dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Evaluation We have assessed the benefits of our proposed combination of VTLs and urban platoons via simulation. The scenario consisted of a Manhattan grid urban environment of 7 × 7 intersections and 1375 × 1375 m with two lanes per road (one in each direction) and a maximum speed of 50 Km/h. To simulate an application in which the exchange of information among the vehicles may affect their movements, we resorted to VEINS  [33] , a tool that combines an event-based network simulator (OMNeT++) and a road traffic simulator (SUMO). The architecture, including the modules from our own works, is shown in Figure  6. Our goal was to compare the performance of four different configurations: • Configuration 0 (traditional traffic lights only) provides the performance baseline, managing no platoons and no VTLs.
2 and 4. 3. For configurations 2 and 3, we developed the VTL & platoon management procedures within VEINS, and reused implementations of the VaNetLayer and VNIBR protocols as OMNeT++ modules (The space between intersections in the simulation scenario allowed two L2VNs and one L3VN along each road segment). The Platooning Extension for VEINS  [36]  was used to implement the platooning operations, with precise control on the vehicles' speeds and distances. As an alternative to VNIBR for routing, we wrote an OMNeT++ implementation of VNAODV (Ad hoc On-Demand Distance Vector on Virtual Nodes), an algorithm proposed in  [37]  to demonstrate the capabilities of the virtualization layer that preceded the VaNetLayer (see  [38] ). The comparison between VNAODV and the different flavors of VNIBR serves to highlight the gains from a purely intersection-based routing approach.
The center of the circular specimen is pencil-marked before the bulge test and checked after bulging and unloading, in order to assess precisely the maximum height of the sample, also output from Digital Image Correlation (DIC, Aramis 4M, GOM mbH, Braunschweig, Germany) measures. DIC data is calculated as a post-treatment and cannot be used to control the test. Therefore, the end criterion of the bulge test is defined on the pressure.
Therefore, it leads to an increasing overstress when the pre-strain increases. Inverse identification of the material parameters is carried out using the in-house Matlab toolbox SMAT developed at Xi'an Jiaotong University  [24]. A cost function L (A) is defined and minimized iteratively in the least square sense by Equation  (5). L (A) = N ∑ n=1 L n (A) = N ∑ n=1 1 M n M n ∑ i=1 D n |σ(A, t i ) -σ * (t i )| 2 (5) where N is the number of tests in the database, A is the set of material parameters. For the variable n, numbers of 1, 2 and 3 correspond to the cases of tensile pre-strains of 0. 02, 0. 04 and 0. 08 before thermal treatment. M n is the number of experimental points of the n-th test, σ(A, t i )σ * (t i ) is the gap between experimental value for the stress σ(A, t i ) and simulated stress σ * (t i ) at time t i , and D n is a weighting coefficient for the n-th test.
Material parameters thus identified are given in Table  2. The constitutive equations presented above are implemented in the finite element code Abaqus via a user subroutine, for plane stress states σ i3 = 0, i = 1, 2, 3. The equivalent plastic strain is stored at each integration point during the pre-strain, and in particular at the end of the first step. Then, upon reloading during the subsequent step after bake hardening, the parameter ¯ * is set equal to the last stored value of the equivalent plastic strain during the first step. Comparison of experimental and numerical results for the tensile tests are shown in Figure  10. The numerical model uses only one finite element for the tensile sample, assuming a homogeneous strain field. Several steps are performed: firstly, a pre-strain in tension followed by unloading, using the initial mechanical behavior exhibited in Figure  10a  ( f = 0).
g. , it increases when the pre-strain increases  [7, 10, 16]. The dependence of the proposed model to the pre-strain magnitude follows this trend, as given by Equation (3). Figure  11  compares the magnitude of the bake hardening effect (BH) calculated from experimental and numerical data. It can be seen that from the experimental data, BH does not evolve significantly over the strain range, though it tends slightly to decrease when the pre-strain increases. This slight discrepancy may come from the fact that material parameters are identified by an inverse procedure over all the tests, and optimisation leads to a compromise, whereas BH magnitude is evaluated only upon the highest stress reached upon reloading. These steps are used out of identification purposes. Therefore, a next step is the validation of the model, using a different experimental database.
Numerical Simulation of the Dent Test The aim is now to develop a numerical model for the multi-step process used in this study with the software Abaqus. Though the material is assumed isotropic, the initial circular blank (radius 135 mm) is modelled with one fourth of the geometry. Symmetry conditions are applied on the two straight edges. The tools are defined as analytical surfaces and are considered rigid. Three-node triangular shell elements are used to mesh the blank (total number of 9271). The mesh size is of the order of 4 mm on the outer diameter and in the area that remains flat under the die. It decreases down to 1. 5 mm and is constant in the circular ring for radii in between 40 mm and 100 mm. Then, it further decreases down to 0. 6 mm close to the pole. Numerical simulations of both the bulge and the dent tests were performed using axisymmetric elements, 4-node (regular mesh) and 3-node (random mesh) shell elements.
The disturbance intensity was measured using the relative frequency of Pourouma and Cecropia (Urticaceae) species, which are strict early-successional pioneers whose abundance was demonstrated to depend on canopy gap frequency as assessed from aerial LiDAR data at two test sites. We also measured soil properties on 450 soil profiles to directly assess nutrient availability, niche diversity and long-term ecosystem stability. Geospatial data were also used to account for environmental drivers of regional diversity such as rainfall  13  or geomorphology  17  , which have already shown to be explanatory of broad scale floristic patterns in French Guiana  18, 19.
Disturbance regime and long-term stability significantly explain broad-scale diversity patterns. Applying Bayesian averaging models to the entire dataset (including sites for which no soil analyses were available) or to a subsample of 21 sites for which complete soil analyses were available, we found that disturbance (DI approximated by Urticaceae frequency) was a more important factor than habitat diversity (HD approximated by soil diversity, mean slope, elevation range), resource availability (RA approximated by soil properties), or long-term climatic stability (LS approximated by rainfall, elevation and geographical coordinates) in explaining tree diversity. Indeed, in the first run including the entire dataset, Urticaceae frequency and its quadratic effect best explained Fisher's alpha diversity (posterior inclusion probabilities, i.
Sites were selected in old-growth forests (excluding the man-modified coastal vegetation) across the entire territory that were representative of the different forest types  51  , climate variability  52  within the region, and the diversity of geomorphological landscapes  53  , which influence floristic composition  18, 39. In this study, we distinguished four main categories of landscapes across French Guiana: (i) all-slope reliefs, also locally called mountains (SLO); (ii) plateau or tablelands (PLT); (iii) multiconvex reliefs shaped by successions of hills (MCX) associated with wide valleys (VLL); (iv) coastal plains (PLN) here grouped with multiconcave reliefs (MCV) corresponding to inland plains. Data were collected along 3-km line transects chosen to capture local heterogeneity in terms of topography, geology, and geomorphology within the same type of landscape.
The list of species of these authors was complemented by referring to a regional database containing functional traits  56  and included 60 species from a total of 1,581 species inventoried in French Guiana  55. We also computed the mean wood specific gravity using the global wood density database as reference  57. These indicators were produced by simulating species composition from forest inventories using the same Monte Carlo process than for Fisher's Alpha diversity  54. Soil data. Soil samples were collected with a hand-auger at 450 locations along transects chosen to represent variability in elevation, geology and topographical positions. Core soil samples were collected at 20-cm intervals from the surface to a depth of 120 cm and dried as soon as possible. For each core, seven layers were described in the field (0-10 cm, 10-20 cm and then every 20 cm until 1.
At Trinité, two 3-km-long and 20-m-wide field transects were established on each side of a small mountain. The LiDAR acquisitions covered two areas that were 1-km-wide and 8-km-long. At Itoupé, LiDAR acquisition covered a single area 9-km-long and 8-km-wide. This area covered the four new transects on the two sides of the mountain (as at Trinité) and a central area corresponding to the summit of the mountain, where a network of transects had already been established in 2010. LiDAR data were acquired by aeroplane at an altitude of ~600 metres above ground level. The on-board system comprised a scanning laser altimeter with a rotating mirror mechanism (Riegl LMS-Q560). The wavelength used was 1,550 nm. The scanning angle was +/-20°. The laser altimeter recorded up to 7 reflected pulses with a nominal precision of 10 cm.
The final mean point density was 17 points/m² for a mean emitted density of 12 pulses per m 2. Details on LiDAR data acquisition can be found on the geoportal of the laboratory (http:// vmamapgn-test. mpl. ird. fr:8080/geonetwork/srv/eng/search). First returns were triangulated, and a 1-m raster was built by interpolation of the triangulated surface to obtain a canopy surface model. The digital terrain model was obtained in a similar way by interpolating the triangulated ground points. A canopy height model (CHM) was computed as the difference between the canopy surface model and the digital terrain model.
As we had a large number of potential predictor variables for a few observations, we first used Bayesian model sampling to select the predictor variables considering their posterior inclusion probabilities (package R bms). Then, we introduced the most frequently selected predictor variables in a GLM and stepwise algorithm to select the best model (i. e. , that with the lowest AIC, using the glmulti package). Finally, to test the robustness of the model, especially the quadratic term of the model that caused the unimodal shape of the relationship, we built the following model using the selected predictor variables (equation 1): θ θ θ θ θ σ ≈ + × + × + × + × θ AlphaDiv N U rti U rti R ain A lt ( ; ) 0 1 2 4 5 2 3 where θ 0 :θ 5 and σ are the model parameters to be inferred with uninformative priors, AlphaDiv is the observed Fisher's alpha diversity, Urti is the percentage of Urticaceae, Rain is the normalised annual precipitation and Alt is the normalised altitude.
Because AlphaDiv is not measured but is estimated by simulating species composition from the vernacular nomenclature (see above), uncertainties of AlphaDiv were propagated through the model inference in a Monte Carlo scheme. The full model was then inferred using an adaptive form of Hamiltonian Monte Carlo sampling. Code was then developed using the R language and the Rstan package  63. We also tested the relationship between tree diversity indices and understory using Pearson's product moment correlation coefficient, and we used the same modelling approach (i. e. , selection of predictor variables, selection of the model and test of robustness) separately for understory vegetation diversity.
Acknowledgements This study was carried out as part of the  DYNFORDIV  project  (IRD and  " Parc Amazonien de Guyane " partnership) based on datasets mostly gathered as part of the  HABITATS  project (ONF,  ONCFS  and " Parc Amazonien de Guyane " partnership). DYNFORDIV was supported by a  BGF grant  from the  French Ministry of Ecology (MEDDE)  and an " Investissement d'Avenir " grant managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-0025 ), HABITATS by the  PO-FEDER  grant of the  European Union. We thank the  French Forest Agency (ONF)  and the  "Parc amazonien de Guyane" National Park (PAG)  for supporting the studies and providing facilities. We also thank all the fieldworkers and colleagues who cooperated during these long but exciting field campaigns and thank  GIP ECOFOR , which supported our efforts in the last year.
With the purpose of guiding the user through a correct pipeline for a certified 3D model reconstruction, an operative workflow is proposed, focusing on the first part of the object reconstruction steps (tie-points extraction, camera calibration, and relative orientation). A new GUI (Graphical User Interface) developed for the open source MicMac suite is then presented, and a sample dataset is used for the evaluation of the photogrammetric block orientation using statistically obtained quality descriptors. The results and the future directions are then presented and discussed.
To obtain a reliable estimation of the IOP, the extracted tie points can be filtered, firstly discarding the ones with a high or unacceptable re-projection error and, then, homogenizing their distribution in an automatically generated map of the tie point's displacement, accordingly to the sector of the image with the minimal registered density of tie points. Moreover, the camera distortion parameters can still be retrieved applying the well-known self-calibrating bundle adjustment, but only considering a selected group of from the dataset which fulfil the requirements for a correct self-calibration process. This is especially crucial when consumer-grade digital cameras are employed for a measurement tasks in which the network geometry is not able to give a successful self-calibration. Finally, in case of automatic generation of point clouds, each point can be certified by using the estimated m.
Unlike the commercial solutions a wide range of camera calibration models are available in MicMac, and even more have been recently introduced. Since the MicMac suite is still in development, there are some limits in this research that depends on the current version of the software (v. 10. beta13). MicMac covers the entire photogrammetric pipeline (i. e. does not stop after the bundle adjustment phase); it also generates dense point clouds and orthophotos. The suite works with perspective and push broom images and it allows deformations studies on all types of images  (Rupnik, et al. , 2017).
Use of GUI For the aims of this research, new solutions have been developed for the generation of point clouds, and the possibility to retrieve metric quality descriptors has been implemented using a newly designed GUI (Graphical User Interface), allowing the user to understand and criticise the obtained results systematically. The innovative purpose of GEMINI is to allow the assessment of the data's quality, which is independent from the software that is used and, instead, related with the network geometry, the distribution of tie and control points, the nature of the surveyed object itself, etc. The purpose of GEMINI is not only to present a user-friendly environment; its main aim is to allow the user to understand and criticise the obtained results systematically in a guided way, preserving the automation of the process.
Tie points extraction (Tapioca) The first step of the process computes tie points from all pairs of images. This is the part of the workflow in which currently we have the less control of the quality, as it uses an external solution (SIFT++) that will be eventually changed in the following updates of the suite due to some patent issues for non-academic use (as announced to the user community at the end of 2017). The need of matching features in a group of images has been a shared problem for computer vision and photogrammetrists for years; as a solution, the SIFT key-point detector offers not only the Scale Invariant Feature Transform, but it allows also differences in illumination, viewpoint and rotation of the used images. SIFT and SIFT++ are respectively a MATLAB/C and command line/C++ implementation of the SIFT feature detector and descriptor.
Internal and relative orientation (Tapas) To obtain reliable metric data from images, it is mandatory to calibrate the camera to eliminate the systematic errors generated by the real optics. Calibration is the process that allow gathering the parameters that allows reconstructing the central projection of the images generated by a camera. These parameters are the same for all the images of the survey (e. g. MicMac do not allow the management of photogrammetric blocks formed by images acquired by different camera and optics), as are dependent on the used sensor and, as we are considering the use of non-metric cameras, are not provided by the sensor manufacturer. Several calibration models have been theorized for photogrammetric purposes, but sensor calibration and orientation are usually done via a perspective geometrical model by means of the bundle adjustment  (Brown, 1971).
, 2003)  the following approach is proposed. The aim is to use a restricted set of images to obtain the internal orientation parameters; these images must fit the following self-calibration requisites:  images must converge on the same part of the object's area;  the object's area must represent a 3D volume, to have a better estimation of the focal length;  a given part of the object must figure in different positions in all the images, for a better estimation of the distortion parameters. The so obtained parameters will be then used for the orientation of the whole dataset. The possibility to obtain a quality feedback at the end of the calibration phase is useful to understand whether the network geometry or the employed calibration model are suitable for the object reconstruction. Pursuing this aim, a sample dataset will be used and a synthetic set of statistical indexes for the evaluation of the photogrammetric block orientation will be retrieved; the problem and solution analysis will be then presented and discussed.
THE MICMAC'S QUALITY EVALUATION TOOLS With the aim of experimenting the possibilities offered by the quality evaluation tools embedded in MicMac, that can be performed after the calibration and relative orientation phase, a sample dataset has been used. The dataset is retrievable from the Tutorial page of the MicMac suite  (Girod, 2017) , and was acquired to model a volcano model created by O Galland. It is composed by 4 images acquired with a NIKON D90 using an objective with 26 mm focal length (Figure  5 ).
Another interesting analysis is given by the option ExpImRes of Campari, that generates images representing the spatial repartition of residuals in the sensor plane; this has been performed again for each internal calibration model adopted (Figures  8  and 9 ). The clearer areas represent a higher module of residual. There is also the possibility to obtain the variance of any parameter that is estimated during the bundle adjustment; here we will only focus on the Internal Orientation parameters that could be used as quality descriptors, as they are the more realistic evaluation of the estimation uncertainty. This can be done using the parameter ExportSensib in Campari command. As the two models employ different kind of radial and tangential distortion parameters, the comparison has been done considering the estimated values and uncertainties of the focal length and the coordinates of the principal point, for each calibration model (Table  1 ).
CONCLUSIONS This paper described the importance of valorising the information inherent the photogrammetric software useful for the quality control of the 3D reconstruction process, nowadays used by a wide range of researchers and professionals in different fields. Deepening this study, it appeared that apart from the first step of the pipeline (the automatic tie-points extraction) the MicMac suite already allows a good control of the quality parameters, even if sometimes these are not easily accessible or presented to the user in a clearly understandable way. The purpose of the future researches will be focused to the improvement of the GEMINI interface for enhancing a better user experience (e. g. guiding the user through the whole process) and the graphical feedbacks. Considering that Format angle is a convenient method of distinguishing between different basic lens types, one idea could be sorting the typical lens in 4 groups; telephoto (small angle), normal (default) wide angle and fish-eye.
BRC4Env as a relevant support for research programs The network BRC4Env and its BRCs and collections implement a quality management according to the INRA Quality Framework v2 for optimized functioning, continual improvement, and stakeholder satisfaction. They plan to move to national (NF S 96-900) or international (ISO 9001:2015) certification in the next 2 years. Numerous BRCs and collections ensure the traceability and planning of their animation activities using the software AQ-tools developed by CIRAD (http:// golo. cirad. fr). The network BRC4Env, the CSGR of GenoSol, EP-Coll, and Colisa have been labelled as BRCs by the French organism IBiSA. The BRCs follow the rules of good scientific and ethical practices, policies, and regulations. BRC4Env is committed to provide an operational support to its members regarding the implementation of the Access and Benefit Sharing and the Nagoya protocol, and the staff of the BRC follows training accordingly.
The key action Sys3D (Systematics for the Detection, the Diagnosis and the iDentification; whose goal is to create fast and reliable tools for plant diagnosis) of the  INRA metaprogram SMaCH (Sustainable Management of Crop Health, 2010 -2020)  relies on a part of this Arthropods Collection: sequenced specimens, studied morphologically, preserved with their DNA for traceability purposes  (Laval and Streito 2017). The reference collection of European quarantine Arthropods, an outcome of the European project FP7/Q-BOL/Q-Bank  (Rasplus and Loomans 2017)  is also hosted at CBGP. Informative genes from species selected in EU Directive and EPPO lists are DNA barcoded from vouchered specimens. Methodological outcomes of this program are a non-destructive extraction technique, an amplification protocol using primers multiplexing, a bioinformatics pipeline for sequences validation, and a structured database with a web interface.
This website is automatically updated using the DBMS (Database Managing System) used for the Terrestrial Arthropods collection. New species have recently been described using specimens from this collection. Examples include a phytophagous mite  (Arabuli et al. 2016 ), a seed pest beetle  (Mouttet et al. 2016) , or a new species of darkling beetle  (Soldati et al. 2016 ). The collection is also used by research programs aiming to estimate the relative weights of ecological factors and geographical isolation on the speciation processes within several groups of Arthropods  (Meseguer et al. 2015; Migeon 2015). In return, this collection is regularly enriched by specimens collected via these research programs.