dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition to the data extracted for Vgsc allele frequencies, data were also identified for Ace-1 allele frequencies and metabolic mechanisms of resistance including cytochrome P450s, esterases and glutathione-S-transferases. The volumes of genetic and biochemical data currently available for these mechanisms of resistance did not meet    11. List of the insectides that were tested and are included in the datasets for release. our aim of providing standardised data for a large number of locations across Africa, so no collated datasets for these mechanisms were generated. Dataset 4 consists of results from synergist bioassays so it does, therefore, provide data linked to P450-mediated mechanisms of resistance. Many studies performed both bioassays and genetic tests. If links between the different tests performed on the same sample of mosquitoes were provided by the original study, and providing any subsamples tested were not biased, then it was possible to extract pairs of phenotypic and genotypic measures of resistance for samples from a specific time and place.
Unfortunately, however, when instances of paired phenotypic and genotypic results for an individual species from a single time and place were extracted, only sixty pairs were identified. This volume of data did not meet our aim of providing standardised data for a large number of locations across Africa. The same was true for paired phenotypic and genotypic results for a species complex or subgroup from a single time and place. In addition, the data volumes available for species-level CDC bottle bioassay results, species-level paired bioassays with and without a synergist, and species-level intensity bioassays, were too low to meet our aim of providing standardised data for a large number of locations across Africa.
Usage Notes Each data file released has been designed to provide results for a representative sample of a species complex or subgroup, or an individual species, so users can be confident of what each set of results represents. Older versions of the collated datasets of WHO susceptibility test results and the Vgsc allele frequency have been used in a prior geostatistical analysis that aimed to identify associations among resistance to different insecticides in the An. gambiae species complex  15. The data files have been designed for use in geospatial analyses and, in such analyses, the precise location for each data point is important for two reasons. First, because this allows accurate calculation of the Euclidian distances between points for analyses that exploit spatial correlations in the data  15, 29.
Data on insecticide resistance in the Anopheles vectors of malaria are available from VectorBase, however, VectorBase's aim and scope are much broader than those of the current data release and the data volumes for insecticide resistance in Anopheles vectors are smaller than those provided here  35. Furthermore, these data have not been configured specifically for use in mathematical analyses including geospatial analyses. Insecticide resistance data can also be viewed on interactive maps using the IR Mapper and Malaria Threats websites but these are data visualisation tools  36. The data shown on these sites were not collated in support of mathematical analyses and are not available for download. There are overlaps in all of these databases, including overlaps with the data being released here.
The data released here includes data that were provided to the World Health Organization to support the establishment of the Malaria Threats website  37. In addition, the data being released here were shared with the group behind the IR Mapper site so that both groups could cross-check each other's sources to identify publications that had been missed. A geo-database of insecticide resistance in the Aedes vectors of arboviruses has previously been released but this has much smaller data volumes for Africa, and encompasses a much greater range of insecticides tested at a greater range of concentrations on both adults and larvae  38. In contrast, the data released here provide sufficient volumes of standardised values to support a range of analyses of insecticide resistance in malaria vectors in Africa and are freely available to all.
In addition to the current data release, these data have been shared with the Pan Africa Mosquito Control Association to support the establishment of an Africa -led and -managed data resource. The datasets released here will also be available for download from the IR Mapper website [www. irmapper. com]. In addition, predicted values for the prevalence of resistance (i. e. mortality in a standard WHO susceptibility test) at every location in a ~5 km resolution grid, for each year from 2005 to 2017, will be modelled and released in the coming months.
Introduction Ground-sourced heat pumps (GSHP) can significantly reduce CO2 emissions associated with new buildings. However high investment cost is a limitation to the deployment of this technology. As a consequence in France the number of yearly installed GSHP collapsed from 15,500 to 3,200 between 2010 and 2014  [1]   [2]. Cost-effective systems have to be found to reduce GSHP capital costs. Energy geostructures such as Pile Heat Exchangers (PHE) are one solution, since they couple the structural role of the geostructure with that of ground-sourced heat exchangers. PHE are superficially similar to borehole heat exchangers (BHE), but although BHE sizing tools are available to engineers, including pre-sizing Excel sheets (AHSRAE), bespoke software (EED) and dynamics simulation tools (TRNSYS-DST, FEFLOW), there are few PHE sizing tools, with the commercial software PILESIM  [3]  being the main example.
Li and Lai took a different approach, developing G-functions that dealt with the pile inertia explicitly  [35]. They applied the infinite line-source theory in composite media accounting directly for the contrast in thermal properties of the concrete and the ground. The approach is elegant but requires derivation for every pipe arrangement. Hence it requires a database similar to that of Eskilson for routine implementation  [18]. Analytical models of spiral coils PHE have been developed for homogenous (  [36] ,  [37] ) or heterogeneous  [38]  ground conditions, some of them being able to distinguish ground and concrete properties  [39]. Zarella et al. developed a model for PHE equipped with 6 pipes, which can be generalized to any number of pipe  [40]. However, this model still requires a steady-state resistance, which can be calculated from numerical models or from analytical formulae available in literature.
While finite difference or finite elements are often time-consuming, the accuracy of borehole thermal resistance calculation methods is still an open question  [41]. The final option for pile analysis is the Duct Storage model  [23]  which underpins the software PILESIM  [3]. The model superimposes three solutions, a steady state solution for within the ground heat exchanger, a local ILS, and a global interaction between the underground thermal store and the surrounding soil. While developed for boreholes, but later validated for piles, it is limited by both use of the ILS and a steady resistance within the pile. We present both a new pile G-function and importantly a resistive-capacitive model for a PHE equipped with 4 pipes. This model requires material thermo-physical properties and PHE geometrical properties to compute the evolution of the fluid temperature.
Since an analytical expression of GHFCS Φ0 (t*) seems out of range, GHFCS Φ0 (t*) was established from finite element (FE) simulations, achieved in COMSOL-Multiphysics software on a 2D axisymmetric model (see Figure  2 ). The COMSOL model solves the partial derivative equation (  4 ), that is the normalized heat equation. An adiabatic condition was set on every domain face, except at the ground heat exchanger wall where a constant normalized power was set (Neumann condition). The size of the domain ( ≈ 233 QR * ) was large enough to ensure it did not disturb the heat transfer in the borehole vicinity. The mesh was refined in the vicinity of the borehole wall to account for sharp temperature gradient, with typical length of the triangular elements being 2 cm. Note that a coarser mesh would have been appropriate, however, as the model is 2D, a fine mesh does not compromise the execution time.
//f0f. g M * = \ N h log 8# M * hi8 < h`8 (7) The 20 coefficients of eq. (  7 ) (4 coefficients ai for every 5 parameter Gmax, X, A1, A2, A3) were determined by minimizing the misfit (root mean square error) between the 4251 evaluations of GHFCS(t*) and eq. (  6 ) with the fmincon function of Matlab®. For the five HFCS Φ0 parameters, the coefficients a1, a2, a3 and a4 are given in Table  1. The regression is able to reproduce the original dataset in an excellent way (Figure  4 ). 6 ).
The pile wall temperature Tp is computed through the superposition principle  [18] : ‡ ^- # = 1 ( 8 ^+ \ y78 -y ^iy ^i8 y`8 : (14) Where pb is the linear power (or power by unit length of pile) exchanged at the PHE wall (W. m - 1 ), which is supposed to be constant along the depth, and Tp the temperature at this node. All the nodes B3,i are connected to the PHE wall, leading to: ^= \ ; , ^- k__ < `8 (15) {T n+1 } is determined by solving equation (  13 ) with an implicit Euler scheme implemented in Matlab®, which results in the following linear system: " ^78 … -" ^… ∆ + Λ " ^… = " †… ⇒ H 1 ∆ + Λ J " ^78 … = 1 ∆ " ^… + " †… (16) The influence of time step on the temperature precision was investigated for Δt ranging from 0. 25 h to 1 h. The tested configuration is similar to case a presented in section 2.
4, except that the 500 W power is applied for 6 h followed by 6 h of relaxation (cf. Figure  8 ). The implicit Euler scheme has proven to be stable. Temperatures computed for Δt = 0. 25 h and 0. 5 h are almost superposed, suggesting that a 0. The main advantage of the semi-analytical model is that its execution is fast. For instance, evaluation of 1 000 time-step requires about 10 s on a desktop PC. Its implementation requires no advanced skills in programming and could be implemented in open software like Python, given the detailed flowchart (Figure  7 ). In the author's view, the most significant shortcoming of the SA model is that it considers a single PHE with an adiabatic condition at the surface, corresponding to a building of infinite extension whose floor is perfectly insulated. Recent research shows how the ground thermal regime is affected by the boundary condition at the surface  [46] [47] [48].
Model validation against FE code The SA model was validated against a finite element (FE) model developed in COMSOL-Multiphysics® software for 5 cases (Table  2 ). For all simulations the PHE was 10 m deep and a linear constant power 50 W. m -1 was applied. The parameters used in the simulation are summarized in Table  3. The initial temperature was set to T0 = 0°C. The flow is turbulent; as a result, the convective thermal resistance is an order of magnitude lower than the conductive one. Validation The fluid in every pipe is modelled in one dimension along the pipe axis s. A power balance (W) on an elementary volume V of fluid contained between s and s +ds reads: - *. * 9‰ = Š ‹. 9OE (17) In eq. (  17 In eq. (  18 ), ρfl is the fluid density (kg. m -3 ), and v the fluid velocity (m. s -1 ), given that: = xy • & , (19) The term in the right of eq.
Application to TRT data The SA model was applied to the analysis of a long thermal response test (TRT) carried out on a PHE with a radius of 30 cm and four heat exchange pipes installed, located in London clay, (cf. Table  5 , Table  6  and Figure  14 ). The TRT lasted for 353 hours (14. 7 days, see Table  6 ). Given the short duration of the test (t*< 10), the boundary condition (adiabatic or isotherm) plays a negligible role over the TRT duration on the G-function. An adiabatic condition at the surface was assumed, and S T! UV used (eq. (  6 )) in the SA model. No laboratory measurement of ground and concrete thermal properties was performed, and the determination of distance between pipes s was based on standard construction details rather than as built records. The initial ground temperature T0 was estimated by measuring the temperature of the heat-carrier fluid (water) circulating in the PHE before the TRT heater is switched on.
Lithology The ability of the semi-analytical model to predict the fluid temperature evolution once the model parameters have been fitted was tested. Along with the ground thermal conductivity λm, the concrete thermal conductivity λc was chosen as an effective parameter accounting for internal thermal transfers within the pile. Therefore, the TRT was interpreted by minimizing the root mean square error (RMSE) (°C) between measured and computed outlet temperatures was minimized  [50]  [51]: k•OE- , l = -˜C z{|,™R -z{|,!+ , l , D 9 | š›oe | š•ž QR - ^ (28) The RMSE was minimized with the local optimization algorithm active-set developed in MATLAB®. The SA model was run with a time step Δt = 15 min, which ensured its evaluation within a few seconds. The choice of the TRT duration should result from a compromise between the limitations of costs, leading to shorter TRT, and the reliability of the results, leading to longer TRT.
Acknowledgement The development of the semi-analytical model described in this paper is a part of a research project ''GECKO'', geo-structures and hybrid solar panel coupling for optimized energy storage, (2011-2015) which is supported by a grant from the French National Research Agency (ANR) under reference "ANR-11-SEED-0013-06". The exploitation of the semi-analytical model for TRT analysis was funded by the European Network for Shallow Geothermal Energy Applications in Buildings and Infrastructure COST-GABI, under reference "ECOST-STSM-TU1405-280216-071379 STSM". The TRT data was provided by Crossrail, based on tests conducted by GI Energy. This project would not have been possible without their support. The second author greatfully acknowledges financial support from the Royal Academy of Engineering in the UK.
4 and A. 7 were solved with the ode45 function for ordinary derivative equations in MATLAB® Software. The internal time step used by the ode45 was left up to ode45, with the output with being exported at every normalized time step Δt* = 10 -2 up to t* = 5. t* = 5 was used as it ensured the steady-state to be reached. Similarly, the inner time step used by COMSOL was left to the software, with output being exported on the same period. The reader is referred to the supporting information for further details on this numerical procedure. The objective function ε (eq. A. 1) was minimized when fulfilling equality constraints and with a Particule Swarm Optimization (PSO) algorithm  [54]  (cf. The procedure is iterated over 181 configurations, each configuration being characterized by a value of rp* and s* satisfying geometric constrains c1, c2, c3 as represented in Figure  6.
2 ). Radioactive isotopes were uniformly created in the walls, floors, and steel with the relative production rates determined from the radioassay measurements given in Table  1. The effect of 222 Rn in the air was also included, assuming an activity of 49 ± 15 Bq/m 3 , which is the average indoor radon level in Germany  [26]. The decays of the isotopes and any subsequent daughters were handled by the GEANT4 Radioactive Decay Module, and secular equilibrium was assumed. This module has recently been updated by the GEANT4 collaboration to include newer versions of the Evaluated Nuclear Structure Data File (ENSDF) datasets  [27]  and to better ensure energy conservation for decays  [25]. Available physics processes were set by the Shielding EMZ physics list, which uses the most accurate electromagnetic physics models and is well-suited for shielding simulations  [28].
Acknowledgements We acknowledge the support of  Helmholtz Association (HGF), Ministry for Education and Research BMBF  ( 5A17PDA ,  05A17PM3 ,  05A17PX3 ,  05A17VK2 , and  05A17WO3 ),  Helmholtz Alliance for Astroparticle Physics (HAP) , and  Helmholtz Young Investigator Group  ( VH-NG-1055 ) in Germany;  Ministry of Education, Youth and Sport  ( CANAM-LM2011019 ), cooperation with the JINR Dubna (3+3 grants) 2017-2019 in the Czech Republic; and the  Department of Energy  through grants  DE-FG02-97ER41020 ,  DE-FG02-94ER40818 ,  DE-SC0004036 ,  DE-FG02-97ER41033 ,  DE-FG02-97ER41041 ,  DE-AC02-05CH11231 ,  DE-SC0011091 , and  DE-SC0019304  in the United States.
The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project's achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model.
Over the last decade, increases in computer power have allowed operational use of convection-permitting NWP models, such as the Met Office UK variable resolution (UKV) configuration of the Unified Model  [5, 6]. Although convection-permitting models produce realistic precipitation features, errors grow rapidly (especially at small scales)  [7, 8]  and hence initial condition errors are a key source of uncertainty. To reduce these errors, and provide detail on appropriate scales, the assimilation of observations that have high frequency both in space and time has become increasingly important  [9] [10] [11] [12] [13]. The FRANC (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) project involves fundamental research to improve forecasts of severe weather by exploring new techniques to extract the maximum amount of information from observations of winds, temperature, precipitation and moist processes.
The FRANC project has been funded by the UK Natural Environment Research Council (NERC) Flooding from Intense Rainfall (FFIR) programme. This programme has the objective of reducing the risks of damage and loss of life by surface water and flash floods through improved identification, characterisation and prediction of interacting meteorological, hydrological and hydro-morphological processes that contribute to flooding associated with high intensity rainfall events. FRANC forms the first work-package of the programme and has been carried out as a close collaboration between UK University and Met Office researchers, using Met Office software and operational systems alongside research facilities. The second work-package SINATRA (Susceptibility of catchments to INTense RAinfall and flooding) has the aim of advancing scientific understanding of the hydrological processes determining the probability, incidence, and impacts of FFIR.
We found similar results in a study of the assimilation of Doppler radar winds into the Deutsche Wetterdienst (DWD) convection-permitting system  [59] *. Better use of the high-resolution observations should be accomplished by the inclusion of correlated error statistics in the assimilation, as this allows for less thinning of the data. However, taking account of spatial correlations in a operational system does require a new supercomputing parallelization strategy to avoid excessive communications between processors. Initial forecast trials in the Met Office system with new software developed as part of the FRANC collaboration are described by  [60] *. They demonstrate no significant difference in wall-clock time for the assimilation, despite a fourfold increase in the number of Doppler radar wind observations assimilated each cycle.
Linear Models of Convection In 4D-Var, a nonlinear least-squares problem is solved iteratively via a sequence of linearized problems. A linear dynamical model is used to evolve trajectories through the data assimilation time window. Sharp transitions between the regime of moist thermodynamics inside a cloud and dry thermodynamics outside a cloud can cause problems with minimizing the data assimilation objective function  [64]. In FRANC we tackled the problem of deriving linear models of non-precipitating convection from cloud physics. The objective of this work was to expand on existing linear cloud models by implementing a smooth transition of buoyancy frequency across the cloud boundary. With the new model, we identified a critical relationship between growth rate and buoyancy frequency necessary for cloud solutions to form.
Convective Predictability Previously in this article, we have discussed improvements to radar-retrieval algorithms and convective-scale data assimilation. However, it is important to recognize that the sensitivity of a rainfall forecast to the initial state of the model will depend upon the meterological situation. In particular, the impact from better assimilation of better-quality observations into an NWP system will depend upon the inherent predictability of the convection itself. The predictability of a selection of convective cases has been examined in the FRANC project by constructing ensembles of perturbed convection-permitting forecasts  [66] *. The ensemble members are formed by perturbing the UKV model by introducing small buoyancy fluctuations into the boundary layer. The six cases investigated span a spectrum of convective regimes including quasi-equilibrium and non-equilibrium environmental forcing.
The Met Office made available significant staff time, and provided desktop computers to University staff to allow them to securely access their software, observations and supercomputing facilities on the same basis as Met Office staff. This gave the University researchers the opportunity to flexibly move back and forth as needed between the development of ideas using academic research techniques (such as experimental radar, idealized models and assimilation systems, mathematical theory etc. ) and applying them in the complex operational systems. This has been particularly key for data assimilation research, where the need for both complex operational software and observational databases often excludes immediately applicable research. Early career scientists have particularly benefited from learning about the constraints of operations while carrying out publishable academic research.
Conclusions The NERC Flooding from Intense Rainfall programme aims to reduce the risks of damage and loss of life caused by surface water and flash floods through improved identification, characterisation and prediction of interacting meteorological and hydrological processes that contribute to flooding associated with high-intensity rainfall events. The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in NWP of convective heavy rainfall events via the reduction of initial condition uncertainty. Observations, data assimilation and modelling have been carried out over the UK, but the methods and results are widely applicable. The FRANC work has brought operational impacts in improved radar reflectivity and refractivity observations and developed better data assimilation algorithms to maximize the value of these (and other observations) in data assimilation and convection-permitting weather forecasts.