dataesr/scientific-paragraphs-categorization huggingface dataset:
In the short-term future, 13 new sites will be incorporated (Fig. 8a ). Broadly speaking, up to 82 % of the deployments have been permanent, while a small portion of them were temporarily implemented in the framework of specific time-delimited research projects (Fig. 8b ). Finally, DF HFRs are more abundant than BF systems in this regional domain (Fig. 8c ). In comparison with other regional alliances like the Iberia-Biscay-Ireland Regional Ocean Observing System (IBIROOS), MONGOOS fairly represents 55 % of the total HFR sites in Europe (Fig. 9a ). Several of those MON-GOOS networks (about the 23 %) are already integrated in the EU HFR Node data flow, thus providing standardized and interoperable near-real-time (NRT) datasets to the CMEMS-INSTAC, EMODnet Physics, and SDC distribution platforms (Fig.
TOSCA (2010-2013) The five-country TOSCA (Tracking Oil Spills and Coastal Awareness network) pilot project was part of the MED program and supported by European Regional Development (ERD) funding. It aimed at improving the quality, speed, and effectiveness of the decision-making process in the case of marine accidents in the Mediterranean concerning oil spill pollution and SAR operations  (Berta et al. , 2014; Bellomo et al. , 2015). Among other valuable goals, Lagrangian comparisons of HFR-derived measurements were conducted against the trajectories provided by drifters previously released in high-traffic coastal areas to provide critical information to support policy makers. Ocean Sci. , 18, 761-795, 2022 https://doi. org/10. 5194/os-18-761-2022 The Italian flagship project RITMARE, funded by the Italian Ministry of University and Research, was a national research program focused on (i) the integration of the existing local observing systems toward a unified operational Italian framework and (ii) the harmonization of data collection and data management procedures  (Carrara et al.
HAZADR (2013-2015) This project, funded by the IPA Adriatic Cross-border Cooperation Programme, aimed to upgrade the knowledge framework about the estimated environmental and socioeconomic risks in the most vulnerable areas of the Adriatic Sea due to both natural and human-induced factors. Furthermore, a decision support system was implemented to track the spreading of oil spilled during hazards. The usage of six HFR systems in different applications was part of the project, with some of them (like HFR-Split, shown in Fig. 1 ) being installed during the project, while sharing the data in a common database.
NEURAL (2013-2015) The main objective of NEURAL project, which was supported by the Unity Through Knowledge (UKF) Fund, was to build an efficient, reliable, and innovative prototype ocean surface current forecasting system in coastal areas of the northern Adriatic Sea by using neural network algorithms. The self-organizing map (SOM) neural network was trained jointly by the multi-year surface current fields measured by HFR and mesoscale surface winds simulated by highresolution numerical weather prediction models. Then, based on the weather forecast and the trained SOM solutions, the prediction of surface currents was issued for the following 3 d, for which mesoscale atmospheric models have significant reliability. The SOM-based forecast was verified against an independent dataset, showing slightly higher reliability than the classical ocean forecasting system based on numerical modeling.
JERICO-NEXT (2015-2019) The JERICO-NEXT (Joint European Research Infrastructure network for Coastal Observatory -Novel European eXpertise for coastal observaTories) initiative was developed under the Horizon 2020 (H2020) program INFRAIA. It was carried out by 33 institutions from 15 countries and emphasized that the complexity of the coastal ocean cannot be well understood if interconnection between physics, biogeochemistry, and biology is not guaranteed. Such integration required new technological developments allowing continuous monitoring of a larger set of parameters. JERICO-NEXT consisted of strengthening and enlarging a solid and transparent European network to provide operational services for a timely, continuous, and sustainable delivery of high-quality environmental data and products related to the marine environment in European coastal seas.
In terms of HFR technology, the main aim was not only to harmonize data formats and best practices but also to improve current estimates (by means of advanced quality controls) to study ocean transport and connectivity between coastal and deep open-sea waters. In this context, it is worth mentioning the ongoing JERICO-S3 and JERICO Design Study (DS) projects (2020-2023) as part of the JERICO Research Infrastructure (RI) initiative. JERICO-RI, which is a long-term integrated framework providing high-quality marine data, expertise, and multi-platform infrastructures for Europe's coastal seas, might have a significant impact in terms of integration of HFR among key coastal observing technologies.
EuroSea (2019-2023) The project "EuroSea: Improving and Integrating European Ocean Observing and Forecasting Systems for Sustainable use of the Oceans" is supported by the H2020 program BG2019-1. It works to enhance the European ocean observing and forecasting system in a global context by delivering ocean observations and forecasts to advance scientific knowledge about ocean climate, marine ecosystems, and their vulnerability to human impacts, thereby demonstrating the importance of the ocean for an economically viable and healthy society. It aims at advancing research and innovation towards a user-focused, truly interdisciplinary, and responsive European ocean observing and forecasting system that delivers the essential information needed for human well-being and safety, sustainable development, and a blue economy in a changing world.
10 ) whose main scope was (and still is) to consolidate the HFR as an efficient coastal ocean monitoring technology. Most of the projects are funded by the European Commission in the framework of different Interreg programs, by the EU H2020 Research and Innovation, and by national research programs. In particular, 2020 has been a key year in terms of the wealth of initiatives carried out simultaneously  (10). A relevant number of new HFR sites have been recently deployed and integrated into multiplatform observatories, providing quality-controlled data that are routinely delivered a broad audience and subsequently used for diverse marine applications, including, among others, maritime safety, oil spill accidents or SAR operations (TOSCA, HAZADR, IBISAR, SICOMAR plus, CALYPSO, PANORAMED, SHAREMED, iWaveNET), port and harbor security (SINAPSI), risk prevention, and coastal management (TRADE), as well as marine spatial planning and integrated coastal zone management (RITMARE, IMPACT).
, 2020). In this context, new cross-border agreements should be reached to consolidate the observing infrastructure, following the example of the one signed in 2018 by Spain, France, and Italy in the western Mediterranean Sea  (Roarty et al. , 2019). For instance, the monitoring of the surface circulation through the Strait of Gibraltar could be significantly enhanced thanks to a cross-border agreement between Spain and Morocco to integrate their respective HFR sites into one single network. A complementary aspect would be the implementation of harmonized outage reporting among the HFR community at both European and Mediterranean levels. This would imply the creation of a centralized HFR outage database  (Updyke, 2017)  as an ancillary support tool for operations and maintenance in order to ensure HFR site sustainability (i.
This would help to fulfill the recommended level of data provision: 80 % of the spatial region over 80 % of the time  (Roarty et al. , 2012). Complementarily, the accurate monitoring of transport processes also remains as a prime concern due to its influence on SAR operations and oil spill emergencies. Lagrangian time-dependent approaches with HFR data, such as Lyapunov exponents  (Nolan et al. , 2020)  and Lagrangian coherent structures  (Haller, 2015) , provide a robust framework to resolve coherent flow patterns. However, they are often time-consuming and computationally more expensive as they require trajectory integrations over a complete spatiotemporal velocity dataset. Since hardware or software failures in the HFR system occasionally compromise the availability of data, diverse methodologies have been proposed to fill spatiotemporal gaps in HFR measurements, encompassing self-organizing maps (SOMs), open-boundary modal analysis (OMA), and data interpolating empirical orthogonal functions (DINEOFs), among others.
Data-driven fuel design framework This work introduces a simple but elegant data-driven framework to inversely design fuels satisfying desired target properties. In particular, the AI fuel design tool was built on top of an end-toend DL model based on recurrent and fully connected (FC) layers to predict three combustion properties of pure components and blends, namely, RON, MON, and YSI. Figure  1  shows a schematic of the entire network architecture. The curated database, on which the model was trained, contains single species from 19 molecular classes with oxygenates accounting for >20% of the pure species dataset, surrogate fuels mostly containing 2-10 pure components and complex mixtures, including gasoline. We propose a linear mixing operator (MO) implanted into the training loop, the algorithmic advancement that enables direct connectivity between molecular and mixture representations, thus, fuel screening on a mixture level.
Results and discussion Performance on the test set. We report proposed model's performance in terms of coefficient of determination R 2 and mean absolute error (MAE). Figure  2  shows the parity plots for the model's independent test set, where model demonstrates acceptable performance by reaching R 2 > 0. 92 across all three target properties. Additionally, Table  S1  in Supplementary Material reports R 2 and MAE for each of the two inputs, i. e. , single species and blends. Looking at ON mapping, the model achieves higher prediction accuracy on mixtures, more represented in this database regarding single hydrocarbons. For the sooting index, the model shows good generalization capability in the target estimation of pure components on different numerical scales. Moreover, the model sustains its performance for YSI mixture predictions, although it is being the least represented data type in the database.
Conclusion This work introduced a conceptually simple and fully data-driven framework to design fuel mixtures matching desired combustionrelated properties, enabling high-octane mixtures and low engineout soot emissions. The proposed workflow comprises the jointproperty predictive model and search approach. In the first step, the feed-forward network with RNN and FC layers was trained on a comprehensive database, including RON, MON, and YSI measurements from the literature of single species from 19 molecular classes, surrogate fuels, and complex mixtures. The innovative part of the network is an adaptation of MO, a mechanism generating mixture-hidden representation by performing linear combinations on hidden vectors of pure components. MO enables using a mixture's detailed composition and species' molecular information as a direct input to the model without preprocessing.
After additional screening with RVP, density, viscosity, and LHV, one mixture containing 22 components was preserved as the most promising candidate. In future work, we plan to extend the current database by curating other relevant properties, such as RVP, viscosity, density, and LHV, essential criteria in fuel screening. Ultimately, future comparison of short-listed mixtures experimental properties with predictions will allow us to further improve tool accuracy and valorize the current framework. Moreover, future workflow versions should preclude formulations based on component availability. We expect our simple and practical framework will serve as a solid baseline and help ease future research designing liquid energy carriers.
Methods Data curation. The database of experimentally obtained measurements for the three combustion-related properties (RON, MON, and YSI) for single hydrocarbons and mixtures was curated from many literature sources. Table  5  summarizes the elements in the collected database, where the entire dataset is classified into three subpopulations: pure components, ≤ 10-component blends (mostly surrogates), and complex fuels with more than 10 components. We extended the RON/MON pure components database published by Schweidtmann et al. 15  and the Yale University YSI database  49, 50  by adding measurements for oxygenated compounds  51, 52. The curated data include data for single species from 19 molecular classes with 365 RON, 333 MON, and 451 YSI observations. More interestingly, oxygenated compounds account for approximately 20% and 50% of single component ON and YSI databases, respectively.
For mixtures with 2-10 components, ON data were collected for 372 blends, including 22 hydrocarbon representatives from five molecular classes (n-alkanes, isoalkanes, alkenes, cycloalkanes, and aromatics) blended with five oxygenated hydrocarbons, four alcohols (methanol, ethanol, 2-propanol, and prenol), and MTBE  19, 27, [53] [54] [55] [56] [57]. Furthermore, detailed hydrocarbon analysis and ON measurements, characterizing an ignition quality of 76 real fuels, were extracted from the literature. These complex mixtures include 30 fuels for advanced combustion engines (FACEs) mixed with ethanol  [58] [59] [60] [61]  , Haltermann and Coryton gasoline fuels  62  , 3 FACE F + terpineol 23 , 36 reformulated blendstocks for oxygenated blending blended with prenol/other C 5 alcohols  63  , and five test gasoline fuels  64.
The literature scarcely reported YSI measurements for mixtures for the third property database. Overall, only 40 measurements of the sooting index for mixtures and their detailed compositions were found. These values were identified for diesel, gasoline, and jet fuel surrogates  [65] [66] [67]  and co-optima test gasoline and its surrogates  68. YSI quantification is based on measurement of a maximum soot volume fraction (M i ) directly proportional to the sooting propensity. M i is measured on the centerline of a coflow methane/air nonpremixed flame doped with 400 ppm test fuel and converted to an apparatus-independent YSI using the following equation  69  : YSI i ¼ ðY SI B À YSI A Þ Ã M i À M A M B À M A þ YSI A ð1Þ where A and B are the two reference compounds. In analogy to octane rating, the numerical scale, which is used to translate the measured quantity M i to YSI, is defined by lower and upper endpoint species and the values assigned to them, YSI A and YSI B.
Multiple numerical scales were reported in the literature to accurately assess the YSI of hydrocarbons whose sooting propensity is too different to capture in a single experimental setup. Four ratings were identified in measurements of the curated YSI database, and the summary is shown in Supplementary Table  S3. Furthermore, different experimental techniques were used to measure M i quantity, including color-ratio pyrometry, light extinction measurement, and laser-induced incandescence. Supplementary Fig. S3  depicts the data distribution histograms for ON and YSI databases. Train and test split. In Table  5 , the curated database contains values of three properties for pure components and mixtures; however, only 141 data points have all three measurements available, and the remaining 1018 observations have at least one missing property.
The customized hierarchical stratified sampling was used to split the dataset to ensure that observations from all relevant subpopulations were included in the training/validation and testing sets. The entire population was divided into six nonoverlapping subsets based on the availability of specific properties, e. g. , Subset 1 (Sub 1) contains observations with all three properties (RON, MON, and YSI). Two nonoverlapping strata were defined within each subset: single species and mixture observations. Next, each subset was randomly split into 85% train/validation and 15% test set using stratified sampling in the scikit-learn library  70  to ensure 15% of each stratum (pure species and mixtures) was randomly sampled into the test set. Each subset's final train and test sets were merged, and Table  S4  in Supplementary Material reports the resultant datasets.
Training and test datasets for pure components and mixtures are provided in Supplementary Data 2 and Supplementary Data 3. Predictive DL model Molecular representation. As a molecular input to the predictive model, we used a one-dimensional text representation, SMILES strings, with molecular descriptors calculated using the Mordred platform  71. SMILES nomenclature is based on small and natural grammar, providing rigorous structure notation derived from molecular graph theory principles  72. SMILES strings are widely used to represent molecules for chemical information processing tasks, such as property prediction and inverse molecular design. Aromatic SMILES were identified for 649 pure species using the Chemical Identifier Resolver tool developed by the National Cancer Institute  73. Mordred is an open-source molecular-descriptor-calculation software generating more than 1800 2D and 3D descriptors.
The predictor network comprises three FC layers with rectified linear activation functions and a final linear layer. Several numerical scales in the curated YSI database (Supplementary Table  S3 ) can be an additional bottleneck in modeling the joint-property predictive model. Therefore, to extend the model's capability to evaluate YSI on any given scale, we predict M i , M A and M B from the molecular structural information of the test fuel (i) and two reference compounds (A and B). The last step is postprocessing predictions and calculating the test fuel's YSI value using Eq. (1). Therefore, the model's input includes SMILES, Mordred descriptors of pure components, compositions for mixtures, and the scale on which YSI is estimated, namely, SMILES and Mordred descriptors of lower and upper endpoint species (A, B) and their assigned YSI values.
The proposed model's architecture exhibits numerous hyperparameters to be tuned, including batch size (B), learning rate (lr), predictor architecture, and Extractors 1 and 2. The final output sizes of the latter two determine the optimal dimension of the latent space vector (m) for pure components and mixtures. The hyperparameter tuning was performed using an adaptive experimentation platform using the Bayesian optimization algorithm (https://ax. dev/). The optimal hyperparameter settings were based on the validation set, comprising 15% of the training set. After the tuning, the optimal hidden vector dimension was 24 (i. e. , m = 24), reported in Supplementary Table  S5  with the other parameters. Exploring chemical space: Inverse fuel design. Our primary objectives with the search tool are to design mixtures that To match these goals, we propose a full-scope search, a search procedure performed on the entire chemical space generated from the available database.
Full-scope search. Since DL is a form of a feature learning based on the nonlinear mappings and the resulting problem is highly non-convex, we first provide a set of k candidates which are starting points for the search. These are chosen as the closest points from the curated database (Table  5 ) to the vector in the latent space corresponding to the target properties, where the distance is defined as the MSE. The pseudocode for candidate search is provided in Table  6. We subsequently describe the optimization workflow for the full scope search, further documented in Algorithm 2 in Table  7. The objective is to find a set of optimal composition vectors denoted by x ⋆ , which can be written as the following optimization problem x ? ¼ arg min x2R n PredictorðAxÞ À y 2 s:t: c l ≤ cðxÞ ≤ c u ; x l ≤ x ≤ x u ; ð3Þ where c(x) is a vectorized linear function with its upper and lower bound, e.
g. , ∑ n i¼1 x i ¼ 1 is encoded here. x u and x l are the upper and lower bounds for the composition vector, respectively. Matrix A contains a latent representation of all pure components as columns, therefore, Ax refers to the mixture's representation (see (2)). Vector y contains target properties as entries. We solve this problem using optimize subpackage in the open-source scientific Python computing library-scipy  76. We call the optimize function k times, each time with a different starting point, which were obtained using Algorithm 1 in Table  6. We output all the possible solutions with a loss smaller than the given threshold ϵ. The pseudocode is provided below. For more efficient optimization, we provide an optimizer with a gradient that can be efficiently calculated and extracted using the automatic differentiation module of PyTorch  77  , a Python open-source library used for implementing DL architecture.
Depth-first search recursively conducts an exhaustive search of all nodes by proceeding, if possible, else to backtrack to the neighbors of all upper levels until the solution is found. Visualization of this search approach is illustrated in Supplementary Fig. S4. Constraints and targets. The chemical space was limited to the CHO space to evaluate the search tool. The following criteria were used to exclude species from the search: • alkynes (sooting components), aldehydes (unstable components) • components with molecular weight outside of range 45-150 g/mol, • sooting components with more than one aromatic ring, • sooting components with aromatic ring and and extra unsaturation (e. g. styrene, indene), • sooting components with more than 3 unsaturations (excluding aromatic, e. g. octatetraene) In this work, the considered chemical space includes only molecules that were present as a single component or part of blends in the curated database.
However, in general, any new pure component outside of the database can be added to the chemical space to design mixtures by providing molecular information in terms of SMILES string and molecular descriptors generated by Mordred, i. e. , the inputs to Extractor 1 and 2, without need to know its experimentally measured properties. The default linear constraint corresponds to the requirement that the sum of the compositions in a given mixture, x, must be equal to one ∑ n i¼1 x i ¼ 1. Other requirements in implementing the search approach follow European gasoline specifications  80  and are summarized in Table  S6  in Supplementary Material. An additional constraint was specified to consider a maximum volume threshold (10%) for the transporting fuels containing 3, 4, 7 and 8 aliphatic rings (saturated and unsaturated).
Acknowledgements This paper is based on work supported by the  Saudi Aramco Research and Development Center FUELCOM3 Program  under Master Research Agreement Number  6600024505/ 01. FUELCOM ( Fuel Combustion  for Advanced Engines) is a collaborative research undertaking between  Saudi Aramco and KAUST , intended to address the fundamental aspects of hydrocarbon fuel combustion in engines, and develop fuel/engine design tools suitable for advanced combustion modes.
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently.