Opportunities of artificial neural network generated VGA: Training a Multilayer Perceptron to recognize the underlying structures of space

This paper presents the research conducted with the aim of understanding if new advances in computer science, more specifically a type of supervised, feedforward Artificial Neural Network, a Multilayer Perceptron (MLP) is able to estimate the values of Visibility Graph Analysis (VGA) without the need for expensive calculation. The overarching hypothesis is that an MLP can be setup in a way that it can be trained to learn the relationship between spatial configuration and the VGA (neighbourhood size and clustering coefficient) derived from it. Two hypotheses are stated: firstly, if such an MLP can be created than it will be able to generate spatial configurations for specific VGA values as inputs (mode A); secondly, the network would be able to generate VGA when presented with spatial configuration faster, compared to current method and with negligible error (mode B). The hypotheses were tested by creating unique setups of an MLP for each mode, all of which had a different configuration. As each combination of possible setups were tested, the performance of the networks could be compared to each other and to the traditional method of VGA calculation. Both mode A and mode B was able to achieve satisfying results that prove that an MLP is able to generate –with limitations- configurations based on VGA input and it is able to calculate the neighbourhood size and the clustering coefficient of a 2D layout substantially faster and with negligible error. All MLPs were created at a generic space, therefore the MLP taught once can be adopted universally to most spaces. The implications of the two systems is that spatial analysis can be integrated into the design process, enabling interactive, instant analysis and the possible deployment of optimisation procedures, for instance a Genetic Algorithm

ROMAP: Rosetta Magnetometer and Plasma Monitor

The scientific objectives, design and capabilities of the Rosetta Lander’s ROMAP instrument are presented. ROMAP’s main scientific goals are longterm magnetic field and plasma measurements of the surface of Comet 67P/Churyumov-Gerasimenko in order to study cometary activity as a function of heliocentric distance, and measurements during the Lander’s descent to investigate the structure of the comet’s remanent magnetisation. The ROMAP fluxgate magnetometer, electrostatic analyser and Faraday cup measure the magnetic field from 0 to 32 Hz, ions of up to 8000 keV and electrons of up to 4200 keV. Additional two types of pressure sensors – Penning and Minipirani – cover a pressure range from 108 to 101 mbar. ROMAP’s sensors and electronics are highly integrated, as required by a combined field/plasma instrument with less than 1 W power consumption and 1 kg mass

Venus monitoring camera for Venus express

The Venus Express mission will focus on a global investigation of the Venus atmosphere and plasma environment, while additionally measuring some surface properties from orbit. The instruments PFS and SPICAV inherited from the Mars Express mission and VIRTIS from Rosetta form a powerful spectrometric and spectro-imaging payload suite. Venus Monitoring Camera (VMC)—a miniature wideangle camera with 17.51 field of view—was specifically designed and built to complement these experiments and provide imaging context for the whole mission. VMC will take images of Venus in four narrow band filters (365, 513, 965, and 1000 nm) all sharing one CCD. Spatial resolution on the cloud tops will range from 0.2 km/px at pericentre to 45 km/px at apocentre when the full Venus disc will be in the field of view. VMC will fulfill the following science goals: (1) study of the distribution and nature of the unknown UV absorber; (2) determination of the wind field at the cloud tops (70 km) by tracking the UV features; (3) thermal mapping of the surface in the 1 mm transparency ‘‘window’’ on the night side; (4) determination of the global wind field in the main cloud deck (50 km) by tracking near-IR features; (5) study of the lapse rate and H2O content in the lower 6–10 km; (6) mapping O2 night-glow and its variability