dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Many thanks to the editors as well as to the formal and informal reviewers for their very helpful guidance, which led to significant improvements in the paper. Included among them are  J. Devlin ,  D. Blodgett ,  T. Ferre ,  C. Langevin , and  C. Rivard. The  Federation University of Australia  acknowledges the support of several contributors in the development and testing of  GWML2 , most notably Andrew MacLeod. Funding Information Funding for CSIRO and Bureau of Meteorology work is provided by  Water Information Research and Development Alliance (WIRADA) , and funding for  Federation University Australia work by the 'Centre for eResearch and Digital Innovation'  and the ' Victorian Broadband Enabled Innovation Program '. Funding for BRGM work is provided by its joint  IT Research Center 'INSIDE' dedicated to innovation in Water Information Systems  with the  French National Agency for Biodiversity (AFB).
Methods The insights presented in this paper derive from two phases of investigation. First, we conducted a workshop at the ACM SIGCHI annual conference  [23]  to explore the first-person perspective, attracting some of the leading experts in the field of soma-based design. Second, during the writing of this paper, we continued the analysis of theoretical concerns, engaged in ongoing dialog about our practice and research methods, discussed design exemplars not presented at the workshop, and conducted an extensive literature review. Overall, our research method can be characterized as a Research through Design (RtD) analysis. Let us describe these activities in some more detail before we turn to the data the two phases generated.
What RtD literature does not always acknowledge is the strong role of the designer/user-the subjective attitude, aesthetic sensitivity, politics, taste, values and bodily experiences-governing design choices and use. In the analysis of our design exemplars, design practices and theory contributions, started during the workshop but later continued in the work behind this paper, the role of the designer/user and their felt experience of the interaction were repeatedly emphasized. This can perhaps be contrasted with the current artificial intelligence trend turning towards data analytics as the solution to our design problems. Data analytics can be used as a way to understand user behavior (as in A/B-testing), or even as a way to create designs automatically  [61]. Though we are not opposed to making use of data analytics, we argue strongly in this paper for a continuation and even a deepening of designer-driven experiential practices and shifting of the perspective to instead seeing data as a material in the design-with and through designers and users.
Data: Design Exemplars, Design Practice and Theoretical Underpinnings Let us now turn to the data generated by the two activities (the workshop and the RtD analysis). We divide our data into: an autobiographical account of five design exemplars, jointly experienced at the workshop; first-person design methods and material encounters soma-based interaction practitioners typically engage with; and some of the theoretical underpinnings of our work (that is, not the generic concepts from philosophy introduced in the background above, but the concepts specifically arising when creating or engaging with soma-based designs).
Embodied Encounters Studio The last example system engaged during the workshop was the Embodied Encounters Studio  [72, 73]. Hummels has used this system to visit well over 80 inspirators around the world, envisioning and exploring together hands-on what potential futures can be. The studio consists of an evocative toolbox with three components: (1) the Embodied Ideation Toolkit: a set of tangible magnetic non-descriptive objects, (2) Hexn, a modular tangible notation system existing of hexagonal transparent discs with icons, and (3) Inspiration Cards, small RFID-enhanced laminated cards showing pictures that relate to content in the database, e. g. , slides and videos that are shown on a stationary iPad, see Figure  6.
Embodied Encounters Studio The last example system engaged during the workshop was the Embodied Encounters Studio  [72, 73]. Hummels has used this system to visit well over 80 inspirators around the world, envisioning and exploring together hands-on what potential futures can be. The studio consists of an evocative toolbox with three components: (1) the Embodied Ideation Toolkit: a set of tangible magnetic nondescriptive objects, (2) Hexn, a modular tangible notation system existing of hexagonal transparent discs with icons, and (3) Inspiration Cards, small RFID-enhanced laminated cards showing pictures that relate to content in the database, e. g. , slides and videos that are shown on a stationary iPad, see Figure  6. The Embodied Encounters Studio was available during the interactivity track at the ACM SIGCHI annual conference  [23].
75 mL DMSO-d 6. 2D NMR HSQC data were acquired on a Bruker Avance-400 NMR spectrometer at 300 K based on the method described by Lu and Ralph  [28] , as modified by Rencoret et al. [29]. For the lignin samples, 40 mg sample was dissolved in DMSO-d 6 and HSQC data acquired as described above. Data was analyzed using Bruker TopSpin 3. 2 software. Volume integrals were performed to estimate C-H correlations associated with acetate using Equation (  2 ). The G2 correlation was used as a reference in which the integral of G2 was made to be equal to 1. %Acetate = ppm ppm (ia + iia + iiia. na) ppm ppm (1 + 2 + 3 + • • • n + ia + iia + • • •. na) × 100% (2) where ia, iia,. na are integrals of correlations associated with acetyl groups, while 1, 2,. n are integrals of all correlations assigned in Table  S1  (Supplementary materials).
Acknowledgments: The authors wish to thank Stibal E. , Menana Z. , and Rudolf, A. , for their technical support in Freiburg, Nancy, and Tharandt respectively. Bentrop, D. , helped in acquisition of the HSQC, Isabelle Z. provided feedback on the article. Rhodia Acetow/Accoya  (Freiburg) and  Prof. Frühwald  of  University of Hamburg ,.
A standard waterproof magnetic stirring plate was placed underneath the heating block (Variomag Multipoint 6, Thermo Scientific, Waltham, United-States). Three peristaltic pumps with stepping motors (FZ10, A2V, Gazeran, France) were connected to each chemostat for automated substrate loading, biomass wasting and degassing (Fig 1B ), thus 18 pumps per module. All peristaltic pumps were calibrated before use (S1 Fig). Peristaltic pumps were connected to a controller module (TMCM 6110, Trinamic, Hamburg, Germany) and were piloted via a free software (TMCL-IDE, Trinamic, Hamburg, Germany). The feed, waste and degassing tubes were connected to the 250 mL reactor vessels through stainless steel three-hose connectors inserted into GL45 caps with aperture. The custom-made cap connector (Garaud, Carcassone, France) is gas tight through a rubber joint between the reactor and the insert under the recommended pressure range of the reactor vessels (maximum of 1.
All pressure sensors were calibrated before use (S2 Fig). The pressure sensor tolerates operation in humid environments. Pressure data were constantly monitored by the same controller and software as for the peristaltic pumps. The interval for pressure data collection was set to 20 s, i. e. , every 20 s, the current pressure in the reactors was recorded using a custom code written in Python 2. 7 (https://www. python. org/). For the experiments described here, degassing began when a pressure of 1. 2 bar was reached. Gas was pumped out of the system until the pressure fell to 1. 05 bar. All the equipment for the construction of one module (six reactors) cost about 7000€ in 2017 and are detailed in the Table  1   added. About half of the budget was used for pumps and sensors with their power supplies.
Data analyses All pressure data and statistics were analyzed in the R software environment, version 3. 3. 1  [11]. Raw pressure data were acquired as absolute pressure readings in the range of 1. 05 to 1. 2 bar. Pressure data were converted into biogas volume in three different signal processing steps: first, pressure drops due to automatic degassing after biogas accumulation (1. 2 to 1. 05 bars) were removed to obtain a curve of cumulated absolute pressure. Secondly, occasional sudden sharp increases or decreases in pressure caused by technical problems (i. e. , gas leakage, liquid sampling) were removed so that they did not contribute to the accumulated signal. The limits for this removal were pressure spikes of +/-10 mbarÁmin -1 for one measurement, e. g. , a 20 s time interval. The corrected data were then converted to normalized biogas volume under standard conditions, i.
Long-term operation of the multiplexed chemostats The LAMACs was designed for a long-term operation of numerous anaerobic digesters at a time. The technical objective of this experiment was to challenge the continuous operation of the LAMACs over a long period, i. e. , more than two months. The LAMACs was tested by incubating three inocula in four replicates, therefore twelve anaerobic digesters over nine weeks. Biogas production rates were reported in Fig  4  as a function of inocula origins and time. Results are presented on a weekly basis. Despite having more highly resolved data available,   ). The biological variability of the four replicated reactors is displayed by the size of the boxes in box plots in  Fig 4. This intrinsic biological variability was smaller than the variation of performances over time due to operating conditions, and less than the differences between inocula sources.
Acknowledgements This work was funded by  Junta de Castilla y León  (Projects  VA293U13 , and  VA081U16 ), and  Spanish Government  ( CICYT MAT2010-20441-C02 (01  and  02 ) and  ENE 2014-56069-C4-4-R ). J L Pura  was granted by the  FPU programme (Spanish Government)  ( FPU14/00916 ).
In the frame of C-BORD project (H2020 program of the EU), a Rapidly relocatable Tagged Neutron Inspection System (RRTNIS) is being developed to non-intrusively detect explosives, chemical threats, and other illicit goods in cargo containers. Material identification is performed through gamma spectroscopy, using twenty NaI detectors and four LaBr3 detectors, to determine the different elements composing the inspected item from their specific gamma signatures induced by fast neutrons. This is performed using an unfolding algorithm to decompose the energy spectrum of a suspect item, selected by Xray radiography and on which the RRTNIS inspection is focused, on a database of pure element gamma signatures. This paper reports on simulated signatures for the NaI and LaBr3 detectors, constructed using the MCNP6 code.
A 5 cm thick lead plate is used to shield all gamma detectors from scattered neutrons and gamma rays coming from the polyethylene shield. All elements of the system (neutron generator, gamma detectors, shields, electronics, etc. ) are placed on the same side of the container in a 270  250  220 cm 3 box, fixed on a lifting platform so as to be able to inspect the cargo container at different heights. The total weight of the system is estimated to be of about 5 tons. The gamma spectrum obtained by summing the spectra of the NaI or LaBr3 detectors results from individual gamma signatures of the interrogated elements, weighted by their chemical proportions in the cargo materials. It can be fitted by a linear combination of these individual signatures, using an unfolding algorithm and a database of pure element gamma signatures (see Fig.
The interrogation of a pure element target by the RRTNIS was modeled with MCNP6  [2]  as presented in Fig. 3. The associated particle neutron generator is assimilated to a point-like 14 MeV neutron source with a 20° conical emission. The floor is modeled by a 100 cm thick layer of concrete and the target consists of a 20 × 50 × 50 cm 3 parallelepiped of the pure element of interest, with a density of 1 g/cm 3. The MCNP6 point detector (F5 tally) is used in the front face of each detector to estimate the incoming photon flux. This last is then processed by MODAR software  [3]  to account for detection efficiency, based on MCNP calculations of the energy deposition in NaI or LaBr3 crystals (F8 pulse height tally), and for time and energy broadening due to the resolution of the detectors taken from  [4]   [5].
An example of this procedure is presented in Fig. 4  for a thick carbon target. The time-of-flight (TOF) spectrum of each detector can then be converted into a distance spectrum using the ~5 cm. ns -1 speed of 14 MeV neutrons, and the spectra of all NaI or LaBr3 detectors can be summed to improve statistics. The gamma spectrum of the region of interest can then be plotted by selecting the corresponding spatial (or TOF) region, as can be seen in Fig. 5  for a carbon target. Note that MODAR can also be used to process experimental data, in which case time selection allows suppressing random coincidences (see further in Fig. 9 ), which are not simulated here. Two databases of gamma signatures have been constructed for the NaI and LaBr3 detectors, respectively. An extract of the NaI database is presented in Fig.
A picture of the experimental setup is presented in Fig. 8. A target of pure element (e. g. magnesium in Fig. 8 ) is placed in front of the tagged neutron beam produced by a TPA17 neutron generator from SODERN, similar to the one that will be used in C-BORD project. Signals from the embedded YAP detector and of the gamma detectors are processed and recorded using a digital acquisition system and raw data postprocessing software, developed in a joint effort by CAEN and UNIPD  [10]. From this initial post-processed data, other software developed by CEA Cadarache is used to generate, for each detector, a correlation between the neutron TOF and the deposited gamma energy in the detector. A TOF (or spatial) selection on the target is then performed, as shown in Fig. 9  for a graphite target, to obtain the experimental gamma signature of the interrogated material.
V. CONCLUSION In view to decompose the NaI and LaBr3 gamma spectra of C-BORD Rapidly Relocatable Tagged Neutron Inspection System (RRTNIS) into a weighted sum of elemental spectra, pure element gamma signatures are needed. Therefore, neutron-induced gamma spectra of elements of interest for cargo container inspection have been simulated with MCNP6 Monte Carlo code and post processed with MODAR software. In addition, as differences have been observed in the past between simulation and experimental spectra, such as missing peaks or significant differences in the continuum, the acquisition of neutron-induced gamma signatures is underway at CEA Cadarache. This database of gamma spectra will be used to identify the interrogated materials during RRTNIS tests, first in the laboratory in JRC Ispra (Italy) and CEA Saclay (France) for the commissioning of the system, in 2017, and then for in-situ demonstrations at DCT Gdansk (Poland) and DCA Rotterdam (The Netherlands), in 2018.
Manuscript received June 30, 2017. This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 653323. This text reflects only the author's views and the Commission is not liable for any use that may be made of the information contained therein. A. Sardet, B. Pérot and C. Carasco are with CEA, DEN, Cadarache, Nuclear Measurement Laboratory, F-13108 Saint-Paul-lez-Durance, France (email: alix. sardet@cea. fr). G. Sannié is with CEA, DRT, LIST, Saclay, F-91191 Gif-Sur-Yvette, France. S. Moretto, G. Nebbia, C. Fontana and F. Pino are with Dipartimento di Fisica e Astronomia e INFN di Padova, via Marzolo, 8, I-35131 Padova, Italy. A. Iovene and C. Tintori are with CAEN S. p. A, Via Vetraia, 11, I-55049 Viareggio, Italy.
Problem (  28 ) is a nonlinear nonconvex problem of real dimension 2N + 1. We resort to an off-the-shelf MATLAB optimizer to solve  (28). In more detail, we resort to fmincon  [23] , and we consider a sequential quadratic programming (SQP) method (see, e. g. ,  [6] ) for nonconvex optimization. The gradient of the objective function is estimated through finite difference. Since the problem is nonconvex, we consider 10 initial conditions chosen randomly in P ×G, and then we select (µ j , z j ), j = 1,. , 10, that minimizes the objective. The numerical results are not particularly sensitive to the choice of G: in all our numerical simulations, we first solve a state estimation problem for ḡ using only the observations on Ω d-1 , and then we set G = {z = x + iy : x n ∈ [x n ± 1 2 max(|x n |, tol)], y n ∈ [ŷ n ± 1 2 max(|ŷ n |, tol)]} , where tol = 1, and ẑ = x+iŷ is the vector of coefficients associated with the estimate of ḡ.
The aim of this study is to show how a numerical database constructed using "uncertainty quantification techniques" can be a useful tool for the analysis of the tsunamigenic potential of a seismic zone. This methodology mainly relies on the construction and validation of some "emulators", or "meta-models", used instead of the original models in order to evaluate and quantify the uncertainty and the sensitivity of the tsunamis height at a given location to the seismic source parameters. The proposed approach was tested by building a numerical database of nearly 50,000 tsunamis scenarios generated by the Azores-Gibraltar Fracture Zone (AGFZ) and potentially impacting the French Atlantic Coast. This seismic area was chosen as test-case because of its complexity and the large uncertainty related to its characterization of tsunamigenic earthquake sources.
The construction (and validation) of meta-models representing the maximum tsunami height at four selected location along the French Atlantic Coast is presented in section 3. These emulators permitted to build (section 3) a numerical database of a wide bunch of the maximum possible tsunamis generated by the AGFZ. This tsunamis database is then analysed in section 4 and a hazard level (with the associated uncertainty) from uncertainty quantification is finally proposed. It must be noted that the aim of this study is to show how a numerical database constructed using "uncertainty quantification techniques" can be a useful tool for the analysis of the tsunamigenic potential of a seismic zone. As a consequence, this paper illustrates a methodology through a case study, which is not an operational assessment of tsunami hazard along the French Atlantic Coast.
, 2016). In this study, we use the Gaussian process meta-model (also called kriging)  (Sacks et   al. , 1989) , which good predictive capacities have been demonstrated in many practical situations (see  (Marrel et al. , 2008)  for example). As a consequence, kriging meta-model will be useful both for sensitivity analysis and for a numerical database construction through uncertainty quantification (as reported in the next section). The classical steps for meta-models construction and validation are reported in  (Faivre et al. , 2013)  or  (Saltelli, 2002; Saltelli et al. , 2008) , and can be shortly summarized as follow (see Table  1 ). The step related to the estimation of the meta-model parameters consists in the mathematical construction of the meta-model and it obviously depends on the chosen meta-model.
The initial design for meta-model fitting For computer experiments, selecting a design database (or space filling design) is a key issue in building an efficient and informative meta-model. Several authors have shown that the space filling design are well suited to meta-model fitting  (Iooss et al. , 2010). The design of the experience is the choice of the input parameters (and, consequently, of the initial simulations) to use in order to build the most accurate meta-model at a minimum computational coast (minimum number of model simulations). The design of the experience can depend on the chosen meta-model  (Kleijnen, 2005). For Gaussian emulators as kriging, the most popular design type is the Latin Hypercube Sampling (LHS)  (McKay et al. , 1979). LHS is a simple technique which offers flexible design size for any number of simulation inputs  (Kleijnen, 2005).
The partitioning of the original data base It is generally assumed that the simulations performed to build the meta-model (the design database) can be partitioned in a training set (data used for the estimation of the meta-model) and a validation set (simulations performed to test the ability of the meta-model to reproduce the model results with other data). According to  (Amari et al. , 1997) , to obtain an optimum unbiased meta-model, nearly 80% of the simulations of the design database must be used for the training set and 20% for the validation set. This ratio is used in our study.
Finally, the cross-validation used in this study is evaluated through the mean of the quantity L k computed for each fold:    K k k L K CV L 1 1 _ (12) It must be noted that when K is equal to the number of simulations of the training set, the cross-validation method corresponds to the leave-one-out technique  (Oliveira, 2008). The methodology employed is described in the DiceEval Rpackage  (Dupuy et al. , 2015)  reference-manual. We considered K = 10 and compute the MSE between meta-model approximation and the k-fold observations for each fold (k times). The mean MSE is an index of the mean differences between meta-model predictions and model results. As a consequence, it is an index of the ability of the chosen meta-model to reproduce the original model. The more this indicator approaches zero, the more the meta-model can be considered accurate.
Performance of the meta-model and residual analysis: validation set This last operation is performed on the validation-set (composed of the 20% of the design database). The objectives are to evaluate the meta-models built on the entire testing set (after cross-validation) outside their construction domain (training set) and to estimate the uncertainty related to the use of the meta-model instead of the original model. The first statistical parameter we chose for evaluating the meta-models performance is the correlation coefficient R² (eq. 13), classically used in statistic: Finally, a particular attention was devoted to the analysis of the distribution of the residuals (the difference between model results and meta-model predictions). Especially, the residual plots are some useful tools in evaluating and comparing meta-models predictions as they can help avoiding inadequate meta-models and help adjusting the meta-model for better results.
The Azores Gibraltar Fracture Zone characteristics In order to assess the tsunami hazard induced by the AGFZ through numerical simulations, the seismic characteristics of the AGFZ must be previously defined. With this aim, a preliminary research was conducted in the framework of the ANR-TANDEM research project  (Antoshchenkova et al. , 2016). The variation ranges of the seismic source input parameters issued from this study are reported in Table  5  (in the column "Design database"). The idea was to explore as largely as possible the potential tsunami earthquakes from AGZF integrating all supposed possible sources of the 1755 Lisbon event as well as earthquakes data coming from the following earthquakes catalogues: http://www. isc. ac. uk; http://earthquake. usgs. gov; http://www. globalcmt.
org; http://www. bo. ingv. it/RCMT/searchRCMT. html. The earthquake magnitude distribution associated with these tsunami scenarios varied in the range of [6. 8; 9. 3]. It must be noted that, considering the random and independent choice of the seismic source input parameters reported in Table  5 , this preliminary database is composed of numerous tsunamis scenarios that are not likely from the geological or seismological point of view. Especially, the authors explored extreme faults depths, reaching 180 km. Finally, the database contains nearly 20,000 tsunami scenarios. The considered AGFZ zone extends from 34°to 40°N and from 18 to 7°W, encompassing to the East the southern part of Portugal down to Morocco, and reaching the oceanic sea-floor west of the Madeira tore rise (as reported in Figure  3 ).