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Proprietà dei layer (vettoriali e raster)
Get PDF<p>The present lesson (in Italian language) is related to the annual course of QGIS (base level) held by ISPRA in the frame of its SNPA activities. The lesson concerns layers properties, both raster and vector.</p>
INFN Technology: Life Science
No full text<p>Promo video about INFN's technologies in the life sciences field.</p>
<p>3 versions available:</p>
<ul>
<li>full length</li>
<li>60 seconds</li>
<li>30 seconds</li>
</ul>
Geodatabase Postgis, PARTE I
No full text<p>Geodatabase PostGIS PART I<br>This lesson (in Italian) is part of the annual advanced QGIS course organized by ISPRA as part of its SNPA activities. The lesson covers the following topic: Geodatabase PostGIS – PART I.</p>
Dataset related to article \"The role of limbic structures in financial abilities of mild cognitive impairment patients\
No full text<p>-</p>
Primordial black holes and their gravitational-wave signatures
No full textIn the recent years, primordial black holes (PBHs) have emerged as one of the most interesting and hotly debated topics in cosmology. Among other possibilities, PBHs could explain both some of the signals from binary black hole mergers observed in gravitational-wave detectors and an important component of the dark matter in the Universe. Significant progress has been achieved both on the theory side and from the point of view of observations, including new models and more accurate calculations of PBH formation, evolution, clustering, merger rates, as well as new astrophysical and cosmological probes. In this work, we review, analyze and combine the latest developments in order to perform end-to-end calculations of the various gravitational-wave signatures of PBHs. Different ways to distinguish PBHs from stellar black holes are emphasized. Finally, we discuss their detectability with LISA, the first planned gravitational-wave observatory in space
Solar wind speed estimate with machine learning ensemble models for LISA
No full textIn this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016–2017. We leverage a supervised model trained with the ACE observations and the galactic cosmic-ray flux variation data measured with particle detectors hosted on board the LISA Pathfinder mission also orbiting around L1 during the same years. Missing data in galactic cosmic-ray time series have been filled with the benefit of other machine learning models developed in previous work. The model presented here will be used for the European Space Agency Laser Interferometer Space Antenna (LISA) after its launch in 2035 to estimate the solar wind speed, that will not be measured on board, with the only benefit of galactic cosmic-ray variation measurements. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as the LISA mission and space weather science
Note Illustrative della Carta geologica d'Italia alla scala 1:50.000, F. 185 Ferrara, Servizio Geologico d'Italia - ISPRA
No full text<p>Note illustrative redatte per il Foglio geologico n. 185 Ferrara della Carta Geologica d'Italia alla scala 1:50.000. 144 pp.</p>
Wildfire in Protected Areas (2019-2024)
No full text<p>Il dataset descrive le superfici forestali percorse da incendi all’interno del sistema delle Aree Protette Nazionali.<br>Queste comprendono:</p>
<ul>
<li>
<p>le aree dell’Elenco Ufficiale delle Aree Naturali Protette (EUAP): Parchi Nazionali, Parchi Regionali, Riserve Naturali statali e regionali, oltre ad altre aree protette (es. oasi e monumenti naturali);</p>
</li>
<li>
<p>i siti della Rete Natura 2000 (RN2000): Zone Speciali di Conservazione (ZSC) e Zone di Protezione Speciale (ZPS).</p>
</li>
</ul>
<p>Poiché le diverse tipologie di aree protette si sovrappongono parzialmente tra loro, è stato necessario calcolare le superfici incendiate attraverso l’intersezione tra confini amministrativi e aree percorse dal fuoco.</p>
<p>I dati sono presentati in tabelle annuali, con denominazione e tipologia di appartenenza delle aree interessate.</p>