The Taylor series method for the problem of the rotational motion of a rigid satellite

This paper presents the Taylor series method for integration of differential equations describing the rotational motion of a rigid satellite. We compared the presented algorithm with other methods, and we show that it gives the most accurate results with reasonable efficiency.     

On the gravitational fields of Pandora and Prometheus

Utilizing topographic models of Saturn's F-ring shepherd satellites Prometheus (S16 1980S27) and Pandora (S15 1980S26), derived by Stooke (1994), and supposing that their mass density is constant, we derived basic geometrical and dynamical characteristics of the moons. They include the volume and mass, the mean radii, the tensor of inertia, and Stokes coefficients of the harmonic expansions of external gravitational potential. The best fitting ellipsoid approximations of the topography were calculated. A simple method of determining the gravitational potential on the surface of an irregular satellite is presented. Examples of equipotential surfaces of the satellites are shown     

Solutions homoclinic to regular precessions of a symmetric satellite

The aim of this paper is to study numerically asymptotic manifolds and homoclinic solutions to the regular precessions of a rigid symmetric satellite in a circular orbit.     

Secondary Ion Mass Spectrometry Bias on Isotope Ratios in Dolomite–Ankerite,Part II: δ13C Matrix Effects

This study is Part II of a series that documents the development of a suite of calibration reference materials for in situ SIMS analysis of stable isotope ratios in Ca‐Mg‐Fe carbonates. Part I explored the effects of Fe²⁺ substitution on SIMS δ¹⁸O bias measured from the dolomite–ankerite solid solution series [CaMg(CO₃)₂–CaFe(CO₃)₂], whereas this complementary work explores the compositional dependence of SIMS δ¹³C bias (calibrated range: Fe# = 0.004–0.789, where Fe# = molar Fe/(Mg+Fe)). Under routine operating conditions for carbonate δ¹³C analysis at WiscSIMS (CAMECA IMS 1280), the magnitude of instrumental bias increased exponentially by 2.5–5.5‰ (session‐specific) with increasing Fe‐content in the dolomite structure, but appeared insensitive to minor Mn substitution [< 2.6 mole % Mn/(Ca+Mg+Fe+Mn)]. The compositional dependence of bias (i.e., the matrix effect) was expressed using the Hill equation, yielding calibration residual values ≤ 0.3‰ relative to CRM NBS‐19 for eleven carbonate reference materials (6‐μm‐diameter spot size measurements). Based on the spot‐to‐spot repeatability of a drift monitor material that ‘bracketed’ each set of ten sample‐spot analyses, the analytical precision was ± 0.6–1.2‰ (2s, standard deviations). The analytical uncertainty for individual sample analyses was approximated by combining the precision and calibration residual values (propagated in quadrature), suggesting an uncertainty of ± 1.0–1.5‰ (2s).     

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.