dataesr/scientific-paragraphs-categorization huggingface dataset:
Combined system analysis After modeling, presenting the results, and validating each system, they are coupled based on Fig. 1. At first, the high-quality output heat of the IRSOFC is used for running the FPSE-PMLSM system. Then to increase the temperature and burn the left output gas of the IRSOFC, it passes through a combustion chamber. After that, the reheated gas is used for preheating the input air and fuel of the IRSOFC. Then, it provides the required heat of the DEACH. The final low-quality heat is used to produce the required hot water. The input parameters of the combined system are presented in Table  12. For a better understanding of FPSE input data, its schematic is shown in Fig. 16 , that all the spaces and parameters can be located. Based on these input data and DEACH temperature levels of Table  10 , the systems are combined.
Based on these data and two different methods, the efficiency of the system was calculated. At first, it was assumed that all the productions (electrical power, cooling, required heat for hot water production) were the system's output that was used for efficiency calculation. In this case, the system efficiency was around 72%, which was 40% higher than the standalone IRSOFC with around 32% of efficiency. Then, only the recovered heat (by the heat exchanger and DEACH) was used besides the produced electrical power (by IRSOFC and FPSE-PMLSM) for efficiency calculation. In this case, the total efficiency of the system was around 63%, which was around 32% higher than the standalone IRSOFC. In any case, these results showed the significant effect of adding the FPSE and other auxiliary heat recovery systems to the IRSOFC system.
Multiannual rice garden experiments were thus implemented at AfricaRice and NARS sites across the Sahel region, forming a successful, multi-disciplinary research network (funded by BMZ, German Federal Ministry for Cooperation). Luckily, pest pressure remained low. Although rice crop data were scarce and had to be generated from scratch to support crop modelling, climate data were in good supply. The inter-governmental CILSS (Comité inter-État de lutte contre la sécheresse au Sahel) with its center AGRHYMET in Niamey (Niger Rep. ; http://ccr-agrhymet. cilss. int/) kept a well-curated and up-to-date database of daily weather records for synoptic weather stations all over the Sahel region  (Morel, 1992; Traore et al. , 2014)  reaching back to the 1950s and used since the 1990s for real-time drought forecasting for dryland crops with the SARRA/DHC crop water balance model  (Abdallah, 1999; Baron et al.
It was published as part of a Ph. D. thesis  (Julia, 2012). Over 3000 datasets for panicle and top leaf (flag or the leaf below it) temperature, based on IR imagery and micrometeorological records, were obtained at around flowering stage for four genotypes, different times of day and in three environments: Cold-dry season (CDS) and Hot-dry season (HDS) in Senegal, as well as the dry season in the Philippines (Fig. 3 : only local cv. Sahel 108 is shown). The air temperature at 2 m varied between 20 and 40 • C. The panicle-air temperature difference (Tp-Ta) varied between -12 and +3 • C and was lowest (most negative) at low air relative humidity (RH), indicating transpirational cooling (Fig. 3A ). The humidity effect thereby varied among the environments. However, Tp-Ta followed a common linear function (R 2 = 0.
69) of vapor pressure deficit (VPD) at 2 m, presumably because VPD considers both RH and air temperature (Fig. 3B ). The leaf-air temperature difference responded similarly but transpirational cooling was by one to three degrees stronger, and R 2 was higher (0. 82). These results were derived from a large dataset collected in AfricaRice Senegal study. A similar study using published data from humid to semi-arid environments across the world found a very similar transpirational cooling vs. humidity relation (  van Oort et al. , 2014c). The data indicated that under hot and dry air conditions, and unlimited water availability to the plant, transpirational cooling is a powerful heat avoidance mechanism  (Julia and Dingkuhn, 2013; van Oort et al. , 2014c). For Julia's dataset, covering air temperature ranges of 17-40 • C and VPD ranges of 0.
RIDEV V2 thus simulates two heat avoidance responses of rice panicles, one through transpirational cooling and the other through temporal escape of anthesis from the midday heat. In retrospective, however, this model leaves room for further improvement. In Fig. 3C  we compared Ridev V2 calculations of Tp-Ta with those of IM2PACT, a heat balance based canopy and panicle temperature model developed for rice, based on FACE experiments in Japan  (Yoshimoto et al. , 2011). We apply it here to the same dataset. The IM2PACT model gave better predictions than the empirical equation of RIDEV V2, with slope and R 2 values nearer to 1 when regressed against observed Tp-Ta. Specifically, RIDEV V2 overestimated transpirational cooling at low vapor pressure deficit. It should thus be considered to implement a physics-based model such as IM2PACT in future versions of crop models that simulate heat stress effects on crops.
, 2015b, b)  of an indica-rice diversity panel with 200 accessions. The datasets were generated in a mini rice-garden trial at AfricaRice in Senegal and at different altitudes in Madagascar. RIDEV parameter estimation was then conducted across environments and genome-wide association studies (GWAS) were performed on the estimated genotypic crop parameters of the model  (Dingkuhn et al. , 2017ab). A major result of these genetic studies was that thermal response of phenology and sterility for many cultivars was not the same in all environments. To account for this, an additional model parameter was introduced in RIDEV V2 to simulate hypothetical acclimation effects, e. g. in cases where plants exposed to chilling during reproductive stage had been previously exposed to chilling at juvenile stage.
RIDEV applications within rice growth and yield models RIDEV was incorporated into ORYZA1, a commonly used rice growth model during the 1990s, and along with some other modifications (e. g. , transplanting/direct-seeding options and transplanting shock). The result was called ORYZAS (S for Sahel). ORYZAS also simulated thermal effects on kernel weight (Fig. 2B ) and leaf/stem assimilate partitioning  (Dingkuhn and Sow, 1997). ORYZAS was then calibrated on rice garden data and used to simulate climate, sowing date, location and cultivar dependent yield potential  (Dingkuhn and Sow, 1997). Typical patterns of sowing-date dependent phenology and yield are shown in Fig. 5. Yield potential was greatest at about 10 t ha -1 in the hot dry season which has the longest days, but also in the wet season when sowing was done late but not too late to risk cold-induced sterility.
ORYZAS cannot simulate water limited yields, whereas approximately 74 % of the cultivated rice area in Africa is rainfed  (Diagne et al. , 2013). SAMARA can simulate rainfed conditions and deficit irrigation but due to its focus on phenotypic plasticity, it is unnecessarily complex for large-scale yield gap studies  (Kumar et al. , 2016 (Kumar et al. , , 2017)). Cropping systems models like DSSAT and APSIM simulate like ORYZA2000 both potential and water-limited yield. However, limited access to their source code in the early 2010s would have made modifications difficult. Testing across diverse environments brought to light weaknesses in ORYZA2000, stimulating further research and concurrent model improvements.
Irrigated rice 3. 1. 1. Testing and improving ORYZA2000 in Senegal: Phenology and thermal sterility ORYZA2000 was developed in the 1990s by the international Rice Research Institute (IRRI) and AB-DLO (later incorporated into Wageningen University and Research, WUR). The scientific basis and code is well documented  (Bouman et al. , 2001). The calibration program DRATES (provided along with the ORYZA2000 model) uses default cardinal temperatures of 8, 30 and 42 • C. Zhang et al. (2008)  questioned the validity of these values, notably with respect to climate change. van Oort et al. (2011)  used a large dataset from Senegal (de  Vries et al. , 2011)  to develop the calibration program pheno_opt_rice and applied it to optimize cardinal temperatures that were compatible with the ORYZA2000 phenology sub-model.
Without these the original ORYZA2000v2n13 model predicted no cold stress at all, while in reality when planted in October, complete crop failure occurred due to cold sterility. These improvements were confirmed later with an independent dataset from the same site  (Stuerz et al. , 2014b) , resulting in similar model accuracies (  van Oort et al. , 2016). While ORYZA2000 improvements significantly increased model accuracy, substantial uncertainties remained for the simulation of spikelet number per hectare, which can strongly affect yield. Simulations with observed phenology and spikelet number showed model efficiencies of up to 0. 70, while with both traits simulated the model efficiencies decreased to 0. 50. It is not clear whether uncertainties in the spikelet number simulations are due to uncertainty in observed data, in the model or both.
This can also be done with cropping systems models like APSIM or DSSAT. However, for high numbers of combinations of varieties, years and sowing dates, computational speed would become an issue, and a customized interface would be required for mass simulations and scenario extraction/optimization. In CCC, three modules were implemented to perform the following tasks (van  Oort et al. , 2016)  The CCC model was used to optimize sowing dates and traits in the Fanaye site of Senegal (  van Oort et al. , 2016). It showed that triple cropping (rice-rice-rice or rice-rice-vegetable) is virtually impossible and that in rice-rice and rice-vegetable systems, rice yields can be increased by implementing earlier sowing and switching from short to medium duration varieties. Researchers and extension service staff were trained in using the CCC tool in Senegal and Madagascar.
, 2001)  was not modified in the ORYZA2000v2n14 'AfricaRice' version. Rainfed rice yields simulated by ORYZA2000 are like potential yields (assuming ample nutrients, no weeds, pests and diseases) but with yield reductions due to drought. ORYZA2000 can simulate effects of soil water holding capacity (which depends on soil texture), bunding and groundwater level. A hardpan is simulated by assigning a low hydraulic conductivity to the respective soil layer, reducing percolation. In turn, this causes more run-off (if there are no bunds) or higher pond water levels (if bunds are present). Being a 'point based' model, ORYZA2000 does not simulate lateral flow. It thus can simulate water run-off but not run-on, nor can it simulate below-ground lateral flow. Previous studies showed ORYZA2000 can accurately simulate field level water dynamics if fed with accurate data on irrigation, groundwater, bunding and soil parameters  (Wopereis et al.
If the crop duration is shorter than the wet season, flexibility for cropping calendars and varietal duration types may be elements to improve productivity. Terminal drought, or in bimodal rainfall environments also midseason drought, are risk factors that can be evaluated by multiannual analyses. A model like ORYZA2000 can be used to simulate yield gains and risks related to calendar and variety options as a baseline for regional research priorities. However, for any location specific studies, simulations would require high quality input data on all soil parameters and local dynamics of soil hydrology.
Impacts, causes and uncertainties After extensive model testing and improving weather data bases, ORYZA2000v2n14 was used for a climate change impact study on rice in Africa. van Oort and Zwart (2018)  showed that without varietal adaptation, yields would decrease due to shortening of crop duration. With varietal adaptation (longer duration varieties) yields might increase by +4 to +8% depending on the climate change scenario. The single exception was the hot dry season in the non-coastal Sahel zone, which was the hottest climate considered. In the current climate, daily Tmax frequently exceeds 40 • C and occasionally attains >45 • C. According to 2050 scenarios (van  Oort and Zwart, 2018) , daily Tmax will increase further by 3. 6 • C compared to the year 2000. For this scenario, simulations predicted yields dropping by 60-80 % due to a collapse in photosynthesis due to heat.
A model experiment with the "photosynthesis collapse" disabled showed hardly any yield decline. The study identified a need for further research on photosynthesis at extreme temperatures. The ORYZA2000v2n14 version used accounted for effects of transpirational cooling on sterility but not on photosynthesis. The origin of the photosynthesis x temperature response function in ORYZA2000 (also used in ORYZA2000v2n14) is not documented in  (Bouman et al. , 2001)  or  (Matthews et al. , 1995 (Matthews et al. , , 1997)). We lack on data on rice photosynthesis at extreme temperatures. Field photosynthesis measurements in the hot dry season in Senegal  (Stuerz et al. , 2014a)  were just below the temperature threshold at which, ORYZA2000v2n14 predicts a collapse in photosynthesis. Does rice leaf net photosynthesis "collapse" under extreme heat? (Scafaro et al.
Scientific Given the large diversity of African rice environments and systems, the limited crop and environment data, the limited availability of suitable modelling tools and the presence of only one modeler at AfricaRice in the 1990s and 2010s, hard choices had to be madeboth in terms of priorities and simplifications/assumptions with respect to the systems' complexity. In an applied research context expected to generate impact in a reasonable time, the choices are further constrained. The main modelling foci implemented were (1) thermal constraints in irrigated rice vs. genotype and cropping calendars and (2) mapping potential and water-limited yields across environment typologies, regions and scenarios of climate change. The two foci used fundamentally different approaches: experiments and custom-made models for (1), application of existing models and expert assumptions to collated, largescale data resources for (2).
Biotic stresses were generally not modelled. Approach (1) concretely impacted varietal development and adoption and also generated new physiological and genetic knowledge. A major scientific learning was the role of leaf and panicle transpirational cooling as a very effective heat avoidance mechanism. Approach (2) probably influenced the institute's and also NARS' research agenda (through the regional Task Force network: www. africarice. org/africa-wide-ri ce-task-forces), but in the first place it exposed massive data and knowledge gaps particularly on soil-and hydrology-based yield limitations in rainfed systems, raising a humbling host of research questions. Both approaches were complementary, e. g. in stimulating attempts to scale up plot-level stress simulations to regional or cropping systems scales.
• As compared to the work of Mémoli et al. [15], we pay more attention to the intrinsic surface properties in implicit brain imaging, that have been shown to be more relevant to brain morphometry studies  [34]. • We perform multileaved comparisons between implicit and explicit methods, in particular for curvature estimation tasks. Their robustness is evaluated using neonatal brain MRI data 2  from the Developing Human Connectome Project 3  (dHCP)  [35] ,  [36]. • We provide an open access code (to estimate curvatures, cortical thickness and sulcal depth), dataset and visualization tools.
A. IMPLEMENTATION TOOLS AND COMPUTATIONAL ASSESSMENT The NIfTI (Neuroimaging Informatics Technology Initiative) file format is used very commonly in imaging informatics for neuroscience  [49] , neuroradiology research, and even musculoskeletal  [7]  and organ motion investigation  [4] ,  [50]. In this context, and to avoid duplication of efforts such as mesh post-processing steps (tessellation and refinement), usually required to perform brain feature extraction with Freesurfer (see Section IV-C), we provide an algorithm acting directly on segmented volumes (encoded as NIfTI files), and generating a smooth surface mesh (triangular but not necessarily regular), together with its texture feature maps (per-vertex curvature). A fast implementation of parameterization-free curvature estimators for manifolds represented as implicit surfaces is then performed in Python.
The algorithm described in Fig. 3  takes only a few seconds to calculate curvatures for both synthetic and realistic 3D shapes. We also provide tools to display the computed surfaces and features in an object oriented way through the use of the Visvis Python library. The source code and data files used in this work are open source and available on GitHub at: https://github. com/k16makki/ Medima_tools. A smooth (twice-continuously differentiable) SGD map is used for surface representation in all experiments which allowed smooth surface mesh extraction using the marching cubes algorithm. It is important to note that a high-quality reconstruction of surface mesh is required to perform objective comparisons between implicit methods and other algorithms for estimating curvature on triangular meshes.
For the last method, which has been shown to possess a high level of accuracy, in particular for highly resolved regular meshes  [23] , we employed two different versions of its implementation (both available on GitHub 6  , 7  ). For both mean (see Fig. 7 ) and Gaussian (see Fig. 8 ) curvatures, the performed comparisons show that the proposed algorithm is largely the fastest. In general, the computation time (indicated in each figure caption) decreases by a factor of up to 20 when using a bounding-box around the shape mask. It can often be used to handle large datasets in medical studies. Furthermore, the implicit and explicit curvature methods are numerically and visually distinguishable. Unlike existing mesh-based methods, our algorithm can generate smooth and accurate curvature maps. This suggests that performing the computations in the ambient space leads to less sensitivity to noise and irregularities.
C. CURVATURE ESTIMATION FOR FREESURFER OUTPUT: AN ADULT BRAIN CASE As mentioned in Section IV-A, the marching cubes algorithm generates a topologically correct meshes, but without guaranteeing their regularity which reduces the quality of geometric approximation for mesh-based algorithms. To address this issue, we modified our algorithm (used for method comparisons in Section IV-B) inorder to compare it with meshbased curvature methods on a quasi-regular mesh generated by Freesurfer. A second aim of this experiment is to evaluate the flexibility of the proposed algorithm on a mesh which was not directly extracted from the scalar signed distance function from which the feature maps were obtained. FreeSurfer is an open source software platform providing powerful tools which are commonly used in neuroimaging to process and visualize structural and functional neuroimaging data from cross-sectional or longitudinal studies  [55] -  [57].
Three methods were used to calculate extrinsic/intrinsic curvature per vertex of the right hemisphere surface (a locally refined triangle mesh composed of a set of n = 144k vertices). The corresponding results are presented in Fig. 10. In this figure, we show that implicit methods can deal more robustly with Freesurfer outputs. Our algorithm provided a more realistic measures of curvatures for the white matter: the intrinsic curvature mostly ranged between -0. 07 and 8 Subject was selected from the Open Access Series of Imaging Studies (OASIS) database (www. oasis-brains. org). Subject participated in accordance with guidelines of the Washington University Human Studies Committee. 0. 07 mm -2 over the surface, while the estimated mean curvature ranged over [-0. 5, 0. 6]mm -1 in average. In contrast, popular mesh-based methods such as the Rusinkiewicz method which was recently used in  [23] , and the Trimesh method, failed to deal with some irregular vertices, leading to an undesirable out of range of curvature values.
Only in areas where non-ground features are not present, such as a well-maintained lawn, will the DSM include ground points. DSMs are rarely used in archaeology. When LiDAR data is used, the DSM is generated from the first return points. However, the use of the terms DEM and DTM in general practice depends on the sensors and methods used for the data acquisition, the methods of data processing, the type of representation, and the countries or environments in which these datasets are used. Moreover, the definition of these terms changes over time (note the change over 35 years between  [25, 26]  and  [23] ). The translation of these terms between different languages is also problematic. The current generally accepted definition that we adhere to states that DEM is an umbrella term for DSMs and DTMs, while the DTM represents the bare ground  [23].
Test Sites Four test sites were selected in order to simulate the most common archaeological uses of airborne LiDAR data (Figure  2 ). Due to the availability of data and first-hand experience, all of the datasets are from Europe. For the comparison of the different steps of data processing, these are the same test sites we have already used and outlined in other studies  [1, 7]. The description of the test sites is therefore only briefly repeated. data processing, these are the same test sites we have already used and outlined in other studies  [1, 7]. The description of the test sites is therefore only briefly repeated. The test data from Austria (AT), Slovenia (SI1, SI2), and Spain (ES) were selected based on their archaeological and morphological similarity. Each site has a hilltop settlement (archaeology), buildings (modern), vegetation on steep slopes, and sharp discontinuities.
DFM Quality Assessment The fact that significant areas of DFMs derived from airborne LiDAR data are interpolated from undersampled points has important implications for all subsequent steps in the workflow. Most vulnerable to these implications are interpretative mapping and certain methods of "deep" interpretation-for example, hydrological analysis. An experienced operator is able to visually identify undersampled areas and, to some extent, the severity of the undersampling. By observing undersampled areas, the interpolator, or at least the family of interpolators, can also be identified in most cases. However, in order to achieve the necessary scientific integrity for archaeological interpretation, the quality of the DFM must be quantified. Given the high internal variability of DFMs, quality assessment must be done on a cell-by-cell basis.
The test data from Austria (AT), Slovenia (SI1, SI2), and Spain (ES) were selected based on their archaeological and morphological similarity. Each site has a hilltop settlement (archaeology), buildings (modern), vegetation on steep slopes, and sharp discontinuities. An additional test site (SI2) was selected as an example of relentlessly dense low vegetation. Each test site is 1000 × 1000 m, but only the most relevant windows are shown in figures. All datasets are from nationwide data acquisitions and are in the public domain under various licences. The main difference between them is the average point density (Table  1 ). Therefore, the datasets represent, high, medium, and low point density scenarios, respectively.