dataesr/scientific-paragraphs-categorization huggingface dataset:
Hereafter, we refer to these parameters as sampling bias parameters, as they may bias the intensity estimates as a result of the process of sampling the data. The key advancement of the combined likelihood approach is that the environmental response, parameterised by β, is informed by both the presence-only and presence-absence data. Such an approach implicitly assumes that the data sets are statistically independent, which allows for the combined log-likelihood to be expressed as a sum of the single-source log-likelihoods. Other combinations may be done in similar fashion. For example,  Koshkina et al. (2017)  considered a combination of presence-only data with site-occupancy data, and Pacifici While these papers clearly advance the practice of fitting SDMs in important ways, they do not address some common challenges that arise in real datasets.
Implementation in R To fit models with the combined penalised log-likelihood in eqn 1, we have developed a set of functions in R inspired by the optim function and ppmlasso package  (Renner & Warton, 2013). The main function comb lasso takes as an input a list of species data, associated environmental data, and formulae for the environmental trend and sampling bias trends for each component, along with details such as type of presence-only likelihoods to use, the type of penalty, the number of models to fit, and the tuning parameter criterion. The function applies the coordinate descent algorithm of  Osborne et al. (2000). This requires the derivatives of the component likelihoods (also known as "score equations") to be computed. Analytical score equations are supplied directly to the optim function, which serves as the machinery of the optimisation.
Lynx Locations s w s d s c Altitude was averaged over 1km × 1km cells from data available in the raster package in R, while human population density was averaged over 1 km × 1km cells taken from version 4 of the Gridded Population of the World data repository (Center for International Earth Science Information Network (CIESIN) -Columbia University, 2016). Distances from the nearest main road and railway were computed from shapefiles from Version 151 of the ROUTE 500 database, accessible at http://professionnels. ign. fr/route500. We fitted initial separate IPPPMs to the wild records s w and the domestic records s d using linear, quadratic, and interaction terms for the four environmental covariates, and linear terms for the sampling bias covariates. From these models, we are able to assess whether We estimate the wild and domestic intensities from area-interaction models, and as the observed values of the cross K-function fall within the envelope boundaries, this suggests that there is no clustering across the two data sets.
The resulting likelihood expression would be: where ξ(y) ∼ MVN(0, Σ). Models of this type are typically fitted in a Bayesian framework. We could reduce the dimension of ξ through methods like fixed rank kriging or induce sparsity in Σ through lasso-type penalties such that the likelihood in eqn 4 could be fitted with software such as Template Model Builder (TMB,  Kristensen et al. , 2016). Another way to achieve sparsity is with the stochastic partial differential equation approach (SPDE,  Lindgren et al. , 2011) , as implemented in the inlabru package  (Bachl et al. , 2019).
Agent's structure is given in the Figure  4  and includes states of the worker with the corresponding transitions between the states. Optimisation and coordination agent as a higher hierarchy agent creates the global schedule and initialize processing and breaks. Detailed information about agent characteristics is given in the Table  2. {please insert Figure  4  here} {please insert Table  2  here} The structure of the model is presented in Figure  5. Two main stages can be distinguished: the first stage dedicated to global optimisation followed by simulation optimisation method for dynamic rescheduling in the second stage. The process starts with the first stage that generates the schedule and ends with the second stage that simulates the production and performs necessary rescheduling. {please insert Figure  5  here} At the beginning of the first stage, information about the parts to be produced (processing times) and time margin strategy (represented through lower and upper time margins boundary) is loaded from the database.
The loaded information is assigned to one part agent per dataset. Following Step 1 of global optimization, part agent calculates and sends values of and to optimisation agent. Optimization agent then performs Step 2 of the global optimization algorithm and as a result the schedule is created. Upon schedule finalization, optimization agent coordinates the simulation stage by sending necessary information about the first part to be produced (part ID, type, , time margin lower and upper boundary) and sends the message to the worker agent to start the simulation. After receiving the message, the second stage starts. The worker agent goes from not working to processing state. Before entering this state, the processing time calculation is performed based on equations (  4 )-(  10 ). After performing the processing, fatigue needs to be calculated and time margin value needs to be set before the worker enters the next state, time margin state.
Experimental verification The goal of the performed work has been to create unique solution that will connect ergonomic and production research -to provide recommendation for the production systems' designer to firstly take into account the workers in the production system and then to optimize the system through time margins so that both, optimisation objective function (makespan), fatigue level and human error probability are to be satisfied. The tool has been developed in AnyLogic PLE 7. 3. 6 using Java programing language. It comprises elements described in the methodology and model sections and it is dedicated to the two-worker flow system analysis. The experiments, presented in this section, have been conducted using the tool. As underlined in Section 2, integration of ergonomics with production or operation research represented through the scheduling problem, haven't been identified in the current literature.
Thermodynamic model of the system The model was developed using the software Dymola, with the object-oriented language Modelica. The Radau solver with a tolerance of 1e -6 was selected to solve the equations. Several usual assumptions for modeling of thermodynamic cycles at a system scale were made: • Ideal gas assumption (with temperature-dependent specific heat, thermal conductivity and dynamic viscosity). • Adiabatic compression/expansion processes with constant isentropic efficiencies. • No thermal losses for the heat exchangers. • Steady state is assumed (at the exception of the thermal storage). The transient behavior of the storage is considered because it is expected that it will influence the behavior of the downstream system. In terms of dynamics, the time length of the cycles -from three hours for the discharge to five hours for the charge -is long enough to neglect the start-ups and shutdowns of turbomachinery.
This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance -relying on a priori expert judgement-, the costs, and possible risks to construct some technical solutions, to finally identify the costefficient ones.
Outside the EU, crop area estimation is a necessary objective and thus requires a dedicated system component, especially relevant for those countries where interannual production variability is largely influenced by sown area. The last two objectives 5 and 6 relate to the dissemination of the system outputs. Regular bulletins constitute the reference documents containing the results of the monitoring activities: reports on weather and crop growth monitoring, remote sensing analysis, yield forecasts and area estimates. Therefore, the release of regular bulletins has a high priority for stakeholders. Moreover, all the indicators of the system shall be publicly available with an appropriate infrastructure (e. g. internet data portal).
System components and workflow The monitoring system to be built, following the principles of the European MCYFS, is composed of six components: A) An infrastructure to acquire and process meteorological data. B) An infrastructure to process remote sensing data. C) A software platform to calibrate and run crop growth models. D) A statistical toolbox to perform yield forecasts, including an archive of crop area and yield statistics. E) A team of analysts in charge of the quantitative and qualitative analysis of crop growth and yield. F) A specific component to estimate crop area from remote sensing and field sampling. A schematic representation of the system and the different components is given in Fig. 1 , whereas Fig. 2  describes the data and processes workflow. The meteorological data infrastructure acquires and processes in near-real time (NRT) weather data from primary sources: weather stations and atmospheric models.
, 2003) , STICS  (Brisson et al. , 2009) ; and the crop-specific models CaneGro  (Singels et al. , 2008)  for sugarcane, and WARM  (Confalonieri et al. , 2009)  for rice. Moreover, this third component includes, in addition to the BioMA platform, a dataset of soil physical properties from existing soil maps -necessary to simulate soil water balance-and crop experimental data (e. g. Boons -Prins, 1993) used for crop model calibration. The indicators provided by the meteorological, remote sensing and crop modelling components are derived and aggregated at a 25 km resolution reference grid. Then, they are also aggregated at administrative unit level (following the Global Administrative Units Layers, GAUL, from FAO) to produce regional indicators. The aggregation is based on land use -taken from existing land cover maps such as GLO-BCOVER  (Arino et al.
, 2008)  or GLC 2000  (Bartholomé and Belward, 2005) , and crop area regional statistics to weight sub-national administrative units according to crop occurrence. As an example, a detailed description of the aggregation of remote sensing indicators can be found in  Genovese et al. (2001). The mentioned three components include a historic archive of grid and regional indicators, where additional ones such as long-term and medium-term averages, quantiles, etc. needed for anomaly detection, are regularly calculated and updated. Regional indicators produced by the first three components aggregated at different administrative levels are analysed statistically in the fourth component of the MCYFS-like system, the statistical database and toolbox, to produce a yield forecast (Fig. 2 ). This component has two main elements: a repository of historical time-series of official crop yield and area statistics; and a software called CoBo (Control Board) where different statistical methods are implemented to produce yield forecasts.
These methods try to identify the statistical relationship between the different indicators and yields statistics in the past -through the analysis of yield trends, different regression methods, and similarity analysis of time-series-to produce a yield figure. Moreover, CoBo, developed for the European MCYFS, is also an archive of the forecasting exercises and a management tool to guarantee the traceability of the yield figures produced. The analysis team is the fifth component of the system, and has a central role for the accomplishment of all the system objectives explained in Section 2. 1, with the exception of area estimation. The European MCYFS is a data driven decision-support system, where the role of the analyst is key. The analysts investigate the indicators provided by the rest of the components and identify the ones that better explain actual crop growth and yields.
They select the appropriate statistical methods to produce a reliable yield forecast and report the results of the mentioned analysis in the form of regular bulletins (see https://ec. europa. eu/jrc/en/mars/bulletins). Moreover, the analysts in the MCYFS have responsibilities for the system maintenance: highlighting possible caveats and limitations in some of the components; proposing scientific and technical solutions to improve them; but also maintaining links with local institutions of countries to exchange data and resources. The analysis team is multidisciplinary, with professionals having a scientific/technical specialization -e. g. on agro-meteorology, agronomy, crop modelling, remote sensing, statistics…-that favours the development and maintenance of the system components. Each analyst is also specialized in a regional context (countries, group of countries), which permits the analysts to reach an expertise on the local agro-climatic conditions, to better understand how the crop indicators produced by the different system components describe actual yield variations in that regional context, and to consolidate the relationship with local partners.
The area estimation component relies on the use of high-resolution remote sensing data -in some cases combined with field surveys, see Section 2. 3-as proposed by Gallego   2010 )) and has specific staff for photointerpretation, digital classification, and statistical methods for crop area survey design and area estimation. The successful implementation of this component of the MCYFS-like system outside the EU requires collaboration with local institutions, especially when planning and conducting field surveys.
In the meteorological data infrastructure the minimum solution relies on the use of modelled weather data at global level: reanalysis and forecast weather data from the European Centre of Medium-Range Weather Forecast, (ERA-Interim and HRES products from ECMWF, see  Dee et al. , 2011) , acquired and post-processed (projection, spatial aggregation of indicators) daily in NRT. ECMWF products were preferred to other NRT reanalysis datasets like the JRA-55, from the Japan Meteorological Analysis  (Kobayashi et al. , 2015)  since it permits the use of processing chains already implemented in the European MCYFS, which uses currently ECMWF data. Other alternative reanalysis data like MERRA, from NASA  (Rienecker et al. , 2011)  were discarded as they are not NRT products. The intermediate and advanced solutions would incorporate additionally data from meteorological weather stations to the ECMWF dataset, having thus two production lines of meteorological indicators based, respectively, on modelled and observed weather data.
The use of observed data requires the implementation of a processing chain to pre-process (error checking, computation of daily indicators…) and interpolate spatially weather data (Micale and  Genovese, 2004 ). The intermediate one will rely on available open access weather data, e. g. from the GHCN (Global Historical Climatological Network, see https://www. ncdc. noaa. gov/ghcn-daily-description). In the advanced level, an additional number of observations from local weather station networks (e. g. from national meteorological institutes) are added to the open datasets, increasing the density of observations to reach a density similar to the European MCYFS -> 2500 active stations in the EU territory-thus improving the quality of the interpolated weather indicators. In the remote sensing component, the minimum technical alternative consists in the use of sensor-specific low-resolution products (e.
g. simple NDVI), which are calculated from top-of-canopy reflectance, provided in NRT in the form of 10-day composites. The intermediate alternative is an upgrade of the minimum component using long-term product archives based on inter-operable, multi-platform products. The objective of this alternative is guaranteeing, at least, a product archive of at least 15 years, beyond the end life cycle of remote sensing platforms. This long-term archive is necessary when using the products to detect crop growth anomalies  (Baruth et al. , 2006)  against an average year or as yield predictor  (López-Lozano et al. , 2015). A product example within this intermediate level are the NRT Copernicus land biophysical products (e. g. fAPAR, LAI, see  Verger et al. , 2014) , which use reflectance data from SPOT-VEGETATION and PROBA-V platforms since 1998 until present.
g. WOFOST, CropSyst, STICS, WARM, see Section 2. 2), will be used to simulate the growth of individual crops. Crop growth models have the advantage against simple vegetation models of a more detailed simulation of the different processes: light interception, carbon assimilation, biomass partitioning, evapotranspiration, etc. In the intermediate implementation the calibration of these models will be performed based on agronomic data extracted from scientific literature. Soil properties computed from global soil datasets (e. g. Batjes, 2008)  will be used as input for the soil water balance model. Additionally, local agro-management data including irrigated/rainfed arable land and single/multiple cropping distribution are collected enabling models to simulate crop growth under realistic agro-management conditions.
In the advanced solution, the calibration of crop models is conducted using field information collected from local experiments, similar to the work of Boons -Prins (1993) for the European MCYFS. Detailed soil datasets will be used as input for soil water balance simulations, and local data on fertilizing practices and possible pressure of pest and diseases will be collected as well to improve the reliability of crop model runs. In the statistical component, the three technical solutions constitute a gradient in the sophistication of the statistical methods derived for crop yield forecasting, and the quality of the statistical data archive (Table  2 ). In the minimum solution, the statistical methods used are the simplest ones: trend analysis and linear regressions, applied at national scale.
important efforts (e. g. economic). • High risk. This data is not likely to be available, or is not reliable. The replies from all the MCYFS Europe analysts on the individual scores and risks were collected, and the most frequent and risk per component and country was selected. In the crop modelling and statistical components, the analysts attributed the attainment scores and risks to the sub-components (Table  2 ): the crop models used -including agronomic data for model calibration-, the soil data available to simulate soil water balance, the additional local crop management data, the statistical methods, and the statistical database. Considering sub-components in these two cases is important since, in some countries a high risk may be attributed only to one sub-component (e. g. experimental field data, or reliable crop statistics, are not accessible), but it can jeopardize the performance of the whole component and eventually the system.
Cost estimation Costs are estimated for every technical solution (Table  2 ) under the financial rules governing the European Commission as of 2011. Four categories where considered: data acquisition; staff; software and IT infrastructure; and contracted costs. The costs are established separately for the set-up (una tantum) and operational phases (yearly cost, including maintenance). Data acquisition costs are calculated directly from public chart prices when available (e. g. subscription to ECMWF forecast data in NRT, acquisition of very high resolution satellite imagery). When price lists are not publicly available (e. g. statistical data for some countries, station weather data in local national networks) these costs are estimated by expert judgement from unit costs (e. g. costs per station, per dataset) remunerated in the operational MCYFS in Europe for data acquisition.
Software (e. g. database server software, remote sensing programs, GIS packages, mathematical applications and IT development environments) and hardware (workstations, FTP servers, etc. ) are taken based on the actual market prices. Additionally, information storage and backup devices are estimated from the amount of hard disk space necessary for the input data and output products (meteorological data, satellite imagery, crop modelling products at different spatial units) and assuming a yearly cost per terabyte of information, derived from the actual expenses of the MCYFS system. The investment needed in staff is based on current fares for the European Commission statutory staff (both permanent and temporary) and external (IT consultants). Contracted costs are for those tasks that we recommend to outsource to external tenderers: e.
Algorithm for evaluating system performance and selection of the costefficient ones An automatic algorithm has been specifically programmed in Matlab (Mathworks Inc. , US) to run the cost-efficiency analysis of the MCYFS-like system for the different countries covered by GLOBCAST. The algorithm first computes the overall attainment score in the six objectives for all the possible systems (a total of 729 possible combinations of the technical solutions for the system components). Then, a set of rules is applied taking into account the system attainment scores and costs to identify the cost-efficient ones.
The resulting scores S o, c for all components of the system are then used to compute the total system score S o for all the six objectives o = 1, …, 6. The formulae to derive S o from S o, c shown in Fig. 3  try to reproduce the actual interaction of the components in the MCYFS workflow (2. 2) to the achievable attainment of each objective. In objectives 1, 2 and 3, where more than one objective is involved, the nature of that interaction is twofold: either constraining -identified with a minus sign in Fig. 3 -or synergistic -plus sign in Fig. 3. In a constraining interaction, the component with the minimum score of the interaction acts as a bottleneck for all the others, and the resulting score is the minimum of the components interacting. This happens, for instance, when meteorological data and crop modelling interact in the MCYFS for crop monitoring and yield forecasting.
If the meteorological data is not reliable, it will limit the performance given by the crop modelling -using meteorological data as an input-no matter how accurately the crop model has been calibrated and the quality of additional soil and management information, and vice versa. Something similar can be expected when the analysis team and the statistical component interact with the outputs from the meteorological, crop modelling and remote sensing components to produce reliable crop monitoring analysis and yield forecasts (Fig. 3 ). A synergy occurs with the result of the interaction between the crop modelling and meteorological components, on one side, and the remote sensing, on the other side (formulae with the plus sign in Fig. 3 ). As crop modelling and remote sensing are used complementarily in the MCYFS for growth monitoring and yield forecasting (objectives 1-3), the performance of the system is not affected if only one of the two components has a low reliability.