dataesr/scientific-paragraphs-categorization huggingface dataset:
Still, the dearth of academic work and absence of empirical regularities is mainly due to the fact that many ASM are operated informally or illegally, resulting in very limited information on this sector at the local level. In this paper, we develop a suitable strategy to map locations and estimate the size of the ASM activities by combining state-of-the-art machine learning techniques and remote sensing data. More specifically, we use a convolutional neural network (CNN) for pixel-level classification, a modified version of the deep learning model U-Net  [6] , which aims to detect surface mining using satellite data (Sentinel-2). One major challenge is handling the high-dimensional features of images and the scalability of a possible automatic solution. We train the U-Net model with millions of input data from high-resolution satellite images to output a probability for each pixel to be a mine.
Labelled dataset creation process A crucial step of our methodology is the creation of a labelled dataset to train, tune and evaluate our model. For instance, during the training step, every time the model predicts a previously labelled region, it compares the ground truth label to the prediction made. The model parameters are then optimized by minimizing the error between these two. Even if qualitative initiatives exist to map mining activities  [9] , available data contains no information on location at pixel level, or the exact date of the labelling is missing or imprecise. The creation of our labelled dataset is particularly challenging as the identification of ASM is related to class imbalance problem: while the number of ASM has increased in the last years, they are still sparse and highly dispersed across space, likely representing less than 0.
1% of the African continent. Facing this challenge, we follow a sixth-steps strategy to create our reference dataset. First, we divide the region into a grid, each cell covering 14. 5 km × 14. 5 km (210 km 2 : 2. 1 million pixels), with an overlap of 1 km on each side (S2 Fig). Second, to get a first idea about the potential ASM distribution, we retrieve for each cell the density of "quarry" labels from the Open Street Map contributions. Indeed, pre-identifying boxes with a high density of "quarry" labels helps to overcome the challenge of the positive class' sparseness. Third, we annotate a mixture of randomly chosen boxes with and without ASM, looking for the right compromise between two conflicted purposes: i) including a subsequent number and variety of mining examples, while ii) maintaining our labelled dataset (relatively) representative of the class imbalance reality.
After empirical exercises testing various ASM proportions, and in coherency with our objective to first minimize the wrong predictions, we finally aimed to reach approximately 1% of ASM in our dataset. However, it should not necessarily be the same value in other contexts (e. g. in other regions, or in fine-tuning an already pre-trained algorithm). Fourth, drawing lessons from our recurrent wrong prediction observations, we refine our dataset by manually select boxes containing particular features, such as rivers with sandy shores, railways and highways, and buildings under construction. Note that these three combined steps -2, 3 and 4-were not successive but iteratively operated together. In theory, they might be continually further-improved, by gradually enlarging and refining the current labelled dataset with a wide variety of landscapes, features and ASM types.
Dataset partition To train and estimate the model performance, the labelled dataset is divided into three different partitions: i) the training set (55% / 13,035 km 2 ) used to train the model; ii) the validation set (20% / 4,625 km 2 ), with images the model has never seen during the training, which is used to tune the model parameters and to avoid over-fitting the training set. Also, the best weights are selected based on validation metrics only and iii) the test set (25% / 5,677 km 2 ) with images the model has never seen before, to estimate the model's performance. Given the limited amount of labelled data and the scarcity of ASM areas in comparison to other landscapes, we select boxes for the test set to ensure that we have sufficient ASM-in terms of variety of geological features and surface-to compute coherent metrics (see Results section), while keeping the maximum number of boxes in the training set.
Performances along ASM size classification Another important feature of our model is its ability to detect ASM independently from their ground surface range (in hectares). In our labelled dataset, the average ASM size is around 13 hectares, while the standard deviation is around 70 hectares and 75% of the labelled ASM cover less than 7 hectares (S3 Fig). This indicates that our model is trained on a wide range of ASM sizes and not only large-scale (or industrial) mines. To investigate its ability to effectively detect this wide range of ASM, we make use of the labelled dataset to create equally distributed minesized classes and compute the recall for each of them. We also add a last class to separately represent the industrial mining activities, since their size and features are distinct from ASM.
Furthermore, as the Mangroves area is not well predicted by the algorithm, if the user's region of interest stands within the Tropical and sub-Tropical moist broad-leaf forests biome, predictions even get much greater: a precision of 85% while retrieving 46% of the ASM and a satisfying MCC of 0. 62. This last finding is particularly reassuring, suggesting our model should perform well on larger African regions or other continents. Additionally, we compare our predictions to the most exhaustive existing dataset on the large-scale mines locations (S&P Global-SNL Metals and Mining). We detect all of the 265 listed large-scale mines located within our region of interest. Moreover, we detect 700 clusters of ASM, originally unrecorded, in a radius of 5km around the locations of the large-scale mines included in the S&P Global dataset.
Robust regression formulations In our experiments, we follow the setup of Carmon et al. [2]  to produce our problem data. Each problem instance corresponds to a dataset {(a i , b i )} m i=1 , where a i ∈ R n and b i ∈ R m , with n = 30 and m = 60. Every vector a i is generated according to a Gaussian distribution of zero mean and identity covariance matrix, which we denote by a i ∼ N (0, I n ), where I n is the identity matrix in R n×n. Letting A = [a T i ] ∈ R m×n and b = [b i ] m i=1 , we generate the vector b according to the following formula: b = Az + 3ν 1 + ν 2 , where z ∼ N (0, 4I n ), ν 1 ∼ N (0, I m ), and ν 2 ∈ R m has i. i. d. components drawn following a Bernoulli distribution of parameter 0. 3. Given a dataset {(a i , b i )} m i=1 of this form, we consider the robust regression problem: min x∈R d f SB (x) := 1 m m i=1 φ(a T i x -b i ), (23) where φ : R → [0, ∞) is a smoothed biweight loss function defined by φ(t) = t 2 1 + t 2.
Comparing semi-adaptive GD and Armijo GD allows to assess the impact of this initialization technique. All the algorithms were implemented in Python 3. 7. 4 using NumPy version 1. 17     (7). The best value for the smoothed biweight loss problems is p = 0. 55 while p = 0. 65 is best for the Tukey loss problems. restarting process may be applied without modifying the overall algorithmic behavior: in that case, we can thus provide stronger decrease guarantees for non-restarted iterations, as shown by our complexity analysis. Further results are reported in Tables  4  and 5. Those confirm that values of p smaller than 0. 5 not only increase the number of restarted iterations, but also reduce the ability of the methods to solve all problems in the prescribed budget. We also present our results in the form of data profiles  [10].
EXAFS measurements and data processing XAFS experiments at the U L III edge (E (2p 3/2 ) = 17. 166 keV) were performed at the INE-Beamline  (Rothe et al. , 2012)  of the Karlsruhe Research Accelerator (KARA, Karlsruhe, Germany). The INE-Beamline is dedicated to the investigation of radionuclide materials with hard X-ray spectroscopy techniques. The EXAFS spectra were recorded on bulk samples conditioned as pellets with a diameter equal to 5 mm. The pellets were encapsulated by double-containment sample holders and cooled near 115 K using a LN 2 cryostat (Oxford Instruments OptistatDN) during data acquisition. Because of the low concentration of the samples, EXAFS measurements were performed in fluorescence yield detection mode averaging the U L α1 fluorescence signal (~13. 61 keV) recorded by a 4-element Vortex-ME4 SDD and a 1-element Vortex-60EX SDD (SIINT).
EXAFS data fitting Data processing was conducted using the ATHENA code  (Ravel and Newville, 2005). The e o energy was identified at the maximum of the absorption edge for data fitting. Fourier transformation (FT) in k 2 was performed between 3 and 12 Å -1 with Hanning windows using the ARTEMIS code  (Ravel and Newville, 2005) , except for the C11-14. 5 thin section B sample, which is very noisy, and FT had to be performed between 3 and 8 Å -1. Only one global amplitude factor S 0 2 and one energy threshold correction factor ΔE 0 were used in all paths. Phases and amplitudes were calculated using the FEFF6 simulation code  (Rehr et al. , 2010)  included in ARTEMIS and based on the modified structure of menasite  (Plášil et al. , 2013) , brannerite  (Szymanski and Scott, 1982)  and parsonsite  (Locock et al.
EXAFS spectroscopy performed on the bulk samples provided valuable data about dominant U speciation in the samples. HR-XRD allowed us to describe the paragenesis and identify uranium-bearing minerals. Micromorphology, microgeochemical analyses, and micro-EXAFS spectroscopy performed on petrographic thin sections of the samples made possible the characterizations of specific uranium phases, inherited or newly formed, and highlighted mechanisms ruling the uranium immobilization. The formation of uranyl (U VI ) groups was detected in the U-tailings samples by EXAFS spectroscopy. In both samples, uranyl is mainly associated with sulfate groups (Table  1 ), which is consistent with the chemical and environmental conditions occurring in the U-tailings pile of Cominak. This pile is ruled by acidic pH and anthropic sulfur rich environments.
Credit author statement Florian Lahrouch made the EXAFS, HR-XRD and WDS studies + wrote the manuscript. Benoit Baptiste manages the BAG program to perform HR-XRD characterizations on CRISTAL Beamline and helped to the HR-XRD data recording. Kathy Dardenne and Jörg Rothe are the INE beamline Scientists who helped to the EXAFS data recording. Erik Elkaim is the CRISTAL beamline Scientists who helped to the HR-XRD data recording. Michael Descostes supplied the samples, and brought his knowledge of post mining environment. Martine Gerard made the EXAFS, HR-XRD and WDS studies + wrote the manuscript.
Acknowledgments The authors acknowledge the  KIT  light source for provision of beamtime at the  INE-Beamline  operated by the  Institute for Nuclear Waste Disposal  and would like to thank the  Institute for Beam Physics and Technology (IBPT)  for the operation of the storage ring, the  Karlsruhe Research Accelerator (KARA). In the framework of the  CNRS Я ÉCIPROCS network , this work has been accepted for synchrotron beamtime by the Soleil scientific proposal committee (BAG proposals 20191509). The authors would like to thank the  SOLEIL Synchrotron Facility  for use of radiation facilities. The authors also sincerely thank  Omar Boudouma  ( Plateforme MEB-OSU, Sorbonne Université ) for acquisition of SEM images and  Michel Fialin  and  Nicolas Rividi  ( CAMPARIS, Sorbonne Université ) for wavelength-dispersive spectroscopy (WDS) measurements.
Uncertainty in the system is considered by quantifying the dependence between variables in the form of conditional probabilities. The use of BNs has been investigated recently in agri-food domains  (Baudrit et al. 2015; Drury et al. 2017; Chapman et al. 2018). This article discusses a numerical workflow to treat data and knowledge that combines ontologies, databases and computer calculation tools based on the theory of belief functions and BNs. The workflow developed is based on a pluridisciplinary collective study involving experts in the domains of food processing and artificial intelligence, and comprises three sequential steps (see Fig. 1 ). The first step consists of elicitation, structuring and assessment of knowledge related to a food process of interest. More precisely, experimental data published in scientific articles are annotated using an ontology, and their reliability is assessed by experts in food processing.
Data from scientific articles are annotated in a simple tabular format file that is semi-automatically generated using the ontology (see step 1. 1 in Fig. 1 ). Then, in step 1. 2, the file is uploaded and annotated data are stored in a Resource Description Framework (RDF) database. The complete annotation data set used in this paper is available from  (Buche et al. 2021 ) and the database can be queried in open access using a SPARQL Protocol and RDF Query Language (SPARQL) end-point. Relationships between variables from expert opinion are structured by a BN through its associated graph. The second step consists of extracting annotated data and associated reliability scores from the database using a dedicated querying system guided by the ontology to learn the BN parameters (i. e. conditional probability tables).
RELATED STUDIES AND THE UTILITy FOR MICROFILTRATION PLANTS Ontology is a formal representation of knowledge that structures the domain of knowledge via hierarchical specialisation organisation of concepts and the relationships between them  (Staab and Studer, 2010). Supported by a description logic language such as the Web Ontology Language (OWL), ontology-based information systems enable automatic logical reasoning. Multiple approaches have been developed to manage probabilistic reasoning in ontology,  (Setiawan et al. 2017). Several languages have emerged to tackle BNs in ontologies such as PR-OWL  (Carvalho et al. 2017) , BayesOWL  (Pan et al. , 2005)  or OntoBayes  (Yang and Calmet, 2005)  extending OWL. PR-OWL designs ontologies that contain probabilistic information by considering the properties of OWL classes as random variables; it relies on the framework of Multi Entity Bayesian Networks  (Laskey, 2008)  which combines the first-order logic principles with BNs.
BayesOWL and OntoBayes (its successor) propose an approach to directly express OWL ontologies as BNs where classes are translated into nodes and relations are represented as conditional probabilities. Unfortunately, these frameworks remain prototypical, cannot query in SPARQL and lose the richness of the ontology framework. There is a lack of standardised tools capable of dynamically and automatically updating ontology or supporting BN structural adjustment and parameter learning. This leads to a weak scalability of the aforementioned languages  (Patnaikuni et al. 2017). Bayesian ontologies obtained with these approaches cannot integrate all information 4 contained in the ontology because BNs cannot represent relational information. Thus, the two models must be combined. A variety of approaches have been used to translate ontologies into BNs; for example,  Devitt et al.
The final objective is to address questions provided by experts, which require reason in the face of uncertainty. Consequently, a simple-to-use generic ontological model was selected which has already been used successfully for food  (Guillard et al. 2017 , Yun et al. 2018 ) and bio-based products  (Lousteau-Cazalet et al. 2016)  to manage heterogeneous literature sources. Representing relations between data in this ontological core model as n-ary relations  (Buche et al. 2013 ) guarantees acceptance by end-users who are familiar with entering and manipulating data in spreadsheets. The core ontology model has been implemented in @Web software (AtWeb 2021) which permits to (1) annotate data from heterogeneous sources in a simple tabular format that is semi-automatically generated by @Web using the ontology and (2) import the annotated tables into the RDF database.
2020 ). However, the databases of these equipment manufacturers do not contain enough data on fraction composition, operating parameters or plant design to accurately predict the performances of microfiltration under a wide range of operating conditions. To our knowledge, they do not use information in the scientific literature. This lack of data integration makes it difficult to predict performances and compare the membrane technologies available on the market, and hinders the optimisation of microfiltration plants. Some studies have focused on developing computational fluid dynamics approaches to predict and improve the performance of microfiltration. For example, Jalivand et al. (  2014 ) simulated crossflow microfiltration of whey suspensions by solving Navier-Stockes equations combined with Darcy's law.
HETEROGENEOUS DATA SOURCE ANNOTATION AND RELIABILITy ASSESSMENT Using ontologies is one relevant solution to integrate heterogeneous sources of scientific data  (Buron et al. (2020) ). Although relational models are widely popular and have been used since the 1980s for storing and retrieving data, their dependence on a rigid schema and the explosive growth of available data result in the reduction of their interest and importance. Indeed, the schema of a relational database makes it difficult to add new relationships between the objects or to manage the interoperability between different sources of databases. Implementing complex systems in relational databases requires introducing associative tables (also known as join tables) when many-to-many relationships occur in the model and this is expensive to be calculated.
To overcome these limits, RDF graph-oriented models propose a versatile framework capable of flexible extensions to take into account new sources of information. Moreover, contrary to relational databases, multiple RDF databases may be queried simultaneously on the Web thanks to federated SPARQL queries that facilitate interoperable data reuse in an Open Science perspective. Finally, domain ontologies, which may be natively used in RDF databases to annotate data, promote the use of aligned and standardized vocabularies, which reduce ambiguities in human and machine understanding. @Web tool has been used to implement the first step of Fig. 1. This includes semantic annotation of data from scientific articles guided by an ontology  (Buche et al. (2013) ), data source reliability assessment (Destercke et al.
(  2013 )) and querying of the annotated data stored in a RDF database available on the Web. @Web is based on a generic ontological and terminological resource (OTR) which is used to annotate and query scientific data. OTR is divided in two parts: a generic part (i. e. core ontology) and a specific part targeted to a given domain of application (i. e. domain ontology). As the core ontology lies at the heart of the workflow of capitalising on scientific data, only the domain ontology must be determined to reuse @Web in a new application domain. By example,  Lousteau-Cazalet et al. (2016)  applied it to the biomass process domains. The core ontology is structured to model scientific experiments in a given domain in annotated data tables. It is indeed a simple solution, easily understandable by annotators to structure an experimental result comprising the observed phenomenon and the associated relevant parameters, which are modelled by n-ary relation concepts.
Relation and Argument are generic concepts defined in the core ontology to model n-ary relations and arguments related by these relations. Concepts in a given application domain are defined as specialisations in the concepts of the core ontology. The set of terms that describe the application domain in different languages and which is associated with concepts in the conceptual portion used to annotate data is represented in the terminological part of the OTR. More details on the structure of the OTR can be found in  (Buche et al. (2013) ). @Web is fully compliant with semantic web standard languages:the ontological part of OTR is modelizedin OWLthe terminological part in SKOS, annotated tables in RDF, and the querying module based on SPARQL. When collecting data from multiple sources, the reliability of both data and scientific articles rapidly becomes an issue.
Parameters θ ij are initialised by using Dirichlet prior distributions and are successively updated each time new information is made available and can be formulated into a frequentist form. This approach is used in step 2 of the workflow (Fig. 1 ) and enables weighting the importance of the multiple sources of knowledge, as follows: θ α α ijk m ijk p m ijk ij k ij k D D s p N p N p | , , / 1 1 ( ) = + ( ) ( ) ( ) =           ∑    = + ( ) ∑ p m s p 1 (3) where [. ] denotes a sum, α α α ij ij ijr i = ( , , ) 1 are the hyperparameters of the Dirichlet prior distribution, which can be thought of as the size of a virtual database which corresponds to a belief of experts rather than on experiments. Database D p can correspond to simulated or experimental data, where counts N p ij ( ) , and s p ( ) are the confidence level (reliability rank) of source of knowledge p.
In this study, the reliability rank is estimated from the scores established in section 3 and N p ij ( ) corresponds to the events in the dataset extracted from @Web. Using BNs, known as inference, consists of estimating the conditional probability P X X Q E ( | ) of a set of variables X Q given a set of evidence variables X E. For further details about inference, see  Salmeron et al. (2018) , who show different kinds of inference algorithms (exact and approximate inference) based on the complexity and size of the BN  (Cooper, 1990).
wORKFLOw APPLICATION FOR SKIMMED MILK MICROFILTRATION To demonstrate the workflow developed (Fig. 1 ) in the context of food processing, the study focused on the hydraulic performance of milk microfiltration with ceramic membranes and did not consider protein transmission or the quality of fractions. As no ontology of milk microfiltration was found on existing portals or in the literature, experts designed a new domain ontology. By using Competency Questions  (Uschold & Gruninger 1996 , natural-language questions that outline the scope of knowledge represented by an ontology), the milk microfiltration ontology has been designed and adapted to the @Web OTR format and is available on AgroPortal and the INRAE dataverse (MICROFILTRATION ontology). It currently contains 20 relation concepts involving 152 symbolic concepts and 73 quantity concepts associated with 49 units of measurement.
Symbolic) concept. Time and Input product are arguments of the microfiltration_controlled_parameter_evolution_relation presented in Fig. 2. The database created with the milk microfiltration OTRconsists of 52 scientific articles in the domain of milk microfiltration, for a total of 220 documents. Other articles were excluded because they performed microfiltration using innovative methods (e. g. gas injection, rotating membranes) that did not reflect traditional microfiltration. Several of the articles initially identified studied the influence of microfiltration on the cheese making but did not give enough details about operating conditions. Other documents did not provide the required characterization of product and permeate and retentate fractions. The data included in the set of 52 selected articles represent: • 190 process experiments • 1572 controlled variables at several sampling times • 731 product characterisations Two excerpts of annotated tables which include data from experiments of  Jorgensen et al.