dataesr/scientific-paragraphs-categorization huggingface dataset:
■ ACKNOWLEDGMENTS We thank The  Netherlands Organization for Scientific Research (NWO; TOP  grant to H. S. O. ), the  European Research Council  ( ERC-2011-AdG-290836  " Chembiosphing " to H. S. O. , and  ERC-2012-AdG-32294  "Glycopoise" to G. J. D. ), the  Biotechnology and Biological Sciences Research Council (BBSRC ;  BB/R001162/1  and  BB/M011151/1  grants to G. J. D. ), the  Natural Sciences and Engineering Research Council of Canada  ( Post-Doctoral Fellowship  to N. G. S. M. ), the  Spanish Ministry Science, Innovation and Universities (MICINN ,  CTQ2017-85496-P  to C. R. ),  Generalitat de Catalunya-AGAUR  ( 2017SGR-1189  to C. R. ), the  Spanish Structures of Excellence Mari ́a de Maeztu  ( MDM-2017-0767  to C. R. ), and the  French National Research Agency  ( Funlock  Project  ANR-13-BIME-0002-01  to J-G.
B. ) for financial support. G. J. D. thanks the  Royal Society  for the  Ken Murray Research Professorship. The authors gratefully acknowledge the computer resources at  MareNostrum  and the technical support provided by  BSC-CNS  ( RES-QCM-2016-3-00017 ). We thank  Diamond Light Source  for access to beamlines I03 and I04 and (proposal mx-13587), which contributed to the results presented here. Proteomics data were collected at the  York Centre of Excellence in Mass Spectrometry , which was created thanks to a major capital investment through  Science City York , supported by  Yorkshire Forward  with funds from the  Northern Way Initiative , and subsequent support from  EPSRC  ( EP/K039660/1 ;  EP/M028127/1 ).
Measurement at the EAR-2 The three C 6 D 6 detectors were placed at 5 cm from the sample and perpendicular to the beam. In addition, three additional detectors were used for monitoring the beam. Two of them measured the intensity of the proton beam. The third one was the SiMon  [8] , an array of four silicon detectors facing a thin enriched lithium fluoride foil, for monitoring the neutron beam. To obtain accurate weighting functions required for the PHWT  [9]  technique, a very detailed description of the experimental set-up has been implemented in the Geant4 toolkit  [10]. The simulated response functions have been validated with experimental data obtained with several calibration sources ( 133 Ba, 137 Cs, 60 Co, 88 Y, AmBe and CmC). The geometry implemented in Geant4 and one of the simulated response functions are shown in Figure  1.
Acknowledgments The  University of Littoral -Côte D'Opale (ULCO) , the  Lebanese University , and the  Agence Universitaire de la Francophonie -Région du Moyen-Orient (AUF)  are gratefully acknowledged for financial supports. The authors would also like to thank the  ARCUS E2D2  project, the  French Ministry of Foreign Affairs , and the  «Région Hauts de France»  for financial support.
Sinter SPS-650 of Sumitomo Metals, Japan. Sintering for all the samples was carried out at 1373 K with a dwell time of 5 min with an applied pressure of 50 MPa at a heating rate of 100 K/min to generate samples of 20 mm diameter. The alloy was characterized by X-ray diffraction (XRD) using X'Pert Pro PanAlytical unit with Cu-Kα radiation. The single peak profile analysis using pseudo-Voigt function was used for crystallite size analysis from XRD patterns. Rietveld refinement was carried out using FULLPROF software. The thermal stability of the phases in the sintered samples were studied using a SETARAM LabSys evo differential thermal analyzer (DTA). The compositions of the alloys were estimated by energy dispersive X-ray spectroscopy (EDS) unit attached to FEI Helios G4 UX Dual Beam scanning electron microscope (SEM).
The transmission electron microscopy (TEM) analysis was carried out using FEI Titan G2 60-300 high resolution TEM. The Seebeck coefficient (S) and electrical resistivity (ρ) were measured by the differential method and four probe method, respectively, using Linseis LSR-3 from room temperature to 873 K using 10 mm × 2 mm × 2 mm samples. Thermal diffusivity measurements were carried out using laser flash technique in a Netzsch LFA-427. The needle shaped APT specimens were annular milled using dual beam FEI Helios G4 UX focused ion beam/scanning electron microscopy (FIB/SEM). Atom probe characterization were performed by CAMECA LEAP 5000 XR using laser mode with a pulse frequency of 250 kHz, a focus laser energy of 30 pJ. These conditions led to individual LEAP datasets with up to 40 million atoms.
Acknowledgements The authors gratefully acknowledge  National Facility for Atom Probe Tomography (NFAPT)  for carrying out APT studies. The major funding came from  Department of Science and Technology (DST)  under  Nano-mission program  ( SR/NM/NS-1041/2016 ). The authors would like to thank  NFAPT  project officer  Mr. Manu Mathai  for his help during FIB sample preparation and APT studies. The authors would like to acknowledge  ARCI CAEM  for allowing the usage of vacuum arc melting facility.
Finally, as a significant part of this work, we implemented all our methods using a software-defined radio (SDR) and performed over-the-air (OTA) testing in the 2. 4-GHz ISM band using standard WiFi and ZigBee frames. Results of OTA tests fall in complete agreement with our simulations indicating the practical applicability of our methods. Our methods apply to all the standards and related radio transmission techniques which are based on OFDM and face narrowband co-channel interference. Additionally, since our work focuses only on receiver side modifications, they can be integrated with the existing infrastructure with minimal modifications.
Contributions Interference mitigation in unmanaged networks is still a challenging problem. In continuation to our previous works on interference mitigation for single-antenna WiFi receivers  [9]  and multi-antenna WiFi receivers  [10] , in this work, we have primarily focused on their applicability in real-time by implementing them using software-defined radio (SDR). Our main contributions in this work are summarized as follows: 1 We propose soft-bit maximal-ratio combiner with LLR scaling (SB-MLSC) for mitigating narrowband interference in a multi-antenna WiFi receiver. SB-MLSC performs equivalently to MLSC; however, it is easy to prototype SB-MLSC in the existing SDR software packages. 2 We implemented our single antenna contribution  [9]  using Ettus Universal Software Radio Peripheral (USRP)  [25]  and a combination of GNU Radio  [26]  and OpenAirInterface  [27]  SDR software packages.
Further, we performed over-the-air (OTA) testing of our methods against standard compliant WiFi (IEEE 802. 11g) frames being interfered by standard compliant ZigBee (IEEE 802. 15. 4) frames 3. 3 We implemented our dual-antenna interference mitigation method for WiFi, i. e. , SB-MLSC using USRP and a combination of GNU Radio and OpenAirInterface followed by OTA testing against standard compliant WiFi and ZigBee frames. Results of the OTA tests fall in close agreement with our simulation results showing the practical applicability of our proposed methods. All our proposed methods 3 We took the binary dump of WiFi and ZigBee frames generated by MATLAB and transmitted them using USRP SDR. The frames were detected by commercial WiFi and ZigBee nodes which established the standard compliance of the WiFi and ZigBee frames generated by MATLAB toolboxes for WiFi are also applicable to wideband OFDM-based systems which face co-channel narrowband interference.
Soft-bit maximal-ratio combiner The idea behind the usage of MRC in MLSC is to achieve diversity gain apart from interference mitigation. Conventional MRC, as discussed in Section 2. 2. 2, is a Symbol Level Diversity Combiner where the bit metrics are generated after the complex samples are combined from different antenna branches. Although, simulation results in  [10]  showed the effectiveness of MLSC; however, a working module of MRC for WiFi is not available in GNU Radio WiFi package  [35]. A somewhat different but simpler way to perform diversity combining, which is more popular in distributed systems, is soft-bit maximal-ratio combining (SBMRC)  [36, 37]. In contrast to the conventional MRC which combines complex samples obtained from the different antenna branches, an SBMRC combines the LLRs from individual antenna branches.
Achieving diversity combining by just adding the LLRs simplifies the way it can be implemented in SDR software. In the simulation section, we see that the performance of SBMRC is equivalent to MRC for all the experimented WiFi MCS under the same channel conditions. The reason being MRC attempts to maximize the SNR of the complex samples obtained from multiple antennas which in turn makes LLRs more strong; in contrast, SBMRC tries to maximize the LLRs directly as explained in  [36]. In the following, we discuss our method to implement MLSC using SBMRC.
Simulations To validate our methods, we perform baseband Monte-Carlo simulations using standard compliant IEEE 802. 11g (WiFi) and IEEE 802. 15. 4 (ZigBee) MATLAB packages available in release 2017b of MATLAB. We simulate the worst case scenario, i. e. , as if there is no CSMA/CA, creating 100% chance of collision. For all the experiments, we iterated until statistical reliability was achieved (in our case, until 500+ frames were erroneous).
Results We first plot the comparison result of MRC and SBMRC in Fig. 15a. For the 10% PER criterion, we observe that both of them essentially perform the same for WiFi MCS 0 and 2 while SBMRC shows a slight gain at higher WiFi MCS, i. e. , 4 and 6. This equivalence in performance between MRC and SBMRC encourages our choice of using SBMRC instead of MRC for achieving diversity gain as the implementation of SBMRC is simpler using SDR software packages. Next, we plot the comparison of MRC, MLSC, and SB-MLSC in Fig. 15b  in the presence of a single interferer. We observe that both MLSC and SB-MLSC outperform MRC for the 10% PER criterion in the presence of interference for all the WiFi MCS we experimented. The reason being that in the presence of interference, MRC does not take any measure to mitigate it; however, both MLSC and SB-MLSC apply LNV-Sc.
Software-defined radio implementation For the practical applicability of our methods and real-time verification of simulation results, we prototyped our methods in a software-defined radio (SDR). For SDR hardware, we used Universal Software Radio Peripheral  [25]  which is one the most popular FPGAbased hardware for wireless prototyping. On the software side, we used a combination of GNU Radio  [26]  and OpenAirInterface (OAI)  [27]. In the following, we discuss our SDR implementations followed by over-the-air (OTA) experiments details and test set-up.
SDR implementation of a single-antenna interference mitigating WiFi receiver For this implementation, first, we developed the softdecision WiFi receiver using a combination of GNU Radio and OpenAirInterface. Both GNU Radio  [35]  and OpenAirInterface 5 contain standard compliant WiFi receivers. WiFi package available in GNU Radio, i. e. , gr-ieee 802. 11g contains hard-decision Viterbi decoder (HDVD) in the WiFi receiver. Hence, first, we changed Fig. 14  Interference detection: a noise level ratio of 6 dB is observed for an interference TXP as low as -100 dBm the GNU Radio WiFi receiver to output LLRs as we have to perform LLR scaling for all our singleantenna interference mitigating methods. In the next step, we scaled the LLRs with their corresponding LNV estimates. Moreover, we integrated soft-decision Viterbi decoder (SDVD) available in OpenAirInterface WiFi receiver to decode the scaled LLRs outputted by GNU Radio.
ACKNOWLEDGMENTS This work was supported by public grants overseen by the  Comisión de Investigaciones Científicas de la Provincia de Buenos-Aires (CICPBA)  through a " Proyecto de Fortalecimiento de Centros " (Reference No. FCCIC16 ) and by the  French National Research Agency (ANR)  through  (i) ANR Bacflow  No. AAPG 2015  and (ii) the " Laboratoire d'Excellence Physics Atom Light Matter" (LabEx PALM) as part of the "Investissements d'Avenir" program  (No. ANR-10-LABX-0039-PALM ). We also acknowledge support by the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , the  Franco-Argentinian International Associated Laboratory in the Physics and Mechanics of Fluids (LIA PMF-FMF) , and the  International Research Project  " Ingéniéries Vertes en Mécanique des Fluides" (IRP-IVMF ).
Its main aim is to reduce production and maintenance, repair and operating (MRO) costs of CC parts in aeronautics by introducing a new family of enhanced thermoset composites that preserve all the advantages of conventional thermosets, while showing new unprecedented features such as Re-processability, Reparability and Recyclability. AIRPOXY is a European Consortium project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769274. Within the project, numerical simulation is becoming a powerful tool which can reduce design time and, moreover, time wasted on trial-error process to achieve the technical requirement and the quality required at the end of the manufacturing process. In AIRPOXY, simulation will address the process feasibility regarding material behaviour and process parameters in order to set up (in one shot) the correct process parameters.
In particular, the environmental studies performed along the project are following the recommendations set in the ILCD Handbook published by the European Commission  [17]  as well as the recommendations set in the 14. 040 ISO standards  [18]. A parametric tool developed in Excel® spreadsheet, coupled to specific LCA software is being elaborated to determine the environmental profile of any scenario analysed. This approach allows obtaining a decision support tool which helps to include environmental criteria during process development and the quantification of the environmental gain expected from the innovative alternatives. This is especially relevant in that case and allows to, as described at the beginning, identify possible improvements to goods and services in the form of lower environmental impact and lower economical cost across all life cycle stages.
5. The coupled system has two pairs of conjugate roots when calibrated by the fixed point theory and H 2 optimization criterion, while the two pairs of conjugate roots coalesce into a double pair of conjugate roots for the maximum damping strategy and the absolute value of real parts of its roots is the largest. The performance indices for three calibration procedures can be read directly as: Λ fpt = 0. 186, Λ h2 = 0. 137, Λ mdc = 0. 321. A numerical simulation is finally performed to investigate the transient vibration response of the EMSD. In this study, the EMSD is supposed to undergo vibration freely, i. e. f (t) = 0, and an initial displacement condition is imposed instead. The transient response of system can be then obtained by solving the dimensionless ordinary differential equations (  5 ) with the ode45 solver built in Matlab.
95 t / ha instead of 1. 25 t / ha) was used. The lifespan of jatropha plantations was assumed to be 20 years. The purchase price of urea and NPK fertilizer is 12 000 XOF/50kg each  (MAAH, 2017). For jatropha harvesting, the model proposed by  (Borman, von Maltitz, Tiwari, & Scholes, 2013)  for the calculation of a harvester seed picking rate (in kg/man. day) was used to determine the labor requirement. Cropping systems, cultivation and harvesting data and costs are listed in Table  5. Fruit harvesting labor cost (XOF/ha) 29300 31655 Pre-processing operations consist of de-husking jatropha fruits and pulping and de-hulling B. aegyptiaca fruits. Due to the difficult access to electricity in rural areas in Burkina Faso, and due to large quantities of feedstock to be collected at the biofuel production unit, it was assumed that pre-processing is manual when it takes place at biomass production sites and motorized when it takes place at the biofuel production unit.
Results and discussion The model was implemented and solved in the optimization software Xpress IVE 8. 3. The experiments were performed on an Intel Core i7-5500U CPU 2. 4 GHz with 8 GB RAM on a 64-bit platform. The solution was obtained using the branch and bound algorithms available in the software solver. The model determines the number of biomass production sites, the optimal surface area to be harvested as well as the optimal cropping systems for biomass cultivation; the location and allocation of biomass production sites; the number, location and allocation of collection points; the quantities of fruit to be harvested and pre-processed; the location of pre-processing; the quantities of fruit or seed to be transported, handled and stored depending on the pre-processing location; total supply costs.
Instrumentation for the liquid phase velocity measurements The vertical and the horizontal component of the liquid phase velocity are measured by means of Laser Doppler Anemometry (LDA) in backscatter mode. The flow is seeded with polyamid seeding particles (diameter 5 µm, density 1050 kg/m 3 ). The LDA system used consists of DopplerPower DPSS (Diode Pumped Solid State) laser and a Dantec burst spectrum analyser (BSA). It has been shown previously that the LDA in backscatter mode measures predominantly the liquid velocity  [29] [30] [31]. Therefore, no post-processing was performed on the data. Measurements of 30 minutes were performed for each measurement point with a data rate of O(100) Hz.
Acknowledgments This work is part of the  Industrial Partnership Programme i36 Dense Bubbly Flows  that is carried out under an agreement between  Akzo Nobel Chemicals International B. V. , DSM Innovation Center B. V. , SABIC Global Technologies B. V. , Shell Global Solutions B. V. , TATA Steel Nederland Technology B. V. and the  Netherlands Organisation for Scientific Research (NWO). Chao Sun  acknowledges the financial support from  Natural Science Foundation of China  under Grant No. 11672156. This work was also supported by  The Netherlands Center for Multiscale Catalytic Energy Conversion (MCEC) , an  NWO Gravitation Programme  funded by the  Ministry of Education, Culture and Science of the government of The Netherlands.
Acknowledgments. We thank  Tomáš Neuman ,  Javier Aizpurua , and  Guillaume Schull  for stimulating discussions. This work was supported by  IDEX Bordeaux  (No. ANR-10-IDEX-03-02 ) and  Euskampus Transnational Common Laboratory QuantumChemPhys. T. F. acknowledges grant  FIS2017-83780-P  from the  Spanish Ministerio de Economía y Competitividad. R. A. acknowledges financial support by the  Agence Nationale de la Recherche  project  CERCa ,  ANR-18-CE30-0006. bunching (g (2) (0) > 1) to antibunching (g (2) (0) < 1) of photon-emission.
Indeed, protein domain organization changes drastically between pro-aerolysin soluble dimers and the heptameric nanopore conformation (Figure  1C ). Intermediates between both states have also been identified by combining classical X-ray crystallography, cryo-EM, molecular dynamics and computational modelling  46. The applications of aerolysin as a channel forming protein that can oligomerize to form a heptameric structure into lipid bilayers are developed since 1990s  [51] [52] [53]. This toxin is synthetized as an inactive precursor, pro-aerolysin 52 kDa, that stabilize as dimers  54  (PDB code: 1PRE, Figure  1C  left). Activation is initiated by C-terminal cleavage of the pro-peptide sequence (25 amino acids) followed by oligomerization of the toxin protein  50. The first low resolution (25 Å)   structural studies of membrane inserted aerolysin where obtained by Parker and colleagues  48.
After these studies, development of a mutant version of aerolysin  55  allowed to further understand the heptameric structure of the channel. This mutant assembled into a hydrophilic oligomer with a mushroom-shaped structure akin to its wild type counterpart. The combination of X-ray crystallography, cryo-EM, molecular dynamics and computational modelling was necessary to achieve the structure of the membrane inserted nanopore  46  (PDB: 5JZT). In this study, several transition structures were identified due to a reorganization of the pre-stem loop between domains 3 and 4 after proteolytic cleavage of the pro-peptide sequence at the lipid membrane proximity that finally contributes to create a transmembrane β-barrel (Figure  1C  middle and right). Some of these intermediate structures have also been deposited in the Protein Data Bank as pre-pore (PDB: 5JZH), post-pore and quasi-pore (PDB: 5JZW) structures.
At the left, structure of monomeric pro-aerolysin (PDB: 1PRE). Structural domains are color coded as follows: domain 1 in grey, domain 2 in orange, domain 3 in green and domain 4 in yellow. The C-terminal pro-peptide is shown in blue and the pre-stem domain, enlarged in the inset, is shown in tan. In the middle, ribbon representation of aerolysin monomer as observed of the model of the prepore and the membrane-inserted (solid) state. During transition, domains 3 and 4 rotate and flatten to a position almost parallel to the membrane plane. During transition, the pre-pore stem loop is extracted and flips around to form the transmembrane β-barrel. At the left, ribbon and space-filling (for two representative monomers) representation of the prepore and the membrane-inserted state, illustrating overall swirling movement undergone by the complex (Reprinted with permission from reference46, Copyright 2013 Springer Nature).
The Python package fluidsim is introduced in this article as an extensible framework for Computational Fluid Mechanics (CFD) solvers. It is developed as a part of Fluid-Dyn project  (Augier et al. 2018) , an effort to promote open-source and open-science collaboration within fluid mechanics community and intended for both educational as well as research purposes. Solvers in fluidsim are scalable, High-Performance Computing (HPC) codes which are powered under the hood by the rich, scientific Python ecosystem and the Application Programming Interfaces (API) provided by fluiddyn and fluidfft packages (Mohanan et al. 2018 ). The present article describes the design aspects of fluidsim, viz. use of Python as the main language; focus on the ease of use, reuse and maintenance of the code without compromising performance.
Introduction Designed as a specialized package of the FluidDyn project for computational fluid mechanics (CFD), fluidsim is a comprehensive solution to address the needs of a fluid mechanics student and researcher alike -by providing scalable high performance solvers, on-the-fly postprocessing, and plotting functionalities under one umbrella. In the open-science paradigm, scientists will be both users and developers of the tools at the same time. An advantage of fluidsim is that, most of the users just have to read and write Python code. fluidsim ensures that all critical modules, classes and functions are well documented -both as inline comments and as standalone documentation, complete with examples and tutorials. For these reasons fluidsim can become a true collaborative code and has the potential to replace some in-house pseudo-spectral codes written in more conventional languages.
Balance between runtime efficiency and cost of development In today's world where clusters are moving from petascale to exascale performance, computing power is aplenty. In such a scenario, it becomes apparent that man-hours are more expensive than computing time. In other words, the cost of development outweighs the cost of computing time. Therefore, developers should be willing to make small sacrifices in efficiency, to improve development time, code maintainability and clarity in general. For the above reasons, majority of fluidsim's code-base, in terms of line of code, is written using pure Python syntax. However, this is done without compromising performance, by making use of libraries such as Numpy, and optimized compilers such as Cython and Pythran. Numpy functions and data types are sufficient for applications such as initialization and postprocessing operations, since these functions are used sparingly.