dataesr/scientific-paragraphs-categorization huggingface dataset:
| Field data The study area (Figure  1 ) is located in the Province of Bolzano/ Bozen (Italy) in the municipality of San Genesio/Jenesien (46. 55° N, 11. 32° E). Twenty 1-ha plots (100 m × 100 m) were randomly chosen within a dense coniferous forest at 1,100 m a. s. l. characterized by a high canopy cover. Following previous study designs  (Oldeland et al. , 2010; Torresani et al. , 2018) , the center and corners of all plots were geo-referenced with a global positioning system (GPS) device (spatial accuracy of ±3 m; Garmin, USA). From June to August 2017, trees with a diameter at breast high (DBH) of at least 5 cm were identified at species level. Among the measured trees, 95% were coniferous species, dominated by Pinus sylvestris, followed by Larix decidua and Picea abies. The remaining 5% were deciduous species such as Betula alba, Corylus avellana, Salix caprea and Sorbus aucuparia.
| Remote-sensing data The canopy reflectance signal of the vegetation plots was derived from the Sentinel-2 satellite data. This sensor system measures the solar electromagnetic spectrum with two satellites (Sentinel-2A and Sentinel-2B) from 457 nm to 2,280 nm with 13 bands. All images available for the years 2017 (n = 11 images available), 2018 (n = 12), and 2019 (n = 13) were used for this purpose, excluding images with suboptimal conditions of acquisition (e. g. presence of snow, shadows, clouds, aerosols). For the three considered years, images from the 90th to the 310th day of the year (DOY) were available. The images were acquired from the multispectral instrument (MSI) on board of both satellites between 10:00 and 11:00 am (local time). Sentinel-2A and 2B satellite images acquired with the relative orbit numbers R022 and R065 and provided as 32TPS were downloaded from ESA's Sentinel Scientific Data Hub.
| OTIs estimation The inversion approach used to retrieve the OTI values for each plot from the canopy spectra of each Sentinel-2 images followed the in forward mode  (Schlerf & Atzberger, 2006; Jean-Baptiste et al. , 2008). The combination of input parameters was based on random sampling and followed the ranges defined in Tables  1  and 2. The range of the parameters was derived according to the species composition of the plots following the information contained in the Leaf Optical Properties Experiment Database (LOPEX;  Hosgood et al. , 1995; Féret et al. , 2019)  and in other related studies  (Meroni et al. , 2004; Schlerf & Atzberger, 2012; Navarro-Cerrillo et al. , 2014). The spatial resolution of the scenes simulated with INFORM was set to 10 m × 10 m to match the Sentinel-2 data. Successively, we used the lookup table to train the random-forest regression model  (Pal, 2005)  to predict values of each parameter from the spectra.
| OTI heterogeneity The Rao's Q index was used to calculate the OTI heterogeneity for each plot for all the images in the considered years following Equation (2). Where The distance matrix d ij can be built in different dimensions, allowing the consideration of more than one band or raster at a time. Q rs = Rao's Q applied to In our case, d ij was calculated as a simple Euclidean distance based on the single band (raster trait retrieved from the radiative transfer models). We used and implemented the R package function spectralrao() (available in  Rocchini et al. , 2017)  to retrieve a Rao's Q value for each 100 m × 100 m plot representing our local landscape. The resulting values of the OTI heterogeneity were then compared with the field Shannon's H based on linear correlation. Since our analysis was based on multiple analyses (multiple tests made using different images), we corrected the p-values with the Benjamini-Hochberg correction to get an unbiased measure of significance  (Benjamini & Hochberg, 1995).
complex regions to guide field sampling and localization of biodiversity hotspots. ACK N OWLED G EM ENTS M. T. and D. R. were partially supported the H2020 Project SHOWCASE (Grant agreement No862480). D. R. was also partially supported by the H2020 COST Action CA17134 Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO). J. -B. Feret acknowledges financial support from Agence Nationale de la Recherche (BioCop project-ANR-17-CE32-0001) and TOSCA program grant of the French Space Agency (CNES) (HyperTropik/HyperBIO projects). AUTH O R CO NTR I B UTI O N SMT, HF, J-BF and DR conceived of the presented idea; HF designed the study and performed statistical analyses; MT collected the data;.
Ecuador's NDC itself was indeed informed with long-term scenarios modelled with the LEAP platform  [25]. However, only the energy sector was modelled without considering interlinkages with the AFOLU sector. The current expansion and operation planning of the Ecuadorian electric power system  [26] , carried out by the Ministry of Energy is based on two computational tools: OPTGEN (model for generation expansion planning and regional interconnections), and SDDP (stochastic hydrothermal dispatch with network restrictions), both commercial software provided by PSR  [27]. The OPTGEN model starts with an exogenous demand forecast and project inventory and determines the least-cost expansion plan (investment, operation and maintenance). These results are subsequently integrated into the SDDP model, which considers the uncertainty of runoff and the operational restrictions of generation plants.
For every time step, national food production is calculated by adding total national consumption and exports and excluding food imports. Food imports and exports forecast are determined by means of a linear projection of historic values obtained from FAOSTAT  [72]. From the FAO database, 18 categories containing relevant agricultural products for Ecuador are considered (See Appendix A-2). The food supply projection is calculated to account for dietary changes and food waste  [73]  evolution, which is important in decarbonisation scenarios. Food supply projection is driven by population growth and an increased caloric content diet that would reach current developed countries food intake levels until 2050. The annual demand (metric-tons) of a certain product is calculated by multiplying the share of the product in the daily diet, the daily diet (kcal/day/person), population, 365 days, the inverse of the percentage for its food waste, and an appropriate conversion factor  [73].
Nevertheless, these narratives contained qualitative storylines that had to be transformed into quantitative data to be implemented in ELENA. This quantitative data shapes the model in the form of demands, restrictions, or technological parameters (e. g. efficiency changes over time). One of the main constraints used to shape the DDP scenarios was the national carbon budget, limiting cumulative CO 2 emissions from 2010 to 2050. This carbon budget is specific for Ecuador and it was calculated based on the South America region's carbon budget, defined by the global COFFEE model  [17]. That carbon budget was distributed to South American countries based on the GDP per capita 10 proxy. Due to uncertainties in the global carbon budgets calculations, there is an    [65, 66]. 10 Several exercises were done to distribute the South America carbon budget by country: by total GDP, population, GDP per capita, etc.
In the case of solar resources, simulations using the System Advisor Model (SAM), which uses the information of the National Renewable Energy Laboratory (NREL)  [82] , allowed to determine the global horizontal irradiance. A major challenge was to determine the final energy use for the industrial sector. At first, the overall final energy consumed was obtained from the National Energy Balance report  [40]. Then, it was attributed to the industrial production, using the reports of companies, which led to the specific energy required by metric-ton of product  [83] [84] [85]. Finally, since the input variable into ELENA is useful energy, energy conversion efficiencies were defined considering different processes and fuels. For the AFOLU sector, a variety of data was collected. In order to set the base year, crops' yield and livestock densities were required  [86].
Historical averages for reforestation and deforestation from 1990 were analysed to create the required model constraints. As explained before, the mapping of different crops were created to analyse the land-use change. To determine the food production demand, FAO database was considered  [72]. Besides, as explained before, geoprocessed data in Arcgis and Edaphoclimatic conditions data were used to create the land use category classes that determine land use interactions. In order to project the data through the entire studied period, different techniques were applied. In the residential and commercial sector, a bottom-up approach was developed using demographic parameters. For the transport sector, a dashboard created by IDDRI at the DDP project was used considering the different goals for each scenario  [87].
For the industrial sector, the projection was driven by the GDP, with adjustments made according to induced changes on efficiency considering figures from developed countries taken from the RETScreen database  [88]. Regarding food production, the FAO database provides a historical tendency for agricultural production. It was linearly extrapolated, maintaining a coherence between the increase in productivity and the limits for the crop yield improvements. In addition, food losses  [73]  and dietary parameters were included in the calculations, which allowed to perform variations in dietary tendencies and include its effects on the food demand. The food projections considered a dietary intake as the base for the calculations, allowing to reach developed countries dietary level. Fig. 6. Representation of the energy and land use system conversion chain in the ELENA model.
By running the ELENA model, it was possible to carry out this trade-off analysis between two decarbonisation scenarios. Being it a game changer situation, further studies should deepen this discussion. For example, a reforestation program must account for the economic benefit that the forest delivers. Forest ecosystem services, provide resources, regulates cycles (in water, soil and weather) and encompasses cultural services  [97]. All these benefits must be included from an economic perspective to better evaluate the DDP High Refo scenario, while the DDP scenarios relying on BECCS should account for its advantages (e. g. oil substitution, labour and income creation) and risks (not yet mature and regulated high-cost option).
Acknowledgement We thank the  Deep Decarbonisation Pathways in Latin America  and the  Caribbean (DDP-LAC)  project, financed by the  Inter-American Development Bank (IADB) Sustainable Energy and Climate Change Initiative fund  ( RG-T3028 ), the  IADB French Climate Fund  ( RG-T3193 ), the 2050  Pathways Platform , and the  Agence Française de Développement (AFD). We thank  Institut du Développement Durable Et des Relations Internationales (IDDRI)  by their technical support and coordination during the  DDP  project. We thank to  Alejandra Guevara ,  Eduardo Noboa  and  Freddy Ordóñez  for their support during initial stages of this work. We also value the information and feedback to our work provided by  Verónica Guayanlema  and  Paúl Melo. We thank the  Ministry of Environment (MAE) ,  Ministry of Energy and Non-Renewable Natural Resources (MERNNR) , Operator of the Ecuadorian Power System (CENACE) and  Electric Corporation of Ecuador (CELEC)  for sharing technical data.
Internet of Media Things (IoMT) The ISO/IEC 23093 series (a. k. a. MPEG-IoMT) provides an architecture, specifies APIs, and details compressed representations of data flowing between MThings  [2] [3] [4]. Various MThings (MCameras, MMicrophones, MDispalys, MAnalysers, or MStorages) can thus be designed, orchestrated, and operated in a large variety of tasks such as acquisition, rendering, processing, or multimedia content storage. The IoMT standards also make provision for the use of blockchain solutions in conjunction with MThings. In fact, the standard already specifies the APIs and the usage of digital coins or legal tender to ensure the payment of Mthings for their use, as detailed hereafter for the case of an MCamera (similar mechanisms exist for every MThing).
Advanced solution The main innovation disclosed by our study is a generic, blockchain-agnostic architectural framework and its specified, designed, and implemented underlying modules. The architecture is centered around the automatic production and execution of Smart Contracts from IoMT information, as presented in Fig. 2. It combines expertise from both IoMT and blockchain Experts and it is composed of three main components: the IoMT Parser, the Smart Contract Developer, and the Blockchain Manager. The process starts with an IoMT expert who deploys and/or exploits an IoMT system in which at least one blockchain is required for the operation of at least one MThing. Hence, the IoMT expert designs and develops the software application based on the blockchain-specific IoMT APIs and data formats, as illustrated in Section 2.
2. Yet, the IoMT expert is unaware of the technical complexity of blockchain operations. The IoMT Parser extracts the relevant specifications for a blockchain operation (e. g. , Fig. 1 ) and returns a structured set of transactional rules and conditions. This module is independent of the blockchain, and its output can be accessed through a generic API. In parallel, the Blockchain Expert (with deep skills in the blockchain technology but with limited knowledge regarding IoMT standards) elaborates a set of specific Smart Contract Prototypes for IoMT usage. Although these Smart Contract Prototypes are logically independent with respect to the blockchain solution, their code depends on the specific programming language supported by the blockchain in context. By combining IoMT and blockchain information (Blockchain Governance and Smart Contract prototypes), the Smart Contract Developer produces the Smart Contract specification.
Testing environment The experiments reported in this paper use a 3-node, EEA (Enterprise Ethereum Alliance)-compliant Proof of Authority private blockchain deployed on an AWS server. The 3 nodes are validators and run on different ports of the same server using Hyperledger Besu. All 3 are complemented with their own Orion nodes that act as Private Transaction managers. Users can communicate with the nodes through EthSigner proxies that have access to the private keys of 3 accounts made available for testing purposes. Alethio Lite Explorer is used to help us monitor blockchain activity. The architecture and its modules are implemented in Python and JavaScript: the software is made available as open source under Apache license (upon paper publication). Illustrative parts of the corresponding data formats/files are included in Section 4.
Workflow illustration This section illustrates the generic information flow related to the conversion of an IoMT specification (for an MThing or a collection of MThings) into a Smart Contract, for the case of an MCamera and the previously specified EEA blockchain. We assume the architectural modules to be deployed on the blockchain infrastructure and the Blockchain Expert to have already created the Smart Contract Prototype. This prototype is composed of a set of functions matching individual IoMT specifications to their blockchain counterparts, cf. Fig. 3. Assuming the IoMT Expert creates a new application around the video stream produced by an MCamera, the IoMT Parser automatically extracts the set of information required by the blockchain and presents it to the Smart Contract Developer through a generic API, as illustrated in Fig.
Acknowledgments Mohamed Allouche's contribution to this paper was mainly achieved during his MS internship at  Telecom SudParis. He  is currently a Ph. D. student with Vidmizer (https://vidmizer. com/en/) and  Telecom SudParis. Alexandre Moreaux' Ph. D. thesis is developed under the framework of a tight R&D partnership between Blockchain Secure (https://blockchainsecure. io) and  Telecom SudParis.
Environmental flows are generally characterized by complex bed morphology and high current speeds. Such configurations favor the formation of vortex structures that strongly affect hydrody-namics and sediment transport. Large-Eddy Simulation (LES) enables investigation of the dynam-ics of the largest turbulence scales and, thanks to enhanced calculation resources, has now become applicable for simulating environmental flows. In this paper, a LES approach is developed in a CFD code (TELEMAC-3D), which was originally developed to simulate free surface flows using RANS methods. The present developments involve implementing subgrid models, boundary con-ditions and numerical schemes suitable for LES. The LES version of TELEMAC-3D was validated by comparing results on the model with experimental data for flow past a cylinder.
Sometimes, the large-scale eddies move up to the free surface and create a boil when they bloom at the free surface. Some large-scale eddies may also hit the bottom and contribute to bed erosion. In this context of studying the effect of turbulence on solids transport, the objective of the current paper is to present a new LES model dedicated to natural flow. The model is derived from the widely used RANS model TELEMAC-3D, designed for simulating environmental flows  (Hervouet, 2007; Jankowski, 1999). Before addressing sediment motion with a dedicated module, it is appropriate to compare how well an LES approach evaluates bottom shear stress. Flow over asymmetric dunes is used as a case study. The current paper is organized as follows: Section 2 includes the description of the TELEMAC-3D model, the developments required for implementing an LES approach, and model validation for a test case representing flow past a cylinder.
Results: hydrodynamic statistics and flow structures The RANS results and hydrodynamic statistics obtained using an LES approach are given about 10 flow cycles). Flow statistics were   computed after a transient phase of 40 s (corresponding to about 10 flow cycles). The time-averaging period was 110 s. First, the influence of grid resolution was investigated. Fig. 4  shows the average stream-wise velocity and Reynolds stresses < u , u , > and < w , w , > obtained with LES using three grids along the profile L 1. Comparison with experimental data showed that, with the three space discretizations, the model gives satisfactory average velocity profiles. However, concerning the second-order statistics, the coarser grids did not accurately model the Reynolds stresses, and particularly the <w , w , > component.
Conclusions A Large-Eddy Simulation approach was developed under the environmental software application TELEMAC-3D, a Navier-Stokes solver dedicated to environmental flow modeling. The turbulence characteristics predicted by the model compared well with experimental measurements of (i) flow past a cylinder and (ii) flow over idealized dunes. Numerical investigation of flow over dunes showed that large turbulent structures propa-gate in the inner layer and strongly affect bottom shear stress. Locally and sporadically, very high peaks of shear stress were identified. It is expected that these turbulenceinduced in-creases in shear stress have important implications in terms of sediment mobility. Further studies will therefore assess the transport rate induced by the turbulence.
Confocal Microscopy Confocal images of 3D-CCMs were acquired with a LEICA SP5 confocal system. Cell nuclei and the plasma membrane were stained for 30 min with a 1 µmol/L Hoescht 33342 solution (exc: 350 nm/em: 461 nm) (Thermo Fisher Scientific©) and Cell Mask TM Deep Red Actin tracking Stain (Invitrogen TM ) respectively. Data acquisition was performed with a scan speed of 400 Hz. FOV and pixel sizes used were reported in the legend of the figures. For each sample, transmission images were captured together with the fluorescent images.
The signals were collected in epidetection mode by a photomultiplier tube (H6780-01, Hamamatsu) and separated from the laser light by a dichroic mirror (T695lpxr, Chroma) and an interference filter (FF01-450-70 or FF01-460-80, Semrock). Data acquisition was performed on a 350 µm square field of view with a pixel size of 0. 192 µm and an acquisition frequency of 400 Hz. Images were captured with a z-step of 1 µm.
Image Processing Two parameters were investigated to determine the reproducibility of the production method: sample thickness and cell distribution. The volume and shape of 3D models are known to be sources of variability that can lead to different treatment responses  (Zanoni et al. , 2016). Thus, size uniformity is a key parameter that was evaluated from sample thickness measurements. This parameter was calculated from the acquired SHG stacks according to the determination of a z-range containing a detectable collagen signal. In parallel, we developed a Python code (V. 3. 7) to analyze the cell distribution inside 3D-CCMs. Our program is open-access, available online on Zenodo (doi: 10. 5281/zenodo. 381436) and described in detail in the Appendices, Section A. Briefly, each 2PEF image stacks were thresholded to create binary images.
, 2011; Porcel et al. , 2014). Irradiation. Irradiation was performed under atmospheric conditions with a 662 keV Cesium ( 137 Cs) gamma source located at Institut Curie, Orsay (GSR-D1, RadeXp). Clonogenic assay. Samples were then disaggregated according to the protocol given in Section "Cell Viability. " For each irradiation dose, three Petri dishes were prepared. Cells were then incubated for 15 days before fixation and staining in a solution of 50% methanol/50% methylene blue. Colonies consisting of at least 50 cells were counted. The survival fractions (SFs) were determined as the number of colonies counted divided by the product of the plating efficiency (PE) with the cell seeding. Statistical analysis. A statistical analysis of the colony formation assay results was performed with the software package CFAssay for R (R Core Team, 2017), R: A Language and Environment for Statistical Computing.
ACKNOWLEDGMENTS The authors want to thank the  Centre de Photonique pour la Biologie et les Matériaux (CPBM)  (https://www. pluginlabsuniversiteparissaclay. fr/fr/entity/917033-lumat-fr-2764-centrede-photonique-biomedicale-cpbm) at ISMO and particularly  Ludivine Houël-Renault  for the confocal microscope facilities and biological advices. In addition, Multiphoton imaging was performed at the Polytechnique bioimaging facility with financial support from  Région Ile-de-France  and  Agence Nationale de la Recherche  (contracts  ANR-11-EQPX-0029 Morphoscope2  and  ANR-10-INBS-04 France BioImaging ). This research was funded by  Université Paris Saclay  via the" Inititative de Recherche Stratégique" program , grant  IRS   NanoTheRad  project. The authors thank  Isabelle Lamarre  (LOB) for access to the  L2 biology laboratory.
RESULTS To illustrate our method, we carry out simulated experiments for single-and two-qubit gate sets, as well as use our RL-GST procedure to characterize three 5-qubit IBM quantum computing devices available for free access. We compare the performance of our method against that of standard GST. For implementing the GST protocol, we make use of the Python package 'pygsti' from Ref. [22]. Single-qubit simulations. To assess the performance of our RL-GST procedure, it is important to begin with a situation where we know the true noise in the gates. We simulate noisy single-qubit gate operations by attaching to an ideal gate a noise map of the form e(e) ≡ AD(e 0 ) • Pauli(e 1 , e 2 , e 3 ) • R x (e 4 ) • R y (e 5 ) • R z (e 6 ). Here, AD(e 0 ) is the amplitude-damping channel AD(e 0 )(•) = (|0 0| + √ 1 -e 0 |1 1|)(•)(|0 0| + √ 1 -e 0 |1 1|) + e 0 |0 1|(•)|1 0|, Pauli(e 1 , e 2 , e 3 ) is the Pauli channel Pauli(e 1 , e 2 , e 3 )(•) = [1 -(e 1 + e 2 + e 3 )](•) + a=1 3 e a σ a (•)σ a , and R X(Y,Z) (e 4  (5, 6)  ) is the rotation about the x(y, z)-axis of the Bloch sphere by angle e 4  (5, 6).