dataesr/scientific-paragraphs-categorization huggingface dataset:
2008; Newhall and Hoblitt 2002). Altough we suggest that this time window can be extended when the hazard assessment is considered for very long-term development of infrasturcture projects. At Node 1 (N1), we want to estimate the probability of entering a new unrest phase within the next time window. Unrest is a diverse and complex phenomenon described as a deviation from the background activity of a volcano or by anomalies in monitoring data  (Gottsmann et al. 2019; Phillipson et al. 2013). Although subjective, this generalized de nition cannot be readily applied to active volcanoes that are poorly monitored or that are not at all monitored. Ceboruco is under-monitored, therefore, we rely on geological information to de ne unrest, recognizing that this lack of reliable monitoring data limits our analysis to long-term PVHA.
Occurrence of VT events indicate intra-crustal stress accommodation, either from a magmatic body or the regional tectonic setting (i. e. , the half-graben within which the edi ce is located). Recently,  Núñez-Cornú et al. (2020)  used data from four stations around Ceboruco (deployed 2012 -2014) and concluded that most of the recorded events follow shallow structural lineaments and have shallow hypocenters (<10 km). Although insightful, these geophysical signals cannot serve as basis for a wider de nition of background activity and therefore unrest, since the network was small and deployed temporarily. Sánchez et al. (2009)  suggest Ceboruco might be in an intra-eruptive phase while  Núñez-Cornú et al. (2020)  suggest that the seismic activity indicates local tectonic stresses. No other observations that may indicate changes in Ceboruco's behavior have been recorded.
The past data describes our likelihood function  (Marzocchi et al. 2008)  and is estimated from geological information. We assume that the Jala eruptive period, ~AD 912 -1142 (including the Jala Plinian eruption and pre-and-post Jala effusive activity) and the AD 1783 -1894 period (including the AD 1870-'75 eruption) represent two long-lived unrest episodes. The long dormancy (quiescence) interval between the two episodes indicates an inter-eruptive period with no clear signs of magma intrusion but disturbed twice by large tectonic earthquakes that may have perturbed the magmatic system at Ceboruco. In total we count four episodes of unrest, magmatic or amagmatic including seismic, that began in time windows during the past ~1108 years. Therefore, the sample proportion, which is the mean of the likelihood function, is 4/1108.
g. , tectonic). Given its activity of the past ~1000 years and the relatively short time since its most recent eruption, it is reasonable that unrest could be associated with magma. Ceboruco, however, lies within a tectonically active area within which large-magnitude tectonic earthquakes are common, like the two earthquakes reported in the 16 th century. Given the variability of both volcanic systems and tectonic regions we assume a prior probability distribution with Beta = 0. 5 and  = 1, indicating maximum ignorance (i. e. , equal chances of having magmatic or amagmatic unrest). The likelihood function is described by the past data according to which two of the four unrest episodes were clearly magmatic in origin. As the sample proportion, 2/4, is equal to the mean of the prior probability, 0.
e. , Titan2D;  Patra et al. 2005)  for ows and the Energy Cone module in LaharZ  (Schilling 1998)  for surges) to estimate posterior probabilities for these hazardous phenomena. The PDC map shows the highest probabilities (>5 x 10 -4 ) within 10 km of the vent. The valleys and some inter uves at >10 km from the vent have a lower probability of inundation attributed to the simulated surges and eld data from past large magmatic eruptions. The cities of Ahuacatlán and Jala, both at ~10 km from the vent, along with several smaller communities, have relatively elevated probabilities (~ 1 -3 x 10 -4 ) of being affected by PDC, along with existing infrastructure. Tephra fallout hazard is evaluated for all three eruption sizes. Sieron et al. (2019 b)  describe in detail the methodology used for simulations of tephra fallout that serve as prior models in our analysis.
Lahars (syn-and-post eruptive) are considered for both S2 and S3 eruption scenarios due to the availability of fresh pyroclastic material available with such events and the availability of water during the rainy season at Ceboruco or a passing tropical storm. Lahar deposits have been identi ed in the eld and associated with the large Jala eruption  (Sieron et al. 2019 a). We use the extent of past lahars and simulation with LaharZ as input data in PyBetVH. The average probability map of lahar inundation is shown in Figure  4b. Unlike  Sieron et al. (2019 b)  who considered lahar hazards as far north as Grande de Santiago river, we focus our assessment on Ceboruco and its main drainage network and tributaries to the Ahuacatlán river. The highest probabilities are within the drainage network of Ceboruco (>1.
0018 to 0. 00243. The uncertainties in these estimates can be reduced with more informed prior distributions. For example, by using Beta = 0. 64 in node 3 (based on the  Phillipson et al. 2013 catalogue)  the nal estimate for probability of eruption decreases slightly (dotted line in Fig. 5b ). The PVHA at Ceboruco is sensitive to the de nition of unrest and the prior probability estimates, and both depend on geophysical monitoring. Without consistent data from a developed and continuous monitoring network to inform the hazard assessment, the nal probability estimates are relatively higher. The uncertainties in both the prior and likelihood functions are propagated to the posterior probability estimates; the only solution to re ne the PVHA and deal with the uncertainty is the development of a realtime and continuous monitoring network and to conduct more detailed eld studies.
Conclusions And Final Remarks We conduct a PVHA for Ceboruco, a poorly monitored and active volcano on the western part of the TMVB. Based on the eruptive scenarios proposed by  Sieron et al. (2019 b) , we create a generic ET for Ceboruco to account for magmatic and amagmatic activity. For the magmatic eruption branch of the ET, we choose three scenarios: i) small (effusive), ii) moderate (vulcanian/sub-Plinian) and iii) large (Plinian); with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars, and lava ows. We use PyBetVH, an e-tool based on the Bayesian event tree methodology, to create probabilistic hazard maps for each of the selected eruption scenarios. Using geological data with outputs from other numerical and theoretical models in PyBetVH, we estimate the probability of a magmatic eruption at Ceboruco within the next time window (i.
These estimates are associated with high uncertainties which can only be reduced by re ning the prior distribution functions with information from geophysical monitoring. We recognize the numerous efforts by academic and government institutions, and we recommend the set-up of at least a minimal permanent monitoring network at Ceboruco with the capabilities to provide real-time geophysical data continuously. Another important consideration at Ceboruco is to extend the event tree presented here to include the surrounding monogenetic eld. Ethics approval and consent to participate. Not applicable. Consent for publication. applicable. Competing interests. We declare no competing interests.
Calculation geometry and physics models The geometry model is identical for the three calculation codes. It is setup as defined in the Annex G of ICRP116 (2010). A cube of skin of 10 × 10 × 10 cm 3 is located in vacuum (see figure  1 ). The used skin composition is defined in ICRP110 (  2009 ) and shown in table 1. A circular source beam of 7 cm diameter is normally incident to the skin cube. Depending on the code used, the simulated source consists of a beam of mono-energetic neutrons (MCNP) or a beam of neutrons with energies samples from a uniform distribution over a given interval (group formalism for doses in GEANT4 and FLUKA). More details about the neutron groups are provided below in section 3. A cylindrical scoring volume is located between 50 and 100 µm depth in the skin cube, with a normal surface of 1 cm 2.
3. 3. html as of 22 February 2021. 4 https://bugzilla-geant4. kek. jp/show_bug. cgi?id=2307 as of 22 February 2021. 5 https://bugzilla-geant4. kek. jp/show_bug. cgi?id=2333 as of 22 February 2021. calculated on the fly by sampling the velocity of the neutron-collided nucleus for each interaction. This process is done at a fixed temperature in the data files for FLUKA and MCNP. G4ThermalScattering and thermal scattering correction for H bound with water is implemented as suggested in  Ribeiro and Souza-Santos (2017). The authors show that the High Precision physics list with the thermal scattering correction from chemically bound atoms is recommended for neutrons in the energy range 1 meV to 15 MeV. The same particle types as for FLUKA simulations are scored. No energy threshold is applied and the standard electromagnetic physics is used for secondary particles.
Below 1 × 10 -3 MeV, ions are mainly produced by recoil protons whereas above 1 × 10 -3 MeV, they mainly come from neutron reactions that produce ions. That is the reason why they are included in the 'Fluka_Kerma dose' for this energy range. Finally, we draw the attention of the reader that alpha particles, positrons and photons contributions have also been studied. They are included in the LSD-CC values (for each code result) with contributions with less than 0. 1% to the LSD-CC. The slight discrepancies observed between the three codes could originate from: • The nuclear database cross-section used for the calculation, regarding neutron transport. Indeed, MCNP calculations rely on ENDF/BVII for neutrons and ENF/BVI. 8 for photons, GEANT4 results are produced with G4NDL4. 6 which is mainly based on JEFF-3.
Conclusions We use simulation codes (MCNP, FLUKA and GEANT4) to compute a set of 'LSD per Fluence' conversion coefficients (LSD-CC) for neutrons in the energy range of 1 meV to 15 MeV following the ICRP116 energy points. These factors are provided for the first time as they are not given in the literature. They are necessary to calculate the protection quantity of local skin dose induced by neutron exposure. A comparison between three code results is performed. The results are both qualitatively and quantitatively consistent. However, differences up to 19% are observed between GEANT4 and MCNP at 10 keV, 18% between GEANT4 and FLUKA at 500 eV and 15% between FLUKA and MCNP at 20 keV. GEANT4 results are slightly above the two others at energies below 1 × 10 -8 MeV. Explaining these discrepancies is not the scope of this paper but they could originate from the different nuclear databases used in the various codes and from the physics models (electromagnetic particle transport, Kerma approximation).
Aerospace production systems face increasing requirements for flexibility and reconfiguration, along with considerations of cost, utilisation, and efficiency. This drives a need for systems with a small number of automation platforms (e. g. industrial robots) that can make use of a larger number of end effectors that are potentially flexible or multifunctional. This leads to the challenge of ensuring that the configuration and location of each end effector is tracked by the system at all times, even in the face of manual adjustments, to ensure that the correct processes are applied to the product at the right time. We present a solution based on a Data Distribution Service that provides the system with full awareness of the context of its automation platforms and end effectors. The solution is grounded with an example use case from WingLIFT, a research programme led by a large aerospace manufacturer.
! WingLIFT focusses on a specific instantiation of part of this concept in order to prove its applicability in an industrial environment. The WingLIFT approach involves the aspects of data sharing that enable smart integration of dynamic modular production systems through the use of distributed intelligence. This distributed intelligence is presumed to be based on multi-agent technology, but an agent is not a "hard requirement" for every resource in the system, as will be discussed later. Other parts of the WingLIFT project include some aspects of pervasive metrology, but they will not be included in this paper.
4. 4 Hardware / Software Stack Physical hardware resources in the WingLIFT architecture are usually controlled by intelligent agents deployed on embedded computers. Each agent is connected to the resource using a resource-specific translation layer and to the rest of the system using a DDS. In the EAS project this was the most common type of stack, but Wing-LIFT aims to address a wider range of resource types. Fig. 4  shows this more complete view: the physical resource may provide a software interface, or may require a hardware interface; the system can also interface with humans though software running on portable devices; and the system may include software services running on other computing hardware. There should be no difference to the architecture whether the resource being managed is physical automation, a human, or a software service.
DDS databus PLC on which a module can be installed to communicate with the agent). WingLIFT uses an intelligent agent to virtualise each resource. Agent. The WingLIFT architecture uses an agent-based approach to integrate resources that do not support intelligent integration. Each agent is based around a streamlined version of the JADE agent platform  [27]. The JADE Abstract Architecture and Application frameworks have been utilized, but the Agent Communication and Agent Message Transport has been replaced by the RTI Connext DDS Pro publish-subscribe databus. Each WingLIFT agent extends the abstract JADE agent class, to provide structure for agent data management and behaviours. Communication. The WingLIFT agent communicates along two main channels: via the agent-resource translation layer, and via the inter-agent communication method (e.
Our research question differs from this earlier literature. We ignore the (relevant) question of the convergence or divergence of cultures in the digital era, and instead, we study whether in each country digitization makes music sound more similar or more diverse, irrespective of the differences in the country of origin or musical genres. A second contribution of our paper is the introduction of a measure of similarity of music consumption, which goes beyond artists' nationality or musical genres. 5 he remainder of the paper is organized as follows. In Section 2 we discuss the potential impact of digitization on music diversity using the relevant literature, and we also discuss further our contribution. Section 3 is devoted to the presentation of our datasets. In Section 4 we describe our empirical strategy and our estimation results.
Weekly music charts The first dataset represents commercially successful music in a number of well-established music markets in North America, Europe and Australasia. Studies of music consumption have since decades back (e. g. Peterson and Berger 1975; Ferreira and Waldfogel 2013; George and Peukert 2016; Ordanini and Nunes, 2016)  relied on "record charts" or "music charts" as a proxy for music consumption. These music charts list the best-selling or most-played songs (or albums) in a certain territory during a certain time period, normally a week. Charts play an important role in the music industry since they serve as a performance indicator, as a promotion tool, and as content for numerous media shows based on the chart as its fundamental structure (e. g. BBC's Top of the Pops in the UK aired between.
Music charts have existed since the early days of the modern pop music industry and methodologies and practices for generating the charts have evolved along with the evolution of technologies and practices in the business for recorded music. Throughout these decades of change, major music markets across the globe have continued to publish a weekly chart, which the leading stakeholders in that market have collectively recognized as the "Official". In this study, we are using such Official Charts from ten music markets as proxies of music consumption during the period 1990 to 2015. These markets represented 56% of worldwide recorded music sales in 2015 (including three of the top five markets), and thus can be considered as fairly representative of the whole industry. We built web scrapers for each chart and collected data from their online archives.
Acoustic features of recorded music The second dataset in this study contains data on "acoustic features" of recorded music. This data has been accessed from the world's leading streaming music service, Spotify. The data is generated by an audio analysis algorithm developed by Berenszeig, Logan, Ellis and  Whitman (2004)  and commercialised by The Echo Nest  (Whitman and Jehan 2011) , a company that in 2015 was acquired by Spotify. Analysing acoustic features of recorded music is still a relatively novel research approach but generating a dataset from Spotify's audio features API is becoming an established method as exemplified by  Askin and Mauskapf (2017)  or  Chinoy and Ma (2018). Spotify, and originally The Echo Nest, presents the audio analysis method as: "…the world's only 'music listening' API.
For instance, instrumental, acoustic songs are not as likely to enter mainstream weekly music charts as vocal, upbeat, pop and rock music. The histograms in Figure  1  illustrate the distribution of the ten acoustic features for the songs in the dataset and Table  3  presents some basic descriptive statistics for these distributions. More about these features can be read at: https://developer. spotify. com/documentation/webapi/reference/tracks/get-audio-features/ 4a  shows when, where and by whom the songs were recorded and on what album the songs were released. To further illustrate the concept of acoustic features and acoustic proximity between songs we present radar charts (Figure  2  below) for each song example, but for clarity, we are only using the dimensions with significant variability: acousticness, valence, tempo, liveness, energy and danceability.
Merging the two datasets The websites (see Table  1 ) where the charts used in this study are published, generally only present metadata such as the name of the charted songs and the name of the artist or band that made the recordings. There are no unique song identifiers available that would enable researchers to establish unambiguous links between a song in the chart data with its corresponding metadata accessible from other datasets. This means that when linking songs in our two datasets (Weekly music charts and Acoustic features) we are required to search for matching combinations of song name and artist name in each dataset. This is a straightforward and common approach, but due to typos, spelling ambiguities, etc. , it is not one without flaws. Some of the songs in the chart dataset could for that reason not be confidently matched with acoustic features metadata, which led to our decision to delete the unmatched items (11%) from the original dataset.
An aggregated measure of acoustic diversity Diversity measures are per definition calculated over a set (or sets) of elements, in this case a set of songs. A song set in this study is defined as the songs that are listed in a chart in a specific country during a specific Time Window. Since the music charts in this study are published on a weekly basis, the minimum Time Window is one week. This Time Window is iteratively moved forward one Time Step at a time over the period of analysis  in order to build the time series representing the dynamics of the acoustic diversity measure. In all calculations of the acoustic diversity measure, we used the smallest possible Time Window (1 week) and the shortest possible Time Step (1 week). This means that a complete time series that covers the entire period of analysis from 1 January 1990 to 31 December 2015 consists of (26 years x 52 weeks) 1,352 data points.
Empirical strategy and results We first provide a basic descriptive analysis of the evolution of acoustic diversity over the period on the whole dataset, and then an econometric analysis taking into account our panel of ten countries and the different periods of the digitization era. To account for the changes in distribution channels during the digitization era we argue that five periods, corresponding to four turning points, are significant and we run regressions that allow us to take into account the possible existence of different trends in these various periods. Note that we control for two dimensions that could have an impact on acoustic diversity within the music listened in a specific country: the dominant musical genres and the country of origin of songs that entered the tops.
Global trend of the evolution of acoustic diversity Plotting the evolution of the mean annual value of acoustic diversity over the period 1990-2015 in our whole sample highlights a seemingly negative trend (see Figure  3 ). While the acoustic diversity follows a similar overall trend in all ten countries (see Figure  A. 1 in appendix), there are nevertheless some distinct variations between individual countries. However, it is necessary to recognise that in this study we have no ambition to explain the variations between countries, but merely note that the data underscores the well-established idiosyncratic nature of national music markets. Beyond the global trend of the evolution of acoustic diversity it is also interesting to study the evolution over the period of the ten individual acoustic features (Acousticness, Danceability, Energy, Instrumentalness, Liveness, Speechiness, Valence, Tempo, Loudness and Duration).
For instance, Spotify uses genres as labels attached to artists, rather than to songs and lists a number of very specific genres to assist music discovery and association. In this study, we are using this approach and collect data from Spotify about the genres associated with the artists in the dataset. Since these Spotify's genres are very niche (e. g. Italian trance, chill groove, zapstep), we traced these back to a small number of well-established key genres that were common in our dataset, namely hip hop, electronic dance music (edm), pop, dance pop, rock, rhythm & blues (r&b), country. Not all songs could be traced back to these genres, which required to add an "other/undefined" genre as well. Figure  4  plots the weekly market share of each musical genre within the charts of the whole set of countries.
It shows that the diversity within the three dominant genres over the whole period ("dance pop", "pop" and "rock") follows a similar trend. It seems thus hard to explain the decrease in acoustic diversity over time by the increase of "pop" and "dance pop" songs at the expense of "rock" songs. However, the role of the "other/undefined" songs is potentially more important. On the one hand, the level of acoustic diversity is unsurprisingly higher for this genre than for the others. On the other hand, the market share of this genre has collapsed which has mechanically driven down the acoustic diversity in our dataset. However, if this can contribute to explain the decrease of acoustic diversity between 2000 and 2009, it cannot explain the increase in acoustic diversity at the end of the period! To sum up, we argue that the trend of acoustic diversity from the beginning of digitization could hardly be driven by the solely changes in dominant musical genres.