dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank SUNLAB group at the  University of Ottawa ,  K. Schulte  from  NREL  and  M. Rondeau  for fruitful discussions,  D. Troadec  for his help in TEM sample preparation,  G. Beaudin  for 3D illustrations,  E. Paradis  and  S. Ecoffey  for their help,  NSERC  strategic project grant program; and  RQMP and FQRNT  for financial support.
Results & discussion This section is devoted to the validation of the  ∞ ∕  active antiroll bar control by using the TruckSim ®1 simulation software. TruckSim ® delivers accurate, detailed, and efficient methods for simulating the performance of multi-axle commercial vehicles such as 4×2, 6×4 tractors as well as box trucks, buses and trailers. Let us note that it has been used in the literature in  [18]  to assess the effectiveness of the active roll control system of heavy vehicles. To survey the proposed  ∞ ∕  controller for the active anti-roll bar system with a nonlinear vehicle model, we use the co-simulation between Matlab ® / Simulink ® and TruckSim ® software, see Fig. 8. The nonlinear vehicle model is determined from the TruckSim ® software,  integrated as a S-function in Simulink.
Meanwhile, the controller and the actuators are built directly in the Matlab ® /Simulink ® environment. In the sequel, the proposed  ∞ ∕  controller is tested with two different types of vehicles: a fully loaded tour bus and a fully loaded LCF Fig. 7. Effect of the forward velocity on the maximum absolute value of the normalised load transfers at the rear axle. truck using the solid suspension system. The engine is mounted at the rear of the vehicle for the tour bus and at the front for the LCF truck. To evaluate the rollover of vehicles, we consider the tyre force (  ) in the Z direction at the each wheel (rollover occurs when   = 0). Note that Here, the vehicle's trajectories in the cases of passive and active anti-roll bar systems, will follow the target path which fits the driver's wishes.
The physical properties of each image pixel are modeled using the concept of projection domain material decomposition and reconstructed from SPCCT data  14. The concentration of contrast agents at regions-of-interest can then be quantified. Hence, the material decomposition into water, iodine and gadolinium make it possible to quantify the spatial distribution of the contrast agent on a pixel basis. In a more mathematical description, SPCCT therein enables the analysis in a 3-dimensional vector space, the first and second dimensions being the two basis functions independent of the K-edge and the third being provided by K-edge imaging  17, 29. Further details of the reconstruction process can be found in one previous study  10. Overlay images of the SPCCT and contrast material maps were generated using an image processing software (ImageJ, National Institutes of Health, United States)  30.
The data obtained represented the absolute mean concentration of gadolinium and iodine in mg/mL ± standard deviation. Contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR) among the four rabbits in each protocol were calculated as follows: The standard deviation of the water bag attenuation placed on the rabbit was used as a noise reference. = | - | CNR Cluster analysis. The simultaneous acquisition of arterial and portal venous phase enables a pixel-by-pixel analysis. Resulting maps from material decomposition (e. g. iodine and gadolinium) may be processed by cluster analysis or support vector machines. In our case, iodine, gadolinium and water can be understood as a three-dimensional (3D) vector space  26. By plotting the data in 3D space for different ROIs (hepatic artery, portal vein and liver as described above), clusters can be detected which represent a similar tissue type.
Characterization of fresh and spent adsorbents Elemental analyses were performed at the Service d'Analyses Elementaires at University of Lorraine (France). Small Angle X-ray Scattering (SAXS). SAXS measurements were performed using a SAXSess mc 2 instrument (Anton Paar). Samples placed between two Kapton ® sheets were exposed to a CuKα radiation (λ = 0. 1542 nm). Scattered beams were registered by a CCD detector placed at 309 mm away from the sample. The resultant 2D image was integrated by SAXSQuant software (Anton Paar) into one-dimensional variation between the scattering intensities I(q) and the scattering vector q = (4π/λ) sin(θ), where 2θ is the total scattering angle, such that all intensities were normalized according to an attenuated primary beam (q = 0). The data were finally corrected for the background scattering from Kapton ®.
0533 and b = 3. 0). Because the amounts of PEI in some of the prepared materials were noticeably high, porosimetric data reported in Table  1  and Fig. 3  were normalized according to the SiO2 content in each PEI-impregnated materials. Hence, it allows to describe the evolution of textural properties of PEI-impregnated SBA-15 sorbents on a PEI-free basis. Thermogravimetric Analysis (TGA). TG measurements were carried out in order to gain insights both on the thermal stability of the impregnated PEI and the stability of adsorbed iodine species. The mass losses underwent by 10- Technologies equipped with a double monochromator and DRA 900 integrating sphere. DR-UV-Vis spectra were registered between 200 and 800 nm, with a scan rate of 600 nm/min and a resolution of 2 nm, with the parent SBA-15 used as a reference sample.
The incident light intensity was calibrated by measuring the reflected light intensity from a quartz substrate and calculated with the known quartz refractive indices. Later the diffused light from the SCs was collected and divided by the incident intensity to obtain the diffuse reflectance spectra. The measured data are consistent with the commercial one at room temperature. Raman scattering spectroscopy. The infrared Raman measurement (808 nm) was carried out using a XY-Dilor spectrometer associated with a Ti-Sapphire (Ti: Al 2 O 3 ) tunable laser which was pumped by an Argon laser. At this wavelength, the sample is transparent and no heating effect has been observed. We have used a long working distance ×40 objective with a numerical aperture of 0. 4 and selected a laser power under objective of 7 mW.
Acknowledgements Financial support from  Nanyang Technological University  start-up grant  M4080514 ; the  Ministry of Education AcRF Tier  1 grants  RG101/15  and  RG173/16  and Tier 2 grants  MOE2015-T2-2-015  and  MOE2016-T2-1-034 ; the  NTU-A*STAR Silicon Technologies Center of Excellence Program  Grant  11235100003 ; the  US Office of Naval Research  ( ONRGNICOP-N62909-17-1-2155 ) and from the  Singapore National Research Foundation  (Programs  NRF-CRP14-2014-03  and  NRF2018-ITC001-001 ) is gratefully acknowledged. M. B. J. R. and J. H. acknowledge financial support from the  Research Foundation-Flanders  ( ZW15_09-GOH6316 ,  AKUL/15/15-G0H0816N, G. 098319N ),  KU Leuven Research Fund  ( C14/15/053 ), the  Flemish government  through long-term structural funding Methusalem ( CASAS2 ,  Meth/15/04 ) and the  Hercules foundation  ( HER/11/14 ).
Several open source IGA codes are available nowadays, such as PetIGA  [24] , and IGA implementations in commercial simulation software exist such as in Abaqus or LS-Dyna. Let us also quote a related method, proposed in  [79]  and denoted NURBS enhanced FEM (NEFEM), that involves NURBS functions to construct the boundary of the mechanical structure alone while the interior remains discretized using FEA Lagrange basis functions. Despite its performance, and as for any numerical method, IGA is associated with discretization error that needs to be effectively assessed to control the quality of the approximation and drive automated adaptive procedures. This verification step is a crucial engineering topic as the massive use of virtual prototyping, with possibly very complex models, relies on the capacity to warrant the relevance of the numerical solutions usually obtained from limited computational resources.
IGA approximation and discretization error In its original formulation, IGA consists of searching an approximate solution to (3) by means of a Galerkin formulation and a finite dimensional subspace of U spanned by the same set of B-Splines/NURBS basis functions as these describing the geometry of Ω. We thus introduce the functional space U h = Span{ϕ I , 1 ≤ I ≤ n} ∩ U, where ϕ I are defined from multi-variate B-Splines or NURBS functions introduced in Section 2. 2 (e. g. N p 1 ,p 2 ,p 3 A ijk or R p 1 ,p 2 ,p 3 A ijk in 3D ). The IGA formulation then reads: Find u h ∈ U h such that a(u h , v) = l(v) ∀v ∈ U h (9) and leads to a linear system to solve. This formulation shares similarities with FEA as: (i) it is still based on an isoparametric formulation through the geometrical mapping F; (ii) imposing boundary conditions is standard, provided open knot vectors are considered; (iii) the code architecture remains identical.
Construction of an admissible flux field We detail in this section a procedure that enables to recover a relevant admissible flux field qh ∈ S from a post-processing of the approximate IGA flux field q h at hand, and using conventional tools available in commercial software. A similar procedure applies for the recovery of an admissible adjoint flux field qh ∈ S from qh. This procedure essentially follows the same principles as the hybrid-flux (or EET) technique developed in the FEA context  [54, 55, 71, 74]. Nevertheless, as IGA shape functions ϕ I are associated with noninterpolating control points I (instead of physical nodes in FEA), and may be supported by a large number of knot spans (compared to FEA where the support of the shape function ϕ I associated to node I is made of elements connected to this node alone), the EET technique requires specific modifications.
Performance with local mesh refinement We now consider a locally refined mesh. It is constructed from an initial 10×10 mesh, after a first adaptation step from the error distribution shown in Fig. 18 , and it involves hierarchical NURBS. It is obtained using hierarchical refinement tools available in the pack-  age available at https://github. com/canitesc/IGAPack. After applying the CRE approach to this mesh, we represent in Fig. 22  the spatial distribution of the error estimate and local effectivity indices. The global effectivity index is 1. 054. Results indicate that the the CRE strategy still applies and performs well with a locally refined mesh.
Consequently, the proposed work constitutes a practical tool for engineers aiming at controlling and adapting IGA computations efficiently. Forthcoming works will concentrate on the following points: (1) full analysis of the CRE estimate on complex nonlinear models such as damage models used for composite materials, for which IGA was shown to tremendously improve the design and analysis in recent works  [27] ; (2) goal-oriented error estimation using the CRE concept for nonlinear problems solved with IGA; (3) optimization of the constructed admissible fields when considering highly graded meshes; (4) implementation of the CRE error estimate into commercial software; (5) related to the previous point, potential combination of the approach with the facilitating Bézier extraction technology developed in  [11, 77] , in order to directly reuse hybrid-flux procedures implemented for FEA;  (6)  application with local refinement strategies based on recent technologies such as T-splines.
Extractable quantitative imaging biomarkers with potential to support clinical decision-making Quantitative imaging biomarkers that characterise tissue features (e. g. calcium, fat and iron deposition, cellularity, perfusion, hypoxia, diffusion, necrosis, metabolism, lung airspace density, fibrosis) can provide information that characterises a disease state and reflects histopathology. Multiple quantitative features can be incorporated into algorithms for recognising disease and its change over time (both natural course and in response to therapy). This involves an informatics style approach with data built from atlases derived from validated cases. Curation of anatomical databases annotated according to disease presence, phenotype and grade can then be used with the clinical data to build predictive models that act as decision-support tools.
They may be therefore better suited to decision support in terms of treatment selection and risk stratification. Current radiomics research in X-ray mammography  [133]  and cross-sectional imaging (lung, head and neck, prostate, GI tract, brain) has shown promising results  [134] , leading to extrapolation in non-malignant disease. Image quality optimisation and standardisation of data acquisition are mandatory for widespread application. At present, individual research groups derive differing versions of a similar signature and there is a tendency to change the signature from study to study. Since radiomic signatures are typically multi-dimensional data, they are an ideal input for advanced machine learning techniques, such as artificial neural networks, especially when big multicentric datasets are available.
Selecting and translating appropriate imaging biomarkers to support clinical decision-making Automated quantitative assessments rather than scoring systems are easier to incorporate into artificial intelligence systems. For this, threshold values need to be established and a probability function of the likelihood of disease vs. no disease derived from the absolute quantitation (e. g. bone density measurements)  [137]. Alternatively, ratios of values to adjacent healthy tissue can be used to recognise disease. Similarly, for prognostic information, thresholds established from large databases will define action limits for altering management based on the likelihood of a good or poor outcome predicted by imaging data. This will enable the clinical community to move towards using imaging as a "virtual biopsy".
• Inventory of imaging biomarkers accessible through quantitation by inclusion into Integration of imaging biomarkers into trials is a web-based portal would inform the inclusion and clinical trials dependent on what is available routinely to utilisation of imaging biomarkers within trials (The non-imagers in the clinic, rather than exploiting European Imaging Biomarkers Alliance initiative). an imaging technique to its optimal potential. • Certified biomarkers conforming to set standards (Quantitative Imaging Biomarkers Alliance initiative) Validate against pathology or Several major databanks hold imaging and • Large data collection for validation of imaging clinical outcomes to make clinical or pathology data and pathology imaging a "virtual biopsy" • CaBIG (USA) • Curation in imaging biobanks • UK MRC Biobank (UK) • German National Cohort Study (Germany) Select appropriate quality Trials with embedded QA/QC procedures have • Ensure curation and archiving of longitudinal imaging assured quantitative IB indicated good reproducibility of quantitative data with outcomes within trials imaging biomarkers (e.
4. Acknowledgements The authors are indebted to the participants in ESSνSB for the presentation of the material in this article. The  ESSnuSB  project is supported by the  COST Action CA15139  " Combining forces for a novel European facility for neutrino-antineutrino symmetry-violation discovery '' (EuroNuNet). It has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  777419.
Modelling strategy This section presents the three-dimensional finite element model developed to simulate the unfolding of L-and M-shaped specimens. The preprocessing, processing and analysis were performed through the use of scripts written in Python and of Abaqus/Standard finite element program. Each ply was represented as a homogeneous orthotropic medium with one layer of three-dimensional, 8 nodes and fully integrated linear finite elements (C3D8). The width of the specimens was taken equal to W=30 mm, in order to limit the computational cost and in agreement with what is often practiced in literature  [12, 15]. The thickness of the elements was set to 0. 18 mm. In order to take into account the material orientation, a specific coordinate system aligned with the fibres was affected to each element of the plies.
Emerging Low Power Wide Area Networks (LPWAN) represent a real breakthrough for monitoring applications, since they give the possibility to generate and transmit data over dozens of kilometers while consuming few energy. To further increase the autonomy of such wireless systems, the present paper proposes an original methodology to correctly dimension the key elements of an energy autonomous node, namely, the supercapacitor and the battery that mainly give the form factor of the node. Among the LPWAN candidates, LoRa is chosen for real field experiments with a custom wireless platform that proves its energy neutrality over a finite horizon. Different LoRa configurations are explored, leading to adequate dimensioning. As an example, it is shown that, for the same quality of service, the size of the solar panel needed to keep a LoRa node autonomous in the South of France is less than half of the size required in North of France.
To overcome these drawbacks, the present paper proposes a methodology to reduce as much as possible the energy harvesting and storage elements while guarantying the desired quality of service, namely, the data rate. Our contributions are the following: (i) An energy autonomous node architecture with LoRa communication capabilities. (ii) An optimization methodology for the energy harvesting and storage elements of the wireless node. (iii) The exploration of both application requirements and light conditions and the impact on the node dimensioning. The remainder of this paper is organized as follows. Section 2 presents related works on existing long-range oriented standards and energy harvesting. Our wireless node architecture is introduced in Section 3. Section 4 focuses on the LoRa standard and our energy consumption characterization process, followed by a experimental validation in Section 5.
However, the problem of these devices is a lack of methodology for efficiently choosing both harvester and energy storage device according to a target quality of service. Indeed, most of them are designed for a given data and/or sensing rate. However, new LPWAN standards such as LoRa allow a wide range of data and packet rates. The node energy consumption of the node can consequently vary of several orders of magnitude from one configuration to another. Powering LoRa based devices with energy harvesting is, as far as we know, still a research work  [17] [18] [19]. Reference  [17]  presents an energy-efficient multi-sensing platform that exploits energy harvesting, long-range, communication and ultra-low-power short-range wake-up radio to achieve self sustainability in a kilometer range network.
The proposed methodology is quite generic and does not depend on the types of radio transceiver, MCU, and sensors. Thanks to power characterization using microbenchmarks, any technology can be used in this framework. In our experimentation, the LoRa LPWAN will be addressed but other radio standards can be used. All parameters of the proposed model are given in Table  1  for the required input values and in Table  2  for the intermediate results and the outputs. Dimensioning Energy Harvesting Components. Energy Consumption Model of the Platform. In order to dimension energy harvesting components, a duty-cycle model is proposed in which the node switches between a sleep state (the radio is turned off) and an awaken state for transmitting the data according to a periodical schedule. The time interval between two consecutive transmissions is called the wake up interval and is denoted.
A trade-off between energy consumption and the range of the communication must be considered  [22]. This trade-off is controlled by the spreading factor (SF), the bandwidth (B), the coding rate (CR), and the transmission power (  ). The CR parameter corresponds to the additional data overhead ratio incurred by the cyclic error coding to perform forward error detection and correction and takes value in the range between 4/8 and 4/5. The SF takes value in the range between 6 and 12 and BW between 125 kHz and 500 kHz. The bitrate denoted   , for a LoRa communication, is calculated as follows: 𝑅 𝑏 = 𝑆𝐹 ⋅ 𝐵 2 𝑆𝐹 ⋅ 𝐶𝑅. (12) The modification of just one of the four parameters can significantly change the time on air of a LoRa communication and therefore the energy consumption. Because the space defined by these parameters is large, 3 setups corresponding, respectively, to the highest bit-rate setup (SH), the LoRa default setup (SD), and the lowest bit-rate setup (SL) were considered in this work.
In this work, a mechanical constitutive law is proposed for a plastic-bonded explosive (PBX) which is a quasi-brittle energetic aggregate material, submitted to quasistatic loadings. Such material, sensitive to hydrostatic pressure, presents viscoelastic behaviour with damage induced anisotropy and irreversible strains. Previous models developed for this kind of material were mainly devoted for transient dynamic behaviour. The few existing quasi-static models considered the mechanical behaviour either isotropic or only elastoplastic. So, a new viscoelastic Microplane model with the damage induced anisotropy is proposed and implemented in commercial Finite Element software. The viscoelasticity was taken into account using a Maxwell generalized model with Microplane approach. The model consists in implementing the viscoelasticity inside the microplanes defining thus a Maxwell model on every direction.
Validation of the viscoelastic Microplane model The viscoelastic behaviour was taken into account in the Microplane approach and the resulting model was successfully implemented in the commercial finite element software "Abaqus", using Umats and following the internal approach described above. The standard 21 symmetric directions on a hemisphere, proposed by Bazant and Oh  (Bazant & Oh, 1986) , were used to define the directions of microplanes. A 3-dimensional, 8 nodes element with a reduced integration (element C3D8R) was considered. The model is simple and fast to be implemented, since variables are projected on the microplanes and, so, only scalar variables or vectors are managed. However, it requires many state variables to be saved (~900 SDV in this study), which makes the simulation times consuming.
The new tracking system presented herein doesn't emit any AIS signal, it is only a receiver system composed by an omni-directional VHF antenna installed below the aircraft. Assuming a flight level of 15 000 ft, the onboard system allows collecting cooperative signal emitted by vessel more than 30 NM away. ONERA developed a modular and reliable system, called "ExaAIS", which is able to read AIS information from serial port and transferring data on Ethernet network. This software is based on open source library for decoding maritime automatic identification system messages "libAIS", python project.
IV. TARGET SELECTION AND PREDICTION Once a maritime target has been selected (identified by its MMSI), a software has been designed to predict the trajectory that will be followed by the vessel. It is called "ExaTRAJ" and relies on the use of the boat's previous position to interpolate its future localization. The following information is displayed onboard (Fig. 5 ):  All marine cooperative targets on a map.  Current aircraft position.  Selection of a single target of interest.  Plot of predicted trajectories. This software "ExaTRAJ" collects network data provided by "ExaAIS". Moreover, it process information provided by the aircraft inertial navigation system to display all this information on a single screen. After selecting a target of interest, we have to predict the beginning of the ship trajectory, depending on the localization and the velocity of both the vessel and the aircraft.