dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental/benchmarking setupA specific aspect of this work consists in the period of data available because the NATL60 native run is only one-year long which is relatively short in comparison with the training period typically used in the previous related work mentioned in Introduction. To get around this issue, we decide to build four 20-days long validation period homogeneously distributed along this one-year dataset (see the starting dates reported on Figures5 and 6), supposed to be representative of the different seasonality effects that may be encountered during the year. Regarding the metrics used in the intercomparison exercise, daily normalized RMSE (nRMSE) time series are first provided: they give a quick overview of the potential gain obtained with the data-driven interpolators.
over the Mediterranean sea, which may indicate AnDA responds differently to the along-track aggregation strategy depending on the energetic dynamical regime of the region. On the other hand, both FP-ConvAE and FP-GENN interpolators performs better (not shown here) by aggregating nadir data over a 5-day time window. As a consequence, the results presented in what follows will use value of d = 0 for AnDA and VE-DINEOF and d = 5 for FP-ConvAE and FP-GENN. Next, to evaluate the behaviour of the different interpolators on both along-track nadir samplings and their fusion with wide-swath SWOT datasets and make the comparison possible, we have to preliminary define if the NN-based interpolator will be used under a supervised or unsupervised learning strategy. Figure5depicts how the FP-GENN interpolator performs using nadir data (a) or their joint use with SWOT (b), according to the input and target data used for the training.
4. 1. 1 Experimental/benchmarking setupA specific aspect of this work consists in the period of data available because the NATL60 native run is only one-year long which is relatively short in comparison with the training period typically used in the previous related work mentioned in Introduction. To get around this issue, we decide to build four 20-days long validation period homogeneously distributed along this one-year dataset (see the starting dates reported on Figures5 and 6), supposed to be representative of the different seasonality effects that may be encountered during the year. Regarding the metrics used in the intercomparison exercise, daily normalized RMSE (nRMSE) time series are first provided: they give a quick overview of the potential gain obtained with the data-driven interpolators.
[ 5 ] 5 over the Mediterranean sea, which may indicate AnDA responds differently to the along-track aggregation strategy depending on the energetic dynamical regime of the region. On the other hand, both FP-ConvAE and FP-GENN interpolators performs better (not shown here) by aggregating nadir data over a 5-day time window. As a consequence, the results presented in what follows will use value of d = 0 for AnDA and VE-DINEOF and d = 5 for FP-ConvAE and FP-GENN. Next, to evaluate the behaviour of the different interpolators on both along-track nadir samplings and their fusion with wide-swath SWOT datasets and make the comparison possible, we have to preliminary define if the NN-based interpolator will be used under a supervised or unsupervised learning strategy. Figure5depicts how the FP-GENN interpolator performs using nadir data (a) or their joint use with SWOT (b), according to the input and target data used for the training.
Maxime Beauchamp, Ronan Fablet, Clément Ubelmann, Maxime Ballarotta and Bertrand Chapron ; writing-original draft preparation, Maxime Beauchamp; Supplementary Materials: The code is available on https://github. com/CIA-Oceanix/DINAE_keras with additional informations provided in the ReadMe file to describe the architecture of the code and how to use it Author Contributions: "Conceptualization, Ronan Fablet and Maxime Beauchamp; methodology, Ronan Fablet and Maxime Beauchamp; software, Ronan Fablet and Maxime Beauchamp; validation, Maxime Beauchamp; investigation,.
Implementation SHiNeMaS has been developed using the Python language[15] and the Django framework 1 , a free and open source Python web framework. Django provides an API to interact with the database and migration tools that generates scripts automatically when the relational model is updated. It is an important feature as we aim to develop and distribute further versions of the tool. SHiN-eMaS is deployed on GQE-Le Moulon's server with Post-greSQL DBMS, but, according to Django's API, MySQL, Oracle and SQLite DBMS can also be used. SHiNeMaS is distributed by two way, (i) a Docker container which makes possible to use the software as a desktop application or a server based application, but it works only with Linux, (ii) the code source which will need IT knowledge to deploy in your web server (often Apache or Nginx).
Database structure The SHiNeMaS relational schema is provided as Additional file 1. The first part of the schema describes the genetic material. Seed_lot is the main table of the schema while Update_Quantity stores stock inventory events. Quantities are managed using gram as the unit. A seed lot is linked to the tables Germplasm and Location. The table Germplasm_type manages the type of the genetic material. SHiNeMaS offers a flexible way to manage inbreed lines, hybrids, populations or any kind of genetic material you want to describe. The name of the species is also an attribute of the genetic material. A second part of the schema describes the network of participants in the breeding program. The table Person describes the actors of the network, it can be farmers, facilitators, researchers etc.
Manage data with files SHiNeMaS provides interfaces to load (Fig. 2 , step 1) relational information between seed lots and the associated data concerning phenotypes, environments and cultural practices with tabulated format files. SHiNeMaS accounts for five kinds of events to describe the type of relationship between seed lots: diffusion, reproduction, cross, mixture and selection resulting in five files format with specific and mandatory headers. A sixth file format describes individual measures. In addition to the mandatory headers, users can add headers corresponding to the variables measured (phenotyping, environment description, cultural practices). The associated data submitted are linked to a relation, a sown seed lot, a harvested seed lot etc. according to the type of the variable and the type of event.
A date can be associated to each measure respecting the ISO 8601 2 standard (YYYY-MM-DD). The format DD/MM/YYYY is also accepted in SHiNeMaS. After each file submission, a report is displayed to inform the user whether the data has been successfully inserted into the database and, if not, what was wrong with the file. If the submission is valid, SHiNeMaS will create automatically the output seed lot of each relation (Fig. 2 , step 2, for example, the harvested seed lots of a reproduction) with a specific pattern to create the name of the resulting seed lot. Currently, SHiNeMaS does not provide any native connection to mobile devices. However, the tool we describe here uses tabulated text files to record data. This kind of file can be easily transferred to any mobile device and can be filled with usual editors on this device; In this case 1 https ://www.
Prepare files with wizard tools Because preparing a large file in a spreadsheet software can be a fastidious task, SHiNeMaS provides a helpful assistant to the creation of files using the seed lots or relations already recorded in the database (Fig. 2 , step 3). By executing successive queries from a simple form, a user can build a list of seed lots or relations and export it in one of the file format described below. Thus, the user will only have to fill the remaining cells (Fig. 2 , step 4) before the next submit.
Data management with single form SHiNeMaS provides form interfaces to create single relation events (Fig. 3  shows a form to create a new reproduction event). As for the files, a different form has been designed for each event type. When creating a new single event, the user can also add all the data measured, like when submitting a file. Forms are also provided to manage any other type of information recorded in the database (projects, variables, methods, etc. ).
Data querying SHiNeMaS provides query interfaces to retrieve information about seed lots such as (1) parentage, (2) generation number (on the current location and in total) and the quality of information, (3) data regarding variables measured during a given period of time. The query can be filtered according to various criteria and the results are displayed in three tables showing the data related to seed lots, the data related to the relations and individuals data. From any query result, the user can access the seed lot card (Fig. 4 ). It displays information about the seed lot (its name, stock location, creation year, germplasm etc. ), how it was created and used and its stock evolution. The stock is computed using initial quantity of the lot, all quantities used in relations as a parent and the inventories of the stock.
The user can also access to the relationship card (Fig. 5 ) between two seed lots. It displays all the data that has been measured for this event and the associated seed lots. From any card the data tables can be downloaded in tabulated text file. 1 ) the application will create the resulting seed lots for each event  (2). These seed lots can be used later to create and export a new file (3). Thus, the user only need to fill the remaining cells in a spreadsheet software (4) before a new submission. A user can also export a file with recorded relations and use it to run a deletion process  (5)  Lastly, SHiNeMaS provides a global search bar enabling a quick access to the seed lots, relations or germplasms cards. This search bar is implemented with an auto-complete feature.
Support and community SHiNeMaS source code is hosted on the French national forge for research and higher education, SourceSup  3. Through this forge, our aim is to provide tools to the community such as an issue tracker, forums or wiki site. This forge provides anyone who wants to contribute to the development of our project the usual tools of popular forges. Currently, the web interfaces of SHiNeMaS are available in English or French but, with the help of the community, it would be easy to translate the application in any other language. A virtual machine is also available to run SHiNeMaS' tutorial  4.
Future developments Major future developments of SHiNeMaS will focus on the management of new data types (climatic data and plant pictures). In addition to storing data on seed lots and relations between seed lots, it will be possible to store data on germplasm. A web service will be developed to provide features ensuring interoperability with external tools such as R packages  [7]. Efforts will also be made in terms of controlled vocabulary by complying with the Plant Science community of Elixir 5 and we aim to implement methods of the Breeding API 6. The version 2. 0 of this API defines new methods related to seed lot and seed lot transactions which would be relevant for our tool. Moreover, we will ensure an audit of submitted files to keep track of the submissions. In a future version of SHiNeMaS, we will focus on the use of controlled vocabulary and ensure the interoperability with standard ontologies.
Conclusion We developed SHiNeMaS to ensure seed lots traceability and to manage phenotypes, environments and cultural practices data for various genetic resources, varieties of many crop species. This web application is a key feature as it enables data administration at different scales (files, or single forms). SHiNeMaS is especially adapted for field experimentation in a network of farms or experimental stations and that requires tracking seed lots history. It is available under AGPL licence and uses free technologies. SHiNeMaS has been used in production in our laboratory for 5 years and some farmers' organizations facilitators manage their own information in the system.
Funding This work has been supported by  INRAE. It has been funded by (1) the  European Community 's  Seventh Framework Program  (Grant Agreement No 245058, Solibam), (2) the  European Union 's  Horizon 2020 research and innovation program  (Frant Agreement No 633571, DIVERSIFOOD), (3) the  Fondation de France , the  French Region Centre  ( SPEAL  project), the  French Ministry of Agriculture and Food (ProABioDiv and Covalience CASDAR projects).
[6] We perform the estimation at the country level. Bilateral migrant flows data are derived from 1990 to 2015 stock estimates from the World Bank, available for 5-y periods, using two methods of derivation: ref. From our gravity model specification, we also capture trends related to convergence or divergence in economic development levels, as well as rough demographic trends (changes in population sizes). Yet we do not explicitly capture trends linked to geopolitical events. This limits our ability to fully capture nonstationarity. The resulting estimation is presented in Table  1. Results imply that migration flows between two countries increase with origin population size and with the existence of a common language and decrease with distance between countries, as suggested in other studies using similar models (e.
g. , ref. 25). Migration flows also tend to increase with destination population size and per capita GDP. The effects of bilateral characteristics (distance, remittances shares and costs, common official language) are as we would expect. Unsurprisingly, remittance costs reduce migration-yet the effect is # For a detailed description of each impact module, see https://www. fund-model. org. We use the version of FUND implemented with Mimi. Mimi is a Julia package that provides a component model for integrated assessment models. It is being developed by a team led by University of California, Berkeley's David Anthoff, in connection with Resources for the Future's Social Cost of Carbon Initiative. † † We thus use secondary data on bilateral migration flows. These datasets have the benefit of exhaustivity, crucial for our purpose.
Existing primary datasets on international bilateral flows, often based on data from the Organization for Economic Cooperation and Development  (37) , omit many migration corridors, in particular for South-South migration. not significant. Conversely, the variable measuring remittances as share of income has a positive effect on migration, hinting at the effects of migration networks on the persistence of migration flows: People tend to move to areas where strong remittance traditions with their home countries exist. Alternatively, if the migration is a collective household decision, this positive effect might reflect the desirability to the household as a whole of the extra income. We provide a first robustness check by estimating parameters using data from ref. 36  (Table  1 , column 2) and find that our projections are virtually not affected by the data source.
Note that including origin and destination fixed effects in our estimation would imply that we are considering effects that are inferred from the deviations of migration flows from origin-and destination-specific long-run equilibria and as such can be thought of as summarizing short-to mediumrun elasticities. The effects on changes in the long-run equilibria, on the other hand, are factored into the parameter estimates of the model without these country fixed effects. For that reason we consider the model with only year fixed effects to be the more appropriate for theprojection exercise. In addition, projecting country fixed effects would lead to flow differences across countries remaining constant in the long run, hiding potential long-term effects of our socioeconomic covariates. For information, we provide estimations with country fixed effects on both datasets in SI Appendix, Table  S2.
We find that it does not, as the squared-term coefficient is positive. Sixth, we include regional exposure and vulnerability levels in our gravity model (SI Appendix, Table  S8 ). We find no robust effect of origin exposure level on migration flows ‡ ‡ In this part of the literature, migration models feature such a constraint: Income per capita at origin and that at destination have effects of the same magnitude and opposite signs on migration flows. For instance, ref. 17 calibrates this elasticity following ref. 38 , which uses data on migration to Spain for this purpose. In our estimation, we allow for coefficients on income at origin and destination to be different and find in our global dataset that origin income actually has a positive, albeit smaller effect on migration than destination income.
[8] We perform the estimation at the country level. The share of income sent as remittance is computed as follows: ρ od = remit od /(ypc mig,od * stock od ), with ypc mig,od = max((ypco + ypc d )/2, ypc d ) as in Eq. 4. For that purpose, we use bilateral remittance matrices available from the World Bank. Those are estimates of amounts in dollars sent between countries as remittances in a given year; they are estimated by the World Bank using migrant stocks as well as host and origin country incomes. We also use data on bilateral estimates of migrant stocks from the World Bank and on per capita income also from the World Bank. We use the last year for which all three datasets are available, which is 2017. For independent variables, we use data on income per capita from the World Bank, as well as data on the cost of sending remittances (expressed in percentage of amount sent) available from the World Bank's database Remittance Prices Worldwide  (39) , all for the year 2017.
Remittance parameter φ is transposed from country to region level as described below. Residuals , used both in our remittance and in our gravity models (Eqs and 3), are aggregated at the region level as described in SI Appendix. Calibration of Other Migration Parameters. Other remittance parameters are calibrated as follows. First, duration of migrants' stay in the destination region is computed as the difference between life expectancy λ in the destination region and age of migrants at time of migration µ, both calibrated using SSP projections available at the country level. We assume constant parameters after 2100 (note that here we show only results for 2015 to 2100, so this assumption does not affect our results). For both parameters, we transpose from country to region level by weighting countries within each region by 2015 population size, using data from the Wittgenstein Center As our migration dynamics model starts only in 2015, we also include an initial stock of migrants for that year.
For that purpose, we use data on bilateral migrant stocks from the World Bank for 2017, the closest available year, and assign an age distribution built as the average of two distributions: the age distribution of migrants at time of migration in the period 2015 to 2020 and the age distribution of the overall destination population in the same period, both sourced from the SSP population projections. Second, we estimate the share of a migrant's income ρ sent as remittances as described above. We also assume that the cost φ of sending remittances is constant over time and across the SSP narrative, but specific to the origin/destination pair. We calibrate this parameter using data on the cost of sending remittances (expressed in percentage of amount sent) available from the World Bank's database Remittance Prices Worldwide  (39)  for the year 2017.
We transpose it from country to region level by weighting migration corridors within each region by remittance flows. Remittance cost values range from 2 to 17% of the amount sent as remittance, depending on the corridor. Finally, the risk of dying while attempting to migrate δ is also assumed constant over time and across the SSP narrative, but specific to the origin/destination pair. We calculate this risk as the ratio of missing migrants to the migrant flows on that journey. We calibrate this parameter using data on missing migrants for the period 2014 to 2018 from the International Organization for Migration, as well as data on migration flows between regions in the period 2010 to 2015 from ref. 34. Input Scenarios: The SSPs without Migration. FUND requires input scenarios of population, economic, energy, and carbon intensity growth.
ACKNOWLEDGMENTS. We thank  David Anthoff ,  Joel Cohen ,  Johannes Emmerling ,  Brian Jones ,  Robert McLeman , Aur élie M éjean,  St éphane Zuber , and participants of the  Association of Environmental  and  Resource Economists  and  European Association of Environmental  and Resource Economists 2020 conferences for helpful feedback on earlier stages of this analysis. We also thank  Guy Abel ,  Matthew Gidden , and  Samir KC  for data, as well as  David Anthoff ,  Frank Errickson ,  Cora Kingdon , and  Lisa Rennels  for support with the FUND model.
Front-end electronics Cherenkov light from particle showers in the atmosphere is detected and digitized in the front-end of the camera. The light sensors are 960 PMTs, organized into 60 modules, called "drawers" (Fig. 3 ). The drawers are arranged in a 9 × 8 rectangular matrix, with each corner of the matrix devoid of 3 drawers. A drawer consists of 16 PMTs, two 8-channel analogue boards, and a slow control board (see Fig. 3 , top). The analogue boards host the electronics components responsible for the amplification, discrimination and digitization of the PMT signals (see Fig. 4 , left); the slow control board hosts an FPGA (Altera Cyclone IV) controlling the whole drawer, an ARM9-based single-board computer (TaskIt Stamp9G45), the power regulators and the sockets for the PMT HV bases. The drawers are supported by a mechanical structure which separates the front from the back-end of the camera.
Slow Control and Connection boards The FPGA and ARM computer of each front-end drawer are located on the slow control board. They are connected via a 100 Mbit/s memory bus, with a 16 bit word width; the ARM computer has a 100 Mbit/s Ethernet interface and acts as a device node of the distributed camera control software. The FPGA reads out sampling data from the NECTAr chip, collects other monitoring data such as PMT currents and L0 trigger counters, and directly controls all the electronics inside the drawer. The ARM computer runs a slow control server accessing the FPGA registers, reads out all FPGA data, buffers it and sends it over the network to a central camera server via TCP/IP using the ØMQ library  [7]. The central camera server controls the drawer by means of remote procedure calls implemented using the Apache Thrift library  [8].
Cabling The cabling uses industry solutions such as standard Ethernet twistedpair cables wherever possible to ensure ease of procurement and replacement. The data (both readout and slow control) between drawers and backplane is transmitted via TCP/IP over Ethernet by means of standard Cat. 6 cables. The LVDS pulse-amplitude modulated trigger, readout control and 10 MHz clock signals are transmitted on standard Cat. 6 A Ethernet cables of equal length (with a tolerance of ±40 mm, corresponding to ±0. 2 ns). The power is delivered to to each drawer on 4-wire cables terminated with threaded 4-pin M8 connectors. Ethernet and power cables are bundled in special pre-built cable spines (see Fig. 5 , right). Auxiliary sensors and actuators are connected using 3-wire or 4-wire electrical cables and standard threaded M8 connectors, except for the GPS antenna which is connected via a standard coaxial cable and SMA connector.