ASIME 2018 White Paper. In-Space Utilisation of Asteroids: Asteroid Composition -- Answers to Questions from the Asteroid Miners

In keeping with the Luxembourg government's initiative to support the future use of space resources, ASIME 2018 was held in Belval, Luxembourg on April 16-17, 2018. The goal of ASIME 2018: Asteroid Intersections with Mine Engineering, was to focus on asteroid composition for advancing the asteroid in-space resource utilisation domain. What do we know about asteroid composition from remote-sensing observations? What are the potential caveats in the interpretation of Earth-based spectral observations? What are the next steps to improve our knowledge on asteroid composition by means of ground-based and space-based observations and asteroid rendez-vous and sample return missions? How can asteroid mining companies use this knowledge? ASIME 2018 was a two-day workshop of almost 70 scientists and engineers in the context of the engineering needs of space missions with in-space asteroid utilisation. The 21 Questions from the asteroid mining companies were sorted into the four asteroid science themes: 1) Potential Targets, 2) Asteroid-Meteorite Links, 3) In-Situ Measurements and 4) Laboratory Measurements. The Answers to those Questions were provided by the scientists with their conference presentations and collected by A. Graps or edited directly into an open-access collaborative Google document or inserted by A. Graps using additional reference materials. During the ASIME 2018, first day and second day Wrap-Ups, the answers to the questions were discussed further. New readers to the asteroid mining topic may find the Conversation boxes and the Mission Design discussions especially interesting.Comment: Outcome from the ASIME 2018: Asteroid Intersections with Mine Engineering, Luxembourg. April 16-17, 2018. 65 Pages. arXiv admin note: substantial text overlap with arXiv:1612.0070

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

Geoscience and a Lunar Base: A Comprehensive Plan for Lunar Exploration

This document represents the proceedings of the Workshop on Geoscience from a Lunar Base. It describes a comprehensive plan for the geologic exploration of the Moon. The document begins by explaining the scientific importance of studying the Moon and outlines the many unsolved problems in lunar science. Subsequent chapters detail different, complementary approaches to geologic studies: global surveys, including orbiting spacecraft such as Lunar Observer and installation of a global geophysical network; reconnaissance sample return mission, by either automated rovers or landers, or by piloted forays; detailed field studies, which involve astronauts and teleoperated robotic field geologists. The document then develops a flexible scenario for exploration and sketches the technological developments needed to carry out the exploration scenario

Navigating Diverse Datasets in the Face of Uncertainty

When exploring big volumes of data, one of the challenging aspects is their diversity of origin. Multiple files that have not yet been ingested into a database system may contain information of interest to a researcher, who must curate, understand and sieve their content before being able to extract knowledge. Performance is one of the greatest difficulties in exploring these datasets. On the one hand, examining non-indexed, unprocessed files can be inefficient. On the other hand, any processing before its understanding introduces latency and potentially un- necessary work if the chosen schema matches poorly the data. We have surveyed the state-of-the-art and, fortunately, there exist multiple proposal of solutions to handle data in-situ performantly. Another major difficulty is matching files from multiple origins since their schema and layout may not be compatible or properly documented. Most surveyed solutions overlook this problem, especially for numeric, uncertain data, as is typical in fields like astronomy. The main objective of our research is to assist data scientists during the exploration of unprocessed, numerical, raw data distributed across multiple files based solely on its intrinsic distribution. In this thesis, we first introduce the concept of Equally-Distributed Dependencies, which provides the foundations to match this kind of dataset. We propose PresQ, a novel algorithm that finds quasi-cliques on hypergraphs based on their expected statistical properties. The probabilistic approach of PresQ can be successfully exploited to mine EDD between diverse datasets when the underlying populations can be assumed to be the same. Finally, we propose a two-sample statistical test based on Self-Organizing Maps (SOM). This method can outperform, in terms of power, other classifier-based two- sample tests, being in some cases comparable to kernel-based methods, with the advantage of being interpretable. Both PresQ and the SOM-based statistical test can provide insights that drive serendipitous discoveries

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided