Urban airspace environments present exciting new opportunities for delivering drone services to an increasingly large global market, including: information gathering; package delivery; air-taxi services. A key challenge is how to model airspace environments over densely populated urban spaces, coupled with the design and development of scalable traffic management systems that may need to handle potentially hundreds to thousands of drone movements per hour. This paper explores the background to Urban unmanned traffic management (UTM), examining high-level initiatives, such as the USA’s Unmanned Air Traffic (UTM) systems and Europe’s U-Space services, as well as a number of contemporary research activities in this area. The main body of the paper describes the initial research outputs of the U-Flyte R&D group, based at Maynooth University in Ireland, who have focused on developing an integrated approach to airspace modelling and traffic management platforms for operating large drone fleets over urban environments. This work proposes pragmatic and innovative approaches to expedite the roll-out of these much-needed urban UTM solutions. These approaches include the certification of drones for urban operation, the adoption of a collaborative and democratic approach to designing urban airspace, the development of a scalable traffic management and the replacement of direct human involvement in operating drones and coordinating drone traffic with machines. The key fundamental elements of airspace architecture and traffic management for busy drone operations in urban environments are described together with initial UTM performance results from simulation studies.
For any group G the involutions I in G form a G-set under conjugation. The corresponding kG-permutation module kI is known as the involution module of G, with k an algebraically closed field of characteristic two. In this paper we discuss the involution module of the special linear group SL 2 ð2 f Þ. We describe the vertices and the Loewy and Socle Series of all its components.
We determine all indecomposable symplectic modules for the Klein-four group K 4 over a perfect field of characteristic 2 and classify the symplectic forms up to isometry. We also determine all K 4-invariant quadratic forms which polarize to a given symplectic form and classify such quadratic forms up to isometry.
This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.
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