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This paper presents initial results from the ESA-funded ``SUPPPPRESS'' project, which aims to develop high-performance liquid-crystal coronagraphs for direct imaging of Earth-like exoplanets in reflected light. The project focuses on addressing the significant challenge of polarization leakage in vector vortex coronagraphs (VVCs). We utilize newly manufactured multi-grating, liquid-crystal VVCs, consisting of a two- or three-element stack of vortex and grating patterns, to reduce this leakage to the $10^{-10}$ contrast level. We detail the experimental setups, including calibration techniques with polarization microscopes and Mueller matrix ellipsometers to enhance the direct-write accuracy of the liquid-crystal patterns. The performance testing of these coronagraph masks will be conducted on the THD2 high-contrast imaging testbed in Paris., Comment: 14 pages, 10 figures

Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing. The objective is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature. Moreover, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. The specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency (ESA) OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants monitoring on the Phisat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are discussed and in progress and future work introduced., Comment: 11 pages, 9 figures, submitted to IEEE JSTARS

Optical Bound States in the Continuum are peculiar localized states within the continuous spectrum that are unaffected by any far-field radiation and intrinsic absorption, therefore possessing infinite mode lifetime and Q-factor. To date they have been widely studied in dielectric structures whereas their exploitation in lossy media, i.e. plasmonic nanostructures, still remains a challenge. Here, we show the emergence of a hybrid BIC state in a 2D system of silver-filled dimers, quasi-embedded in a high-index dielectric waveguide. The hybrid BIC onset is found to be highly dependent on the bare modes' spectral and spatial overlap, but particularly on the plasmonic field's intensity. By tailoring the hybridizing plasmonic/photonic fractions we select an ideal coupling regime for which the mode exhibits both, high Q-factor values and strong near-field enhancement tightly confined in the nanogap and a consequently extremely small modal volume. We demonstrate that this optical layout can be exploited in a proof-of-concept experiment for the detection of TAR DNA-binding protein 43, which outperforms the sensitivity of current label-free biosensing platforms, reaching the zeptomolar range of concentration.

The formation of small droplets and bubbles in turbulent flows is a crucial process in geophysics and engineering, whose underlying physical mechanism remains a puzzle. In this letter, we address this problem by means of high-resolution numerical simulations, comparing a realistic multiphase configuration with a numerical experiment in which we attenuate the presence of strong velocity gradients either across the whole mixture or in the disperse phase only. Our results show unambiguously that the formation of small droplets is governed by the internal dynamics which occur during the break-up of large drops and that the high vorticity and the extreme dissipation associated to these events are the consequence and not the cause of the breakup.

Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting water quality. Its accurate assessment is crucial for safeguarding ecosystems and human consumption, demanding meticulous attention and action. For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A. Traditional methods are labor-intensive while CatBoost offers an efficient solution, excelling in predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data through the Google Earth Engine (GEE), our study contributes to scalable and precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong contaminants monitoring stations enriches our study, providing region-specific insights. Results showcase the viability of this integrated approach, laying the foundation for adopting advanced techniques in global water quality management., Comment: 4 pages, 3 figures, IGARSS2024

In this paper, we present the data preparation activities that we performed for the Digital Experience Platform (DXP) project, commissioned and supervised by Doxee S.p.A.. DXP manages the billing data of the users of different companies operating in various sectors (electricity and gas, telephony, pay TV, etc.). This data has to be processed to provide services to the users (e.g., interactive billing), but mainly to provide analytics to the companies (e.g., churn prediction or user segmentation). We focus on the design of the data preparation pipeline, describing the challenges that we had to overcome in order to get the billing data ready to perform analysis on it. We illustrate the lessons learned by highlighting the key points that could be transferred to similar projects. Moreover, we report some interesting results and considerations derived from the preliminary analysis of the prepared data, also pointing out some possible future directions for the ongoing project, spacing from big data integration to privacy-preserving temporal record linkage.

The increasing spreading of small commercial Unmanned Aerial Vehicles (UAVs, aka drones) presents serious threats for critical areas such as airports, power plants, governmental and military facilities. In fact, such UAVs can easily disturb or jam radio communications, collide with other flying objects, perform espionage activity, and carry offensive payloads, e.g., weapons or explosives. A central problem when designing surveillance solutions for the localization of unauthorized UAVs in critical areas is to decide how many triangulating sensors to use, and where to deploy them to optimise both coverage and cost effectiveness. In this article, we compute deployments of triangulating sensors for UAV localization, optimizing a given blend of metrics, namely: coverage under multiple sensing quality levels, cost-effectiveness, fault-tolerance. We focus on large, complex 3D regions, which exhibit obstacles (e.g., buildings), varying terrain elevation, different coverage priorities, constraints on possible sensors placement. Our novel approach relies on computational geometry and statistical model checking, and enables the effective use of off-the-shelf AI-based black-box optimizers. Moreover, our method allows us to compute a closed-form, analytical representation of the region uncovered by a sensor deployment, which provides the means for rigorous, formal certification of the quality of the latter. We show the practical feasibility of our approach by computing optimal sensor deployments for UAV localization in two large, complex 3D critical regions, the Rome Leonardo Da Vinci International Airport (FCO) and the Vienna International Center (VIC), using NOMAD as our state-of-the-art underlying optimization engine. Results show that we can compute optimal sensor deployments within a few hours on a standard workstation and within minutes on a small parallel infrastructure.

Ultrasound (US) imaging is widely used in diagnosing and staging abdominal diseases due to its lack of non-ionizing radiation and prevalent availability. However, significant inter-operator variability and inconsistent image acquisition hinder the widespread adoption of extensive screening programs. Robotic ultrasound systems have emerged as a promising solution, offering standardized acquisition protocols and the possibility of automated acquisition. Additionally, these systems enable access to 3D data via robotic tracking, enhancing volumetric reconstruction for improved ultrasound interpretation and precise disease diagnosis. However, the interpretability of 3D US reconstruction of abdominal images can be affected by the patient's breathing motion. This study introduces a method to compensate for breathing motion in 3D US compounding by leveraging implicit neural representations. Our approach employs a robotic ultrasound system for automated screenings. To demonstrate the method's effectiveness, we evaluate our proposed method for the diagnosis and monitoring of abdominal aorta aneurysms as a representative use case. Our experiments demonstrate that our proposed pipeline facilitates robust automated robotic acquisition, mitigating artifacts from breathing motion, and yields smoother 3D reconstructions for enhanced screening and medical diagnosis., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We investigate the statistics of turbulence in emulsions of two-immiscible fluids of same density. We compute for the first time velocity increments between points conditioned to be located in the same phase or in different phases and examine their probability density functions (PDF) and the associated structure functions (SF). This enables us to demonstrate that the the presence of the interface reduces the skewness of the PDF at scales below the Kolmogorov-Hinze scale and therefore the magnitude of the energy flux towards the dissipative scales, which is quantified by the third-order SF. The analysis of the higher order SFs shows that multiphase turbulence is more intermittent than single-phase turbulence. In particular, the local scaling exponents of the SFs display a saturation about the Kolmogorov-Hinze scale and below, which indicates the presence of large velocity gradients across the interface. Interestingly, the statistics approach of classic homogeneous isotropic turbulence when significantly increasing the viscosity of the dispersed phase.

Chiral lattice modes are hybrid states arising from chiral plasmonic particles assembled in ordered arrays with opportune periodicity. These resonances exhibit dependence on excitation handedness, and their observation in plasmonic lattices is strictly related to the chiroptical features of the fundamental plasmonic unit. Here, we show the emergence of chiral surface lattice resonances in properly engineered arrays of nanohelices, fully 3D chiral nano-objects fabricated by focused ion beam processing. By tuning the relative weight of plasmonic and photonic components in the hybrid mode, we analyze the physical mechanism of strong diffractive coupling leading to the emergence of the lattice modes, opening the way to the engineering of chiral plasmonic systems for sensing applications. In particular, we identify a coupling regime where the combination of a large intrinsic circular dichroism of the plasmonic resonance with a well-defined balance between the photonic quality factor and the plasmonic field enhancement maximizes the capability of the system to discriminate refractive index changes in the surrounding medium. Our results lay the foundation for exploiting circular dichroism in plasmonic lattices for high performance biosensing., Comment: 9 pages, 4 figures + supporting

The dynamics of droplet fragmentation in turbulence is described in the Kolmogorov-Hinze framework. Yet, a quantitative theory is lacking at higher concentrations when strong interactions between the phases and coalescence become relevant, which is common in most flows. Here, we address this issue through a fully-coupled numerical study of the droplet dynamics in a turbulent flow at high Reynolds number. By means of time-space spectral statistics, not currently accessible to experiments, we demonstrate that the characteristic scale of the process, the Hinze scale, can be precisely identified as the scale at which the net energy exchange due to capillarity is zero. Droplets larger than this scale preferentially break up absorbing energy from the flow; smaller droplets, instead, undergo rapid oscillations and tend to coalesce releasing energy to the flow. Further, we link the droplet-size-distribution with the probability distribution of the turbulent dissipation. This shows that key in the fragmentation process is the local flux of energy which dominates the process at large scales, vindicating its locality.

We present the Fluid Transport Accelerated Solver, FluTAS, a scalable GPU code for multiphase flows with thermal effects. The code solves the incompressible Navier-Stokes equation for two-fluid systems, with a direct FFT-based Poisson solver for the pressure equation. The interface between the two fluids is represented with the Volume of Fluid (VoF) method, which is mass conserving and well suited for complex flows thanks to its capacity of handling topological changes. The energy equation is explicitly solved and coupled with the momentum equation through the Boussinesq approximation. The code is conceived in a modular fashion so that different numerical methods can be used independently, the existing routines can be modified, and new ones can be included in a straightforward and sustainable manner. FluTAS is written in modern Fortran and parallelized using hybrid MPI/OpenMP in the CPU-only version and accelerated with OpenACC directives in the GPU implementation. We present different benchmarks to validate the code, and two large-scale simulations of fundamental interest in turbulent multiphase flows: isothermal emulsions in HIT and two-layer Rayleigh-B\'enard convection. FluTAS is distributed through a MIT license and arises from a collaborative effort of several scientists, aiming to become a flexible tool to study complex multiphase flows.

Fine control of the chiral light-matter interaction at the nanoscale, by exploiting designed metamaterial architecture, represents a cutting-edge craft in the field of biosensing, quantum and classic nanophotonics. Recently, artificially engineered 3D nanohelices have demonstrated programmable wide chiroptical properties by tuning materials and architecture, but fundamental diffractive aspects that are to the origin of chiral resonances still remain elusive. Here, we proposed a novel concept of three-dimensional chiral MetaCrystal, where the chiroptical properties are finely tuned by in-plane and out-of-plane diffractive coupling. Different chiral dipolar modes can be excited along the helix arms, generating far field optical resonances and radiation pattern with in-plane side lobes and suggesting that a combination of efficient dipole excitation and diffractive coupling matching controls the collective oscillations among the neighbor helices in the chiral MetaCrystal. This concept enables the tailorability of chiral properties in a broad spectral range for a plethora of forefront applications, since the proposed compact chiral MetaCrystal can be suitable for integration with quantum emitters and can open perspectives in novel schemes of enantiomeric detection., Comment: 22 pages (article + supporting information) 5 figures

We present a numerical study of emulsions in homogeneous and isotropic turbulence at $Re_\lambda=137$. The problem is addressed via Direct Numerical Simulations (DNS), where the Volume of Fluid (VOF) is used to represent the complex features of the liquid-liquid interface. We consider a mixture of two iso-density fluids, where fluid properties are varied with the goal of understanding their role in turbulence modulation, in particular the volume fraction ($0.03<\alpha<0.5$), viscosity ratio ($0.01<\mu_d/\mu_c<100$) and large scale Weber number ($10.6

The size of drops generated by the capillary-driven disintegration of liquid ligaments plays a fundamental role in several important natural phenomena, ranging from heat and mass transfer at the ocean-atmosphere interface to pathogen transmission. The inherent non-linearity of the equations governing the ligament destabilization leads to significant differences in the resulting drop sizes, owing to small fluctuations in the myriad initial conditions. Previous experiments and simulations reveal a variety of drop size distributions, corresponding to competing underlying physical interpretations. Here, we perform numerical simulations of individual ligaments, the deterministic breakup of which is triggered by random initial surface corrugations. The simulations are grouped in a large ensemble, each corresponding to a random initial configuration. The resulting probability distributions reveal three stable drop sizes, generated via a sequence of two distinct stages of breakup. Four different distributions are tested, volume-based Poisson, Gaussian, Gamma and Log-Normal. Depending on the time, range of droplet sizes and criteria for success, each distribution has successes and failures. However the Log-Normal distribution roughly describes the data when fitting both the primary peak and the tail of the distribution while the number of droplets generated is the highest, while the Gamma and Log-Normal distributions perform equally well when fitting the tail. The study demonstrates a precisely controllable and reproducible framework, which can be employed to investigate the mechanisms responsible for the polydispersity of drop sizes found in complex fluid fragmentation scenarios.

Highly sensitive and selective label free devices for real-time identification of specific biomarkers are expected to significantly impact the biosensing field. The ability of plasmonic systems to confine the light in nanometer volume and to manipulate it by tuning the size, shape and material features of the nanostructures, makes these systems promising candidates for biomedical devices. In this work we demonstrate the engineered sensing capabilities of a compact array of 3D metal dielectric core-shell chiral metamaterial. The intrinsic chirality of the nano-helices makes the system circular polarization dependent and unaffected by the background interferences, allowing to work even in complex environment. The core-shell architecture enhances the sensing properties of the chiral metamaterial on both in the far and near field, offering also a large surface to molecular immobilization. With our system we recorded sensitivity of about 800nm/RIU and FOM= 1276 RIU-1. The sensing abilities of the system is demonstrated with the detection of the of the TAR DNA-binding protein 43 (TDP-43) , a critical biomarker for the screening of neurodegenerative diseases. In particular, the sensor was tested in different environments, such as human serum, with concentrations ranging from 1pM down to 10fM, opening new perspectives for novel diagnostic tools., Comment: 13 pages 6 figures plus supporting

We study the kinetics of the two-dimensional q > 4-state Potts model after a shallow quench slightly below the critical temperature and above the pseudo spinodal. We use numerical methods and we focus on intermediate values of q, 4 < q < 100. We show that, initially, the system evolves as if it were quenched to the critical temperature. The further decay from the metastable state occurs by nucleation of k out of the q possible phases. For a given quench temperature, k is a logarithmically increasing function of the system size. This unusual finite size dependence is a consequence of a scaling symmetry underlying the nucleation phenomenon for these parameters., Comment: 17 pages, 8 figures

Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatability of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error ($\pm$ SD) in the simulation are $1.16\pm0.1~mm$ and $2.7\pm3.3^{\circ}$, respectively. On a gel phantom, these errors are $1.95\pm2.02~mm$ and $3.3\pm2.4^{\circ}$. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time., Comment: Accepted for publication in IEEE Transactions on Industrial Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5ik

LOUPE, the Lunar Observatory for Unresolved Polarimetry of the Earth, is a small, robust spectro-polarimeter with a mission to observe the Earth as an exoplanet. Detecting Earth-like planets in stellar habitable zones is one of the key challenges of modern exoplanetary science. Characterising such planets and searching for traces of life requires the direct detection of their signals. LOUPE provides unique spectral flux and polarisation data of sunlight reflected by the Earth, the only planet known to harbor life. This data will be used to test numerical codes to predict signals of Earth-like exoplanets, to test algorithms that retrieve planet properties, and to fine-tune the design and observational strategies of future space observatories. From the Moon, LOUPE will continuously see the entire Earth, enabling it to monitor the signal changes due to the planet's daily rotation, weather patterns, and seasons, across all phase angles. Here, we present both the science case and the technology behind LOUPE's instrumental and mission design., Comment: 13 pages, 5 figures. Accepted for publication in Royal Society Philosophical Transactions A. Corrected typos in v2

In recent years significant efforts have been made to design and fabricate functional nanomaterials for biomedical applications based on the control of light matter interaction at the nanometer scale. Among many other artificial materials, hyperbolic dispersion metamaterials allow to access unprecedented physical effects and mechanisms due to the extreme anisotropy of their optical constants. The unbound isofrequency surface of hyperbolic metamaterials (HMMs) enable the possibility to support a virtually infinite density of states and ultra-high confinement of electromagnetic fields, allowing perfect absorption of light and extreme sensing properties. Optical sensor technology based on plasmonic metamaterials offers significant opportunities in the field of clinical diagnostics, particularly for the detection of low-molecular-weight biomolecules in highly diluted solutions. In this context, we present a computational effort to engineer a biosensing platform based on hyperbolic metamaterials, supporting highly confined bulk plasmon modes integrated with out-of-plane chiral metasurfaces. The role of the helicoidal chiral metasurface is manifold: i) as a diffractive element to increase the momentum of the incoming light to excite the plasmon sensing modes with linearly and circularly polarized light; ii) as out-of-plane extended sensing surface to capture target analytes away from the substrate thereby the diffusion limit; iii) as a plamonic chiral nanostructure with enhanced sensing performance over circularly polarized reflectance light.

Using large-scale numerical simulations we studied the kinetics of the 2d q-Potts model for q > 4 after a shallow subcritical quench from a high-temperature homogeneous configuration. This protocol drives the system across a first-order phase transition. The initial state is metastable after the quench and, for final temperatures close to the critical one, the system escapes from it via a multi-nucleation process. The ensuing relaxation towards equilibrium proceeds through coarsening with competition between the equivalent ground states. This process has been analyzed for different choices of the parameters such as the number of states and the final quench temperature., Comment: 7 pages, 5 figures

Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods for feature fusion at different resolutions have been scarcely investigated for medical applications. In this work we propose to implement segmentation via an encoder-decoder architecture which differs from any other previously published method since (i) it employs a very deep architecture based on residual learning and (ii) combines features via a convolutional Long Short Term Memory (LSTM), instead of concatenation or summation. The intuition is that the memory mechanism implemented by LSTMs can better integrate features from different scales through a coarse-to-fine strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate the remarkable advantages of this approach on two datasets: the Montgomery county lung segmentation dataset, and the EndoVis 2015 challenge dataset for surgical instrument segmentation., Comment: Accepted for presentation at MICCAI 2018