dataesr/scientific-paragraphs-categorization huggingface dataset:
Implemented Machine Learning Methods This subsection focuses on the machine learning (ML) aspect of the developed tool. Indeed, supervised learning approaches are ML techniques based on input and output data (labeled data) and are employed for classification. The objective is to automatically generate knowledge rules from a database containing "samples" of inputs and corresponding outputs so that with a new input data, the output variable can be predicted (as represented by Figure  5 ). Regarding supervised learning approaches, they can be divided into two categories  [31] : • Classification methods, which dispatch the input observations in categorical groups and lead to the construction of predictive models for discrete responses.
K-Nearest Neighbors Algorithm The k-nearest neighbor (kNN) is a supervised ML algorithm that can be used in both classification and regression models. For classification purposes, kNN is a non-parametric method that supports non-linear solutions and can only provide labels as an output. By assuming a value k for the number of nearest neighbors, kNN algorithm identifies the training observations N closest to the new prediction point x, as represented in Figure  6. Each new observation x is compared to those that already exist in the input dataset by using a distance calculation (such as Euclidean distance, cosine of the angle formed by the two observations, etc. ). Then, the class with the smallest distance is assigned to x. The algorithm therefore requires knowing k, the number of neighbors to consider.
The kNN algorithm also has the advantage of being a versatile and easy to understand and implement method with no need for initial assumptions. However, when the volume of samples in the dataset (so-called predictors) increases, the kNN algorithm tends to become slower. Even if there are more precise classification algorithms, kNN remains a first-choice and simple algorithm to model a classification problem and can achieve a high classification accuracy in problems with unknown distributions, while familiarizing with the available database. For this study, the kNN algorithm has been implemented by keeping the Euclidean distance as the employed distance measure. For this study, the Euclidean distance has been employed as the distance measure because of the ease of calculations and possible manual checking of results.
A decision tree is a commonly used and highly understandable machine learning method. It is a reliable algorithm for separating a dataset (predictor variables set) into several given classes by providing some clear indications about the most relevant predictors. For classification problems, a DT algorithm does not need much computation and does not rely on functional assumptions (i. e. , it is not affected by any non-linearity) while it can build very complex trees and encounter an overfitting problem. Additionally, the creation of optimal decision trees can be obstructed by the presence of dominate classes. DT accuracy reduces, however, when the number of training examples to the number of classes is low. Decision trees are widely used algorithms that give high-quality results with the data, which mostly depends on the conditions  [35, 36].
It is a reliable algorithm for separating a dataset (predictor variables set) into several given classes by providing some clear indications about the most relevant predictors. For classification problems, a DT algorithm does not need much computation and does not rely on functional assumptions (i. e. , it is not affected by any non-linearity) while it can build very complex trees and encounter an overfitting problem. Additionally, the creation of optimal decision trees can be obstructed by the presence of dominate classes. DT accuracy reduces, however, when the number of training examples to the number of classes is low. Decision trees are widely used algorithms that give high-quality results with the data, which mostly depends on the conditions  [35, 36]. In electric power system applications, DT is used in load consumption prediction and load forecasting, preventive and corrective control, power systems security assessment, etc.
The key point is then to find the right values for parameters θ (θ being a vector of the same size as the observation vector x) by solving a minimization problem: min θ J(θ) (17) with J(θ) = 1 M M ∑ m=1 cost h θ x (m) , y (m) ( 18 ) where J is a cost function, M is the total number of observations in the dataset and cost is the quadratic classification error that is expressed as follows  [39] : cost(h θ (x), y) = 1 2 1 1 + e -(θ T x) -y 2 (19) cost(h θ (x), y) = -log(h θ (x)) i f y = 1 -log(1 -h θ (x)) i f y = 0 ( 20 ) The cost function to be minimized will be equal to: J(θ) = - 1 M M ∑ m=1 y (m) log h θ x (m) + 1 -y (m) log 1 -h θ x (m) (21) The logistic regression method is the go-to method for binary classification problems (problems with two class values). LR is easy to implement, fast and very efficient to train.
The input node (i. e. , node 2) is connected to customer C1 while the output node (i. e. , node 3) is connected to customer C2. The first node (i. e. , node 1) connected to the secondary side of the transformer is supposed to be at the 230 V reference value. In this study, a month of SM data is used to build the dataset. For each day, 96 measurements are made. The total number of observations is thus equal to 2880 measurements (i. e. , 30 × 96). Those 2880 observations are created while ensuring uniformity of the two classes in the synthetic dataset. Table  3  shows how the cable states are distributed in the working database.
Training and Validation Sets Supervised machine learning algorithms consist of two phases-a training phase and testing phase. During the training phase, the training samples and the class labels of these samples are stored in a subset. The algorithm to learn and to create the right output from the data uses this subset. While training, the algorithm modifies the training parameters. In this phase, the algorithm is said to be learning. During the testing phase, the remaining observations from the original dataset are stored in a subset without the associated output. Then, a prediction is made on those samples to check how well the algorithm predicts the desired output. To fit those two phases, the original dataset has been reduced in two subsets: the training subset and the test subset. The training subset is used to train the algorithm and the test subset is used to make some predictions for the resulting model validation.
To select the observations in each data subset, a random logical selection was made. Tables  4  and 5  summarize the repartition of the data used in each classification algorithm. For the sake of simplicity, the analysis of this paper is carried out on a portion of the network shown in Figure  9 , which is in the upward direction of node 3. The input node (i. e. , node 2) is connected to customer C1 while the output node (i. e. , node 3) is connected to customer C2. The first node (i. e. , node 1) connected to the secondary side of the transformer is supposed to be at the 230 V reference value. In this study, a month of SM data is used to build the dataset. For each day, 96 measurements are made. The total number of observations is thus equal to 2880 measurements (i. e. , 30 × 96). Those 2880 observations are created while ensuring uniformity of the two classes in the synthetic dataset.
To select the observations in each data subset, a random logical selection was made. Tables  4  and 5  summarize the repartition of the data used in each classification algorithm. As explained in Section 2, the main purpose of this work is to identify if the monitored cable section (i. e. , the one between nodes 2 and 3) is either in the healthy working condition (class H) or has any insulation wear (class M). This classification will be made by various ML methods using an input dataset built from the provided smart meter data and computed nodal voltage variations. Figure  10  presents the flowchart of the implemented tool for solving that classification problem while Figures  11  and 12  show the specified classification process for each implemented algorithm. Energies 2021, 14, x FOR PEER REVIEW 13 of 20 As explained in Section 2, the main purpose of this work is to identify if the monitored cable section (i.
e. , the one between nodes 2 and 3) is either in the healthy working condition (class H) or has any insulation wear (class M). This classification will be made by various ML methods using an input dataset built from the provided smart meter data and computed nodal voltage variations. Figure  10  presents the flowchart of the implemented tool for solving that classification problem while Figures  11  and 12  show the specified classification process for each implemented algorithm. Training subset ND1, ND2, Voltage Output y.
Hyper-parameters adaptation Error    As explained in Section 2, the main purpose of this work is to identify if the monitored cable section (i. e. , the one between nodes 2 and 3) is either in the healthy working condition (class H) or has any insulation wear (class M). This classification will be made by various ML methods using an input dataset built from the provided smart meter data and computed nodal voltage variations. Figure  10  presents the flowchart of the implemented tool for solving that classification problem while Figures  11  and 12  show the specified classification process for each implemented algorithm.
Case 1: Impact of the Net Demand and the Thickness Variation The first application case will evaluate the impact of the net demand and the thickness variation on the model training and the prediction result. In this case, the net demand (ND) and the nodal voltage (V) of both the input node (named ND1 and V1) and output node (named ND2 and V2) are given to the classification input dataset. This helps the algorithm in its learning process. The algorithm will understand if any variation in the data is related to a cable degradation (based on the net demand/voltage level compromise) or to the client net demand.
Case 1: Impact of the Net Demand and the Thickness Variation The first application case will evaluate the impact of the net demand and the thickness variation on the model training and the prediction result. In this case, the net demand (ND) and the nodal voltage (V) of both the input node (named ND 1 and V 1 ) and output node (named ND 2 and V 2 ) are given to the classification input dataset. This helps the algorithm in its learning process. The algorithm will understand if any variation in the data is related to a cable degradation (based on the net demand/voltage level compromise) or to the client net demand.
By comparing these results, it can be concluded that the LR and decision tree are great binary classification tools, while the kNN method leads to less accurate predictions. Figure  15  represents the confusion matrix of LR and DT methods for the first application case in order to visualize the quality of the classifiers output (see if the predictions really match the real associated classes for validating the prediction counts in Table  6 ) in a three-dimensional plot. In Figure  15 , the axes yPred and yvalid correspond, respectively, to the outputs of the classifier (the predictions) and to the known cable conditions (real classes from the original dataset). Only a few damaged cable conditions could not be predicted with either LR or DT algorithms (small blue block corresponding to 30 observations in Figure  15a  and four observations in Figure  15b ).
Particularly, this work demonstrates the ability of different classification algorithms to identify, on the basis of only ND and voltage variation, the LV network cable condition assessment. However, this presented work should not be directly extended for other practical applications or be generalized, for two reasons. Firstly, the resistance of the insulation material is calculated (in Section 3) within consideration of some LV cable electrical properties specific to each manufacturer. Secondly, machine-learning techniques have been developed here for the degradation detection in operating domains where the causes of observed variations are difficult to interpret. Hence, to avoid a direct median separation in the observations, the input database has been built (in Section 5. 1) by excluding the cases of extreme degradation scenarios (severe faults) because they are easily detected without any advanced techniques.
Conclusions In this study, a machine learning-based framework is proposed for the identification of low voltage cable degradation due to the insulation material wear. To this end, a probabilistic tool was first developed to generate scenarios for the uncertain nature and degree of the cable insulation degradation. Those scenarios were then associated with the load demand and PV generation variations and used to build the nodal voltage database by performing probabilistic load flow calculations. Different supervised learning methods were finally applied to the generated database. In the first (training) stage, the studied classification methods learned from the given inputs, its associated cable condition status in order to be able to predict, in the second (test) phase, the cable condition corresponding to each given network operating point.
Photo-bioreactor Hydrogen producing experiments were carried out in a laboratorymade, rectangular-shaped 1 L bioreactor operated in the fed batch mode  [28]. Anaerobic conditions were obtained by initially purging the bioreactor with sterile argon gas. Temperature and pH values were acquired by two pH and temperature probes connected to a computer via an acquisition card (Measurement Computing SA, PMD1208 LS). The pH was controlled with a pH regulator and pumps, by automatic addition of sodium hydroxide (2 M). The hydrogen flow rate was measured by a mass flow meter (Mac Millan, model 50D, 0-20 ml H 2 h À1 ) linked to the data acquisition card. Hydrogen volume was obtained by time-integrating the hydrogen flow rate furnished by the flow-meter. The substrate flow during fed-batch culture was controlled by a single pump functioning in both directions and electro-valves (Sirai, Type Z130A, V12).
The entry flow was identical to the output flow in order to maintain a constant culture volume. The pump and electro-valves were controlled using the digital channels of the acquisition card, interfaced with a lab-made software based on Visual Studio. The bioreactor was illuminated from one side by a sodium-vapor lamp (OSRAM, Plantastar, 600 W). The light intensity at the surface of the reactor was varied by changing the distance between the light source and the illuminated surface. The light intensity was measured with a digital lux meter (Meter, RO 1332). The dynamics of the bacterial population and the hydrogen production were measured in quasi-continuous mode. The culture was started in batch mode, then when a constant bacterial protein concentration was reached ca. 0. 45 g/l for B10 and ca.
Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret. org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0. 03-0. 76 min and interval width of 0. 33-8. 78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS.
Introduction The dark matter in metabolomics refers to the large fraction of molecular signals that are detected with untargeted analyses but remain unidentified. Part of this dark matter corresponds to the food metabolome. Currently, >26,000 compounds have been described in foods (https://foodb. ca), and upon ingestion and digestion, these food components are further transformed into various metabolites  (Scalbert et al. , 2014) , many of which are not identified or inventoried yet in databases  (Barabási, Menichetti, & Loscalzo, 2020). Plant food bioactive compounds (also referred as dietary phytochemicals, e. g. , (poly)phenols, carotenoids, glucosinolates, alkaloids) and their phase I, -II and gut microbial metabolites represent an important class of the food metabolome that receive widespread interest for their protective health effects and more recently, for their usefulness as food intake biomarkers.
They cover a large chemical space ranging from highly polar to lipophilic compounds, and their identification in untargeted methods remains a challenging feat. Identification of unknowns in untargeted metabolomics combines multiple types of information and tools, such as matching of exact mass in compound databases, comparison of experimental to reference MS n spectral data and chromatographic retention time (RT) of authentic standards for Metabolomics Standards Initiative (MSI) level I identification, or to publicly available spectral databases for MSI level II  (Sumner et al. , 2007). But searches in databases often return an excessive number of structurally similar hypotheses  (Hall, Hill, Cawley, Hall, Chen, & Grant, 2018) , and purchasing all corresponding standards is not feasible due to limited availability and high cost.
However, these prediction models are limited in their application, as RT data are specific to one chromatographic system (CS) and the models do not provide accurate predictions outside the trained conditions. As analytical methods are not harmonised and most laboratories tend to have their own routine semi-targeted or untargeted LC methods for covering plant food bioactive compounds in various types of matrices (serum, plasma, urine, digestive fluids, food materials), it is ideal that RT prediction models be customisable across CSs. PredRet  (Stanstrup et al. , 2015)  represents an original approach that enables users of the scientific community to benefit from RT data sharing through its open access RT database, and obtain predictions in their own CS if the RT of a compound has been experimentally determined by another user or laboratory.
In this aspect, PredRet is relevantly applicable for transposing RTs between CSs differing in mobile phase composition, gradient, flow rate and column dimensions. In the framework of the COST Action POSITIVe (https://www6. inra. fr/cost-positive, FA1403), we evaluated the performance of PredRet to predict the RTs of plant food bioactive compounds and their metabolites in a multi-laboratory test involving 19 laboratories across Europe, using 24 gradient-based reversed-phase CSs. We also expanded PredRet database with experimental RTs of 467 plant food compounds.
Experimental RT datasets containing compound name, InChI and/or chemical structure were provided by the involved laboratories. InChIs were used as unambiguous identifiers for recognition of identical compounds between CSs and compound names were harmonised across laboratories. For polyphenol metabolites, we applied the new reference KCC nomenclature  (Kay et al. , 2020). InChIs were either extracted from databases such as PhytoHub (http://phytohub. eu), PubChem (https://pubchem. ncbi. nlm. nih. gov) (Kim et al. , 2019) , HMDB v4. 0 (www. hmdb. ca) (Wishart et al. , 2018)  or computed from chemical structures using Marvin v19. 7, 2019, ChemAxon (https://www. chemaxon. com). LogP values were computed using ALOGPS v2. 1 (http://www. vcclab. org/lab/alogps/) (Tetko et al. , 2005; VCCLAB, 2005)  after conversion of InChIs to SMILES via InChIToSMILES (http://www.
Chromatographic systems Experimental RT data were collected from 24 CSs across 19 laboratories. These CSs were not intentionally optimised for the RT prediction test but rather represent the routine semi-targeted or untargeted metabolomic methods of the various laboratories. A full description of instrument, column and analytical conditions used in the 24 CSs is provided in Table  1. Overall, 15 C18 reversephase (RP) columns from various manufacturers were used with dimensions ranging from 0. 5 to 4. 6 mm (internal diameter), 50 to 250 mm (length) and 1. 6 to 5 m (particle size). HPLC or UHPLC methods were used in acidic conditions. Water and acetonitrile acidified with formic acid (0. 1-0. 9%) or trifluoroacetic acid (0. 1%) were most commonly used as mobile phases A and B, while three CSs used methanol or acetone as mobile phase B.
Prediction of retention times Experimentally measured RTs (Table  S3 ) were entered in PredRet for the 467 compounds listed in Table  S1. The number of measured RTs by CS varied from 29 in CS9 to 103 in CS14. For each CS, the compound names, InChI and experimentally measured RTs were entered into PredRet web interface (http://predret. org) along with a description of the respective CS method. PredRet is then able to predict RTs for compounds that have not been previously experimentally measured in one CS but have been determined in some other CS. The prediction is achieved by constructing GAMs between all pairs of CSs in the PredRet database using the compounds that were measured in both CSs. Empirical prediction intervals (PI) were established via bootstrapping of GAMs, as described in more details by  Stanstrup et al (2015).
Validation of predicted retention times A validation test was conducted on  CSs 1, 2, 4, 5, 14, 18, 19 and 22, which   data, n = 79, 71, 73, 67, 82, 63, 63  and 78   compounds respectively)  and test sets (   20% data, n = 20, 18, 18, 17, 21, 16, 16 and 19 compounds respectively). For selection of compounds in the test sets, the datasets were split into three equal sections covering the beginning, middle and end of the chromatographic run, and then 20% of the compounds were randomly selected from the three sections to ensure a uniform distribution of RT along the entire chromatographic run. Another criterion was to select, in the test set, the same proportion of unique compounds as in the whole dataset of the selected CSs. Validation of RT predictions for each of the eight selected CSs was performed in conditions where the complete datasets of the remaining 23 CSs were entered into the PredRet database.
48 to 10. 40 and with monoisotopic masses from 95. 0371 to 934. 0712 daltons (Figure  1 ). The PredRet database is growing continuously with addition of new compounds and associated RT data by registered users. At the time of our experiment, a limited number of plant food compounds was present in PredRet, and our datasets represented a major update for this category of compounds. The number of CSs in which each compound was analysed is provided in Table  S1. Of the 467 entered compounds, 212 were analysed in one CS only, while 4'-hydroxy-3'-methoxycinnamic (ferulic), 4-hydroxy-3-methoxybenzoic (vanillic), 3,4-dihydroxybenzoic (protocatechuic), 5-Ocaffeoylquinic and 4'-hydroxycinnamic (p-coumaric) acids were most commonly measured in 20 of the 24 CSs (Figure  S2 ). The size of the datasets varied from 29 to 103 experimental RTs.
e. , UHPLC or HPLC) of the LC gradient as well as number of common compounds shared with other CSs. For example, infrequently used mobile phases may limit the predictability of a CS. The entry of 29 compounds for CS9 was not sufficient to obtain RT predictions. However, despite relatively small RT datasets (35 to 46 compounds) were entered for CSs 11, 15 and 16, they had a high prediction rate, explained by a versatile CS and/or good combination of compounds.