dataesr/scientific-paragraphs-categorization huggingface dataset:
The focus will therefore be on comparing the results of two European system-wide models EU TIMES and NEMESIS (which model the whole energy system and individual sectors) with a sectoral model of each of the three end-use sectors industry (FORE-CAST), transport (ALADIN) and buildings (FORECAST). The models were selected from a set of seven models within the European Horizon 2020 project PARIS REINFORCE based on availability of data output required to perform our analysis. The sector models differ by their sector coverage and modelling approach, as do the two system-wide models: one is an energy-system model and the other is a macro-econometric model. While the comparison of system models is not the focus of this work, our method nonetheless provides some insights on how the decarbonization pathways differ between these two types of system models.
This is driven by a combination of policy (the Energy Efficiency Directive and the Renewable Energy Directive) and cost-competiveness (based on carbon price). In addition, the model includes a database on all EU production sites, considering new investor or manufacturer capacity announcements. Hence, for NZE, only some additional effective implementation in circular-economy-related measures remain as additional levers to reduce energy intensity. Overall, the trends of the three models' NZE scenarios are relatively similar. Looking at the IDA results, energy efficiency contributes -38% to decarbonization in FORECAST NZE. The NEMESIS energy intensity indicators lie slightly below the FORECAST corridor, which suggests optimistic assumptions. This is mirrored in the high contribution of energy and material efficiency as well as circularity to decarbonization in the IDA for NEMESIS (-51%).
Data can be found in the annex Table  A9. trucks almost 100% electric by 2050, under the assumption that infrastructure includes fast chargers or overhead lines. This is a strong regional assumption, on which the system-wide model NEMESIS is comparably more conservative. NEMESIS electrification rate stays substantially lower than in NZE than in WWH, and which suggests that the model does not sufficiently reflect the decarbonization potential of decarbonization. This should be further investigated with regard to the lower technology detail in NEMESIS. The available decarbonization levers depend on the transport mode (i. e. maritime, aviation, road or rail). EU TIMES covers the sectors road, rail, shipping and aviation, but exclude transport outside of the EU. For example, in EU TIMES, electrification is mostly applicable in road transport for passengers (cars, motorbikes and buses), while trucks employ electricity, hydrogen and natural gas to lower emissions.
The sectoral perspective from ALADIN however shows that the transport sector can be decarbonized by itself but this is only achieved with a full electrification of the road sector in the next 30 years requiring strong infrastructure development for fast charging and Fig. 6. Buildings sectoral benchmark indicators (CO 2 intensity and energy consumption per capita compared to 2020, electrification rate and district heat rate). Data can be found in the annex Table  A9. overhead lines. For EU TIMES, takeaways from the sectoral perspective mostly concern the use of energy efficiency as a decarbonization lever, as the model currently assumes no decrease in transport energy use. A third of transport fuels are fossil in 2050 in EU TIMES. The insights from the sectoral perspective for NEMESIS are different.
6 EJ for all models, and thus does not differ much. It can be inferred that energy efficiency measures in the building sector do not increase substantially with higher climate change mitigation ambition for the sector model, and the system models can be considered to be still in the plausible range from this sector perspective. The FORECAST corridor for electrification rate is also very small between WWH and NZE. The IDA results reveal that electrification is only the third largest decarbonization lever in FORECAST, and the Fig. 7. Building sector decomposition 2020 to 2050 (relative values). Data can be found in the annex Table  A10. model relies mostly on renewables (-46%, including mostly biomass and some solar thermal and ambient heat) and district heat (-21%) to decarbonize the building sector.
15. Applying this simple regression rule to the dataset of the AlCuFeCrNi HENAs, the energy of mixing is found to be minimized for Additional Monte Carlo simulations were performed for these specific ternary and quaternary compositions, the results of which are superimposed in Fig. 15(a). The outcome of these simulations agrees with the expectations in both cases, indicating that these specific HENA particles have optimal energies or entropies of mixing with respect to the coarse dataset. The results obtained by considering the pair correlation entropy instead of the thermodynamical mixing entropy, shown in Fig. S3  of the ESI, † are essentially similar. S pc appears here particularly sensitive to the number of chemical elements present in the mixture, the correlation plots showing clear progressions in S pc as the number of elements increases.
Introduction Geophysical integration has been gaining traction in recent years, be it from the joint or cooperative point of 35 views. A number of approaches for the joint modelling have been developed with the goal of exploiting the complementarities between different datasets (see for instance the reviews of  Lelièvre and Farquharson, 2016, and Moorkamp et al. , 2016, and references therein). As summarized in the review of  Ren and Kalscheuer (2019) , "joint inversion of multiple geophysical datasets can significantly reduce uncertainty and improve resolution of the resulting models", be it for the modelling of a single property (e. g. , resistivity for joint controlled-source 40 electromagnetic and magnetotelluric MT data, or density for joint gravity anomaly and gradiometry data), or of multiple properties (e.
g. , the joint inversion of seismic and gravity data to model P-velocity and density). In the second case, joint inversion approaches can be grouped into two main categories based on the hypothesis they rely on. Structural approaches allow to jointly invert datasets with differing sensitivities to the properties of the subsurface through the premise that geology is such that spatial changes in inverted properties should be 45 collocated. Structural constraints can then be used as a way to link two or more datasets jointly inverted for by encouraging structural similarity between the inverted models  (Haber and Oldenburg, 1997; Gallardo and Meju, 2003). Alternatively, petrophysical approaches utilise prior petrophysical information (e. g. , from outcrops, boreholes, or the literature) to enforce certain statistics in the recovered model so that it resembles the petrophysical measurements'  (Lelièvre et al.
, 2012; Sun and Li, 2015; Giraud et al. , 2017; Astic and Oldenburg, 50 2019). Whereas structural and petrophysical approaches are well suited to exploit complementarities between datasets in a quantitative manner, running joint inversion might be, in practice, challenging and requires significantly more computing power than the separate inversions. In this contribution, we present a new multidisciplinary modelling workflow that, instead of the more common simultaneous joint inversions, relies on sequential, cooperative modelling. It follows the same objectives as the 55 two categories of joint inversion mentioned above in that structural information is passed from one domain to the other and it uses petrophysical information to link domains. The development of the sequential inversion scheme we present was motivated by a similar idea as  Lines et al.
(1988)  who states that "the inversion for a particular data set provides the input or initial model estimate for the inversion of a second data set". A further motivation was to design a workflow capable of integrating the inversion of two or more datasets quantitatively using 60 standalone modelling engines that run independently. The general concept behind the methodology we present is summarised in Figure  1. In this paper, the workflow is applied to the sequential inversion of magnetotelluric (MT) followed by magnetic data, taking into account the importance of robustly constraining the thickness of the regolith in hard rock imaging and mineral exploration. This is motivated by the relative paucity of works considering cooperative workflows to 65 integrate MT and magnetic data together with the recent surge in interest for the characterisation of the depth-tobasement interface in mineral exploration, despite these two geophysical methods being part of the geoscientists' toolkit for depth-to-basement imaging.
Historically, MT has often been integrated with other electromagnetic methods or with seismic data, and with gravity to a lower extent (see review of the topic of  Moorkamp, 2017). It is, however, seldom modelled jointly with magnetic data unless a third dataset is considered (e. g. ,  Oliver-Ocaño 70 et al. , 2019 ; Zhang et al. , 2020; Gallardo et al. , 2012; Le Pape et al. , 2017). We surmise that this is because (1) the interest for integrating MT with other disciplines arose primarily in oil and gas and geothermal studies and https://doi. org/10. 5194/se-2021-124 Preprint. Discussion started: 3 November 2021 c Author(s) 2021. CC BY 4. 0 License. relied on structural similarity constraints for reservoir or (sub)salt imaging, (2) of the difference in terms of spatial coverage between the two methods elsewhere, (3) the differences in terms of sensitivity to exploration targets and (4) the difficulty to robustly correlate electrical conductivity and magnetic susceptibility.
MT inversion for interface probability 105 The MT method is a natural source electromagnetic method. Simultaneous measurements of the fluctuations of the magnetic and electric fields are recorded at the Earth's surface under the assumption of a source plane wave. The relationship between the input magnetic field H and the induced electrical field E, which depends on the distribution of the electrical conductivities in the subsurface, is described by the impedance tensor Z, as follows: 𝐄 = 𝐙 • 𝐇. (1) Resistivity models derived from MT data are found by forward modelling and inversion of the impedance tensor 110 Z, generally using gradient-based deterministic methods (see, e. g. ,  Rodi et al. , 2012). Deterministic approaches provide a single solution which minimizes the objective function considered during the inversion, but limited information of the uncertainty around this model can be derived.
Survey setup The structural geological model was derived from foliations and contact points using the Geomodeller® software  (Calcagno et al. , 2008; Guillen et al. , 2008; Lajaunie et al. , 1997). It is constituted of a sedimentary syncline abutting a faulted contact with a folded basement. The model's complexity was increased with the addition of a fault and an ultramafic intrusion. Details about the original 3D geological model are provided in  Pakyuz-Charrier 235 et al. (2018b). Here, we increased the maximum depth of the model to 3150 m and added padding in both horizontal directions. Figure  2a  shows the non-padded 2D section extracted from the reference 3D geological model. We assigned magnetic susceptibility in the model considering non-magnetic sedimentary rocks in the basin units (lithologies 3, 5 and 6 in Table  1 ) and literature values (see  Lampinen et al.
MT data The synthetic MT data is computed using the complete 3D resistivity model derived from the 3D geological 280 model, to simulate the influence of 3D effects. The core of the electrical conductivity model used the same discretization as the magnetic susceptibility model. Relationships between geological units and electrical resistivities follow Table  1. A padding zone was added in both horizontal directions and vertically, in order to satisfy the boundary conditions required by the forward solver. The final mesh has 160 x 160 x 62 cells in the 𝑥, 𝑦 and 𝑧 directions, respectively. The ModEM 3D forward modelling code  (Egbert and Kelbert, 2012; Kelbert et 285 al. , 2014)   evenly spaced 1. 016 km on a 16 × 16 grid (see inset in Figure  2 ). The frequency range spans from 10 KHz to 0. 01 Hz, with 6 frequencies per decades, for a total of 37 frequencies; 5% magnitude Gaussian white noise was added to the synthetic data before running the 1D inversions.
1D Probabilistic inversion of MT data and derivation of cover-basement interface probabilities We perform the 1D MT inversions of each MT sounding independently, using a 1D trans-dimensional Markov 295 chain Monte Carlo sampler  (Seillé and Visser, 2020). The presence of 2D and 3D effects was compensated for using larger uncertainties, calculated using the workflow introduced in Seillé and Visser (2020) and summarized in Sect. 2. 1. All the 1D trans-dimensional Bayesian inversions ran using 60 Markov chains with 10 6 iterations each. For each chain, a burn-in period of 750,000 samples (75% of the total) was applied to ensure convergence, after which we 300 recorded 100 models equidistantly spaced. The model ensembles are then constituted by 6000 models for each MT site. An example of the posterior distribution for three MT sites is shown in Figure  4.
Field case study We propose an application example illustrating potential utilisations of the proposed sequential inversion workflow. Using observations made in the synthetic case, our aim here is to integrate MT with magnetic data 385 inversion using the case relying on MT-derived ADMM bound constraints with homogenous starting model and smoothing constraints. We note that, to the best of our knowledge, this is the first time that the ADMM algorithm is used to constrain magnetic data inversion. We used existing results of the depth to basement derived using MT within a probabilistic workflow  (Seillé et al. , 2021)  in the Cloncurry area in Queensland (see Figure  9 ). These results are used to constrain the magnetic 390 inversion.
Discussion We have presented a workflow for sequential joint modelling of geophysical data, and applied it to synthetic and field measurements. In this study we used constraints in the form of interface probabilities derived from a probabilistic workflow driven by MT data, but it is general in nature and is not limited to a particular geological 575 or geophysical modelling method to generate the inputs. This has allowed us to report the utilisation of the ADMM algorithm to constrain magnetic data inversion for the first time This workflow presents several advantages. It is computationally inexpensive by use of standalone inversions. The inversion of the MT dataset used to derive the constraints was performed only once. Then, a series of constrained magnetic inversions was run to test different geophysical and petrophysical hypotheses.
The 1D MT modelling and interpolation schemes present excellent scalability. The Tomofast-x engine  (Giraud et al. , 2021b; Ogarko et al. , 2021b)  is implemented using 3D grids. It presents good scalability and it offers the possibility to reduce the size of the computation domain to save memory when calculating the sensitivity matrix in the same fashion as  Čuma et al. (2012) , and  Čuma and Zhdanov (2014) , for large-scale potential field 615 data modelling. On this basis, the workflow presented here might be applicable to large datasets using, for discover tectonic discontinuities and terrane interfaces. Current investigations on Tomofast-x comprise the application of wavelet compression operators to accelerate the inversion in the same way as  Li and Oldenburg (2003)  and  Martin et al.
However, this would 640 make the modelling process more demanding combined with limitations based on cases where determining the causitive relationships between petrophysics supporting joint approaches poses a challenge. The workflow we propose here presents a few advantages over a joint inversion scheme, in the sense that it does not require both datasets to be inverted simultaneously under a defined set of petrophysical and/or structural constraints. The time required to run a joint inversion being limited by the running time of the more computationally expensive 645 technique, it can limit the range of tests to be performed. In this study, we could run rapidly many 2D constrained magnetic inversions, even if the 1D probabilistic inversions of the MT data (and posterior fusion) required significant longer running time compared to the 2D constrained magnetic inversion.
This point would particularly https://doi. org/10. 5194/se-2021-124 Preprint. Discussion started: 3 November 2021 c Author(s) 2021. CC BY 4. 0 License. be relevant in the case of large 3D datasets. This approach may represent a step in the modelling workflow which that is useful to explore, understand and refine structural and petrophysical relationships between different 650 physical parameters before undertaking more demanding joint inversions. In the field application case presented here, the probabilistic depth to basement map to derive constraint assumed lateral continuity of the depth to basement estimates at a large scale, not accounting for small-scale lateral variations. Thus, uncertainty for depth to basement may be underestimated at some locations, in particular in between MT sites as shallow depths.
The model offers insights on complex phenomena which are out of reach in experiments. This includes the variability in space and time of the fields of stress and effective friction along the fault, the progressive thickening of the damaged region of rock around the interface, and the build-up of a granular layer of gouge accommodating shear. We present in detail several typical sliding events, we illustrate the fault heterogeneity, and we analyse quantitatively the damage rate in the numerical samples. Some limitations of the model are pointed out, as well as ideas of future improvements, and several research directions are proposed in order to further explore the large numerical dataset produced by these simulations.
Both of these thicknesses increase with the confining stress. Quantitative estimates are broadly consistent with values infered from field and lab studies. The simulations depicted in this paper produced a large amount of data, of which only a small proportion was described above. These data include (i) the detailed motion and damage of each grain composing the gouge layer, (ii) the detailed history of the kinetic and strain energies of the continuous regions of the sample, (iii) the high-frequency (in space and time) record of the displacement and velocity fields of the surfaces bounding the discrete region of the sample, and (iv) the same type of fields recordings for the full stress tensor. Future works will therefore be to exploit this dataset in order to improve our understanding of the causes and consequences of the stress and strength heterogeneities we pointed out.
Objective: From patient and phantom studies, we aimed to highlight an original implementation process and share a two-years experience clinical feedback on xSPECT (xS), xSPECT Bone (xB) and Broadquant quantification (Siemens) for 99m Tc-bone and 177 Lu-NET (neuroendocrine tumors) imaging. Methods: Firstly, we checked the relevance of implemented protocols and Broadquant module on the basis of literature and with a homogeneous phantom study respectively. Then, we described xS and xB behaviours with reconstruction parameters (10i-0mm to 40i-20mm) and optimized the protocols through a blinded survey (7 physicians). Finally, the preferred 99m Tc-bone reconstruction was assessed through an IEC NEMA phantom including liquid bone spheres. Conventional SNR, CNR, spatial resolution, Q. %error, and recovery curves; and innovative NPS, TTF and detectability score d' were performed (ImQuest software).
Background Single-photon metabolic imaging has constantly evolved, from analog planar imaging to digital single photon emission computed tomography (SPECT) coupled with X-ray computed tomography (CT)  [1] [2] [3] [4] [5] [6] [7] [8]. The advent of SPECT-CT increased diagnostic performances but also opened new fields of study such as quantification  [9] [10] [11]  and internal dosimetry, with numerous publications to date, whether preclinical  [12, 13] , diagnostic or theranostic  [14] [15] [16] [17] [18]. The evolutions have not only concerned the hardware but also the software with the emergence of new reconstruction algorithms including Flash3D (F3D), xSPECT (xS) and xSPECT Bone (xB), and the quantification module Broadquant (Siemens, Erlangen, Germany)  [19]. F3D involves the conventional ordered subset expectation maximization (OSEM)-based algorithm with more recent depth dependent 3D resolution recovery using the gaussian point-spread function (PSF).
Relevance of installed xS and xB reconstructions, and Broadquant quantification module The first step was to check that the installed new tools were consistent with the constructor white paper and the literature. We also assessed quantitative F3D (handmade quantification), xS and xB (Broadquant) in a large range of activity: the 6800 mL cylindrical phantom was filled with a homogeneous 99m Tc solution; 5 acquisitions were performed with the optimized protocol at t = 0, 3. 2, 7. 4, 9. 6 and 22. 5 h leading to 706, 487, 302, 235 and 53 MBq respectively. On Syngo®, we performed a 2000 cm 3 spherical VOI in the center of the phantom. For xS and xB, activity concentration and SUV were directly displayed. For F3D, activity concentrations and SUV were computed from the displayed counts and the Equation (  2 ) and (3).
For a deeper comparison, innovative metrics NPS (radial profile of the 2D Fourier transform of a homogeneous image), TTF (averaged edge spread function of a clinical task) and d' detectability score were computed using ImQuest software (V7. 1): -From 125 mm × 125 mm ROIs in 30 axial slices in the background, normalized NPS curves (nNPS) allowed to compare noise texture [Fig. 7a ]. Peak frequency (f peak ) and average frequency (f av ) were computed. -TTF was estmated placing a circular ROI around the 37 mm sphere on the single slice corresponding to the mid-plane of the spheres (inner circle of the ROI at the FWHM of the sphere signal) [Fig. 7b ]; task based spatial resolution was computed as 1/frequency(TTF 10% ). -d' of each sphere was computed from the given NPS and TTF, a task function describing the sphere (model, diameter, profile exponent, image matrix and voxel size), the sphere to background contrast, and a 2D non-prewhitening (NPW) matched filter observer detectability model  [30].
In this particular case, we showed the need to optimize xS and xB in terms of acquisition and reconstruction parameters, and pointed out Broadquant computed activity concentration was decay corrected at the time of administration. If this does not impact SUV accuracy on Syngo®, this must be taken into account while performing several time points acquisitions in the context of time activity curve measurement to compute organ or tumor dosimetry  [34] , or if the SUV is computed on another software from the decay corrected activity concentration. These different results, shared with the constructor, highlighted the added value of medical physicists in the collaborative clinical validation process of industrial technology  [35, 36]. Steffie M. B. Peters et al. [37]  showed Broadquant and xSPECT Quant had equivalent performances.