dataesr/scientific-paragraphs-categorization huggingface dataset:
Very Large Array Observations (VLASPECS) The CO(1-0) transition in the ASPECS galaxies between z = 1. 99-2. 70 was observed with the Karl G. Jansky Very Large Array (VLA) as part of the VLASPECS survey  (Riechers et al. 2020; VLA program ID: 19B-131; PI: Riechers). Two pointings were conducted with the D array in the Ka band, over a continuous bandwidth of 30. 593-38. 662 GHz at 2 MHz spectral resolution, resulting in a 17 km s -1 resolution (at 35 GHz). The naturally-weighted cube has an average rms noise level of ≈ 0. 1 mJy beam -1 channel -1 (increasing by about and Band 6 at different redshift ranges (colored just to make them more easily distinguishable). We highlight the samples at z = 1. 0 -1. 6 (blue) and z = 2. 0 -2. 7 (green), for which we have coverage of both a low-J and a mid/high-J CO line.
All the lines in the continuum-free spectrum are modeled by Gaussian line shapes, whose central frequencies are tied together by a single redshift. Fitting the sources with the highest signal-to-noise (S/N) spectra, we find that the widths of the different transitions are consistent in most cases. This is illustrated in Fig. 2 , where we show the line width measured in the CO(2-1) and CO(3-2) lines in Band 3 against the line width of the other CO and [C i] lines. Here we only include sources with S/N > 3 in all the relevant lines in the free fit (which is more conservative than for the fits where the line widths are tied together, because of 1 https://lmfit. github. io/lmfit-py/ the additional degrees of freedom). We therefore model all the lines with a single line width. Although this assumption is not strictly necessary, this often improves the fitting of lines with lower signal-to-noise, where the line width can be better constrained by the strongest lines.
FW acknowledges support from  ERC  grant  740246  (Cosmic Gas). Este trabajo cont ó con el apoyo de  CONICYT + PCI + INSTITUTO MAX PLANCK DE ASTRONOMIA MPG190030. DR acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107. DR also acknowledges support from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. IRS acknowledges support from.
It applies the policies listed in order and allows an activity for a given individual if and only if it is allowed by all policies. • ForwardingPolicy is a policy that embeds another policy and can change its enablement dynamically (for example, the specialized SpatialPolicy restricts the application of its target policy in a given geographical space, while TemporaryPolicy does it within a limited period of time). Among the different policies delivered with the standard version of COMOKIT is the one that explicitly links to the epidemiological sub-model ESM (without being necessary for its operation). This is the HospitalizationPolicy, which depends on the existence of at least one hospital Building in the dataset, and which takes care of the Individuals that need to be hospitalized after a certain period of time following symptom onset, given by a distribution, and remain hospitalized until they are recovered or dead.
Spatial Data The initialization of the spatial environment of the model requires one main input: a shapefile describing the buildings of the studied area. This shapefile must obligatorily contain two attributes: the type of building, which will be used in the definition of activities (each type of activity will be linked to one or more types of buildings), and the number of apartments per building, which is used to locate the households inside (one household per apartment). COMOKIT provides a spatial data generation tool, allowing, from a spatial boundary given as a shapefile, to download existing OSM 2  data of the area and put it in the right format so that it can be directly used in the simulations. The existing tool also allows the vectorization of images (e. g. , GoogleMap) to enrich the OSM data.
To initialize the simulations, we first obtained spatial data on the buildings of the commune from the buildings present in Google Map and Bing data. The population input data file was generated using the Gen* library to produce a set of individuals grouped into households. We then used an approach based on combinatorial optimization to find a tradeoff between maintaining the consistency of the microdata sample at the household and individual levels, while trying to match the census marginals (e. g. , number of men/women, frequency of age category). In our case study, we used the IPUMS sample of individuals in households available for the whole Vietnam in 2014 (15% of the total population) with age, sex, household identification and employment status. Then, we randomly selected households with corresponding individuals in the sample to match the age and sex distribution at the individual level that we found in the 2019 Vietnamese census for Son Loi.
In this paper, we presented an agent-based modeling software built on the GAMA platform called Covid-19 Modeling Kit (COMOKIT), designed to be generic, scalable, and thus portable in a variety of social and geographical contexts. COMOKIT is an integrated model, presented in detail in section 3, which combines a direct person-to-person transmission sub-model, an environmental transmission submodel across the built environment, a policy design submodel, and a person mobility and activity model based on a 1-h time step agenda. As shown in section 4, COMOKIT offers many guarantees in terms of reproducibility of results and sensitivity to input parameters. In addition, as we have demonstrated by implementing and comparing different policies and policy combinations, COMOKIT is modular and flexible enough to allow modelers to represent different strategies and study their impacts under several social, epidemiological or economic scenarios.
It should be noted that although it comes with a predefined set of policies and activities for individual agents (e. g. , buying, studying, working, etc. ), adapted to most contexts, it can easily be extended to new agents, policies or activities by editing the models written in GAML. Thanks to this inner flexibility and genericity, and to the increasing availability of open data, new case studies can be processed in COMOKIT within a few hours, allowing it to be used in a variety of contexts and by a majority of decision-makers. In fact, as shown in section 3, the model can work with only a minimal (usually open) initial dataset: the built environment and administrative boundaries of the study area can be extracted from OpenStreetMap, while a statistically consistent synthetic population can be generated by the Genstar Toolkit from IPUMS datasets.
More accurate and sophisticated data can of course be mobilized to support the design of more complex models if required, and this can be done in a progressive and incremental fashion. This first version of COMOKIT (version v1. 0, released in May 2020) has however some limitations that are already identified and that we think we can gradually remove with the help of other modelers: • Scaling up: in computational terms, an agent-based approach will always be more expensive than an aggregate approach, not only in terms of execution time, but also in terms of the necessary replications (with respect to deterministic mathematical models). In its version 1. 0, COMOKIT can reasonably (i. e. , in less than 10 min on an average laptop with a graphical user interface enabled) simulate several months of pandemic fighting in cities with 10-20,000 inhabitants.
The differences between countries and territories in terms of the occurrence of the virus and the number of victims are striking, as are the approaches of governments and their effectiveness in combating the pandemic. In such a context, it is important to recognize the increasing importance of data-based modeling approaches in the design of public health strategies. Platforms, such as COMOKIT can contribute to this effort, provided, as in this case, that they are open, transparent, easily explorable and testable, and above all built on sound theoretical and computational foundations. COMOKIT run on their GAIA supercomputer. They finally wish to acknowledge the important contribution, in terms of data and directions, of Prof. Nguyen Ngoc Doanh (Thuyloi University) and Dr. Pham Quang Thai (NIHE), on behalf of the Rapid Response Team of the Vietnam Steering Committee against COVID-19.
DATA AVAILABILITY STATEMENT The version of COMOKIT used in this paper is labeled V1. 0. The source code of this version, together with the input dataset and all the experiments presented in this paper, are available in the repository of the project: https://github. com/COMOKIT/ COMOKIT-Model. To be executed, COMOKIT requires GAMA, an open-source modeling and simulation platform, in its version 1. 8. 1. GAMA is available at: https://gama-platform. org. In order to make it as easy as possible for readers to run COMOKIT, an all-in-one release, including a Java virtual machine, the correct version of the GAMA platform and COMOKIT version 1. 0 with all the datasets, is available here: https://github. com/COMOKIT/ COMOKIT-Model/releases.
g. ,  Bálint et al. , 2018; Parducci, Bennett, Ficetola, Alsos, Suyama, Wood, & Pedersen, 2017). However, these reviews have mostly focused on the issues of SedaDNA data production (e. g. , DNA extraction; amplification and sequencing; authentication of ancient molecules; bioinformatics analyses) and on the application of SedaDNA data within the ecological framework. The numerical aspects of ecological analyses based on SedaDNA have been rarely discussed, in spite of the needs and benefits discussed above. In this review, we focus on the ecological analysis of SedaDNA, which is performed after the bioinformatics steps  (Parducci, Bennett, Ficetola, Alsos, Suyama, Wood, & Pedersen, 2017; Zinger et al. , 2019). The aim of this work was to present a range of statistical approaches that can be particularly useful to multispecies ecological analysis on SedaDNA, especially for biodiversity studies on macroorganisms (e.
Occupancy is a parameter of key ecological significance, which is used, to name a few, to trace the shift in a species' presence in a particular area through time  (Schmidt et al. , 2013)  and to estimate the habitat patchiness of a species at multiple locations within a particular time span  (Bailey et al. , 2004). Second, the detection probability of target DNA generally increases with its concentration; thus, the frequency F I G U R E 1 Ecological analysis to study past biodiversity based on sedimentary ancient DNA (SedaDNA). Rounded squares represent data forms that can be generated from SedaDNA data, with background colors indicating the related section (blue for those with Section 2 and green for Section 3). Arrows indicate data processing flow. Each arrow is accompanied by squares indicating possible approaches, with the related subsection numbers in parentheses at the end, to the respective data analysis step of successful PCR amplifications increases with eDNA abundance.
, 2019). Insufficient knowledge of the precise area represented by SedaDNA can make data analysis and interpretation difficult, as multiple, spatially distinct communities can coexist in a catchment area. Therefore, it is important to carefully discuss the potential spatial coverage of DNA samples. Several R packages and independent software are currently available to run SODM on eDNA data, such as unmarked  (Fiske & Chandler, 2015)  and EDNAOCCUPANCY  (Dorazio & Erickson, 2018). EDNAOCCUPANCY was specifically designed for eDNA data analysis and provides tools to fit multiscale occupancy models with various settings, in a Bayesian framework. Furthermore, several authors provided scripts and examples to run Bayesian analyses in dedicated software such as WINBUGS  (Schmidt et al. , 2013) , JAGS  (Guillera-Arroita, Lahoz-Monfort, Rooyen, et al.
| Clustering samples to find hidden structure In many biodiversity studies, a key objective is to find hidden structure of species assemblages across multiple sites or samples, by comparing them along some spatial/temporal or environmental gradients. Such comparisons can be performed using two major types of techniques: clustering and ordination  (Legendre & Legendre, 2012). Cluster analysis is the partitioning of a collection of objects (e. g. , species assemblages) under study, with the aim to find discontinuities in a dataset. Generally speaking, the main purpose of cluster analysis is to bring out unseen structures in data, although the ecological relevance of the result clusters is subject to the user's interpretation  (Borcard et al. , 2011). There is a plethora of clustering methods, each of which adapts to some specific data types and meets specific needs.
| Looking for main trends by ordination In contrast to cluster analysis, which identifies discontinuities in a dataset, ordination analysis arranges object points along one or more continuous axes, each of which representing an ordered relationship  (Legendre & Legendre, 2012). Ordination methods are a group of multivariate techniques that, in ecology, can be applied to a speciessite matrix or on a distance/dissimilarity matrix. These methods arrange objects (i. e. , sites or samples) along one or more axes based on their compositions and then display them in a coordinate frame so that the higher-dimensional relationships among them are easy to inspect  (Legendre & Legendre, 2012; Pielou 1984). Therefore, ordination methods provide a convenient way to visualize multidimensional biodiversity datasets such as a species abundance-site matrices.
Second, there is no simple rule to determine in advance how many dimensions need to be retained in nMDS. Usually, two to three axes are enough, but it is recommended to check the stress (the penalty function minimized by the nMDS algorithm) in several alternatives in order to help deciding. Third, although its goodness of fit can be assessed by the abovementioned stress, nMDS provides no intuitive measure of the representativeness of its axes upon the original dataset, in contrast with PCA, which comes with the amount of variance explained by each of its axes (indicated by the respective eigenvalues). Therefore, the interpretation of the axes of nMDS requires different strategies. For example, environmental variables can be fitted on the nMDS by linear regression. The passive explanation of axes using environmental variables is a post hoc approach that can be applied to both nMDS and PCA, and allows the interpretation of an ordination using external variables.
In the special case of SedaDNA, an even greater challenge is to deal with the unevenness of data quality along time, usually caused by DNA degradation  (Parducci, Bennett, Ficetola, Alsos, Suyama, Wood, & Pedersen, 2017; Pedersen et al. , 2015). A potential solution could be to statistically model the degradation process and compensate (e. g. , weight the data along a degradation gradient) its effects on the final dataset. Unknown taphonomy of SedaDNA may also confound the interpretation of results  (Bálint et al. , 2018; Giguet-Covex et al. , 2019) ; therefore, inspecting the sedimentological and geochemical data of the sediment core(s) is a necessary (but not always sufficient) step to control for such effects.
Ecological phenomena often involve multiple tangled links. It is therefore crucial to resolve their confounding effects in analysis. In linear regression, it is possible to estimate the effects of some explanatory variables, while controlling for the effects of some covariates. Similar approaches exist in constrained ordination, for example, partial RDA and partial CCA  (Legendre & Legendre, 2012; ter Braak, 1988). Especially, SedaDNA data are often temporally structured, so that some parts of the variation may have originated from intrinsic temporal processes. Such processes may generate effects that confound with those caused by extrinsic environmental factors, that is, resulting from niche-based processes  (Legendre & Legendre, 2012). Using partial constrained ordinations, it is possible to partition the variance of the composition dataset into four categories: (a) environmental variance independent of temporal structure; (b) variance shared between temporal variation and environmental drivers; (c) variance explained by the temporal structure only; and (d) variance explained neither by the temporal structure nor by the environmental factors  (Borcard et al.
, 2019). One must therefore carefully check the data to control any confounding effects before comparing. Here, we have presented some of the most promising applications of multivariate statistical methods to SedaDNA data. Paliy & Shankar 2016 provided a further guidance on the application of abovementioned techniques (PCA, CA, nMDS, CCA, RDA, etc. ), as well as some others not discussed here, to the ecological analysis of data obtained through metabarcoding, with a chart to guide the choice of methods (see figure  8  in Paliy & Shankar 2016). Although their main focus was on the applications for microbial ecology, these discussions are broadly applicable to data from high-throughput sequencing and can also be relevant for SedaDNA data.
Calibration The NNSC method estimates the shear responses and biases of individual galaxies from the measurements of 27 properties applied to the images. The shear bias and the corresponding calibration were made for a previously chosen shape measurement. Any shape measurement algorithm can be chosen for this purpose. We used the estimation from the KSB method using the software shapelens. When these estimations were completed, we applied the shear calibration over the statistics of interest, in our case, the estimated shear from Eq. 1. The bias calibration was applied as R -1 e obsc , where R and c are the estimated average shear response and additive bias, respectively (see  Sheldon & Huff 2017). This calibration is similar to other common approaches, and we expect similar behaviours for the post-calibration bias as discussed in  Gillis & Taylor (2019).
Learning input data The image properties that are used to estimate the shear bias can be chosen depending on the interest. The NNSC learns to estimate shear bias as a function of these properties, which means that the more properties we use, the more capable the NNSC is to learn complex dependences (if we use the appropriate training). We used 27 measured properties as input for the NNSC. These properties correspond to the output from the gFIT  (Gentile et al. 2012; Mandelbaum et al. 2015)  software (properties such as ellipticities, fluxes, sizes, fitted disc fraction, and other fitting statistics), the shapelens  (Viola et al. 2011 ) KSB implementation (ellipticities, S/N, and the size of the window function) and SExtractor  (Bertin & Arnouts 1996)  software (properties such as flux, size, S/N, and magnitude from both the galaxy and the PSF).
This would involve applying the same selection process to all galaxies independently of the shear and measuring the shear responses to this selection, as described in  Sheldon & Huff (2017)  and in Sect. 7. 2 of PKSB19: R αβ ≈ e obs,+ α -e obs,- α 2∆g β , (8) where the ellipticities are measured for the case with no shear, and the + andsuperscripts refer to the applied selection, corresponding to the catalogues obtained from the positive and negative shear versions, respectively. A supervised training could be applied to learn the shear response on selection. This would involve adapting the method so that the selection is specified in the input data (e. g. with weights specifying the selection for each shear version). Then the cost function involves the average selection shear response over a subset of the catalogue, as C = 1 b s b s i=0 2 α=1 2 β=1 ( w + i e obs i,α -w - i e obs i,α 2∆g β -R e i,αβ ) 2 , ( 9 ) where w +,- i specifies the selection of galaxy i for the cases with positive or negative shear (it can be a weight from 0 for undetected cases to 1 for detected cases with the full signal), e obs i,α is the observed ellipticity for the case with no shear, and R e i,αβ is the output estimated shear response of the training.
On the other hand, the method depends on the numerical processes involving deconvolution, reconvolution, and the treatment of noise. MetaCalibration allows for different implementations regarding the characterisation of the PSF, including a Gaussian parameter fitting of the PSF (so that a combination of Gaussian profiles is used as the PSF), a symmetrisation (where three different rotations of the PSF are stacked to avoid a contribution of its ellipticity), and using the true PSF directly. We used the true PSF, but we found very similar results using the symmetrisation. Moreover, MetaCalibration can be applied for any shape measurement algorithm for which the method calibrates its shear bias. We used MetaCalibration to calibrate the KSB mesurements from the software shapelens.
Acknowledgements AP, FS and JB acknowledge support from a  European Research Council   Starting Grant  ( LENA-678282 ). This work has been supported by  MINECO  grants  AYA2015-71825 ,  PGC2018-102021-B-100  and  Juan de la Cierva  fellowship, LACEGAL and  EWC Marie Sklodowska-Curie grant  No  734374  and no. 776247 with ERDF funds from the  EU   Horizon 2020 Programme. IEEC is partially funded by the  CERCA program  of the  Generalitat de Catalunya.
Analysis of the influence of the slope on the BIPV triangle thermal behavior In order to analyze the impact of the slope of the BIPV triangle on its thermal behavior, the PV module heating related to each configuration was estimated and corresponds to the gradient between the PV module mean temperature and the outdoor ambient air temperature. The analysis of this data permits to overcome the fact that the tests were not realized in parallel. The scattering of points on graphs is due to the momentary presence of clouds, fog or shadings during the test campaign. Figure  19  presents the PV module heating according to the solar radiation received in the plane of the BIPV triangle for the four slopes considered. Moreover, in each case, a first evaluation of the global thermal resistance of the BIPV triangle (or Ross coefficient) k was realized based on the PV module heating correlation (see Equation  11 ) in order to observe its variations according to the system slope.
Analysis of the predicted electrical and thermal performance Due to the reduced number of the measured data, the validated numerical model has been used to predict the thermal behaviour and the electrical performance of the BIPV triangle (with a nominal electrical power of 15. 27Wp) on a full year. For this, a Meteonorm weather data file of Lyon Satolas (France) was selected in TRNSYS17 software library since it is at a latitude close to the one of Le Bourget du Lac (45. 75° of latitude and 4. 85° of longitude). Lyon has also a temperate climate with warm summers and mild winters. The warmest month of the year is July with a slightly higher mean monthly temperature of 22. 2°C and the coldest month is January with a mean monthly temperature of 3. 4°C. The simulation time step considered was of 10min.
This work has been supported by Solar Design European project. It has received funding from the European Commission FP7-NMP Programme, under the Grant Agreement No. 310220. Authors would like to thank project partners: TUW, EURAC, UNIMIB, MUAS, SUN, SIARQ, RHP, INN, F3 and GAIA, especially Eric Pilat from CEA. the i material (J/(kg. K)) dt discretization time step (s) Eac PV module electrical energy production after inverter (AC) (Wh) Esol incident global solar energy received (Wh/m²) Ta outdoor ambient air temperature (°C) Ti temperature at node i (°C) TAij, temperatures nodes on the front side of the PV triangle (°C) TBij, temperatures nodes on the backside of the PV triangle (°C) TCij temperatures nodes on the backside of the PVC textile element (°C) Tpvc (pv) temperature of areas of the PVC layer in contact with the PV module (°C).