dataesr/scientific-paragraphs-categorization huggingface dataset:
Application to the Alemania Sequence 4 in the Cabral Corral area The 3D reservoir grid has a horizontal regular resolution of 100 m and a vertical one of 10 cm. The top and bottom surfaces were constructed with the Gocad/Skua ® geomodelling software by Emerson. They are based upon the stratigraphic surface markers  [Deschamps et al. , 2020]  of the sequence boundaries. The top surface is the flattened reference surface and corresponds to the Sequence Boundary SB5 surface of  Deschamps et al. [2020]. The bottom surface was constructed based on the Sequence Boundary SB4 surface of  Deschamps et al. [2020]  (Figure  1 ). The grid has the same vertical resolution (0. 1 m) than the sections which ensures no loss of information during the numerical conversion. There is no horizontal spatial trend but a clear vertical organization visible in all wells.
, 2000, Durieux and Brown, 2007]. This study of an outcrop is not completely relevant in terms of porosity values for subsurface similar reservoir but interesting as a methodological example performed with a coherent dataset. Furthermore, it would be interesting to study the diagenesis imprints, timing and processes to better understand its spatial distribution which might be controlled by primary sedimentary and microbialite heterogeneity and initial porosity and permeability. Structural or mechanical constraints may also have an important impact on the final porosity and hydraulic conductivity, through fracturing to be specific. It may be relevant to consider it and requires the definition of relationship between fracturing and the petrophysical properties. In the modelled area, some fractures and micro-fractures, mostly localized, have been observed at outcrop and in thin sections.
The distribution of the significant structural elements could be added as an extra-step in the modelling workflow. These fractures would be modelled as DFN (Discrete Fracture Network) based on specific descriptive structural elements such as maps, fracture density, persistence. The simulation of the DFN may also be further controlled by larger structures such as faults or facies as shown by  Lapponi et al. [2011]. It requires an important specific dataset gathered for this purpose and the identification of existing relationships between larger structures or facies which is not the case in this study.
Acknowledgments Part of this work was supported by companies in the frame of the  Joint Industrial Project COMPAS  led by  IFP Energies nouvelles :  BHP Billiton ,  BP ,  Chevron ,  Cobalt Inc. ,  Ecopetrol ,  ENI ,  ExxonMobil ,  GalpEnergia ,  Total ,  MaerskOil ,  Petrobras ,  Repsol ,  YPF  S. A. We also really appreciated the support of  José Salfity  during several field excursions. Developments of the used prototype embedding the bi-PGS method were supported by  Repsol. We would like also to thank the anonymous reviewer whose constructive comments helped in improving this paper.
Space discretization In the sequel, we use cylindrical coordinates (r, θ, z) as indicated in Figure  1. We assume that the domain Ω is axisymmetric which allows us to use a Fourier decomposition in the azimuthal direction θ as it is done in the SFEMaNS code used to perform all the numerical simulations reported in this paper. The Fourier components of the electric potential can then be approximated on a meridian section Ω 2D of the domain Ω using finite elements. We refer to  [14] [15] [16]  for more information on the SFEMaNS code It leads us to introduce a regular family of non-overlapping quadratic triangle meshes that we denote by (E h ) h > 0 and the meridian finite element space: X 2D h = v ∈ L 2 Ω 2D ; v |Ω 2D i ∈ C 0 Ω 2D i , for i = 1, 2; v(T K ) |K ∈ P 2 , ∀ K ∈ E h , where h represents the mesh size (i.
Numerical investigations In this section, we run two sets of tests using manufactured solutions to ensure the good behavior of the SFEMaNS code when solving the above weak formulation. First, a series of tests with ϕ jump = 0 is performed, then, another series of tests is done with a given jump in electric potential around the interface Σ. All tests are performed on the domain Ω = {(r, θ, z); 0 ≤ r ≤ R, 0 ≤ θ < 2π, H 0 ≤ z ≤ H 1 } with the interface Σ = {(r, θ, z) ∈ Ω; z = H } where we set R = 0. 5, H 0 = 0, H 1 = 1, H = 0. 5. The manufactured solutions used in this section are determined using the method of separation of variables. They involve the first kind Bessel functions denoted by J m and the n th root of J ′ m denoted by κ ′ mn. In the following, our numerical tests involve the roots κ ′ 01 , κ ′ 11 and κ ′ 21 whose approximate values are reported below for completeness: κ ′ 01 = 3.
Characterization The morphology of synthesized NPs was characterized by transmission electron microscopy (TEM, JEOL 1011). A droplet of NPs dispersion (in water or in hexane) was deposited on a carbon-coated copper grid and air-dried at room temperature. Lengths, diameters and aspect ratios were measured using ImageJ software (n ≥200) and the data obtained were analysed as the mean ± standard deviation (normal law). Scanning electronic microscopy (SEM) was performed on a SEM-FEG Hitachi SU-70. Dry samples of NRs were analysed by X-ray diffraction on a Rigaku Ultima IV. X-ray absorption near edge spectroscopy (XANES) at Fe Kedge was performed at Synchrotron SOLEIL on the ROCK beamline in 1 mm diameter glass capillary. The total iron concentrations of all samples were measured by atomic absorption spectroscopy (AAS, PinAAcle 500, Perkin Elmer).
Acknowledgements We acknowledge  SOLEIL  for provision of synchrotron radiation facilities and we would like to thank  Juliette Sirieix-Plénet  and  Anne-Laure Rollet  for their assistance in XANES measurements and  Valérie Briois  for assistance in using beamline ROCK. We are thankful to  David Montero  for his assistance in SEM imaging, to  David Hrabovsky  at the  MPBT (Physical Measurements at Low Temperatures ) platform of  Sorbonne Université  for his assistance with magnetism measurements, to  Aude Michel-Tourgis  and  Delphine Talbot  for their help during total iron concentration measurements by AAS.
Note that we separate sets HUdata/sdata , DMUdata , Ydata and DAdata , which correspond to the specific FJSP settings described in Table  2. We include these benchmarks for reference since they are commonly adopted in previous studies on the FJSP. Currently, best known results for these problem instances are summarized in Section 1 of the supplementary materials. According to the solution quality on the well-known benchmark problem instances, studies on the FJSP to minimize makespan can be classified into the two categories: Papers using most benchmark instances discussed in Section 3. 2 and papers using a small selected set of benchmark instances discussed in Section 3. 3. Note that many papers have focused on or are considering the makespan criterion while taking account other criteria, additional constraints or other characteristics, as discussed in Sections 4, 5 and 6.
Papers using most benchmark instances Let us first consider studies where the proposed solution methods are thoroughly tested on the benchmark instances, resulting in new best-known upper bounds. Following the integrated approach of Dauzère-Pérès & Paulli (1997) ; Mastrolilli & Gambardella (20 0 0) introduce a Tabu Search (TS) with two neighborhood functions. An extensive computational study is conducted on the BRdata, BCdata, DPdata, and HUdata benchmark instances, and competitive baseline results are established. Pezzella et al. (2008)  propose a Genetic Algorithm (GA), and experiments on the BRdata, BCdata, DPdata , and HUdata benchmark instances show that the GA obtains comparable results with respect to other best known algorithms. A two double-level parallel metaheuristic algorithm based on a new method for neighborhood determination is developed in  Bozejko et al.
(2010). Previous best known solutions were determined for the BRdata, BCdata, and HUdata benchmark instances with the aid of graphics processing units. Hmida et al. (2010)  develop a variant of the climbing discrepancy search approach with various neighborhood structures, whose computational results on the BRdata, BCdata, DPdata , and HUdata benchmark instances show that the proposed approach outperforms previous algorithms on some types of benchmark instances and is competitive on the others. Zhang et al. (2011)  propose a GA, whose computational results on the BCdata, BRdata , and DPdata benchmark instances show the same level or even better results in terms of computational times and quality compared to other GAs. Yuan & Xu (2013a)  develop a hybrid differential evolution algorithm combined with a local search algorithm.
Extensive computational experiments on the KCdata, BRdata, BCdata , and HUdata benchmark instances are convincing, and new best known solutions for some benchmark instances are determined. González et al. (2015)  develop a scatter search algorithm which determines new upper bounds for a number of instances in the BRdata, BCdata, DPdata , and HUdata benchmark instances. A GA hybridised with TS and heuristic seeding is presented in  Palacios et al. (2015)  , leading to some new best known solutions for the BCdata, BRdata , and DPdata benchmark instances. Li & Gao (2016)  present an algorithm hybridizing a GA and a TS algorithm, which provides some new best known solutions for the BRdata, BCdata, DPdata , and HUdata benchmark instances. A multistart multi-level evolutionary local search is proposed in  Kemmoé-Tchomté et al.
Metaheuristics When machine flexibility is very high, i. e. , when operations can be performed on many machines, finding very good or even optimal solutions is easier. The benchmark instances of  Hurink et al. (1994)  discussed in Section 3 are a well-known example, where the most flexible instances HUdata/vdata are easier to solve compared to instances HUdata/edata and HUdata/rdata. This can be explained by the fact that the total number of solutions increases with machine flexibility, but also the number of very good solutions which are thus easier to reach. To solve the most challenging FJSP instances, advanced metaheuristics are the most appropriate approaches. As mentioned above, the number of solutions to explore is huge, and major contributions in metaheuristics are focusing on: • Deriving important structural properties to guide the search process in relevant and promising solution areas; • Integrating metaheuristic components that are extended and improved specifically for the FJSP.
EDS analysis confirmed that Zr and Ti substitute each other in the D0 22 and D0 23 phases, whereas Si substitutes in Al sites. Acceptance of Si inside the D0 23 phase was not expected according to FTlite (FactSage) and TCAL7 (Thermo-Calc) databases. Additionally, Si was found to enhance the formation of (Al,Si) 3 (Zr,Ti)-D0 22 intermetallics with high Zr-content, contrary to FactSage 7. 3 predictions. TEM results showed intermetallic/FCC crystal coherency for samples S2 and S6, implying that these intermetallics acted as nucleation sites for the Al-phase due to their small lattice mismatch. Furthermore, Si site occupancy was calculated for both (Al,Si) 3 Ti-D0 22 and (Al,Si) 3 Zr-D0 23 phases via DFT, showing that sites 2b and 4e are the most favorable for Si occupation, respectively. Finally, a thermodynamic model is derived to describe Si substitution upon solidification.
Silicon is one of such important elements. It is present in various amounts in the majority of aluminium alloys in the form of either an impurity or as an alloying element for both wrought and cast alloys (e. g. 4xxx alloys  [36] , 6xxx alloys  [37] , A356 alloy  [38] , etc. ). Therefore, understanding the effect of Si on the formation of Al 3 Ti and Al 3 Zr primary phases becomes a focal point in alloy design. Engineers and scientists could fine-tune its presence to either promote or suppress the formation of key primary intermetallics in aluminium alloys. A reliable thermodynamic description of Si within the crystal structure of Al 3 Zr-D0 23 and Al 3 Ti-D0 22 solid solutions is therefore required for designing and fine-tuning the chemistry of the next-generation of aluminium alloys. CALPHAD-like thermodynamic databases such as the FTlite light alloy database available in the FactSage  [39] , currently account for Si substitution on Al sublattice sites for the thermodynamic description of the Al 3 Ti-D0 22 solid solution via the Compound Energy Formalism (CEF)  [40].
Sample S4 has the lowest content of Ti; conversely, sample S6 has the largest concentration of Ti. Moreover, samples S4 and S6 were intentionally doped with ∼1 wt% of Si (Table  1 ). Fig. 2  shows the equilibrium phase diagrams for samples S2 (a), S4 (b) and S6 (c), computed with the FTlite database of FactSage 7. 3  [39]  and by neglecting Fe, Ni, Ga and V impurities due to their relatively small amount within the alloys. Al 3 Zr-D0 23 is the primary precipitate for all compositions according to thermodynamic calculations. Al 3 Zr-D0 23 is the equilibrium phase in the molten metal for the imposed equilibration temperature in samples S2 and S4 (dark square in Figs. 2a  and 2b , respectively). For sample S6, an equilibrium with both Al 3 Zr-D0  23  and Al 3 Ti-D0 22 phases is predicted (dark square in Fig.
Numerical methods DFT simulations were carried out by means of the VASP software  [47, 48]  based on the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation function  [49]. Pseudopotentials with the projector augmented wave (PAW) method were employed with a 600 eV cut-off energy. Lattice parameter relaxation was performed for both D0 22 and D0 23 structures. The atomic positions were also relaxed in the case of the D0 23 structure. The relaxation continues until the Hellman-Feynman forces are lower than 1 meV/Å. For k-point sampling of the Brillouin zone, a dense grid of 11 × 11 × 5 and 13 × 13 × 3 was used for the D0 22 and D0 23 structures, respectively. The total energy of different ordered configurations simulating the substitution of Al by Si was calculated.
EDS analysis is presented in further sections to identify the chemistry of the phases. Thermochemical calculations, performed with the FTlite database of FactSage  [39] , show that the amounts of intermetallics to be obtained are: 2. 66 g of Al 3 Zr-D0 23 /kg of alloy-S2; 3. 11 g of Al 3 Zr-D0 23 /kg of alloy-S4 and (5. 86 g of Al 3 Zr-D0 23 + 1. 19 g of Al 3 Ti-D0 22 )/kg of alloy-S6. Therefore, the methodology employed in this work is useful to precipitate and develop big intermetallic particles whose mass fraction is low compared to the 𝛼-Al phase (Al-Si-Ti-Zr diluted alloys). i. e. , considering reasonable amounts of alloying elements comparable to industrial alloys. In this way, the formation of undesired phases can be investigated. This is an issue frequently observed in the industry when the produced batch's chemistry differs from the specification due to differences in the raw materials.
Implications on the solidification process via sheil -cooling simulations of aluminium-rich Al-Si-Ti-Zr alloys As presented in the experimental part of this work, Si can be found in both D0 22 /D0 23 intermetallics. Therefore, a proper modulation of Si partitioning is required to correctly predict the phase assemblage in the Al-Si-Zr-Ti system. In this matter, a thermodynamic model was constructed using the end-members from Tables E2 (D0 22 phase) and E3 (D0 23 phase) employing the CALPHAD approach. The DFT models for the D0 22 and D0 22 solid solutions were coupled with the stoichiometric compounds available in the FTLite database of FactSage  [39]. The FCC, liquid and all available solutions from this dataset were also considered. Due to the non-equilibrium compositions observed in the intermetallic phases of this study, the Scheil-cooling approach was applied to tailor the new D0 22 /D0 23 thermodynamic models.
Acknowledgments This research was supported by funds from the  Natural Sciences and Engineering Research Council of Canada (NSERC) , [funding reference number  ALLRP 560998-2020 ],  Constellium ,  Alcoa ,  Hydro Aluminum, Rio Tinto Aluminum, Elysis  and the  CRITM  (funding reference number  13-2019-056 ). This research was enabled in part by support provided by  Calcul-Québec  (calculquebec. ca) and the  Digital Research Alliance of Canada  (alliancecan. ca). We would like to express our gratitude to  Dr. Daniel Chartrand  at the  Department of Chemistry, University of Montreal , for his assistance with XRD analysis and Rietveld refinement. We also thank  Stéphan Claude  and  Christine Nardin  (at  Constellium ) for their support in alloy preparation and characterization.
Funding: This work was nancially supported by the  Centre National de la Recherche Scienti que (CNRS) , the  STAE-RTRA foundation (Toulouse, France) , the latter under the  RTRA-STAE/2014/P/VIMA/12  project grant, the  National Science and Engineering Research Council  in the context of the Alliance project grant, and  Prima-Québec. Financial support from  CNRS ,  Université de Montréal  and  Université de Toulouse III -Paul Sabatier  through their contributions to the  International Research Network on Controlled Multifunctional Nanomaterials (IRN NMC)  is also acknowledged.
S INCE Cormack and Hounsfield's Nobel prize-winning breakthrough, X-ray CT is extensively used in medical applications and produces a huge number of gray-scale CT images. However, these images are often insufficient to distinguish crucial differences between biological tissues and contrast agents. From the perspective of physics, the Xray spectrum from a medical device is polychromatic, and interactions between X-rays and biological tissues depend on the X-ray energy, which suggests the feasibility to obtain spectral, multi-energy, or true-color, CT images. Over the past decade, spectral CT has been rapidly developed as a new generation of CT technology. DECT and PCCT are the two main forms of spectral CT. DECT is a method of acquiring two projection datasets at different energy levels. PCCT, on the other hand, uses detectors that measure individual photons and their energy, promising significantly better performance with major improvements in energy resolution, spatial resolution and dose efficiency  [1] ,  [2].
Although the 90degree phase shift between the two scans creates a slight temporal offset, the two X-ray sources can select independent X-ray energy spectra to optimize the spectral separation for material differentiation in the data and/or image domains. A dual-layer detector or a combination of two detector layers of scintillation material is also a good solution for DECT  [20] -  [23]. In this approach, low-and high-energy datasets are collected simultaneously by the two detector layers with perfect spatial alignment and excellent synchronicity. This advantage simplifies direct data-domain material decomposition. Fast kVp-switching DECT is yet another technology that uses a highly specialized X-ray generator that can rapidly switch the tube voltage between low-and high-kVp settings during data acquisition.
The latent variable z represents the patient which connects the different channels. The penalty function plays the role of a discriminator by promoting images originating from the generative model and by penalizing images that deviate from it, in a similar fashion to the relaxed synthesis model  (11). Most of this subsection will address DL, i. e. , F k (z) = D k z for some dictionary matrix D k , as it is the most prevalent learned penalty used in synergistic multichannel image reconstruction. Convolutional dictionary learning (CDL) will also be discussed in a short paragraph. Finally, we will discuss recent work that uses deep NN models. In this subsection µ tr = {µ tr k } denotes a random spectral CT image whose joint distribution corresponds to the empirical distribution derived from a training dataset of L spectral CT images µ tr, [1] ,.
) are replaced by transformations such as convolution layers with parameters that are trained end-to-end. Other works rapidly followed for regularized inverse problems in general and image reconstruction in particular. Learned proximal operators, which consist of replacing the update  (9)  with a trainable convolutional neural network (CNN)  [109] ,  [110]. In a similar fashion, Chun et al. , proposed BCD-Net  [111]  and its accelerated version Momentum-Net  [112]  which consists in unrolling a variable-splitting algorithm and replace the image regularization step with a CNN. Adler et al. [113]  proposed a trainable unrolled version of the primaldual (Chambolle-Pock) algorithm  [53]. A synergistic reconstruction algorithm such as given by Eq. (  14 ) and Eq. (  15 ) may also be unrolled in a trainable deep multi-branch architecture by merging the mappings L k θ q,k at each layer q into a single multichannel mapping L Θq : R J K → R J K that depends on the entire binned projection dataset y = {y k } and on some parameter Θ q.
(  40 ). These techniques are similar in essence to those described in Section III-D1 except that dictionary learning uses decomposed images for spatially regularizing the decomposed images. NNs may be used instead to improve the linear model in Eq. (  40 )  [126]. As in many other fields of research on image processing, deep CNNs have demonstrated their ability to solve image-based decomposition with a more satisfactory solution than the one produced by a pixel-by-pixel approach. Several deep learning architectures, previously designed to solve other image processing tasks, have been deployed for image-based decomposition. Most works are based on a supervised learning approach where a dataset of manually segmented basis material images are available: fully convolutional network  [127] , U-Net  [128] -  [133] , Butterfly-Net  [134] , visual geometry group  [132] ,  [135] , Incept-net  [136] ,  [137] , generative adversarial network (GAN)  [138] , Dense-net  [139].
Eguizabal et al. [168]  adapted the projection-based unrolling algorithm of  [157]  to one-step reconstruction. The same group has used machine learning to improve the physical model in Eq. (  38 ) by modeling charge sharing  [169]. Another approach is to insert a backprojection step into the network architecture, i. e. the adjoint of the line integral operator in Eq. (38) , to account for this knowledge in the network architecture  [170] ,  [171]. Finally, machine learning may be used at each iteration for denoising the images, e. g. with a dictionary approach  [172]. A self-supervised approach named Noise2Noise prior  [173] , which does not require manually segmented ground truth materials images, has been applied to one-step decomposition using a training dataset consisting of sinograms paired with their noisy counterpart obtained by sinogram splitting.
[243]. Additionally, the effect of direct denoising on VMIs has been investigated. In a study of Lee et al. [263]  the post-processed VMI using ClariCT. AI (a FDA-approved vendor-agnostic imaging denoising software) is compared with original standard VMI in the assessment of hypoenhancing hepatic metastasis. The results suggest denoising leads to better image quality and lesion detectability. A similar conclusion was achieved by Seo et al. [264]  with the same post-denoising method for the evaluation of hypervascular liver lesions. 3) Contrast Agent Dose Reduction: Iodine-enhanced CT is essential for diagnosing various diseases. However, iodinebased contrast media can cause significant side effects, including allergic reactions in certain patients, and dose-dependent kidney injury and thyroid dysfunction.