Foundation for Research and Technology Hellas

ABSTRACT We present HECATEv2, the second release of the Heraklion Extragalactic Catalogue (HECATE), an all-sky, value-added galaxy catalogue comprising 204 733 galaxies from the Hyper Lyon-Meudon Extragalactic Database (HyperLEDA) with recession velocity $\lt 14\, 000 \, \rm {km\, s^{-1}}$ (D $\lesssim$ 200 Mpc). This release focuses on qualitative upgrades of the provided information while maintaining the same parent galaxy sample as HECATEv1. Improvements include a new cosmology-based distance framework, expanded and homogenized optical and mid-infrared (IR) photometry from SDSS-DR17/NSA, PS1-DR2, and All Wide-field Infrared Survey Explorer (AllWISE), and new quality-control flags for stellar contamination, incorrect photometry, and coordinate inconsistencies. We also extend the galaxy-size coverage and derive stellar population parameters for a substantially larger fraction of the sample. Star formation rates ($\mathrm{SFR}$) and stellar masses (${{M}_{\star }}$) are now available for $\gt 70$ per cent of galaxies using updated mid-IR/optical calibrations that account for stellar population age and dust attenuation, while gas-phase metallicities are derived for $\sim 90~{{\ \rm per\ cent}}$. Activity classifications are provided for $\gt 50$ per cent of galaxies based on spectroscopic and/or photometric diagnostics, and supermassive black hole (SMBH) masses for $\sim 86~{{\ \rm per\ cent}}$. In terms of $L_{B}$, $L_{Ks}$, $\mathrm{SFR}$, and ${{M}_{\star }}$, HECATEv2 is among the most complete local-universe catalogues with spectroscopic redshifts. We also provide spatial completeness maps as a function of distance and luminosity, highlighting variations across the sky. Compared to other catalogues (e.g. GLADE+ and NED-LVS), HECATEv2 offers broader (optical, near- and far-IR photometry, metallicity, and activity classifications) or comparable (mid-IR photometry, $\mathrm{SFR}$, and ${{M}_{\star }}$) coverage, making it a robust reference for studies of SMBH–host galaxy connections, gravitational-wave and high-energy transient hosts, population analyses, and rare galaxy subpopulations.

The Black Sea and the Sea of Azov are high-risk regions for major oil spill incidents due to heavy maritime tanker traffic and potential pipeline leaks. As a result, the Black Sea is currently considered the most oil-polluted marginal sea. In this work, we present a series of forecasting simulations to predict the dispersion of oil following an accidental 4,000-ton mazut release from the tanker Volgoneft 212 in the Kerch Strait from December 15 to December 25, 2024. The Kerch Strait is located in the southern part of the Sea of Azov, connecting this small, shallow, brackish body to the northeastern part of the Black Sea. The Strait is 40 km long and 4-5 km wide, and very shallow (3–5 m) at its center, deepening steadily to depths of 10 and 20 m at its northern and southern parts. Circulation through the Kerch Strait is not steady or unidirectional; it exhibits large synoptic variability in intensity and direction, governed by episodic wind-forcing events.The Lagrangian particle-tracking numerical models MEDSLIK, OpenDrift, and MEDSLIK II were used in hindcast mode. The CMEMS hydrodynamic fields, the CYCOFOS wave fields, the NOAA-GFS, the ECMWF, and the SKIRON meteorological forecasts were used to force the oil spill models. Sentinel-1 SAR images were used to assess the impact of the oil spill and to evaluate model results. Satellite imagery and modelling results indicate that the spillage significantly affected more than 60 km of the NE Black Sea coastline, from Veselovka to Anapa. Due to the extreme weather conditions, with wave heights between 3 and 5 m, the available SAR imagery was limited. Combined SAR data and local media reports were first compared with the results of the oil spill models. Backtracking modelling and stochastic analysis were implemented to assess the exact location of the oil leak. The oil spill predictions from all models show good agreement with the reported on-site observations regarding the impacted coastal areas, the large extent of the impacted area, and the chronology of oil deposition along the coast.Upper panels: SAR images of oil spillage in the Kerch Strait on 18/12/2024 (left) and 19/12/2024 (right); Lower panels: Superimposed MEDSLIK oil spill predictions on 18/12/2024 (left) and 19/12/2024 (right).

With the ever-increasing realisation that measures against climate change have to be local, many cities opted to become NetZero (i.e., CO2-Neutral, emissions compensated) in the near future. Especially within the EU, a large-scale project takes place driven in close collaboration between the EU and cities. The “EU Cities Mission” facilitates urban transition via supporting actions and strategies towards neutrality by offering official labelling to cities that create successful climate city contracts and commit to achieve CO2-Neutrality by 2030. The Horizon project CLMS-Cities targets to support cities with quantifying and trenching their CO2 emissions based on existing and freely available Copernicus Services, in particular within CLMS (Copernicus Land Monitoring Service Cities) and in-situ data such as GNSS based mobility data. Within CLMS-Cities a CO2 exchange model for local-scale scope 1 CO2 emissions is developed at 10m resolution at hourly scale for the five sectors: mobility, buildings, industrial sources, AFOLU (Agriculture, Forestry and other land uses), and human respiration following closely typical city inventories. We here present first results and the background of the model for the case study city Vitoria-Gasteiz, Spain (Tier 1 city). The model is mainly based on the Urban Atlas and produces estimates of CO2 exchange by integrating relevant Copernicus services, satellite products and third-party mobility data. To ensure robustness, the model is paired with local-scale eddy covariance observations in the city centre of Vitoria-Gasteiz. Following this validation process, the model will be extended and rolled out to ten additional EU Mission cities to ensure that it accommodates a wide range of spatial, urban and environmental contexts, that is seen across the real cities in the EU. A co-design approach underpins this work, with continuous engagement with cities to ensure that their requirements are fully integrated in the design, development and operationalisation of the model.

Emission controls in Europe have substantially reduced SOx and NOx but left NH3 largely unchanged. This imbalance between acidic and basic species may shift aerosol acidity and its impacts, including toxicity, particulate matter (PM) composition, and the deposition of reactive nitrogen (Nr). The atmospheric deposition of Nr plays a critical role in ecosystem productivity and PM formation, with impacts that vary across spatial and temporal scales.In this study, long-term observations (2008–2024) of atmospheric gases and aerosols from Swiss monitoring sites ware analyzed to assess changes under current emission reductions. Aerosol pH was estimated using the ISORROPIA-lite thermodynamic model1 and interpreted using SHapley Additive exPlanations (SHAP) to quantify key drivers. Annual mean aerosol pH shows a slight increasing trend, with consistently lower values in summer than in winter. SHAP results indicate that temperature controls seasonal pH variability at agricultural sites, whereas total ammonia (NH3T) dominates at the semi-alpine site.Dry deposition regimes of HNO3 and NH3 were investigated in relation to aerosol liquid water content and acidity following the approach of Nenes et al. (2021).2 The findings indicate that NH3 deposition is rapid across both the lowland and Alpine regions, suggesting localized nitrogen burdens near emission sources. In contrast, PM has become increasingly insensitive to NH3 and more sensitive to HNO3, particularly in the agricultural sites. These results highlight that, although HNO3 precursor controls have effectively reduced PM pollution without the need for NH3 reductions, evermore significant ecological concerns remain from a lack of NH3 control. This underscores the need for coordinated reductions in both NOx and NH3 emissions.

This work presents a fast neural surrogate capable of reconstructing fully three-dimensional hemodynamic velocity fields in stenotic and bifurcating microvascular geometries with satisfactory accuracy, avoiding repeated, computationally demanding computational fluid dynamics (CFD) simulations. A Fourier-augmented, coordinate-neural surrogate is presented and assessed for rapid computation of three-dimensional blood-flow fields in a sample geometry. The model is trained on detailed CFD data across a parameter set of stenosis severities that feed a direct mapping from spatial coordinates to velocity components. To mitigate spectral bias and improve accuracy in regions of steep gradients, the input space is embedded with random Fourier features and compared against a conventional multilayer perceptron (MLP) backbone. Predictive ability is assessed upon strict hold-out testing, during which certain arteriolar stenosis cases are excluded from training and treated with the Fourier surrogate. Direct comparison with CFD results reveals that the Fourier MLP achieves nearly CFD fidelity with the coefficient of determination R2 ≥ 0.994 and offers more than 80% reduction in the normalized errors as provided by conventional MLP, with the precise improvement depending on the severity of stenosis. Centerline velocity and cross-sectional profiles further show that the Fourier MLP reconstructs stenosis speed-up and radial profiles more reliably compared to conventional MLP. These results indicate that Fourier feature embedding provides a simple and effective route to robust full-field hemodynamic surrogates for efficient screening of stenosis configurations without resorting to repeated, heavily demanding CFD simulations.

Detecting surface potsherds using low-altitude remote sensing is challenging due to severe class imbalance and limited training data. This study develops and validates a semi-automatic detection methodology that adapts threshold-optimized boosting classifiers (AdaBoost, XGBoost) to maximize ceramic detection recall under extreme class imbalance in the Western Megaris archeological landscape, Greece. Models were trained on only 15% of the available data to simulate realistic field conditions. Evaluation emphasized recall-oriented metrics (precision, recall, F1-score, AUC) for the minority class, addressing the accuracy paradox where high overall accuracy masks poor rare-class performance. Threshold optimization enabled AdaBoost and XGBoost to achieve substantially improved recall compared to baseline methods, with detection-to-ground-truth ratios of 2.5 and 3.2, respectively, reflecting deliberate prioritization of recall over precision for exploratory survey purposes. The results demonstrate that this methodological framework provides archeologically interpretable screening tools for identifying high-probability ceramic locations, supporting more efficient field survey design and heritage documentation workflows in Mediterranean landscapes.

The behavior and properties of polymer-resin blends are critical for the design of advanced polymeric systems in a wide range of applications. In this work, we present an atomistic molecular dynamics study of the effects of hydrogenated dicyclopentadiene (H-DCPD) resin on the structural, dynamical, and viscoelastic properties of polymer matrices. Two different systems are examined: linear 1,4-cis polyisoprene (PI) and a four-component styrene-butadiene rubber (SBR) copolymer. The results reveal lower miscibility of H-DCPD resin in PI compared to that in SBR, as evidenced by the formation of resin-rich domains, revealed by the magnitude of local peaks in resin-resin radial distribution functions. Temperature-dependent analysis shows that structural and dynamical properties are most sensitive at ambient conditions (T = 300 K, P = 1 atm), with PI exhibiting significantly reduced molecular mobility due to its proximity to the glass transition temperature. This restricted dynamics directly influences the viscoelastic response, leading to increased structural rigidity upon resin addition. Furthermore, the effect of resin concentration is systematically assessed, demonstrating that, while 17 vol% resin has a negligible impact on both systems, increasing the concentration to 34 vol% in PI, results in pronounced changes in its structural and viscoelastic behaviors. Overall, this study highlights atomistic molecular dynamics simulations as powerful predictive tools for the rational design of resin-elastomer blends.

Laser-assisted reduction of graphene oxide (GO) has emerged as a promising method for fabricating conductive films and interdigitated electrodes onto flexible substrates, paving the way for high-performance flexible electronics and smart textiles. In this study, we demonstrate laser-reduced graphene oxide (LrGO) with an unprecedentedly low defect density (average D/G Raman band ratio of 0.1) onto bare heat-sensitive paper substrates. Under optimal lasing conditions, the paper-supported LrGO films exhibit a very low sheet resistance (<50 Ohm sq-1) and maintain good conductivity after multiple cycles of bending deformation. Increasing the laser power beyond the threshold required for GO reduction leads to a gradual decrease in defect density (as measured by Raman spectroscopy), while the surface chemistry (analyzed via X-ray Photoelectron Spectroscopy) remains largely unaffected. This suggests that excess photon energy primarily contributes to defect healing rather than further extending the reduction of LrGO. Three-electrode characterization in a 6 M KOH electrolyte revealed a maximum capacitance of 31.8 mF cm-2 at 10 mV s-1, while the capacitance of an interdigitated supercapacitor with PVA:H2SO4 electrolyte was 5.2 mF cm-2 at 0.02 mA cm-2. These values surpass those reported in recent studies on symmetric supercapacitors using LrGO electrodes on protected paper and other polymeric substrates, underlining the superior quality and potential of the electrodes developed in this work.