dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008; Vidal et al. , 2010)  and the Spanish  (Quintana-Seguí et al. , 2016; 2017)  implementations of SAFRAN. It has a temporal resolution of one hour and a spatial resolution of 2. 5 km. The LSM used by SASER is SURFEX (Surface Externalisée, in French)  (Masson et al. , 2013)  version 8. 1. It uses the Interaction Soil-Biospère-Atmosphère (ISBA) scheme  (Habets et al. , 2008)  to simulate natural surfaces, which describes the vertical processes in the soil column and generates a runoff and a drainage that flows through the hydrosystem using Eaudysée  (Saleh et al. , 2011; Vergnes & Habets, 2018)  and the Routing Application for Parallel Computation of Discharge (RAPID) scheme  (David et al. , 2011). We used the SURFEX simulations of the PIRAGUA_hydro_analysis dataset  (Beguería et al. 2022) , which were performed using the same grid as the PIRAGUA_atmos_analysis.
Eaudysée and RAPID were run at a resolution of 1 km, using the HydroSheds database  (Lehner & Grill, 2013)  to describe the stream network. Some limitations of the current SASER implementations are that: (i) There is no lateral flow between SURFEX grid cells; (ii) there is no bidirectional interaction between the river and the alluvial aquifer; and (iii) groundwater processes are not simulated. This translates into a fast reaction between runoff and precipitation. Therefore, a better representation of groundwater storage is necessary to account for the slow processes (groundwater and snow melting).
Physiography In this study, we used the SURFEX's default physiographic database ECOCLIMAP II  (Faroux et al. , 2013). It has a spatial resolution of 1 km and includes an ecosystem classification as well as a consistent set of land surface parameters. In contrast to commonly used land cover products like Corine Land Cover and Global Land Cover, ECOCLIMAP-II has a new division of the existing classes with a better regional character obtained from the climatic environment (latitude, proximity to the sea, topography). The land cover parameters provided in this dataset include root depth, minimal stomatal resistance, albedo, and Leaf Area Index (LAI). The temporal variables are represented using a climatology (i. e. a mean annual cycle). Additionally, we used the Copernicus NDVI product, which was obtained from (https://land.
, 2001; Donohue et al. , 2007; Peel, 2009). The snow affects the land cover and has an important role in streamflow generation in mountain regions, and also on the slow component of the flow. • Finally, SL, the slope; SND, soil sand content; and CLY, soil clay content, are variables related to the topography and the soil. The slope predictor was included due to the good general correlation between surface slope and soil depth  (Tesfa et al. , 2009) , and the soil texture has a strong influence on all soil related processes, including subsurface runoff. (Price, 2011). Most of these variables were obtained from the ECOCLIMAP II database. Although these descriptors are not directly associated with groundwater, they help determine a landscape that can be prone or not to groundwater.
Since the reservoir is implemented as an external module to postprocess the SURFEX drainage before the routing step, there is no need to run SURFEX within the genetic algorithm (see Fig. 3 ). This is a key benefit, as SURFEX is a computationally-expensive model. Furthermore, given the size of the catchments there is no need to run the routing scheme (Eaudyssee-RAPID) at each iteration (see Fig. 4 ). This allows us to undertake a very high number of simulations, as we only run the very simple reservoir model, which consists of a few lines of Python code. The transfer equations that link the predictors with the predictands are expressed as follows: MP i =w i1 ARI + w i2 MAP + w i3 PET + w i4 NDVI + w i5 SNW + w i6 SL + w i7 SND + w i8 CLY + w i9 (7) where MP i are the model parameters (k and L max ) and w i are the coefficients that will be optimized.
Low flow indices In addition to calculating the KGE scores, and to evaluate how the conceptual reservoir implementation (local calibration and regionalization approach) impacted the simulated low flows, we calculate two low flow indices. Different low flow indices can be estimated  (Gustard et al. , 1992; Smakhtin, 2001 ), but we focused on two common ones. First, the ratio (Q 90 /Q 50 ), where Q 50 is the median and the Q 90 is the low value that is Fig. 5. KGE(Q 1/2 ) scores between observed data and the default simulation, for the whole period. Larger circles with a black border indicate stations defined as natural or near-natural. The other stations are considered as influenced. observed 1/10th of the time, at daily time step. This index is interpreted as the proportion of streamflow originating from groundwater stores, excluding the effects of the catchment area  (Smakhtin, 2001).
The median daily KGE value of the default simulation was 0. 41 and the median daily KGE value of the local calibration was 0. 53, while for the regionalization it was 0. 52, its value is close to the obtained by the local calibration. Furthermore, we obtained improvements in 79 % of the validation catchments. This confirms the robustness of the regionalization approach to improve low flows in the catchments that were not used in the training dataset, also the KGE scores are as good as using the local calibration. The three additional experiments (last three boxes in Fig. 12 ) where performed to see how the eight experiments can be combined to obtain a final result. In the first additional experiment, we calculate the median time series of the eight previous experiments. In the second, we run the reservoir scheme with the median value of the k and L max parameter maps (median value of the parameters for each grid point) and calculate streamflow.
Computing natural streamflow with the improved model. Once the maps of the two parameters of the reservoir model were obtained using the median value of the parameter maps of the eight experiments (Fig. A1 ), we proceed to calculate the final streamflow using the river routing scheme. Fig. 14  presents the streamflow simulation performance obtained using the median values of the eight regionalization experiments for the entire record (1979-2014) over the complete database. Of the total stations, 47 % showed higher KGE values than 0. 5 (5 % more compared to the default simulation). Fig. 14(b)  shows the improvement, in terms of ΔKGE, due to regionalization for the whole period. For comparison purposes, we calculated the KGE values over the same periods that were used for local the local calibration  (1979-1997) and validation (1997-2014).
REAGENT or RESOURCE SOURCE IDENTIFIER Our BECCS modeling and optimization approach is inspired from Fajardy et al. ,  12, [57] [58] [59]  a global-scaled model, and the BuSCH model  45  a BECCS optimization at the UK national level. We consider bioenergy dedicated biomass crops are grown on marginal lands-an economic term, designing lands with little agricultural values. These lands are often well suited for bioenergy crops. 60 We account for hypothetical future water risks by using the Overall Water Risk (OWR) factor made public by the World Resource Institute in the Aqueduct dataset. 38 This dataset gives a regional index between 0 and 5 (low to extremely high risk) and we assume no bioenergy crops are grown on lands with high (4) and extremely high (5) risks. Equations 5-9 incorporate the GHG assessment of BECCS supply chain.
The TNG50 Cosmological Simulation The IllustrisTNG Project  (Marinacci et al. 2018; Naiman et al. 2018; Nelson et al. 2018; Pillepich et al. 2018; Springel et al. 2018)  is the follow-up of the original Illustris simulation  (Genel et al. 2014; Vogelsberger et al. 2014; Sijacki et al. 2015)  and comprises three suites of magnetohydrodynamic cosmological simulations (TNG300, TNG100, and TNG50) evolved with the moving-mesh code AREPO  (Springel 2010). Our analysis is based on TNG50-1 (hereafter TNG50;  Nelson et al. 2019; Pillepich et al. 2019) , the latest and highestresolution version of the IllustrisTNG simulation suite. TNG50 follows the evolution of 2 × 2160 3 total initial resolution elements and includes dark matter, gas, stars, black holes, and magnetic fields within a uniform periodic-boundary cube of 51.
Synthetic Images In this section, we describe the main steps taken to produce synthetic images tailored for JWST observations starting from cosmological simulations (Figure  2 ). We first detail the methodology for post-processing galaxies in cosmological simulations to get noiseless images for NIRCam and MIRI in Section 3. 1. We focus on the CEERS observational strategy and describe how to create NIRCam-like raw data in Section 3. 2. Finally, we briefly outline the main steps for reducing the JWST imaging observations in Section 3. 3.
NIRCam and MIRI Noiseless Images In order to produce noiseless synthetic images of TNG50 high-z galaxies, we modeled both the light distribution produced by stellar populations and star-forming regions and the effects of dust on radiation using the most recent version of the SKIRT Monte Carlo radiative transfer code (v9. 0;  Camps & Baes 2015; Verstocken et al. 2017; Camps & Baes 2020). For each galaxy and each snapshot described in Section 2. 2. 1, we extract the corresponding sets of sub-halo star particles and gas cells, while stellar wind particles are ignored. Each image has a side equal to twice the total halfmass-radius of the corresponding galaxy, but greater than 2 arcsec (see Section 2. 2. 1). The number of pixels is chosen so  that the resulting pixel scale matches that of the JWST instruments.
Synthetic Data Products The post-processing of TNG50 galaxies allows us to create a set of synthetic observables comprised of noiseless images in multiple bands at different spatial resolutions, including spatially integrated fluxes of the total galaxy and each source component (i. e. , primary and secondary emissions). In detail, we take advantage of SKIRT's flexibility and observe each galaxy from 20 configurations. It is worth noticing that each additional configuration increases both the simulation run time and the memory consumption. At every observer position, we place a detector to mimic both NIRCam and MIRI observations. Each instrument provides both the spatially integrated flux and the images for each of the NIRCam and MIRI filters (narrow, medium, and broadband) at the corresponding pixel scale (see Section 2.
Data Reduction and Calibration Data simulated with mirage can be run through the JWST calibration pipeline v1. 4. 6 (CRDS v11. 10. 1),  25  mimicking the data reduction strategy to be used for in-flight data. The calibration and reduction procedure takes place in three different stages, which are briefly detailed in the following sections (Figure  2 ). The step-by-step description provided hereafter concerns the images obtained with detectors B1 and B5, which are used for the NIRCam filters F200W and F356W, respectively.
Morphological Measurements We derive the parametric and nonparametric morphology of our sample galaxies using the standard configuration of statmorph, 27  a Python package developed by  Rodriguez-Gomez et al. (2019)  and optimized to compute the optical morphologies of galaxies. All the details about the different sets of parameters derived with statmorph as well as a discussion about their definitions are fully explained by  Rodriguez-Gomez et al. (2019). Regarding the data quality, statmorph flags "bad measurements" either if there is a problem with the basic measurements (e. g. , artifacts in the image or bad sky subtraction) or if there is a problem during the Sérsic fit, which could happen for galaxies with very irregular morphologies. Furthermore, we visually flag galaxies that are poorly detected or which have highly irregular segmentation maps.
Summary and Conclusions In this work, we created mock observations tailored for forthcoming JWST observations of high-z galaxies using a state-of-the-art cosmological simulation (i. e. , TNG50). The main goal is to provide predictions for observable morphological parameters (i. e. , by forward modeling stellar light), focusing on the galaxy sizes, to be compared right away with the first JWST data sets. We generated ∼25,000 synthetic images of M å 10 9 M e galaxies from the TNG50 cosmological simulation at z = 3, z = 4, z = 5, and z = 6. Noiseless images were generated with the radiative transfer code SKIRT v9. 0, including the effects of dust attenuation and scattering. The images are available in all the filters of the NIRCam and MIRI instruments of JWST, at the corresponding angular resolution, i.
This research has been funded by grant Nos. PGC2018-093499-B-I00 and MDM-2017-0737 Unidad de Excelencia "Maria de Maeztu"-Centro de Astrobiología (INTA-CSIC) by the Spanish Ministry of Science and Innovation/State Agency of Research MCIN/AEI/ 10. 13039/501100011033 and by "ERDF A way of making Europe. " L. C. acknowledges financial support from the Comunidad de Madrid under Atracción de Talento grant 2018-T2/TIC-11612. A. Y. is supported by an appointment to the NASA Postdoctoral Program (NPP) at NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA. F. B. also acknowledges support from the grants PID2020-116188GA-I00 and PID2019-107427GB-C32 by the Spanish Ministry of Science and Innovation.
IV. HEURISTIC TASK DEPLOYMENT ALGORITHM Since the MINLP-based PP is linearized to the MILP-based PP1, the optimal solution to PP1 can be found by the existing solver, e. g. , CPLEX or Gurobi  [9]. However, finding the optimal solution is still time-consuming, especially when the size of the problem is large. The complexity of an optimization problem is related to the number of variables and constraints. Based on the idea of problem decomposition, we propose a novel heuristic method for solving PP to improve the scalability of the proposed method. The reasons for solving PP rather than PP1 are that 1) these two problems are equivalent, but the problem size of PP is smaller than PP1; and 2) the heuristic method considers the variables of PP in sequence, and thus, the coupling among variables can be avoided.
Thus, the proposed method is still applicable to other system parameters. Next, we first evaluate the influence of system parameters on task deployment results. Then, we compare the system performance (i. e. , system QoS, problem schedulability, and computation time) of the proposed optimal (OPT), the heuristic (HEU) methods, and other state-of-the-art deployment schemes: i) task deployment without task migration (WTM)  [24] , ii) task deployment without DVFS (WDV)  [20] , i. e. , each processor has a fixed voltage/frequency, and iii) task deployment with fixed task allocation (WTA)  [5] , i. e. , task-to-processor allocation is fixed. The simulations are performed on a workstation with 32 processors and 64 GB RAM, and the algorithms are implemented in Matlab 2019a with optimization solver Gurobi 9.
This work was supported in part by the  National Key Research and Development Program of China  under Grant  2022YFF0902800 , in part by the  Fundamental Research Funds for the Central Universities of China  under Grants  2242021R10113  and  2242022K30038 , in part by the  Natural Science Foundation of Jiangsu Province of China  under Grant  BK20202006 , in part by the  Southeast University "Zhishan Scholars" Projects  under Grant  2242021R40003 , and in part by the  Jiangsu Provincial Key Research and Development Program  under Grant  BE2022135.
The authors say that their approach outperforms all state-of-the-art stereo-based 3D detectors on the KITTI benchmark. Another interesting solution presented in the literature is the combination of LiDAR and a stereo camera. These methods exploit the fact that LiDAR will complete the vision and information provided by the camera, by adding notions of size and distance to the different objects that make up the environment. For example, the proposed method in  [8]  takes advantage of the fact that it is possible to reconstruct a 3D environment using images from stereo cameras, making it possible to extract a depth map from stereo camera information and enrich it with the data provided by the LiDAR sensor (height, width, length, and heading angle). In  [9] , a new method proposed by us, called SLS-Fusion (sparse LiDAR and stereo fusion network), is presented.
This is an architecture based on DeepLiDAR  [10]  as a backbone network and the pseudo-LiDAR pipeline  [8]  to fuse information coming from a four-beam LiDAR and a stereo camera via a neural network. Fusion was carried out to improve depth estimation, resulting in better dense depth maps and, thereby, improving 3D object detection performance. This architecture is extremely attractive in terms of costeffectiveness, since 4-beam LiDAR is much cheaper than 64-beam LiDAR (the price of a 4-beam model is around USD 600  [11] ). Results presented in Table  1  and in  [9]  show that the performance offered by the 64-beam LiDAR (results were obtained with PointRCNN  [12]  from testing on the KITTI dataset [13], for "Car" objects with IoU = 0. 5 on three levels of difficulty (defined in  [14] ): easy (fully visible, max.
truncation-15%) = 97. 3, moderate (partly occluded, max. truncation-30%) = 89. 9, hard (difficult to see, max. truncation-50%) = 89. 4) is not far from the one reached by the stereo camera and the four-beam LiDAR model (with the best results obtained from testing on the KITTI dataset, for "Car" objects with IoU = 0. 5 on three levels of difficulty: easy = 93. 16 and moderate = 88. 81 with SLS-Fusion, and hard = 84. 6 with Pseudo-LiDAR++). For this comparison, the satisfactory results obtained by the 64-beam LiDAR were modeled directly in PointRCNN, while the combination of video and LiDAR requires the generation of a new point cloud, usually referred to as the pseudo-point cloud. There are other solutions presented in the literature based only on stereo cameras, such as the CG-Stereo method presented in  [7] , which achieves outstanding results on easy mode (level of difficulty: easy = 97.
Camera-Based Methods Some of the first algorithms were keypoint/shape-based methods. In 2017,  Chabot et al. [21]  presented Deep MANTA, one of the first works on camera-based 3D object detection. This architecture recognizes 2D keypoints and uses 2D-to-3D matching. This algorithm has two steps: (1) a RCNN architecture to detect and refine 2D bounding boxes and keypoints, and (2) a predicted template similarity to pick the best matching 3D model inside a 3D dataset. However, the main disadvantage of this method is the excessive time required for 2D/3D matching. On the other hand, the pseudo-point cloud-based methods arose from the idea of simulating LiDAR from a stereo camera. These methods typically convert data from 2D to 3D by using extrinsic calibration information between the camera and LiDAR.
The most common types of projection are bird's eye view (BEV), front view (FV)  [16] , range view (RV)  [29] , and spherical view (SV)  [30]. The voxel-based method maps a point cloud into 3D grids (voxels) as an initial stage. In 2017, Engelcke et al. [31]  presented their LiDAR-based detector Vote3Deep. In this method, LiDAR point clouds are discretized into a sparse 3D grid. Then, items are detected using a sliding-window search with a fixed-size window with N different orientations. In each window, a CNN performs binary classification. Point-based methods usually deal with the raw point cloud directly instead of converting the point cloud to a regular structure. Qi et al. [25]  introduced PointNet, a pioneering study on deep learning-based architectures for processing point cloud raw data for classification and semantic segmentation.
Analysis of the Role of Each Sensor in the 3D Object Detection Task SLS-Fusion is a fusion method for LiDAR and stereo cameras based on a deep neural network for the detection of 3D objects (see Figure  2 ). Firstly, an encoder-decoder based on a ResNet network is designed to extract and fuse left/right features from stereo camera images and project the LiDAR depth map. Secondly, the decoder network constructs a left and right depth map of optimized features through a depth cost volume model to predict the corrected depth. After the expected dense depth map is obtained, a pseudo-point cloud is generated using calibrated cameras. Finally, a LiDAR-based method for detecting 3D objects (PointRCNN  [12] ) is applied to the predicted pseudo-point cloud. Section 1 shows previous results of SLS-Fusion on the KITTI dataset, which uses the refined work of PoinRCNN to predict the 3D bounding boxes of detected objects.
ResNet is a group of residual neural network blocks and each residual block is a stack of layers placed in such a way that the output of one layer is taken and added to another deeper layer within the block, as shown in Figure  4. The main advantage of ResNet is its ability to prevent the accuracy from saturating and degrading rapidly during the training of deeper neural networks (networks with more than 20 layers). This advantage helps in choosing a network to be as deep as needed for the problem at hand. What we needed in this case, was to extract as much detailed features as possible from sparse LiDAR data and high-resolution stereo images. This process considerably assisted the decoder network to fuse the extracted features well. The network of the decoder consists of adding the functions of both LiDAR and stereo encoders, then up-projecting the result to progressively increase the resolution of the features and generate a dense depth map as a decoder output.
Assessment of the Different Network Architectures Implemented To assess the operation of the SLS-Fusion system, the KITTI dataset  [13, 41] , one of the most common dataset for autonomous driving is used to train the neural network for dense depth estimation, pseudo-point cloud generation, and 3D objection detection. It has 7481 training samples and 7518 testing samples for both stereo and LiDAR. In this section, the results obtained with each component of the SLS-Fusion model (stereo camera, 4-and 64-beam LiDAR) are presented to understand the impact of each component on the ultimate detection performance of 3D objects and show how results are affected. To do this, a complete ablation study was performed by disabling each component as previously explained, or by changing the number of LiDAR model component beams.
As shown in Figure  5 , increasing the number of LiDAR beams will increase the number of points that represent the targets detected by the LiDAR. The aim of this illustration is to show the difficulty when dealing with LiDAR data processing. Depending on the environment, some areas are full of detected points, while others are empty. Consequently, the LiDAR contribution to the performance of the object detection model will be enhanced. However, as shown in Table  2 , increasing the number of beams from 4 to 64 beams will significantly increase the cost of the LiDAR sensor. An optimized solution involves selecting the appropriate number of corresponding LiDAR beams, which can provide a desired performance level. For a more comprehensive survey of the LiDARs available on the market, the reader is referred to  [42].
Easy The obtained results could be related to the dataset processed. Thus, to deepen this analysis, other datasets other than KITTI must be used. This is perspective work. In any case, the best solution is obtained by fusing both sensors. This proves that each component of the SLS-Fusion architecture effectively contributes to the final performance of the model, and we cannot eliminate these components of the neural network architecture in all possible cases: low-cost, medium-cost, or high-cost LiDAR sensors.