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Ingredients for Semantic Sensor Networks

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  • The where clasue for both SPARQL extensions is the same
  • Transcript

    • 1. Ingredients for the Semantic Sensor Web
      Jožef Stefan Institute
      Ljubljana, Slovenia
      September 23rd 2011
      Oscar Corcho
      Facultad de Informática,Universidad Politécnica de Madrid
      Campus de Montegancedosn, 28660 Boadilla del Monte, Madrid
      http://www.oeg-upm.net
      ocorcho@fi.upm.es
      Phone: 34.91.3366605
      Fax: 34.91.3524819
    • 2. Index
      PART I. Motivation
      From Sensor Networks… … to the Sensor Web / Internet of Things… … to Semantic Sensor Web and Linked Stream/Sensor Data
    • 3. Sensor Networks
      Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources
      Source: Antonis Deligiannakis
    • 4. Anexample: SmartCities
      4
      Environmentalsensor nodes
      Parking sensor nodes
      Santander
    • 5. Sensor Networks and Streaming Data
      5
      • Streaming Data
      • 6. Continuously appended data
      • 7. Potentially infinite
      • 8. Time-stamped tuples
      • 9. Continuous queries
      • 10. Latest used in queries
      • 11. Time and tuple-based windows
      (t9, a1, a2, ... , an)
      (t8, a1, a2, ... , an)
      (t7, a1, a2, ... , an)
      ...
      ...
      (t1, a1, a2, ... , an)
      ...
      ...
      Window [t7 - t9]
      Streaming Data
      • Sensor Networks
      • 12. Cheap, Noisy, Unreliable (depends)
      • 13. Low computational, power resources, storage
      • 14. Distributed query execution
      • 15. Routing, Optimization
      Query
      EnablingSemanticIntegration of Streaming Data Sources
    • 16. Who are theendusers of sensor networks?
      Theclimatechangeexpert, or a simple citizen
      Source: Dave de Roure
    • 17. Notonlyenvironmentalsensors, butmanyothers…
      7
      Weather Sensors
      Sensor Dataset
      GPS Sensors
      Satellite Sensors
      Camera Sensors
      Source: H Patni, C Henson, A Sheth
    • 18. How do wemakethesesensors more accessible?
      8
      Source: SemsorGrid4Env consortium
    • 19. The Sensor Web (relatedto Internet of Things)
      Universal, web-based access to sensor data
      Some sensor networkproperties:
      Networked
      Mostlywireless
      Each network with some kind of authority and administration
      Sometimes noisy
      9
      Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009
    • 20. Should we care as computer scientists?
      They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines]
      Hence interesting for those computer scientists interested on helping these users… We are many ;-)
      But they are also interesting for “pure” computer scientists
      They address an important set of “grand challenge” Computer Science issues including:
      Heterogeneity
      Scale
      Scalability
      Autonomic behaviour
      Persistence, evolution
      Deployment challenges
      Mobility
      Source: Dave de Roure
    • 21. A semanticperspectiveonthesechallenges
      Sensor data querying and (pre-)processing
      Data heterogeneity
      Data quality
      New inferencecapabilitiesrequiredtodealwith sensor information
      Sensor data modelrepresentation and management
      For data publication, integration and discovery
      Bridgingbetween sensor data and ontologicalrepresentationsfor data integration
      Ontologies: Observations and measurements, time series, etc.
      Eventmodels
      Userinteractionwith sensor data
    • 22. Vision (aftersomeiterations, and more to come)
      12
      RWI WorkingGrouponIoT: NetworkedKnowledgeGluhak et al, 2011. AnArchitecturalBlueprintfor a Real-World Internet', FutureInternet Assembly
    • 23. Semantic Sensor Web / LinkedStream-Sensor Data (LSD)
      A representation of sensor/streamdata followingthestandards of LinkedData
      ButwhatisLinked Data?
    • 24. WhatisLinked Data?
      14
      • An extension of the current Web…
      • 25. … where data are given well-defined and explicitly represented meaning, …
      • 26. … so that it can be shared and used by humans and machines, ...
      • 27. ... better enabling them to work in cooperation
      • 28. And clear principles on how to publish data
    • The fourprinciples (Tim Berners Lee, 2006)
      Use URIs as names for things
      Use HTTP URIs so that people can look up those names.
      When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)
      Include links to other URIs, so that they can discover more things.
      http://www.w3.org/DesignIssues/LinkedData.html
      15
      http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
      http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
    • 29. Semantic Sensor Web / LinkedStream-Sensor Data (LSD)
      A representation of sensor/streamdata followingthestandards of LinkedData
      Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data source
      Usingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections
      Earlyreferences…
      AmitSheth, CoryHenson, and SatyaSahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83
      Sequeda J, Corcho O. LinkedStream Data: A Position Paper. Proceedingsof the 2nd International WorkshoponSemantic Sensor Networks, SSN 09
      Le-Phuoc D, Parreira JX, Hauswirth M. Challengesin LinkedStream Data Processing: A Position Paper. Proceedingsof the3rd International WorkshoponSemantic Sensor Networks, SSN 10
    • 30. Let’schecksomeexamples
      Meteorological data in Spain: automaticweatherstations
      http://aemet.linkeddata.es/
      Paperunder open review at theSemantic Web Journal
      http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data
      Live sensors in Slovenia
      http://sensors.ijs.si/
      ChannelCoastalObservatory in Southern UK
      http://webgis1.geodata.soton.ac.uk/flood.html
      And some more from DERI Galway, Knoesis, CSIRO, etc.
      17
    • 31. AEMET Linked Data
      18
    • 32. JSI Sensors
      19
    • 33. Coastal Channel Observatory and other sources
      20
      Sensors, Mappings and Queries
      Work with Flood environmental sensor data.
      SemSorGrid4Env project www.semsorgrid4env.eu.
    • 34. PART II
      How to create, publish and consume Linked Stream Data
    • 35. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 36.
      • Several efforts since approx. 2005
      • 37. State of the art on sensor network ontologies in the report below
      • 38. In 2009, a W3C incubator group was started, which has just finished
      • 39. Lots of good people there
      • 40. Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
      • 41. Ontology: http://purl.oclc.org/NET/ssnx/ssn
      • 42. A good number of internal and external references to SSN Ontology
      • 43. http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography
      • 44. SSN Ontology paper submitted to Journal of Web Semantics
      SSN ontologies. History
    • 45. Deployment
      System
      OperatingRestriction
      Process
      Device
      PlatformSite
      Data
      Skeleton
      ConstraintBlock
      MeasuringCapability
      Overview of the SSN ontology modules
    • 46. deploymentProcesPart only
      Deployment
      System
      OperatingRestriction
      hasSubsystem only, some
      hasSurvivalRange only
      SurvivalRange
      DeploymentRelatedProcess
      hasDeployment only
      System
      OperatingRange
      Deployment
      hasOperatingRange only
      deployedSystem only
      deployedOnPlatform only
      Process
      hasInput only
      inDeployment only
      Device
      Input
      Device
      Process
      onPlatform only
      PlatformSite
      Output
      Platform
      hasOutput only, some
      attachedSystem only
      Data
      Skeleton
      implements some
      isProducedBy some
      Sensor
      Sensing
      hasValue some
      SensorOutput
      sensingMethodUsed only
      detects only
      SensingDevice
      observes only
      SensorInput
      ObservationValue
      isProxyFor only
      Property
      isPropertyOf some
      includesEvent some
      observedProperty only
      observationResult only
      hasProperty only, some
      observedBy only
      Observation
      FeatureOfInterest
      featureOfInterest only
      ConstraintBlock
      MeasuringCapability
      hasMeasurementCapability only
      forProperty only
      inCondition only
      inCondition only
      Condition
      MeasurementCapability
      Overview of the SSN ontologies
    • 47. SSN Ontology. Sensor and environmental properties
      Skeleton
      Property
      Communication
      MeasuringCapability
      hasMeasurementProperty only
      MeasurementCapability
      MeasurementProperty
      Accuracy
      Frequency
      Precision
      Resolution
      Selectivity
      Latency
      DetectionLimit
      Drift
      MeasurementRange
      ResponseTime
      Sensitivity
      EnergyRestriction
      OperatingRestriction
      hasOperatingProperty only
      OperatingProperty
      OperatingRange
      EnvironmentalOperatingProperty
      MaintenanceSchedule
      OperatingPowerRange
      hasSurvivalProperty only
      SurvivalRange
      SurvivalProperty
      EnvironmentalSurvivalProperty
      SystemLifetime
      BatteryLifetime
    • 48. A usageexample
      Upper
      SWEET
      DOLCE UltraLite
      SSG4Env
      infrastructure
      SSN
      Schema
      Service
      External
      OrdnanceSurvey
      FOAF
      Flood domain
      CoastalDefences
      AdditionalRegions
      Role
      27
    • 49. AEMET Ontology Network
      83 classes
      102 objectproperties
      80 datatypeproperties
      19 instances
      SROIQ(D)
    • 50. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 51. Goodpractices in URI Definition
      Sorry, no clearpracticesyet…
    • 52. Goodpractices in URI Definition
      Wehavetoidentify…
      Sensors
      Features of interest
      Properties
      Observations
      Debate betweenbeingobservationor sensor-centric
      Observation-centricseemsto be thewinner
      Forsomedetails of sensor-centric, check [Sequeda and Corcho, 2009]
    • 53. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 54. Queries to Sensor/Stream Data
      SNEEql
      RSTREAM SELECT id, speed, direction
      FROM wind[NOW];
      Streaming SPARQL
      PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
      SELECT ?sensor ?speed ?direction
      FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS
      WHERE {
      ?sensor a fire:WindSensor;
      fire:hasMeasurements ?WindSpeed, ?WindDirection.
      ?WindSpeed a fire:WindSpeedMeasurement;
      fire:hasSpeedValue ?speed;
      fire:hasTimestampValue ?wsTime.
      ?WindDirection a fire:WindDirectionMeasurement;
      fire:hasDirectionValue ?direction;
      fire:hasTimestampValue ?dirTime.
      FILTER (?wsTime == ?dirTime)
      }
      C-SPARQL
      REGISTER QUERY WindSpeedAndDirection AS
      PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
      SELECT ?sensor ?speed ?direction
      FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]
      WHERE { …
      33
      Semantically Integrating Streaming and Stored Data
    • 55. SPARQL-STR v1
      34
      Sensors, Mappings and Queries
      SELECT ?waveheight
      FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
      [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE]
      WHERE {
      ?WaveObs a sea:WaveHeightObservation;
      sea:hasValue ?waveheight; }
      SELECT measuredFROM wavesamples [NOW -10 MIN]
      conceptmap-def WaveHeightMeasurement
      virtualStream <http://ssg4env.eu/Readings.srdf>
      uri-as concat('ssg4env:WaveSM_',
      wavesamples.sensorid,wavesamples.ts)
      attributemap-defhasValue
      operation constant
      has-columnwavesamples.measured
      dbrelationmap-def isProducedBy
      toConcept Sensor
      joins-via condition equals
      has-column sensors.sensorid
      has-columnwavesamples.sensorid
      conceptmap-def Sensor
      uri-as concat('ssg4env:Sensor_',sensors.sensorid)
      attributemap-def hasSensorid
      operation constant
      has-column sensors.sensorid
      Query
      translation
      SNEEql
      SPARQLStream
      Query Processing
      Stream-to-Ontology
      mappings
      Client
      Sensor Network
      Data
      translation
      [tuples]
      [triples]
      S2O Mappings
      Source: EnablingOntology-based Access toStreaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010
    • 56. SPARQL-STR v2
      SPARQLStream algebra(S1 S2 Sm)
      GSN
      Query
      translation
      q
      SNEEql, GSN API
      Sensor Network (S1)
      SPARQLStream (Og)
      Relational DB (S2)
      Query Evaluator
      Stream-to-Ontology
      Mappings (R2RML)
      Client
      Stream Engine (S3)
      RDF Store (Sm)
      Data
      translation
      [tuples]
      [triples]
      Ontology-based Streaming Data Access Service
      Source: PlanetDatadeliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu
    • 57. CreatingMappings
      36
      Sensors, Mappings and Queries
      ssn:observedProperty
      ssn:Observation
      ssn:Property
      http://swissex.ch/data#
      Wan7/WindSpeed/Observation{timed}   
      sweetSpeed:WindSpeed
      ssn:observationResult
      wan7
      ssn:SensorOutput
      timed: datetime PK
      sp_wind: float
      http://swissex.ch/data#
      Wan7/ WindSpeed/ ObsOutput{timed}   
      ssn:hasValue
      ssn:ObservationValue
      http://swissex.ch/data#
      Wan7/WindSpeed/ObsValue{timed}
      qudt:numericValue
      xsd:decimal
      sp_wind
    • 58. R2RML
      RDB2RDF W3C Group, R2RML Mappinglanguage:
      http://www.w3.org/2001/sw/rdb2rdf/r2rml/
      37
      Sensors, Mappings and Queries
      :Wan4WindSpeed a rr:TriplesMapClass;
      rr:tableName "wan7";
      rr:subjectMap [ rr:template
      "http://swissex.ch/ns#WindSpeed/Wan7/{timed}";
      rr:classssn:ObservationValue; rr:graphssg:swissexsnow.srdf ];
      rr:predicateObjectMap [ rr:predicateMap [ rr:predicatessn:hasQuantityValue];
      rr:objectMap[ rr:column "sp_wind" ] ]; .
      <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 >
      a ssn:ObservationValue
      <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 >
      ssn:hasQuantityValue "4.5"
    • 59. 38
      Red de Ontologías para el Camino de Santiago
      QueryTransformationSemantics
      • ConjunctiveQueries
      • 60. Mapping
      expression
      overstreamingsources
      conjunctive
      query
    • 61. Algebra expressions transformed to GSN API
      39
      Sensors, Mappings and Queries
      π
      SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10
      timed,
      sp_wind
      σ
      sp_wind>10
      ω
      5 Hour
      http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &
      field [0]= sp_wind &
      from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&
      c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10
      wan7
    • 62. Algebra construction
      40
      Sensors, Mappings and Queries
      π
      timed,
      sp_wind
      windsensor1
      σ
      windsensor2
      sp_wind>10
      ω
      5 Hour
      wan7
    • 63. Staticoptimization
      41
      Sensors, Mappings and Queries
      π
      π
      π
      timed,
      sp_wind
      timed,
      windvalue
      timed,
      windvalue
      σ
      σ
      σ
      sp_wind>10
      windvalue>10
      windvalue>10
      ω
      ω
      ω
      5 Hour
      5 Hour
      5 Hour
      wan7
      windsensor1
      windsensor2
    • 64. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 65. Data Modeling: stRDF
      • stRDF
      • 66. Temporal/spatial data are represented by linear constraints, representing as literals of type strdf:semiLinearPointSet.
      • 67. OGC simple feature geometries (points, polylines, polygons etc.) using the Well-known Text representation
      floodInstances:ModelledFloodIngressDataset
      rdf:typeinfo:Dataset;
      rdfs:label "Modelled flood ingress Dataset" ; time:hasTemporalExtent "[NOW,NOW+12]"^^RegistryOntology:TemporalInterval;
      Services:coversRegionAdditionalRegions:CoastalDefencePartnershipModelledArea; Services:includesFeatureTypeCoastalDefences:FloodPlain ; Services:includesPropertyTypeCoastalDefences:WaterDepth.
      AdditionalRegions:CoastalDefencePartnershipModelledArea
      rdf:typespace:Region; Services:hasSpatialExtent "POLYGON((625145.2823357487 5624227.2548582135, 625145.2823357487 5637255.203057151, 647383.6564885917 5637255.203057151, 647383.6564885917 5624227.2548582135, 625145.2823357487 5624227.2548582135))"^^RegistryOntology:WKT.
      Source: Our NKUA partners at SemsorGrid4Env
      2nd Year Review Meeting - Brussels, 16-17 Nov. 2010
      43
    • 68. Querying: stSPARQL
      Find all WMS services with FOI flood plain that cover the Coastal Defence Partnership modelled area and provide valid information for the next 12 hours
      select distinct ?ENDPOINT
      where { ?SERVICE rdf:typeServices:WebService .
      ?SERVICE Services:hasEndpointReference ?ENDPOINT .
      ?SERVICE Services:hasServiceTypeServices:WMS .
      ?SERVICE Services:hasDataset ?DATASET .
      ?DATASET Services:includesFeatureTypeCoastalDefences:FloodPlain.
      ?DATASET time:hasTemporalExtent ?TIME .
      filter(?TIME contains “[NOW,NOW+12]"^^RegistryOntology:TemporalInterval) .
      ?DATASET Services:coversRegion ?SERVICEREGION .
      ?SERVICEREGION Services:hasSpatialExtent ?SERVICEREGIONGEO .
      AdditionalRegions:CoastalDefencePartnershipModelledArea
      Services:hasSpatialExtent ?COSTALGEO .
      filter(?SERVICEREGIONGEO contains ?COSTALGEO) }
      Source: Our NKUA partners at SemsorGrid4Env
      2nd Year Review Meeting - Brussels, 16-17 Nov. 2010
      44
    • 69. Implementation: STRABON
      SupportforstRDF and SPARQL, plus
      Topologicaloperators in spatialfilters
      DISJOINT, TOUCH, EQUALS, CONTAINS, COVERS, COVERED BY, OVERLAP
      ConstructSpatialGeometries
      e.g. ?geo1 union ?geo2
      Projectionoperation
      e.g. ?geo[1,2]
      Renameoperator
      ConversionFunctionsforexportinggeometries:
      e.g. ToWKT(?geo) AS ?geoAsWKT
      Library thatreturns SPARQL results as a KML document
      45
      Source: Our NKUA partners at SemsorGrid4Env
    • 70. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 71. Sensor High-level API
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 72. Sensor High-level API
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 73. API definition
      Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
    • 74. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 75. SwissEx
      51
      Sensors, Mappings and Queries
      Global Sensor Networks, deployment for SwissEx.
      Distributedenvironment: GSN Davos, GSN Zurich, etc.
      In each site, a number of sensorsavailable
      Each one withdifferentschema
      Metadatastored in wiki
      Federatedmetadata management:
      Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T., Lehning, M.EffectiveMetadata Management in federatedSensor Networks.  in SUTC, 2010
      Sensor observations
      Sensormetadata
    • 76. Gettingthingsdone
      Transformed wiki metadata to SSN instances in RDF
      Generated R2RML mappings for all sensors
      Implementation of Ontology-basedquerying over GSN
      Fronting GSN with SPARQL-Stream queries
      Numbers:
      28 Deployments
      Aprox. 50 sensors in eachdeployment
      More than 1500 sensors
      Live updates. Lowfrequency
      Access to all metadata/not all data
      52
      Sensors, Mappings and Queries
    • 77. SensorMetadata
      53
      Sensors, Mappings and Queries
      station
      location
      sensors
      model
      properties
    • 78. Sensor Data: Observations
      54
      Sensors, Mappings and Queries
      Heterogeneity
      Integration
    • 79. SPARQL-STR + GSN
    • 80. HowtodealwithLinkedStream/Sensor Data
      Ingredients
      Anontologymodel
      Goodpractices in URI definition
      Supportingsemantictechnology
      SPARQL extensions
      Tohandle time and tuplewindows
      Tohandlespatio-temporal constraints
      REST APIstoaccessit
      Anotherexample: semanticallyenriching GSN
      A couple of lessonslearned
    • 81. LessonsLearned
      High-level (part I)
      Sensor data isyetanothergoodsource of data withsomespecialproperties
      Everythingthatwe do withourrelationaldatasetsorother data sources can be done with sensor data
      Practicallessonslearned (part II)
      Manageseparatelydata and metadata of thesensors
      Data shouldalways be separatedbetweenrealtime-data and historical-data
      Use the time formatxsd:dateTimeand the time zone
      Graphicalrepresentation of data forweeksormonthsisnot trivial anyway
    • 82. Ingredients for the Semantic Sensor Web
      Jožef Stefan Institute
      Ljubljana, Slovenia
      September 23rd 2011
      Oscar Corcho
      Acknowledgments: allthoseidentified in slides + the SemsorGrid4Env team (Jean Paul Calbimonte, Alasdair Gray, Kevin Page, etc.), the AEMET team at OEG-UPM (GhislainAtemezing, Daniel Garijo, José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET)