dataesr/scientific-paragraphs-categorization huggingface dataset:
PENTAGON Data Model The PENTAGON data model is made up of different types of information: the topology of the different networks with associated parameters and the configuration parameters of the optimization problem (constraints and objective function). The information is managed by the PENTAGON-model. All the parameters, even those of the nonlinear model of Smart Operation, are part of the PENTAGON data model. None are managed in an external way by a module. Each of the considered test cases is fully represented by a database set and switching from one test case to another is thus automatic. The configuration of the model for each test case is provided in an Excel template, which is then loaded into the database. All parameters of the optimization problem (bounds, limits, prices, objective function type, constraints type, …) are included in that template.
Security Requirements PENTAGON project deals with sensitive data (i. e. information of energy consumption, etc. ) and therefore user authentication will be employed to only allow authorised access. All requests to any service will need the client to be authenticated, and will require users to enter their username and password. Communication security will be further enhanced by using the widely used secure communication HTTPS protocol. It consists of communication over HTTP within a connection encrypted by TLS (Transport Layer Security) or SSL (Secure Socket Layer). It is worth mentioning that the approach described here applied only to the communication between PENTAGON components. However, communication between third-party's services/components (e. g. , DEMS, public weather services, etc.
The heating network is simulated using the equation-based object-oriented language Modelica along with the simulation platform Dymola. The dynamic heating network model is based on detailed physical representation of the system by gathering component models that were previously developed and validated. The components models are taken from a CEA in-house Modelica library named DistrictHeating  [19]. At the production plant level, the model enables the control of supply temperature and differential pressure, and takes into account heat propagation delays, heat losses, tube thermal inertia, and pressure losses. At the consumer level, the buildings are not explicitly modelled, but represented by their substation, which is the unit in which the energy is distributed from a high level to a low level.
Conclusions This paper has introduced the PENTAGON platform for intelligent management of multi-vector energy networks. The platform consists of a collaborative network of modules coordinated through a shared, semantic understanding of the given energy networks. Vitally, the platform allows a paradigm shift from reactive, rule-based control to predictive, anticipatory control and management to attain energy and emissions reductions and maximise human comfort. This is achieved through a combination of real-time data retrieval and organisation through the smart connector, prediction of pivotal variables through leveraging artificial intelligence, deployment of advanced optimisation techniques and adjustment to real-time grid constraints though simulation all underpinned by a central, coordinating ICT infrastructure.
Coupled numerical finite element model The simulation of ultrasonic thermography experiments requires a coupled thermo-mechanic model consisting of three components in order to simulate the behaviour of an ultrasonically excited contact where heat is generated at the contact interface: a crack model, a solid mechanics unit and a thermal unit. The crack model takes into account the behaviour of the normal and tangential parameters at the contact interface. The solid mechanics unit is needed to solve the elastic wave equations for the ultrasonic wave passing through the specimen and the thermal unit is required to define the contact interface as a heat source and to solve the thermal diffusion equation. In this work, the commercially available software package COMSOL Multiphysics®, which uses finite elements to solve the differential equations, is used.
More particularly, the above described MMD is used to solve the contact problem, through a direct link between the FEM-model and an external procedure implemented in Matlab®. The Structural Mechanics module of COMSOL®  [9]  is used to solve the elastic wave propagation problem and the Heat Transfer module  [10]  is used to solve the heat diffusion equation. This approach is an extension of the implementation described in Delrue et al. [3] , where the mechanical contact problem was considered without the account for thermal phenomena.
Conclusion The development of realistic thermo-mechanical models for elastic wave propagation in structures with internal defects is important for the field of thermographic non-destructive testing and evaluation as it allows internal defects not only to be detected, but also to be localised and characterised by comparing numerical and experimental data. In the present paper, a 2D thermo-mechanical finite element model, implemented in the commercially available software package Comsol Multiphysics®, has been proposed in which the particular mechanical and thermal behaviour of vibrating contacts is described using the Method of Memory Diagrams. A particular example of an ultrasonic shear wave propagating in an aluminium sample with internal contact defect has been considered. It was shown that during different contact states (full sliding and partial slip), friction-induced heat is generated at the defect interface.
KAT-7 observations and reduction The KAT-7 observations were conducted between late 2014 and early 2015, and three different phase calibrators were observed for each galaxy. The observing times are summarised in Table  2 , and the correlator mode used provided a total bandwidth of 12. 5 MHz over 4096 channels, which corresponds to a velocity resolution of about 0. 65 km s -1. The observation technique consists in alternating between the target and a phase calibrator about every 20 min to calibrate the phase stability, and observe the flux calibrator two or three times per session. Given the large primary beam of the telescope (∼ 1 • ), a single pointing was sufficient to individually cover the galaxy candidates. The complete data reduction was performed with the miriad package  (Sault et al.
So the RFI inspection had to be manually done to remove eventual artifacts from the data, including the effects of Galactic Hi on low velocity sources. The observing sessions were individually reduced and for each session a second order polynomial fit over linefree channels was used to perform continuum subtraction in the uv plane using the miriad's task uvlin. The emission from the galaxies typically spans less than 100 channels, providing a large number of line-free channels for the baseline fits. We then proceeded to correct the velocity coordinates of the continuum-subtracted uv data with the cvel task in aips  (Greisen 2003). This is because KAT-7 does not use Doppler tracking and, as mentioned before, the miriad package does not contain the telescope's parameters. The resulting datasets for a source's individual sessions were then combined and a datacube was produced with a natural weighting.
GBT observations and reduction The GBT telescope was used as an alternate instrument to observe the rest of the sample galaxies after KAT-7 became unavailable. Out of the 25 remaining galaxies, a total of 16 observable from the GBT were selected for mapping. 3. 9 • × 3. 9 • 2 • × 2 • ∼ 2 • × 2 • RMS noise (mJy beam -1 ) 7. 0 10 1. 7 N Hi (1σ × 10 18 cm - 2  , over 16. 5 km s -1 ) a 3. 2 0. 59 3. 8 Table  3. Summary of the KAT-7, GBT and MeerKAT image cubes. The channel width listed for the MeerKAT data cube is the width obtained after Hanning-smoothing. Note: a the N Hi limit is assuming that any emission completely fills the beam. sions 2 and for each session, an area of 2 • × 2 • around each galaxy was observed for 10 hours, requiring a total observing time of 160 hours for the whole sample.
The reduction was done with the same script as in  Pingel et al. (2018). The data were then flagged to remove recurrent RFIs at ∼ 1416 MHz. The flagged data were then smoothed to a 6. 4 km s -1 resolution using a boxcar kernel, and exported into aips via IdlToSdfits 4  where they were combined by source, before being imaged into data cubes. For most sources, the baseline subtraction did not succeed to completely remove the strong continuum sources in the field. For these, an additional continuum subtraction was performed using a fourthorder polynomial with the aips task xbasl. The typical noise achieved per galaxy candidate is σ ∼ 20 mK over 6. 4 km s -1 , equivalent to 2. 3 × 10 17 cm -2 assuming optically thin emission. This corresponds to a flux sensitivity of σ ∼ 10 mJy/beam, or a 5σ Hi mass detection limit of M Hi = 4.
MeerKAT commissioning observations of NGC 3621 As of April 8th 2017, date of NGC 3621's observations, only half of the 32 then completed MeerKAT's antennas were available since the ROACH2 correlator then used was limited to a total of 32 inputs (see  Camilo et al. 2018  Overall, after flagging, the total observing time of the target is 253 mins, that of the flux calibrators is 73 mins, while the phase calibrator received 110 mins. The data were reduced with miriad following the same procedure as the KAT-7 data. In general, the MeerKAT data cannot be reduced with miriad because of the w-projection algorithm not being available in the package. However, the w-projection is only necessary when the observations are affected by the non-coplanar baselines effect, which happens when the Fresnel number, defined as N F = D 2 /Bλ (where D is the antenna diameter, B the maximum baseline and λ the observing wavelength), is N F < 1  (Cornwell et al.
4 ± 0. 7 Jy km s -1 in the GBT data, comparable to 121. 6 Jy km s -1 from the HIPASS data. Moreover, the Parkes flux of UGCA 320, as reported in  Pisano et al. (2011) , is 107. 3 ± 0. 3 Jy km s -1 , about ∼ 12% lower than the value derived in the present work. The total Hi intensity maps of the GBT data are presented in Appendix A.
Search for Hi clouds The MHONGOOSE galaxies were selected to exclude strongly interacting galaxies, and dense group and cluster environments, to minimise the gas contribution of companion galaxies. This way, the contribution of the cold gas accretion should be more apparent. We searched, in the whole observed sample, for Hi clouds associated with the galaxies that may be signs of gas accretion. However, none of the candidates present such structures down to a 3σ level (for any emission that fills the beam) of ∼ 2. 2 × 10 18 cm -2 over a 20 km s -1 line width, with a beam size of 9. 1. However, the particular case of suspected cloud near the galaxy UGCA 250 caught our attention; a cloud-like structure was seen but could not be confirmed due to artefacts in which it was embedded. The structure was located south-west of the galaxy, in a region of the data that was affected by RFIs and where the noise was higher.
This value is consistent with the value of 939. 0 ± 1. 5 km s -1 derived from the global profile of the galaxy using the miriad task mbspect, where the error is taken to be the difference between the line centres at 20% and 50% respectively. Because of the low inclination of the galaxy, the variation in both the inclination and the position angle is high in certain regions, especially in the inner 2 and beyond 11 (Fig. 8 ). We then use the average values of φ = 239 ± 8 • and i = 29 ± 3 • over the region 2 to 11 to derive the rotation curve of the galaxy. The resulting curve is given in the right panel of Fig. 8  where, as in  Carignan et al. (2013) , the error on v rot is a combination of the dispersion σ(v) in each ring and half the difference between the velocities of the approaching and the receding sides: ∆v rot = σ 2 (v) + 1 2 (v rec -v app ) 2 (3) Because the obtained rotation curve is greatly affected by beam smearing -especially in the inner regions of the galaxy -we used the automatic 3d Barolo package (Di Teodoro & Fraternali 2015) to re-derive the curve from the 3-dimensional data cube, instead of the 2-dimensional velocity field as is done with rotcur.
SUMMARY & DISCUSSIONS The test observations of the MHONGOOSE galaxies allowed to identify the best calibrators for each of the five galaxies observed. Three different flux calibrators  (PKS 1934-638, 0407-658 and 3C138)  were used in total, two of which were used per individual galaxy. All the flux calibrators performed well overall, with the flux being quite constant. However, due to the low observing time that the sessions with 3C138 have received, we cannot fully assess the quality of the calibrator. As for the phase calibrators, we successfully identified the best of three candidate sources for each galaxy. The most extreme case of source quality variation was observed between the phase calibrators 0039-445 and 0201-440, where the quality difference of the respective data cubes of the target galaxy was very significant.
ACKNOWLEDGMENTS The  KAT-7  and MeerKAT telescopes are operated by the  South African Radio Astronomy Observatory (SARAO) , which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Technology (DST). We thank the  West Virginia University Research Office  for its support of the operations of the GBT. We also thank  Tom Jarrett  who provided the WISE photometric data of the galaxies NGC 7424 and NGC 3621 used in this work, and  Danielle Lucero  who helped with the KAT-7 observations. The work of CC is based upon research supported by the  South African Research Chairs Initiative (SARChI)  of the  Department of Science and Technology (DST) ,  SARAO  and  NRF. The research of AS and MK has been supported by  SARChI  fellowship. DJP,  NMP , and  Amy Sardone  acknowledge partial support by  NSF CA-REER  grant  AST-1149491.
The upwind scheme is used on a 400 × 400 Cartesian grid with a CFL equal to Numerical simulations performed in this section are in good agreement with the properties verified by the scheme. The discretization proposed in this paper has been implemented on a Computational Fluid Dynamics software called P 2 REMICS  [17]. It will be used on a complex model with reactive flows to simulate the flame front propagation in the explosion phenomenon, for nuclear safety issues. Work on this model and its related numerical simulations is underway. By taking y = x -∇φ(x, t) |∇φ(x, t)| h we see that the previous upper-bound is reached. Therefore , ∂ t φ(x, t) + |∇φ(x, t)| + o(1) ≤ 0, and passing to the limit when h → 0 leads to the desired result.
Manual scoring is observer-dependent and can be arduous when multiple flies are involved  [23]. Schneider J, et al  [27]  proposed a rule-based automated alternative to classify a number of well-defined interactions based on distance, duration and approach angle. However, the approach described in their study is not generic and cannot be applied to classify other behaviors. Also, the natural behavior of a fly is difficult to describe by a set of rules. An interactive machine learning based approach (JAABA) was recently proposed by Kabras et al  [28] , in which a user iteratively labels specific behaviors, and a machine learning algorithm uses these as training examples to predict future occurrences of the same behavior. However, the iterative process of creating the training dataset might be tedious for long videos.
Thus, our approach differs from the above approach both in the classification strategy and in way that the training dataset is extracted. Schneider et al  [20]  were the first to study social interactions of Drosophila at the group level by generating unweighted dynamic networks. Based on binary social network parameters, they concluded that social isolation had no effect on the networks. On the other hand, Simon et al  [5]  used a static parameter called the social space index, denoting the distance between resting non-interacting flies, to conclude that 7 days of isolation enlarges the social space between young (3-4 days old) flies. Our aim in this work is to extend these initial studies on social isolation, by investigating the effect of isolation on the social structure of flies at group-level over a long period of time.
Detection of touch interactions between flies After video acquisition and tracking, a supervised machine learning-based method as described by Nath et al  [32]  was used to detect the social interactions between male flies. Social interactions were defined as close physical contacts between two or more flies. In this paper, only "head to-tail touch" events (see Methods for a detailed definition) were considered (Fig 1E, S7 Video). A "touch" was defined as a head-to tail touch event in a single frame. A "touch interaction" is obtained when a touch lasts for at least 15 consecutive frames (~0. 5s). A gap of at least 15 frames between two consecutive interactions was required to separate the two interactions. A training and test dataset was generated by a human expert, annotating frame-by-frame touch events in the video.
Data acquisition in the large group walking arena Flyworld We constructed Flyworld (Peira bvba, Turnhout, Belgium) to record multiple flies in a walking arena  [29]  under different illumination conditions, with a high-speed infrared camera (Fig  1A ). The 3D models of the arena and key supporting materials (S1 File) are developed using SolidWorks 2012 which can be read and measured using a free version software, eDrawing. The IR LED panel underneath the arena was made by Peira. The main parts including arena and camera cost approximately 3000 euros. On the day of recording, flies were taken out of vials and put into the fly chamber of Flyworld using cold anesthetization 2-4 hours after the start of light cycle. After a one-minute recovery period, the locomotion activities of the flies were recorded for 60 minutes in complete darkness.
Flytracker a reliable tracker for multiple walking flies The Flytracker is developed mainly using MATLAB (version 2015b, tested on both Mac OS Sierra and Windows 7 systems, now available for download and development at github (https://github. com/flytracker/flytracker). Flytracker's reliability in tracking multiple flies is based on one particular assumption that shapes the algorithm: the number of flies remains constant throughout the video. Since the field of view of the camera in Flyworld covers the whole walking arena, this assumption is automatically met. As in Ctrax  [23] , tracking is achieved in two steps: fly detection and identity assignment. During fly detection, each frame is segmented to separate flies from the background. Each fly is detected as a set of white connected components on a black background.
For this, a number of features was calculated from the fly tracks. In order to be able to distinct a single frame head-to-tail touch event from other touch events, these features need to describe the specific action dynamics from the fly. For this, 22 temporal features are defined, i. e. the Euclidean distances between the interactor and interacted flies over 11 time frames centered around the time frame under consideration, and the relative displacements of the interactor fly over the same time window. A training data set was built by manually annotating touch events. The training data set contained 1000 frames of touch events and an equal number of no-touch events. Testing data sets were built by manually annotating 3 different fly-pairs exhibiting the two events. Each testing data set contains 1000 events, to be classified into one of the two behavioral classes.
Acknowledgments The authors would like to thank  Dr. Brijnesh Johannes Jain ,  Technische Universita ¨t Berlin, Berlin  for providing the Java implementation of computing the average network. The authors would also like to thank  Patrick Callaerts Lab, VIB of Katholieke Universiteit Leuven, Leuven, Belgium  for providing the precision balance. B. A. H is an Allen Distinguished Investigator and an  Einstein Visiting Fellow.
This work was supported by  VIB  (http:// www. vib. be/en/Pages/default. aspx, no award number) to BAH, FWO (http://www. fwo. be/en/, G. 0543FP7 People :  Marie-Curie Actions  (https:// ec. europa. eu/research/fp7/index_en. cfm,  289941 ) to BAH and HN, EMBO (http://www. embo. org, ALTF 1591-2014 ) to GAL,  FP7 People :  Marie-Curie Marie Skłodowska Curie Fellowship   656824 ) to GAL, ICM (https://icm-institute. org/en/,  BBT2 ) to BAH, and  PGAFF  (https://www. alleninstitute. org/what-we-do/frontiers-group/, 12202 ) to BAH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses. We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independant datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketting.
Material and methods ASICS is an R package available on Bioconductor 2  (Gentleman et al. (2004) ) that combines all the steps of the analysis of 1 H NMR spectra (library of pure spectra management, preprocessing, quantification, post-quantification statistical analyses). The package also includes functions to directly perform statistical analyses on buckets and diagnosis tools to assess the quality of the quantification. All functionalities of the ASICS package are summarized in Figure  1  and described in the next sections.
3/14 Baseline correction Most of 1 H NMR spectra have baseline distortions coming from various sources like instrument instability. These distortions can induce an increase or a decrease in peak intensities and skew the results of quantification. Wang et al. (2013)  developed a method to estimate the baseline for a spectrum by classifying each point as a signal or a noise point and by using a linear interpolation between noise points to construct the baseline. Then, the baseline is subtracted from its spectrum. Peak alignment Due to pH or temperature variations between the acquisition of multiple spectra, peak positions of the same metabolite can change between spectra. It is better to align all peaks before analyses, especially if a binning algorithm is used. Vu et al. (2011)  developed an algorithm, implemented in the R package speaq, to carry out this alignment.
A two step procedure is used instead (Figure  2 ): 1. First, reference library spectra are aligned with the complex mixture spectrum of interest by maximizing the Fast Fourier Transform cross-correlation  (Wong et al. (2005) ). The algorithm that finds the best shift (with a maximum allowed shift equal to M ) is taken from the R package speaq  (Vu et al. (2011) ). 2. Second, every peak of each library spectrum taken individually is aligned by minimizing residuals of the linear regression between the spectrum of interest and the reference library spectrum. To perform local distortions of the chemical shift grid for each peak, ASICS uses the function φ(x) = ax(1 -x) + x, where x ∈ [0, 1], corresponds to the rescaled initial grid, φ(x) ∈ [0, 1] to the newly scaled grid and a ∈ - m w 2 -w 1 0.