dataesr/scientific-paragraphs-categorization huggingface dataset:
Store 500 mL aliquots at À20 C up to 1 year. 2-mercapto-ethanol is seriously irritating and toxic if swallowed or inhaled, so it is advised to handle it in an active fume hood. Continued REAGENT or RESOURCE Reagent SOURCE Final concentration IDENTIFIER Amount I-TASSER (Iterative Threading Tris base 125 mM Yang et al. 21 RRID:SCR_014627; https://zhanggroup. 747 mg ASSEmbly Refinement) SDS 4% org/I-TASSER/ 2 g NetMHC -4. 0 Glycerol 20% Andreatta and Nielsen 2016 RRID:SCR_021651; https://services. healthtech. 10 mL OpenProt Protein Database 1. 6 2-mercapto-ethanol OriginPro, Version 2022b Bromophenol blue HCl 10% N/A N/A Brunet et al. 5 OriginLab Corporation dtu. dk/service. php?NetMHC-4. 0 5 mL https://www. openprot. org/p/ng/Home 100 mg RRID:SCR_014212; https://www. originlab. com/ Up to pH jj6.
05 FDR. vii. Fragment mass tolerance: 0. 02 Da. viii. Validation is done with Percolator using strict FDR = 0. 01 and relaxed FDR = 0. 05 b. For RefProts identification. i. Protein database: UniProtKB v. 2022_02 reviewed and unreviewed. (77,895 sequences, downloaded from Uniprot website, 25 feb 2022) ii. At least two peptides per sequence. c. For AltProts identification. i. Protein database: Homo sapiens OpenProt v1. 6 (184,706 sequences), containing RefProts and predicted AltProts detected in mass spectrometry experiments with at least one unique peptide. ii. At least one peptide per sequence.
Method of Fault Scarp Mapping We mapped the scarps of the Tambomachay Fault using three independent data sets. For the western section of the fault, we used Pléiades 0. 5 m panchromatic and 0. 7 m multispectral images acquired on 15 th February 2019, plus a high-resolution DEM formed from the panchromatic stereo-image pair of the same images built with the photogrammetry software Ames Stereo Pipeline  (Broxton and Edwards, 2008)  following the procedure of  Beyer et al. (2018). The resulting DEM has a point cloud density of 1. 53 m/px and covers the region between Sencca Hill and Larapa. We also formed a 0. 2 m/px DEM over the Sencca Hill area from aerial photographs collected using an eBee senseFly drone and the structure-from-motion technique implemented in Agisoft PhotoScan  (Agisoft, 2018).
The highest-probability Gaussians give peaks at 14. 51 ± 0. 58 kyr (1 outlier) and 14. 15 ± 0. 47 kyr (2 outliers) for the eastern and the western moraines, respectively. As the values are indistinguishable within their uncertainties, this indicates that the two moraines edifices have the same age, which is consistent with the facts that they are close to each other in space (700 m) and that they both derive from similar glacial cirque areas at same elevation (Figure  5c ). Processing the whole dataset with P-CAAT gives a similar result, with the same three outliers are detected (T3, T4 and T11) and a Gaussian distribution peak at 14. 2 ± 0. 5 kyr (Figure  5d ). The distribution of exposure ages is thus well clustered (Figure  5d ), which supports the view that unaccounted-for sources of uncertainty (such as differential denudation between boulders, partial shielding, surface lowering since the glacier retreat), if any, would be very low (<5%) and thus negligible.
Acknowledgements This work was supported by  INGEMMET (Geological Survey of Peru)  and the  CuscoPATA Project "Paleoseismology, archaeoseismology and active tectonics " (grant  006-2016-FONDECYT ). The sample processing and chemical extraction of the 10Be were performed at the  GTC plateform (ISTerre, Grenoble). We gratefully thank  Francis Coeur  for the sample processing. The 10 Be measurements were performed at the  ASTER AMS national facility  (CEREGE,  Aix en Provence)  which is supported by the  INSU/CNRS , the  ANR  through the " Projets thématiques d'excellence" program  for the " Equipements d'excellence "  ASTER-CEREGE  action and IRD. We acknowledge the IRD contribution to the LMC14 ARTEMIS instrument ( CEA-CNRS-IRD-IRSN -MCC). SW was funded by the  Denman Baynes Junior Research Fellowship  at  Clare College, Cambridge.
Experimental design The 200 sentences were equally distributed into 4 blocks with items each. Sentences were recorded by a French native speaker who is a semi-professional actor. He was instructed to keep the same intonation as far as possible when reading both the grammatical and the ungrammatical sentences. We used the software Audacity (Audacity® software is copyright © 1999-2021 Audacity Team) to record the sentences, and PRAAT  (Boersma and Weenink, 2023)  to apply the cross-splicing technique and control/correct pitch variations in the ungrammatical sentences condition (i. e. , the incongruent condition). Participants listened to the sentences in a pseudo-randomized order and after sentence offset were asked to indicate if the sentences presented were acceptable or unacceptable in French by pressing a green or a red button respectively.
EEG acquisition and processing The EEG was recorded with 64-channel headcap (10/20 international system), using Biosemi ActiveTwo AD box (BioSemi, Amsterdam, Netherlands), with a sampling rate of 512 Hz. An online bandpass filter was applied with a low-pass cutoff at 100 Hz and a highpass cutoff at 0. 05 Hz. To detect eye movements, two electrodes were applied for the electrooculogram (EOG); measurements were obtained from below versus above the left eye (vertical EOG). Two additional flat electrodes were placed over the left versus right lateral mastoids. After recording, data were analysed offline in MATLAB (Version 2021a) using the MATLAB toolboxes EEGLAB (Version 2019_0;  Delorme and Makeig, 2004)  and ERPLAB (Version 8. 01; Lopez-Calderon and Luck, 2014). First, during pre-processing, continuous EEG data were filtered offline using a bandpass filter from 0.
05 to 45 Hz. We removed bad electrodes by the clean_rawdata option in EEGLAB. Furthermore, we performed an ICA (Independent Component Analysis) in EEGLAB to identify blink components. Components identified as blinks (see  Debener et al. , 2010)  or muscle movements (probability > 0. 9) were removed from the data. Then, continuous data were cleaned for bursts using ASR (Artifact Subspace Reconstruction) [Maximum tolerated flatline duration: 5 s; Transition band for high-pass filter: [0. 25 0. 75] Hz; Minimum channel correlation: 0. 8; Line Noise Criterion: 4; Standard deviation cutoff for removal of bursts (via ASR): 30; bad data periods were repaired instead of rejected]. Then, we performed the interpolation of the bad channels and data were re-referenced to average electrodes (see  Molinaro et al.
g. ,  Lagos et al. 2018). However, the results from  Pelliccia et al. (2019)  and  Pérez-Martínez et al. (2021)  to compare galaxies from different surveys and observed with various instruments (e. g. ,  Pérez-Martínez et al. 2021)  to reach these conclusions. As in  Abril-Melgarejo et al. (2021)  and  Mercier et al. (2022) , we argue that there might be systematic effects when doing so, mainly driven by different selection functions between different datasets. This paper is the first of a series. In this analysis, we propose to study the impact of the environment on the stellar angular momentum of galaxies in the MUSE-gAlaxy Groups In COSMOS (MAGIC) survey  (Epinat et al. , in prep. ). As shown in  Mercier et al. (2022) , this survey is ideal to probe the impact of the environment on galaxy dynamics at z ∼ 1 because it allows for the simultaneous observation of galaxies located in structures of various masses (mostly galaxy groups) and foreground and background galaxies in more rarefied environments, as well as to analyse them in a consistent manner.
Deriving the stellar angular momentum We are interested in deriving as precisely as possible the stellar angular momentum of the galaxies. We therefore implement a method, discussed in Sects. 3. 1 and 3. 2, that makes full use of the morphological information contained in the highresolution HST images and of the best-fit gas kinematics (velocity field and dispersion map) derived from the MUSE data cubes in the previous section. We assume that the ionised gas kinematics traces sufficiently well the stellar kinematics to derive the stellar angular momentum. This assumption is based on comparisons performed between the kinematics of the stellar and ionised gas components of galaxies at intermediate redshift (e. g. ,  Guérou et al. 2017  and is discussed in more details in Sect. 3. 3). The formalism presented below only applies for (potentially thick) stellar disks.
Acknowledgements. We thank the referee for providing useful and constructive comments on the submitted version of this paper. We dedicate this article in memory of  Hayley Finley. We acknowledge  David Carton  for his investment in the build-up of the project. This work was supported by the  Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU  with INP and  IN2P3 , co-funded by  CEA  and  CNES. This work has been carried out through the support of the  ANR   FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ), and the  A*MIDEX  project ( ANR-11-IDEX-0001-02 ), which are funded by the  " Investissements d'avenir" French government program.
1 W. During our experiments, we used the power meter with a sampling frequency of 1 Hz, which we consider sufficient for our study. B. Scenario 1 with technological leverages : Temporary power capping at data center scale 1) Global context: The electricity provisioning for a data center is challenging and is constrained by power bounds. In the current energy context in Europe, where the lack of electricity can cause global power outages, the ability to temporarily limit the instant power consumption at the scale of a data center can be an important requirement. The most trivial and intuitive way to limit the instant power consumption of a data center is to use the shutdown leverage on a set of compute nodes. However, the direct use of shutdown leverage not only has an impact on the user by canceling his workload but also has a significant cost in terms of energy and time during switch on and off  Raïs et al.
S2A ). Instead, activation of DNA_damage leads to the activation of the apoptotic pathway (see Supplementary Material). The pressure due to cell crowding activates the DNA_damage node, which leads to the secretion of MMPs and the remodeling and degradation of ECM  (39). In the absence of DNA damage, a high probability of cells with the EMT/Migration/ECM_adh phenotypes is observed, corresponding to a high invasive potential. This is also true in the absence of oxygen and growth factor. The presence of DNA damage inhibits EMT while promoting secretion of MMPs (Fig. S2C ). The combination of the absence of GF and DNA damage leads to an apoptotic state (see Supplementary Material). Different combinations of initial states can be simulated using our dedicated Jupyter notebook (supplementary material) and in the GitHub repository https://github.
However, to be more precise in the model predictions, more genes and pathways could be included in the future based on new experiments. The choice of these pathways could be suggested by identifying differentially expressed genes or molecular signatures for patients with high metastatic potential, exploring public patient datasets such as The Cancer Genome Atlas (TCGA) data for which clinical data about tumor invasiveness are provided. In future releases, we also plan to include more cell types such as cancer associated fibroblast (CAFs) and T-cells to represent the role of the immune response. In this setting, each cell type will have its own intracellular model and would interact with other cell types according to biologically-relevant rules. The current model has shown its predictive potential but there are several limitations.
This paper presents a novel design and robust control for wind conversion systems using DFIG. The system is designed to reduce the problems related to the sudden variation of the wind speed and to improve the sensitivity of the DFIG to grid faults to avoid disconnection of the wind system from the electrical grid. To enhance the DFIG behavior, power fluctuation and to protect power devices under symmetrical faults, a specific superconducting magnetic energy storage (SMES) scheme and its control are proposed. To validate this study, the control structure and strategies were implemented in the MATLAB/Simulink environment. The results obtained by simulation were compared with those using traditional control strategies, they highlight an improvement in the functioning of wind conversion systems of this type, showing the rigor and effectiveness of the proposed strategy.
In the literature, we find two categories of solutions; first is the contribution in command algorithm, the second is use of an additional equipment  [8]. In this paper, we will be presented of a novel strategy that can ensure; improving the behavior of the DFIG system, protection of the electronics power device of the DFIG system, and participate in system services; this proposal is a new design of an additional energy magnetic storage system (SMES), and its robust control. This proposed system ensures a highly efficient energy storage, optimal time response and power flow regulation  [16] ,  [17]. The SMES is used to sustain the wind system during the grid disturbed regime, keep the DFIG connected to the electrical system and smooth power output. In the MATLAB/Simulink environment, the model in question has been developed, and the simulations obtained validate the effectiveness of our model.
RESULTS AND DISCUSSIONS The Table  1  presents the parameters of the generator used in the wind farm composed of six turbines, so that we can start our command we have chosen two scenarios. The first will be between the instant 0. 8 s and 1. 4 s, this is the duration of a fault that occurs on the network. The fault occurs at t0=0. 457 between 1. 4 s and 2 s the duration of the normal operation, this is the duration where there is no fault on the network show Figure  6  The moment of the recovery voltage is tf=1. 4 s, it's two scenarios will be well under a variable wind speed between 6 m/s and 24 m/s, what is true in most cases see  [25]  shown in Figure  7. The validation of the performance of the new SMES design is done by developing a model under the MATLAB/Simulink environment. the proposed model is applied to a wind farm composed of six DFIG wind turbines.
Data In this paper, we analyse 19 galaxies observed with MUSE  (Bacon et al. 2010)  as part of the PHANGS-MUSE ESO Large Program (P. I. Schinnerer;  Emsellem et al. 2022). The galaxies in the sample are all nearby (D < 20 Mpc), star-forming and mildly inclined (i < 75 • ), to minimise the effect of extinction and blending. Each galaxy was observed with a variable number of pointings (from 3 to 15), to map a significant fraction of its starforming disk. All the targets were observed in wide field mode (WFM). All the data have been reduced using pymusepipe 3  , a python wrapper of the ESO data reduction pipeline  (Weilbacher et al. 2020) , specifically developed for the reduction of PHANGS data. Then, the reduced data have been combined, to produce a single mosaicked datacube for each target galaxy.
Classification Here, we develop a new classification algorithm based on comparing the regions' morphological and spectral characteristics with models of nebular properties using the principle of the odds ratio. This new algorithm is designed to overcome most of the issues of the traditional classification criteria: (1) it does a comparative classification, (2) it associates a probability to each classification, (3) it consistently considers non-detections and upper limits, (4) it can take advantage of the plethora of morphological and spectroscopic information that can be recovered from modern high spatial resolution integral field spectroscopic datasets.
For the inner radius of the nebula and the gas density, we selected the intermediate values between the set provided in the grids. This left as our free parameters the temperature and luminosity of the ionising star. Finally, we select the fast shock models by  Allen et al. (2008)  to represent SNRs. These are a set of simulations of shocks produced using the MAPPINGS photo-ionisation and shock modelling code typically used to study SNRs (e. g. Dopita et al. 2010; Sabin et al. 2013; Blair et al. 2014; Micelotta et al. 2016; Kopsacheili et al. 2020 ). As we did for the H II regions and PNe grids, we selected a sub-sample of the available models representing standard conditions found in SNRs (e. g. Dopita et al. 2010). One problem with these models is that they have been extensively used in the literature to also describe the emission of the narrow-line region and extended narrow-line region of AGN A148, page 9 of 56 A&A 672, A148 (  2023 ) 2 1 0 log([N II]/H ) 2 1 0 1 2 log([O III]/H ) H II reg.
g. NGC 1365). So, it might be possible that some of the regions classified using these models are AGN ionised regions and not real SNRs. For this reason, we decided to identify the nebulae classified by these models as shocks or shock-ionised nebulae, and we leave to Sect. 7. 7 a discussion of how other types of nebulae are contaminating our sample of SNRs. The Delgado-Inglada et al. (  2014 ) and Pérez-Montero (2014) models have been downloaded from the Mexican Million Model database 6  (3MdB). They are a rerun of the original grids performed with Cloudy v13  (Ferland et al. 2013 ) and Cloudy v17. 01  (Ferland et al. 2017 ) respectively. The  Allen et al. (2008)  models are downloaded from the shock section of the 3MdB 7  , and they are a rerun of the original grid with MAPPINGS V  (Sutherland et al.
Extinction estimates Having a reliable estimate of the reddening in each region is essential when measuring quantities involving lines with wide wavelength separations (e. g. the ionisation parameter from the [S III]λ9068/[S II]λλ6717, 6731 or [O III]λ5007/[O II]λ3727 ratios) or intrinsic extinction-corrected luminosities (e. g. star formation rate from Hα). The reddening is typically measured by comparing the observed Hα/Hβ ratio with its theoretical value for a nebula with a temperature of 10 000 K, low-density limit and Case B conditions (Hα/Hβ = 2. 86,  Osterbrock & Ferland 2006). We use this technique, as implemented in the pyneb python package  (Luridiana et al. 2015) , to estimate the colour excess (E(B -V)) in all nebulae where both Hα and Hβ are detected at better than 3σ, and we compute it both before and after the DIG correction.
Physical properties of the nebulae Thanks to the complete set of spectral information included in our nebulae catalogue, we can compute some regions' physical properties and study how their distribution changes depending on the selection criteria and the classification scheme. This could help us identify biases introduced by different approaches to classifying and selecting sources when investigating ionised nebulae. Some additional information could be recovered by comparing the values directly measured from the data with those obtained by IZI as we did for the extinction. However, we decided not to perform this comparison since the set of models we are using for the classification is limited, and it has not been chosen to obtain precise and reliable estimates of the nebulae' physical parameters.
Comparison with Santoro et al. (2022) S22 recently used the PHANGS-MUSE data to compile a nebulae catalogue to study the H II regions luminosity function (LF). While based on the same data, S22 aimed to produce a very clean sample of H II regions, resulting in several differences between their catalogue and the one presented in this work. Firstly, the S22 catalogue is based only on the Hα maps produced by the DAP, boosting the detection of H II regions but reducing the possibility of detecting other classes of nebulae. Secondly, the catalogue has been created using HIIPHOT  (Thilker et al. 2000) , a common source detection algorithm specifically developed to detect H II regions in two-dimensional images. HIIPHOT detects nebulae by looking for morphologies typical of H II regions in narrow-band images.
Thanks to its comparative and probabilistic nature, the algorithm overcomes some of the main limitations characterising the criteria traditionally used in the literature to classify nebulae, such as their binarity, the sharp limits used for the classification and the limited amount of information that they rely on to perform the classification. Given the large volume of data on ionised nebulae that will be produced in the near future by instruments like MUSE and surveys like the Local Volume Mapper survey, this algorithm is an important step toward more complex and robust automatic approaches to the classification of any type of nebulae. The analysis of the catalogue and the comparison between the results of the model-comparison-based classification with what can be obtained from the traditional criteria reveals that: -The DIG emission can significantly alter the observed line ratios of the nebulae, particularly in the faintest ones where the DIG emission is dominant.
Acknowledgements. The authors of the paper want to thank the anonymous referee for their helpful suggestions. Based on observations collected at the  European Southern Observatory  under ESO programmes 1100. B-0651 ,  095. C-0473 , and  094. C-0623  ( PHANGS-MUSE ;  PI: Schinnerer ), as well as  094. B-0321  (MAGNUM;  PI: Marconi ),  099. B-0242 ,  0100. B-0116 ,  098. B-0551  (MAD; PI:  Carollo ) and  097. B-0640  (TIMER; PI: Gadotti). Science- level MUSE mosaicked  datacubes and high-level analysis products are provided via the ESO archive phase 3 interface (https://archive. eso. org/scienceportal/ home?data_collection=PHANGS). A full description of the first PHANGS data release is presented in Emsellem et al. (2022). This work was carried out as part of the PHANGS collaboration. E. C. and G.
A. B. acknowledge support from  ANID Basal  projects  ACE210002  and  FB210003. K. K. , F. S. , and O. E. gratefully acknowledge funding from the  German Research Foundation (DFG)  in the form of an  Emmy Noether Research Group  (grant number  KR4598/2-1 , PI Kreckel). H. A. P. acknowledges support by the  National Science and Technology Council of Taiwan  under grant  110-2112-M-032-020-MY3. S. C. O. G. acknowledges funding from the  European Research Council  via the  ERC Synergy Grant  " ECOGAL -Understanding our Galactic ecosystem: From the disk of the Milky Way to the formation sites of stars and planets " (project  ID 855130 ) and from the  Heidelberg Cluster of Excellence  ( EXC 2181 -390900948 ) " STRUCTURES: A unifying approach to emergent phenomena in the physical world, mathematics, and complex data ", funded by the  German Excellence Strategy.
In comparison, the in-situ magnets have their static field reinforced inside their bore center and canceled outside of the structure. Thus, their magnetic field is roughly homogeneous inside the structure. But drawbacks still exist and can be amplified, especially the in depth problem of Signal/Noise. Calculations, in a microscopic point of view, are develop, first applied to resistances and inductances imbedded in this circuit. Before, from any point source, we calculate the impedance spreading out. For this, our approach is using Transmission Line Model, over or into a multi-layered substrate; it can be also derived by solving Poisson's equation analytically to obtain the associated Green's kernel. We implement our algorithms in MATLAB. Thus, it permits to extract impedances between any two embedded contacts, real or virtual, of any shape or thickness.