dataesr/scientific-paragraphs-categorization huggingface dataset:
Features extraction and building of the database One of the most challenging issues in clustering and classification problems is to extract informative features from measurements. Wavelet analysis has demonstrated high efficiency for the extraction of relevant features from PD data hence the reason it is commonly applied to PD denoising in HV equipment  [16, 17, 18]  and defect recognition  [19]. A typical Discrete Wavelet Transform (DWT) decomposition equation can be formulated as: ∑ = ⎛ ⎝ -⎞ ⎠ = - * DWT m k a s n g n b a ( , ) 1 ( ) n N 0 1 (1) where s n ( ) is the original signal, N is the number of samples in the windowed signal, g (. ) is the mother wavelet function, = a 2 m and = b k2 m are the scaling and translation parameters where m is the de- composition level index and  ∈ k. * denotes the complex conjugate.
In fact, to avoid biases choices, it is important to first consider a large panel of features, also features that were not considered to be relevant in a first approach. After, feature selection techniques will be applied to choose the most relevant variables. In this contribution, each of the 4993 signals in the dataset was decomposed by using the 'db10′ version of Daubechies wavelet  [20]  and the detail coefficient distributions up to the fifth level; cD1, cD2, cD3, cD4 and cD5 were used as signal features. This large data set was further reduced in dimensionality by representing the cD i vectors by their statistical moments mean, standard deviation, skewness and kurtosis. The mean and standard deviation are defined as followed: ∑ = = x N cD n 1 ( ) cD n N i j 1 , i j ( , ) (2) ∑ = - = σ N cD n x 1 [ ( ) ] cD n N i j cD 1 , 2 i j i j ( , ) ( , ) (3) where cD i j , is the n-th detail coefficient at level j, extracted from the i-th signal and N is the total number of detail coefficients at level j.
The distributions of the detail coefficients at each level of decomposition have different shapes that can be described using the skewness and kurtosis. If the skewness is positive, the coefficients are positively skewed, meaning that the right tail of the distribution is longer than the left. If the skewness is negative, the coefficients are negatively skewed, meaning that the left tail is longer. If skewness = 0, the distribution is symmetric. The kurtosis can be explained in terms of the central peak of the distribution. Higher values indicate a higher, sharper peak while lower values indicate a lower, less distinctive peak. Thus, the original feature dataset for each signal was reduced from 5xn cD (n cD the number of detail coefficients) to 5 × 4 features.
k-means Algorithm K-means is a commonly used clustering algorithm in Partial Discharge studies  [23]. This algorithm requires the user to specify the number of clusters k to be generated. Since the objective is to separate noise from PD signals regardless of the possible several sub-categories inside the PD and noise groups, we assume that the number of clusters is two. Silhouette analysis  [24]  is used to verify this assumption given our dataset. It measures the separation distance between the resulting clusters for different values of k. The Silhouette index has a range of [-1,1] where a high value indicates that the object is well matched to its own cluster and poorly matched to the others. It is defined as follows: = ⎧ ⎨ ⎪ ⎩ ⎪ - < = - > s i if a i b i if a i b i if a i b i ( ) 1 , ( ) ( ) 0, ( ) ( ) 1, ( ) ( ) a i b i b i a i ( ) ( ) ( ) ( ) (5) Where a i ( ) is the average distance between i and all other data points in the same cluster.
∑ = - ∈ ≠ a i C d i j ( ) 1 | 1| ( , ) i j C i j , i ( 6 ) d i j ( , ) is the distance between data points i and j in the cluster C i. The smaller the value ofa i ( ), the better is i assigned to its cluster. b i ( ) is the smallest average distance of i to all points in the other cluster (of which i doesn't belong to). b i ( ) is a measure of how dissimilar i is to its neighboring cluster. A large value means that i is badly mat- ched to its neighboring cluster. ∑ = ≠ ∈ b i C d i j ( ) min 1 | | ( , ) i j j j Cj  (7)  Fig. 3  illustrates the average Silhouette values of all data points according to the number of clusters, from k = 2 to k = 6. The average Silhouette value is optimized for k = 2. Thus, the partitioning of our dataset into k = 2 sub-groups seems to be the best natural way to cluster the data, minimizing the risk of cluster overlapping and assignment errors.
1. Normalize the dataset Define a set of n hyper-parameters C = [C1…. …. Cn] Define a value p = 10 random partitioning of the data into the folds 2. -for i = 1 : NMax, with NMax, the number of available features -First, consider each feature separately as input of the TSVM for j = 1 : n -Consider hyperparameter Cj for hyperparameter Cj for l = 1 : p -Draw one random partitioning of the data into the five folds -For k = 1:5 Set fold k as the validation set Train model on remaining k-1 folds Compute and store validation score on fold k -End for k Compute and store average validation score over the 5 folds -End for l -Compute and store average cross-validation score over the 10 random partitioning of the data over the folds -End for j -Select and store hyperparameter Cj with best average cross-validation score over the 10 partitioning -End for i -Select feature with hyperparameter Cj that gives the best average cross-validation score over the 10 partitioning 3.
Labeling of data In this work, the labeling of the 200 data is assisted by the peak amplitude-charge cluster graph reported in  [33]. In this cluster graph, the peak amplitude of the signal is represented in the ordinate axis. The PD current signal is approximated by dividing the HFCT's voltage output over the sensor gain. The discrete time integration of the main peak of this current signal is an estimation of the charge of the PD pulse  [14]. The charge is represented in the abscissa axis of the cluster graph leading to the result of Fig. 9. By using the software PDflex  [34] , it is possible to retrieve the waveform of the signals as the user hovers the pointer over the cluster graph and check visually if that given signal corresponds to a PD or noise signal. Due to the compactness of the test set-up shown in Fig.
Conclusion In this work, unsupervised as well as semi-supervised classification methods were applied to the classification of PD and noise signals collected from a test cell under 40 kVDC. Experimental data were transformed using DWT and decomposed up to five levels. A set of 20 numerical features formed by the mean, variance, skewness and kurtosis of the wavelet detail coefficient distribution at each level of decomposition were extracted from each acquired signal. A first unsupervised framework was proposed for feature selection and for the determination of the optimal number of clusters based on the Dunn index. The use of feature n°4, which is the kurtosis of the distribution of the detail coefficients at level one, as input of a k-means algorithm resulted in clearly well-separated clusters.
Since the unsupervised framework does not provide any method for the validation of the results, then a semi-supervised learning approach was applied on the same dataset using Transductive SVMs. 4% of the total dataset was labeled as PD or noise and this manual labeling process was assisted by checking the waveforms of the signals. A fraction of this labeled dataset was then used for automatic testing of the classifier performance. In this test set, some PD signals with very low SNR and EMI were labeled as noise signals by the user The results obtained using the semi-supervised approach showed a successful separation of PD and noise signals according to criteria defined in 4. 2. 1, with 80% of accuracy and a reduced set of features (feature n°4 alone or couple (n°4, n°7)), thus decreasing considerably the size of data to be processed as well as the computation time required.
The detector was placed 1 m away from the sample, giving access to full Debye-Scherrer rings with a maximum 2θ angle of 12°. The Q&P heat treatments were reproduced using the dilatometer (see Figure  1 ). Then, the samples were first heated up to 900 °C at 5 °C/s and held for 5 min at this temperature in order to achieve a full austenitic state. The samples were successively cooled down rapidly at 50 °C/s to three different quenching temperature (QT = 260, 230, and 200 °C). This step was followed by a homogenization step of 5 s at QT and then reheated at about 30 °C/s to a partitioning temperature (PT) of 400 °C, held at PT for 200 s, and finally quenched down rapidly to room temperature. The corresponding thermal paths are given in Figure  2. The 2D diffraction patterns obtained with the detector were integrated circularly using the Fit2D software to obtain 1D diffractograms.
In order to determine the nature and volume fractions of the phases formed during Q&P heat treatments, the diffractograms were analyzed with a full Rietveld refinement procedure  [13]. Diffraction peaks were modeled using pseudo-Voigt functions using FullProf software with 20 degrees of freedom for each record (background, phase fraction, lattice parameters, shape of peaks, and temperature effects). The phase fractions were extracted from the peak integral breaths. The uncertainty on both phase fractions and lattice parameters were estimated to be roughly ±1% and 0. 005 Å, respectively. Two phases were considered in the refinement: austenite and ferrite. While the symmetry used to describe austenite is straightforward, i. e face-centered cubic (FCC), Fm-3m, two different symmetries were tested to fit the ferritic phase: a body-centered cubic (BCC, Im-3m) and a bodycentered tetragonal (BCT, I4/mmm).
Metallographic Analysis The SEM image in Figure  7a  corresponds to a sample that underwent a Q&P treatment with QT = 230 °C and PT/t = 400 °C/0 s. The time spent at the PT is very brief, but as the HEXRD experiments showed (Figure  5 ), both martensite and the new BCT phase were formed. The topological aspect of phases was the first discriminating criterion used to sort out our Q&P microstructures. The image analysis software ImageJ was used in this study. The use of automatic selection tools based on differences in pixel brightness was made difficult; thus, every martensite/austenite (MA) island was delineated manually, as shown in Figure  7b. The volume fraction of MA islands and the other BCT phases were measured to be 34% and 66%, respectively. So for now, a two phase-group classification was obtained, and some characteristics can be extracted from the image analysis.
The prior austenite grain boundary (PAGB) was determined with MERENGUE 2 (see Figure  12b ) in yellow lines). As the only two phases given to the EBSD software to index were FCC and BCC, all the microconstituents that are not red are either martensite (tempered or fresh) or bainite (carbides were too small to be probed with the current mapping settings). Retained austenite was present in two different morphologies: either as blocky RA as in Zone 1 or as thin lath-like RA as in Zone 2 (see Figure  12 ). It is interesting to note that ultra-fine RA laths were rarely detected via EBSD. This led to an underestimation of the amount of RA (9. 7% via EBSD, 14% via XRD). Bainite was present in both zones as small carbide-free laths (green arrows on Figures  13  and 14 ) and was very often surrounded by retained austenite.
, 2018; Marin et al. , 2016; Santos et al. , 2013). In each case, the initial kinetics are studied to estimate the activation energy. Two articles studied the kinetics based on the mechanisms of Langmuir-Hinshelwood and Eley-Rideal  (Marin et al. , 2016; Santos et al. , 2013). However, the rate laws developed are different in the two articles. The aim of this study is to develop a kinetic model for the synthesis of DEC and PC, for which no data has yet been found in the literature (Eq 1 and 2). A parametric study was performed using the catalyst CeO 2 , which gives good yields and very high selectivity  (Yoshida et al. , 2006). Thermodynamic constants were evaluated experimentally for different experimental conditions and compared with thermodynamic data found in the literature. Several kinetic models were developed based on different mechanisms, and rate constants were identified with the experimental data.
, 1976; Gardner and Hussain, 1972; Gardner and Hussain, 1972; Moureu and Dode, 1937; Zaripov, 1982; Choi and Joncich, 1971; Vasil'eva et al. , 1972; Leino et al. , 2011; Shukla and Srivastava, 2018). For the synthesis of DEC, values given by Leino and colleagues and Shukla and colleagues are different. The data of Leino and colleagues are for gas components, and the data of Shukla and colleagues are for the liquid components, except for CO 2 which is considered as a gas in both cases. Liquid heat capacity was used for DEC, water and ethanol, and the gaseous heat capacity was used for CO 2. These data were found in the National Institute of Standards and Technology database (NIST). The influence of the value of heat capacity on the calculation of the equilibrium constant is negligible compared to the enthalpy and entropy.
Eq 25 The reaction rate depends on the concentration of DEC (or PC) as described in Eq 26: Eq 26 Differential equations were solved in Matlab, using a method based on differentiation (numerical differentiation formulas, ODE15S). The objective function used is given in Eq 27. represents the number of experimental points. For the synthesis of DEC, we used 16 experiments, which represent 103 experimental points. Eq 27.
metabolism of microorganisms  (5)  such as methylotrophs  (6) (7) (8). Briefly, 13 C-MFA exploits a metabolic model and 13 C-isotope patterns measured from key metabolites to estimate reaction rates consistent with the observed labeling patterns (see references 9 and 10 for reviews). Specifically, cells are grown on a 13 C-labeled carbon source, metabolites of the central metabolism or constituents of the biomass such as proteinogenic amino acids are sampled and quenched, and the labeling patterns of metabolites are monitored by mass spectrometry (MS) and/or nuclear magnetic resonance (NMR). With NMR, carbon isotopomers are determined; hence, positional information is provided. With MS, isotopologues are identified. A metabolic model is then used to fit these measurements to theoretical data that are simulated by optimizing reaction flux values from the metabolic model.
49 Ϯ 0. 06 U mg Ϫ1 ) and 12 times higher than for AtlF with Xyl5P (0. 11 Ϯ 0. 01 U mg Ϫ1 ) (Table  1 ). NADH was the preferred cofactor over NADPH, with a 10 times lower K m (0. 01 Ϯ 0. 01 versus 0. 11 Ϯ 0. 09 mM). The affinity and NADPH-dependent activity (0. 24 Ϯ 0. 06 U mg Ϫ1 ) could only be determined with AtlD and Xyl5P, as no significant activity was detected for any of the other reactions. Liquid chromatography-mass spectrometry (LC-MS) analyses were performed to confirm the formation of arabitol phosphate in the enzyme reactions catalyzed by AtlD. Although arabitol 1-phosphate and arabitol 5-phosphate could not be distinguished, arabitol phosphate was clearly produced with both Xyl5P and Ribu5P as the substrates (see Fig. S2 ). Overall, these data suggest that AtlD has a major role in arabitol catabolism in vitro and that AtlD and AtlF both prefer Xyl5P.
To address this point, we used a dedicated program, IsoDesign (34), using as input metabolic models of each carbon source and several labels as described in Materials and Methods. For arabitol and mannitol, good flux precision was obtained with mass spectrometry labeling data from proteinogenic amino acids. This is advantageous for two reasons. First, the experimental setup is simpler than for intracellular metabolites, because no quenching is required and the cellular pellet can just be collected by centrifugation. Second, the labeling can be measured by NMR and MS, providing crucial positional information to distinguish between the two pathways (i. e. , via arabitol 5-phosphate or via arabitol 1-phosphate dehydrogenase). Among the different label inputs tested, 100% [5- 13 C]arabitol appeared ideal to identify whether arabitol is converted into arabitol 1-phosphate only or into arabitol 5-phosphate only.
e. , glucose 6-phosphate dehydrogenase [zwf]) of 0. 5909 Ϯ 0. 0578 and 0. 6919 Ϯ 0. 0959 mol/g (dry cell weight)/s for the two replicates. Next, those data were used to constraint the flux through zwf (as a "measured flux") through the influx_si optimization process. Measurement of physiological parameters. Assessment of physiological parameters is a prerequisite for flux calculation. Here, B. methanolicus was grown in batches on three different carbon sources. For each cultivation, the growth, consumption, and production rates were determined. Results are given in Table  2. No significant differences between the physiological parameters obtained for the culture replicates were observed. On methanol (batch cultures at 50°C), around 35% of the carbon source was directly evaporated, and biomass and carbon dioxide were the only products formed in detectable amounts.
Temperature, pH, and stirring speed were maintained at 50°C, pH 6. 8 (with KOH 1 M), and 800 rpm, respectively. The N 2 , O 2 , Argon, CO 2 , 13 C-CO 2 , and methanol concentrations in the bioreactors off-gas were measured on-line with the mass spectrometer. To perform the pulse of tracer, 100 mM 13 C-methanol (99% 13 C; Euriso-Top, France) was added to the cultures at an optical density of 600 nm (OD 600 ) of 2. 5. Growth curves are available in Data Set S1 in the supplemental material. (ii) Quantification of cells and supernatant NMR analysis. For determination of the dry weight of cells, a conversion factor of 0. 389 g/liter (dry weight) of cells per OD 600 unit was used. Supernatant samples were taken to analyze substrate methanol consumption as well as by-product formation by subtracting 1 ml of culture and centrifuging it at 13,000 ϫ g for 60 s.
Liquid anion-exchange chromatography was performed as described previously  (59). (iv) Sampling and MS analysis of labeled metabolites. Label enrichments in the intracellular metabolites were followed after performing a pulse of 100 mM 13 C-methanol at an OD 600 of 2. 5. Whole broth (WB; internal plus external pools) and culture filtrate (CF; external pools) were sampled to indirectly track label incorporation in the intracellular metabolites. Specifically, 13 WB and 3 CF samples were collected in 3. 5 min from each bioreactor. Exact sampling times can be seen in Data Set S1. IC-MS/MS quantification was used to analyze the isotopologues of each metabolite as described in reference 59. The metabolites analyzed were phosphoenolpyruvate (PEP), ribose 5-phosphate (Rib5P) plus Ribu5P plus Xyl5P, sedoheptulose 7-phosphate (Sed7P), 6-phosphogluconate (Gnt6P), glucose 6-phosphate (Glc6P), fructose 1,6-bisphosphate (FruBP), fructose 6-phosphate (Fru6P), glycerol 3-phosphate (Gly3P), 1,3-diphosphateglycerate (13PG), 3-phospho-D-glycerate (G3P) plus phosphoglycerate (PGA), citrate (Cit), aconitate (Aco), fumarate (Fum), malate (Mal), and succinate (Suc).
After manual peak integration, the raw peak areas were corrected for the contribution of all naturally abundant isotopes using IsoCor software  (60). Some cross-contamination was found in the isotopologues M ϩ 4 of Aco and M ϩ 2 and M ϩ 3 of Gnt6P that were subsequently removed from the analysis (Data Set S1). Additionally, the exact ratio between 13 C-methanol and 12 C-methanol after the pulse was measured by 1D 1 H NMR as well as the evolution of 12 CO 2 and 13 CO 2 by the mass gas analyzer. (v) Mass balance. Experimental data consistency of the measured rates was verified using standard data reconciliation procedures under the elemental mass balance constraints  (61, 62). The biomass elemental composition used in the reconciliation procedure was taken from the closely related nonmethylotrophic bacterium Bacillus subtilis, CH 1.
MS analyses were performed in positive Fourier transform MS (FTMS) mode at a resolution of 60,000 (at 400 m/z) in full-scan mode, with the following source parameters: capillary temperature, 275°C; source heater temperature, 250°C; sheath gas flow rate, 45 AU (arbitrary unit); auxiliary gas flow rate, 20 AU; S-lens radio frequency (RF) level, 40%; source voltage, 5 kV. Isotopic clusters were determined by extracting the exact mass of all isotopologues, with a tolerance of 5 ppm. Experimental carbon isotopologue distributions (CIDs) of alanine, glycine, valine, serine, threonine, phenylalanine, aspartate, glutamate, histidine, isoleucine, leucine, lysine, arginine, tyrosine, proline, and methionine were obtained after correction of raw MS data for naturally occurring isotopes other than carbon, using IsoCor  (60).
1% DCl). The positional isotopomer distribution of alanine C-2 and C-3 was extracted from the analysis of 13 C- 13 C couplings in twodimensional (2D) 1 H- 13 C heteronuclear single quantum coherence (HSQC) experiments  (66). The carbon isotopic enrichments of alanine (C-2 and C-3) and histidine (C-2 and C-5) were extracted from the analysis of 1 H-13 C couplings using 2D zero quantum filtered-total correlation spectroscopy (ZQF-TOCSY)  (67). NMR spectra were collected on an Avance III 800 MHz spectrometer (Bruker, Germany) equipped with a 5-mm z-gradient QPCI cryoprobe. Every acquisition, 1D, 2D, and absolute quantifications were performed on 1D 1 H spectra using TopSpin 3. 5 (Bruker, Germany). Models and simulations. (i) Metabolic flux analysis software. All simulations needed for metabolic flux analysis (MFA) on methanol, mannitol, and arabitol were performed with influx_si software v4.
4. 4  (68) , either in stationary or nonstationary mode. "influx" has the advantages of allowing for the integration of labeling data coming from different experimental setups (MS and NMR) and supporting several integration strategies (stationary, nonstationary, and parallel labeling). All input and output files needed for reproducibility are available in an archive in https://fairdomhub. org/data_files/3269?versionϭ2. Importantly, this includes the models in FTBL and SBML file formats. (ii) Metabolic models. We designed the models to cover central carbon metabolism and biomass needs of B. methanolicus. influx_si uses a nonstandard legacy file format (FTBL) to encode the metabolic network and the associated atom-atom transitions. This format centralizes the metabolic network with all biological measurements, i.
e. , metabolite pool sizes, fluxes, and carbon isotopologue distribution. Consequently, we developed distinct model files for each carbon source. Models share the same nomenclature and the same general topology between them, which is displayed in Fig. 2  and detailed in Data Set S1. Assimilation pathways of methanol, mannitol, and arabitol were included when relevant to explain the incorporation of the tracer. The CO 2 pool was explicitly modeled within the system to allow for reincorporation of tracer via CO 2. Amino acid synthesis pathways were modeled as part of biomass needs along with important precursors. The biomass equation was borrowed from a Bacillus subtilis genomescale model  (69). The reaction for phosphoenolpyruvate carboxykinase (BMMGA3_RS13120) was not included in the final model, as the associated expression level was rather low in proteomics  (26)  and transcriptomics  (21, 25)  studies.
Unless otherwise stated, each culture replicate was processed independently to estimate fluxes for their respective carbon source condition. For MFA on mannitol, we exploited carbon isotopologue distribution data of proteinogenic alanine, glycine, valine, serine, threonine, phenylalanine, aspartate, glutamate, histidine, isoleucine, leucine, lysine, arginine, tyrosine, proline, and methionine. Acetate production was modeled with an export flux from acetyl-CoA, to which we associated the acetate flux measured from supernatant data. Acetyl-CoA was further constrained using acetate positional labeling measured by 1D 1 H NMR spectroscopy from the supernatant. For MFA on arabitol, we averaged the proteinogenic labeling measurements of the [5-13 C]arabitol experiment and exploited it as a parallel labeling data set for each biological replicate of the [1/2-13 C]arabitol experiment.