dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments:  Michael Hemming  was a PhD student at the  University of East Anglia  and  Sorbonne University  under the supervision of  Prof. Jan Kaiser ,  Prof. Karen J. Heywood ,  Dr Dorothee Bakker  and  Dr Jacqueline Boutin , funded by the  Defence, Science and Technology Laboratory  (Contract no. DSTLX1000092277 ) with cooperation with  Direction Générale de l'Armement (DGA) -. The assistance of the UEA Glider Group in keeping the glider flying is also gratefully acknowledged. The BOUSSOLE time series project is funded by the  Centre National d'Etudes Spatiales (CNES)  and the  European Space Agency (ESA/ESRIN  contract  4000119096/17/I-BG ). The authors acknowledge  Météo-France  for supplying the data from the Azur buoy and the HyMeX database teams (ESPRI/IPSL and  SEDOO/Observatoire Midi-Pyrénées)  for their help in accessing their data.
Observations and reanalysis The climate models are compared against a suite of observational and reanalysis products. These include near-surface air temperature (SAT) and tropospheric zonal winds from the ERA5 reanalysis  (Hersbach et al. , 2020) , precipitation rate from the version-2 GPCP dataset  (Adler et al. , 2003) , cloud cover from the version-3 ESA Cloud_cci dataset (ESA CCI-CLOUD;  Stengel et al. , 2020) , and net cloud radiative effect computed from the CERES-EBAF dataset  (Kato et al. , 2018; Loeb et al. , 2018). The net cloud radiative effect is computed as the difference between the top-of-theatmosphere upward net flux and the clear-sky component; it represents the net effect of clouds on the radiation budget at the top of the atmosphere, with negative mean values for cloud-induced cooling, and vice versa (Fig.
Acknowledgements. This research has been supported by the  Hori-zon2020  project  PRIMAVERA  ( H2020 GA 641727 ) and IS-ENES3 ( H2020 GA 824084 ). Eduardo Moreno-Chamarro  acknowledges funding from the  Spanish Science and Innovation Ministry (Ministerio de Ciencia e Innovación)  via the  STREAM  project ( PID2020-114746GB-I00 ) and from the  ESA  contract  CMUG-CCI3-TECHPROP. Etienne Tourigny  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 748750  ( SPFireSD  project). Financial support. This research has been supported by the  Horizon 2020 (PRIMAVERA  (grant no. 641727 ), IS-ENES3 (grant no. 824084 ), and  SPFireSD  (grant no. 748750 )), the  European Space Agency  (grant no. CMUG-CCI3-TECHPROP ), and the  Spanish Science and Innovation Ministry (Ministerio de Ciencia e Innovación)  (STREAM (grant no.
More recently,  Bakalem et al. (2020)  identified a total of 534 species on the Algerian coasts, eight of which are non-indigenous species. However, it is difficult to compare the diversity of polychaetes between the northern and the southern sides of the Mediterranean Sea due to insufficient data for the southern part: i. e. , a scarcity of data from the Libyan coasts, as well as fragmentary and incomplete data from the Egyptian coasts  (Abd-Elnaby, 2009; Dorgham et al. , 2014). Faced with environmental changes, a detailed knowledge of biodiversity appears necessary, in particular by targeting non-indigenous species (NIS). In recent years, research on NIS has increased, reflecting the scientific interest in these species due to their ecological and economic impact on ecosystems  (Zenetos et al.
, 2005 (Zenetos et al. , , 2010 (Zenetos et al. , , 2012)). The European Union's Marine Strategy Framework Directive (MSFD) and the Commission for the Scientific Exploration of the Mediterranean recommend the monitoring and creation of a database on NIS to improve updating of our knowledge of biodiversity in the Mediterranean Sea  (Zenetos et al. , 2010 (Zenetos et al. , , 2012)). The MSFD approach was adopted to protect the marine environment and is based on geographical and ecological criteria. Four sub-regions were subsequently defined: 1) the Western Mediterranean Sea (WMED); 2) the Central Mediterranean Sea (CMED); 3) the Adriatic Sea (ADRIA); and 4) the Eastern Mediterranean Sea (EMED)  (Zenetos et al. , 2010). The Tunisian coasts make up a hinge or transition zone between the WMED and CMED regions.
Data sources 1-Study of tidal channels in the Gulf of Gabès (Southern Tunisia) Tidal channels in the Gulf of Gabès were investigated in the framework of A. Fersi's PhD thesis with the aim of establishing the organization of its benthic communities. Four tidal channels were chosen: the Mimoun Channel (CM), the Kneiss Channel (CK), the Maltine Channel (CML) and the Ben Khelaf Channel (CP) (Fig. 1 ). Other details of the sampling procedure and location are given elsewhere in  Fersi et al. (2018)  and  Dauvin et al. (2021). For each polychaete species identified, the total number of individuals was counted for the whole sampling year and given as results for all tidal channels in Table  1. 2-Polychaete collection: Based on some recent work dealing with the taxonomic identification of pseudo-cryptic species (morphologically very similar), we examined some polychaete species lodged at the collection of the National Institute of Marine Science and Technology (INSTM) (Tunisia).
Results In total, 52 families containing 390 species were counted, of which 49 species belong to a complex of cryptic or pseudo-cryptic species; this means it is not known whether specimens belong to the concerned species sensu stricto or to a different species of the same genus. A total of 29 new species are added based on the present study, including 23 species reported from the tidal channels of the Gulf of Gabès from March 2016 to January 2017 and six species newly recorded after examination of polychaete species lodged in the collections of the INSTM. Revision of the available data from research conducted over the past decade allowed us to list 92 new species. Among them, only 21 were reported from the southern part of the region (CMED). In total, 37 species previously recorded are excluded from the final list of Tunisian polychaetes because they are in fact absent from the area but were misidentified as valid present species.
Acknowledgements The authors acknowledge financial support from  Sfax University (Tunisia ) and  UMR M2C  ( Normandy University, France ) for  Abir Fersi  four-month stay in Caen from September to December 2017. Dr M. S. N. Carpenter  post-edited the English style and grammar. We are also thankful to two referees and editors of  Mediterranean Marine Science  ( Dr. Langeneck Joachim  and  Dr. Argyro Zenetos ) for their helpful suggestions and comments.
Lidar system The multiwavelength Mie-Raman lidar LILAS (LIlle Lidar AtmosphereS) is based on a tripled Nd:YAG laser with a 20 Hz repetition rate and pulse energy of 70 mJ at 355 nm. Backscattered light is collected by a 40 cm aperture Newtonian telescope, and the lidar signals are digitized with Licel transient recorders with a 7. 5 m range resolution, allowing simultaneous detection in the analog and photon counting mode. The system is designed for the detection of elastic and Raman backscattering, allowing the so-called 3β + 2α + 3δ data configuration, including three particle backscattering values (β 355 , β 532 , β 1064 ) and two extinction (α 355 , α 532 ) coefficients, along with three particle depolarization ratios (δ 355 , δ 532 , δ 1064 ). The particle depolarization ratio, determined as a ratio of cross-and co-polarized components of the particle backscattering coefficient, was calculated and calibrated in the same way as described in  Freudenthaler et al.
(2009). Many calibration and operation procedures have been automated for the LILAS system to improve the overall performance of the lidar in terms of observation frequency and data quality. The aerosol extinction and backscattering coefficients at 355 and 532 nm were calculated from Mie-Raman observations  (Ansmann et al. , 1992) , while β 1064 was derived by using the  Klett (1985)  method. The full geometrical overlap was achieved at approximately 750 m range. For the calculation of α and β at 532 nm, we use the rotational Raman scattering instead of the vibrational one  (Veselovskii et al. , 2015) , which allows us to increase the power of Raman backscatter and to decrease separation between the wavelengths of elastic and Raman components. Additional information about atmospheric parameters was available from radiosonde measurements performed at Herstmonceux (UK) and Beauvechain (Belgium) stations, located 160 and 80 km away from the observation site, respectively.
Classification of spatiotemporal observations The input parameters in our classification scheme are the spatiotemporal distributions of β 532 , δ 532 , and G F , which are presented as matrices β i,j 532 , δ i,j 532 , and G i,j F , where i = 1,. , N T ; j = 1,. , N H. Values N T and N H are the numbers of temporal and height intervals in the analyzed dataset. In a single measurement, we accumulate 2 × 10 3 laser pulses, so  the temporal resolution of the measurements is about 100 s, while the height resolution is 7. 5 m. The particle intensive parameters cannot be evaluated reliably when the backscattering coefficient is low. Thus, we set a threshold value for β 532 (normally 0. 2 Mm -1 sr -1 ), namely when β i,j 532 < 0. 2 Mm -1 sr -1 , then the elements of the matrices δ i,j 532 and G i,j F are classified as low signal and ignored.
Acknowledgements. We acknowledge funding from the  CaPPA  project, funded by the  ANR  through the  PIA  (contract no. ANR-11-LABX-0005-01 ), the  "Hauts de France" Regional Council  (project  CLIMIBIO ), and the  European Regional Development Fund (FEDER). The  ESA / QA4EO program  is greatly acknowledged, for supporting the observation activity at LOA and the  OBS4CLIM Equipex project  funded by  ANR. Development of the algorithm for aerosol classification was supported by the  Russian Science Foundation  (project  21-17-00114 ). The SILAM model is acknowledged, for providing pollen simulations. Financial support. This research has been supported by the  Centre National de la Recherche Scientifique  (grant no. ANR-11-LABX-0005-01 ), the  "Hauts de France" Regional Council  (project  CLIMIBIO ), the  European Regional Development Fund (FEDER)  and the  Russian Science Foundation  (project  21-17-00114 ).
In this chapter, we offer a brief introduction to the main concepts associated with Land Use Cover (LUC) mapping, Land Use Cover Change (LUCC) modelling and the uncertainty and validation of LUC and LUCC data and model outputs. The chapter summarizes the theoretical fundamentals required to understand the rest of the book. First, we define Land Use and Land Cover concepts that have been extensively discussed and debated in the literature (Sect. 2). Second, we review the history of LUC mapping, from the first manually produced maps to the advent of aerial and satellite imagery and the production of new datasets with much greater detail and accuracy (Sect. 3). Third, we address the usefulness of LUC data and LUCC analysis for society (Sect. 4), contextualizing all these studies and efforts within the framework of Land Change Science (Sect.
Introduction Land Use and Land Cover (LUC) data is an important source of information for a wide range of users from different backgrounds and scientific disciplines. It provides an overview of the different covers on the Earth's surface (e. g. vegetation, agricultural fields, rocks, water, artificial sur-faces…) and how they evolve over time. It also traces how these covers are used (land use) and how this use changes. LUC data can be very useful in an array of different fields. It is especially valuable for understanding the impact that many natural and human-induced processes, such as climate change, deforestation and urbanization, can have on the Earth's surface. As a result, LUC research has been receiving increasing attention over recent decades, and the number of fields making use of this data is on the rise.
In recent years, it has been applied, for example, in studies analysing habitat distribution and ecosystem services  (Jacob et al. 2003; Brown 2013) , spatial patterns of biodiversity  (Zimmermann et al. 2010; Tuanmu and Jetz 2014) , and ecosystem status and biogeochemical cycling  (Johnson and Patil 1998; Lawrence et al. 2012) , etc. A wide variety of processes are also studied using LUC data. Bielecka (2019) review some of the most common processes analysed through the CORINE Land Cover database. These include agricultural abandonment, urbanization, afforestation, deforestation, landscape fragmentation, etc.
2016). Moreover, the validity of a specific dataset will depend on how it is used  (Castilla and Hay 2007 ). An LUC map considered invalid for a specific type of study could be a reliable source of information for another study at another scale and with different aims. Maps like these are often described as "fit for use" or "fit for purpose"  (Chrisman 2010 ). In addition, users often process the datasets in some way, so introducing sources of uncertainty that need to be evaluated  (Nienkemper and Menz 2016). When using a series of LUC maps, additional uncertainties may arise. As  Olofsson et al. (2013)  noted, even when two independent maps are both very accurate, it is possible that the accuracy of the change map obtained by post-classification comparison will be low due to error propagation.
Combining these unique multisensorial data with modelling, we show that the dust devil was around 25 m large, at least 118 m tall, and passed directly over the rover travelling at approximately 5 m s -1. Acoustic signals of grain impacts recorded during the vortex encounter provide quantitative information about the number density of particles in the vortex. The sound of a Martian dust devil was inaccessible until SuperCam microphone recordings. This chance dust devil encounter demonstrates the potential of acoustic data for resolving the rapid wind structure of the Martian atmosphere and for directly quantifying wind-blown grain fluxes on Mars.
Multisensorial recordings of a dust devil encounter The 167 s SuperCam microphone acoustic recording on Perseverance sol 215 shows two 2-3 s periods of low-frequency signal content (20-60 Hz) close to the start of the recording, with an ~3 s very quiet period in between and after them (Fig. 1 ). Such low-frequency content is typical of microphone signals associated with wind gusts  37, 38. The Root-Mean-Square (RMS) of the sound amplitude (calculated in 2 s windows-see "Microphone data and data processing") has a similar double-lobed structure as predicted for very close encounter vortex signals recorded on the microphone  44  , and is similar to acoustic data recorded on Earth during terrestrial vortex encounters (see "Acoustic recordings of terrestrial vortices"). The two peaks correspond to the stronger winds at the vortex walls resulting in a larger amplitude microphone signal, while between the peaks the rover is in the calm eye of the vortex.
With a sampling rate of up to two samples per second, the wind sensor is not well suited to resolve the rapid wind fluctuations associated with this convective vortex. However, the high microphone sampling rate (25 kHz) allows the faster dynamics to be studied. The drop in the wind speed between two gusts, coincident with the pressure minimum, indicates that this dust devil passed directly over Perseverance. The initial wind gust visible in the microphone data corresponds to the leading vortex wall, and the second wind gust to the trailing vortex wall. In between these gusts, Perseverance is in the calm eye of the vortex with a wind speed <2 m s -1 (the limit of sensitivity of the microphone to wind speed  38  , Supplementary Fig. 7 ). In the final Navcam image, the amount of dust is close to half that in the penultimate image thus providing further evidence that the image is taken from the middle of the dust devil.
Such investigations would also permit on-board data compression schemes to be formulated to more efficiently extract information on aeolian processes from microphone data, which as presently implemented on Perseverance requires substantial data volumes to be downlinked (8 MB per 167 s recording). Additional microphone recordings during the convective period on Mars may provide the opportunity to observe more dust devils as the Perseverance mission continues. This would allow comparative studies to be performed of the dust-lifting capabilities of different vortices and at different geographic sites.
Microphone data and data processing SuperCam's microphone records air pressure fluctuations from 20 Hz to 12. 5 kHz at a 25 kHz sampling frequency, and up to 50 kHz when the 100 kHz sampling mode is used. The analog signal from the microphone, ranging from 0 to 5 V, is digitized (12-bit depth) using one of 4 electronic gains to boost the sensitivity from 0. 6 to 21 V/Pa and the resolution from 2 to 0. 06 mPa. For atmospheric recordings such as the one on Perseverance sol 215, the highest gain level is used (21 V/Pa), corresponding to an amplification factor of 972. The microphone's electronic response function is used to correct raw spectra below 100 Hz. The spectrogram of the microphone sound pressure level is calculated using a window size of 0. 2 s in order to resolve the fine details in the acoustic data.
Wind speed, direction, and pressure data The wind and pressure data are recorded from the Mars Environmental Dynamics Analyzer (MEDA) instrument  42. These data are acquired at 1 sample per second during this event. Wind speed and direction are independently acquired from two separate booms, both at the height of 1. 5 m above the ground and separated by 120°(termed boom 1 and boom 2). Data from one boom is generally superior to the other, for a given wind direction, depending on the rover elements obstructing the flow to each boom. The accuracy of the wind speed measurement is ±1 m s -1 (up to 10 m s -1 ) and 10% of the wind speed over 10 m s -1 and the resolution is ±0. 5 m s -1 (up to 10 m s -1 ) and 10% of the wind speed over 10 m s -1. The wind direction is resolved with an accuracy of ±15°.
Temperature data during the vortex encounter Data from the five Atmospheric Temperature Sensors  42  (ATS) and from the Thermal Infrared Sensor (TIRS) ground and 40 m temperature retrievals are provided in Supplementary Fig. 5. ATS1-3 are mounted at 1. 45 m on the rover's remote sensing mast, while ATS4 and 5 are mounted at 0. 85 m on either side of the front of the rover. ATS1 and ATS2 have good measuring conditions and are the best proxy for the near-surface air temperature at the time of this vortex encounter. ATS4 does not show fluctuations because it is "sheltered" from the environment at this time and records the temperature within the rover's thermal boundary layer. This is also the case for ATS3, but to a lesser extent, which records a constant temperature over most of this period.
The abrupt and asymmetric change in ATS5 is due to the wind direction change, as the temperature sensor is cooled when the sensor points toward the wind direction. This effect is likely not related directly to the vortex thermodynamics but the wind direction change is a consequence of the vortex winds. There are no vortex features in the ground temperature data provided by TIRS. The TIRS sensor measuring air temperatures at a height (z) of approximately 40 m showed a similar response to ATS1 and 2, with a temperature peak well above the noise level at the time of the vortex passage. Winds during the vortex encounter were such that heat from the rover's Radioisotope Thermonuclear Generator (located at the rear of the rover), should have been advected away from the vortex, allowing a good measurement of temperatures.
Navcam image interpretation The rover orientation and location on Perseverance sol 215 are shown in Supplementary Figs. 8  and 9 , respectively. The Navcam images place the dust devil as coming from an azimuth of approximately 163°(162°f rom the Navcam image, 164°from a comparison with orbital data using the Multi Mission Geographic Information System-MMGIS), initially as a 2 m dust cloud 110 m away, and then as an 11 m diameter cloud in subsequent images taken over the following 21 s (Fig. 3  and Supplementary Figs. 10-14), suggesting that either the vortex was still growing, or dust was being initially entrained into the vortex, when it was first observed. The diameters are based on hand drawings of the edge of the dust cloud. The distance in the 30-m image is well-constrained, since it is just about to cross rover tracks, which are welldefined in length.
Acknowledgements We are most grateful for the support of the  Mars 2020  project team, including hardware and teams. This project was supported in the US by the  NASA   Mars Exploration Program , and in France by  CNES. It is based on observations with SuperCam embarked on Perseverance (Mars2020). The research carried out at the  Jet Propulsion Laboratory, California Institute of Technology , is under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). The JPL co-author (M. T. ) acknowledges funding from  NASA's Space Technology Mission Directorate  and the  Science Mission Directorate. A. V-R is supported by the  Spanish State Research Agency (AEI)  Project No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (INTA-CSIC ), and by the  Comunidad de Madrid  Project  S2018/NMT-4291  ( TEC2-SPACE-CM ).
R. H. and A. S-L. were supported by Grant  PID2019-109467GB-I00  funded by  MCIN/AEI/10. 13039/501100011033 /and by  Grupos Gobierno Vasco   IT1742-22. A. M. was supported by Grant  PRE2020-092562  funded by  MCIN/AEI/10. 13039/501100011033and  by " ESF Investing in your future ". R. L. acknowledges  InSight PSP Grant   80NSSC18K1626  as well as the Mars 2020 project. B. C. is supported by the  Director's Postdoctoral Fellowship  from the  Los Alamos National Laboratory , grant  20210960PRD3. JA. RM. , M. M, J. T and J. G-E were supported by  MCIN/AEI 's Grant  RTI2018-098728-B-C31.
Data availability The Mars2020 data used in this study is publicly available at the Planetary Data System Geosciences Node: https://pds-geosciences. wustl. edu/missions/mars2020/. The acoustic and MEDA data used here can be found at https://doi. org/10. 17189/1522646 and https://pdsatmospheres. nmsu. edu/PDS/data/PDS4/Mars2020/mars2020_meda/, respectively. The images used in this paper are available at https:// mars. nasa. gov/mars2020/multimedia/raw-images/. Source data are provided with this paper.
Moreover, we analyzed a set of core top and sediment cores collected from several locations of the Northwestern Mediterranean that are considered to represent foraminifera assemblages sedimented during the preindustrial Holocene era (Table  1 ). Heussner et al. , (2006) , and data concerning MR 3. 1. A can be found in  Cisneros et al. , (2016). Conventional 14 C ages and 1sigma uncertainties have been rounded according to convention. 3. 37° E bulloides 3. 2. Sediment core samples processing A total of 2 sediment samples from Planier core top, 7 from Lacaze-Duthiers sediment core and 40 from Minorca sediment core were weighed (Table  1 ). Dry bulk sediment samples from all sites was weighed using a Sartorius CP124S balance (precision= 0. 1mg). The samples were then wet-sieved in order to separate the <63µm fraction and dry sieved to separate the bigger fractions (>150 µm and >300 µm).