dataesr/scientific-paragraphs-categorization huggingface dataset:
The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2. 0 dataset (https://doi. org/10. 5270/esa-sea_level_cci-1993_2015-v_2. 0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset.
The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3. 2 mm yr -1 during 1992-2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1. 1 of the SL_cci dataset, the new rate of Published by Copernicus Publications. 558 G. D. Quartly et al. : A new phase in the production of quality-controlled sea level data change is 0. 2 mm yr -1 less during 1993 to 2001 and 0. 2 mm yr -1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections.
Together these effects and sea level rise amplify the vulnerability of coastal regions, producing major societal impacts. Finally, sea level variations need to be precisely monitored at the mesoscale (50-200 km) as the variability associated with eddies and current fluctuations provides many of the mechanisms for transporting and mixing water masses, with attendant effects on primary productivity. The European Space Agency (ESA) set up the Climate Change Initiative (CCI) in 2010 to develop consistent longterm datasets of many of the recognised essential climate variables (ECVs), with one using satellite altimetry to provide sea level data over most of the open ocean, with the aim of addressing part of the aforementioned wide range of scientific and societal needs. The initial (v1. 0) dataset spanned 1993-2010  (Ablain et al.
, 2015 (Ablain et al. , , 2016)) ; the second phase of the  CCI (2014 CCI ( -2016) )  has not only extended the data duration (up to end of 2015) but also revisited many aspects of the data processing and corrections to improve the quality of the dataset for global, regional and mesoscale applications. This paper details the processing options selected for the production of the v2. 0 dataset. The whole dataset is based on the concept of altimetry, i. e. that a satellite flying in a near-polar orbit measures the ocean surface topography by recording the time taken for radar pulses emitted by the satellite to reflect off the surface and be recorded on the satellite. There are many technical details to the measurement of this distance to within a few centimetres from a satellite ∼ 720-1350 km above the Earth's surface, which are described in  Chelton et al.
Thus the required oceanographic parameter, the sea level anomaly (SLA), is defined as SLA = Orbit -(Range + DTC + WTC + Iono) -DAC -Tides -SSB -MSS, (1) where the mean sea surface (MSS) is the sum of the geoid (the geopotential surface indicating the level that would be recorded for a motionless ocean) and the mean dynamic topography (MDT), which corresponds to the topographic variations associated with the mean circulation of the ocean. Values for these corrections are supplied in the geophysical data records (GDRs) provided by the space and meteorological agencies; however, there is a need to review whether new ones are more accurate, and also to establish a consistent selection across all missions used. Gridded altimeter products combine information from two sets of altimeters -the "reference missions" (TOPEX/Poseidon, Jason-1, Jason-2 etc.
05-day) and CryoSat-2 (88 • inclination, geodetic orbit). The periods indicated by white bars with red outlines are not used in the production of CCI v2. 0 product. (b) Annual amount of independent altimeter data used in the production of the v2. 0 dataset. (Note, for example, that during the 6-month "tandem" phases between successive "reference missions" the contribution of one of the pair to the sea level record is redundant. ) several of which (ERS-1, ERS-2, Envisat) have been in a 35-day repeat orbit. In progressing from the Sea Level CCI (SL_cci) v1. 1 product to the v2. 0 product, the length of the dataset has been extended and two new sources of altimeter data have been included (SARAL/AltiKa and CryoSat-2; see Fig. 1 ), and all the corrections have been reappraised to ensure that they are the most appropriate for establishing a consistent and stable long-term record for use at global, regional and mesoscale.
Note that the SL_cci Algorithms Theoretical Basis Document  (Ablain et al. , 2016)  provides the details on all algorithms used to compute the 1 Hz along-track measurements. This paper deals with each of these correction terms, documenting the selections made and their justification; subsequent papers will exploit the SL_cci v2. 0 data to improve our understanding of present-day sea level variations at global and regional scales, and their causes. The assessment of new corrections has been carried out by a formal validation protocol using a common set of diagnoses defined to fulfil the sea level accuracy and precision requirements, as defined by the Global Climate Observing System  (GCOS, 2011). This protocol consists of comparing new al-timeter corrections with previous ones through their impact on the sea level calculation.
Wet tropospheric correction The University of Porto has developed a robust method for determining the WTC by data combination through spacetime objective analysis of various data types: valid measurements from the on-board MWR (whenever available) and third-party observations from GNSS and scanning imaging MWR. The latest version of these corrections, designated GNSS-derived Path Delay Plus (GPD+; see  Fernandes and Lázaro, 2016) , includes improved calibration of all radiometers on altimetric satellites by comparing them with the known stable performance of the SSMI and SSMIS. In addition to the calibration with respect to SSMI and SSMIS, the original GPD solution  (Fernandes et al. , 2015)  has been augmented by adding new datasets (from scanning imaging radiometers) and improved selection criteria for selecting valid MWR observations.
The third effect relates to the algorithms used to find the range -this effect will vary with each retracker implemented, but should be the same for identical instruments. However, there are always slight differences between sister instruments, e. g. ERS-1 and ERS-2, so the overall sea state bias model is usually determined independently for each altimeter plus retracker. In practice, all three of these effects scale roughly with wave height, so the overall sea state bias is expressed as a multiplier of wave height that is a weakly varying function of sea state conditions. Although the first two components of SSB should be the same for all Ku-band observing systems, a separate total SSB solution has to be derived for each individual altimeter. For each dataset, minimisation procedures are used to express SSB in terms of wave height and wind speed, leading to the least variance at crossovers.
Corrections for short-term atmospheric and oceanographic phenomena Our concern within the sea level CCI project is to provide the best dataset for observing climate scale variations in sea level and changes associated with geostrophic currents. The temporal sampling by altimeters is insufficient to resolve all timescales, so high-frequency ocean variability is aliased to longer timescales, thus polluting climate estimations if not adequately corrected. Thus, short-term effects have to be removed using accurate physical ocean models, which are expected to be independent of satellite missions.
Next, there is the "pole tide", a term describing the small long-period oscillations associated with the movement of the Earth's rotational axis. The recent advance by  Desai et al. (2015)  takes into account self-gravitation, loading, conservation of mass, and geocentre motion. Moreover, this new model includes a bias and a drift, which means that the new computed pole tide does not include the effects of the Earth's displacement response to that mean pole drift. Removing the long-term mean pole drift has a significant impact on the regional MSL trend estimation; this impact has been validated by comparisons with an Argo database over the time span of the Envisat mission (ASM, 2015a;  Legeais et al. , 2017). Thus the recent model of  Desai et al. (2015)  is the one implemented in SL_cci v2.
Reference surfaces For some applications, it is useful to estimate sea level anomalies with respect to the mean sea surface (MSS), which is the sum of the geoid and mean dynamic topography (see Eq. 1). Frequent updates of the MSS are provided as new data become available, in particular from CryoSat-2 at high latitudes, and from the "end of life" geodetic phases of recent missions. The SL_cci v2. 0 is referenced to the DTU15 MSS (ASM, 2015e), and corresponds to a mean over the period 1993-2012. The DTU datasets provide a complete global coverage (including the high-latitude Arctic). This version is an improvement on earlier versions (DTU10 & DTU13, see  Andersen et al. , 2015)  in that it makes use of 4 years of CryoSat-2 data but gives less weighting to data from IceSat (whose large errors gave an unrealistic stripiness to derived MSS fields).
Finally, output checks and quality control are performed and the multi-satellite along-track data are mapped to generate gridded sea level products. The sensitivity of the gridded products to the mapping algorithms is described in detail in  Pujol (2012). Different mapping methods were tested in order to assess their ability to accurately reproduce climate signals. This evaluation has been carried out separating the different temporal and spatial scales related to climate applications. A monthly optimal interpolation is applied (including additional weighted information from part of the previous and following months) to produce maps of sea level on a 0. 25 • grid for the middle of each month. Note that this approach differs from the one used in the production of the DU-ACS dataset  (Pujol et al.
Conclusions During phase 2 of the ESA Sea Level CCI project, the consortium has reappraised all the corrections to be used in the production of the v2. 0 dataset. In some cases, e. g. Earth tide, there has been no change in the recommended correction; in others, such as the pole tide, a new model has become available that is readily endorsed since it significantly improves the accuracy. For many other terms, there was a choice of two or three corrections: the project evaluated these through a variety of techniques including minimisation of mono-mission crossovers, comparison between different altimeter missions, and validation with in situ data. This paper has documented the choices made (Table  1 ). The v2. 0 dataset was released in December 2016, with details provided at http://www. esa-sealevel-cci.
05-day) and CryoSat-2 (88 • inclination, geodetic orbit). The periods indicated by white bars with red outlines are not used in the production of CCI v2. 0 product. (b) Annual amount of independent altimeter data used in the production of the v2. 0 dataset. (Note, for example, that during the 6-month "tandem" phases between successive "reference missions" the contribution of one of the pair to the sea level record is redundant. ).
The other complementary missions are GFO (72 • inclination, 17. 05-day) and CryoSat-2 (88 • inclination, geodetic orbit). The periods indicated by white bars with red outlines are not used in the production of CCI v2. 0 product. (b) Annual amount of independent altimeter data used in the production of the v2. 0 dataset. (Note, for example, that during the 6-month "tandem" phases between successive "reference missions" the contribution of one of the pair to the sea level record is redundant. ).
An active knowledge base (Oro), conveniently thought as a semantic blackboard, connects most of the modules: the geometric reasoning module (Spark) produces at relatively high frequency symbolic assertions describing the state of the robot environment and its evolution over time. These logical statements are stored in the knowledge base, and queried back, when necessary, by the language processing module (Dialogs), the symbolic task planner (HATP) and the execution controller (Shary or pyRobots). The output of the language processing module and the activities managed by the robot controller are stored back as symbolic statements as well. For instance, a book laying on a furniture might be picked up by Spark and represented in symbolic terms as BOOK1 type Book, BOOK1 isOn TABLE. These symbolic statements are stored in the knowledge base Oro and made available to the other cognitive modules.
Knowledge Model In our architecture, knowledge manipulation relies on a central server (the Oro server  [28] , Figure  5  top) which stores knowledge as it is produced by each of the other deliberative components (the clients). It exposes a json-based RPC API to query the knowledge base  [29]. We represent knowledge as RDF triples in the OWL sub-language 2. Every time triples are added or removed from the knowledge base, a Description Logics reasoner (Pellet  [30] ) classifies the whole ontology and inserts all possible inferred triples. The clients of the Oro server are in charge of managing themselves the knowledge (when to add, when to update, when to retract knowledge) as no meta-semantics are carried over that let the server manage itself these dynamics. 3 his architecture design (a central knowledge base that essentially appears as a passive component to the rest of the system -even though it actually actively processes the knowledge pool in the background to perform inferences) departs from other approaches like the CAST model  [31]  where knowledge is represented as a diffuse, pervasive resource, or the CRAM/KnowRob architecture  [32]  where the knowledge base is an active hub that pro-actively queries perceptual components to acquire knowledge.
Alternatives have been proposed (like KnowRob  [33] ) that interleave RDF with more expressive logic languages like Prolog with however other limitations, like closed-world reasoning. Classification performance is another issue: in our experience, an ontology sized for a typical study (about 100 classes and 200 instances), classification takes around 100ms, which may introduce perceptible delays during interactions. Besides, the performances are difficult to predict: the insertion of seemingly simple statements may change abruptly the logical complexity of the knowledge model and lead to a noticeable degradation of classification time. This knowledge model also largely excludes representation of continuous phenomena (like time) or uncertain phenomena. When required (for instance for action recognition), these are managed within dedicated components (like Spark, discussed in Section 3.
2), and are not represented in the knowledge base. Alternative formalisms have been successfully investigated in robotics to address some of these restrictions. Besides the Prolog/OWL combination relied upon by KnowRob, Answer Set Programming has been used in robotics  [35, 36]  for instance to better supports non-monotonic reasoning. We have also pointed in  [37]  how modal logics like the epistemic logics could be relevant to the particular field of social human-robot interaction as they allow for the representation of alternative mental models. First-order logic and OWL ontologies have however proved so far a simple, effective and sufficient symbolic framework for our experimental applications. Incidentally, and because ontologies and RDF statements remain conceptually simple (compared to full logical languages like Prolog or modal logics), their adoption has also effectively helped to grow awareness amongst colleagues on the significance of the "semantic level" when developing new components for the robot.
This upper part of the ontology is pictured on Figure  3. All the classes visible in this figure belong to the OpenCyc namespace. Aligning the upper part of the ontology on OpenCyc (as done by other knowledge representation systems, like KnowRob  [33]  or PEIS K&R  [39] ) has multiple advantages. First the design of this part of the ontology is generally difficult: it pertains to abstract concepts whose mutual relations comes to philosophical debates. The upper taxonomy of OpenCyc represents a relative consensus, at least within the semantic Web community. Then, because it is a well established project with numerous links to other on-line databases (like Wikipedia or WordNet), the reuse of key OpenCyc concepts ensures that the knowledge stored by the robot can be shared or extended with well-defined semantics.
These labels are used in priority during the grounding of verbal interactions. Figure  3  also illustrates the fundamental disjunction in the Oro model between temporal and spatial entities (formally, (TemporalThing SpatialThing) I = ∅, with I the interpretation of our model). On this same figure, the class PurposefulAction represents the superset of all the actions that are purposefully performed by the robot (or another agent). Actual actions (i. e. subclasses of PurposefulAction like Give or LookAt) are not initially asserted in the common-sense ontology. They are instead added at run-time by the execution controller (in link with the symbolic task planner) and the natural language processor based on what the robot is actually able to perform and/or to interpret in the current context (i.
We instead opportunistically extend the common-sense knowledge when required by studies. Lastly, the Oro common-sense ontology contains several rules and class expressions that encode non-trivial inferences. The definition of Bottle as found in the Oro ontology is a typical example: Bottle ≡ Container ∧ GraspableObject ∧ hasShape ∈ CylinderShape I ∧ hasMainDimension ∈ [0. 1, 0. 3] If a human informs the robot that a given object is indeed a bottle, the robot can consequently derive more information on the object. Conversely, if the human affirms that "a car is a bottle", the reasoner may detect logical contradictions (like inconsistent sizes) and reject the assertion. The Dialogs module relies on such logical consistency checks when processing natural language inputs, both to ensure that the verbal input has been correctly acquired and parsed, and also to verify that what the human says is logically consistent.
The Oro server features a mechanism to mimic only minimalistic forms of memory families. When new statements are inserted in the knowledge base, a memory profile is attached to them. Three such profiles are predefined: short term, episodic and long term. They are currently attached to different lifetime for the statements (respectively 10 seconds, 5 minutes and no time limit). After this duration, the statements are automatically removed. This approach is limited. In particular, episodic memory should primarily refer to the semantics of the statements (that is expected to be related to an event) and not to a specific lifespan. We rely however on this mechanism in certain cases: some modules like the natural language processor use the short term memory profile to mark concepts that are currently manipulated by the robot as active concepts: if a human asks the robot "Give me all red objects", the human, the Give action, and every red objects that are found are marked as active concepts by inserting statements such as HUMAN type ActiveConcept in the short-term memory (which can be considered, in this case, to be a working memory).
However, asking for "the keys on your left" gives enough information to the listener to understand where the speaker is referring to. On the contrary, when using an exact, unambiguous term of reference to describe a location (e. g. "go north") no ambiguity arises. In Spark, agent-dependent spatial relations are computed from the frame of reference of each agent. Symbolic Locations. Humans commonly refer to the positions of objects with symbolic descriptors (like on, next to. ) instead of precise, absolute positions (qualitative spatial reasoning). These type of descriptors have been extensively studied in the context of language grounding  [68, 69, 70, 71, 72]. Spark distinguishes between agent-independent symbolic locations (allocentric spatial relations) and agent-dependent, relative locations (egocentric spatial relations).
Limitations In its current form, our situation assessment module makes two assumptions: the objects are known in advance (hence, we can rely on proper 3D CAD model for spatial reasoning) and the robot benefits of an nearly perfect perception, made possible by the use of fiducial markers. Each object receives a unique tag which enables an accurate localisation in 3D and prevents recognition ambiguities that would be otherwise reflected in the knowledge base. While Spark algorithms do not concern themselves with the nature of the input sources, and would work equally well with a full object recognition stack, we did not investigate this research area so far. Additionally, temporal reasoning (essential for accurate action recognition for instance) is not generally addressed in the current state of our system.
Natural Language Grounding Natural language is a basic interaction modality that we use in our system both as an input (processing of the human speech) and as an output (verbalization of the robot intentions, as well as human-robot shared plans). Natural language processing is facilitated as our architecture manipulates semantics that are close to the human level. This section presents the main features of our speech processor, Dialogs, that include semantic and multi-modal grounding, and interactive disambiguation. Algorithms and implementation details are provided in  [74]. We acquire natural speech input from the human participants through a custom Android-based interface. The interface relies on the Google speech recognition API for speech-to-text (ASR) and relays the textual transcript to the robot.
Robot Execution Control While parts of the architecture (Spark, Oro) have been deployed with external execution controllers (like Cram  [32]  or the BERT platform  [82] , as reported in  [28] ), we have also developed dedicated robot controllers which integrate into the deliberative architecture introduced in Figure  2. Shary  [83]  is the main one, written in the Procedural Reasoning System (PRS) language  [84]. We have also developed the Python-based pyRobots  [85]  that provides a large set of high-level actions and an event-based architecture well suited for prototyping. They both rely on extensive integration with the knowledge base, that serves as the primary source of semantics for the decision making process. One of the main roles of Shary is to control the production and execution of shared plans.