dataesr/scientific-paragraphs-categorization huggingface dataset:
This demonstrates that one can reliably use this technique to study the reaccumulation process with a discrete-element method much more effectively. Furthermore, we argue that despite the reduction in resolution between the SPH and N-body segments, the outcomes should still robustly represent the asteroid family formation process. The resolution in DEM does not represent the same thing as it does in a hydrocode  (Asphaug et al. 2015). For DEM, there is a 'correct' resolution to the problem, since every particle represents a discrete body. For these simulations, the correct resolution is set by the average boulder size of real rubble-pile asteroids. The influence of smaller grains (asteroid regolith) on the reaccumulation process is parameterized through the SSDEM contact parameters. In contrast, increasing resolution only serves to improve the realism of an SPH simulation, as damage propagation and energy dissipation can be better solved.
To the best of our knowledge, there is no general criteria, such as L-curve, to estimate the TV regularization parameter. Moreover, the nonquadratic properties of the TV norm hinders the integration with the linear conjugate gradient  [47]. To solve the above equation, we followed the so-called superiorization approach  [48, 49] , where the penalty term is replaced by perturbation steps between iterations, to shift the path toward an intermediate solution  [50]. This shift is subjected to the second criterion, defined by the TV. Here we implemented the S-CG-K pseudo-code in Ref. [49]  in Matlab. There are two questions that need to be addressed here: (a) can S be represented sparsely in the formed basis; and (b) whether the enforced sparsity suits the sampling patterns. The answer might lie in the principle of incoherence, which demands a sampling pattern in which the induced artifacts spread through the sparse domain such that their corresponding coefficients can be easily penalized by λ TV  [51].
88°, E 10. 88°); it uses the FAO AquaCrop biophysical model to estimate production in selected fields and scale up the results to the watershed level. Maps of soil, slope, and land use are combined by a GIS tool to obtain a database of averaged field properties and occupations. Three categories of texture, depths, and slopes were considered to classify the 313 fields of the watershed into 27 soil classes and determine their respective area-weighting factor. The systematic method considering all fields and the proposed method considering the 27 representative fields were used to estimate the watershed production for dominant crops: wheat, barley, and faba bean. Results show a good correlation between both methods with values of relative RMSD in the range of 0. 5-2% for biomass and 2-5% for grain yield.
2014; White et al. 2011). In productivity assessment and prediction studies, the choice between using complex and time-consuming DHMs, based on simplified crop models, or up-scaling results of dedicated biophysical crop models, depends on the available data and the level of precision needed, but remains problematic for highly heterogeneous watersheds. In this work, we propose a simplified approach based on the use of AquaCrop and straightforward up-scaling method combining terrain variables and land use. All fields of the watershed are classified into a limited number of classes according to slope, soil texture, and soil depth. The weight of each class within the cropped area is determined for dominant crops using land use maps and a GIS tool. Crop productivity is estimated by the AquaCrop model for all fields and in one representative field of each soil class.
Datasets and used maps The available data on soil are the soil map of  Zante et al. (2005)  which contains descriptive soil units and some quantitative data about soil profiles realized locally  (IAO 2002; Mekki 2003; Alaya et al. 2017). Quantitative information on texture, wilting point, field capacity, and hydraulic conductivity at saturation needed by AquaCrop are available for only a limited number of fields. We used the pedotransfer functions to derive these properties for all soil units from the soil description in the soil map and the available quantitative data. The  Saxton and Rawls' (2006)  pedotransfer functions were adopted for this task as they were evaluated in a previous work for the soils of the region and showed a good performance in estimating field capacity and permanent wilting  (Alaya et al.
2017). Soil class depth is derived from soil map. The information concerning texture, bulk density, hydraulic properties, and depth are then added to each unit of the soil map. The resulting shapefile was combined with the fields' ownership map to determine mean values for each field. A 10-m digital elevation model is used with the QGIS software to map the watershed slopes and to calculate the average slope of each field. Land use maps are available for 13 seasons between 1996 and 2012  (Mekki 2003 ;O M E R E2017) with heterogeneous field limits and legend. Those corresponding to the period 1996-2001 contain only general information about fields' occupation, namely cereals, pulses, arboriculture, grassland, and scrublands while the survey for the period 2004-2012 delineates the cultivated species and their area limits when more than one species are cultivated on a field.
Land use maps were used to identify the dominant cultivated crops, the change of their areas, and the crop rotations practiced in each field. Then, they were homogenized and combined into a single shapefile with information on field limits and legend, used in a representative field selection procedure. Daily climatic data observed at the watershed by the Mediterranean environmental and water resources observatory (OMERE) during the period 2004-2013 were used to determine reference evapotranspiration by the Penman-Monteith method and used in the simulations. Field observations carried out in 2009-2010 and 2012-2013 on wheat barley were used for AquaCrop model evaluation  (Aloui et al. 2012; Boudhinaetal. 2019).
The coefficient of determination (R 2 ) is a measure of the variability explained by the model. The root mean square difference is a measure of the average deviation between the proposed and the systematic methods. The rRMSD is a dimensionless criterion that expresses error as a fraction of average value, which is more convenient for comparing errors based on different datasets with different average responses. Model performance can be classified according to rRMSD values as excellent (< 10%), good (10-20%), fair (20-30%), and poor (> 30%) (Van Gaelen 2016). The Willmott trend index developed as a standardized metrics is a measure of model precision  (Wilmott 1981)  and varies between 0 and 1  (Holzkämper et al. 2015). These statistical performance indicators, generally used in modeling tasks, were used by  Hsiao et al.
In situ validation of the AquaCrop model Performance of AquaCrop is tested for wheat and barley using in situ monitoring data carried out in 2009/2010 by  Aloui et al. (2012). During this experiment, agronomic observations were carried out in three fields of wheat and a single field of barley. Data from experimental field monitoring obtained by  Boudhina et al. (2019)  in three fields of wheat during 2013 were also considered for model verification. Simulation by AquaCrop was performed using observed climatic data and calibrated parameters of  Sghaier et al. (2014)  for wheat and El  Mokh et al. (2017)  for barley given in Table  2. Figure  1  gives a comparison between observed and simulated values of total biomass. Linear regression between observed and simulated values shows that yields simulated by the model are overestimated for wheat and underestimated for barley.
Simulation of the watershed production An R script (R core Team 2016) was written and used to generate for all fields' input files required by AquaCrop: crop and soil parameter files, soil water initial condition files, and simulation projects. Simulation was performed using the AquaCrop plug-in in continuous run mode for 10 years  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  to take into account the soil water content variations in the offseason. For each simulation project, AquaCrop generates two output files with daily and seasonal output data. A second R script was written and used to summarize the results of the output files and up-scale field results to the watershed. The model was run for wheat, barley, and faba bean crops on all fields of the considered occupation category.
The use of more dedicated biophysical models like AquaCrop requires a lot of efforts to prepare data for all fields in appropriate file format. Development of tools to automate the management of data and parameter files and to control the simulation runs in AquaCrop-GIS package introduces spatial dimension to the presentation of the model's results  (Lorite et al. 2013 (Lorite et al. , 2015)). AquaCrop-GIS tool has been designed to facilitate the use of the AquaCrop model when a high number of simulations are needed, simplifying the task of generating input and project files and the management of output files. It prepares the required inputs, executes AquaCrop, elaborates the results, and shows them in a geographic information system. AquaCrop-GIS allows a drastic reduction of processing time, but preliminary work to build data and parameter files remains an important and time-consuming task.
Conclusion The proposed methodology, based on the use of AquaCrop and the classification/aggregation of topographic and soil properties for estimating productivity of hilly, highly fragmented watersheds, gave reasonably good results in comparison with the systematic method considering all fields and their actual soil properties. Its use for long-term simulation of productivity and its performance in climate change studies should be considered with reference to the more complex and time-consuming GIS-based models using lumped crop models like AquaCrop-GIS or the semi-distributed hydrological model like SWAT. The recent release of an open-source version of AquaCrop which can be run on multiple programming languages and operating systems and support parallel execution will certainly facilitate the model linkage and integration in distributed hydrological models for a better simulation of both crop and hydrologic processes.
Introduction As the subdetector in closest proximity to the interaction point, the ATLAS pixel detector will be exposed to an unprecedented amount of radiation over its lifetime. The modules comprising the detector are designed to be radiation tolerant, but their performance will still degrade over time. It is therefore of crucial importance to model the impact of radiation damage for an accurate simulation of charged-particle interactions with the detector and the reconstruction of their trajectories (tracks). Modelling radiation damage effects is especially relevant for the high-luminosity (HL) upgrade of the Large Hadron Collider (LHC); the instantaneous and integrated luminosities will exceed current values by factors of 5 and 10, respectively. The simulations for the present (Run 1: 2010-12, Run 2: 2015-18) and future ATLAS detectors currently do not model the effect of silicon sensor radiation damage  [1, 2].
This article documents the physics and validation of the pixel radiation damage models that will be incorporated into the ATLAS simulation. Section 2 briefly introduces the specifications of the ATLAS pixel detector and provides an overview of the impact of radiation damage effects. Measurements of the fluence and depletion voltage are presented in Section 3. A model of charge deposition and measurement that includes radiation damage effects is documented in Section 4. Comparisons and validation of the simulation with data are presented in Section 5 and conclusions are given in Section 6.
Consequently, radiation damage effects depend on both the irradiation and temperature history. The silicon bulk of the IBL planar sensors underwent type inversion after about 3 fb - 1  of data collected in 2015 and the second innermost layer (B-layer) inverted in the 2012 run after about 5 fb -1. The outer two layers inverted between Runs 1 and 2. In ATLAS, complex radiation fields are simulated by propagating inelastic proton-proton interactions, generated by Pythia 8  [13, 14]  using the MSTW2008LO parton distribution functions  [15]  and the A2 set of tuned parameters  [16] , through the ATLAS detector material using the particle transport code FLUKA  [17, 18]. Particles are transported down to an energy of 100 keV, except for photons  (30 keV)  and neutrons (thermal). It is important to model as accurately as possible all the inner detector and calorimeter geometry details because high-energy hadron cascades in the material lead to increased particle fluences in the inner detector, especially neutrons.
A description of the ATLAS FLUKA simulation framework can be found in Ref. [19]. Predictions of the 1 MeV neutron-equivalent fluences2 per fb -1 for silicon in the ATLAS FLUKA inner detector geometry are shown in Figure  1(a). The dominant contribution is from charged pions originating directly from the proton-proton collisions. The fluence values averaged over all barrel modules for the four pixel layers starting from the innermost one are 6. 1 × 10 12 , 2. 9 × 10 12 , 1. 2 × 10 12 and 7. 8 × 10 11 n eq /cm 2  /fb -1 , respectively. The fluence depends on the z position as the material and particle composition are η-dependent. For example, in the IBL the maximum predicted value of 6. 6 × 10 12 n eq /cm 2 /fb -1 in the central location is about 10% higher than in the end regions (studied further in Section 3.
For the IBL, the lifetime fluence is only due to Run 2 and for the other layers, the fluence includes all of Run 1. The IBL curve represents both the fluence on the IBL (left axis) as well as the delivered integrated luminosity in Run 2 (right axis). The goal of this paper is to present a model for radiation damage to silicon sensors that is fast enough to be incorporated directly into the digitization step of the ATLAS Monte Carlo (MC) simulation, i. e. the conversion from energy depositions from charged particles to digital signals sent from module front ends to the detector read-out system. In the context of the full ATLAS simulation chain  [1] , digitization occurs after the generation of outgoing particles from the hard-scatter collision and the simulation of their interactions with the detector and before event reconstruction, which is the same for data and simulation.
The hardness factors used here are from the RD50 database  [32] [33] [34] [35] [36] , but all of these values are without uncertainty and many are based only on simulation. The uncertainty in the hardness factors affects both the prediction and the Hamburg model (through the α parameters). Future collision data may be able to constrain these hardness factors. As shown in Fig. 3 , most of the damage is due to charged pions, protons, and neutrons, so the larger uncertainties on other particle species is a subdominant source of total uncertainty for the hardness factors. The remainder of this paper focuses on central |z| ≈ 0, using the FLUKA simulations without modification for the central value, but with a 15% uncertainty in the fluence taken from this leakage current study. The ATLAS tracking acceptance is |η| < 2.
While the introduction rates g C , g A , and g Y have been measured elsewhere (e. g. Ref. [6] ), the reported values vary significantly amongst different materials and irradiation types, and so are fit with depletion voltage data from the ATLAS pixel detector. The notion of full depletion is not well-defined for highly irradiated sensors where the regions inside the sensor bulk can have a very low field (see Section 4. 2. 2). However, at moderate fluences, the depletion region is well-defined and is important for calibrating the 7 Where k i (T) = k i,0 e -E i /k B T , where k A,0 = 2. 4 +1. 2 -0. 8 × 10 13 /s, k Y,0 = 1. 5 +3. 4 -1. 1 × 10 15 /s, E A = (1. 09 ± 0. 03) eV and E Y = (1. 33 ± 0. 3) eV N eff (0) = N non-removable D (0) + N removable D (0). parameters of the Hamburg model specifically for the ATLAS pixel sensors, as the full depletion voltage (V depl ) is calculated in terms of the effective doping concentration: V depl = |N eff | • ed 2 2 0 , where d is the sensor thickness, e is the charge of the electron, is the dielectric constant, and 0 is the vacuum permittivity.
Details for the 3D sensor simulation can be found in Section 4. 7. Investigations of the electric field profile in the bulk of irradiated silicon sensors have shown that the electric field is no longer linear with the bulk depth after irradiation (see, for example, Refs. [43, 44] ). Irradiated planar sensors with non-linear profiles are simulated using the Chiochia model  [44] , implemented in the Silvaco TCAD package  [42, 45]. The Petasecca  [46]  n-type model was also investigated, but was found to not predict space-charge sign inversion below Φ = 1 × 10 14 n eq /cm 2 and was therefore not considered further. The simulation is performed over an area that corresponds to a quarter of an ATLAS IBL pixel sensor cell, to take advantage of symmetry. The electric field is computed at T = -10 • C using an effective doping concentration of 1.
The trap energy level E t varies by 10% of thermal energy k B T (see Section 4. 2. 3) are found to be consistent with naive temperature variations that bracket all Run 2 operational temperatures (-15 • C to +20 • C) and therefore provide a conservative bound on the predictions presented in Section 5. In the future, high-statistics collision data may be used to tune models in situ and avoid this complication. The Chiochia model is a double-trap model with one acceptor and one donor trap with activation energies set to E c -0. 525 eV and E v + 0. 48 eV  [43]  for the conduction band energy level E c and the valence band energy level E v , respectively. This model was developed using CMS diffusion oxygenated float zone n + -in-n pixel module prototypes and was chosen since the bulk material type is the same as in the ATLAS IBL and pixel layers and the initial effective doping concentration is similar: 50 V depletion voltage for the ATLAS IBL and 75 V for CMS with slightly thicker sensors.
Radiation damage effects for the 3D sensor are implemented in the Perugia model  [53]  (the Chiochia model cannot be used, as it is designed for n-type bulk) with the Synopsys TCAD package  [54]. As shown in Figure  16  one-eighth of the sensor is simulated to take advantage of the symmetry within the pixel. In the Perugia model, there are two acceptor traps and one donor trap, with activation energies given by E c -0. 42 eV, E c -0. 46 eV, and E v + 0. 36 eV, respectively. The density of traps is predicted to increase linearly with fluence Φ, so for each trap an introduction rate (g int ) is defined as: g int = N/Φ, where N is the trap concentration. In Table  6  the values used for the simulation of 3D sensors reported in this paper are summarized.
Data and simulation The models presented in the previous sections are validated by comparing the simulations with data, both in terms of physics predictions. This section presents two key observables for studying radiation damage: the charge collection efficiency and the Lorentz angle. These two quantities are measured as a function of time in Run 2 for the IBL planar sensors. The IBL is well-suited for this test because at the start of Run 2, it was unirradiated. The data were collected in the fall of 2015 and throughout 2016 and 2017. Charged-particle tracks are reconstructed from hits in the pixel detector, silicon strip detector, and transition radiation tracker. Clusters on the innermost pixel layer associated with tracks are considered for further analysis. The IBL is operated with a bias voltage of 80 V, 150 V, or 350 V at a temperature ranging from -15 • C to +20 • C.
The analogue threshold is 2550 e with 4 bits of ToT for the digital charge read-out  [5]. The ToT is calibrated so that a ToT of 8 corresponds to 16 ke -and a digital threshold of ToT > 1 is applied. The analogue-to-digital conversion is modelled using the same charge-to-ToT conversion. In practice, the charge-to-ToT conversion is non-linear, especially for low charge. This is most prominent at low charge and therefore required care in interpreting data-to-simulation comparisons in this regime. Simulated datasets are based on Geant4  [31]  with digitization implemented in Allpix  [56] , which is a lightweight wrapper of Geant4 that is optimized for test-beam analysis and is a powerful test-bench for digitizer development. The figures in this section refer to this set-up as the 'Stand-alone Simulation.
Lorentz angle The Lorentz angle is determined by performing a fit to the transverse cluster size F as function of the incidence angle of the associated track using the following functional form: F(α) = [a × | tan α -tan θ L | + b/ √ cos α] ⊗ G(α| µ = 0, σ), where α is the incidence angle,17 θ L is the fitted Lorentz angle, G is a Gaussian probability distribution evaluated at α with mean 0 and standard deviation σ, and a and b are two additional fit parameters related to the depletion depth and the minimum cluster size, respectively. An example input to the fit is shown in Figure  22 (a). In general, the simulation does not match the data at very low and high incidence angles, since the simulated points depend on many features of the simulation, but the position of the minimum should depend only on the Lorentz angle.
Conclusions and future outlook This paper presents a digitization model for ATLAS planar and 3D sensors that includes radiation damage effects. Predictions for the fluence at a given integrated luminosity are validated using leakage current data and stand-alone Hamburg model-based calculations. TCAD simulations with effective traps in the silicon bulk are used to model distortions in the electric field caused by exposure to radiation. The impact of annealing is studied by using predictions of the effective doping concentration to adjust the concentration of defect levels in the TCAD simulation. Systematic uncertainties in all aspects of the luminosity-to-fluence conversion and radiation damage model are estimated. Comparisons between simulations using the radiation damage model and collision data indicate that within the current precision, the fluence-dependence is well-reproduced.
Another related challenge is that TCAD models such as the Chiochia one need to be improved in order to accommodate an accurate temperature dependence. While future collision data may be used to tune the radiation damage models, additional work may be required to combine the best of the Hamburg and TCAD models. The flexibility of the digitizer model will allow collision data to be used to validate and test new ideas in the future. A version of the digitizer model is publicly available on GitHub  [59]  and the model is also implemented in the ATLAS software framework (ATHENA) in releases designed for simulating the data in 2016 and beyond. The ATLAS code can be found on GitLab  [60]. Even though the innermost pixel layers have only been exposed to Φ ≈ ×10 15 n eq /cm 2 , radiation damage effects are already measurable.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT we propose an original approach to address this issue. The aim was to design reproducible experiments, so that the results of the experiments could be refuted or replicated by our peers. We identified two main challenges. The first challenge was that designing such an experimental setting meant handling concepts and objects used at various levels of abstraction (the concepts from the modelling language used to build the model, the model used during the ComMod process, the model that we used in the experiment to represent the model used during the ComMod process, etc. ). We thus needed a robust conceptual framework, in order to specify rigorously the hypotheses we wanted to test and see how they could be refuted or confirmed. In order to take up this first challenge, we used a generic framework for meta-modelling called the Minsky triad conceptual framework  (Bonté et al.