dataesr/scientific-paragraphs-categorization huggingface dataset:
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if existing data are being re-used (if any) Public workflows, scripts, molecular and crystal structures from open source databases are re-used. The infrastructure developed here keeps the provenance of the data, making it possible to trace back where it was obtained from. Calculation metadata Information about a calculation such as who carried it out and using the software which version of Users are free to create this autogenerate it manually or have script to A (possibly nested) dictionary of keys and values. json Table WP 1. 25. Types and formats of experimental data, mostly collected from WP5 Data category What does the data describe Methods used for data collecting the Type Format What layout for measurement Experimental areas on a Digital Journals Table WP 1. 24. Data collected from simulations layouts and materials library is JSON describing Text,.
json Text, pictures, Data category information materials and where they data describe materials are used What does the used, what data collecting the description type Methods used for in key value Type. json Format Calculations are sources AiiDA calculations and workflows ASE structures or calculations Measurement recipes Measurement results typically deliver structural data and materials properties, while workflows detail the methodology used Structures generated in or resulting from ASE calculations What kind and how a measurement was performed by listing all the measurement parameters and conditions The result of a measurement performed according to AiiDA automatically records the full provenance graph as a workflow executes Machines require input to perform a measurement either that is stored or it is stored in the saved measurement data Data is manually stored in an ASE database Machine saved the data -Electronic structure -Molecular dynamics -Quantum chemistry As above As above As above -AiiDA export file (see specs below) -.
json file with relevant parsed inputs and outputs As above. sqlite (ASE database format) The. 3dx file Binary, sometimes proprietary Structures, certain parameters format, but reader 3DEXPERIENCE platform / Pipeline pilot raw data and calculation results Analyses A general description of data flow is illustrated in Fig. WP 1. 18. computational data from calculations carried out with Pipeline Pilot protocols, and workflows codified into Pipeline Pilot protocols The Pipeline Pilot protocols can be tailored to store A code runs over Data derived from analyzing a the measurement data and saves the measurement result structures and computational data plus any additional meta data required 1. 9. 4 Specify -Quantum mechanical -Molecular As above dynamics -Coarse grained molecular dynamics and writer components will be created to export data to As above commonly accepted formats facilitating exchange with Materials Cloud and other databases Raw Quantum ESPRESSO output Electronic structure calculations A tarball is created from the calculation folder, including relevant inputs and outputs Simulation.
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json,. hdf5 feedback loop the BIG models autonomous data for training Data to run WP10's cycling Library of scripts that extract pertinent FOM and or labels from experimental data Tarball files can be Code, database, weights. tar. gz (with Active learning measurement data from Tools that analyse experiments mostly in WP4 and WP5 to a format that can be used for training the BIG models and for models in active learning Curated atomistic data and joint in-silico/experimental feedback loops created with post-processed data and parameters of interest. databases and reproducible data sheets) WP10 will deliver Table WP 1. 28. Data delivery from WP10 to other WPs Data analysis tools WP2 python Active learning tools Tools that analyse measurement data from experiments mostly in WP4 and WP5 to a format that can be used WP3 Domain Workflow interface between Tarball, WP What Suggested Usable for type format GB MB-GB size Knowledge WP2 and wp10, uncompressed raw.
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txt, html or java KB, MB scales WP11. Data collected Storage, WP9 Standardization for data assimilation and storage from external sources has been collection and delivery of Infrastructure Python code MB generated for data All of the above various other purposes and outside BIG- WP11 will deliver MAP Library of scripts Data that convert processing tools Primarily contributed by Tools that process data to a format that can be used for training the BIG models different data format suitable into a standardized hierarchical Code Python codes in gitlab MB WP7/9 for generative deep learning Curated databased A curated database holding interface multiple length scales structure data at Database linked access interface to high speed DB Hdf or. json TB.
The electrical responses are shown in Figs 7-10 (dotted lines). They respect the specifications, showing the ability of the topology to resolve this particular filtering problematic. The insertion losses, between 0. 2 and 0. 45 dB, are better than those imposed by the specifications (<0. 5 dB). Figure  11  presents the simulation of the multipactor threshold of the filter at the central frequency, f 0 = 1. 54 GHz, obtained with Spark3d software. The initial parameters used for this Spark3d simulation were 300 and 700 W for initial and final power, respectively, and the initial number of electrons was fixed at 10 000. The multipactor threshold of the designed filter was 410 W, in accordance with the requested specifications, thus validating the preliminary work presented in the Section "Multipactor threshold evaluation.
Multipactor measurements Multipactor measurements were performed for three different frequency points: the center frequency f 0 , and the frequencies at the group delay maxima f TPG , for which energy storage was maximal within the filter (Fig. 10 ). The comparison between the measurements and the simulation performed with Spark3d software is    shown in Table  4. The test bench used was limited to a maximum input power P max = 450 W. At the center frequency of the filter, neither detection was observed. For the two other frequencies, a multipactor discharge was registered by the phase nulling detection method. As shown in Table  4 , there is a difference of 5 dB between the measured and simulated values. Several hypotheses can explain this difference: a lack of precautions during the machining could have affected the roughness of the filter.
This study integrated hydrological optimization models with an IOM to measure broader economic impacts, linking them to allocation decisions in the management of water resources, such as reservoir volumes and water allocations at the basin level. The optimization models available in the platform maximized different hydrological objective functions (see Figure  2 ) making it possible to provide values for water decision variables simultaneously in all time periods, considering all inflows related to the full-time horizon period under analysis  (2011) (2012) (2013) , when a severe drought in the study region occurred. Figure  2  shows the modeling concept as flow chart of the used methods and materials in an illustrative manner. Water supply and demand data was used together with institutional directives [constraints, objective functions (OF)] to identify water allocation of all water users.
Application of ensemble weather forecast to the Fukushima study The ECMWF-ENS operational ensemble used in this study was retrieved from the ECMWF archive in 2017. It contains 50 members with 0. 25 ∘ of horizontal resolution and 3-h time step, the domain spans 40 × 40 grid boxes that cover a great part of Honshu island. The vertical resolution of the data is coarse, with only five pressure levels to cover the boundary layer height: 50,000, 70,000, 85,000, 92,500, 100,000 Pa. For dispersion purposes, the meteorological variables are then interpolated on a finer grid adopted to our needs (see section 3. 2). The ECMWF operational ensemble forecast is representative of the kind of data that could be retrieved and used on a relatively short term for emergency response. Ensembles of finer resolution have been made operational for weather forecast at national level as computer efficiency increases, but for a given computational burden, there is a balance between the respective benefit of increasing resolution compared to using more ensemble members to represent uncertainty  (Raynaud and Bouttier, 2017).
To accurately represent precipitation, the spatial and temporal scales are crucial. The subgrid effect described in Section 2. 2. 1 can lead to significant bias, due to the discrepancy between the cell average and the observation on station. Besides, the 3-h time step may lead to a delay in the beginning or end of the rain episode, since rainfall rates are interpolated between two time steps. These representativeness errors are not well taken into account by the ensemble approach, and are the main limitation of this dataset.
All source terms in the literature are based on more or less sophisticated inverse or reverse modeling techniques, which combine environmental observation data, meteorological and dispersion modeling. An account of the different source terms available in the literature and the various techniques and observation datasets used to obtain them is available in  Mathieu et al. (2018). Here, six source terms have been retained. Some source terms were not selected, either because they did not significantly differ from another source term (to avoid introducing bias in the ensemble), because they were not deemed relevant due to the spatial and temporal scales of interest in the study, or because insufficient information was available. Although they are all a posteriori source terms based on observations, the variability in the overall released quantities is important.
Air activity concentration In this paper, we make use of the hourly concentrations of 137 Cs retrieved by  Oura et al. (2015). The air activity concentration is given in Bq. m -3. The data was obtained from the air quality automated monitoring network measuring suspended particulate matter (SPM) on filter tapes. They were measured too late to detect short-lived radionuclides, but they give crucial information on the temporal variation of 137 Cs concentration close to ground level, therefore indicating the passage of different plumes. Fig. 7  shows the spatial distribution of the 108 stations; unfortunately the repartition is not uniform, especially over the mostly contaminated area (in grey).
Gamma dose rate The gamma dose rate measurements were provided by automated stations as early as March 11 st , with a 10-min time step. The dose rate measurements are available from the IAEA database IAEA (2012). There are 88 stations spread over Japan, although the spatial coverage is heterogeneous (Fig. 9 ). The dose rate readings have the advantage of measuring the contribution from all radionuclides, including the short-lived species that could not be detected by the other monitoring systems. It is composed of two parts: the direct plume contribution ("cloud-shine") and the gamma-ray emitted by radionuclides deposited on the ground ("ground-shine"). Hence, the cloud-shine is usually responsible for peak values observed during the plume passage, whereas ground-shine corresponds to a lasting contribution that lingers after the plume has left the area, and decreases because of the radioactive decay.
For each of the 200 ensemble simulations, these indicators can be computed. Then, the maximum, minimum and median values of this score over the 200 simulations are presented. This gives an idea of the performance of the different members of the ensemble by comparison to observations. It may indicate whether the contamination predicted by one particular selected member, or the ensemble median, would be appropriate for use as a tool for decision making. However, the main goal of the ensemble is to give an estimation of the results uncertainty, which is usually given by the ensemble's spread. Probabilistic scores, such as rank diagrams, BS and DRPS, along with time evolutions (similar to the meteograms shown in section 2. 2) are therefore computed to evaluate the ensemble's quality. All these indicators are computed for the three different observation datasets described in section 3.
Therefore, stations inside the plume give measurements that differ from those given by stations outside the plume, whereas the simulations are identical. This can produce a significant model-to-data discrepancy. 3. 4. 3. 1. RMSE. Table  7  presents for air concentrations: the best RMSE among all ensemble members, the RMSE of the ensemble average and that of the median. Overall, the "best" RMSE is about 3-4 times the observational average, which confirms that the ensemble members do not show a very good performance for air concentrations, compared to the other observation datasets. The RMSE can vary a lot from one member to another (from 13. 43 Bq m -3 and 116. 06 Bq m -3 ), which is a sign of high variability in the ensemble. There are very few members whose RMSE values are higher than 40 Bq m -3.
A. BASIC PRINCIPLES OF SCMA We consider a synchronous SCMA system with a BS and J separate users so-called layers and K OFDM subcarriers, so-called REs. A SCMA transmitter encodes log 2 (M j ) data bits of user j and maps them into a K-dimensional codeword, x j , which is selected from a distinct codebook C j of size M j. Each codebook presents the signature of the corresponding user. The codebooks are built based on multi-dimensional constellation, C = {C j , 1 ≤ j ≤ J}, whose shaping gain enables it to outperform the traditional spread code based schemes  [68]. As to the codewords of SCMA, they are sparse, i. e. , only N j K of their entries are nonzero and the rest are zeros, N j is called codebook sparsity degree. The sparsity key of SCMA that all codewords corresponding to the j th SCMA layer have a unique location of non-zero entries at the same (K -N j ) positions.
B. IRREGULAR SCMA Most existing SCMA propositions are based on regular structure where users are treated equally that is why these propositions can not be adapted to users' business needs or conditions in the same scenario, this is not ideal. So new irregular SCMA systems are needed to manage different requirements such as capacity, connectivity, and data rate, and to also exploit the knowledge of channel state information (CSI)  [69]. Some existing works tried to solve this problem, for instance an irregular SCMA codebook design was proposed in  [70]  to assign different codebooks with various dimensions according to different users' requirements. Simulations show that using an irregular codebook design does improve the system performance in terms of BER, however the authors did not take into consideration the impact of the correlation among users.
For the time being, this axe of research was rarely studied  [169] -  [173] , that is why it is deemed as promising and attractive. Some deep architectures such as deep convolutional neural networks, recurrent neural networks and Bayesian neural networks are yet to be associated to SCMA, this requires some investigations. In addition, deep reinforcement learning is never used in the SCMA case, this mechanism can optimize the performance of SCMA detectors. Furthermore, existing works employ simulated data for training which is not ideal, therefore it is important to collect a large amount of real SCMA data in order to build a training database which allows a more powerful offline training. However, the offline training can be aided by an online training which is based on transmitting some pilot signals in real-time, this is a significant research issue for the future.
FR Efficiency on the SORIC TF2 SORIC TF2 was first treated solely with different FR additives and exposed to mass loss cone calorimeter test, to select the most efficient FRs for later incorporation in the laminate. Table  2  and Figure  3  present the composition of the flame-retarded SORIC in terms of total, FR and binder add-ons, as well as the data (TTI, PHRR and THR) obtained from the cone calorimeter. Phosphate, phosphonate, phosphinate and intumescent blends were evaluated, and their performances were compared to those of untreated SORIC TF2. lay the ignition and also efficiently shield the subtract, leading to a lower PHRR, incomplete combustion and enabling more residue to remain. With AP422, both gas phase and condensed phase actions take place (dilution of the flammable gases with ammonia, intumescence with the binder), which could explain the better fire properties that were obtained  [17].
Evaluation of Laminates Made Using AP422 Treated SORIC TF2 Full-glass laminates and sandwich composites made with untreated SORIC and AP422 262 g/m 2 treated SORIC were prepared using the UPR containing 160 phr of ATH. The cross-sections of the samples were observed using EPMA, and elemental X-ray mappings were carried out to highlight the presence of glass fibres (Si element), ATH (Al element) and AP422 (P element) (Figure  4 ). In the FG laminate, the glass fibres are clearly detectable using Si X-ray mapping, and the presence of ATH in the matrix is also clearly evidenced by Al mapping. The fire behaviour of these laminates was then evaluated using the mass-loss ca rimeter. The curves obtained for each sample are presented in Figure  5  and the data Table  3. Full-glass laminates ignite after 120 s exposure.
The heat release reaches a ma For the composite containing the untreated SORIC, the first picture shows the interface between the glass layer and the SORIC layer. Glass fibres and the patterns of the SORIC can be observed. The patterns, made of fibres and holes, are not filled in with the resin, and they consequently do not contain any ATH, as evidenced by the Al mapping. For the AP422-treated SORIC, the observed pattern is not totally visible because the cross-section cut was performed close to its surface. However, it is still clear that the presence of Al and P can be observed in the resin and on the pattern, respectively. The fire behaviour of these laminates was then evaluated using the mass-loss calorimeter. The curves obtained for each sample are presented in Figure  5  and the data in Table  3.
To identify the nature of these interactions, XRD and 31 P solid-state NMR analyses were performed on the residue (Figure  7 ). XRD analysis shows the presence of decomposition products of ATH such as aluminum oxide and boehmite (identified using the database), as well as the presence of aluminosphosphate (identified according to a previous To identify the nature of these interactions, XRD and 31 P solid-state NMR analyses were performed on the residue (Figure  7 ). XRD analysis shows the presence of decomposition products of ATH such as aluminum oxide and boehmite (identified using the database), as well as the presence of aluminosphosphate (identified according to a previous study)  [18]. 31 P NMR confirms the presence of this species. The spectrum of raw aluminophosphate indeed fits one of the peaks (around -30 ppm) in the spectrum.
nd Al mapping in the AP422 SORIC laminate residue obtained after the cone calorimeter test. To identify the nature of these interactions, XRD and 31 P solid-state NMR analyses were performed on the residue (Figure  7 ). XRD analysis shows the presence of decomposition products of ATH such as aluminum oxide and boehmite (identified using the database), as well as the presence of aluminosphosphate (identified according to a previous study)  [18]. 31 P NMR confirms the presence of this species. The spectrum of raw aluminophosphate indeed fits one of the peaks (around -30 ppm) in the spectrum. Other peaks are detected that have not been identified yet. The presence of aluminophosphate indicates a reaction between AP422 and ATH. Such a reaction has already been reported in the literature  [19].