dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset was collected by means of a dedicated ROS package running onboard the embedded CPU (Odroid XU4). This package manages communication with and data logging from the eventbased camera, the 6-axes IMU, and the radar sensor. The embedded CPU clock allows to time-synchronize ROS bags. The ROS drivers for the DVS240 camera are provided by Robotics Perception Group 2 [13]. The events streamed by the camera consist of a set of pixel coordinates (x, y), a polarity p ∈ {-1, +1}, and the corresponding timestamp. Radar data are saved as raw ADC samples: the (I, Q) values of antennas 1 and 2 respectively, with 128 samples per chirp, 16 chirps per frame, and 300 microsecond per chirp as default settings. As a result, for each timestamp, the raw output consists of 4 vectors (2 antennas ×(I, Q) vectors), each vector containing 16 × 128 = 2048 samples.
py Python scripts for data loading & visualization. Sensors: 'dvs', 'imu', 'radar', and 'optitrack'. Formats: 'ros', or 'csv'. 12 Code. /sample_visualization_<format>. py Python scripts for data loading & visualization. All sensors rendered in one unique frame. Formats: 'ros', or 'csv'. 13 Code. /convert_rosbags_to_csv. py Python script for conversion of ROS bags to CSV. 14 Text. /Readme. md Detailed description on how to use the dataset.
Data sources From 23 to 30 April, each service member was phoned by investigators with experience conducting a retrospective survey, using a standardised questionnaire. We chose this type of investigation because service members were isolated or quarantined. The questions were about personal (sex, age, body mass index (BMI), active smoking, blood group and rhesus factor), clinical (medical history, presence of symptoms, and dates of symptom onset/disappearance), and epidemiological data (date of boarding, places where they slept/had meals/worked, possible exposure to COVID-19, 1-to-3 score (never/sometimes, often and always) for application of physical distancing > 1 m, face mask wearing, and hand hygiene). When necessary, we obtained additional information from medical records and consultation registers.
Viral genome sequencing Viral genome sequencing was performed on the first 60 positive samples taken from the service members on 8 April to contribute to molecular epidemiological studies (method in Supplementary Material: viral genome sequencing). Viral sequences are available online in the GISAID database (https://www. gisaid. org). The anonymous individual identifiers, GISAID accession numbers, and other data can be found in the Supplementary Table  S1.
Acknowledgements We wish to thank  Laurent Richecoeur ,  Isabelle Delplanque ,  Patrick Augier ,  Simon Cauchemez ,  Christian Hupin , and  Rémy Michel , as well as the  French Navy  for making available additional personnel for the investigation. We acknowledge the authors, originating and submitting laboratories of the sequences from GISAID's EpiFlu Database on which this research is based (see Figure 4). All submitters of data may be contacted directly via the GISAID website: www. gisaid. org.
This species is native to Japan  (Clausen et al. , 1927)  and was first detected outside of its native range when individuals, most likely from Gunma, Ibaraki and Tochigi Prefectures on the Honshu island  (Strangi et al. , 2022) , were found in the United States of America in 1916  (Fleming, 1972). Since this accidental introduction, the beetle has spread throughout North America and Canada. It was then introduced to the Azores archipelago on Terceira island in the 1970's  (Martins et al. , 1988; Vieira, 2008)  and has spread to all of the Azores islands with the exception of Santa Maria. More recently, Pj has been detected in Northern Italy in 2014  (EPPO Global Database, 2014)  and Switzerland in 2017 (EPPO Global Database, 2017). Other interceptions have been observed in Central Europe, while stable populations are only found in the Italian regions of Lombardy and Piedmont and the Ticino region in Switzerland  (Poggi et al.
The SDM is trained using a classification machine-learning algorithm based on presence-only data from native and long-invaded ranges (Japan, North America, the Azores archipelago) and the predicted suitability is projected to the entire Northern Hemisphere. More than 30 000 presence data have been gathered from standardized surveys and citizen-science platforms, whereas the target-group method  (Phillips et al. , 2009)  has been used to generate pseudo-absences in the sake of balancing the presence-only dataset and to account for the sampling bias inherently associated to opportunistic citizen-science data. Finally, we structured the dataset into environmental blocks and sampled pseudo-absences within blocks using environmental distance to presence sites to enforce representation of diverse environmental conditions and to ensure independence of validation data in the cross-validation procedure.
Japanese beetle presence data We collected geo-referenced Pj occurrences from both citizen-science platforms and standardized surveys conducted by phytosanitary services (Figure  1A  and Supplementary Information 1). For opportunistic citizenscience observations, data sources and pre-processing are described in the Supplementary Information 1. We also gathered ~10 000 presence data from standardized surveys conducted by local authorities and phytosanitary agencies in the Azores, Canada, Italy and Switzerland. We did not consider absence data reported by these surveys since the Pj invasion is ongoing in these areas and absence data are unlikely to reflect real absences  (Cianfrani et al. , 2010). Even if the first record of Pj occurrence dates back to August 1917 in New Jersey (Supplementary Information 1, Figure  1 ), we considered only presence data starting from 2010 to match the timeframe of most of environmental predictors, hence removing 8% of the presence data before that period.
Predictor variables More than 300 variables were collected from different data sources to describe environmental conditions associated with the presence of Pj (Supplementary Information 2). These predictors encompass climatic variables, soil characteristics, land uses, and human-related features such as population and road densities. We considered these variables because of their relevance to the Japanese beetle's biology  (Fleming, 1972; Kistner-Thomas, 2019; Zhu et al. , 2017) , and we did not restrain our analysis to a limited set of variables identified as a priori important. Source data had different spatial resolutions ranging from 20 m to 4 km (Supplementary Information 2). We aggregated monthly climatic variables at the seasonal scale (Spring: March to May, Summer: June to August, Fall: September to November, Winter: December to February), and removed variables for which a very high pairwise correlation was found.
Spatial data aggregation & data availability In order to harmonize the dataset, we upscaled all data to a 4 km x 4 km resolution (the coarser resolution amongst the environmental predictors), averaging values contained within each cell when necessary  (Bierkens et al. , 2000). The aggregation rule for presence and pseudo-absence data was defined as follows: a grid cell is labelled as "presence" if at least one observation of the Japanese beetle data is found within the cell, whereas it is considered as "absence" if at least one Coleoptera and no Japanese beetle have been reported in the cell. This results in 6844 presence cells and 49010 pseudo-absence cells. The entire dataset, including presence/pseudoabsence data and environmental variables, is available in raster format at a 4km x 4km resolution  (Borner et al.
, 2010; Liu et al. , 2020; Thuiller et al. , 2005). Thus, we intentionally excluded data from Continental Europe during model fitting because they represent a recent and ongoing invasion process (less than 10 years since first detection) and considered only observations from either a native or long-occupied invaded area, i. e. Japan, North America (USA and Canada) and the Azores Archipelago. Furthermore, for cross-validation purposes, we trained the SDM only on data from the 2010-2019 period, leaving the remaining data in a separate dataset for validation. In total, 53210 raster cells  Machine learning provides an efficient framework to develop predictive models such as SDM. In this study we fitted a random forest model (hereafter RF) as it has been shown to be a good candidate for species distribution modelling and to be more robust against collinearity among predictors  (Cutler et al.
Cross-validation, model fitting and evaluation Imbalanced dataset (49010 pseudo-absences vs. 4200 presences, Figure  2 , panels A-B) are known to affect RF predictions  (Khalilia et al. , 2011). Therefore, it is recommended to use repeated random down-sampling, i. e. repeatedly fit the model on a subset of the original data containing all presence data and a random subsample of the same number of pseudo-absences  (Japkowicz & Stephen, 2002; Valavi et al. , 2022). As we subsample 7% of pseudo-absences among a globally distributed dataset, which displays spatial and underlying environmental structure (Figure  2 , panels A-B), this structure is likely to be reflected in the output of random sampling and affect model predictions. Furthermore, model evaluation using standard cross-validation based on random sampling of observations is known to perform poorly when applied to ecological data with spatial or temporal structure  (Ploton et al.
, 2020; Roberts et al. , 2017). Thus, we split our dataset into blocks based on environmental dissimilarity among observations (Figure  2 , panels C-D) and used this structure both in down-sampling and in an environmental blocks cross-validation strategy  (Roberts et al. , 2017; Valavi et al. , 2019). Using the environmental blocks structure in down-sampling enforces sampling of pseudo-absences in diverse environmental groups while ensuring equal representation with presence data. In the cross-validation strategy, it allows to increase independence of validation data (held-out from training) and improves predictions error estimates  (Ploton et al. , 2020; Roberts et al. , 2017). The construction of the environmental blocks is described in detail in Supplementary Information 3 and eventually resulted in the definition of 7 environmental blocks (Figure  2 , panels C-D).
We repeatedly fit 100 RF models using the randomForest R package 4. 6-14  (Liaw & Wiener, 2002)  to predict the probability of any observation to be a presence record, using environmental variables as predictors (number of trees = 1000). At each repetition of model fitting, we applied a 7-fold cross-validation strategy using the environmental blocks as folds, i. e. , one block is held out, while the model is trained on the remaining 6 blocks. Within each block we sampled an approximatively equal number of pseudo-absences than the number of presences in the block, whereas pseudo-absences are sampled using a weighting scheme that prioritize observations that are further apart from presences according to the environmental dissimilarity (see Supplementary Information 3). These precautions ensure that data are balanced both within and between blocks, and that pseudo-absences (for which we have less confidence) are sampled at a certain environmental distance from presence data.
These classes should reflect model calibration and more objectively represent the levels of habitat suitability. This method, based on the shape and confidence interval around the curves, provides a more honest discretization of the unit interval than an arbitrary choice of thresholds  (Arshad et al. , 2012; Hirzel et al. , 2006; Lee-Yaw et al. , 2022; Sattler et al. , 2007; Vaughan & Ormerod, 2005) , see Figure  1  in Supplementary Information 4 for more details). We used an independent test dataset, set aside from model fitting and performance evaluation, to provide an unbiased evaluation of the suitability maps (median of models' predictions). This test dataset includes all Japanese beetle presence data in Continental Europe, as well as presence data recorded in the years 2020-2021 in North America, Japan and the Azores (4098 presence raster cells).
67 -0. 84]) and more than half of models predict at least 10 times more presences than expected by chance (S5 = [0. 84 -1. 00]). Overall, the proportion of presences from the test dataset increases along classes of increasing predicted suitability, and 93% of presences from the independent dataset are located within cells of the last three classes of suitability (13. 5% in S3, 27. 5% in S4 and 52% in S5, respectively). Only 0. 6% of test presences are located within cells of class S0.
SDM of invasive species A critical point for the training of SDM is to constitute a database that is well representative of the environmental conditions in regions where the organism is present, as well as where it is absent, allowing the model to neatly separate those environments that are favourable from those that are not. Since we are modelling the distribution of an invasive species, we excluded recently infested regions (i. e. , Continental Europe) from model training, as species distribution in these regions mostly reflects dispersal capacities rather than conditions which are favourable to the species. Although previous authors did include data from Continental Europe in model training  (Della Rocca & Milanesi, 2022; Kistner-Thomas, 2019) , our decision not to include them is supported by recent ecological literature  (Liu et al.
Citizen science data, sampling bias and target-group strategy for pseudo-absences In the lack of a standardized and comprehensive database with Pj presences at global scale, we used opportunistic citizen-science observations to constitute the presence-only database, which, on one hand, ensured a global coverage with tens of thousands of presence records, but that, on the other hand, may affect the quality of the results  (Dobson et al. , 2020; Steen et al. , 2019). In particular, opportunistic citizen-science data are likely to be geographically biased towards easily-accessible locations, typically urban, agricultural and recreational areas  (August et al. , 2020). To compensate for the sampling bias and lack of absence data, we chose to generate pseudo-absence data using the target-group strategy  (Phillips et al.
Projection of suitability in Continental Europe and comparison with previous studies Our predictions depict a complex scenario for Europe (Figure  3C ), with two main clusters of high predicted suitability: the first in the foothills of the Alps (both North and South) and in the Northern Balkans, and the second on the eastern shores of the Black Sea. (Della Rocca & Milanesi, 2022; Kistner-Thomas, 2019; Zhu et al. , 2017) , with the only exception of the Iberic Peninsula, the peaks of the Alps and Scandinavia. As expected, those models that were overestimating the suitability in the training dataset (mostly North America), maintained that tendency when extrapolating to Europe. Our maps highlight a more contrasted suitability scenario for Europe, which is an improvement for prioritization in surveillance.
Acknowledgements This research was supported by the  IPM-Popillia  project, funded by the  European Union   Horizon 2020 research and innovation programme  under grant agreement No  861852. Authors would like to thank  Servizio Fitosanitario Ticino ,  Plant Health Service -Piedmont Region (Italy) ,  Secretaria Regional da Agricultura e do Desenvolvimento Rural do Governo dos Açores , and the  Canadian Food Inspection Agency , for providing standardized survey presence data included in analyses. Authors would also like to thank citizen science platforms and all citizens who take part in citizen science surveys, for their contribution to the advancement of research.
More extensive literature is available for the intercrop algorithms in STICS, called STICS-IC (for InterCropping) in this manuscript. STICS-IC generally performs correctly compared to observations, thus providing the first relevant basis for simulating bi-specific intercrops  (Brisson et al. , 2004; Kherif et al. , 2022; Launay et al. , 2009) , but several inconsistencies were identified in some cases. Shili-Touzi et al. (2010)  applied the model on a winter wheat-red fescue intercrop and found a tendency to overestimate N uptake for the fescue. Corre-Hellou et al. (2007 , 2009)  had difficulties in computing light competition related to poor simulation of plant height, an issue also found in APSIM. We also identified some discrepancies between observations and simulations for STICS-IC using a database from works published by  Bedoussac (2009)  and  Bedoussac and Justes (2010) , indicating that the model needs further improvements.
github. io/STICS-IC-paper. The challenge of properly simulating intercrops with formalisms that are easy to integrate into soil-crop models, with few parameters, high accuracy and genericity involve designing or revisiting both scientific concepts and software algorithms. Consequently, the three-fold objectives of this study are to: (1) Review the formalisms in the initial soil-crop STICS model related to intercropping and evaluate the consistency of the algorithms; (2) Propose new simple, yet powerful novel formalisms (STICS-IC) to improve the simulation of the main processes in the initial version, which were considered unsatisfactory, and evaluate their relevance; (3) Evaluate the genericity and validity domain of these new formalisms implemented in STICS-IC using both a conceptual assessment and a comparison with observations for various types of arable bi-specific intercrops: winter and spring legume-based intercrops associated with cereal or sunflower with a wide range of measured plant traits.
e. whether the dominant crop grows more because it has priority in resource acquisition each day as it is simulated first. Our hypothesis is that the maximum delay of one-day between the plants has a very low impact on the simulation, i. e. the dominated plant can also be considered having priority over the dominant plant because it acquired resources last on day i-1. Nevertheless, this assumption needed to be evaluated. Second, we used data from two crops either grown as sole crops or intercropped, and simulated both cases to evaluate the ability of STICS-IC to reproduce the interspecific interactions as well as the intraspecific interactions. Third, we evaluated the model using experimental data of intercrops with contrasting species mixtures and spatial heterogeneity, at contrasting sites, to investigate its genericity and the domain of validity of STICS-IC.
And second, the partial land equivalent ratio () computed after  Willey and Osiru (1972) : pLER = Yield ic Yield sc (19) where   is the grain yield in intercrop and   is the grain yield in sole crop. A value of  above 0. 5 indicates a higher per-area performance in the intercrop than grown as a sole crop, which is most often called over-yielding. The evaluation of the overall intercrop performance is then made using the LER by summing up the  of each crop in the intercrop and comparison is made to the reference value of one (for replacement half density design), i. e. no difference compared to sole crops. Graphical evaluations and statistics were computed using the CroPlotR package  (Vezy et al. , 2021a)  in order to evaluate the quality of calibration of sole crops and evaluation of intercrops.
Acknowledgements We thank  Mme Tilly Gaillard  for her professional English reviewing of the manuscript. This research was supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  in the framework of the ReMIX (Redesigning European cropping systems based on species mixtures, https://www. remix-intercrops. eu/)project from 2017 to 2021 [grant number  727217 ].
Data and code availability All data and code needed to replicate the simulations, statistics and figures of this study are available from a Github repository (https://github. com/VEZY/STICS-IC-paper) and fully archived on Zenodo  (Vezy et al. , 2022). The parameter values are all available from the specific input files for each species, soil, site, meteorology and crop management. The simulations, parameter value optimizations, analyses, and graphical visualizations were performed using the "SticsRPacks" suite of R packages  (Vezy et al. , 2021b).
Thus, it seems reasonable to see that rate constants for the process HD(v = 1, j = 6) → HD(v ′ = 1, j ′ ) are closer to those for HD(v = 0, j = 11) → HD (v ′ = 0, j ′ ) than to rates for HD(v = 1, j = 6) → HD(v ′ = 0, j ′ ). As for the case of Sec. III A, the full set of state-to-state rate coefficients for reaction 2 are included in the supplementary material and will be made available in the EMAA database at https://emaa. osug. fr/. The good performance of the SQM method observed in the present investigation for the H + + HD → H + + HD and H + + HD → D + + H 2 reactions reaction is as good as the results seen for the H + + H 2 → H + + H 2. 34 The comparison of the statistical predictions with the EQM results, when available, shows the complex-forming character of the overall dynamics of both processes.
In what follows, protons and atomic hydrogen are considered separately, with a fixed density at n(H + ) = n(H) = 10 -2 cm -3. This range corresponds to the "low-density regime" where HD is predominantly in the ground and first rotational states j = 0 and j = 1, respectively. The kinetic temperature, T, was varied in the range 5-3000 K for protons and 5-5000 K for atomic hydrogen. The normalized HD level populations, nj, were computed at steady-state with the public code RADEX 42 using our SQM collisional rate constants for HD-H + , the recent TIQM rate constants of Desrousseaux et al. 41  for HD-H, and spectroscopic data (including both dipole and quadrupole transitions) from the HITRAN database. 43 The statistical equilibrium equations were solved for HD levels up to (v, j) = (0, 11) for collisions with H + (see Fig.
Exhaustive explanatory variables We use a set of 23 publicly available spatially continuous environmental covariates. The covariates provide information on the main biotic and abiotic factors which are known to affect SOC stocks. The main sources of covariates was the database from BioClim, SoilGrids and a SRTM elevation model with derivatives. We also used MODIS products from which the vegetation index NDVI map was calculated from the NIR and RED spectral bands and a high-resolution landcover map with 7 classes obtained by Sentinel-2 images. Covariates were further grouped into six categories representing 1) average climate condition, 2) climate seasonality, 3) extreme climate conditions, 4) topography and terrain derivatives, 5) soil properties and 6) organisms/vegetation. The list of factors, covariates, along with their unit and reference is provided in Table  1.
Modelling and mapping of SOC stocks 135 The SOC stocks data and their matching values of environmental covariates were used to build a regression matrix. The modelling approach to establish the relationship between the SOC stocks and environmental covariates is based on random forest. Random forest (RF,  Breiman, 2001 ) is an ensemble machine learning algorithm based on decision trees. A single decision tree is fitted by partitioning the covariate values of the calibration dataset. Partitions are evaluated based a splitting metric and the 140 partition providing the optimal value of the metric is selected. This procedure is repeated until the a user-defined value of node size is reached. The prediction value of a single tree is taken as the average prediction of all nodes at the terminal leaf.