dataesr/scientific-paragraphs-categorization huggingface dataset:
Ab initio calculations The first-principles calculations were based on the projector-augmented wave (PAW) method  43  as implemented in the VASP package  [44] [45] [46]  using the generalized gradient approximation 47. We used cubic unit cell with a F 3m space group for CoN. The full structural relaxation was performed until the forces became smaller than 1 meV Å Figure 2 Cyclability. Magneto-ionic cyclability of the Co3O4 and CoN lms subjected to -4 V/+4 V and -8 V/+8 V, and to -4 V/+4 V, respectively, with pulses of short duration (≈ 8. 5 min/cycle). The pale yellow lines represent the average curves. Horizontal lines (red, green) are guides to the eye and span 1 emu cm-3. The data are shifted in M-axis to make them distinguishable. Cyclability was carried out under the application of 5 kOe to ensure being above the anisotropy eld and, thus, in saturation.
Acknowledgements Financial support by the  European Research Council  ( 2014-Consolidator Grant Agreement Nº 648454  and  2019-Proof of Concept Grant Agreement Nº 875018 ), the  Spanish Government  ( MAT2017-86357-C3-1-R ), the  Generalitat de Catalunya  ( 2017-SGR-292  and  2018-LLAV-00032 ), the  French ANR (Project "FEOrgSpin")  and  FEDER  ( MAT2017-86357-C3-1-R  and  2018-LLAV-00032 ) is acknowledged. ICN2 is funded by the  CERCA programme/Generalitat de Catalunya  and supported by the  Severo Ochoa Centres of Excellence programme (Spanish Research Agency , grant no. SEV-2017-0706 ). Work at  GU  has been supported by  SMART  ( 2018-NE-2861 ), one of seven centers of nCORE, a Semiconductor Research Corporation program sponsored by  NIST , and  NSF  ( DMR-1905468 ,  DMR-1828420 ). The PALS measurements were carried out at ELBE at the  Helmholtz-Zentrum Dresden -Rossendorf  e.
There are exact solutions which use contour integration in the complex plane, though such methods are beyond the scope of introductory quantum mechanics courses 7. Numerical solutions of models can be precise to whatever number of decimal places one wishes to carry out the calculations. The article by Jelic and Marsiglio  [9]  gives a thorough numerical solution to the double potential well problem. That article facilitates implementations through software tools such as Mathematica and Maple. However, a shortcoming of numerical solutions appears when one wishes to explore the sensitivity of the problem to parameter changes. Each contemplated parameter change can require an excess of calculation to determine derivatives empirically instead of by analysis. Graphical solutions of models are easier to understand, but one may not be confident in the validity of the results, particularly when the problem parameters are near a transition point where a state may appear or disappear in the solution as a result of some slight change in the device properties or its environment.
Acknowledgments We would like to thank the anonymous reviewers for their constructive suggestions to improve the manuscript. Professor P C Deshmukh  acknowledges a  Shastri Indo-Canadian   Institute Research Grant  and  Professor S R Valluri  acknowledges the  Natural Sciences Engineering Research Council (NSERC)  for a  Discovery Grant  during the duration of this work. Prof Valluri  would also like to thank  Professor K N Satyanarayana , Director of  IIT Tirupati  for gracious hospitality, where a part of this work was completed. Dr R B Scott  acknowledges funding from the  Centre National de la Recherche Scientifique (CNRS).
[9]  The MCA slot introduces a sensing period at the beginning of each slot for Clear Channel Assessment (CCA) with two different purposes. When MCA is enabled, all nodes in the SmartBAN shall perform a CCA before transmitting data in their scheduled slot during SAP, or before transmitting a data or control and management information during the C/M period, in order to detect a possible emergency transmission. In addition, nodes having outstanding packet in their queue can access the channel during every time slot in the SAP, i. e. , before the slots scheduled for them. In this case, the nodes shall perform two CCAs; one for the possible emergency transmission, and one for the possible slot owner transmission. For busy channel case, the standard defines a back-off mechanism for transmission reattempt.
IV. ETSI TC SMARTBAN INTEROPERABILITY One of the main objectives of ETSI TC SmartBAN is to provide solutions for heterogeneity and interoperability management for BANs. In particular, data/informational interoperability, technical interoperability (i. e. , network and syntactic) and semantic interoperability should be guaranteed. The aim is therefore to develop and standardize a SmartBAN-dedicated global framework. This framework is provided with associated enablers/facilitators and application protocol interfaces. There will be a support for interoperability management, which includes cooperation between different SmartBANs and cross-domain use cases. The procedures for secure and unified interaction and access to any BAN data/information/entities are specified. For these purposes, the proposed SmartBAN solution concerns to the following aspects: • The specification, formalization and standardization of an open reference semantic model for BAN data, entities and environment, associated with corresponding unified metadata and reference modular ontology  [14] : Measurements and control/monitoring data are included in the proposal.
The SmartBAN Reference Model is a key element for BAN's data/information, device and semantic interoperability handling. It will also enable embedded semantic analytics, and thus, ease the implementation of automated monitoring and control strategies. • The specification and formalization of a SmartBAN service/application layer reference model extension, associated with the corresponding sub-ontology  [14] : This SmartBAN reference service model, coupled with a Web of Things (WoT) approach, will in particular enable management of the full semantic interoperability, device discovery, data sharing in application level, and cross domain application handling. • The specification and validation of generic Multi-Agent based enablers and global IoT reference architecture with technical and semantic interoperability management, and embedded semantic analytics handling  [15] : This SmartBAN reference architecture is providing generic service-level enablers for semantic data/information management, as well as distributed monitoring and control operations.
The SmartBAN reference data and service models, as well as reference IoT architecture, are described in the following sub-sections in more details. A. SMARTBAN REFERENCE MODEL Fig. 3  presents the high-level view of SmartBAN semantic Reference Model and ontology under discussion in  [9] ,  [10] , i. e. , the associated main modelled concepts (objects or classes) with their links (object properties). Within Fig. 3 , the following conventions are used: • Concepts, i. e. objects or classes, are denoted by rectangles. • Sub-class relations are denoted by plain arrows with white triangles (the arrow origin is the sub-class of the class being the arrow destination). • Relations between objects (concepts) are called object properties and are denoted by dashed arrows labelled by the object properties identifiers.
SmartBAN semantic Reference Model was designed by using the modularity principle and the associated SmartBAN ontology and thus it has been modularized in the following fully decoupled modules: • BAN module that models all the concepts related to BANs (blue classes drawn in Fig. 3 ). A BAN logically contains nodes and has both a predefined communications process (periodic, event driven or on demand) and one or multiple contacts, as depicted in Fig. 3. A contact is a person, i. e. the one that uses the BAN and whose node can be attached to. This person can mainly be a responsible party (legal entity responsible for the BAN) or a patient, etc. • Node module that models all the concepts related to BAN nodes (orange classes drawn in Fig. 3 ). A BAN node can mainly be a sensor, an actuator, the BAN hub or the BAN coordinator.
This node has an interface and a type, as depicted in Fig. 3  (Interface and NodeType classes). • Process module that models all the concepts related to measurement/actuation process and measures (green classes depicted in Fig. 3 ). A process has Action and Data, as depicted in Fig. 3 , and finally, a Service module that models all concepts related to services that are offered by the SmartBAN nodes (white classes depicted in Fig. 3 ). Those modules are less complex to process in an embedded device than the full SmartBAN ontology, thus opening a door for device-embedded semantic data analytics and edge computing. This is also mandatory for local alarm management and caregiver/patient/helper support tools' implementation. Fig. 3  shows that the SmartBAN ontology first introduces unified metadata (i.
Those agents are directly deriving the strategy adopted for the WoT, and they offer full semantic interoperability handling  [9] ,  [10]. The services are semantically described through the SmartBAN ontology service module already introduced in sub-section IVA and specified in  [9] ,  [10]. This module provides BAN devices/data (sensors, wearables, actuators, etc. ) automatic discovery and sharing, as services and via dedicated agents of the SmartBAN Service sub-Layer (see Fig. 4 ). Finally, the SmartBAN application layer depicted in Fig. 4  consists of several IoT application entities. Those entities are implementing SmartBAN application logic, such as monitoring of vital data, evaluation results of certain patient, caregiver decision support or patient notification.
A. USING THE INTERFERENCE-BASED CHANNEL MODEL The comparative analysis between SmartBAN and BLE v4. 0 with the measurement-based interference model was studied under the ETSI Specialist Task Force (STF) 511 project. The STF 511 group developed a simulator using MAT-LAB Simulink environment, implementing both the physical and MAC layers of SmartBAN and BLE. MATLAB multipath fading channel model for indoor environment was used and 2. 4 GHz ISM band was considered. Additionally, STF 511 has included into the simulator a block that simulates the interference in an hospital environment. The analytical model of the interference was developed by part of the authors in  [12] , based on the measurement campaigns in a real modern hospital. In particular, the lowest and highest interference scenarios were implemented into the interference block of the simulator.
Our smart coordinator (and the other capable edge devices) may processes this data locally and provide us with tailored feedback. The feedback is based on the knowledge of our personal case and the collective wisdom established via AI-based analysis of big data gathered over years from all the persons in the system. It may also perform predictive simulations and projections in reply to ''what if'' questions that we may pose, or which it poses on our behalf, and it supports personalized medicine (e. g. , monitoring of dosage and drug combinations). The smart coordinator receives reference data from the system and returns anonymized data to the system, enabling it to continuously refine and update the global database. The smart coordinator is also capable of interacting with sensors in the environment and cooperating with other AI-based devices.
In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting.
Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.
The paper is organized as follows: Gaussian processes as well as commonly used covariance functions and ways of combining these functions are defined in Section 2. In Section 3, the principles of Gaussian process regression are introduced, including both online and online sparse models, as well as the estimation of their parameters and hyperparameters. The datasets used to train and validate the models included in the comparative study, derived from GHI measurements taken at the laboratory PROMES-CNRS in Perpignan (southern France), are described in Section 4. In Section 5, the forecasting accuracy of the developed OGPR and OSGPR models is evaluated. The paper ends with a conclusion. The hyperparameters' estimated values and detailed numerical results can be found in Appendices A and B, respectively.
Data Description The data used in this comparative study originate from a rotating shadowband irradiometer (RSI), whose uncertainty range is about ±5%, housed at the laboratory PROMES-CNRS in Perpignan, roughly 20 km west of the Mediterranean Sea. This device consists of two horizontally leveled silicon photodiode radiation detectors, situated in the center of a spherically curved shadowband. The region is characterised by strong winds resulting in an often clear sky, in addition to mild winters and hot and dry summers. The comparative study enables the evaluation of GPR models through two GHI datasets, each sampled at a 30 min rate. The first one, hereafter referred to as the "summer" dataset, covers a period of 45 days, from 5 June to 18 July 2015. The second one, hereafter referred to as the "winter" dataset, also covers a period of 45 days, from 1 November to 15 December 2015.
The motivation for using two datasets instead of one larger dataset is twofold. On one hand, clear-sky days are frequent during summertime, while in winter, more complicated situations occur (overcast, broken clouds, etc. ); as will be seen in the forecasting results (see Section 5. 3), GHI is thus easier to predict in summer, while the winter dataset allows the highlighting of the models' robustness in complex atmospheric conditions. On the other hand, this method allows us to show that it is not necessary to gather a huge dataset to obtain good forecasting results. If not specified otherwise, the first 30 days and the last 15 days of each GHI dataset are used for training (see Section 5. 1) and to evaluate the models' generalisation ability, respectively. Finally, the forecast horizons considered in this comparative study range from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases.
Results show that hyperparameters' estimates, when using k SE , are indifferent to the prior distributions, as opposed to k Per , whose hyperparameters' estimates differ along with the priors. This leads to the conclusion that prior distributions have a great influence on the hyperparameters' estimates in the case of a periodic kernel. As mentioned in Section 4, the two GHI datasets (i. e. , the summer and winter datasets) are split into a training subset and a testing subset that cover periods of 30 days and 15 days, respectively. As for the initial values of the hyperparameters θ, we have made the following choices.
Forecasting Results Using OGPR Forecasts of GHI obtained at different time horizons are compared to enable the assessment of the models' global performance. The persistence model is used as a reference. For all the OGPR models catalogued in Section 5. 1, the nRMSE vs. the forecast horizon is displayed in Figure  1. Further numerical results for the summer dataset (Tables  A2  and A3 ) and the winter dataset (Tables  A4  and A5 ) are presented in Appendix B. Broadly speaking, there are three classes of models, each possessing a different performance level. The worst performance is exhibited by the persistence model, and improved performance is witnessed when using OGPR models based on simple kernels; lastly, considerably better performance is exhibited by OGPR models based on quasiperiodic kernels, particularly considering higher forecast horizons.
For both datasets, even at the lowest forecast horizon (30 min), OGPR models based on simple kernels give forecasts comparable to those given by the persistence model (nRMSE 0. 22 in summer and nRMSE 0. 30 in winter), while OGPR models based on quasiperiodic kernels already give better forecasts (nRMSE 0. 18 in summer and nRMSE 0. 21 in winter). As the forecast horizon increases, the persistence model's performance degradation is more rapid than that of OGPR models. At the highest forecast horizon (5 h), the persistence model gives nRMSE 1. 11 in summer and nRMSE 1. 50 in winter; for OGPR models based on simple kernels, nRMSE 0. 55 in summer and nRMSE 0. 70 in winter; for OGPR models based on quasiperiodic kernels, nRMSE 0. 30 in summer and nRMSE 0. 40 in winter. Regarding OGPR models based on simple kernels, no best-performing models have been found.
However, OGPR models based on quasiperiodic kernels-these kernels combine a periodic kernel with a non-periodic kernel-possess the advantage of the periodic kernel, while still managing to predict rapid changes in GHI during the day. Among quasiperiodic kernels, k Per×RQ surpasses other kernels for the summer dataset, with k Per+M 3 /2 , k Per+M 5 /2 and k Per+E all coming in a close second (see Table  A2 ); for the winter dataset, however, there is no clear best-performing kernel, as those four kernels alternatively take the first place (see Table  A4 ). An in-depth analysis of the temporal evolution of GHI during the models' training and testing phases sheds more light on their performance. Once more, the persistence model serves as a reference. Three OGPR models are selected: the classic k SE -based OGPR model and two of the best-performing models based on quasiperiodic kernels; i.
e. , the k Per×RQ -based OGPR model and the k Per+SE -based OGPR model. Here, a dataset of nine days (selected from the summer dataset) is used, split into a seven-day training subset and a two-day testing subset. In Figures  2 3 4 , 30 min, 4 h, and 48 h forecasts are shown. P e r s is. Recall that, while each data sample is used during training, during testing, a new observation is added to the observation set only each whole multiple of the forecast horizon. This means that, first, for all three figures, the training results are identical; second, the coefficients α i in Equation (  16 ) are updated every 30 min in Figure  2  and every 4 h in Figure  3 , while for the 48-h forecast horizon, no update occurs. k E k R Q k M 3 / 2 k M 5 / 2 k S k E k R Q k M 3 / 2 k M 5 / 2 k S An inspection of the seven-day training phase reveals that the data are well-fitted by every OGPR model.
As soon as a new observation is made, the model fits to the data, but in the absence of an update, it converges to the constant mean value learned during training (around 280 W m -2 ). In Figure  4a , this behaviour is more obvious: no update is made throughout the entire two-day testing period, and without additional information, the OGPR model simply makes an "educated guess", consisting of this constant mean value associated with a large confidence interval. Quasiperiodic kernels, however, do possess additional information on GHI, showing that it has a daily pattern. As with OGPR models based on simple kernels, when a new observation is added to the observation set, they fit to the data (see Figure  3b, d ). In the opposite case, OGPR models based on quasiperiodic kernels reproduce the periodic value learned during training (see Figure  4b, d ), giving a result that is distinctly more faithful to the desired behaviour than a constant mean value.
This explains why the performance of OGPR models based on quasiperiodic kernels degrades more slowly when the forecast horizon increases, as seen in Figure  1. Based on the results shown in Figure  3b, d, k  Per×RQ is the best choice among quasiperiodic kernels as it permits sharper and more drastic changes in the modelled signal. The nRMSE gain relative to the persistence model is presented in Table  1 , for OGPR models based on the classical k SE and the three best-performing quasiperiodic kernels (winter dataset). Table  1. Performance comparison, in terms of nRMSE, between the persistence model and OGPR models based on different kernels: the classical k SE and the three best-performing quasiperiodic kernels (winter dataset). The forecast horizon ranges between 30 min and 5 h. The best results are highlighted in green.
Forecasting Results Using OSGPR The impact of training data sparsity on forecasting accuracy (generalisation) is assessed in this section of the paper. The subset of data technique is used; this technique simply consists of ignoring a part of the data available for training. Lowering the quantity of data used to train the models reduces computation time during training and also during testing, since the number of parameters is reduced (see Equation (  16 )). In addition, this allows us to evaluate the models' ability to handle missing data in the training sequence (i. e. , their generalisation ability in the case of missing data in that sequence). Thirty minute, 4 h and 48 h forecasts given by both the k SE -based OSGPR model and the k Per×RQ -based OSGPR model are displayed in Figure  5. The dataset constitutes the same measurements of GHI (nine days) that were considered with OGPR models (see Section 5.
3). Here, however, only 20% of the available data-randomly selected from the summer dataset-have been used during training. With this low number of training points, the OSGPR model based on k SE does not provide acceptable results. Compare, for example, the third and fourth days of training: when given sufficient data, a good fit is obtained, but if the only training points are at the beginning and the end of the day (as during the fourth day), the OSGPR model based on k SE simply makes the best inference, which, in this case, is not sufficient. In contrast, the OSGPR model based on k Per×RQ still manages to give an acceptable fit. The key here is that even a low number of training points is enough for the models based on quasiperiodic kernels to learn the daily periodic behavior of GHI. As expected, the forecast results are good when considering the 30 min horizon, with an advantage shown for the k SE -based OSGPR model.
25-quartile and 0. 75-quartile, and whiskers corresponding to ±1. 5 times the interquartile range) in Figures  6 7 8. As can be seen, if during training, nRMSE decreases steadily with data sparsity, during testing, it quickly reaches a threshold level, and dispersion also quickly vanishes. In this study, this threshold seems to be around 70%. As a consequence, using only 30% of the available training data appears to be sufficient to achieve the same nRMSE results to those obtained when using the whole training dataset. The gain in computation time is significant, as it falls from a median of 175 s at a sparsity level of 0% to a median of 17 s at a sparsity level of 70%, which amounts to around a 90% reduction.