dataesr/scientific-paragraphs-categorization huggingface dataset:
The DSM process used in this study is based on the well-known scorpan model  [3]  that uses quantitative relationships between targeted soil property and environmental variables also called "covariates". The selection of the landscape covariates (Table  2 ) had been performed for a previous DSM application in the region  [11]. It was based on two criteria: (i) the covariates could be derived from freely available geodatasets, at least at the French national level, and (ii) they have a logical and deterministic relationship to the soil properties, according to the literature. Classical geomorphometric indicators found in the DSM literature were computed from the global Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM): the elevation, slope, plan curvature, profile curvature, set of multiresolution valley bottom flatness (MRVBF), set of multiresolution ridge top flatness (MRRTF), topographic position index, and topographic wetness index.
The basic climate data (maximum temperature, minimum temperature, and precipitation) were extracted from the Global Climate Database at a resolution of 1 km 2  [26]. Two aridity indexes were derived from these data, namely, the De Martonne index and the Emberger index (see details in Vaysse and Lagacherie  [11] ). Additionally, we added to the covariate set the regional-scale soil map (1:250,000) that regroups the major landscape types across the region.
, k. We let w i (x) be the average of w i (T) over such a collection of trees: w i (x) = k -1 n t=1 w i (x, θ t ) (7) The final predictions are the average of predictions of individual trees [29-31]: μ(x) = n i=1 w i (x)Y i (8) Recently, Wright, and Ziegler (2015) developed a fast implementation of Breiman's random forest [29] and Meinshausen's quantile regression forest  [27] , for high-dimensional data, which is available as the ranger R package  [32]. This implementation is faster than QRF in terms of i) superior scaling of the number of samples, trees, and descriptive variables tried for splitting and ii) runtime and memory usage (see details in Wright and Ziegler  [32] ). QRF was applied for mapping all possible soil properties considered in the inference trajectories. In particular, the thicknesses of soil layers (SLh i in Equation (  4 )) were mapped separately so that all trajectories could be applied.
Evaluation Protocol The performances of digital mapping of SAWC and of all its components (primary soil properties, hydraulic properties (soil water contents at FC and PWP for different soil layers)) were evaluated by a k-fold cross validation. This evaluation procedure involves randomly dividing the data into k subsets. Then, the holdout method is repeated k times, such that each time one of the k subsets is used as the evaluation set, the other k-1 subsets are merged to form the calibration set. Following this procedure, every data point is included in an evaluation set exactly once and is included in a calibration set k-1 times. In our case, we choose k = 10 and, to increase the robustness of the evaluation, the 10-fold cross-validation was repeated 20 times. The k-fold cross validation was performed using the cvTools R package  [33].
The advent of renewable energy systems has led to an increase in decentralised energy systems. Consequently, the last 10 years have seen development of specialised software such as HOMER, iHOGA, EnergyPro, RETScreen and TRNSYS to analyse these systems. This study compares these software in detail especially in terms of the environmental assessment. It is concluded that these software do not adequately include environmental analysis since only 1 out of 5 software considers more than one life cycle stage, neglecting other upstream/downstream emissions. Furthermore, the emphasis is on emissions such as NOx and SO2 that are usually associated with fossil fuel utilization. As the energy systems are becoming increasingly complex, especially with storage technologies such as hydrogen and batteries, emissions 'shift' away from the operating stage.
They also provide guidelines on the selection of tools according to their needs. Zhou et al  [4]  conducted research on the latest trends in optimization of hybrid solar-wind energy generation systems. As a part of this investigation, general methodology and merits-demerits of the software, HOMER, HYBRID 2, HOGA and HYBRIDS were also reviewed. Connolly et al  [1]  carried out a comprehensive review of software tools for integrating renewable energy systems. They also classified tools according to geographical area, number of users, level of detail, and energy sectors modelled. Along with this, a detailed overview of the selected software was also completed. Bernal-Agustin and Dufo-Lopez  [5]  studied simulation and optimization models in the published scientific literature. Furthermore, a brief description of software for designing hybrid energy systems and their classification in terms of features is also presented.
Since this article has a broad scope, information presented about individual models was limited. Erdinc and Uzunoglu  [9]  reviewed commercially available software and optimization techniques in scientific literature for optimum sizing of hybrid energy systems. Finally, Sinha and Chandel  [10] , is the most recent publication that reviews software for designing hybrid renewable energy systems with a goal of helping researchers identify the ideal tool according to the requirements. Apart from discussing software, they also use a case study to identify differences between HOMER and RETScreen. As we can see, the review of available software for energy systems, their classification, guidelines for use according to user needs and other techniques has been already well documented in the scientific literature.
HOMER The main inputs required by the software consist of technical and economic information about load characteristics, specifications of energy component and resource data. The latter includes both renewable and nonrenewable resource such as solar data, wind data, fuel composition, etc. Once all the information is fed in the software, HOMER performs calculations in the following steps: • An energy balance between available production options and load to be satisfied is performed at each time step throughout the year. Time step can vary from 1 minute to 1 hour. • Out of the possible options, one with the lowest operating cost is selected at each time step. This calculation is repeated for all possible system configurations and after taking into account other factors such as capital cost, interest rates, project lifetime, etc.
, NPC (Net Present Cost) of all systems is calculated. • A second algorithm sorts the feasible systems according to the increasing NPC or other available variables. In the standard version, HOMER does not consider annual variations hence NPC is calculated for the project life based on the same data but taking into account the interest rate. Environmental impact is only calculated for direct emissions of CO2, CO, NOx, SO2, particulate matter and unburnt hydrocarbons from fuel combustion technologies. Emissions are calculated in HOMER as the product of emission factor and annual consumption of fuel. Emission factor is grams of pollutant emitted per unit fuel consumed. Emission factor for carbon dioxide is not required, as it is calculated using fuel consumption and simple mass balance assumptions.
iHOGA Improved Hybrid Optimization by Genetic Algorithm also known as iHOGA is also a techno-economic simulation and optimization tool developed by the University of Zaragoza  [13]. The primary objective of iHOGA, which is simulation and optimization of hybrid energy systems, is similar to HOMER. Hence, it is used in same design phase of sizing and feasibility analysis of energy systems. The major difference between the two software lies in the optimization algorithm. Along with simulating all possible solutions, iHOGA also provides the option of utilizing genetic algorithm to find the optimum solution. The calculation methodology of the latter can be described as follows: • Selection of a limited, random set of possible solutions called 'Population'. In this context, an example of population could be solar panels (0-10kW), battery bank (0-100Ah) and wind turbine (0-10kW) in certain combinations.
Main advantage of this algorithm is that, it takes considerably less time for convergence. Furthermore, iHOGA can also perform multi-criteria optimization taking into account other factors such as lifecycle CO2 emissions, % unmet load, human development index and job creation. A maximum of three variables can be simultaneously optimized. iHOGA is the only software in this review where the user can enter lifecycle emissions. However, only CO2 equivalent emissions can be calculated. There is no possibility to add other emissions. Total CO2 equivalents is the sum of emissions in operating stage plus total emissions in other lifecycle stages (Manufacturing, recycling, transport, etc). Both these values are entered as emission factors that scale according to per unit fuel consumed or size of the equipment.
EnergyPro EnergyPro is developed and maintained since the last 20 years by a Danish Company called EMD International  [14]. EnergyPro is slightly different to the previous two software because of its diverging goal of optimizing 'daily operation' of energy systems rather than its configuration. EnergyPro is thus well suited to simulate performance of an energy system and to minimize its operating cost. Even though there will be discrepancies between load, demand, weather conditions, electricity market, etc. with real time values, the software can provide insight into the system operation. The inputs for the software remain the same again, although, there is more flexibility since values can be entered as formulae based on other variables in the software. The calculation methodology can be described as: • The priority of energy component dispatch to satisfy a load is calculated based on the operating cost.
RETScreen Expert RETScreen has been developed by Ministry of Natural Resources, Government of Canada  [15]. The unique selling point of this software is rapid feasibility analysis of energy systems and its comparison with industrial benchmarks. RETScreen makes it possible by providing large amount of background information in the databases. Even if scarce information is available about the project, it can still be analyzed using weather database, hydrology data and typical energy component specifications along with their prices. Optimization is not available; hence, it can only be used for single project simulations. The calculation methodology operates in the following steps: • First step is selection of location. This enables automatic addition of comprehensive climate data to the specific location.
• Technical and economic data for the project are entered. Interesting thing here is, depending on the data available, project specifications can be entered at different levels of detail or even with default values in the software. • Finally, detailed financial, technical and risk analysis reports are generated. A comparison between the current system and planned facility can also be made in the software based on these reports. Furthermore, average energy cost and efficiencies of different energy technologies with respect to various locations are shown to calculate savings. Energy efficiency and demand management can also be analyzed in this software. Emissions in RETScreen are also estimated using emission factors per unit energy generated. For example, kg CO2/GJ energy produced. In the highest level of granularity, emissions available are CO2, CH4 and N2O.
TRNSYS TRNSYS is a transient system simulation software developed by University of Wisconsin and University of Colorado  [16]. It was first developed for thermal systems 35 years ago but the utility has been expanded to various sectors since. TRNSYS enables detailed technical simulation of energy systems and the built environment. With a possible time step of 0. 01s and detailed models of components, it enables users to find any issues, particularly about the transient state. Although TRNSYS is a paid software, it has open source code. Moreover, building custom models is possible and consequently it has a large component library. Hence, it is attractive to users when the standard models do not simulate the real world performances. This can be when the models are not detailed enough or when dealing with a novel technology.
Additionally, TRNSYS allows the user to expand boundaries of the simulation. For example, modelling the energy demand of a building depending on various factors such as geography, architecture, weather conditions, etc. The software is divided into two parts, kernel and model library. Kernel performs the calculations for the system whereas the model library consists of various component models that can be added to the system. Typical steps of simulating energy systems using TRNSYS are presented below: • the required components of the energy system from the library • Import the corresponding weather data from the database. Complete the interconnections between components and ensure their compatibility. • Kernel computes the detailed simulation and the results can be seen in a variety of graphs and figures.
Numerous specialized software based on TRNSYS such as HYDROGEMS (stand-alone hydrogen), NREL SDHW (solar water heater), TRANSOL (solar thermal), etc. are also available. Some of them available free for TRNSYS users. Estimation of emission does not exist in the basic version of TRNSYS. The specialized software based on TRNSYS were not investigated. However, since TRNSYS offers open source code and custom models, addition of basic emission factors at least should be straightforward.
Summary As suggested by Turcotte et al  [6] , the five software can also be classified into the design stages for which they are relevant as done in Figure  1. RETScreen is used in the prefeasibility stage when scarce data is available to designers. The purpose at this stage is to check whether it is interesting to evaluate a project further given the local conditions. In the next stage, when more data is collected, HOMER and iHOGA provide the optimized component size and the ability to compare various system architecture. In the next step when the choice of a system is down to a few options, EnergyPRO gives us the operation strategy to minimize the net production cost with respect to the system and its corresponding constraints. Finally, TRNSYS can be used for detailed technical simulation or to improve the standard models.
It allows the user to modify algorithms and specific component interactions. TRNSYS is also comparatively better equipped to model a novel technology and include external effects. Hence, it can also be used at an early stage of technology modelling, which is not shown in Figure  1. It should be noted however, software are more flexible to be used in different stages than defined in this section. For example, HOMER and iHOGA can also be used for optimizing daily operations or single project simulations. The environmental analysis included in the reviewed software is judged inadequate in its current form. There are three primary reasons identified for the same. First, the energy scenario is changing rapidly. To analyze this, we look at greenhouse emissions according to lifecycle stages for wind turbines, solar PV, conventional natural gas and coal plants in Figure  2.
For wind turbines and solar PV, we consult a paper investigating 153 LCA studies by Nugent ✔ ✔ ✔ ✔ ✔ ✔ - ✔ iHOGA ✔ ✔ kg pollutant/unit fuel consumed & kg pollutant/size of equipment ✔ - - - - - - - EnergyPRO ✔ - kg pollutant/unit fuel consumed ✔ ✔ ✔ - - - ✔ ✔ RETScreen Expert ✔ - kg pollutant/unit energy produced ✔ ✔ - - ✔ - - - TRNSYS - - - - - - - - - ✔ - and Sovacool  [17]. Data for electricity production from natural gas and coal plants are taken from the LCA harmonization studies Donoughue et al  [18]  and Whitaker et al  [19]  respectively. Three life cycle stages: Upstream, Operation and Downstream are considered here. These emissions are further disaggregated in their primary studies, however for comparison, further breakdown was not possible. The upstream stage consist of all emissions resulting from construction of energy production facilities including material extraction, assembly and transport.
Targets and transport system The targets were water enriched in 18 O. The water used in this work was supplied by Campro Scientific [10]. The water had been irradiated by protons to produce 18 F and had been recycled. It consequently contained a residual of acetonitrile (CH 3 CN) solvant. The respective fractions of oxygen isotopes and solvant were obtained with a gas chromatograph and weighting. The fraction of 18 O atoms in the targets was 68(3)%. The target holder attached to the transport chain was of similar design as the one used in ref. [9] , but was bigger and made of aluminum. It had a cavity of 2. 5 by 2. 5 cm facing the beam and of 3 mm thickness closed with a 1 mm mylar foil. Several water targets of about 2. 3 g weight have been used in the course of the experiment. The exact amount of water was obtained by weighting before and after the data had been taken.
Acquisition and data correction The γ-rays have been counted by a Ge-detector monocrystal belonging to the phase 1 of the JUROGAM array at JYFL. The counting distance from the detector cap to the center of the 3 mm thick stack or target was 10. 2 cm. Corrections for position of the foils in the stack, volume and absorption of the γ-rays have been carried out in the same way as described in our former measurements (ref. [8]  and references therein). The energy spectra have been recorded as gated singles with a Canberra Multi-Channel-Analyser running the Canberra Genie 2000 software. A CAEN two-parameter system  [11]  has also been used. This system directly analyses the pulses from the preamplifier into histograms. It accepts higher input rates and provides better energy resolution than the conventional analogue acquisition chain.
Verification via the 16 O(n, p) 16 N overlaps The cross-section of 16 O(n, p) 16 N is well known up to 20 MeV according to the compilation in NNDC. Evaluations shown in the ENDF library and experimental values shown in the EXFOR library agree. Only one evaluation, namely EAF-2010, and a set of experimental points  [14]  list values above 30 MeV. The evaluation seems to drop too quickly, whereas the original experimental data points do not connect well neither with the experimental values from other authors nor with the evaluations. We kept the energy dependance of the experimental high-energy tail but connected them to the values below 30 MeV by using a multiplication factor. The uncertainty on the tail of σ( 16 O(n, p) 16 N) has no visible impact on the overlaps owing to the small fraction of neutrons contributing to the σ n overlaps.
Spectral Energy Distribution Modeling We use the extensive multi-wavelength photometric data available in the XMM-LSS field to determine the IR, dust, and stellar properties of HXMM05 via SED modeling. Previously,  Wardlow et al. (2013)  modeled the dust SED of HXMM05 by fitting a simple modified blackbody to the photometry measured at (sub-)mm wavebands (Herschel-SMA-MAMBO), assuming a dust emissivity index of β = 1. 5. This model suggests an IR luminosity (rest-frame λ rest = 8-1000 µm) of L IR = (3. 2 ± 0. 4) × 10 13 L and a dust temperature of T d = (45 ± 1) K. Here, we update the SED with more photometric data obtained since, covering UV through radio wavelengths (see Table  2 ). We model the observed dust SED using a modified blackbody (MBB) and the full SED using the MAGPHYS code  (da Cunha et al.
MAGPHYS model To determine the stellar mass of HXMM05, we fit models to its full SED, sampled by the FUV-to-radio wavelength photometry using the high-z extension of MAGPHYS  (da Cunha et al. 2008 (da Cunha et al. , 2015)). This code exploits a large library of optical and IR templates that are linked together physically through energy balance, such that the UV-to-optical starlight is absorbed by dust and re-radiated in the FIR. A detailed explanation of the MAGPHYS code and the model priors are given by da  Cunha et al. (2015). Following da  Cunha et al. (2015) , upper limits are taken into account by setting the input flux densities to zero and uncertainties to upper limits. The best-fit SED is shown in Figure  9  and the resulting best-fit parameters are listed in Table 6. Since in the best-fit model, the Herschel/PACS 160 µm measurement forces the dust peak to shorter wavelengths and  worsens the fit at long wavelengths (similar to the MBB fit), we re-model the SED excluding this outlier.
Dynamical Modeling We fit dynamical models to the 1-kpc resolution [CII] data obtained with ALMA to study the gas dynamics of HXMM05 (more specifically, X-Main). The monotonic velocity gradient observed in  [CII]  suggests that HXMM05 is a rotating disk galaxy, an interpretation further supported by the analysis of §4. 2. 1 below. Assuming that the disk is circular and infinitesimally thin, we use the kinematic major and minor axes to estimate the inclination angle, which yields i = 46 ± 8 •. This is slightly different from the value estimated using the morphological axes, which yields i = 35 ± 5 • , but the two are consistent within the error bars. We initialize the inclination angle in the following analyses based on these estimates.
K m ≡ A 2 m + B 2 m and ψ m ≡ arctan A m B m. ( 3 ) Given the modest inclination of HXMM05, we fit tiltedring models  (Begeman 1989)  to the observed velocity field using the task ROTCUR provided in the GIPSY software package to analyze the gas dynamics of HXMM05 due to bulk motions (i. e. , driven by the gravitational potential). The tiltedring model assumes that the gas is in a circular, rotating thin disk, and describes the disk using a series of concentric rings, where each ring can have an independent inclination angle (i), major axis PA, rotation velocity (v rot ), and expansion velocity (v exp ). The rotation velocity is related to the projected LOS velocity via v LOS = v sys + v rot cos (ψ) sin (i) + v exp sin (ψ) sin (i). (4) Here, we assume that the observed LOS velocity is due entirely to disk rotation and ignore any radial motions (e.
( , 2006) )   22 , we constrain the globally-averaged G 0 , hydrogen density (n), and surface temperature for the PDRs in HXMM05 24. We adopt CO grids from an updated version of the code  (Hollenbach et al. 2012 ; M. Wolfire 2017, private communication) that is merged with the MEUDON code (Le  Petit et al. 2006 ) for a more detailed treatment of H 2 chemistry and thermal balance. The observed line ratios are shown in Figure  12  as functions of G 0 and n. Since a fraction of the [CII] emission in the ISM arises outside of PDRs, and we lack other diagnostic lines to determine this fraction in HXMM05, we adopt a canonical value of 30% to account for this non-PDR contribution  (Carral et al. 1994; Colbert et al. 1999; Malhotra et al. 2001; Oberst et al. 2006 ; see also  Pavesi et al. 2016 Pavesi et al.
However, its extended massive gas disk, monotonic velocity gradient, G 0 , and gas and SFR surface densities at z = 3 suggest additional mechanisms such as proposed in the cold mode accretion model may also play an important role in shaping the existence and subsequent evolution of massive DSFGs. HXMM05 could thus be a rare example of such system showing both mechanisms at play. We thank the referee for providing detailed and constructive comments that have significantly improved the clarify of this manuscript. We thank Mark Lacy and the data analysts at the North American ALMA Science Center (NAASC) for assistance with the ALMA data reduction. T. K. D. L. thanks Amit Vishwas and Drew Brisbin for helpful discussions, Carlos Gómez Guijarro for assistance with the IRAC flux extraction and setting up the GALFIT software, and Gregory Hallenback and Luke Leisman for helpful discussions on dynamical modeling.