dataesr/scientific-paragraphs-categorization huggingface dataset:
To measure immigration attitudes, we use the European Social Survey (ESS) data from rounds 5 to 9 and build two indices about natives' attitudes to immigration. Firstly, their policy preference regarding levels of immigration. Secondly, their assessment of the economic, cultural, and overall contribution of immigration to their country. The data cover  97, 193  individual respondents surveyed between 2010 and 2019 in 183 regions across 21 European countries. Our measure of regional migrant flows captures short-term variations in the share of foreign-born individuals at the NUTS2 regional level, obtained from the European Labour Force Survey. We also build on the recent literature on the determinants of public attitudes to immigration and control for individual drivers as well as contextual, region-specific factors such as GDP, unemployment rate and population density.
Methods In this paper, we combine data from multiple sources to create a dataset that includes individual-level information on native individuals' attitudes toward immigration and several regional variables. At the individual level, the present analysis relies on biannual data from the European Social Survey (ESS). It contains 97,193 respondents from 21 European countries across 183 regions. Because our primary objective is to identify how public opinion reacts to short-term, within-region changes in attitudes to immigration, we only include in our analysis countries surveyed by the ESS at least twice over the time period under scrutiny (2010-2019). 2 Using the ESS allows us to disentangle attitudes to immigration across a number of European regions and within regions across time because people of the same region are observed at different time periods.
We integrate the micro-attitudinal data from the ESS with contextual data at the regional and region-year level to capture the size and composition of the foreign-born population. These regional-level variables are gathered from various sources, particularly the European Labour Force Survey (EULFS) and the OECD's database, which are described in more detail below. The EULFS Data was received and processed through the European University Institute library following approval of research proposal number RPP 47/2021-LFby Eurostat Microdata Access Team. The data was stored and processed on a single designated computer located in a locked office of the EUI premises. Data analysis was conducted using Stata 15 and supported by the EULFS codebook and user guide. The EULFS microdata are anonymized according to anonymisation and aggregation criteria agreed between Eurostat and the National Statistical Institutes in order to enable Eurostat to make EU LFS microdata available to researchers.
Regional migration data We use repeated, cross-sectional data from the European Labour Force Survey (EULFS) to construct variables that capture the average and the short-term variations in the regional share of migrants at the NUT2 level. 5  These level and change variables are assigned to each ESS respondent based on the year they were interviewed and his or her region of residence. 6 Besides demographic information, the EULFS also reports the birthplace of each individual, distinguishing fifteen different regions of origin 7. We use all foreign-born individuals to compute a measure of the share of immigrants as a share of the total population at the regional level: , , , s r t s r t r t M S Pop = where M is the total stock of migrants in region r born in a foreign country, with skills (tertiary educated or not) and/or origin (Europe or non-European), or gender (male or female) in year t.
We also collect data about GDP and unemployment rate from the OECD database and Eurostat to use as controls for time-varying differences across regions that could influence individuals' attitudes to immigration. A contextual variable regarding regional population density was also added to the model. Since these are not of our primary interest, only between-region (and not also within-region) macro indicators are included. A similar approach has been used by  McLaren (2012)  and Jeannet (  2020 ) for country-level controls. We do not control specifically for any country-level characteristics apart from countries being either part of Western or Central and Eastern Europe. Nevertheless, we assume that individuals from the same country are significantly more similar in their attitudes to immigration than individuals from different countries.
I believe that the work will make a fine contribution about the regional/contextual effects of migrants in individual-level preferences on migration policies. including the whole sample and probably controlling for the effect of being native or not. The choice can be reasonable but need to be better justified. Also, they should report the share of nonnatives that were included in the original sample. 3. On p. 7 the authors mention data from both EULFS and OECD, but in the end, they refer only to EULFS on p. 8, please clarify on the use of OECD data. 4. The description of the main independent variable on p. 8 refers to migrants' skills in the formula used to calculate the share of migrants, yet it is not clear how this data is calculated and whether it is used in the analyses (it seems not).
Data availability Underlying data ESS Data used in this study are extracted from round 4, 5, 6, 7 and 8 of the European social survey. This data is publicly available from the European Social Survey (ESS) data portal: https://doi. org/10. 21338/NSD-ESS-CUMULATIVE. Access to microdata from the European Union Labour Force Survey (EU-LFS) is granted for legitimate research purposes only. To protect the anonymity of respondents (persons, organisations), the access to microdata is restricted and requires filing a research proposal to the Eurostat Microdata Access Team. A guide for how to apply for dataset access is available at: https://ec. europa. eu/eurostat/documents/203647/771732/How_to_ apply_for_microdata_access. pdf Data from the OECD is publicly available from the following link: https://stats.
Country Region 2010 2012 2014 2018 Total CZ04 235 0 256 266 757 CZ05 345 0 347 328 1020 CZ06 388 0 281 375 1044 CZ07 310 0 191 256 757 CZ08 293 0 275 267 835 Germany DE1 255 0 295 228 778 (DE) DE2 371 0 355 356 1082 DE3 89 0 140 73 302 DE4 224 0 179 62 465 DE5 22 0 22 6 50 DE6 21 0 41 42 104 DE7 162 0 162 124 448 DE8 116 0 120 51 287 DE9 168 0 222 244 634 DEA 487 0 427 414 1328 DEB 117 0 112 97 326 DEC 19 0 30 23 72 DED 248 0 261 133 642 DEE 177 0 146 66 389 DEF 77 0 78 79 234 DEG 192 0 156 58 406 Denmark DK01 364 0 354 172 890 (DK) DK02 200 0 201 351 752 DK03 332 0 341 349 1022 DK04 382 0 346 393 1121 DK05 197 0 142 204 543.
Country Region 2010 2012 2014 2016 2018 Total Country Region 2010 2012 2014 2016 2018 Total ITH4 0 5 0 69 0 74 Sweden SE11 212 0 347 0 276 835 ITH5 0 55 0 257 0 312 (SE) ITI1 0 68 0 97 0 165 SE12 247 0 220 0 192 659 ITI2 0 13 0 37 0 50 SE21 125 0 155 0 113 393 ITI3 0 19 0 87 0 106 SE22 196 0 205 0 185 586 ITI4 0 74 0 171 0 245 SE23 280 0 301 0 260 841 Lithuania LT01 0 0 527 0 409 936 SE31 128 0 137 0 129 394 (LT) SE32 58 0 87 0 61 206 LT02 0 0 1648 0 1370 3018 SE33 78 0 102 0 98 278 Norway NO01 273 0 282 0 301 856 Slovenia SI03 761 0 687 0 669 2117 (NO) (SI) NO02 102 0 93 0 79 274 SI04 507 0 439 0 494 1440 NO03 272 0 256 0 219 747 Slovakia SK01 218 0 0 0 75 293 NO04 222 0 155 0 177 554 (SK) NO05 252 0 227 0 238 717 SK02 594 0 0 0 360 954 NO06 143 0 130 0 109 382 SK03 455 0 0 0 317 772 NO07 132 0 124 0 134 390 SK04 539 0 0 0 313 852 Poland (PL) PL12 227 0 225 0 174 626 (UK) Kingdom United UKC 103 0 107 0 106 316 PL21 141 0 162 0 152 455 UKD 257 0 230 0 226 713 PL22 204 0 175 0 220 599 UKE 211 0 159 0 190 560 PL41 146 0 139 0 107 392 UKF 166 0 159 0 149 474 PL42 79 0 64 0 51 194 UKG 197 0 166 0 125 488 PL43 39 0 43 0 34 116 UKH 204 0 188 0 199 591 PL51 109 0 85 0 102 296 UKI 120 0 116 0 114 350 PL52 50 0 31 0 33 114 UKJ 295 0 275 0 288 858 PL61 101 0 96 0 77 274 UKK 177 0 180 0 187 544 PL62 64 0 50 0 62 176 UKL 134 0 121 0 90 345 PL63 115 0 80 0 78 273 UKM 227 0 192 0 169 588 PL71 125 0 129 0 118 372 UKN 60 0 54 0 62 176 PL72 69 0 62 0 48 179 Total 31879 890 29382 2395 32647 97193 PL81 112 0 96 0 83 291 PL82 95 0 105 0 103 303 PL84 50 0 57 0 46 153 Portugal PT11 818 0 456 0 332 1606 (PT) PT15 80 0 60 0 37 177 PT16 368 0 289 0 221 878 PT17 663 0 256 0 231 1150 PT18 75 0 109 0 103 287.
Environmental covariates Following the Scorpan framework  (McBratney et al. , 2003) , 392 environmental covariates relevant to SOCD were investigated and listed in Table  1. The environmental covariates were derived from multiple sources: Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM + ), Landsat 8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), products from Moderate Resolution Imaging Spectroradiometer (MODIS), and legacy digital soil maps. All available Landsat 5, 7 and 8 surface reflectance images at 30 m resolution covering the entire study area in the Collection 2 Tier1 Level 2 database were collected from 2003 to 2017 (https://www. usgs. gov/). These images received radiometric, geometric and atmospheric corrections with Landsat Ecosystem Disturbance Adaptive Processing System (Landsat 5 TM and Landsat 7 ETM+) and Land Surface Reflectance Code (Landsat 8 OLI).
Evaluation of model performance and computation efficiency The whole dataset (402 samples) was randomly split into calibration (281 samples) and validation (121 samples) sets at a ratio of 70 % to 30 % with 50 repeats. The R 2 , Lin's concordance correlation coefficient (LCCC), and RMSE were used to evaluate model accuracy on the validation set. Higher R 2 and LCCC close to 1 and a lower RMSE close to 0 mean better model accuracy. R 2 = 1 - ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ n i (ŷ i -y i ) 2 √ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ∑ n i (y i -y) 2 √ (5) RMSE = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ n i (ŷ i -y i ) 2 n √ √ √ √ √ (6) LCCC = 2ρσ y σ ŷ (y -ŷ) 2 + σ 2 y + σ 2 ŷ (7) where y i and ŷi are observation and prediction for sample i, y and ŷ are the mean of all the observations and predictions, n is the number of samples, ρ is the correlation coefficient, σ y and σ ŷ are the variances of all the observations and predictions.
Statistical summary of soil properties Table  2  shows the statistical summary of SOCD in all, calibration and validation datasets. For all dataset, SOCD ranged from 10. 41 to 149. 04 t ha -1. The median and mean of SOCD were 41. 29 and 46. 26 t ha -1 at 0-30 cm. According to the coefficient of variation (47. 65 %), SOCD had high heterogeneity. The skewness of 1. 61 and kurtosis of 6. 36 indicated that SOCD had a slightly positive and leptokurtic distribution in all dataset. The calibration and validation for SOCD had similar distributions to all dataset, showing that the repeated random data split (50 times) was reasonable for proper model calibration and validation.
0. The estimated SOC stock was 2. 89 Pg for the study area in our product while the SOC stock estimation from SoilGrids 2. 0 product was 24. 2 % lower (2. 19 Pg). The poor SOCD estimate of SoilGrids 2. 0 resulted from the fact that the soil database (>140,000 locations) used for producing SoilGrids 2. 0 only had<80 sampling sites in the study area, leading to a limited weight in the spatial predictive model to represent the unique pedo-climatic condition for the study area. Therefore, when using the global soil maps for regional or national studies, it is necessary to correct them by the regional or national soil observations, especially for these regions with limited soil observations in producing the global soil maps. For this purpose, previous studies have proved that model averaging was a potential solution for producing more accurate "local" digital soil property maps by coupling "global" soil maps and "local" soil observations  (Malone et al.
Limitations and perspectives One limitation of MGFS is that it may take a long time to select the optimal variables for a large number of covariates (e. g. , n > 50) as it needs to run the model (n 2 + n)/2 times. Therefore, the early stop is suggested in this study (see details in section 2. 3. 3) when using MGFS for a large set of covariates. It should be noted that the use of early stop may result in a local optimum which can be slightly different from the global optimum. From our personal experience, the local optimum was quite close to the global optimum but it still needs to be confirmed by other datasets  (Xiao et al. , 2022). From theory, MGFS method can work on all the machine learning methods, such as RF, support vector machine, gradient boosted machine, XGBoost. In this study, we only evaluated its performance on RF, so its applicability remains to be tested for other machine learning methods in future studies.
60, LCCC of 0. 74, and RMSE of 13. 80 t ha -1 ) in SOCD mapping than VIF, Boruta and RFE. When compared to the model using all 392 covariates (R 2 of 0. 54, LCCC of 0. 70, RMSE of 14. 79 t ha -1 ), MGFS had an increase of R 2 and LCCC at 11. 1 %, 5. 7 %  and decrease of RMSE at 6. 8 %. In addition, MGFS had the lowest global uncertainty (UI of 0. 86) and the highest computation efficiency among all the models. Accordingly, we concluded that MGFS was superior to RFE, Boruta, VIF, and Full model in terms of model parsimony, model performance, map uncertainty and computation efficiency; therefore, it has a high potential in fine-resolution DSM studies at broad scales. Theoretically, MGFS can work on all machine learning methods; however, further tests on other datasets and machine learning models are still needed to evaluate its robustness.
Acknowledgements This study was supported by the  National Natural Science Foundation of China  (No. 41930754 ,  42001047 ,  41901055 ) and  Ten-thousand Talents Plan of Zhejiang Province, China  ( 2019R52004 ). Dominique Arrouays (DA) Anne C. Richer-de-Forges (ARdF)  are members of the research consortium GLADSOILMAP supported by  LE STUDIUM Institute of Advanced Research Studies, France. ARdF is coordinator and DA is member of the  French "Centre d'Expertise Scientifique Cartographie Numérique des sols " granted by the  CNES-TOSCA program. We also would like to acknowledge two anonymous reviewers for their constructive suggestions which improved our manuscript a lot.
Tree leaf gas exchanges Leaf level gas exchange was measured using two portable photosynthesis system (LI-6400XT) for all trees at all dates except the second one due to breakdown of the greenhouse system. Measurements were done between 11 am to 3 pm, period during which PAR in the green house is highest and stable (between 600 and 1000 µmol. m -². s - 1 ). Licor chamber conditions were set to keep close to the greenhouse while providing non-limiting conditions: PAR was set at 1000 µmol. m -². s -1 , the block temperature was set at 25°C, flow rate and scrubbing were adjusted to maintain RH between 60 and 80%. The leaves were allowed to acclimate for at least 3 minutes in the chamber before measurement, to ensure gas exchange stability. For each leaf (Q. ilex) or needle bunch (P. halepensis), ten values were recorded during one minute and the average was used in the data analysis.
The Rb correction is considered robust for samples with low 85 Rb: 88 Sr ratios (<0. 002), which included all samples in this study. Krypton interference ( 86 Kr) originating from the argon supply is corrected using on-peak subtraction before each analysis. Potential analytical interferences caused by polyatomic molecules were monitored by measuring strontium oxide on mass 104 ( 88 Sr + 16 O) and 87 Sr/ 86 Sr isotope ratios of known marine reference materials. Processing of the geochemical data is performed using the IsoFishR application  (Willmes et al. , 2018a,b). In short, a 5-point average is applied to the raw data collected by the mass spectrometer with an integration time of 0. 2 s resulting in 1 datapoint per second, and outliers were removed based on 2SD outlier criterion using a 10-point moving average window.
Isoscape modelling and spatial assignment Plant and soil sample data were taken from published 87 Sr/ 86 Sr databases covering France  (Willmes et al. , 2014 (Willmes et al. , , 2018a) )  and northern Italy  (Bataille et al. , 2018; Willmes et al. , 2018a; Lugli et al. , 2022). We used kriging with external drift to model the bioavailble 87 Sr/ 86 Sr isoscape near Lazaret Cave, following the methods outlined in  Willmes et al. (2018a). Kriging is a geostatistical approach to interpolate continuous distributions of 87 Sr/ 86 Sr values across the landscape between known data points. Kriging with external drift improves the accuracy of the isoscape by accounting for additional spatial trends defined by an auxiliary variable  (Britton et al. , 2020; Holt et al. , 2021; Lazzerini et al. , 2021) , here, the underlying lithology.
In order to define the auxiliary variable, we used the GLiM map (Global Lithological Map, Supplementary Figure  1 ; Hartmann and Moosdorf, 2012) which regroups geological formations into broader geological units (metamorphic rocks, volcanic/plutonic felsic/intermediate/mafic rocks, carbonate/siliclastic sedimentary rocks). We used 954 sampling location to build the 87 Sr/ 86 Sr isoscape (France: n = 516, northern Italy: n = 438). When several plant and soil samples were available at a given sampling location, we used the average 87 Sr/ 86 Sr value. The GLiM geological unit of each sampling location was retrieved using ArcGIS 10. 5. Kriging was carried out with the gstat package in R (krige function,  Gräler et al. , 2016)  using an exponential variogram model and a search neighbourhood set to min.
= 5 and max. = 50  (Willmes et al. , 2018a). Spatial assignment was performed using the assignR package in R  (Ma et al. , 2020). In order to assess the most probable locality of summer and winter ranges, maximum and minimum 87 Sr/ 86 Sr values were determined for each red deer molars and we used the δ 18 O results to identify the season associated with these values (Supplementary Table  3 ). These time-correlated values were then assigned to the 87 Sr/ 86 Sr bioavailable isoscape within 100 km of Lazaret. We produced a posterior probabilities of origin raster for each sample. We then extracted the area corresponding to 90% of the probability density to identify possible locations of red deer in summer and winter periods identified in each tooth. Given the lack of the variation within the ibex M1-M3 samples, we used the mean 87 Sr/ 86 Sr value of the teeth to establish the year-round range of the individual.
This study focuses on the creation of a digital twin of a space exploration rover to perform damage detection. The digital twin incorporates various subsystems of real rovers to accurately simulate the rover's behaviour. Damage detection is performed by introducing damages into the digital twin and comparing signals obtained in healthy and damaged conditions. By using the multiphysics model created by integrating different subsystems, the effect of damages can be observed in other subsystems of the rover. The study aims to demonstrate the potentiality of a digital twin for damage detection, reducing the risk of mission failure and data loss.
INTRODUCTION In recent years, space exploration efforts have increasingly focused on the surface exploration of planets and satellites such as Mars and the Moon. This has been made possible through the use of rovers, which are capable of travelling across celestial bodies and conducting research activities. However, completing missions can be challenging, and problems must be addressed promptly to avoid the loss of scientific data or even the rover itself. Given the limited communication capabilities with Mars (Olson, Matthies, Wright, Li, & Di, ), for instance, anomalies must be detected quickly, as there is no possibility of on-site human intervention. To face this problem, NASA started developing physical twins of its rovers, such as MAGGIE and OPTIMISM for Curiosity and Perseverance, respectively  (Cook, C.
In this framework, the project "DIGital twin di sistemi di Esplorazione lunare" (DIGES), in collaboration between Politecnico di Milano and Agenzia Spaziale Italiana, proposes a feasibility study for the application of digital twins for future space exploration rovers. Therefore, this study aims to create a digital twin of a space exploration rover using the MATLAB®-Simulink® environment and to use it to perform damage detection by simulating damages within the model. This strategy helps the interpretability of future, and maybe black-box, damage detection algorithms since the latter will be based on multiphysics databases, generated ad-hoc by injecting damage models in the digital twin. The digital twin incorporates various subsystems of real rovers, such as the energy harvesting system (batteries and solar panels), the driveline, the robotic arm, and the heating and cooling system, with all their interdependencies modelled.
By doing so, it is possible to simulate the rover's behaviour in a lunar environment accurately, even if the model can be easily adapted to different celestial bodies. Damage detection is performed by introducing damages into the digital twin and comparing the signals obtained in healthy conditions with those obtained in damaged conditions. By using the multiphysics model created by integrating the different subsystems, the effect of damages and anomalies can be observed in the other subsystems of the rover as well. In summary, this study aims to demonstrate the effectiveness of a digital twin for damage detection in space exploration rovers, which could significantly reduce the risk of mission failure and data loss. Tokyo, Japan, September 11 -14, 2023  OS02-07.
SPACE EXPLORATION ROVER DIGITAL TWIN A digital twin can be defined as a virtual representation of a system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making  (Armstrong, ). For this study, a real space exploration rover, e. g. , Curiosity, has been taken as a reference and then it has been modified according to the characteristics of the lunar environment. The digital twin has been developed in the MATLAB®-Simulink® environment to exploit the advantages given by the block-based modelling approach to represent real system behaviour at a very detailed level and to build multiphysics models thanks to the Sim-scape® toolbox.
DISCUSSION This framework proves that it is possible to detect anomalies by exploiting a digital twin capable of real-time operations for damage detection purposes and that the latter can be used to create a database and train machine learning algorithms, such as support vector machines, to detect damages in real time. The simulation results show that an anomaly in one motor affects the other motors, even those devoted to steering instead of driving. It is worth noticing that the digital twin seems to be very sensitive concerning failures of the rear motors and that one can distinguish an anomaly of the current sensor from motors' anomalies by looking at the current absorbed by all the motors. Moving on to the solar panels, the rover proved to be robust with respect to anomalies such as lunar regolith deposition thanks to a redundant design, since the rover is still able to complete the mission even with a severe efficiency loss.
CONCLUSION This work focused on the usage of digital twins for damage detection purposes in space exploration rover applications, which are high costs and high risks systems due to their operative condition. The results presented address the anomalous conditions of driving motors, solar panels, and electric heaters, but the digital twin described can be used for implementing several damages to the various subsystems. Future applications may be regarding the creation of a database for machine learning algorithms to perform damage detection or implementing model updating algorithms to enable real-time autonomous damage detection. In general, this study proposes a fertile framework for developing digital twins to help understand the behaviour of real systems, with the help of existing physical twins.