeds a given duration and total flow of lava. The composite probabilities do not differ considerably from the pure ones. Paralleling a well established application to seismic events, extreme value theory can be profitably used in volcanic risk estimates, provided that appropriate account is also taken of all other variables.     

Size distribution models of fog and cloud droplets and their volume extinction coefficients at visible and infrared wavelengths

Based on the average variability of the skewness with respect to the droplet mode radius, a wide set of mean size-distribution models is presented in terms of the modified gamma function for fog and stratified cloud droplets. These models appear appropriate for giving reliable size-distribution curves relative to the various formation stages of the droplet population both in fogs and in stratus and stratocumulus clouds.The corresponding volume extinction coefficient has been computed at various wavelengths from 0.4 to 17 m using Van de Hulst's (1957) approximation multiplied by Deirmendjian's (1960) correction factors. This set of theoretical extinction data has been compared with experimental extinction measurements performed in atmospheres characterized by a marked thermal inversion for describing the evolutionary features of the water droplet size distribution within the whole ground layer.     

Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing

ABSTRACT

Geospatial information acquired with Unmanned Aerial Vehicles (UAV) provides valuable decision-making support in many different domains, and technological advances coincide with a demand for ever more sophisticated data products. One consequence is a research and development focus on more accurately referenced images and derivatives, which has long been a weakness especially of low to medium cost UAV systems equipped with relatively inexpensive inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) receivers. This research evaluates the positional accuracy of the real-time kinematics (RTK) GNSS on the DJI Matrice 600 Pro, one of the first available and widely used UAVs with potentially surveying-grade performance. Although a very high positional accuracy of the drone itself of 2 to 3 cm is claimed by DJI, the actual accuracy of the drone RTK for positioning the images and for using it for mapping purposes without additional ground control is not known. To begin with, the actual GNSS RTK position of reference center (the physical point on the antenna) on the drone is not indicated, and uncertainty regarding this also exists among the professional user community. In this study the reference center was determined through a set of experiments using the dual frequency static Leica GNSS with RTK capability. The RTK positioning data from the drone were then used for direct georeferencing, and its results were evaluated. Test flights were carried out over a 70 x 70 m area with an altitude of 40 m above the ground, with a ground sampling distance of 1.3 cm. Evaluated against ground control points, the planimetric accuracy of direct georeferencing for the photogrammetric product ranged between 30 and 60 cm. Analysis of direct georeferencing results showed a time delay of up to 0.28 seconds between the drone GNSS RTK and camera image acquisition affecting direct georeferencing results.     

Detection of seismic façade damages with multi-temporal oblique aerial imagery

ABSTRACT

Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used.     

Cyclostratigraphy and high-frequency carbon isotope fluctuations in Upper Cretaceous shallow-water carbonates, southern Italy

Abstract A detailed carbon isotope study has been carried out on a Santonian (Upper Cretaceous) carbonate platform succession that crops out at Monte Sant'Erasmo (southern Italy). Previous centimetre‐scale studies on this succession have shown that high‐frequency eustatic changes, resulting from the Earth's orbital fluctuations, controlled the hierarchical organization of the depositional and early diagenetic features in elementary cycles, bundles (groups of three to five elementary cycles) and superbundles (groups of three or four bundles). The elementary cycles, which correspond to single beds, suggest a control caused by Earth's precession; the bundles and superbundles record the short (≈ 100 kyr) and long (≈ 400 kyr) eccentricity periodicity respectively. The δ¹³C signal of the Monte Sant'Erasmo succession is cyclic in nature and may be considered to be a reliable proxy for the sedimentary evolution (and related sea‐level history) of the analysed sequence. The carbon isotope cyclicity is recorded at bundle and superbundle level, but it is not evident at the scale of the elementary cycles, at least with the sampling interval used in this study. Spectral analysis of the δ¹³C record shows two main peaks corresponding to the short‐ and long‐eccentricity periodicity, whereas the precession signal is not evident in the power spectrum. In addition, lithofacies analysis shows that, in each bundle (and superbundle), higher C isotope values occur in sediments characterized by marine cements, whereas lower values are normally found in more restricted deposits overprinted by early meteoric diagenesis. Early diagenesis, driven by periodic sea‐level fluctuations, developed in either shallow‐subtidal (marine diagenesis) or subaerial‐exposed (meteoric overprint) sedimentary environments and directly influenced the carbon isotope signature. As a consequence, the δ¹³C record at Monte Sant'Erasmo reflects high‐frequency climatic oscillations controlling both environmental and early diagenetic changes. The long‐term isotopic record is similar to that of contemporaneous pelagic sections in England and elsewhere in Italy. It is concluded that the δ¹³C signature of shallow‐water carbonates, such as those of Monte Sant'Erasmo, offers great potential for correlation with coeval sections, including those of the pelagic realm.     

Linear relationship between thermo-dehydroxylation and induced-strain by mechanical processing in vacuum: The case of industrial kaolinite, talc and montmorillonite

The thermal behaviour and the structural properties of three important industrial phyllosilicates, such as kaolinite, talc and Ca-rich montmorillonite, have been examined after mechanical treatment in a specifically built planetary ball mill working in vacuum (P = 0.13 Pa) at room temperature (25 °C). It is found that, on increasing the grinding time, the temperature of the dehydroxylation reaction decreases linearly as a result of a decrease of the crystallite size and structural order. To be noted that the mechanical treatment in our milling conditions did not induce significant amorphization. The temperature at which there is the maximum of dehydroxylation and the weight losses of the intralayer OH are linearly related to the increase of the FWHM of the 001 basal plane. These results are useful for predicting the thermal behaviour of layer silicates to be subjected to mechanical processing in industrial application.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.