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Construction of this dataset is described in the peer-reviewed publication: Calcagno, P., Trumpy, E., Gutiérrez-Negrín, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0 The geomodel is available in the form of the following files and formats: Metadata sheet description pdf format GeoModeller project format PDF3D format TSurf format VTK format {"references": ["Calcagno, P., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0"]}

Nearly all major glaciers in Greenland have reduced in size over the last two decades. An increase in the amount of ice transported from the Greenland ice sheet to the oceans is predicted following an increase in Arctic air and ocean temperatures. One of the last glaciers with a floating ice shelf and draining a substantial area of the Greenland ice sheet is the Petermann glacier in North West Greenland. With two major calving events in 2010 and 2012 the extent of its floating ice shelf was reduced to only half of that prior to 2010 and since 2016 new fractures indicate a new calving event is predicted to reduce the length of the glacier by ~14 km. Multiple studies have indicated that after the major calving event of 2012 the glacier accelerated and a new increase in the velocity, possibly linked to the next calving event, has already been observed. With every part of the glacier’s ice shelf that is lost the resistive force that holds the glacier back is reduced and the amount of ice drained to the ocean increases. Losing its entire ice shelf could lead to a significant increase in the contribution of the Petermann glacier to global sea level rise as the Petermann fjord extends inlands below sea level for nearly a hundred kilometers. This study uses ice thickness and surface elevation data combined with velocity data from different sources to analyze the current and future stability of the Petermann glacier. Ice thickness and the velocity data is used as input in a fracture model in order to investigate the different contributions of stress, thinning and an increase in the availability of surface water to the depth crevasses can reach. The areas on the glacier that show locations where crevasses penetrate deep into the ice indicate that the glacier is vulnerable to fracturing in those spots. Connected weak spots might indicate further potential for future calving events. The results derived from the thickness data and the subsequent melt rates show that near the grounding line the glacier is experiencing ...

The environmental crisis drives the integration of renewable energy sources into grids, leading to the emergence of microgrids for power supply in rural and urban areas. However, microgrid adoption faces stability and energy management challenges that require resilient control structures and intelligent mechanisms. This thesis proposes a hierarchical control structure for a standalone residential microgrid system, combining solar, fuel cell, and hybrid energy storage (battery and supercapacitor). The control technique employed is model-free super twisting sliding mode control with anti-windup for saturation. The control structure aims to ensure a stable and resilient microgrid with desired DC bus voltage stability, validated through simulation tests and hardware-in-the-loop experiments. Additionally, the thesis proposes a novel optimized energy management strategy (EMS) focusing on power sharing and battery state of charge regulation, considering the tradeoff between battery usage and hydrogen consumption costs. Simulation tests confirm the successful achievement of the intended goals by the EMS. ; La crise environnementale pousse à l’intégration des énergies renouvelables dans les micro-réseaux pour les zones rurales et urbaines. Cependant, l’adoption des micro réseaux rencontre des défis de stabilité et de gestion de l’énergie nécessitant des contrôles résilients. Cette thèse propose une structure de contrôle hiérarchique pour un micro-réseau autonome résidentiel, combinant énergie solaire,pile à combustible et stockage hybride(Battery et supercondensateur). La technique de contrôle utilisée est le contrôle en mode glissant super twisting sans modèle avec antidépassement pour la saturation. La structure de contrôle vise à assurer un micro-réseaustable et résilient avec une stabilité souhaité ede la tension du bus continu, validée par des tests de simulation et des expériences en boucle fermée. De plus, la thèse propose une nouvelle stratégie de gestion optimisée de l’énergie (EMS) axée sur le partage de ...

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CNRM-CERFACS.CNRM-ESM2-1.amip' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-ESM2-1 climate model, released in 2017, includes the following components: aerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Les trois familles catalytiques à base de perovskites contenant du Ni: massives [[LnFe1-x-yNiyMxO3-δ] (Ln=La, Pr; B=Co, Mn, Ru), sur support [mLnNi0.9Ru0.1О3/nMg-γ-Al2O3] (Ln = La, Pr) et structuré [mLaNi0.9Ru0.1О3/nMg-γ-Al2O3/mousses structurées] ont été synthétisés, caractérisés et testés dans les réactions de vaporeformage de l'éthanol et de glycérol. Les effets de la composition chimique et de la méthode de synthèse sur les propriétés structurelles et texturales, ainsi que sur la réductibilité des échantillons initiaux ont été évalués. L'utilisation préférentielle de Pr, Ni et Ru dans la composition de catalyseur a été démontrée pour toutes les familles. Le rôle essentiel de la modification du support γ-Al2O3 avec ≥ 10%mass de Mg introduit par imprégnation humide pour le catalyseur supporté a également été prouvé. Des catalyseurs de la composition optimale fournissant une activité élevée dans le vaporeformage de l'éthanol et du glycérol à T = 650 °C ont été trouvés: le meilleur catalyseur massif à base du précurseur PrFe0.6Ni0.3Ru0.1O3 fournit une activité élevée pendant au moins 7 h, grâce à la facilité de leur réduction et les propriétés d'oxydoréduction de l'oxyde de praséodyme formé. Les catalyseurs sur support 10-20% PrNi0.9Ru0.1O3/10-15%Mg-γ-Al2O3 fournissent le meilleur rendement en hydrogène (~ 90%) et la stabilité pendant ~ 20 heures. Le catalyseur structuré optimisé à base de la plaquette Ni-Al métallique fournit le rendement stable en hydrogène 80-87% dans l’oxy-vaporeformage d'éthanol dans les mélanges concentrés (concentration d'éthanol de 30%) dans un réacteur pilote pendant 40 heures. Les résultats obtenus rendent ces systèmes catalytiques structurés très prometteurs à utiliser dans les générateurs électrochimiques à base de piles à combustible avec l'utilisation de ressources renouvelables peu coûteuses comme bio-huile. ; The three catalytic families based on Ni-containing perovskites: massive [LnFe1-x-yNiyMxO3-δ] (Ln=La, Pr; B=Co, Mn, Ru), supported [mLnNi0.9Ru0.1О3/nMg-γ-Al2O3] (Ln = La, ...

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed soybean.

The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed sorghum.

This file contains the raw dataset used in the manuscript "Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy" published in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659) Data has been acquired using Nion Swift (https://nionswift.readthedocs.io/en/stable/). Experimental details can be found in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659). The dataset has been analyzed using the following Python libraries: Numpy, Scipy, Hyperspy, Matplotlib EELS hyperspectral images have been aligned using the Hyperspy "align1D" method. Aligned EELS hyperspectral images are saved in files finished with "_Aligned.hspy": For the strong coupling experiments: Tip 1 is on hBN Tip 2 is on vacuum For each of the nanowires tips, a file with the fitted coefficients are available, as well as a plot of the data and the fitted curve. Datasets have been fitted with gaussian and/or lorentizan functions, as described in the published text. Any question can be forwarded to the corresponding authors of the published text. Other funding: 1) National Agency for Researchunder the program of future investment TEMPOS-CHROMATEM (reference no. ANR-10-EQPX-50); 2) Spanish MINECO (MAT2017-88492-R and SEV2015-0522); 3) the Catalan CERCA Program; 4) Fundació Privada Celle;