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In the present study, we present the aerosol optical properties and radiative forcing (RF) of the tropospheric and stratospheric smoke layers, observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, during the ...
Assessing the impact of violent conflict on Syrian agriculture is challenging given data limitations and attributability issues. Using satellite data at 30 m spatial resolution, we found that the extent of productive cropland showed greater interannual var ...
This paper proposes a novel technique for the design of miniaturized Ka-band filters based on locally resonant metamaterials (LRMs), which are fabricated monolithically using lost wax casting. We implement ultra-small metamaterial filters by exploiting a n ...
The Global Navigation Satellite System (GNSS) refers to a constellation of satellites emitting signals from space, used to provide Position, Navigation and Timing (PNT) services to the receivers on Earth. Nowadays, the GNSS is exploited in a wide range of ...
Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) ...
Lunar resources is one of the many new putative business models that may transform space logistics. Yet it competes with Earth-based resources, in a complex trade-off involving both tech development & socioeconomic dynamics. This study models the size vs. ...
This paper proposes an alternative technique for the design of miniaturized waveguide filters based on locally resonant metamaterials (LRMs). We implement ultrasmall metamaterial filters (metafilters) by exploiting a subwavelength (sub-λ) guiding mechanism ...
As the pace of space activities accelerates and societies become more reliant on space-based systems, the associated risks deserve more attention. Satellites form a critical infrastructure on which military, civil and commercial entities rely. The services ...
Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial a ...
Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unravel ...
