dataesr/scientific-paragraphs-categorization huggingface dataset:
III. LTE SCHEDULING FOR ENERGY AUTOMATION In this section, we propose a novel LTE scheduler that enables IEC 61850 communication services. The IEC 61850 standard was initially defined for local intra-substation automation services using switched Ethernet in the data link layer  [2]. However, to extend the reach of automation information beyond substation boundaries, IEC 61850 packets would have to be translated to a different wide-area protocol through supporting routing mechanisms. Thus, the set of IEC 61850 services have to be compatible with the LTE radio protocol architecture. Since LTE was not initially designed to support energy automation tasks, adaptation protocols have to be introduced above the transport layer in the OSI reference model, as illustrated in Fig. 3. LTE standard defines different QoS classes to support a diverse set of services.
This class-based QoS mechanism relies on the concepts of data flows and bearers. Data flows are mapped to bearers, a set of multiple QoS requirements which are indicated by the QoS Class Identifier (QCI). As illustrated Fig. 3. MMS and GOOSE protocol stacks and their association with OSI layers  [2] ,  [10]. Adaptation protocols are added to ensure seamless integration with LTE radio protocol stack. Based on QCI value, the LTE scheduler could schedule traffic flows so as to achieve the corresponding QoS targets. Fig. 4  illustrates the proposed uplink scheduling framework. Consider C traffic classes, where a smaller class index corresponds to higher priority traffic, as presented in Table  II. We model class i arrivals as a Poisson process of rate λ i and are characterized by a payload size which is an exponential random variable with mean b i bytes.
A. Scenario 1: Centralized scenario Table  IV  provides the details of the simulated client-server scenario. We assume that remote control traffic between the IEDs and the control center follows a periodic traffic pattern with constant inter-arrival times of 6 ms and a payload of 150 bytes. These traffic characteristics are typical for control tasks  [11]. In overload all IEDs that belong to the same substation LAN are active and send data to the control center. Fig. 5  presents the CDF of the delay experienced by MMS messages, measured at the control center. The proposed scheduler outperforms the conventional LTE one. In particular, our scheduler guarantees that even in overload only 0. 5% of the messages experience delay slightly above the maximum acceptable threshold of 100 ms whereas the corresponding percentage for the native LTE scheduler is 21.
Thus, the main challenges for the fourth phase of PMIP include designing experiments that are suitable for all of its participants, addressing sufficiently fundamental questions to be of interest to the EMIC community, defining adequately focussed scope for the feasible participation of the latest generation of Earth system models, and prescribing flexible model setups that can be implemented in this range of models, whilst maintaining the ability to robustly compare results. In addition, a continuing challenge for PMIP is to assemble suitable palaeoclimatic data sets for comparison to model results. One of the most recent working groups to be established in PMIP is the Last Deglaciation Working Group. With the aim of coordinating transient simulations of the last deglaciation, the challenge of including the full range of PMIP models is at the forefront of our experiment design.
However, as  Schmidt et al. (2011)  pointed out, the choice of one particular configuration from a range of plausible boundary conditions and forcings is often arbitrary and does not account for uncertainties in the data used for developing the forcings/boundary conditions. Moreover, in designing the PMIP last deglaciation experiment, we have attempted to strike a balance between establishing a framework within which to assess model differences and performance, and taking the opportunity to utilise the full range of PMIP climate models (Earth system, general circulation and intermediate complexity) to examine uncertainties in deglacial forcings, trigger mechanisms and dynamic feedbacks. In short, when we do not precisely know the climate forcing for an event, or the temporal evolution of model boundary conditions, it is more efficient to compare the results from models that use different forcings with geological and palaeoclimatic data than to run one scenario with all models and all scenarios with all models.
2. 2), atmospheric trace gases (Sect. 2. 3), ice sheets (Sect. 2. 4), meltwater fluxes (Sect. 2. 5), and orography/bathymetry (Sect. 2. 6), as summarised in Table  2. Boundary condition data for version 1 of the Core experiment are provided on the PMIP4 wiki (PMIP Last Deglaciation Working Group, 2016; https://pmip4. lsce. ipsl. fr/doku. php/exp_design:degla).
2. 4). The associated topography and coastlines should be used as per the chosen ice sheet reconstruction. Beyond maintaining consistency with the coastlines, it is optional whether or not to implement the associated bathymetry and participants should adapt the bathymetry according to their model's capabilities (for example, depending on whether the spatial resolution allows for it or makes this a useful adaptation). These data will be provided with the ice sheet reconstructions. Whichever ice sheet reconstruction is chosen for the LGM spin-up should be carried through to the Core transient simulation. -Global ocean salinity should be +1 psu, compared to preindustrial, to account for the increased terrestrial ice mass at the LGM (PMIP LGM Working Group, 2015). On the freshwater budget, PMIP advises groups to "carefully check the fresh water budget in their LGM experiments in order to avoid unnecessary drifts of the ocean salinity.
However, caution is advised. Some of the boundary conditions for previous PMIP LGM simulations are different to the set-up outlined here, specifically in relation to ice sheets and trace gases concentrations, and therefore need to be adapted to match these requirements. The protocol for the PMIP4-CMIP6 (being finalised at the time of writing) is currently compatible with the LGM spin-up described here. Therefore, provided that either the ICE-6G_C or GLAC-1D ice sheet reconstruction is used for both the LGM spin-up and transient run, the PMIP4-CMIP6 LGM simulation can be used to initialise transient simulations of the last deglaciation without alteration. Please provide time series data for the diagnosis of model (dis)equilibrium at 21 ka (introduction to Sect.
Atmospheric trace gases (21-9 ka) For the deglaciation, chlorofluorocarbons (CFCs) should be fixed at 0, and O 3 should be set to PMIP3-CMIP5 preindustrial values (e. g. 10 DU), as used for the LGM. When a model is not running with dynamic atmospheric chemistry, the remaining trace gases should be time evolving, with CO 2 following  Bereiter et al. (2015) , CH 4 following  Loulergue et al. (2008)  and N 2 O following  Schilt et al. (2010) , all adjusted to the AICC2012 chronology  (Veres et al. , 2013) ; see Fig. 1d-f. The atmospheric CO 2 concentrations provided by Bereiter et al. (  2015 ) is a composite data set, combining previous Antarctic ice-core records and composites (for the period 26-0 ka:  Ahn and Brook, 2014; Lüthi et al. , 2008; MacFarling Meure et al. , 2006; Marcott et al. , 2014; Rubino et al.
2. 7) with appropriate consideration of soil characteristics. Where ice is replaced by ocean, it is advised to follow the procedure for changing coastlines described in Sect. 2. 7. Inland lakes can be prescribed based on the ice sheet and topography reconstructions, but this is not compulsory. It is also optional to include changes in river routing basins (i. e. catchments) and outlets, which can either be calculated from the provided topography and land-sea mask data (see Sect. 2. 6), or can be manually set to follow routing maps provided on the PMIP4 last deglaciation wiki (PMIP Last Deglaciation Working Group, 2016). Groups are free to choose how often to update ice extent and elevation. This could be done at regular intervals (e. g. the sub-1000-year time slices provided) or at specific times during the deglaciation, as was done in the TraCE-21 ka experiment  (Liu et al.
Topography, bathymetry, coastlines and rivers Participants are recommended to note the advice set out by  Kageyama et al. (2016)  for implementing these boundary conditions in PMIP4-CMIP6 Last Glacial Maximum and Pliocene equilibrium-type experiments. Changes in the ice sheets and their glacial eustatic and isostatic influence affected continental topography and ocean bathymetry, which in turn shifted the coordinates of river mouths and the coastal outline throughout the deglaciation. Hence, time-varying topographic, bathymetric and land-sea mask fields that match the chosen ice sheet from Sect. 2. 4 (i. e. ICE-6G_C or GLAC-1D) should be used; moreover, these are provided within the ice sheet reconstruction data sets. Topography should be updated at the same time as the model's ice sheet is updated; this is mainly implicit to implementing the ice sheet reconstruction because the major orographic changes through the deglaciation relate directly to ice sheet evolution.
Acknowledgements. Ruza F. Ivanovic  is funded by a  NERC   Independent Research Fellowship  (no. NE/K008536/1 ). Data processing for boundary condition preparation was carried out using the computational facilities of the  Palaeo@Leeds modelling group, University of Leeds, UK. All authors would like to thank everyone who has taken the time to discuss the Working Group's aims and experiments with us. We are especially grateful to  Jean-Yves Peterschmitt  ( Laboratoire des Sciences du Climat et de l'Environnement, France ) for managing and archiving the boundary conditions, as well as setting up and maintaining the PMIP wiki pages; to  Emilie Capron  ( British Antarctic Survey, UK ) for help with the ice-core data and to  Bette Otto-Bliesner  ( National Center for Atmospheric Research, USA ) for useful comments on an earlier version of this manuscript.
Other histological characteristics that can be observed from the data are cell spaces and Sharpey's fibers that, when combined with the vascularization, could help to provide insights into the growth of the structure. The 3D data of the scales from Lophosteus superbus is similar to comparable data from other fossil osteichthyans, and the morphology of the reconstructed buried odontodes from this species is identical to scale material of Lophosteus ohesaarensis, casting doubt on the validity of that species. The 3D data presented in this paper is the first for fossil fin spines and so comparable data is not yet available. However, the overall morphology and histology seems to be similar to the structure of placoderm dermal plates. The 3D datasets presented here provide show that microtomography is a powerful tool for investigating the three-dimensional microstructure of fossils, which is difficult to study using traditional histological methods.
The depth-length ratio (>1. 5) of the scanned scale (GIT 727-1) indicates it is probably a central or anterior trunk scale based on comparison to squamation of other early osteichthyans  (Chen et al. , 2012; Jessen, 1968; Qu, Zhu & Wang, 2013; Trinajstic, 1999). The scan of the scale is incomplete, missing the most dorsal and ventral part (Fig. 1 ). The Lophosteus fin spine (GIT 727-2) scan is also incomplete and only includes the most distal part of the structure. However, this does not affect the study of its general growth pattern and 3D architecture (see 'Results'). The synchrotron data will be made available through the ESRF palaeontology database (http://paleo. esrf. eu).
The samples were fixed at a propagation distance of 30 mm from the detector. Two thousand projections were performed during continuous rotation over 180 degrees. The time of exposure per projection was of 0. 3 s. The field of view at high resolution was restricted to 1. 4 mm, and therefore only specific regions of the scale and spine were imaged. The data obtained in edge detection mode were reconstructed using a classical filtered back-projection algorithm (PyHST software, ESRF). Acquisition artifacts-such as ring artifacts-were filtered while processing the data. Segmentation and rendering were done using the software VG StudioMax 2. 2 (Volume Graphics, Heidelberg), following the protocols established by  Qu et al. (2015a).
Lophosteus spine and scale histology comparison The 3D reconstructions of the current data have revealed several new characters that are shared between the scale and the spine of Lophosteus that otherwise might not have been identified through traditional investigation. Datasets confirm the earlier descriptions made by  Gross (1969)  with the spine and scale being constructed similarly, with a loosely calcified bony base that is covered in dentine odontodes (Figs. 1  and 6 ). Within the bony tissue we have identified large void spaces that most likely represent clusters of fibers (Figs. 1D-1F and 11). We also validate the claim that the surface of each structure is not covered by a continuous layer of dentine  (Gross, 1969) ; rather, bony surfaces can be identified between each odontode (Figs.
, 2013; Qu et al. , 2016)  for the Lophosteus scale, there is no directly comparable 3D data yet published relating to the spine. Soler-Gijón (1999) provided a three-dimensional model of fin spine histology from a xenacanth shark to discuss the structure's growth, but this was created from a stack of spaced 2D thin sections for which details can sometimes remain uncertain (e. g. , osteostracans histology;  Qu et al. , 2015a). There are a large number of 2D histological descriptions of early vertebrate spines  (Soler-Gijón, 1999; Jerve et al. , 2014; Burrow et al. , 2016)  that can be compared to the thousands of virtual thin sections that comprise a synchrotron dataset, some of which we will briefly summarize here. However, it is not possible to comment in great detail on the morphology of structures that are only known from the 3D reconstructions (i.
CONCLUSIONS The description of the scale and spine tip of Lophosteus presented here shows that there is a great deal of histological information that can be derived from high-resolution 3D datasets. Not only are we able to confirm the previously published characteristics from this taxon by  Gross (1969)  and others, but we have also shown that 3D synchrotron data can aid in identifying new morphological and potentially important paleohistological features. The following are the main novel features uncovered by the study: • The spatial organization of dermal odontode addition on the scale and spine. These are broadly similar, both based on a gap-filling system and lacking the type of unidirectional 'dentition-like' addition seen on the scale of Andreolepis  (Qu et al. , 2013) , but the scale shows a greater number of odontode generations.
ACKNOWLEDGEMENTS Our sincerest gratitude goes to  Paul Tafforeau  ( ESRF ) for facilitating the synchrotron beamtime provided by the  ESRF. We would also like to acknowledge  Tiiu Märss  ( Institute of Geology at the Tallinn University of Technology, Estonia ), for access to material and to the field site on Saaremaa. Many thanks to  Barbro Bornsäter Mellbin  and  Vincent Dupret  ( Australian National University ) for scanning the material, and  Tatjana Haitina  ( Uppsala University ),  Daniel Snitting  ( Uppsala University ),  Michael D Gottfried  ( Michigan State University ) for all technical, administrative, and editorial support. Thank you to  Git Klintvik  ( Lund University, Sweden ) for acid preparations of the samples. Many thanks to  Min Zhu ,  John Long , and  Hans-Peter Schultze  for providing reviews of this work.
The impact of MHD research will be explored using bibliometric analyses of scientific publications, clinical guidelines and newspaper stories reporting on research initiatives. Finally, these research inputs and outputs will be considered in light of various metrics that have been proposed to inform priorities for the allocation of research funds, including burden of disease, treatment gaps and cost of illness. Discussion: Given the growing burden of MHDs, a clear and broad view of the current state of MHD research is needed to ensure that limited resources are directed to evidence-based priority areas. MHDs pose a particular challenge in mapping the research landscape due to their complex nature, high co-morbidity and varying diagnostic criteria. Undertaking such an effort across 31 countries is further challenged by differences in data collection, healthcare systems, reimbursement rates and clinical practices, as well as cultural and socioeconomic diversity.
The Mapping_NCD project will develop a multi-focal view of the MHD research landscape by soliciting the perspectives of European funding entities, analysing the types of research undertaken in the public, voluntary/ not-for-profit and commercial sectors, and contextualizing the collected data through expert interviews. In a second phase, the outputs and impact of MHD research will be explored using bibliometric analyses of scientific publications, clinical guidelines and newspaper stories reporting on research initiatives. Finally, these research inputs and outputs will be considered in light of various metrics that have been proposed to inform the allocation of research funds, including burden of disease, treatment gaps and cost of illness.
Evidence bases for mapping research activity in Europe Using an approach that combines quantitative and qualitative data, the study will develop multiple evidence bases with the goal of mapping the current scope and scale of MHD research funding in Europe. A variety of tools will be used to capture a broad-based view that encompasses the different participants and perspectives (Fig. 1 ). The key element of the initial phase of the project will be an online survey of RFOs that will gather data on overall funding levels, disease focus, funding sources and priorities, types of research funded both currently and planned for the future, and expectations regarding outputs. The tool will be developed and tested in a pilot phase before being implemented across the study area. The relevant public, non-profit and private RFOs at the regional, national, supranational and EU levels will be identified through a systematic search by all consortium members across the five NCD categories.
This organizational structure facilitates the identification of RFOs and the collection of survey data within countries and will enable consortium partners to overcome language barriers and ensure that data across the five disease categories are collected on a standardized basis. A baseline threshold of €0. 5 million to €1 million in annual investment has been set with the goal of identifying funding that could be expected to influence the content or direction of major research programmes. Participation in the survey will be solicited by email or Internet-based contacts. Additional website interrogations will be undertaken to gather available funding data for the identified RFOs. All non-responding RFOs will be followed-up until the close of survey, approximately 9 months after its initiation.
After the data are gathered on a territorial basis, the consortium members will deliver the survey results to the members charged with the analysis of each of the five disease categories. While the overall funding levels of RFOs can provide a macro level view of MHD research, project-based funding data provide a rich source of information regarding disease focus, types of research and project funding levels. The broad-scale view of European RFOs involved in MHD research provided by the survey will be complemented by a purposive sample of publicly-funded projects in the five largest MSs for the period 2006-2013 in order to obtain a more focussed picture of research activity in recent years. The 20 most highly-funded MHD research projects from a key public research funder in each of the five countries will be identified and analysed.
Given the strong influence of the European Commission in research priority setting, MHD projects funded at the European level for the same period will be analysed. Data will be extracted from the Cordis database (http://cordis. europa. eu/home_en. html), including title, timeframe, MHD focus, type of research and level of funding. Our analysis will also include country participation, given that previous studies have found relatively low levels of participation by the newest MSs in European health-related research  [3]. Commercial investment in MHD research is an important component of the overall research picture, although obtaining detailed and specific data on research and development (R&D) expenditure is challenging due to lack of transparency of information considered proprietary. We will focus our efforts on identifying the MHD products in the pharmaceutical and medical device pipelines of European and United States companies for the period 2011-2014.
To measure and compare the commitment of European and United States pharmaceutical companies to R&D investment in MHDs, including the specific disease focus, we will examine the general trends in R&D expenditure, including total R&D expenditure and as a percentage of sales or revenues. We will also explore their research pipelines in terms of MHD molecules in Phase I, II or III trials. Data for publicly-traded companies will be collected from the four most recent annual reports available on the companies' websites (2011-2014). For privately-held companies, it is generally possible to obtain information regarding drug R&D through clinical trial databases, such as ClinicalTrials. gov. We will consider the pharmaceutical pipelines of European and United States companies at three levels: the top 20 companies in terms of R&D investment as identified by the 2014 EU Industrial R&D Investment Scoreboard (http://iri.
jrc. ec. europa. eu/ scoreboard14. html); companies ranked 100 to 2500 for R&D on the European Union Scoreboard; and smaller, mostly privately-held companies. This will enable us to better understand whether the disease focus and level of implication in MHD research varies depending on the size and investment capacity of the company. To explore the medical device pipelines of the top medical device manufacturers as ranked by total revenues (http://www. mddionline. com/article/top-40-medica l-device-companies), data regarding ongoing or completed trials will be obtained from ClinicalTrials. gov for the period 2011-2014. Data for medical devices with FDA pre-market approval or de novo classification will also be searched for the same period. EUDAMED, the European database of CE marked products, has existed since 2009 but is only accessible to government agencies charged with market surveillance in each country.
Thus, we will search the database of the EuroScan International Network, a global network of publicly funded early awareness and alert systems for health technologies, in order to identify relevant MHD devices  [4]. For both the pharmaceutical and medical device pipelines, terminated trials and those for which trial status is unknown or not verified will be excluded. In order to contextualize the data and knowledge obtained through the preceding methods, we will undertake semi-structured interviews with key MHD experts in Europe to obtain their views on the strengths and weaknesses of the MHD research environment in Europe. We will use an interview guide with questions regarding priorities and funding trends in mental health research, the respective roles and priorities of the public and private entities, the issue of coordination and redundancy, and initiatives beyond funding that could improve MHD research in Europe.