dataesr/scientific-paragraphs-categorization huggingface dataset:
The specification of the FEBA test facility and experimental data from the Series I were distributed among participants. •Phase II: Identification and initial quantification of important input uncertainties for reflood prediction. This was performed on the basis of the results of Test 216 of series I in reflood FEBA experiments. •Phase III: Quantification of physical model uncertainties identified in Phase II, using measured data from 6 FEBA tests of the series I (including Test 216) and selected quantification method. •Phase IV: Verification and validation of the results of Phase III, by propagation of the input uncertainties through simulation model (uncertainty analyses of reflood test simulations) and comparison to the measured data of the chosen reflood experiments. At first, the consistency of quantified uncertainty was verified by uncertainty analyses of the 6 FEBA tests used in the quantification.
-Participants who developed their own method in parallel with PRE-MIUM participation: VTT, Tractebel, IRSN, GRS, KAERI -Participants who used an expert-judgment based method improved with methods of fitting of data: PSI In the course of the benchmark the participants who declared development of their own methods undertaken this effort and developed new methodologies. KAERI developed and applied MCDA (Model Calibration through Data Assimilation method  (Heo et al. , 2014). IRSN developed DIPE (Determination of Input Parameters Empirical properties) method  (Joucla and Probst, 2008). TRACTEBEL has developed a sampling based inverse uncertainty quantification (IUQ) method using of DAKOTA software  (Zhang et al. , 2019). GRS developed a method based on combination of separate test evaluation and inverted uncertainty method for combined effect test evaluation  (Skorek, 2017).
Phase II: Identification of influential input parameters. The main goal of Phase II was the identification of input parameters (IP) important for reflood simulation. The influential IP identification has been performed based on the Test 216 of FEBA experimental program. The first step was development of input data and performing of the reference calculation for the selected test 216. Thirteen organizations were involved in Phase II, using 8 different codes (see Table  1 ). All of them were 1D system TH codes, except one, COBRA-TF which is a subchannel module of the system code MARS-KS. Most of participants modelled the test section of FEBA as a single vertical channel and a single heat rod/heat structure. The sole user of TRACE was KIT, and applied a CHAN component, representing a 5 × 5 bundle.
Only one (VTT) included the measured housing temperature. Only one participant (GRS) made use of data from SETs, in order to quantify a part of the model uncertainties (two correlations related to wall heat transfer at dry-out condition). The goal of this procedure was  Skorek et al. Nuclear Engineering and Design xxx (xxxx) xxx-xxx  to benefit from the large database for wall heat transfer correlations to accomplish a better quantification of the uncertainty. U N C O R R E C T E D P R O O F T.
The code RELAP was used by 6 participants. Only UNIPI and SJTU considered the same 4 parameters out of total number of 5 and 4 parameters considered by them respectively. UNIPI and TRACTEBEL considered the same 3 parameters out of total number 5 and 4 parameters. In the case of CATHARE CEA and IRSN considered the same 3 parameters. The third user of CATHARE (Bel V) considered only 1 parameter in common with CEA and IRSN. On the other hand, GRS and VTT using different codes considered three common parameters (parameters related to the same or equivalent models) out of total of six parameters used by each of them. ▪Film The type of probability density functions (pdf's) applied by the participants is clearly method dependent. As it can be seen in Table  5  all the users of the CIRCÉ method and similar data assimilation method applied normal or log-normal probability distribution functions.
The ranges obtained by GRS and TRACTEBEL can be described as moderate. U N C O R R E C T E D P R O O F T. The largest differences between uncertainty ranges can be observed for interfacial friction. The uncertainty ranges are very different for all codes. Some regularity can be found by uncertainty ranges arranged according to the quantification method. The participants using CIRCÉ quantified very small uncertainty ranges with exception of OKBM-2. The application of FFTBM lead also to rather small uncertainty ranges. However, two users of FFTBM used in addition to interfacial friction multiplier droplet diameter, a key parameter available in RELAP input and used in interfacial friction model. GRS and TRACTEBEL obtained moderate uncertainty ranges. IRSN using its own method DIPE obtained for the code CATHARE very large uncertainty range.
The measured value is clearly higher than the predicted ones at the start of the transient. The reason may be a difference between nominal and real initial conditions and/or a measurement error. Since the participants did not use the data measured at the very begin U N C O R R E C T E D P R O O F ning of the transient for quantification, it can be expected that it did not affect the quantified uncertainties. For FEBA test 216, sensitivity analyses have been performed. Most of the participants identified as the most influential parameters for cladding temperature those related to the wall heat transfer at dry-out conditions. Some participants identified parameters related to interfacial heat transfer as the most influential for the cladding temperature. A majority of participants identified the interfacial friction factor as the most influential parameter on pressure drop in the test section.
Quantitative analysis The results of FEBA calculations were quantitatively analysed with IRSN methodology  (Destercke and Chojnacki, 2008) , using the IRSN SUNSET software  (Chojnacki and Ounsy, 1996). Fifteen contributions of 24 scalar outputs (12 related to clad temperatures and 12 to quench times) were collected. First of all, the percentage of uncertainty bands covering the corresponding experimental value was calculated, taking into account all participants. The average percentage is 83%, lower than the values of expected coverage for calculated tolerance bands. The frequency of coverage was better for clad temperatures (88%) than for quench times (77%). The information provided to IRSN for each scalar output was an interval [lower uncertainty bound (LUB), upper uncertainty bound (UUB)] and a reference value (RV).
The validation of the model uncertainties has shown much better results. In the case without calibration the experimental data were generally enveloped by the uncertainty limits. The results have clearly shown that application of the calibration was disadvantageous. For the reasons outlined, CEA changed the CIRCE users' guidelines and recommends the suppression of the recalibrated value estimation, especially when the database used for recalibration is small and does not cover the physical conditions in which the model will be used. In the PREMIUM benchmark, KINS participated with two different methods of model uncertainties quantification. They used CIRCE method and their own MCDA method based on data assimilation. In both cases the validation step was not successful. The obtained uncertainty limits did not envelope the experimental data.
For CIRCE application they used all 6 tests of the FEBA Series I. However, with the IPREM method, the quantified uncertainty ranges were wider than those obtained with CIRCE. The validation step performed with uncertainties obtained with the IPREM method was successful: the experimental data of PERICLES experiment were enveloped by uncertainty limits obtained by uncertainty analysis with model uncertainties quantified with IPREM. Whereas the validation step performed by UPC within the PREMIUM was not successful: the experimental data were partially not bounded by uncertainty limits. The results of this comparison indicate also that the calibration used in the course of uncertainties quantification is disadvantageous. Since the validation step was not successful also either for GRS, the GRS performed a follow-up analysis of the benchmark calculations as well.
User effects PREMIUM has shown that the quantification methods of model parameter uncertainty have a significant user effect. Their application needs the use of engineering judgment, in addition to the well-known user effect issue when using system codes. The existence of user effect can be detected in the different stages of the application of a quantification method. The following features of the quantification, which depend on the judgment and experience of the user, are important for quantification results: 1)Choice of the responses (outputs) on which the quantification is based 2)Choice of the model parameters to be quantified 3)Selection of the database parameters (experimental measurements) used in the quantification 4)Choice of input parameters in the modelling of experiments. This includes the selection of specific process models, coefficients and nodalisation approaches 5)Selection of options or setting criteria by application of quantification methods The selection of model parameters to be quantified is very important.
Extrapolation of experimental database In the course of the benchmark the question arose, if the FEBA experiment is a suitable basis for model uncertainties quantification for reflooding test facility like PERICLES. There is a considerable difference in the treatment of the housing walls of both FEBA and PERICLES test bundles. The housing of the test facility should reproduce the influence of surrounding fuel rods (large array of fuel rods) as in reactor geometry. It is necessary to consider the test bundles as representative for reactor scale geometry. In the FEBA test facility there were thick walls heated initially from the bun  Skorek et al. Nuclear Engineering and Design xxx (xxxx)  xxx-xxx dle and isolated to minimalize the heat losses to environment. In PER-ICLES there were thin walls with heating to keep the wall temperature few degrees above the saturation level.
Quantification database A very important step is the definition of the database for development, validation and quantification of a physical model. The defined database determines the range of validity of the quantified uncertainties. The database used for model uncertainties quantification consists, first of all, of experimental data. However, in some cases, reference model or calculations, e. g. CFD calculations, can be applied as data for evaluation of simpler models. A compromise must be found between general applicability and special application of the developed database. The database is set usually for special case application, in the sense that it is related to the foreseen application of the quantified uncertainties. In other words, if t model uncertainties are needed for calculating a specific scenario in a plant, the database should include experiments related to this scenario.
But the database can be also more general, so that the quantified model uncertainties are applicable to a wide spectrum of simulations. This is rather the case by quantification of model uncertainties in the frame of so called advanced  (Unal et al. , 2011)  or extended  (Skorek, 2018)  code validation. By the uncertainty analyses performed by code users, the database and the following uncertainties quantification are usually for special application case. The experiments included in the experimental database have to be adequate for the foreseen application. The experimental uncertainties should be carefully examined, because they can influence the quantification significantly.
The set of quantified parameters must include the most influential ones on the responses; otherwise the resulting uncertainty may be completely misleading. On the other hand, it is advisable to include in the quantification all potentially important model parameters, not only the  Skorek et al. Nuclear Engineering and Design xxx (xxxx)  xxx-xxx most influential ones, because in other applications the set of dominant parameters may be different. Quantification methods investigated in the benchmark PREMIUM are dedicated to combined effect tests. Nevertheless, some parameters related to the phenomenon may be quantified on the basis of SETs. In such case, it is important to have guidelines about how to proceed: using for quantification only the SET data or combine them with CETs data. U N C O R R E C T E D P R O O F T.
User effect Complex physical models may have a considerable number of physical parameters and produce a large number of responses. The results of the quantification of model parameters uncertainty are very dependent on the selected parameters to be quantified and the selected responses to be used in the process. For the selection of the potentially important parameters, the use of sensitivity studies, the preliminary determination of the investigated parameters uncertainty is of importance. The estimated uncertainty ranges influence a lot the results of sensitivity studies and selection of parameters. The selection of parameters, responses and database are fundamental parts of quantification methods. Guidelines and procedures should be established for such processes. Without these types of guidelines and procedures, the methods will have a strong user effect.
Development of the experimental database Selection and assessment of simulation model: selection of optimal available models for the intended task and qualification of the input data used for simulation of the experiments in the frame of the applied method Application of the method for modelling uncertainties quantification Validation of the results of the uncertainties quantification Assessment of the predictive capability of the evaluated model uncertainties in the context of the intended application The quantification of model uncertainties is a complex procedure and needs to be performed in a systematic way. It can be done by the code users in the course of a particular application. Even better would be, if the model uncertainty quantification is performed by the code developers in the frame of code validation and incorporated in the code documentation.
Coffee is one of the most consumed beverages in the world, and its international market has been growing for many years. Unfortunately, the Brazilian coffee production is threatened by high temperatures projected by climate change models. We evaluated three schemes of low levels of shade, which avoid the loss of production, as a strategy to adapt coffee to possible climate change. Additionally, as field measurements are expensive and often difficult to implement, we used numerical simulation to complement the evaluation. The microclimate simulator software Envi-met is a computer program often used to simulate urban environments, and we tested it on agriculture design. We verified that the shaded schemes assessed in the field decreased the air temperature in 0. 6 C in the studied period and reduced other possible climate stressors such as wind speed, radiation and raised air humidity in the dry period.
, 2010a). One way to increase the knowledge about these systems is the use of mathematical models and numerical simulations  (Van Oijen et al. , 2010b). The Envi-met microclimatic simulation software has emerged as a good microclimate simulator  (Salata et al. , 2016; Skelhorn et al. , 2014) , because of its capacity of simulate several phenomena  (Amborsini et al. , 2014) , specially the impact of vegetation on cities microclimate  (Zolch et al. , 2016; Morakinyo and Lam, 2016; Ambrosini et al. , 2014; Middel et al. , 2014). The software allows the user to design an area (such as trees and buildings) providing real meteorological data as input parameters for simulations, and surface-to-atmosphere interactions. However, the model has never been used to simulate coffee production systems. Considering all the challenges, our research combines field experiments and numeric simulations to assess the climatic variable patterns of three different low levels of shade schemes (up to 30% of shade), in order to analyses these shade arrangement as an adaptation option in one of the most traditional coffee region in Brazil.
We chose 10 coffee bushes and 10 shading trees in each shaded-grown coffee system, and measured the following biophysical parameters to model the coffee system on Envi-met software: (i) plant height (h); (ii) basal circumference at 10 cm from ground level (bc10 cm), (iii) canopy height (ch); (iv) basal canopy diameter (bcd), (v) height of first pair of branches (h1bs), and (vi) canopy volume (cv). The canopy volume was measured considering the coffee geometry  (Favarin et al. , 2002)  by using Eq. (1). cv ¼ p * bcd 2 ch=12 ð1Þ We also measured the following parameters related to the 10 shading trees chosen: (i) basal circumference at 10 cm from ground level (bc10 cm), (ii) diameter at breast height (DBH), (iii) plant height (h), and (iv) leaf area index (LAI). To calculate coffee and shading tree LAI, we used the instrument LI-COR LAI-2000 Plant Canopy Analyzer, operated with one sensor mode.
Envi-met model setup It is possible to simulate the plant-atmosphere interaction, fluxes of shortwave and longwave radiation, pollutant emissions, and complex structures using the ENVImet software suite. The simulated area was designed using a high spatial resolution satellite image (Geoeye-1), having 1. 84 meters of spatial resolution on multispectral bands (Fig. 2 ). The ENVI-met software has a dynamic vegetation model in, and it was parameterized using field data from the experimental area. It was used the version 3. 1 of ENVImet that allows the explicit input of meteorological parameters, enabling accurate control of them. We also used site attributes such as: -farm geographic coordinates (latitude, longitude, and altitude); -characteristics of farm soil (% of clay); -experimental coffee design (coffee spacing, legume and macadamia spacing, and geographic planting orientation), collected at farm level; -planting density (drawn from the high spatial resolution Geoeye-1 satellite image); and -biophysical characteristics of coffee measured at ground level, such as: plant height (h), leaf area index (LAI), basal circumference (bc), basal canopy diameter (bcd), canopy height, and local meteorological data.
Additionally, we added coffee nested grid cells, reproducing the study area to minimize boundary effects. To model coffee plants, we assumed that coffee architecture may be represented by a cylinder (representing the stem), and a cone (representing the canopy), as suggested by  Favarin et al. (2002)  and illustrated in Fig. 3. After that, the tree was divided in ten parts (Fig. 3c ), and the LAI -leaf area index -was proportionally distributed in the canopy parts (part 1 to 7 in Fig. 3c ), as required by Envi-met software. To model shaded trees, we used the Envi-met library, adding real characteristics measured in field area as Leaf Area index (LAI), plant height (h), diameter at breast height (DBH), and basal circumference. The simulation was run for three days, from 23h00 of September 13th to 0h00 of September 17th, to reduce the computation time.
It was performed for the months of coffee blossoming, since the previous knowledge of the planting microclimate on this phase may determine the production of the culture, and the suitable area. We used temperature, relative humidity, and radiation of full sun (control) meteorological station in an hourly basis as initial data input. Meteorological receptors were created in the model for the same locations and at the same height, as those installed in the field, to be comparable. The simulation was done choosing predominantly cloudless days (14 th , 15 th, and 16 th of September). After that, we assessed the model performance, comparing it to real data.
Conclusion The present study investigated how meteorological variables behave in low levels of shade in coffee agrosystems, and the possibility of using an accessible microclimate simulator software (Envi-met) to simulate shaded-grown coffee systems. We also addressed the possibility of conducting coffee in low levels of shade avoiding the productivity losses and acting as an adaptive management to climate change scenarios in the South of Minas Gerais. We concluded that the shaded schemes here with up to 30% of shading, reduced radiation, total temperature and wind speed. The systems reduced the wind speed up to 99%, working as a "wind break". In the driest period of the year, humidity was superior on shaded-grown systems than in full-sun system. All these modifications may minimize the coffee stress in a different climate scenario, as proposed by IPCC.
Many works consider acceleration of these computationally intensive algorithms. Different architectures are used to achieve real-time performance. One is based on field-programmable gate arrays (FPGAs)  [19]. A second alternative is based on graphics hardware using the CUDA language, and is used in many real-time algorithms such as the work of Kowalczuk et al. [20]  and Congote et al. [16]. Different real-time algorithms focus on reducing the complexity associated with cost calculation, at the expense of reduced accuracy. In our work we exploit NVIDIA's GeForce GTX960 computing capabilities to produce an accurate disparity map.
For GFDs, these properties are well respected. The theoretical properties of GFDs are detailed in the work of Smach et al. [22]  and we note their invariance under motion, change of scale, and reflection. In practice, GFDs are obtained for color images using the flowing steps: • decompose the color image into three channels (red, green, and blue); • calculate the Fourier transform and its square modulus for each channel; • vary the radius r and compute the sum of the pixels located along each ray. The final descriptors concatenate the descriptors for each channel. These descriptors give a complete and robust description of the image which can be used for color object recognition and image classification. Smach et al. [22]  evaluated the performance of the GFD on several standard and personal databases.
Experimental results and analysis To evaluate our stereo matching approach it is necessary to use standard databases. We confronted our algorithm with some stereo matching problems involving occlusion and discontinuous regions. The average number of bad pixels in these regions indicates the accuracy of our stereo matching method, and is given by b p = x |D x -GT x | > δ ( 15 ) where D x is the obtained disparity map, GT x is the ground truth, and δ presents a disparity tolerance. To evaluate our approach, we start by evaluating our proposed local function: SGFD can be compared with other local energy functions. We study the effect of the window size and give the evaluation errors. The test was done by changing the window size from (3×3) to (25 × 25) and the errors were calculated in the non-occluded regions.
The images used were ArlL and Teddy from the Middlebury dataset. Results are shows in Fig. 2. The first column in Fig. 2  shows the left image of ArtL and the percentage of bad pixels in non-occluded areas (curves (a) and (b)). The second column presents the Teddy image and the corresponding errors (curves (c) and (d)). The results obtained for all cost functions were calculated without performing any post treatment and errors were calculated using Eq. (  15 ) with δ = 1. We compare SGFD, ZSAD (zero mean sum of absolute differences), and NCC (normalized cross correlation) in curves (a) and (c) for ArtL and Teddy images respectively. Curves (b) and (d) compare SGFD, ENCC (enhanced normalized cross correlation)  [27] , and ZNCC (zero mean normalized cross correlation) for the two stereo pairs.