The space simulation facilities at IAL SPACE

The thermal vacuum facilities of IAL SPACE were tailored for testing of the ESA payloads. They were progressively upgraded for cryogenic payloads including 4 K (liquid helium temperature) experiments. A detailed review of the three vacuum chambers, ranging from 1.5 to 5 m diameter, is presented including the corresponding capabilities in the vacuum, thermal, and optical fields. The various aspects of cleanliness, product assurance, and quality control are also presented

Seismic site classification from the horizontal-to-vertical response spectral ratios: use of the Spanish strong-motion database

Normally, the average of the horizontal-to-vertical (H/V) ratios of the 5% damped response spectra of ground motions is used to classify the site of strong-motion stations. In these cases, only the three-orthogonal as-recorded acceleration components are used in the analysis, and all the vector compositions that can generate a different response for each period oscillator are excluded. In this study, the Spanish strong-motion database was used to classify the sites of accelerometric stations based on the predominant periods through the average horizontal-to-vertical spectral ratios (HVSR) of recorded ground motions. Moreover, the directionality effects using the vector composition of the horizontal components of ground motions were also considered in the estimations of H/V ratios. This consideration is a relevant novelty compared to the traditional H/V ratios methods. Only earthquakes with magnitudes above 3.5 and hypocentral distances below 200 km were selected, which resulted in 692 ground-motion records, corresponding to 86 stations, from events in the period between 1993 and 2017. After the analysis, a predominant-period site classification was assigned to each station. On the whole, the obtained mean and standard deviation values of the spectral ratios are comparable to those shown by other researchers. Therefore, the advantages of the proposed procedure, which takes the directionality effects into account, can be summarized as follows: (a) The obtained information is richer and gives enables more sophisticated and realistic analyses on the basis of percentiles and (b) it is easier to detect anomalous stations, sites, and/or accelerograms. Moreover, the method eliminates the effect of directionality as a contributor to epistemic uncertainty.Peer ReviewedPostprint (published version

IAL SPACE: A test laboratory for the ISO cryogenic payload

The ESA Infrared Space Observatory (ISO) satellite is a 3 axes pointed platform designed to make accurate pointed observations of astronomical objects and sources in the wavelength range between 2.5 and 200 microns. ISO is composed of a service module and a payload module which is a large cylindrical vacuum vessel. The vessel is in fact a cryostat (capacity of 2250 l of liquid He II) which contains the telescope and the four focal scientific instruments. The latter being cooled up to a temperature less than 4 K. The qualification of the payload requires the measurement respectively of: the image quality of the telescope through wave front error (WFE) measurements; and the optical alignment of the scientific instruments with respect to the telescope axis and the telescope focus, and this under cryogenic conditions. Consequently, since 1988, the FOCAL 5 IAL Space facility has been upgraded in order to perform the cryogenic optical tests of the ISO optical subsystems

Alterations in brain connectivity due to plasticity and synaptic delay

Brain plasticity refers to brain's ability to change neuronal connections, as a result of environmental stimuli, new experiences, or damage. In this work, we study the effects of the synaptic delay on both the coupling strengths and synchronisation in a neuronal network with synaptic plasticity. We build a network of Hodgkin-Huxley neurons, where the plasticity is given by the Hebbian rules. We verify that without time delay the excitatory synapses became stronger from the high frequency to low frequency neurons and the inhibitory synapses increases in the opposite way, when the delay is increased the network presents a non-trivial topology. Regarding the synchronisation, only for small values of the synaptic delay this phenomenon is observed

Bailout Embeddings, Targeting of KAM Orbits, and the Control of Hamiltonian Chaos

We present a novel technique, which we term bailout embedding, that can be used to target orbits having particular properties out of all orbits in a flow or map. We explicitly construct a bailout embedding for Hamiltonian systems so as to target KAM orbits. We show how the bailout dynamics is able to lock onto extremely small KAM islands in an ergodic sea.Comment: 3 figures, 9 subpanel

Global Fire Season Severity Analysis and Forecasting

Global fire activity has a huge impact on human lives. In recent years, many fire models have been developed to forecast fire activity. They present good results for some regions but require complex parametrizations and input variables that are not easily obtained or estimated. In this paper, we evaluate the possibility of using historical data from 2003 to 2017 of active fire detections (NASA's MODIS MCD14ML C6) and time series forecasting methods to estimate global fire season severity (FSS), here defined as the accumulated fire detections in a season. We used a hexagonal grid to divide the globe, and we extracted time series of daily fire counts from each cell. We propose a straightforward method to estimate the fire season lengths. Our results show that in 99% of the cells, the fire seasons have lengths shorter than seven months. Given this result, we extracted the fire seasons defined as time windows of seven months centered in the months with the highest fire occurrence. We define fire season severity (FSS) as the accumulated fire detections in a season. A trend analysis suggests a global decrease in length and severity. Since FSS time series are concise, we used the monthly-accumulated fire counts (MA-FC) to train and test the seven forecasting models. Results show low forecasting errors in some areas. Therefore we conclude that many regions present predictable variations in the FSS

Spatiotemporal data analysis with chronological networks

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets

Spatiotemporal data analysis with chronological networks

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet