dataesr/scientific-paragraphs-categorization huggingface dataset:
, see 25 , 26   ( Miller, 2019 )  or  ( Berezutskaya et al. , 2022 ) ). Akin to the ERP-core for EEG (see 27  ), the BrainMap (see 28  ) and Brain Activity Atlas (see 29  ) for fMRI, we further expect the emergence of online meta-databases that will permit examining iEEG results from studies that investigated a certain topic and/or a specific part of the brain. The ERP-core is a free online resource for human ERP research building upon the standardization of ERP paradigms and analysis protocols across studies  ( Kappenman et al. , 2021 ). The BrainMap is a database of published functional and structural neuroimaging experiments  ( Vanasse et al. , 2018 )  and the Brain Activity atlas (BAA) is a battery of functional localizers in a large cohort of healthy adults using task-evoked fMRI. Similarly, a recent initiative, the Human Intracranial EEG Platform, is an open-source platform designed for large scale and optimized collection, storage, curation, sharing and analysis of multiscale human iEEG data at the international level (see 30  and 31  ).
Such meta-database resources will be an efficient way to re-use data, and transfer what has been done from one study to another by different members of the iEEG community. iEEG databases should not be restricted to group studies but also open to single-case studies. It will help test reproducibility (e. g. , if there is a channel at a given location what can we expect to record there?), as well as going back and forth between an individual and a population (i. e. taking into account the richness of data variability). Open databases will be pertinent to combine multiple datasets from different protocols to disambiguate/complement results. Last, knowing "what is happening where " helps to refine hypothesisdriven research (e. g. , to select an experiment with regard to electrode localization, to build some priors in a Bayesian perspective) and it can reveal the "holes in the map " (i.
e. , foster exploratory research). As Open Science practices become more common in general, we expect this to positively impact the sharing of knowledge in iEEG practices. Sharing common iEEG artifacts is one concrete example that would benefit the community (see Sections 1. 3. 4 , 1. 3. 5 and 3. 2 ). Many researchers have gained experience with identifying artifacts in iEEG data, but this knowledge/expertise remains available in somewhat isolated form within smaller communities of researchers (e. g. , in a local research lab). A shared database of iEEG artifact templates (both physiological, e. g. , spikes, and non-physiological), contributed to by different researchers, would be a valuable resource for the community, and especially for the training of early stage researchers in iEEG analyses.
Ideally, in this 'atlas', the iEEG signal would be accompanied by other types of recording to validate the artifacts (e. g. , EOG or EMG recordings) and to better understand iEEG physiology (i. e. passive field spread). An interest for artifact-contaminated data also applies when developing new data analysis methods. For instance, for machine learning approaches both "positive" and "negative" data samples are needed to prevent bias in classification results (see Section 6. 9. 2 ). In the making of such database(s), the contribution by various researchers would align with the concept of "team science" and a live database format would allow information to be updated and improved, differently from the static format of regular peer-reviewed publications. While iEEG can be considered a niche, and as such might be overlooked by researchers working with other approaches, it provides an incomparable opportunity to cross-validate results through a multimodal approach.
In the next decade, we anticipate more translational work between approaches (e. g. , iEEG and fMRI), as well as within iEEG (e. g. , passive recording and electrical stimulation) and with the deepbrain approach used in movement disorders  ( de Hemptinne et al. , 2015 ; Little et al. , 2021 ). With a great deal of precautions regarding ethical issues (see Section 1. 3. 8), clinical information available about the participants (e. g. , electrostimulation, multiscale recordings) may be incorporated, thereby creating multimodal databases similar to those available in the fields of MRI (see for instance the Allen institute initiative, see 32  ). The results of these may fuel brain modeling such as in the Virtual brain project or biophysical models based on iEEG signal model (see 33 and  ( Medani et al.
There the research would simply engage the patients in a scripted set of activities, as in neuropsychological evaluation. To make the most of this approach, we anticipate that iEEG databases will incorporate video recording of natural behavior by means of techniques to replace the patient with an avatar. Finally, the development of live/online-analysis will permit a closed-loop approach  ( Zelmann et al. , 2020 ) , which will improve research investigations by optimizing stimulation approaches using neurally informed personalized targets, as well as direct electrical stimulation protocols and neuromodulatory therapies.
3. 1 and 4. 3. 2 ) and permit drawing neural networks of the patient's brain. Using a ROI-like approach, this method permits grouping sites over different patients which lie within the same brain region and are connected to the same target regions. Using this approach on iEEG data over different centers, and originally acquired for clinical purposes, a large-scale open initiative has resulted in the construction of a Functional Brain Tractography atlas 34   ( Trebaul et al. , 2018 ). Until now, due to the lasting HFS artifacts, the investigation of data with high-frequency train stimulation (HFS) remains less investigated  ( Barborica et al. , 2022 ; Perrone -Bertolotti et al. , 2020 ). Yet, considering the amount of HFS datasets existing in iEEG centers, and more generally of any iES, we anticipate HFS data to be exploited similarly (e.
Pre/postimplantation comparison also allows to assess post-implant tissue displacement due to edema or any injury due to the implant; in the latter case, pre/post-implantation imaging can be further completed with a post-explantation scan (i. e. , after the electrode implants have been removed). This is also known as pre/post-surgical scan and post-explantation scan, where surgery refers to the implantation. The scans can be MRI and/or X-ray CT. • Atlas: An atlas is the result of mapping 3D information (such as population density) on a geometrical template (description + delineation). The represented information can be continuous (such as the amount of rain that falls) but is often restricted to a spatial delineation and geographic description (e. g. , roads, cities, municipalities). The results of such a mapping can be considered an atlas once they are shared with the purpose of re-use for another dataset.
For iEEG this is the output of the analog-to-digital (ADC) conversion from the amplified voltage difference between an electrode contact of interest and a reference electrode. A channel does not have to correspond to a single site or physical electrode. For instance in the case of a bipolar reference: one electrode is rereferenced to its neighbor, the result is generally considered as a channel that is virtually located at mid-distance between the two electrodes. • Signal: A time series of the data being recorded by an analog-digital converter, which can include both brain and non-brain activity. After recording to disk, the signal is subjected to offline preprocessing and analysis with the aim to increase the signal feature(s) related to neural activity. • Acquisition or recording: Storing a signal for either online or offline analysis.
In iEEG analysis this most commonly refers to oscillations, where oscillation power, frequency, and phase are dynamic and change over time. This is especially important given that many oscillations are "bursty "-where non-oscillatory periods are dominated by "aperiodic activity "-and where narrow band filtering can give the appearance of a stable oscillation where one might not exist. • Baseline: The baseline is a concept that relates to signal processing, data visualization and statistics. Considering preprocessing and ERP analysis, the baseline window refers to the time interval (often prestimulus) in which the baseline DC offset is estimated; this estimate can be used to subtract the constant potential offset. Rather than referring to this as baseline correction, in this manuscript we prefer the term DC offset removal.
Acknowledgments We dedicate this review paper to the patients that make this research possible, thanks to their participation they move clinical and fundamental neuroscience forward. We thank the organizers and attendees of the online LiveMEEG 2020 conference for providing a welcoming platform to present and discuss some of the ideas that formed the basis for this manuscript. We thank  Mariana P. Branco  for help with Fig. 4. Last, we acknowledge the three anonymous reviewers for thoroughly reading the manuscript and for their insightful comments. RM is supported by  EU-REA   H2020 MSCA -IF 798853. FT is supported by  NIH/NIBIB   R01EB026299. DH is supported by  NIMH/NIH   R01MH122258. RTK is supported by  NIH/NINDS  2  R01 NS021135 ,  NIH/NINDS   1U19NS107609-01. NFR and MJV are supported by  NIH/NIDCD   U01 DC016686  and  NWO 17619.
The CUSUNORO plot in Figure  4  is obtained using the same dataset, where each curve refers to an input parameter. The magnitudes of the deviations from the zero horizontal line can be used to infer information about the strength of the impact. The CUSU-NORO plot shows a clear agreement with the previous results: the fuel density parameters ! (blue -o-) and & (red -∆-) are significantly more important than the others. Furthermore, curves above the horizontal zero line signal a decreasing effect; curves below the horizontal zero line suggest the opposite. From Figure  4 , one observes that ! has a decreasing effect on the output reactor power, whereas & shows an increasing effect. Furthermore, curves above the horizontal zero line signal a decreasing effect; curves below the horizontal zero line suggest the opposite.
The Surrogate Model The high-fidelity multiphysics model presented in Section 3 can produce very accurate solutions, being able to catch phenomena that could not be described with simpler approaches. However, this accuracy comes at the expense of large computational times, which can be as high as about 24 h in three-dimensional full core simulations. Due to this drawback, the high-fidelity model is not adequate for real-time control and decision support. To overcome this issue, a simplified surrogate model is developed in Matlab ® , suitable for the real time analysis of the MSR. As pointed out by the SA results in Section 5, the effect of the density parameters on reactivity are far more important than all the other model parameters. In the light of this, a surrogate model is developed, only considering the density feedback on reactivity.
The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a generalpurpose algorithm for approximating the Pareto set of multi-objective optimization problems  (Zhang & Li, 2007). It decomposes the original multi-objective problem into a number of single-objective optimization sub-problems and then uses an evolutionary process to optimize these sub-problems simultaneously and cooperatively. MOEA/D is a state-of-the-art algorithm in aggregation-based approaches for multi-objective optimization. The goal of the moead-framework Python package is to provide a modular framework for scientists and researchers interested in experimenting with MOEA/D and its numerous variants.
Statement of Need The MOEA/D algorithm is now considered as a framework. MOEA/D is the basis of many variants that improve or add new components to improve MOEA/D performance. The first version of MOEA/D and its most famous variants  (Li & Zhang, 2009; Zhang et al. , 2009)  are implemented in recent multi-objective optimization software such as pymoo  (Blank & Deb, 2020) , pygmo  (Biscani & Izzo, 2020)  and jMetal  (Nebro et al. , 2015). These implementations offer many state-of-the-art algorithms, visualization tools or parallelization abstraction, but they are not modular enough to test easily all MOEA/D components. The modular R package MOEADr  (Campelo et al. , 2020)  focuses on MOEA/D and allows the definition of different variants for each component of MOEA/D. While some modular frameworks already exist in Python for evolutionary algorithms such as DEAP  (Fortin et al.
, 2012)  or ModEA  (van Rijn et al. , 2016) , these do not (easily) support implementing MOEA/D variants. Instead, they focus mostly on single-objective optimization and CMA-ES variants respectively. With the moead-framework package, we aim to provide the modularity of the MOEADr package by using the flexibility of Python. Indeed, we want to allow the user to update the behavior of MOEA/D components in their research works without being limited by the package itself. The package is focused on a modular architecture for easily adding, updating or testing the components of MOEA/D and for customizing how components interact with each other. Indeed, in contrast with other existing implementations, moead-framework does not limit the users with a limited number of components available as parameters (8 components are available in MOEADr).
Users can easily restructure the 10 existing components of the moead-framework and include new ones to easily add new features without altering existing components. Components are not only customizable with parameters as with MOEADr, but in fact they can be added with the inheritance mechanism on the main run() method of each algorithm. For example, the moead-framework package was used for creating novel sub-problem selection strategies and analyzing them  (Pruvost, Derbel, Liefooghe, Li, et al. , 2020) , and for rewriting.
Chandra Data Analysis We re-analyze deep archival X-ray observations of SNR G327. 1-1. 1 performed with the Advanced CCD Imaging Spectrometer, ACIS-I, on board the Chandra X-ray Observatory for a total exposure time of 350 ks. The standard data reduction and cleaning were performed using the Chandra Interactive Analysis of Observations  (CIAO v. 4. 12; Fruscione et al. 2006 ) software package, resulting in a total clean exposure time of 337. 5 ks. An identical data reduction procedure has already been performed and reported (see  Temim et al. 2015 for details). We re-analyze the X-ray data incorporating two different source extraction regions, following those used to measure the distinct radio "finger" and "body" PWN components from  Ma et al. (2016). Figure  2  shows the regions used for the spectral extraction.
Fermi-LAT Data Analysis We re-analyze the Fermi-LAT data using an optimized technique in order to maximize the source detection and to most accurately measure the MeV-GeV γ-ray spectrum. We perform a binned likelihood analysis using 11. 5 yr (from 2008 August to 2020 January) of P8R3_SOURCE_V3 photons with energy between 300 MeV and 2 TeV from all events. We utilize the latest Fermitools package 8  (v. 2. 0. 8) and FermiPy Python 3 package (v. 1. 0. 1,  Wood et al. 2017)  to perform data reduction and analysis. We organize the events by PSF type, as described above, using evtype=4,8,16,32 to represent PSF0, PSF1, PSF2, and PSF3 components. A binned likelihood analysis is performed on each event type and then combined into a global likelihood function for the region of interest (ROI) to represent all events.
The corresponding broadband photon spectrum can reproduce well the radio, X-ray, and γ-ray data of G327. 1-1. 1 considered by  Temim et al. (2015) , and suggests the remnant is old, τ ∼ 18,000 yr, with distinct particle components apparent in the simulated SED (see Figure  6 ). The resolved PWN morphology in radio and X-ray gives a unique opportunity to study the individual properties of the particle populations through their distinct broadband spectral features, first investigated by  Temim et al. (2015). In this section we attempt to model the particle populations independently in an effort to characterize each component that contributes to the observed broadband emission, incorporating the new Fermi-LAT data, and compare to the model presented in  Temim et al. (2015). We use the NAIMA Python package  (Zabalza 2015) , designed to compute the non-thermal radiation from relativistic particle populations.
Conclusions We have reported the detection of faint γ-ray emission discovered coincident in location with the evolved SNR G327. 1-1. 1. The position and extent is consistent with an origin from the high-energy PWN which is also known to emit TeV γ-rays as HESS J1554-550. We perform a multiwavelength investigation to explore the broadband models supported by observations, utilizing a one-dimensional radiative modeling package NAIMA. We compare the results to an in-depth semianalytic simulation that predicts the evolved particle injection spectrum based on hydrodynamically derived quantities such as supernova explosion energy and ejecta mass and reported in  Temim et al. (2015). We investigate the broadband emission structure based on our understanding of the PWN evolutionary sequence outlined in  Gelfand et al.
We consider the production of a pair of heavy quarks and illustrate the derivation of the MiNNLO PS method to match next-to-next-to-leading order calculations with parton showers (NNLO+PS) for this class of processes. As a first application, we construct an event generator for the fully differential simulation of hadronic top-quark pair production at NNLO+PS and discuss all details of its implementation in a parton shower Monte Carlo framework. We present new phenomenological results for the Large Hadron Collider obtained by including the tree-level decays of the top quarks, while accounting for spin-correlation effects. A comprehensive comparison to LHC measurements shows an excellent description of experimental data across multiple hadronic and leptonic particlelevel observables. The computer code is available for download within the Powheg-Box.
This has the advantage of allowing different scale settings in phase space regions characterised by a large p T , which better describe the momentum transfer in the hard scattering. Suitable candidates are for instance scales proportional to H t t T = m t T + m t T , H t t+ jets T = m t T + m t T + i ∈t p T,i , (4. 22) with m i T = m 2 i + p 2 T i being the transverse mass of particle i. 7  In particular, such settings may be used for the fixed-order part of the calculation by selecting from the inputs of the MiNNLO PS t t code fixedscale 0 and by choosing different values of the parameter largeptscales. Moreover, by setting the parameter whichscale in the input file such scale setting can be chosen for the powers of the coupling constant of the Born squared amplitude, denoted by µ (0) R in the equations given in this section.
Phenomenological results In this section, we present phenomenological results for top-quark pair production at NNLO+PS with and without decays of the top quarks. After introducing our setup in section 5. 1, we perform a comprehensive comparison against LHC data: In section 5. 2 we compare MiNNLO PS predictions for on-shell top quarks against CMS data extrapolated to the inclusive t t phase space. We then continue by considering top-quark decays including spin correlations and we compare novel MiNNLO PS predictions against data from both ATLAS and CMS in various top-decay channels, including fully leptonic (section 5. 3), semi-leptonic (section 5. 4) and hadronic (section 5. 5) top-quark decays.
We update that comparison here using the newest settings, in terms of scales etc. , considered in this paper. We find excellent agreement between MiNNLO PS and fixed-order NNLO predictions for the rapidity distributions, both for the central predictions and for the scale-uncertainty bands. For the other kinematical distributions some differences between MiNNLO PS and NNLO results can be observed, especially in terms of shape, which however are largely covered by the respective scale uncertainties. With respect to MiNLO the MiNNLO PS corrections lead to a significant increase at the level of +10% and a substantial reduction of the scale uncertainties by more than a factor of two. The agreement of MiNNLO PS predictions with data is quite remarkable. Only in the very first bin of the m t t distribution the data deviates beyond the quoted uncertainties from the MiNNLO PS result.
Acknowledgments We are grateful to  Simone Amoroso  for several fruitful discussions and for help with setting up the top-quark decays through MadSpin. We also would like to thank  Rikkert Frederix  for help with requiring on-shell top quarks within MadSpin, and  Massimiliano Grazzini  for comments on the manuscript. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958  in the context of the  KITP programme "New Physics from Precision at High Energies ". P. N. acknowledges support from  Fondazione Cariplo  and  Regione Lombardia , grant  2017-2070 , and from  INFN.
STATE OF THE ART Fault Detection and Diagnosis (FDD) techniques in rocket engines have been studied since the 1970s, and it is a vital part of an HMS. In the early years, the health monitoring of rocket engines was made by observing some important operational parameters with fixed redlines values  (Wu, 2005). In the literature, the Space Shuttle program carried out by NASA is an important source of information concerning HMS in LPRE. In  (Hawman, Galinaitis, Tulpule, & Mattedi, 1990) , different fault detection techniques are tested, and an HMS is proposed. The HMS has one output: yes/no to shut down the engine. According to the report, about 900 Space Shuttle Main Engine (SSME) failure modes can be identified with the database available at that time. The HMS focused on a small group of faults that directly impact the engine safety, but at the same time, they can be detected and treated.
Work performed and remaining work The modeling task has been the main focus of the work carried out so far. A rocket with three engines is used as a reference to our model. The rocket is considered to be powered by three 1000kN class engines, which will use liquid oxygen and liquid methane propellants. The CNES developed one 0D model of the engine on the simulation software CARINS. The models of two subsystems have been derived: the plant dynamics and the propellant feeding lines. The plant dynamics inputs are the force and the position of each engine, and its output is the resulting force of the launcher. The propellant feeding lines are the system that connects the engines with the reservoirs. Its inputs are the inlet temperature and pressure of the propellant and the outlet mass flow. The outputs are the outlet temperature and pressure.
We present a Python module facilitating the pre-and post-processing operations of a modeling workflow based on the widely used USGS MODFLOW6 and MODPATH7 programs. The module handles particle seeding around the sink and estimation of the mixing ratio of water withdrawn from the sink. This ratio is computed with a mixing law from the particle endpoints, accounting for particle velocities and mixing in the source model cells. We investigate the best practice to obtain robust derivatives with this approach, which is a benefit for the screening methods based on linear analysis. We illustrate the interest of the approach with a real world case study, considering a drinking water well field vulnerable to a contaminated stream. The configuration is typical of many other drinking water production sites.