dataesr/scientific-paragraphs-categorization huggingface dataset:
The broadband laser enables to visualize several resonances at the same time, in order to extract the Free Spectral Range (FSR), i. e the pseudo-period of the transduced spectrum. A millimeter tank is positioned on the surface of the chip and filled with water (with fixed heights of 2 and 3 mm, according to the different statistic measurements), in order to perform the sedimentation experiments (Fig. 3 (a) ). Once the tank is placed on the chip, the tested nanoparticles (NPs) suspension is added inside the tank by means of a microlitter pipette (Sartorius Proline Plus pipette, volume 5 µL consisting of NP silica mixture solution with a 10 mg/mL concentration). Throughout each experiment the series of transduced spectra are acquired continuously with a Matlab data acquisition program implemented with the Optical Spectral Analyzer (OSA) or spectrometer (Ocean Optics HR 4000).
I can be noted that the relative variation of the FSR varies from factor 1 to 1. 4 during the process (Fig. 7  (d) between t 1 and t 3 , with FSR-t 3 1. 4 FSR-t 1 ). As shown in Fig. 7 (d) , the maximum of the FFT curve is approximated by the Lagrange's interpolation and thus determined accurately in real time considering said interpolation presenting a single maximum value. During such a process The Matlab code and the man-machine interface specifically developed allow to draw the normalized FSR(t) curves directly onto the screen in real time. Fig. 8  (a), (b) and (c) illustrate the experimental time evolution of the normalized FSR during the dynamic sedimentation process for spherical silica NPs of different radii (R particles = 400, 300 and 250 nm), in an initial water volume of 168 µL corresponding to a water height of 3 mm directly on the device acting with its resonant signal.
Acknowledgment The authors thank the " Fondation d'Entreprise Grand Ouest " (BPGO) plus the  Fondation Rennes1 of the university of Rennes  for its financial support of this work and the  NanoRennes platform  for the DUV process (https://www. ietr. fr/en/nr-nanorennes-platform). This study is also a part of a  PASS_programm 'cordée de la réussite' named 'Pour une Ambition Scolaire Scientifique' (For a Scientific School Ambition ) with the Brittany center ( Sir Cyril Le-Corre  and  Madame Valérie Mesnet at Lycée Fulgence Bienvenüe , and  Collèges Louis Guilloux ,  Paul Eluard ,  Romain Rolland , https://spm. univ-rennes1. fr/les-cordees-de-la-reussite). The authors would like to thank  Pascal Panizza ,  Professor  at the  University of Rennes  for his discussions in fluid mechanics.
Hence, ipole radiative transfer is coordinate and metric independent. ipole has been tested against another polarized ray-tracing code grtrans (see method paper Mościbrodzka & Gammie 2018 and the next subsection) and against a polarized Monte Carlo radiative transfer scheme (see Mościbrodzka 2020 and Appendix A of this work). ipole was used in EHTC VIII for calculating polarized images of models with accelerated electrons.
grtrans The grtrans code 155   (Dexter & Agol 2009; Dexter 2016)  solves the polarized radiative transfer equations along null geodesics in a Kerr spacetime. The radiative transfer equations are integrated either numerically  (Hindmarsh 2019)  or with quadrature methods  (Landi Degl'Innocenti & Landi Degl'Innocenti 1985; Rees et al. 1989). The quadrature methods are the most accurate and efficient for calculations of polarized radiative transfer in Faraday thick problems and are used for the test problems discussed here.
RAPTOR The RAPTOR code  (Bronzwaer et al. 2018 (Bronzwaer et al. , 2020) )   157  is a public code that numerically integrates the equations of motion of light rays in arbitrary spacetimes and then performs polarized radiative transfer calculations along the rays. The code uses an adaptive Runge-Kutta-Fehlberg scheme to integrate the geodesic equation where the Christoffel symbols either can be provided analytically or are numerically computed on the fly by using a fourth-order centered finite-difference method. To integrate the polarized radiative transfer equation, RAPTOR uses a hybrid ImEx integration scheme that switches to an implicit integrator in case of stiffness, in order to solve the equation with optimal speed and accuracy for all possible values of the local optical/Faraday thickness of the plasma.
GRMHD Snapshot The last test consists of imaging the relativistic thermal synchrotron emission at 230 GHz from one snapshot from a GRMHD simulation. This test exercises all aspects of the code, as well as code-specific choices, such as interpolation of fluid state recorded at discrete locations and calculation of the transport coefficients via fitting functions. The standard snapshot file used for this test is taken from a SANE simulation with spin a * = 0. 9375, performed using iharm3D  (Prather et al. 2021 ) with a resolution of 288 × 128 × 128 cells in r, θ, and f respectively. Except for the coordinate system, this simulation exactly reflects the simulations performed with iharm3D as a part of the library used in EHTC V and EHTC VIII, as described in  Wong et al. (2022). Not all GRRT codes can read all GRMHD output, as coordinate systems and fluid state descriptions can differ from code to code-thus, not every polarized radiative transfer code used in the EHTC can be directly compared with this test.
Analytic Comparison Results Results for the analytic integration tests from RAPTOR, Odyssey, ipole, and ipole-IL are shown in Figure  3. Raw output is plotted in the left panels, and differences from the analytic result are plotted in the right panels. This test verifies that the default accuracy parameters of each code allow them to match an analytic solution to within acceptable errors. Note that this is not a good measure of relative code accuracy or convergence-for convergence tests, see the accompanying code papers cited in Section 2. Note that the results from two integrators are shown for RAPTOR as the "RK4" and "IE" variants. In normal integration, RAPTOR uses the "RK4" integrator, reserving the "IE" integrator for the few zones where Faraday rotation is too strong to take steps of an appropriate size with an explicit scheme  (Bronzwaer et al.
21% vs. 1. 5% ), v net (0. 08% vs. 0. 4%), 〈|m|〉 (0. 70% vs. 2. 5%), | | b 2 (0. 0026 vs. 0. 015), and ∠β 2 (3. 4°vs. 17°). In any analysis based on cuts, code differences can shift a few particular images into or out of the final consideration. However, at these uncertainties no image from outside the 1σ detector uncertainty would be consistent with the central observed value. Broadening the comparison to different images and models shows promising similarities. Appendix D presents distributions of the image differences between ipole-IL and grtrans when run over thousands of snapshots of a very different model from the example: they show a wide variance but a smaller difference on average than in the example image, suggesting that image differences, at least between these codes,  Note. v net is omitted since it is uniformly zero, and β 2 magnitude and angle are omitted since the image is not symmetric and thus the magnitude is very small.
The  NRAO  is a facility of the  NSF  operated under cooperative agreement by  AUI. This research used resources of the  Oak Ridge Leadership Computing Facility  at the  Oak Ridge National Laboratory , which is supported by the  Office of Science of the U. S. Department of Energy  under contract No. DE-AC05-00OR22725. We also thank the  Center for Computational Astrophysics, National Astronomical Observatory of Japan. The computing cluster of Shanghai VLBI correlator supported by the  Special Fund  for Astronomy from the  Ministry of Finance in China  is acknowledged. This work was supported by  FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo)  under grant  2021/01183-8. APEX is a collaboration between the  Max-Planck-Institut für Radioastronomie (Germany) ,  ESO , and the  Onsala Space Observatory (Sweden).
The SMA is a joint project between the SAO and  ASIAA  and is funded by the  Smithsonian Institution  and the  Academia Sinica. The JCMT is operated by the  East Asian Observatory  on behalf of the  NAOJ , ASIAA, and KASI, as well as the  Ministry of Finance of   China, Chinese Academy of Sciences , and the  National Key Research and Development Program  (No. 2017YFA0402700 ) of  China and Natural Science Foundation of China  grant  11873028. Additional funding support for the JCMT is provided by the  Science and Technologies Facility Council (UK)  and participating universities in the  UK and Canada. The LMT is a project operated by the  Instituto Nacional de Astrófisica, Óptica ,  y Electrónica (Mexico)  and the  University of Massachusetts at Amherst (USA). The IRAM 30 m telescope on  Pico Veleta , Spain is operated by  IRAM  and supported by  CNRS  ( Centre National de la Recherche Scientifique, France) ,  MPG  ( Max-Planck-Gesellschaft, Germany) , and  IGN (Instituto Geográfico Nacional, Spain).
The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF. Support for SPT participation in the EHT is provided by the  National Science Foundation  through award  OPP-1852617  to the  University of Chicago. Partial support is also provided by the  Kavli Institute of Cosmological Physics at the University of Chicago. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , supported by  NSF  grant  ACI-1548562 , and  CyVerse , supported by  NSF  grants  DBI-0735191 ,  DBI-1265383 , and  DBI-1743442.
This is achieved by the use of an optimal estimation method combined with several aerosol models and other features including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 minutes -the acquisition frequency of the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on MSG-are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI.
In addition, an efficient RTM is used to perform calculations with a good trade-off between precision and speed, which allows iAERUS-GEO to process GEO data in near real time. This article is organized as follows. The iAERUS-GEO algorithm is described in Sect. 2 and the experiments that were conducted to assess its accuracy are detailed in Sect. 3. Results are reported in Sect. 4 and conclusions are drawn in Sect. 5.
Overview iAERUS-GEO retrieves AOD at 635 nm from the cloud-free pixels of each SEVIRI image. This results in the estimation of a map of AOD across the MSG Earth's disk every 15 minutes during daytime. The following data are required as input: • Full-disk images of top-of-atmosphere (TOA) reflectance from channel VIS06 centered at 635 nm and corresponding to the shortest wavelength available on SEVIRI. • Solar and view zenith and azimuth angles. • Static files including maps of latitude and longitude, a land-water mask, a mask for coastal pixels, and a digital elevation model. • Binary cloud mask to limit the processing to cloud-free pixels only. • Fields of surface pressure, total column water vapor, and total column ozone to perform molecular correction. • Fields of surface wind speed and direction to calculate the reflectance of ocean surfaces.
1. According to  Katsev et al. (2010) , MSA allows the estimation of the aerosol layer reflectance with an error lower than 10% except for high AOD and high zenith angles. These reasonable yet perfectible performances are counterbalanced by the high speed of MSA, which is crucial for aerosol retrieval from GEO platforms in near real time, with more than 10 million pixels to process in less than 15 minutes in the instance of SEVIRI. Katsev et al. (2010)  used a truncating angle (𝜉 * ) equal to 45°, which resulted in the discard of the data from the LEO satellite Envisat used in their study with a scattering angle lower than that value. Here, a value of 𝜉 * = 30° is chosen instead to account for the generally lower scattering angles reached by GEO satellites. This truncation angle was found to provide a similar accuracy of MSA than the one reported in  Katsev et al.
Auxiliary aerosol data Ancillary data on aerosols are used in iAERUS-GEO for two purposes. First, the optimal estimation of instantaneous AOD is performed using a priori values of aerosol load coming from a model-based climatology (Sect. 2. 6. 1). Second, radiative transfer calculations are done considering a specific aerosol model, out of seven available (Sect. 2. 6. 2), that is determined for each SEVIRI pixel based on a set of monthly geographic distribution maps (Sect. 2. 6. 3).
Monthly maps of a priori AOD Values of a priori AOD are obtained from the 11-year reanalysis of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service (CAMS,  Bozzo et al. , 2020). This climatological data set was used to calculate monthly averages of total AOD by considering the contribution of each of the five species considered in CAMS: sea salt (SS), dust (DU), organic matter (OM), black carbon (BC), and sulfate (SU). The resulting data sets of total AOD (AODtotal), and those corresponding to each species (AODSS, AODDU, AODOM, AODBC, AODSU), were projected onto the SEVIRI grid. Village and Mongu sites, respectively. Over ocean, Model 0 was built by averaging the microphysical properties reported by  Sayer et al. (2012)  for the sites in Ascension Island, Graciosa, and Bermuda.
Data collection and processing We used a four step procedure to collate and process the global economic cost data of invasive rodents (Fig. 1 ). First, (Fig. 1A ; Appendix 1, 'Rodentia dataset' tab), we selected cost entries identified as Rodentia in the 'Order' column of the most recent version (at the time of writing) of the InvaCost database (version 4. 1, available at https://doi. org/10. 6084/m9. figshare. 12668570, which includes 13,553 cost entries collated from scientific and grey literature in multiple languages;  Angulo et al. , 2021; Diagne et al. ,  Figure  1  Workflow depicting the data collection and filtering process. The expanded subset was obtained through the 'expansion' of the suitable subset using the 'invacost' R package  (Leroy et al. , 2022). The criteria used for generating the conservative subset were based on the 'Implementation' (observed versus potential costs) and 'Method_reliability_final' (high versus low-reliability costs) fields of the InvaCost database (Appendix 1, 'Descriptors' tab).
The number of taxa includes both individual species and undefined species aggregated at the genus level. Costs are expressed in 2017 US$. Full-size DOI: 10. 7717/peerj. 14935/fig-  1  2020b). Each cost entry recorded is standardized to 2017 US dollars and categorized by a range of 65 descriptive fields (Appendix 1, 'Descriptors' tab). We added new data by contacting appropriate experts and agencies working on rodent invasions to seek cost information, and scanning these novel references to discover additional publications or reports using a 'snowball' process. Every new cost record was integrated following the InvaCost template and added to the original Rodentia dataset, so that we obtained our final starting Rodentia dataset (Fig. 1A ; Appendix 1, 'Rodentia dataset' tab). Second, we carefully checked the data to (i) remove overlapping or duplicated costs, (ii) assess the reliability (high or low) of the estimation approach used to provide each cost figure based on evaluation criteria similar to those considered by  Bradshaw et al.
(2016)  and (iii) remove all cost entries without clear information on their duration, calculated as the number of years between the recorded cost entry's starting ('Probable starting year adjusted' column) and ending ('Probable ending year adjusted' column) years (Fig. 1B ). All modifications and additions made here were synthesized in Appendix 1 ('Changes made' tab) and systematically sent to updates@invacost. fr as recommended by the database managers. Third, the resulting subset (Appendix 1, 'Suitable subset' tab) was homogenized using the expandYearlyCosts function of the 'invacost' R package  (Leroy et al. , 2022)  so that all cost entries were considered on an annual basis (hereafter 'annualized cost entries')-which means that costs spanning multiple years were divided according to their duration time (e.
Taxonomic representativeness To evaluate the proportion of invasive rodent species for which cost data were available within each taxonomic family, we compared the list of individual rodent species reported in the 'Rodentia subset' with comprehensive lists of invasive rodents recorded worldwide, following an approach similar to  Cuthbert et al. (2021b). Lists of known invasive rodents were extracted and compiled from the Global Invasive Species Database (GISD; http://www. iucngisd. org/gisd/) and the sTwist database (version 2;  Seebens et al. , 2020). We filtered these databases to select only species belonging to the order Rodentia, using the Backbone Taxonomy from the Global Biodiversity Information facility (https://www. gbif. org/) to standardize species names and remove any duplicates.
Cost calculation and distribution The total cost for each category (see below) was obtained by summing all annualized cost estimates provided in the 'Cost_estimate_per_year_2017_USD_exchange_rate' column of our conservative subset. Again, total costs were obtained by summing all annualized cost entries ('Cost_estimate_per_year_2017_USD_exchange_rate' column). Using key database descriptors (see Appendix 1, 'Descriptors' tab for details on all variables and categories used here), we subsequently investigated the breakdown of cost data and estimates across: (i) Taxa: considering the 'Species' descriptor; undetermined species were therefore aggregated by genus, where possible (e. g. , undefined Rattus sp. and Rattus spp. were grouped under the single category 'Rattus spp. '); (ii) Geography: considering the 'Geographic region' descriptor, with Central America merged with North America and Pacific Islands merged with Oceania; we also considered the insular habitat status (yes or no) using the proposed 'Island2 descriptor; (iii) Type of cost: Damage (economic losses due to direct and indirect impacts of rodents) versus Management (monetary investments to prevent and/or mitigate impacts-further separated according to type of actions undertaken (pre-invasion management, post-invasion management, knowledge funding and mixed management )) and; (iv) The impacted sector: (Agriculture, Authorities-Stakeholders, Environment, Fishery, Forestry, Health, Public and social welfare).
Global costs and temporal dynamics Our analyses revealed that invasive rodents have already cost the global economy at least US$ 3. 6 billion (annually US$ 38. 7 million) between 1930 and 2022, based on the cost estimates reported between 1930 and 2022. This average estimate was increased until US$ 87. 5 million annually when considering the timescale 1980-2020 -i. e. , the period that concentrated most (∼97%) of the data recorded in the conservative subset (Fig. 2 ; Appendix 1). A less conservative approach (i. e. , using also low-reliability and potential cost data as well) produced a global figure of around US$ 297. 4 billion worldwide for the period 1930-2022 (Fig. 1C , Appendix 2). The dynamics of costs showed an exponential increase over time (Fig. 2 ), whatever the nature of cost data considered-while an artifactual decrease can be observed for recent years due to the multi-year delays between the occurrence and reporting of costs in the literature (Appendix 3).
Taxonomic representativeness and distribution of costs Invasion costs were reported for 15 individual rodent species in our conservative data subset, but there are at least 49 invasive alien rodents recorded worldwide (i. e. , across InvaCost, sTwist and GISD; Fig. 3 ). Two further species recorded in the original InvaCost database were not included in our conservative subset (Fig. 3 ). Specifically, costs for Hystrix brachyura and Sciurus niger either reported (for H. brachyura in the UK) or expected (for S. niger, should it arrive in the Netherlands), were respectively deemed as low-reliability and potential estimates and thus conservatively excluded. The most underrepresented rodent families in our subset include Sciuridae (11 species without costs out of 18), Muridae (nine species out of 13) and Cricetidae (five species out of seven) (Fig.
Tremendous, increasing and uneven economic costs Invasive rodents have conservatively cost the global economy at least US$ 3. 6 billion reported between 1930 and 2022, representing an average annual cost of US$ 87. 5 million in the period 1980-2020 (where most data have been reported). Inclusion of all costs through a less conservative data filtering leads to a global amount more than 80-times higher (US$ 297. 4 billion; Fig. 1 ). Importantly, all cost figures shown here should be considered as orders of magnitude rather than exact cost estimates, given the clearly nonexhaustive representation and evolving nature of the cost data considered. Nevertheless, the costs show an undeniable increase over time. While it has been recently shown that costs of biological invasions are rising globally even after accounting for research effort  (Haubrock et al.
We believe the InvaCost consortium (http://invacost. fr/en/accueil/) could serve as a foundation for such a network, in which other existing global efforts to compile information-such as the Database of Island Invasive Species Eradications (DIISE;  Spatz et al. , 2022)  or the Global Register of Introduced and Invasive Species (GRIIS;  Pagad et al. , 2018) -should be valuably integrated.
Funding This work was supported by the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initiative for funding the Invacost project which allowed the construction of the InvaCost database. This work was initiated following a workshop funded by the AXA Research Fund Chair of Invasion Biology. This research was also funded through the 2017-2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND programme. Funds for Elena Angulo and Liliana Ballesteros-Mejia came from the AXA Research Fund Chair of Invasion Biology of University Paris Saclay. Christophe Diagne was funded by the BiodivERsA-Belmont Forum Project ''Alien Scenarios'' (BMBF/PT DLR 01LC1807C). Ross N. Cuthbert received funding from the Leverhulme Trust (ECF-2021-001).
bias in the costs of invasive rodents. Invasive rodent species are those recorded in the InvaCost database, the Global Invasive Species Database (GISD; http://www. iucngisd. org/gisd/) and theFigure 3 (. continued)following their taxonomic family. Species with dichromatic rolls (H. brachyura, S. niger) were in the original Rodentia subset, but were not considered in our conservative subset. Roll height is scaled to the number of species within each group and species silhouettes are sized to scale. Grey bars show total cumulative costs in 2017 US$ (log 10 scale). Except Glis glis, Histrix brachyura, and Tamias sibiricus that were created by the authors, all animal silhouettes were obtained from an open source platform (http://phylopic. org/) where the silhouette of Rattus norvegicus was created by Rebecca Groom.
zibethicus (US$ 328. 6 million (3. 5 million/year); n = 8) was the costliest species, followed by R. norvegicus (US$ 68. 6 million (0. 7 sTwist database (version 2; Seebens et al. , 2020). Species with reported costs are in green rolls, while species without reported costs are in yellow rolls, all grouped (continued on next page. ) Full-size DOI: 10. 7717/peerj. 14935/fig- 3.