dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameter related to fire barrier elements Fire break-door resistance. The aeraulic resistance of the fire break-door is assumed to be independent of the differential pressure exerted on the door (case of the BAUMERT firebreak door tested in the STARMANIA facility in a hot gas flow  [7] ). Characteristic values of fire break-door resistance encountered in nuclear power plants and in tests carried out by IRSN in the STARMANIA facility are used in the study. Thus, the quadratic resistance of the fire break-door ranges from 1000 to 9000 m -4. The aeraulic resistance of fire dampers is not a parameter of the study since the most sensitive fire barrier elements to pressure effects are fire break-doors. A value of 3. 2 10 6 m -4 is set for the resistance of this element in the study, based on fire dampers tested in the STARMANIA program  [7].
The two-zone model SYLVIA The SYLVIA software  [1]  was developed at IRSN. This software is designed to predict the behaviors of mechanical/natural ventilation, fire growth, hot gas and smoke propagation, and airborne contamination transfer in confined and mechanically ventilated enclosures. Indeed, it is a simulation tool for calculating the consequences of multi-compartment fires in nuclear facilities, which are equipped with a full ventilation network (including ducts, room leaks, fire dampers, fans, horizontal/vertical openings, filters, etc. ). The fire design of the SYLVIA software belongs to the well-known two-zone fire modeling  [14]. In such software, each compartment is described with two Lagrangian control volumes separated by a thermal interface (see Fig. 6 ). Mass and energy balances are performed in each zone: the lower zone simulating the fresh gas and the upper zone simulating the combustion products and the gas entrained by the plume.
The size of the database To study a set of configurations, the SYLVIA software is coupled to the SUNSET software, an IRSN statistical tool used in support of risk analysis studies. This coupling directly allows Monte Carlo simulations. For this, a set of variables, known as study parameters, is modeled by random variables. Thus, each study parameter is associated with a variation domain and a distribution function (a uniform distribution function is assumed in the study). For each study parameter, a value is randomly drawn in its range of variation (or imposed as for the closing time of fire dampers), creating a set of values for the parameters characterizing the SYLVIA calculation to be performed. By performing this simulation, one obtains the values of the responses associated with this parametric configuration.
The Monte Carlo method consists in reiterating this procedure a large number of times. The storage of the values taken by the study parameters and by the responses for all the runs constitutes the SYLVIA database. The Monte Carlo simulation provides a means of exploring the whole variation range of the study parameters and to know the impact of these variations on responses of interest. The size of the database depends on the number of influential parameters and on the level of discretization of these parameters. The minimum number of SYLVIA calculations to be performed is given by the product of the highest value of the number of classes of individuals among the responses by a number of runs to have a sufficient statistics for each combination of classes. For a given response, the number of classes of individuals is the sum of all the class combinations of its influential parameters and corresponds to the number of rows of the conditional probability table associated with this response.
The first row of this table is obtained by extracting among the database all the calculations that have, for the seven influential variables, their respective values in their minimal class. In the database, there were 105 SYLVIA runs for this parametric configuration, with the following distribution: 72 (< 20 hPa), 23 (20 -40 10 (40 -60 hPa) and 0 (> 60 hPa). Thus, the first row of the conditional probability table is: 0. 69 (< 20 hPa), 0. 22 (20 -40 hPa), 0. 09 (40 -60 hPa) and 0 (> 60 hPa). By repeating this procedure for each one of the 15,360 parametric configurations, one obtains the conditional probability table associated to the response "maximum pressure in the fire compartment". By proceeding in the same way for each response, it is obtained seven conditional probability tables that are the numerical transcription of the SYLVIA model into a structural model.
Application To illustrate the potential interest of an expert system as an aid tool for safety assessment, we consider the following issue: how to identify the configurations that could lead to a pressure difference at fire break door beyond 60 hPa (∆PFBD > 60 hPa) in the specific case of a medium kinetics fire growth in a compartment with leaks? This pressure difference threshold corresponds to the breaking point of a BAUMERT fire break door (63. 5 hPa) tested in the STARMANIA facility  [7]  and used in the French nuclear power plants. First, if the expert system is used as a prognostic tool or in a forward chaining (see Fig. 8 ), only the knowledge relative to the parameters can be used. In this case, the result of the expert system is rather like a direct exploitation of the database. There are 239,967 runs out of the 1,600,000 computations in the database that meet this parametric configuration and their distribution is 238,170 < 20 hPa, 1602 [20 -40 hPa], 163 [40 -60 hPa] and 32 > 60 hPa, or respectively 99.
Conclusion In fire safety assessment, it is essential to be able to quickly discern configurations at risk in a nuclear facility. For that purpose, an expert system approach was undertaken to take advantage of the SYLVIA software. The proposed methodology is made up of two steps: the building of a knowledge database covering a wide range of configurations, then, the implementation of an inference mechanism corresponding to the reasoning abilities of the expert system. The developed expert system is based on Bayesian networks and can be used to discern configurations of this database useful to one specific case of interest. The expert system was conceived to study the pressure effects on fire barriers in the case of fire in nuclear facilities. The large knowledge database includes the results of 1,600,000 runs of the SYLVIA software.
The values used for the parameters of the study in every SYLVIA calculation were obtained by coupling SYLVIA with SUNSET (IRSN statistical software) and performing Monte-Carlo calculations. The first results confirm the interest of the expert system approach in order to dynamically use large databases as part of a fire safety analysis. As illustrated in the previous section, the expert system can help the identification of configurations increasing the risk for a particular scenario from the exploitation of a large data base of SYLVIA runs. Moreover, the computing time being negligible, this kind of tools can be highly profitable for training. This new software can be considered as a complementary tool of SYLVIA software allowing an analyst to quickly target the configurations of interest in a specific safety assessment.
The perimeter of the knowledge database, which determines the scope of the expert system, is determined by the general framework of the study. If the framework were to change, it would then be necessary to integrate the new generic knowledge (enrichment of the knowledge base). For any other safety assessment needs, the database, the identification of the responses of interest and their influential parameters, as well as the characterization of the conditional probability tables are likely to be different. However, the general framework is generic. Thus, for new issues coming from some fire safety analysis, another study done with SYLVIA / SUNSET software may be necessary, enriching the knowledge base. The algorithmic part (the inference engine), is, in principle, unchanged, but may however need to be adapted in order to take into account the characteristics specific to the new question of interest in terms of parameters and responses.
Most existing experimental FOWT models are designed and manufactured by keeping the Froude number the same, in which the ratio between the inertial and the gravity force is identical between the scaled model and full-scale prototype. However, in this situation, the Reynolds number is not the same. Muller et al. [4]  mentioned in his study that, as most of the scaling laws applied to the FOWT are based on the Froude scaling, the prediction of the flow conditions over the wind turbine varies between the scaled model and full-scale prototype, as the Reynolds number is different. Parallel to the experimental investigation, some simplified analysis tools have been developed, e. g. , Fatigue, Aerodynamics, Structures, and Turbulence (FAST)  [5] , developed at the National Renewable Energy Laboratory (NREL) in the USA  [6] , the Wind Atlas Analysis and Application Program (WAsP)  [7] , and the Horizontal Axis Wind turbine simulation Code 2nd generation (HAWC2)  [8] , developed by the Technical University of Denmark (DTU).
For the numerical wave generation, the "waves2Foam" module based on the multiphase solver "interFoam" in OpenFOAM  [21, 22]  is utilized. In this CFD work, the mooring lines are not directly simulated. Instead, its impact on the floating structure is considered via a dynamic mooring line model  [14]. The present CFD solver has been successfully applied to study FOWT with the regular wave and aeroelastic response of the wind turbine blades as described in our previous work  [9, 11, 14]. The detailed explanations on the flow solver, the moving mesh strategy and the mooring system analysis can be found in the above references. In the following sections, we will only provide brief descriptions of some important points.
Numerical Method In the present study, flow equations are solved by using the open source software OpenFOAM  [30]. PIMPLE (a combination of Pressure Implicit with Splitting of Operator (PISO) and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE)) is utilized to solve the pressure-velocity coupling. The maximum allowed Courant number is set to 1 (wave only) and 75 (wind and wave) respectively. The time step is fixed as low as 0. 002 s when a floating platform is simulated, whereas it is changed to 0. 001 s for the modelling of a FOWT.
Potential Flow Theory (EDF) The results based on a potential flow theory presented in this paper have been obtained using the EDF R&D floating wind turbine aero-hydro-elastic solver CALHYPSO  [24]. In this paper, only the hydrodynamic part of the code is used. The floater is regarded as a rigid body; only the 6 rigid body motions are solved in the time domain, using a Newmark time marching scheme. For the present paper, mooring lines are regarded as quasi-static, and the catenary equation is used to capture their response to floater motion. As shown in  [32] , this assumption is not expected to influence the motions of the floater, but only the tension level in the lines, which are not studied in the paper. The hydrodynamic loads are computed in the frequency domain using the potential flow solver NEMOH, developed by Ecole Centrale de Nantes  [33] , which solves the diffraction and radiation problems.
6 or Cd = 2. Regarding the wave theory, first-order Stokes waves (Airy theory) have been used to compute the focused wave elevation. FFT is applied on the 200 s long free wave elevation time series obtained with the CFD solver at a reference point (centre of the floater at t = 0) in order to get the wave components of the focused wave. About 200 significant wave components are extracted for the cases considered here. Amplitudes and phases are then applied as imposed spectrum into CALHYPSO, and it is verified that the free wave elevation is consistent with the inputs. Finally, the modified Wheeler stretching model is applied to compute the undisturbed wave kinematics in the fluid domain. It is noted that wave kinematics are only used for viscous forces, as all other forces are included in the hydrodynamic database computed by NEMOH.
Acknowledgments: Results were obtained using the  Cirrus UK National Tier-2 HPC Service at EPCC  (http://www. cirrus. ac. uk) funded by the  University of Edinburgh  and  EPSRC  ( EP/P020267/1 ). Also, Results were obtained using the  ARCHIE-WeSt High-Performance Computer  (www. archie-west. ac. uk) based at the  University of Strathclyde. The first author would like to thank  China Scholarship Council (CSC)  for financial support during his Ph. D. study in the UK.
Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.
INTRODUCTION The potential applications of automatic species detection and classification of birds from their sounds are many (e. g. ecological research, biodiversity monitoring, archival)  [1, 2, 3, 4, 5]. In recent decades, there has been an increasing amount of ecological audio datasets that have tags assigned to them to indicate the presence or not of a specific bird species. Utilising these datasets and the provided tags, many authors have proposed methods for bird audio detection  [6, 7]  and bird species classification, e. g. in the context of LifeCLEF classification challenges  [8, 9]  and more  [10, 11]. However, these methods do not predict any information about the temporal location of each event or the number of its occurrences in a recording. Some research has been made into using audio tags in order to predict temporal annotations, labels that contain temporal information about the audio events.
There is a high diversity of animal vocalisations, both in the types of the basic syllables and in the way they are combined  [19, 20]. Also, there is noise present in most habitats, and many bird communities contain multiple bird species that can potentially have overlapping vocalizations  [21, 22, 23]. These factors make detailed annotations laborious to gather, while on the other hand acquiring audio tags takes much less time and effort, since the annotator has to only mark the active sound event classes in a recording and not their exact boundaries. This means that many ecological datasets lack temporal annotations of bird vocalisations even though they are vital to the training of automated methods that predict the temporal annotations which could potentially solve the issue of needing a human annotator.
Recently, BirdVox-full-night  [24] , a dataset containing some temporal and frequency information about flight calls of nocturnally migrating birds, was released. However, BirdVox-full-night only focuses on avian flight calls, a specific type of bird calls, that usually have a very short duration in time. The temporal annotations provided for them don't include any onset, offset or information about the duration of the calls, they simply contain a single time marker at which the flight call is active. Additionally, there is no distinction between the different bird species, hence no specific species annotations are provided, but only the presence of flight calls through the duration of a recording is denoted. Hence, the dataset can provide data to train models for flight call detection but is not efficient for models performing both event detection and classification for a variety of bird vocalisations.
In this paper, we introduce NIPS4Bplus, the first ecological audio dataset that contains bird species tags and temporal annotations  [25] , and can be used for training supervised automated methods that perform bird vocalisation detection and classification and can also be used for evaluating methods that use only audio tags or no annotations for training. The rest of the paper is structured as follows: Section 2 describes the process of collecting and selecting the recordings comprising the dataset, Section 3 presents our approach of acquiring the tags and temporal annotations and provides statistical information about the labels and recordings comprising the dataset followed by example uses of NIPS4Bplus, with the conclusion in Section 4.
AUDIO DATA COLLECTION The recordings that comprise the Neural Information Processing Scaled for Bioacoustics (NIPS4B) 2013 training and testing dataset were collected by recorders placed in 39 different locations, which can be summarised by 7 regions in France and Spain, as depicted in Fig. 1. 20% of the recordings were collected from the Haute-Loire region in Central France, 65% of them were collected from the Pyrénées-Orientales, Aude and Hérault regions in south-central France along the Mediterranean cost and the remaining 15% of the recordings originated from the Granada, Jaén and Almeria regions in eastern Andalusia, Spain. The Haute-Loire area is a more hilly and cold region, while the rest of the regions are mostly along the Mediterranean coast and have a more Mediterranean climate.
leclub-biotope. com/fr/72-sonochiro based on locations and clustering of features, to maximise the diversity in the labelled dataset, resulting in nearly 5000 files being chosen. Following the first stage of selection, manual annotations were produced for the classes active in these 5000 files and any recordings that contained unidentified species' vocalisations were discarded. Furthermore, the training set and testing set recordings were allocated so that the same species were active in both. Finally, for training purposes, only species that could be covered by at least 7 recordings in the training set were included in the final dataset, the rest were considered rare species' occurrences that would make it hard to train any classifier, hence were discarded. The final training and testing set consist of 687 files of total duration of less than an hour, and 1000 files of total duration of nearly two hours, respectively.
Tags The labels for the species active in each recording of the training set were initially created for the NIPS4B 2013 bird song classification challenge  [26]. There is a total of 61 different bird species active in the dataset. For some species we discriminate the song from the call and from the drum. We also include some species living with these birds: 7 insects and an amphibian. This tagging process resulted in 87 classes. A detailed list of the class names and their corresponding species English and scientific names can be found in  [25]. These tags only provide information about the species active in a recording and do not include any temporal information. In addition to the recordings containing bird vocalisations, some training files only contain background noise acquired from the same regions and have no bird song in them, these files can be used to tune a model during training.
Temporal Annotations Temporal annotations for each recording in the training set of the NIPS4B dataset were produced manually using Sonic Visualiser. 2  The temporal annotations were made by a single annotator, Hanna Pamuła, and can be found in  [25]. Table  1  presents the temporal annotation format as is provided in NIPS4Bplus and Fig. 4  depicts the visual representation of the temporal annotations. In concern to the temporal annotations for the dataset, we should mention the following: • The original tags were used for guidance, however some files were judged to have a different set of species than the ones given in the original metadata. • In a few rare occurrences, despite the tags suggesting a bird species active in a recording, the annotator was not able to detect any bird vocalisation.
• An extra 'Unknown' tag was added to the dataset for vocalisations that could not be classified to a class. • An extra 'Human' tag was added to a few recordings that have very obvious human sounds, such as speech, present in them. • Out of the 687 recordings of the training set 100 recordings contain only background noise, hence no temporal annotations were needed for them. • Of the remaining 587 recordings that contain vocalisations, 6 could not be unambiguously labelled due to hard to identify vocalisations, thus no temporal annotation files were produced for them. • An annotation file for any recording containing multiple insects does not differentiate between the insect species and the 'Unknown' label was given to all insect species present. • In the rare case where no birds were active along with the insects no annotation file was provided.
A few examples of the NIPS4Bplus dataset and temporal annotations being used can be found in  [27]  and  [28]. First, in  [27] , we use NIPS4Bplus to carry out the training and evaluation of a newly proposed multi-instance learning (MIL) loss function for audio event detection. And in  [28] , we combine the proposed method of  [27]  and a network trained on the NIPS4Bplus tags that performs audio tagging in a multitask learning (MTL) setting. Additional applications using NIPS4Bplus could include training models for bird species audio event detection and classification, evaluating how generalisable of method trained on a different set of data is, and many more.
The total value delivered by the system is the sum of the value of all subsystems. The value chain activities are separated into primary activities (operations, distribution, trade) and secondary activities (research and development, human resources). To determine the ocean observing status for a certain objective, the activities within the FOO ocean observing value chain are analyzed. The primary activities are (i) requirement processes, (ii) coordination of observational elements, and (iii) data management and information products. These three activities are referred to here as "the FOO pillars. " The secondary activities are identical to those described in economics: research and development, and human resources. To evaluate the maturity of the primary and secondary activities, a readiness level scheme, such as proposed by the FOO, can be applied for each of the FOO pillars.
For example, the observations executed in the context of the Global Ocean Observing System (GOOS) and the Global Climate Observing System (GCOS) are activities that are cosponsored by the Intergovernmental Oceanographic Commission of UNESCO, the World Meteorological Organization, the International Council for Science, and the UN Environment. The sponsoring organizations expect multiple uses of the data. This means, for example, that certain elements of the ocean observing value chain for monitoring the seasonal variability of habitat compression for fisheries advice, which may deliver to operational aspects in the GOOS, may also be used in the ocean observing value chain for long-term trends in deoxygenation, as a clear GCOS topic. The two observing objectives obviously differ in their respective time scales and requirements on data quality, and also the data integrators and users of the products are different, but the observing infrastructure may overlap with each other and also with other GOOS and GCOS observing value chains.