dataesr/scientific-paragraphs-categorization huggingface dataset:
This is surprising given that, as we shall discuss, relations provide the constructs to model complex real-world features, geographic relationships, and representations within the OSM database. Relations play a very important role within the OSM data model. While there has been limited academic investigation into relations in OSM they provide OSM with a means of implementing complex spatial and topological relationships. A relation is one of the core data elements that consists of one or more tags  [2]  and also an ordered list of one or more nodes, ways and/or relations as members which is used to define logical or geographic relationships between these elements. A member of a relation can optionally have a role which describes the part that a particular feature plays within a relation. A simple example of a relation might be a university campus.
The university campus itself contains many ways, representing roads and buildings for example, and nodes representing points of interest around the campus. The relation then expresses the geographic relationship of the campus between these node and way elements. Another very popular type of relation in OSM is called a turn restriction. These relations are usually applied at road intersections, streets, or junctions. Such relations have a set of tags describing the type of turn restriction. Turn restrictions, such as No-left-turn, restriction=only_straight_on and No-U-turn, regulate traffic flow at intersections. The relation then includes the ways and nodes which make up the physical road infrastructure in the junction or intersection. In a similar way to nodes and ways, relations are created and edited by contributors to OSM using software such as JOSM (Java for OpenStreetMap  [3] ).
Routing software and applications which use OSM data then use these relations to create workable optimal routes between locations. We believe that relations play a very important role in modeling complex real-world geographical relationships and phenomena in OSM. Without relations, the OSM database could not be used for applications such as routing  [4, 5] , traffic simulation  [6]  or Web Map Services (WMS)  [7]  to name but a few. Indeed we believe anecdotally that the role of relations is not actually apparent to many of external users and consumers of OSM data. To address this apparent gap in the knowledge of Volunteered Geographic Information (VGI), this paper has three research goals which are outlined as follows:.
• To explore the research question: are the patterns of contribution, editing and tagging for relations similar to those observed by other researcher work on OSM nodes and ways? At the conclusion of this work, we will provide some general observations and conclusions about this work along with a number of suggestions for future research work which could be considered. The remainder of this paper is organized as follows. In Section 2, we discuss some related work on relations and some of the software tools available to work with relations in OSM. In Section 3, we discuss how relations are represented within the OSM database. In Section 4, we outline the analysis we performed on the relations extracted from OSM for four European cities: Madrid, Rome, Vienna and Zurich. In the final section of the paper (Section 5), we draw some conclusions from our experimental analysis and outline a number of suggestions for future research work in this area.
Related Work and Applications As mentioned in the previous section, there are few published articles directly dealing with research on relations in OSM from the academic community. However, there is some attention drawn to relations in a few papers published in the literature. Related Academic Work: Barron et al. [8]  consider a methodology for intrinsic quality analysis of OSM data. Much of the focus of their proposed methodology is on nodes and ways with the authors concluding that future work must involve relations (e. g. , turn restrictions, bus routes, etc. ). Current software, such as software to import the OSM history into a relational database does not support the import of Relations to the database. Subsequently relations are not considered as a part of this proposed methodology. The work of Neis et al.
[9]  on vandalism detection in OSM certainly mentions the usage of relations. However, the work considers relations only from the viewpoint of how many relations have been created or edited by a particular OSM contributor. Eiter et al. [10]  consider the problem of description logics for knowledge representation and reasoning. They mention, without specific implementation or proof, that relations in OSM are spatial objects which have an inherent structure of containment, bordering, and overlapping, which can be exploited to generate topological relations (e. g. , contains). The addition of tagging on relations supplies spatial information allow the relations to be naturally represented as spatial knowledge concepts. Krajzewic et al. [6]  use OSM as an input data source for their open source microscopic traffic simulation package SUMO (Simulation of Urban MObility)  [11].
SUMO uses the relations in OSM to understand traffic flow using turn restrictions, connected routes, street segments, etc. Graser et al. [5]  uses the relations in OSM to extract explicit turn restriction information for their work in development of an Open Source analysis toolbox for street network comparison between geographic datasets. The work by Senaratne et al. [12]  provides an up-to-date review of various quality measures and indicators for selected types of VGI and existing quality assessment methods. In addition to this review the authors also propose a classification of VGI with current methods utilized to assess the quality of selected types of VGI. In a recent edited volume by Capineri et al. [13] , there are several chapters outlining recent work on what motivates citizens to provide VGI and the subsequent factors which govern or predict its validity and quality.
Further consideration of how crowdsourced datasets, such as OSM, evolve over time reveal biases with the mapping patterns. Studies, such as those outlined in Quattrone et al. [16] , develop methods to quantitatively measure the bias introduced by more established users in comparison to occasional contributors in crowdsourced mapping. The authors found strong bias in terms of the geographical areas being mapped. Hecht and Stephens  [17]  also consider urban biases in Volunteered Geographic Information. They argue that, while they have conceptualized the notions of rural and urban as binary at a broad level these categories are more naturally conceived along a continuum. Spielman  [18]  argues that user-generated maps are complex social systems and the quality of a contribution is difficult to assess and subsequently he argues that the structure of groups is more important than the intelligence of group members in VGI projects.
Software and web-based applications: Despite the absence of extensive academic inquiry into relations, the OSM community itself has developed a number of software applications for error checking and quality assurance on relations. The Relation Analyzer (RA)  [19]  analyses OSM relations for several quality-assurance-related aspects (amongst others): relations with gaps accompanied by a list of the segments if there are gaps, a height profile, type of ways included, tags. The RA tries to build a linked series of ways using some general rules and then creates a rating based on the type of relation this object corresponds to. Not all relation types are supported yet. The Keepright tool  [20]  flags the existence of relations which have no type tag and relations which have no tags at all. The Keepright tool can also perform some quality analysis (QA) checking on relations which represent turn restrictions.
The JOSM (Java For OpenStreetMap) tool is one of the most frequently used tools in OSM by contributors to edit the contents of the OSM database. JOSM is usually used by more experienced contributors to OSM. JOSM provides some support for QA of relations  [21]  as they are being created or edited. Two of the most popular software tools for the import of raw OSM data into PostgreSQL PostGIS enabled databases  [22]  are osmconvert  [23]  and osmosis  [24]. Both tools take OSM data and perform extract, transform and load processing into a database with their predefined data model structure. Both osmconvert and osmosis import relations including all members of each relation, all tags and all attribute information. It is then possible, although not always possible or simple, to recreate the geometry of the relation by computing the geometry of all members of the relation and reconstructing the relation structure.
Experimental Analysis In this section, we outline the experimental analysis we performed on the relations extracted from OSM for four European cities: Madrid, Rome, Vienna and Zurich. As this was an exploratory examinations of relations in OSM, we did not impose strict criteria when choosing the case study cities. There are many European, or global cities, we could have chosen. Informally, we choose these four locations from our own preferences. We believe these cities have strong OSM contributor communities, good OSM coverage, different sizes of overall geographical area, and the likelihood of a large number of relation objects. We also felt that it would be important to consider cities where bulk imports of geographic data had been performed on the local OSM database. Bulk imports have been identified as having influence on contribution patterns in OSM  [28].
Relation Data Extraction To perform the data extraction, we carried out the following set of steps. The OSM XML for Madrid, Rome, Vienna and Zurich was downloaded from GeoFabrik on 30 June 2017. We downloaded the whole country files for Spain, Italy, Austria and Switzerland and then extracted the cities directly from this. Using a Python script, we extracted all of the relations for each of the cities and stored these in a PostgreSQL PostGIS database. Java software was developed to perform the analysis of the relations within the PostGIS database. R was used for statistical calculations and visualisations. In summary, the number of relations in each city are as follows: Vienna 9177, Zurich 1855, Rome 13,226 and Madrid 6403. We used the OSM relation representing each city boundary in OSM as a means of extracting the relations in each city in an equivalent manner.
We believe that this may be related to specific tagging strategies for relations related to public transportation or buildings. However, a closer analysis of these objects is required in order to correctly ascertain why this is the case. In Table  3 , we summarize an analysis of tags associated with the five most frequently occurring types of relations. These relations are identified using the type tag key. They are amongst the most frequently mapped relations in the global OpenStreetMap database  [26]. For each polygon, we consider the other tags with co-occur with the type key. The table is organised as follows. For each city, the digit 1 following the city name indicates the most frequently occurring tag key with the corresponding type tag key for that city. The digit 2 following the city name indicates the second most frequently occurring tag key with the corresponding type tag key for that city.
In the case of Vienna, an investigation of change set activity revealed that in the age period 1620 days to 1800 there were 1570 relation objects edited, 90% of which are type = multipolygon. Two users are responsible for over 50% of these edits. This potential bulk import explains the spike in the Vienna distribution during this age period. Another aspect of the edit frequency of relations is contained in the edit version number attribute. Each time an object is edited and submitted to the OSM database, the version number attribute of that objected is incremented by one. Several authors  [5, 8, 39, 40]  have considered the version number of objects in OSM as an important attribute in relation to data quality, contributor pattern analysis and general understanding of OSM mapping. In Figure  5 , we provide the distribution of edit version number of all relations within the four cities.
The total percentage of the current version of all relations edited in each city by the top ten most frequent contributors to relations are as follows: Vienna 53. 44%, Zurich 57. 08%, Madrid 61. 13% and Rome 85. 23%. Through further investigation in the Rome dataset, we found that one particular user (username = Davlak) was the most frequent contributor, and responsible for 32% of the current versions of relations in Rome. It appears that these relations are mostly part of a bulk import or strategic editing of public transportation related relations in Rome.
In their work on analysis of the history of OSM objects, Mooney and Corcoran  [49]  find that over 90% of objects in the their analysis have three or fewer versions. However this requires further investigation in regards to relations. The complexity of relation objects may also have a significant role to play in their actual editing by contributors. Efentakis et al. [50]  argue that in the case of turn restrictions for navigation satellite imagery cannot testify to the existence of restrictions and contributing turning restrictions even for a single road to the OSM dataset may be extremely time-consuming. • Relation Size: The size of relations refers to the number of members contained within the relation itself. In this respect, relations are different to OSM nodes and ways which are single self-contained objects.
Anecdotally, we speculate that relations of small size will not be representing complex spatial relationships or topologies. However, relation size along would not indicate the true nature of the complexity of the relation. It would also be necessary to consider the members themselves within the relation. • Contributors to Relations: In the absence of a full analysis of the history of all edits to relations in each city it is difficult to draw robust conclusions about contribution patterns. However, it was observed that, in the current version of relations in the OSM database for the four cities, a small number of contributors are responsible for a considerable amount of the current version edits. At this stage of our work, it is difficult to accurately quantify the sociological factors most influencing the obtained results.
To indicate the presence of more general trends or patterns we will need to carefully select cities or urban areas based on their urban characteristics or OSM community structure. Ballatore et al. [52]  emphasise the need to ensure that OSM analysis extends beyond the "typical Anglo-American bias". They recommend that analysis considers a diverse set of national and regional OSM communities. We believe that this is the first academic paper to report the results of a study into relations in OSM. Relations in OSM provide database representations of the complexity of the real world for infrastructure such as junctions and road intersections, route structures such as hiking and bike trails, public transport lines and routes, and multipolygons representing a plethora of objects from lakes to university campuses.
are 14 members of this relation. Two of the members are themselves relations while the remaining 12 are ways representing part of the bicycle route ref 66. The first part of the XML for the relation tag has all of the standard OSM attribute information: the current version number of the relation, the timestamp of the last edit, the user ID and username of the last contributor to edit the relation, the change set of edits that this relation edit in contained in, and the OSM ID. Each relation, as is the case for nodes and ways, has its own unique identification number (OSM ID) within the OSM database. If one can access these number then the OSM website provides a simple way to visualise relations and their history within a single API (Application Programming Interface) call. For example the Tiergarten suburb of Berlin, Germany, has an OSM ID of 55750.
Numerical Modelling Using Strand7 The numerical modelling, using the computer software Strand7, presented below, consists of two stages. In the first stage, a benchmark model was carried out using nonlinear static solver for a comparative study. The comparison was based on the failure load values. For the sake of simplicity and saving computational time, the second stage was to investigate the steel bracket connection using linear analysis. The aim was to provide an overview into the structural behaviours of the steel bracket connection when varying the bolthole dimension and bolthole spacing.
Furthermore, extreme precipitations affect the Spanish Mediterranean region every year, which represent a substantial part of the total amounts. Two dams are found in the montainous regions of the Llobregat (Fig. 2 a ). Available raw precipitation consists of 5-minute rainfall data recorded at 81 stations inside or very close to the Llobregat basin (Fig. 2 a ). These pluviometric stations belong either to the Catalan Agency of Water -Automatic Hydrological Information System (ACA-SAIH) or the Spanish Agency of Meteorology (AEMET) networks. Five-minute stream flow data is recorded at four hydrometric sections in the Llobregat River. These stream-gauges are included in the ACA-SAIH network and are deployed in: (i) Súria town, with a dranaige area of 940 km 2 (labelled as Súria); (ii) Sant Sadurní d'Anoia city, with a drainage area of 736 km 2 (Sadurní); and (iii) Castellbell (3340 km 2 ) and (iv) Sant Joan Despí (4915 km 2 ) towns (labelled as Castellbell and Despí, respectively).
A more detailed description of the Llobregat river basin and databases can be found in  Amengual et al. (2007). The first selected event occured in autumn 2011, which has been loaded with heavy precipitation events (HPE) in the north-western Mediterranean  (Hally et al. , 2013; Silvestro et al. , 2012; Rebora et al. , 2013). The northern part of Catalonia was particularly affected at the beginning of November, 2011. On November 2nd, the large-scale situation was dominated by a deep, cold upper-level trough approaching from the North Atlantic Ocean while a southeasterly low-level flow was strengthening over the Catalonia region. Those atmospheric conditions favoured convection and heavy rainfall occured. A maximum amount of daily precipitation of 202. 9 mm was recorded on 3 November over Catalonia and slightly above 150 mm over the Llobregat River basin.
Therefore, this channel partially diverts large runoff discharges from the Guadalentín River into the Mediterranean Sea. Further details on the Guadalentín and databases can be found in  Amengual et al. (2015). The second study case is a classical Spanish Mediterranean HPE that affected Andalusia, Murcia and later on Valencia and Catalonia (even if less intense there) from 27 to 29 September 2012. This case has been documented during the HyMex campaign within an Intensive Observing Period, IOP8  (Amengual et al. , 2015). About ten casualties have been deplored and damage has been estimated to more than 120 Meuros. On 27 September 2012, at upper-level, the synoptic atmospheric situation was controled by a cut-off low centered on the South-West of the Iberian Peninsula moving slowly north-eastward and a lowlevel depression centered upon inner Andalusia.
Furthermore, the distinct hydrological ensemble strategies would prove useful for conveying proper information to civil protection and emergency decision-makers before both floods as [P (Q ≥ q)s] are all well above 0. 2. Note that the different discharge return periods quantify the risk of facing hazardous floodings. The hourly discharges ensemble forecats skills have been assessed computing objective scores on the whole data sample. Specifically, RP SS, RM SE, σ and σ RM SE have been computed by using all the outlets and both cases in order to increase the statistical significance. See, for instance,  Vincendon et al. (2011)  for the scores formulation. Recall that the RP SS assesses the benefit on the studied ensembles compared with the deterministic version of the same model. RP SS score greater than zero means better skill for the ensemble than for the reference.
The lesser spread of the WRF than the AROME ensemble can be attributed to the fact that the synoptic and large mesoscale dynamical and thermodynamical environment is sufficiently accurate in the ECMWF unperturbed member. Therefore, less additional information is conveyed by the perturbed IC/LBC ensemble members. On the other hand, the AROME ensemble is founded on a climatological model error database, accounting not just for inaccuracies in the IC/LBCs, but also in the model formulation. In this case, it would be advisable to also account for inaccuracies in the WRF model physical schemes when using a perturbed IC/LBC ensemble in order to further span the EPS spread. The different spreads in the HEC-HMS and ISBA-TOP models can be attributed to the distinct model infiltration schemes, when initiating runoff as a based-threshold process.
Acknowledgements. This work is a contribution to the HyMeX programme. The  Agència Catalana de l'Aigua (ACA)  and the  Confederación Hidrográfica del Segura (CHS)  are acknowledged for providing data. The  Spanish Agency of Meteorology (AEMET)  is also acknowledged for providing data from the automatic weather stations. This work has been sponsored by the  CGL2014-52199-R  and PCIN-2015-221 Spanish projects, which are partially supported by  FEDER  funds. 16 Hydrol. Earth Syst. Sci. Discuss. , https://doi. org/10. 5194/hess-2017-427Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 18 July 2017 c Author(s)  2017. CC BY 4. 0 License.
This process is called dynamic airspace configuration (DAC). In DAC, airspace configurations are generated so as to reduce the coordination workload between adjacent controlled sectors and to achieve workload balancing between them for each time period of the day. The DAC process also has to ensure that configurations are stable over time periods. Other important aspects of DAC concern the reduction of multiple entries of an aircraft in the same sector and the maximization of the average flight time through the sector. The DAC problem is even more critical in the SESAR or NextGen framework. In comparison with a currently used fixed route network, the SESAR program introduces the user preferred routing (UPR) or free routing concept to enable the airspace users to plan freely 4D trajectories that suit them best.
Motivation Regression and classification techniques, represented in the general class of regularized loss minimization problems  [67] , are among the most central tools in modern big data analysis, machine learning, and signal processing. For these tasks, much effort from both industry and academia has gone into the development of highly tuned and customized solvers. However, with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer fits in the memory of a single computer, and computation must be split across multiple machines in a network  [3, 7, 12, 18, 22, 28, 32, 34, 37, 46, 50, 58, 60, 62, 76]. On typical real-world systems, communicating data between machines is several orders of magnitude slower than reading data from main memory, e.