dataesr/scientific-paragraphs-categorization huggingface dataset:
We detect a fourth emission line, 26. 4 arcsec south of the quasar. The MUSE spectrum shows a featureless continuum object consistent with either CO(2-1) at z=1. 614 or CO(3-2) at z=2. 921 (a higher CO transition would have matching Ly-α line in the MUSE spectrum). Interestingly, one of the continuum spectral window also reveals an emission line at 101. 4 GHz which is identified as CO(2-1) at z=1. 274 thanks to the detection of the [O ii] doublet in the corresponding MUSE spectrum. These findings illustrate the power of combining ALMA with MUSE observations to securely assess the redshift of single line mm detections. Fig. 2  (bottom panel) shows the 0 th moment map of the field. The spectra are extracted within the CASA software and shown in Fig. B2. We also built a continuum image of aggregate bandwidth to check for possible detection of the galaxies in continuum.
Morphological and Kinematical Properties of Group Members MUSE and ALMA observations of the field are also complementary in that they both provide information on the kinematics of the stars and molecular gas respectively. We performed a 3-dimensional morpho-kinematic analysis of the objects detected in MUSE and ALMA, using the method described in  Péroux et al. (2017). In brief, we used the GalPak 3D algorithm  (Bouché et al. 2015)  which compares directly the data cube with a parametric model mapped in x, y, λ coordinates. The algorithm uses a Markov Chain Monte Carlo (MCMC) approach with a proposed distribution of the parameters in order to efficiently probe the parameter space. The code has the advantages that it fits the galaxy in 3-d space and provides a robust description of the morpho-kinematics of the data.
ACKNOWLEDGEMENTS We thank  Thomas Ott ,  Nicolas Bouché  and  Jean-Charles Lambert  for developing and distributing the QFitsView,  GalPak 3D  and glnemo software, respectively. We thank  Chian-Chou Chen  ( TC ) for help with the CO column density calculation. The authors wish to thank the anonymous referee for insightful comments which have led to significant improvement in the quality of the manuscript. CP thanks the  Alexander von Humboldt Foundation  for the granting of a Bessel Research Award held at MPA. CP is also grateful to the  ESO  and the  DFG cluster of excellence "Origin and Structure of the Universe" for support. AK acknowledges STFC  grant  ST/P000541/1  and  Durham University. RA thanks  CNRS  and  CNES  ( Centre National d'Etudes Spatiales ) for support for her PhD. VPK acknowledges partial support from  NASA  grant  NNX14AG74G  and  NASA/Space Telescope Science Institute support for Hubble Space Telescope programs   GO-13733  and  13801.
LAS acknowledges support from  ERC  Grant agreement 278594-GasAroundGalaxies. This work has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. We are grateful to the  Paranal and Garching  staff at  ESO  for performing the observations in service mode and the instrument team for making a reliable instrument. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2016. 1. 01250. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ.
Even if they are very sensitive, the response of most sensors shows interferences with other compounds than the targeted one, depends upon the temperature and humidity, and drifts with time  [19] [20] [21]. Such a preliminary step of characterization and calibration is not necessary for arrays of MOS sensors when used in association with pattern recognition algorithms  [22] [23] [24] [25] [26] [27] , which are methods used in data mining for the extraction of useful information and the exploration of data correlation. Supervised classification approaches  [28]  are based on a classifier built from a training set with a collection of labeled data, and then used to assign new unlabeled data instances. Unsupervised classification  [29]  refers to algorithms that require no training set (blind partitioning), no a priori knowledge of the structure of the dataset, and automatically define the different classes.
However, the physical meaning of these classes needs to be a posteriori interpreted or verified by the expert. In the present study, we investigate the potential of unsupervised classification, or clustering, to analyze the output of selective gas sensors. The dataset used for the analysis has been acquired during a field campaign aiming to investigate the drivers and dynamics of IAQ in a low energy building  [5]. Many clustering algorithms have been developed for data mining, such as reviewed in  [29]. Different clustering algorithms, or even different ways to use them on the same dataset, can lead to different partition results. None of them have proved to be the best technique in a large amount of configurations. Some of these algorithms have been applied to electronic nose data clustering  [30] , each with its respective possibilities and limitations  [25, 31, 32] , depending on the application.
For electronic nose data, hierarchical clustering is commonly used  [33] [34] [35] [36] [37]. It results in a hierarchical structure of the dataset that is more informative about the link between each group of data than the unstructured clusters provided by other techniques. In addition, its representation (dendrograms) allows the selection of the number of clusters. Centroid-based algorithms such as K-means  [38]  are less considered in the micro gas sensor field  [39, 40]  and especially for air quality investigation, though K-means is a simple partitional algorithm and one of the most widely used techniques in data mining thanks to its performances  [41]. K-means algorithms combine simplicity, ease of implementation and of use, speed of convergence, even with a large number of variables and clusters, and ability to process datasets with missing values.
Instruments and measurements settings Measurements used for this work were performed during the Mermaid study  [5]. This field campaign has been carried out in February and March 2015 in a 51 m² (139 m 3 ) classroom of an energy efficient junior high school building in Northern France. Details on the analytical instruments used for this study can be found in  [5]. Only the data from the pollutants from outdoors (NOx and O3), measured with online analyzers (Thermo 42i and Thermo 49i), and of CO2, measured with Testo 480 probe located at the center of the room are considered here. In addition to these instruments, miniature sensors were installed in the center of the classroom, 90 cm above the floor. They monitor nitrogen oxide (electrochemical, Alphasense NO-B4), nitrogen dioxide (electrochemical, Alphasense NO2-B4), ozone (electrochemical, Alphasense O3-B4) and carbon dioxide (NDIR, Alphasense CO2-IRC A1).
Experimental datasets The data used for this work corresponds to a 6-day continuous measurement (28 February 2015 to 6 March 2015). Fig. 1  presents the ventilation status and specific events taking place in the room during this period. These events include three CO2 injections with ventilation ON, one CO2 injection with ventilation OFF, for the determination of the air exchange rate of the room, two periods with people in the room, one NO2 injection, and an accidental release of both NO and NO2. provided by the NDIR sensor, with a 1 min resolution. As previously mentioned, no correction or calibration of the sensors signals was performed.
K-means implementation Numerous extensions of the basic K-means algorithm have been developed in order to improve its partitioning abilities for dedicated applications. Classical K-means performs a direct classification of the full dataset into a number of K clusters, whereas other methods, such as the bisecting Kmeans method  [38] , use a hierarchical method and split the data by iteration. In 2000,  Steinbach et  al. have shown that bisecting K-means, computed as an hybrid approach between the run-time efficiency of conventionnal K-means and the quality efficiency of an agglomerative hierarchical clustering, has higher performances than the conventional algorithm  [44]. It as also been demonstrated that bisecting K-means is relatively insensitive to the initialisation of the clusters centers and has a higher computational efficiency than the conventional K-means algorithm  [45].
In spite of its simplicity, and of its wide use, the reasons behind the efficiency of the K-means algorithm still need to be fully understood. The only required input from the user is the number of clusters into which the dataset must be split. In the present work, we used the K-means procedure with a program written in Julia 0. 4. 0. The raw time series data of each dataset is used for input, except for the carbon dioxide concentration, measured either by the Testo probe or the NDIR sensor, for which the logarithm (base 10) is used, because of the much wider range over which this concentration can vary. Preliminary calculations, not reported here, with the direct CO2 concentration, were performed but the clustering was less efficient than with the log values. The only other parameter for input is the number K of clusters.
Values of K ranging from 2 to 10 have been investigated for each dataset. Some mathematical criteria to determine the optimal number of clusters have been proposed  [44] [45] [46] , but these may have no physical meanings, and we have chosen to leave this determination to the judgment of the expert a posteriori. The program first normalizes all the observations in the dataset, in order to standardize their respective weight on the cluster partition. The clustering process is then initialized by splitting the normalized dataset into two subsets. Then, each datapoint is assigned to its nearest cluster center (centroid) according to the euclidian distance. The process is repeated until the association of all the observations of the dataset to its respective cluster does not change anymore and the sum of the squared errors of the distance is minimized.
The process is iterated by splitting into two new clusters the cluster with the highest sum of squared residuals, following the same procedure, until the required number of clustersis reached. Tests have shown that changing the initial partition does not influence the final results. The output file of the bisecting K-means clustering consists of the raw data tagged with their respective cluster, and of the coordinates of the centroids of each of the K clusters. To compare the outputs of the K-means procedure applied to the two dataset (reference dataset and sensors dataset), we will consider the overlap ratio between a cluster from dataset 2 and its equivalent cluster from the reference dataset 1. It is expressed as the number of correct matches between each datapoint in a defined cluster of each dataset normalized by the number of data points in the reference cluster.
Clustering of the reference online analyzers measurements (dataset 1) main driver of the chemistry in the room  [5]. The specific events described on Fig. 1     Data are successively grouped into new clusters, with the 2 clusters divided into smaller subclusters when K increases. For the initial partition (K=2), the bisecting K-means separates the dataset according to the ozone concentration, with cluster 1 corresponding to the periods with no ozone, i. e. when the ventilation is OFF, while cluster 2 refers to the periods where the ozone concentration is significant, i. e. when the ventilation is ON. This result agrees with the result of the MERMAID campaign  [5] , where the main driver of the IAQ within the room was found to be the ventilation status. When K increases from 3 to 5, the newly defined clusters can be related to the specific known events occurring in the room presented in Fig.
A lower K value will miss some events, and a higher K value will only split classes with physical meaning according to the levels of concentration. K=7 leads to the best compromise between the lowest numbers of cluster and the correct and separate description of every known event. The output results from the partition of dataset 1 into 7 clusters are presented in Table  1 , which summarizes the size (number of data points in the cluster) and centroid coordinates of each cluster, together with their assignment. Cluster 1 groups the majority of the datapoints, with 4480 observations, and is characterized by low levels of every pollutant, as it corresponds to the periods with no ventilation, i. e. no intake of outdoor pollutants. A high ozone level is the dominant parameter that influences cluster 2 (1180 obs.
Clustering of the electrochemical and NDIR sensors measurements (dataset 2) The time series evolution of dataset 2 is presented on Fig. 4. The direct interpretation of the signals from the sensors must be done with caution, because of possible chemical interferences, such as the cross response of NO2 and O3 on electrochemical sensors  [47]  and because the sensors were not calibrated before taking the measurements. This is where unsupervised classification algorithms can really help analyzing the data. The results of the process for each K value between 2 and 10 are displayed on Fig. 5 , illustrating, as for dataset 1, the hierarchical structure induced by the bisecting K-means process. However, the different clusters do not appear in the same order as for dataset 1. K=2 separates the datapoints with high CO2 concentration.
For K=3, the new cluster can be related to the injection of NO2 in the classroom. The accidental release of NO and NO2 is identified in dataset 2 at K=5. The efficiency of the ventilation, impacting the O3 concentrations (oxydants coming from outdoor), is identified by clusters built from K=4 and K=6. At K=6, every known event can be related to a specific cluster, except the distinction between the CO2 injections during the ventilation period or without ventilation, which appears only for the classification into 7 clusters. The clusters obtained for dataset 2 when increasing the value of K up to 10 are difficult to put in regard to the actual events taking place in the room. For instance, cluster 8 cannot be related to any event in the room. Also, cluster 9 is not stable, and partly disappears for K=10.
Still, as the clusters do not appear in the same order when considering the reference dataset or the sensor dataset, it is necessary to explore the analysis with high K values, in order not to miss a specific event. This analysis demonstrates that the bisecting K-means procedure can also be used for sensors, when the activities in the room are known, even when the sensors are not calibrated before the experiment. This comes from the fact that the information does not lie in the absolute value of the measurements, but in the evolution of the intensity of the signals. This holds true also in spite of the poor selectivity, as in the case of the NO2 and O3 sensors. However, the K-means processing of dataset 2 also points to the difficulty of determining the optimal number of clusters, which in the present case could be 7 or 8, depending if we consider the physical meaning of the classes, that is if we use contextual information to supplement the measured data, or 8 if we consider the stability of the clusters, without contextual information.
As discussed when treating previously dataset 1, and considering our goal of comparing the results obtained from the two datasets, we will restrict hereafter our discussion to K=7. The results from the partition of dataset 2 into 7 clusters are presented in Table  2 , summarizing the size and centroid coordinates of each cluster. Cluster 1 (4394 obs. ) is characterized by a background level of the O3-B4 and NO-B4 response, of respectively 21. 2 and 29. 54 mV, a background (uncorrected) CO2 concentration value of 614 ppm, and a low NO2-B4 value of 5. 8 mV. This partition matches well with the period when the ventilation is turned off. Cluster 2 (1246 obs. ) is characterized by a higher response from the NO-B4 sensor (37. 20 mV) and from the NO2-B4 sensor (13. 90 mV). Cluster 3 (1515 obs. ) is characterized by a higher response from the three electrochemical sensors.
Comparison of both clustering results Fig. 6  depicts graphically the clusters obtained from the two datasets. The overlap between the two sets of clusters is summarized in Table  3  (percentage of datapoints from each cluster from the sensors dataset that are assigned to the cluster from the analyzers dataset). There is a general agreement between the clusters from the analyzers and the ones from the sensors. Each cluster pair contains roughly the same number of points (Table  1  and Table  2 ). This 1-to-1 correspondence allows a direct comparison of the two sets of results. For instance, cluster 1 (vent. OFF), cluster 4 (NO+NO2) and cluster 5 (NO2) match very well each other, with only a few mismatched points. These clusters are associated to a well defined event, respectively ventilation OFF, NO+NO2 release, and NO2 injection, that is perfectly singled out by the K-means procedure, leading to an overlap ratio between dataset 2 and dataset 1 higher than 91 % for cluster 1, and of 98% for clusters 4 and 5.
Only about 8% of the datapoints of sensors cluster 1 are associated to the reference cluster 2 (ventilation ON), though no explanation can be advanced. The agreement between the two datasets is slightly poorer for cluster 2 and 3 (vent. ON). The data are split between their respective reference clusters. This might be explained by the relatively low NO2 and O3 concentration amplitudes and the cross sensitivity of the NO-B4, NO2-B4 and O3-B4 sensors  [47]. 393 observations are mismatched and associated to the period without ventilation (cluster 1). Cluster 6 (CO2 vent. OFF) and cluster 7 (CO2 vent. ON) from the sensors dataset are also mainly divided through the two clusters of dataset 1 that describe the high CO2 concentration periods (analyzers clusters 6 and 7), leading to overlaps of 66% at most.
Acknowledgments The authors would like to thank the  French Environment and Energy Management Agency ADEME (Agence de l'Environment et de la Maîtrise de I'Energie)  for their financial support of the  MERMAID  project ( PRIMEQUAL Program ). This work is a contribution to the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-005-01 , and a contribution to the CPER research project CLIMIBIO, with financial support from the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts de France Region  and the  European Funds for Regional Economical Development.
,N i (16) To solve the previous optimization problem, the following algorithm can be used: • Compute the power of all inverter stages, i. e. , obtain P k for the N stages using  (12). • For each sequence w ∈ W evaluate ε 1 , ε 2. ε N and retain ε w,max. • The optimal solution is obtained by: Min w ∈ W ε w,max (17) A MATLAB-based algorithm has been implemented to solve this off-line optimization problem. Possible switching patterns from Table  2  were organized in consecutive order in such a way that the first possibility to obtain each of the 15-th values defines the tested combination. All resulting ε w,max are shown in Figure  10 , where it is possible to identify the minimum error which is near 2. 6%, which corresponds to the power distribution 25. 61%:25. 24%:24. 70%:24. 45% in the inverter.
A. MULTILEVEL INVERTER PROTOTYPE AND EXPERIMENTAL SETUP To validate the proposed method, a 240 VA laboratory prototype was built. The nominal input voltage is 40 VDC and the nominal output voltage is 110 V for a frequency of 60 Hz. The experimental set-up is composed of a programmable power source BK PRECISION XLN6024, a programmable AC load SORENSEN Ametek SLM 300V/4A, an oscilloscope Tektronix TBS1104 with isolated differential voltage probes ADF25, and a Fluke 43B Power Quality Analyzer. The software RT-MLI developed in LabVIEW interacts with the inverter by charging switching patterns using parallel communication, generating analog references for programmable instruments and measuring inverter variables through a DAQ card USB-6002  [22]. The four inverter stages have been implemented using MOSFET IRFZ44 with IRS2004PBF integrated driver circuits.
, 2007)  and TanDEM-X satellites in an approximate 10 m resolution  (Kubanek et al. , 2014). However, these rather rare datasets could hardly be used for year-long monitoring, and were only realized for distinct years and eruptive episodes. The SfM-MVS workflow has also been used to model the summit dome and crater from both optical  (James and Varley, 2012)  and thermal  (Thiele et al. , 2017)  surveys, allowing morphologic studies each time an overflight is realized.
DATA AND METHODOLOGY Volcán de Colima has a continuous seismic monitoring network  (Arámbula-Mendoza et al. , 2018). To obtain an overview of the volcanic activity in the observation period from Jan 2013 to Feb 2016, we employed the records from the seismic station closest to the investigated dome. For this, data from the vertical component of the broadband Guralp CMG-6TD seismic station SOMA, located on the NW flank at 1. 7 km from the summit, were decomposed and normalized employing the discrete wavelet transform (DWT). A 0. 5 Hz high-pass filter was applied to reduce microseismic noise. This type of signal decomposition has been previously used at Volcán de Colima observatory  (Vargas-Bracamontes et al. , 2009)  and, similar to previous seismic studies at Volcán de Colima  (Lamb et al. , 2014) , allows for the identification of dome growth phases as these are associated with characteristic frequency distributions (Figure  2 ).
, 2003)  (Figures  1, 3 ). The resulting TSX footprint captures an area of ∼12 × 10 km around Volcán de Colima, providing a ground resolution of ∼1 m, although distortions  are common due to the nature of the radar acquisition and the steep terrain. The radar images were geocoded based on coregistration with the simulated backscatter from a digital elevation model (DEM) using the GAMMA software  (Werner et al. , 2000). Because the DEM has a major influence on the geometric correction of SAR data, we used a DEM based on a tristereo photogrammetric dataset (acquired 10. 01. 2016) processed in Erdas Imagine. We follow the workflow proposed by  Bagnardi et al. (2016) , i. e. , photogrammetric terrain reconstructed based on three optical high resolution stereo-images. The resulting DEM was resampled at 1 m and allowed accurate geocoding of the TSX data.
High-Resolution Airborne Photogrammetry Flights were conducted by helicopter or plane with flight paths comprised of single or multiple circles around the crater and an oblique viewing geometry (Figure  3 ). Airborne photogrammetric data were acquired at irregular intervals, depending on weather and flight access, though allowing a high spatial resolution. Our photogrammetric survey data consist of 13 flights between 2013 and 2016, each capturing the crater area with digital single-lens reflex handheld cameras (DSLR). The image sizes and resolution varied due to the number of different cameras used  (Canon, Nikon, Sony, Fujifilm)  and are between 1,280 × 960 and 6,000 × 4,000 pixels, while the image to ground distances vary between a few hundred meters to a kilometer. Image input also varied greatly between 61 and 344 photos per survey.