dataesr/scientific-paragraphs-categorization huggingface dataset:
Such degeneracies can result in ⋆ Interactive graphical tool: https://tillkaeufer. github. io/ sedpredictor large uncertainties when the values of single physical parameters are determined. One approach to address this problem is to combine SED fitting with other observations, for example, images at millimetre wavelengths to constrain the outer radius, which effectively reduces the number of free parameters. However, such a procedure will be very specific to a certain combination of multi-kind data, and it is often done only for one or a selected number of individual objects where that particular data exist (e. g. Pinte et al. 2008; Tannirkulam et al. 2008; Dong et al. 2012). Analysing larger samples entails substantial computational efforts (e. g. Sheehan et al. 2022). The statistically correct way to take all the degeneracies in an analysis into account is to calculate the posterior distributions for all parameters via a full Bayesian analysis.
To do so, we generated a large set of SEDs using the Monte Carlo continuum RT code MCFOST  (Pinte et al. 2006 (Pinte et al. , 2009)) , and used NNs to emulate this process of creating SEDs from model parameters. The benefit of this process is the drastic decrease in computational cost to generate SEDs. This makes a full Bayesian analysis that is carried out for 30 well-known protoplanetary disks feasible. This paper is structured as follows. Section 2 introduces the models, emulating process, and the fitting procedure including data selection, data preparation and the Bayesian framework. We present the results of this paper in Sect. 3 including the quality of the SED predictions, the overlap to the DIANA fitting results, the new fits, and the determination of the dust mass. These results are discussed in Sect.
The SED forward model All SEDs are calculated by the Monte Carlo radiative transfer code MCFOST 2. 20. 8  (Pinte et al. 2006 (Pinte et al. , 2009)). MCFOST solves the radiative transfer problem of a dusty disk being illuminated by a star. The disk is assumed to be passive, that is, we assume that all energy is coming from the central star and that at every point the radiative heating of the dust particles equals their radiative cooling. All relevant stellar, disk, and dust parameters for this problem, including disk shape, dust material, size distribution, and settling, are listed in Table  1  and are explained in Sect. 2. 2. The meaning of these parameters is described in detail in  Woitke et al. (2016). MCFOST sets up a 2D density structure with a grid of 150 × 100 points concerning the radial and vertical directions, respectively.
We decided to use the mean squared error between predicted and true logarithmic flux values as our loss function. We use Keras  (Chollet & Others 2015) , which is part of the python package TensorFlow  (Abadi et al. 2016) , to build the multilayer feedforward NNs. Most hyperparameters are determined using hypergrids, which means running multiple NNs to find good settings. A summary of this process is shown in Appendix A. We are aware that we have not found the best combination of hyperparameters, but the change in quality turned out to be small for reasonable settings. This process results in an architecture of 6 hidden layers with 300 neurons for the single zone NN, a prediction of which is seen in Fig. 1. The two-zone NN has 7 layers with 400 neurons. Therefore, both NNs have a number of weights that is comparable to the size of the training sets (single zone: 499 340 weights and 470 379 SEDs in the training set; two-zone: 1 029 340 weights and 1 192 877 SEDs in the training set).
The DIANA sample We apply our machine learning SED-fitting method to a sample of 30 well-studied disks (the so-called DIANA sample, see  Woitke et al. 2019) , for which many multiwavelength observations (photometric fluxes and low-resolution spectra) are available and which have already been fitted using the same RT code (MCFOST). This will allow us to compare our results with a previous study. 27 of these objects are introduced in  Woitke et al. (2019). However, here we expand this set by adding 3 more unpublished SED fits for DN Tau, CQ Tau and PDS 66, which have meanwhile been fitted as well using the same approach as described in  Woitke et al. (2019)   1. In this section, we introduce this sample and explain the modelling done by Woitke et al. 1 https://prodimo. iwf. oeaw. ac. at/models/diana-sedfit (2019) briefly.
Bayesian analysis We use MultiNest  (Feroz & Hobson 2008; Feroz et al. 2009 Feroz et al. , 2019) )  through the PyMultiNest Python package  (Buchner et al. 2014)  to compare the models with observations and determine the posterior distribution for the free parameters. MultiNest is a Bayesian inference tool. It uses a multimodal nested sampling algorithm to compute the Bayesian evidence and posterior distribution from a likelihood function. All details of the algorithm and the numerical implementation are described in the references given above. We use the observational data selected by  Woitke et al. (2019) , which were introduced in Sect. 2. 5. 1. Since the goal of this study is to fit the disk parameters with reasonable assumptions about the star, we exclude all observations shortwards of 0.
Summary and conclusion We used a Bayesian framework to fit the SEDs of 30 protoplanetary disks based on the observational data collected by the FP7 DIANA project 2  , based on single and two-zone full RT MCFOST models, emulated by NNs. The emulation decreases the SED computation time by a factor larger than 10 5. This webpage 3  demonstrates the SED emulation process and (i) shows graphically how individual model parameters affect the SED, and (ii) can be used to compare models easily to observations getting first estimates for well-fitting disk parameters. The main conclusions of this paper are: -Our trained NNs can emulate the SEDs of single and twozone disk models with flux errors of typically less than 5%. This acceleration enables us to fit a large set of objects using Bayesian analysis with or without using additional constraints on the disk properties.
The disk mass and dust composition show the clearest degeneracy. Additionally, the scale height is degenerate with many parameters, like the dust size power law exponent and the flaring index. This study has shown that a combination of complex RT disk models with novel machine learning techniques provide a powerful tool to analyse SED data including all degeneracies. We plan to use this method on more complex thermo-chemical disk models in the future. These models take about a factor of 100 longer than the dust RT models discussed in this paper, making the need for a faster generation of molecular emission spectra even greater.
a b s t r a c t The proposed measured data combines PV plant electrical data with associated solar and meteorological data during normal and faulty conditions. Data are collected regarding a domestic rooftop PV plant of 4 kW, located in the La Réunion Island, in the South-West of Indian Ocean. The present dataset includes healthy behavior and different types of shading faults, identified and labelled by means of a numeric variable. The electrical data (voltage, current and power at AC and DC side as well as produced energy and grid frequency) are collected thanks to PV inverters. Global and diffuse irradiance, PV temperature and ambient temperature are acquired thanks to additional sensors. Electrical and meteorological data sampling frequencies are set to 0. 2 Hz and 1 Hz respectively. At present, 12 months of data are available and the database is still being updated.
The data streams from each connected device require proper techniques to ensure their persistence. To be able to provide both efficient ingestion and retrieval of these time series collections, the NoSQL database management system InfluxDB has been implemented. The whole dataset is available on Zenodo reposi-tory, and can be used, for instance, for PV modeling, PV plant behavior analysis, PV production forecasting and or soiling that provoke drop in performance and premature degradation of the modules. This shading faults implementation on this installation thus provides valuable insights on PV behavior and its electrical production. • PV electrical data (Power, voltage and current at DC and AC sides) complemented by solar and meteorological data (global and diffuse irradiance, ambient and PV cell temperatures), can be used to build PV system models (Ayop et al.
[2]  ), as close as possible to real aging and faulty PV plants. These data are useful for PV plant production forecasters, for con-sidering shading issues, and for Researchers working on PV Fault Detection and Diagnosis (FDD) as Rouani et al. [3]. • Effects of shading faults on PV plants can be observed in detail and analyzed as Bansal et al. [4]. The indication of the type of shading fault (Partial Shading, Uniform Shading …) is directly filled in the database as numeric variable. This carefully labelled indication can be very useful to conduct Machine Learning tools, as supervised classification. • The solar radiation measurements could be used to recalibrate other grided dataset, e. g. , meteorological satellite images, to obtain a better spatial coverage. The solar radiation measurement is of crucial importance for quantifying the variability / intermittency of solar resources.
Objective The data generation takes place in the experimental part of DETECT (Diagnosis onlinE of sTate of hEalth on eleCTrical systems) project at the University of La Réunion. DETECT is a research project aiming to develop a low-cost and low-tech fault diagnosis tool for electrical power systems, specifically PV systems undergoing tropical and humid climates. In the objective to add a minimum of external devices, only already available data on a common PV plant have been collected (data available via PV inverters, and radiometric station) during healthy and faulty behavior. The data are used to test the investigated DETECT fault diagnosis tool. The present data have already been partially used in this way and the results have been published  [1].
Data Description Three datasets are presented below: • Dataset 1: "dt1_solar_and_meteorological_measurement. csv" corresponds to the solar and meteorological measurements from the sensors positioned on the photovoltaic installations ( cf. Fig. 2  ). Variables of the first dataset are described in Table  1. Data are stored by a data logger with a sampling frequency of 1 Hz starting from 2021 to 10-28 for a minimum period of 12 months (YYYY-MM-DD hh:mm:ss; this time format is exclusively used thereafter and in the published dataset in REunion Time zone (RET) corresponding to UTC + 4). • Dataset 2: "dt2_electrical_production_inverter_1_with_faults. csv" provides electrical data from the first inverter, i. e. , the one on which the faults were caused. Variables of the second dataset are described in Table  2  and the labels of the Fault are explained in Table  3.
• Dataset 3: "dt3_electrical_production_inverter_2. csv" contains data from inverter 2, the control inverter. The photovoltaic panels linked to this inverter are functioning normally and no fault has been produced. Variables of this dataset are described in Table  2. Note that, the datasets concerning the electrical data (dataset 2 and 3) are dissociated since they are collected from two separated inverters. Data are stored by a datalogger Energrid with a sampling frequency of 0. 2 Hz starting from 2021 to 10-01 for a minimum period of 12 months. An example of 1-minute data is available for a single day as displayed in Fig. 1  , including only limited variables: Global Tilted Irradiance (red), the two inverter AC powers output for the two photovoltaic panels lines (blue and green) when a uniform shading is generated (cf.
This data pipeline consists of gateways, network services, a database to store the data, and application servers. Electrical data measurements are acquired by an operating and management system for photovoltaic plants connected to the electrical network, coupled with inverters. The frequency of data repatriation has been selected from the telemetric control of the software. In order to monitor the solar radiation, ambient temperature and PV cell temperature, for the correlation of our studies, a pyranometer and two thermocouples have been installed. The Delta-T Devices SPN1 pyranometer, a precision solar radiation measurement instrument, has been installed to monitor the solar quantities needed to correlate our studies ( Fig. 4. a). Ambient air temperature is acquired through a thermocouple type K TC-SA ( Fig.
4. c). photovoltaic cell temperature is measured with a thermocouple type T TC-SA on the back surface of the module ( Fig. 4. b), stuck under the module, in the middle of the second module of the line. All radiometric and meteorological sensors are connected to a Campbell Scientific CR10 0 0X data logger for data collection. The data logger is protected in a waterproof enclosure. The data is transmitted to the server via TCP/IP protocol. In order to evaluate the data in real time, the data is stored at a sampling rate of 1 Hz. Sensors create a voluminous amount of data which places requirements for the servers, and clients too. As our devices broadcast their measurements in intervals of a couple of seconds, storing this data requires hard disk space, and processing larger time series datasets demands high computing power from clients.
The solution uses InfluxDB 2. 0 as a time-series database and the InfluxDB user interface for visualizations. The dashboard can be easily customized and is both locally and remotely accessible from any modern browser. Published studies  [5]  have shown that InfluxDB has advantages over many of the other options in performance, especially in executing aggregation functions over data and faster grouping of query results. To ensure the availability of real-time data, software is written in the Go programming language. It monitors the data files from the various sources and injects data in the form of time series into the InfluxDB database. The program also manages the concatenation of the files locally and their organization through an established tree structure. Accordingly, an end-to-end data pipeline that relies on open source technologies has been developed.
Data from sensors and inverters are periodically sent, collected and stored in a centralized database system based on the Go program for real-time data streaming and InfluxDB for data storage. A set of customized dashboards has been created using the new features available since the last major InfluxDB release, to update real-time monitoring, data visualization and device status control. Shading faults are conducted thanks to different experimental setups during which the second line is kept in normal conditions. Fig. 5  exhibits the different experimental facilities depicted in Table  3  and are conducted as described below: -Uniform shading: the entire line is obscured with an opaque material as shown in Fig. 5.
90126, 55. 48422. Data accessibility Repository name: Zenodo Data identification number: 10. 5281/zenodo. 7157424 Direct URL to data: 10. 5281/zenodo. 7157424 Related research article [1] Lebreton C, Kbidi F, Graillet A, Jegado T, Alicalapa F, Benne M, Damour C. PV System Failures Diagnosis Based on Multiscale Dispersion Entropy. Entropy. 24 (2022) 1311. 10. 3390/e24091311 Value of the Data • PV plant electrical data, combined with irradiance and temperature data, are valuable data in the context of renewable energy generalization. Tropical climate induces worsened ag- ing and increases the difficulties of PV modeling. The high sampling (1 Hz) rate allows to follow up the production as well as irradiance and temperature variations with high accuracy. • Shading faults are common on PV plants, generally caused by vegetation, buildings around.
C. The collection of data for the a priori acceptability study The quantitative part of the a priori acceptability study presented here is based on four self-evaluation questionnaires constructed from data in the literature, in particular Bel's integrative model of behavioral prediction of the use of an unknown technology, and reflections of our research team  [19] [20] [21] [22]. These questionnaires include 46 items grouped into eight themes. The items are assessed using a 7-point Likert scale. The first 15-item questionnaire explores three themes: the person's prior experience and attitudes towards information and communication technologies, and prior experience with health p. 4 Colloque JETSAN 2021 monitoring technologies. The second questionnaire of 12 items explores three themes: the 'expected values of the use of the object' also called a priori utility; the 'subjective norms', meaning the influence of family and friends in initiating ICTs' health monitoring; and the a priori intentions to use the system.
[25]. Various mechanisms have been proposed for the degradation of MAPbI3 in the presence of moisture. [26] H2O, liquid or vapor, decomposes MAPbI3 into MAI, MA, HI and I2. Thermal degradation has been shown to end in CH3I and NH3 and Pb as decomposition products  [27]. These materials under electric field has also been hypothesized to degrade into CH3NH2, HI, CH3 + and NH3  [28]. Table  1 : Few PSC materials and X-ray absorption edges in the soft X-ray regime of the elements involved  [29]  XAS of these materials were simulated with the help of Henke database  [28]  in the soft X-ray regime, from ~100 eV to 2800 eV, and are presented in figures  [1 & 2]. For the simulation, thicknesses are randomly chosen, and the densities used have been those that are available either in the literature or have been roughly estimated.
Acknowledgement: The authors would like to thank  Ecole Doctorale of École Polytechnique and Synchrotron SOLEIL  for their Ph. D. grants assigned to  Haeyeon Jun. Philip Schulz  thanks the  French Agence Nationale de la Recherche (ANR)  for funding under the contract number  ANR-17-MPGA-0012. Ileane Florea  would like to thank the funding from  ANR  under the contract number  ANR-10-EQPX-50  (pole NanoTEM) for the FIB sample preparation on the plateform Centre  Interdisciplinaire de Microscopie électronique de l 'X (CIMEX). The authors would also like to thank  Synchrotron SOLEIL  for providing beamtime at  HERMES beamline.
A detailed theoretical and experimental study of the Iodine M-edge was done by Schubert et al. [32] Following these studies, feature 1 at ~620 eV can be assigned to 3d5/2→5p5/2, 3/2 transition, feature 2 at ~650 eV corresponds to 3d→7p5/2 and feature 3 represents a 3d5/2→σ* transition. Care should be taken while comparing the energy position of the experimental edge jump of Iodine M4,5 with Henke database. As shown in Figure S1 [SI] experimental iodine edge jump shows stronger features only above 650 eV, while the Henke database shows an edge-jump at around 620 eV which following Schuber et al. is a weak 3d5/2→ 5p5/2, 3/2 transition.
Heliophysics and space weather research encompass the effects of solar output on practically the entire Solar System and are fundamentally cross-disciplinary. Cross-domain science investigations, such as in Sun-heliosphere interactions, solar wind-magnetosphere interactions, or magnetosphere-ionosphere coupling, often require the use of data, models, and other digital resources pertaining to different heliophysical domains: the Sun, the solar wind, the magnetosphere, the ionosphere, the thermosphere and the mesosphere. Due to differences in measurement platforms, techniques and instruments, heliophysics data obtained from different domains are diverse and complex, making the resource landscape difficult for untrained users to navigate. Without proper and adequate guidance from domain experts, it is often difficult for early-career scientists and non-domain experts to discover useful datasets and to know from where and how to obtain and understand the data they need to support their research.
Introduction It is often said that because of the large variety of measurement techniques, instrument types, and product types, etc. , involved in supporting heliophysics research, heliophysics data are generally complex and heterogeneous. Data products and other digital resources in different heliophysics domains or disciplines (solar, heliospheric, magnetospheric, and ionospheric) may also be different in the ways they are stored (location, format), served (access protocol), and used (domain knowledge, method of analysis). However, any research task involving the use of heliophysics data, either from observations or models, and other digital resources must begin by acquiring the relevant resources, such as the required datasets, analysis and visualization tools, and the documentation needed to understand the correct use of the resources.
Finally, we should emphasize that the word ''data" in ''data environment", or in this paper for that matter, represents not just data (observational or otherwise), but any digital resource being transferred throughout the data environment and utilized by users. Fig. 1  shows the different components of the heliophysics information architecture (blue boxes), information flows (thick blue arrows) and the interaction pathways (thin arrows) between the users (red oval) and the infrastructure components: data sources (data producers, large databases, and repositories), modeling centers, data visual-Fig. 1. A schematic of information (e. g. , data) flows between different components of the heliophysics information architecture. The numbers associated with each segment of the information flow pathways indicate the operational order of information or data transfer within the heliophysics data environment.
g. , CCMC, VSWMC) Á Support large-scale computer simulations and analysis of modeling results Á Making accumulated modeling results available for post-analysis and reuse by the community Á Proper descriptions (with identifications), storage, tracking, and maintenance of models, model setups for different runs and corresponding modeling outputsÁ Enabling search, access, and understanding of modeling artifacts and ensuring independent use of the resources (''F," ''A," and ''R" in FAIR) Data format (e. g. , CDF, FITS) and service (e. g. , HAPI, TAP) standards sats, rockets, balloons, aircraft, and various types of ground-based instruments. Data are also produced from simulation model runs (section 4. 1. 2), while higher-level products (section 4. 1. 3) can also be generated from various analysis and research projects including datasets built upon multiple sources (e.
g. , the OMNI datasets, https://omniweb. gsfc. nasa. gov/). Consequently, heliophysics data are diverse in data types and in disciplines, complex in terms of the differences in the characteristics of the instruments with which the data were obtained, and otherwise different in terms of their data formats, storage locations and methods of access. Due to the diversity and complexity of heliophysics data, data products produced from one discipline may not be readily accessible or consumed by users in different disciplines without having some familiarity and understanding of the discipline data products. For a given data product to be discoverable, accessible, understandable, and thus usable, particularly by users trained in different disciplines, it is imperative for the product to be uniquely identified and adequately described by the resource producer, who presumably should have the most accurate knowledge and expertise on their data products.