arXiv subjects

Michele Melchiorri

Publications and source records attributed to Michele Melchiorri.

4 recordsLinked to original sources

arXiv2026

A Comparative Review of Methods to Create a Composite Index for Sustainable and Inclusive Wellbeing

Societal goals need to shift from over-reliance on gross domestic product (GDP) to broader aspects of sustainable and inclusive wellbeing (SIW). However, defining SIW and eventually measuring it with a single number is problematic because it involves many subjective and objective contributors that combine in complex, non-linear ways. Conventional approaches either use linear weighted averages or reduce SIW to subjective wellbeing alone. Neither is sufficient. This paper reviews aggregation methods for SIW against nine conditions derived from needs theory and strong sustainability: limited substitutability, penalisation of imbalances, non-linear transformations, respect for environmental ceilings, respect for lower limits, a formative measurement model, no correlation requirement, distributional sensitivity, cross-border spillovers, and intertemporal aggregation. We compare 13 methods, from simple arithmetic means to penalty-based indices, outranking multicriteria, data envelopment analysis, and insights from ecology, neuroscience, and machine learning. Our illustrative example shows that aggregation choices change significantly country rankings. Compensatory methods create similar rankings. No single method satisfies all nine conditions. We conclude that a future SIW composite indicator will require combining methods across levels: non-linear normalisation, non-compensatory aggregation, and measurement-level choices for inclusiveness and spillovers. This paper provides a step towards the headline aggregated indicator advocated by the UN High-Level Expert Group on Beyond GDP.

econ.GN↗

arXiv2021

The XGIS imaging system onboard the THESEUS mission

Within the scientific goals of the THESEUS ESA/M5 candidate mission, a critical item is a fast (within a few s) and accurate (<15 arcmin) Gamma-Ray Burst and high-energy transient location from a few keV up to hard X-ray energy band. For that purpose, the signal multiplexing based on coded masks is the selected option to achieve this goal. This contribution is implemented by the XGIS Imaging System, based on that technique. The XGIS Imaging System has the heritage of previous payload developments: LEGRI/Minisat-01, INTEGRAL, UFFO/Lomonosov and ASIM/ISS. In particular the XGIS Imaging System is an upgrade of the ASIM system in operation since 2018 on the International Space Station. The scientific goal is similar: to detect a gamma-ray transient. But while ASIM focuses on Terrestrial Gamma-ray Flashes, THESEUS aims for the GRBs. For each of the two XGIS Cameras, the coded mask is located at 630 mm from the detector layer. The coding pattern is implemented in a Tungsten plate (1 mm thickness) providing a good multiplexing capability up to 150 keV. In that way both XGIS detector layers (based on Si and CsI detectors) have imaging capabilities at the medium - hard X-ray domain. This is an improvement achieved during the current THESEUS Phase-A. The mask is mounted on top of a collimator that provides the mechanical assembly support, as well as good cosmic X-ray background shielding. The XGIS Imaging System preliminary structural and thermal design, and the corresponding analyses, are included in this contribution, as it is a preliminary performance evaluation.

astro-ph.IM↗

arXiv2021

The X/Gamma-ray Imaging Spectrometer (XGIS) on-board THESEUS: design, main characteristics, and concept of operation

THESEUS is one of the three missions selected by ESA as fifth medium class mission (M5) candidates in its Cosmic Vision science program, currently under assessment in a phase A study with a planned launch date in 2032. THESEUS is designed to carry on-board two wide and deep sky monitoring instruments for X/gamma-ray transients detection: a wide-field soft X-ray monitor with imaging capability (Soft X-ray Imager, SXI, 0.3 - 5 keV), a hard X-ray, partially-imaging spectroscopic instrument (X and Gamma Imaging Spectrometer, XGIS, 2 keV - 10 MeV), and an optical/near-IR telescope with both imaging and spectroscopic capability (InfraRed Telescope, IRT, 0.7 - 1.8 $μ$m). The spacecraft will be capable of performing fast repointing of the IRT to the error region provided by the monitors, thus allowing it to detect and localize the transient sources down to a few arcsec accuracy, for immediate identification and redshift determination. The prime goal of the XGIS will be to detect transient sources, with monitoring timescales down to milliseconds, both independently of, or following, up SXI detections, and identify the sources performing localisation at < 15 arcmin and characterize them over a broad energy band, thus providing also unique clues to their emission physics. The XGIS system consists of two independent but identical coded mask cameras, arranged to cover 2 steradians . The XGIS will exploit an innovative technology coupling Silicon Drift Detectors (SDD) with crystal scintillator bars and a very low-noise distributed front-end electronics (ORION ASICs), which will produce a position sensitive detection plane, with a large effective area over a huge energy band (from soft X-rays to soft gamma-rays) with timing resolution down to a few $μ$s.Here is presented an overview of the XGIS instrument design, its configuration, and capabilities.

astro-ph.IM↗

arXiv2020

Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery

Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed.The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers.The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference data-set of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness.

eess.IV↗