dataesr/scientific-paragraphs-categorization huggingface dataset:
Field Experiments The airborne experiment was conducted on 1 September 2013 in Baoding, Hebei Province, China. The study site is an agricultural area with maize and trees such as toon and elm, and the mean elevation is 16. 8 m. An airborne hyperspectral imaging system designed by Specim Spectral Imaging Oy Ltd. (Oulu, Finland) was carried on the airship (Figure  2a ) in this experiment, including an AisaEAGLE sensor, a data acquisition and power unit (DPU), a high-performance GPS/IMU positioning system (POS) and CaliGeoPRO software (Figure  2b ). The AisaEAGLE sensor is a highperformance airborne visible and near infrared (VNIR) pushbroom hyperspectral system in the 400-970 nm spectral range with 3. 3-nm spectral resolutions. The sensor parameters are listed in Table.
Field Experiments The airborne experiment was conducted on 1 September 2013 in Baoding, Hebei Province, China. The study site is an agricultural area with maize and trees such as toon and elm, and the mean elevation is 16. 8 m. An airborne hyperspectral imaging system designed by Specim Spectral Imaging Oy Ltd. (Oulu, Finland) was carried on the airship (Figure  2a ) in this experiment, including an AisaEAGLE sensor, a data acquisition and power unit (DPU), a high-performance GPS/IMU positioning system (POS) and CaliGeoPRO software (Figure  2b ). The AisaEAGLE sensor is a high-performance airborne Sensors 2016, 16, 480 5 of 19 visible and near infrared (VNIR) pushbroom hyperspectral system in the 400-970 nm spectral range with 3. 3-nm spectral resolutions. The sensor parameters are listed in Table  3.
The POS uses RT3100 produced by Oxford Technical Solutions (city, England), with a positioning accuracy of 0. 5 m. The original image was radiometrically and geometrically corrected using CaliGeoPRO software package. In this work, the reference white board (Figure  2c ) was set in a blank area in the research area. 3. The POS uses RT3100 produced by Oxford Technical Solutions (city, England), with a positioning accuracy of 0. 5 m. The original image was radiometrically and geometrically corrected using CaliGeoPRO software package. In this work, the reference white board (Figure  2c ) was set in a blank area in the research area. The hyperspectral data were acquired from 16:00 to 17:00 UTC + 8 using an AisaEAGLE sensor at a 400-m altitude. At the same time, a high-performance field and laboratory chlorophyll fluorometer (PAM-2500) produced by Heinz Walz GmbH (Effeltrich, Germany) was used to measure the chlorophyll fluorescence parameter on the ground.
A CE318 photometer (Figure  2d ), designed by Cimel Electronique S. A. S (Paris, France) was used to measure the optical properties of the atmosphere. It provided quantification and physical-optical characterization of the aerosols. The SZA, VIS and water content were extracted from the measured data for CE318, and these parameters were then entered into MODTRAN to obtain the atmospheric parameters. The hyperspectral data were acquired from 16:00 to 17:00 UTC + 8 using an AisaEAGLE sensor at a 400-m altitude. At the same time, a high-performance field and laboratory chlorophyll fluorometer (PAM-2500) produced by Heinz Walz GmbH (Effeltrich, Germany) was used to measure the chlorophyll fluorescence parameter on the ground. Several types of leaf samples, which were exposed to direct sunlight, were selected for fluorescence measurements with a measurement time consistent with the flying experiment.
Sensitivity Analysis To illustrate the effects of the atmosphere on TOA radiance, sensitivity analysis was conducted using simulated data to illustrate which atmospheric parameters strongly affected the indicators of fluorescence (Figure  3 ). Gaussian Emulation Machine for Sensitivity Analysis (GEM-SA) software package was used for sensitivity analysis  [53, 54]. In sensitivity analysis, the indicator "total effect" was applied to evaluate the effect of each input parameter on the output. In this study, the following four indicators were selected in sensitivity analysis: (1) the depth of the oxygen absorption band, marked as depth_oxygen_band. The absorption band depth was computed by dividing the value for one band outside of the absorption band by that for one band inside of the oxygen absorption band.
Effects of Atmospheric Parameters on the Oxygen-Absorption Depth in the O2-A and O2-B Bands In the oxygen absorption bands or Fraunhofer lines, SIF partially fills the absorption bands by the sun-excited emission of the luminescent target  [26]. Because the oxygen absorption bands overlap with the peak of the fluorescence spectrum, they are thought to be the best bands for retrieving fluorescence. By determining the depth of the oxygen absorption band, one can estimate fluorescence qualitatively. The depth and shape of the oxygen-absorption bands are determined by the path length of solar irradiation, and they are affected by, e. g. , the solar zenith angle, absorption and scattering of the atmosphere, the surface pressure, the aerosol content, and the water content  [2]. Based on the simulated data generated by the MODTRAN and SCOPE models, the effects of atmospheric parameters on the oxygen-absorption depth in the O2-A and O2-B bands are analyzed in this section.
Effects of Atmospheric Parameters on the Oxygen-Absorption Depth in the O 2 -A and O 2 -B Bands In the oxygen absorption bands or Fraunhofer lines, SIF partially fills the absorption bands by the sun-excited emission of the luminescent target  [26]. Because the oxygen absorption bands overlap with the peak of the fluorescence spectrum, they are thought to be the best bands for retrieving fluorescence. By determining the depth of the oxygen absorption band, one can estimate fluorescence qualitatively. The depth and shape of the oxygen-absorption bands are determined by the path length of solar irradiation, and they are affected by, e. g. , the solar zenith angle, absorption and scattering of the atmosphere, the surface pressure, the aerosol content, and the water content  [2]. Based on the simulated data generated by the MODTRAN and SCOPE models, the effects of atmospheric parameters on the oxygen-absorption depth in the O 2 -A and O 2 -B bands are analyzed in this section.
In the experiment, the PAM is used to measure the steady-state fluorescence of vegetation, whereas the sensor collects data. A comparison between the fluorescence derived from the airborne data and that from PAM-2500 is conducted. R 2 between the retrieved fluorescence using the Damm method and PAM-2500 is 0. 91 (Figure  8a ), whereas that between the retrieved fluorescence using the 3FLD method and PAM-2500 is 0. 65 (Figure  8b ). These results indicate that fluorescence derived from the airborne data is reliable and that results obtained using the Damm method are much closer to the PAM measurements than those obtained using the 3FLD method. Figure  8c  shows that there are differences in fluorescence among the different types of vegetation. The mean fluorescence value for each type of vegetation is used herein to illustrate the trend in the variation of vegetation.
Conclusions Remote sensing of fluorescence from space is currently an important research subject, and it is the core mission objective of ESA FLEX. Preliminary research regarding space-based retrieval of fluorescence has been reported in the literature. Several methods for estimating fluorescence have been developed and applied to GOSAT (TANSO-FTS sensor), MetOp (GOME-2), Envisat (SCIMACHY) and OCO-2 images. Fluorescence retrieval can be performed in the oxygen-absorption bands overlapping the peaks of fluorescence emission using high-spectral resolution data. Combined with atmospheric information, it is feasible to decouple the fluorescence flux from the reflected solar radiation accurately. The effects of the atmosphere are no negligible in the method of retrieving fluorescence. Therefore, in this paper, we first study the effects of the atmospheric parameters on fluorescence in oxygen-absorption bands using simulated data generated by MODTRAN4 and SCOPE.
Acknowledgments: This work was supported by the  National Basic Research Program of China  ( 2013CB733406 ), the  Natural Science Foundation of China  ( 41571409  and  41541043 ), and the  Fundamental Research Funds for the Central Universities  (2014kJJCA02). The authors thank  Ren Huazhong  for supplying the CE318 processing software and  Xijia Zhou  for providing suggestions about the retrieval methods.
Treat-to-target strategy in inflammatory arthritis usually uses clinical measurements of disease activity such as DAS28 or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). While EULAR recommends the use of composite indices to assess disease activity, the American College of Rheumatology (ACR) recommends both composite indices or patient-driven composite tools (PAS, PAS-II or Routine Assessment of Patient Index Data (RAPID) 3). 10  As such, in addition to clinical disease activity measures, the patient's perspective of disease state and burden is increasingly being recognised as an important consideration,  [11] [12] [13]  and the most recent ACR guidelines refer to the use of patient-reported outcomes (PROs) as activity measures. 14 The US Food and Drug Administration defines a PRO as: "any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else.
LITERATURE SEARCH METHODOLOGY Patient self-monitoring and remote monitoring with innovative monitoring tools is a well-accepted and common practice in several chronic disease areas. A literature search was conducted to evaluate the potential role of patient self-monitoring and innovative monitoring tools in optimising disease control in inflammatory arthritis. Readers should note that this was a narrative review and the methods of the review are explained below. The following databases were searched: PubMed ( January 2000 to June 2015), accepted abstracts from ACR and EULAR annual congresses (ACR 2012-2014  and EULAR 2011-2015). Figure  1  shows the search terms used. Searches were performed using a combination of a single primary search term in conjunction with each secondary term ( primary term AND secondary term).
LITERATURE SEARCH RESULTS The literature search retrieved 374 titles/abstracts (354 titles from PubMed, 14 ACR abstracts, 6 EULAR abstracts), of which 278 were excluded by the initial scan and collation (34 duplicates, 244 titles not related to topic). The remaining 96 abstracts/articles were checked for relevance; 85 were excluded as not related to the topic, leaving 11 articles. 20 23 25-33 The findings of the initial literature search were reviewed and the authors suggested additional articles for inclusion that were not found as a result of search terms or human error of the manual searching of databases. After manual addition of these 6 articles, 8 9 34-37 17 articles were selected for inclusion in the review (see online supplementary appendix A and figure  1 ). Key evidence from the literature search is summarised below.
Most patients (62%) expressed a preference for using remote data entry in the future, while 7 (5%) patients felt uncomfortable with the tablet PC due to their rheumatic disease. Disease activity measured by patients and reported with an electronic tool has been shown to correlate well with DAS28 results from a clinical examination. 28  study of 51 patients with rheumatic disease reported a high correlation of 0. 88 for DAS, with moderate correlation (0. 63) for number of tender joints and a lower correlation (0. 41) for number of swollen joints. In 37 (73%) patients, self-monitoring and the clinical examination by the physician resulted in an identical classification for low, moderate or high disease activity, with self-monitoring resulting in a higher classification in 12 (24%) cases and a lower classification in 2 (4%) cases.
Sanoïa (France) Sanoïa (http://www. sanoia. com), launched in 2010, provides online secure health records that allow patients to track and store their own health data. It is available in 14 languages on PCs, tablet computers and smartphones. Forms such as BASDAI, Health Assessment Questionnaire, ASAS-QoL, RAPID3 and treatment trackers are available, and the patient can generate and print reports and graphical summaries. The patient decides whether to allow the physician to see their data. In September 2015, 4695 patients with RA were registered, and patients with AS and PsA started using the tool. A randomised controlled trial is underway to evaluate the effect of the tool on the quality of patient-doctor interactions (http://www. clinicaltrials. gov/ct2/show/NCT02200068).
Andar (Spain) Andar (http://www. proyectoandar. com) is a standardised monitoring tool in which the patient completes the RAPID3 questionnaire and clinical and laboratory measurements can be added by the healthcare professional, allowing calculation of composite indices (DAS28, SDAI, CDAI). Initially, this was developed as a paper-based questionnaire that patients completed before each clinic visit. It has now been developed as a web-based tool. Patients determine their own treatment targets and can view the evaluations. Physicians add blood test results, and nurses decide whether patients need urgent visits on the basis of monthly alerts. iMonitor iMonitor is a web-based software application that allows patients to report information about their disease state for RA, PsA and AS. It can be accessed by PC, tablet or smartphone.
Initial drafting and subsequent medical writing support was provided by  Clare Griffith ,  Synergy, London, UK  and funded by  Pfizer. Competing interests RA has received research grants and honoraria from the speaker's bureau from  Pfizer. BC has received honorarium from  Pfizer. DA has received honoraria from  Pfizer ,  MSD  and  UCB. PB is an employee of  Pfizer. CC is an employee of  Pfizer. GH is a founder and shareholder of the company DiaGraphIT, manufacturing  GoTreatIT Rheuma. BL has received research grants as well as honoraria from  Centocor ,  Abbott ,  Amgen, Aesca, , MSD ,  Pfizer ,  Roche  and  UCB. AR has received honoraria or research grants from  Abbvie ,  BMS ,  Centocor, Eli-Lilly ,  Novartis ,  Pfizer ,  Roche  and  Wyeth. BR has received research grants as well as honoraria from  Centocor ,  Abbott ,  Amgen ,  Aesca ,  UCB ,  Roche ,  MSD ,  Celltrion ,  Schering-Plough ,  Wyeth ,  Pfizer ,  BMS ,  Jannssen-Cilag ,  Eli-Lilly  and  Novartis.
We also consider uncertainty in the measurement process. We define a theoretical framework, to compute influence, based on the conjunctive combination rule for belief functions theory and Smets rule  [7]  to fusion and combine information. The proposed approach is flexible and thus, indirect influence in the graph can also be considered. In this case, the influence assessment considers influence exercised on indirect nodes (e. g. a user may retweet another's tweet indirectly through an intermediate user). This spans the influence on multiple levels based on a multi-level influence graph. An evaluation through experiments is proposed. It is based on real data gathered from Twitter in the TEE 2014 project during the European Election campaign in 2014. We also conduct experiments on the CLEF RepLab 2014 data set, which contains Twitter data including influence-annotated Twitter profiles.
(  6 ), it computes the belief masses combination. After that, Eq. (  6 ) is used again to combine the belief masses for all relations. And finally, using Eq. (  7 ), the belief masses distributions are transformed to pignistic probability. The algorithm returns the final influence degree which is the degree having the maximal pignistic probability. The set of final influence degrees {Inf u : u ∈ U } is denoted by Inf. The source code is available on github. 1 It is the general R code that can be specialized depending on the studied network and used relations. (m ⊗ m ′ ) ⊗ m ′′ � = m ⊗ (m ′ ⊗ m ′′ ). (7) bet(x) = m(x) + m(�) |�| , x ∈ � \ {�} 1 https://www. github. com/kerzol/Influence-assessment-in-twitter. To discuss the complexity of Algorithm 1, we have to determine the complexity of the combination rule (  6 ) and the pignistic probability  (7).
Data description The tweets collection during the election period has build a corpus which is then analyzed. To collect information from Twitter, we used our developed tool SNFreezer 2  [44]. The purpose of gathering is to retrieve tweets mentioning designated users, those containing a hashtag, a word or phrase related to the European Elections (e. g. , the hashtag #UE14), or tweets sent by candidates. Three types of information (generalized under the term "source") are taken as a parameter to query Twitter: user accounts; hashtags and words or phrases. These sources were chosen by political scientists, and among them we find the names of the leading candidates, their Twitter accounts and their parties. The collection allowed us to have a large number of tweets (37 million) retrieved for 50 consecutive days, and to massively process these data.
To deal with this, we perform a rescaling and use the following masses initialization: 2 https://www. github. com/SNFreezer. Retweet � → m retweet (V. Weak) = 0. 55 • 10 -3 m retweet (�) = 1 -0. 55 • 10 -3 The mass of the retweet relation is more important than the others, because we consider that it is a better influence indicator than the other relations. Also, the masses of the indirect relations are a little more important than those initiated for the direct influence relations, because we consider that indirect interactions are good indicator of influence, and this shows that some users are able to diffuse tweets on many levels and they are exercising influence even on users with which they are not connected. Mention � → m mention (V. Weak) = 0. 45 • 10 -3 m mention (�) = 1 -0. 45 • 10 -3 Reply � → m reply (V.
Data description The RepLab data set contains users manually labeled by specialists from Llorente & Cuenca, 4 a leading Spanish e-Reputation firm. These users were annotated according to their perceived real-world influence, and not by considering specifically their Twitter accounts. The annotation is binary: a user is either influential or not-influential. The data set contains a training set of 2500 users, including 796 influentials, and a testing set of 5900 users, including 1563 influentials. It also contains the 600 last tweets ID of each user at the crawling time. These tweets could be either written in English or in Spanish. The data set is publicly available. 5  To evaluate our approach over the RepLab data set, we need to choose relations that will be considered in the assessment, yet the RepLab data set do not provide this kind of information.
We only have the users names with 600 tweets id for each user. Therefore, we should first gather needed information about these tweets; for this, we used Twurl, 6 a tool that enables to collect information about the given tweets from Twitter API. However, Twitter API limits the collection to 180 tweets per 15 minutes. Therefore, it needs 174 days to collect information about all the tweets. For this reason, we limit the evaluation on 500 users with 600 tweets per user. The relations that we can extract from the collected tweets information are: number of retweets, likes, and followers. Table  14  shows the parameters of the used data set.
Experiments Now, we present our experiments on the RepLab data set. The masses initialization is presented as follows: 3 http://www. clef2014. clef-initiative. eu/. 4 http://www. llorenteycuenca. com/. 5 http://www. nlp. uned. es/replab2014/. 6 https://www. github. com/twitter/twurl. The most important masses is given to the retweet relation, because we believe that this relation is a better influence indicator, the follow relation is given the less important belief masses as this indicator gives the popularity of a user, so we use the "popularity" as an influence indicator but we do not give it a very important belief mass. Popular users are not necessarily the most influential ones.
Results and discussion When we applied our method on the Replab data set, we found that the method was able to detect 92 among 144 influential users. Table  15  shows the F-measure results comparison between our method and some existing studies. Our method based on belief fusion has reached 0. 584, and the measure is slightly lower than measures obtained form the state-of-the-art methods. This is due to many reasons: first, 36 % of the tweets were  deleted from Twitter, thus, information about these tweets is no longer available, and this may bias the results. The positive aspects of our results must be modulated by the fact that users having the majority or all their tweets deleted have been assigned to the class , which means that our system is able to express its ignorance about the influence degree of users on which it has not enough information.
Another reason for which the F-measure of our method is lower than the others, is that we do not consider the studied domain, and our method detects influential users regardless the studied domain; hence, many of the detected influential users by our method were not considered as influential in the Replab data set. When we studied these accounts, we found that these accounts seem to be influential in real life, but the reason why they were not labeled as influential in the Replab data set is that these accounts are not influential in the studied domains (automotive and banking).
g. , a user may retweet another's tweet indirectly through an intermediate user). We experimented our approach on real data gathered from Twitter in the context of the project TEE 2014 and the context of the Replab Challenge 2014. The experiments show that relations combination under uncertainty leads to a quite interesting results. Interesting perspectives emerge to further strengthen the proposed approach. The method for users ranking will be improved. Moreover, we will study the content of the tweets to study whether the exercised influence is positive or negative. Besides, we plan to apply the proposed approach on other measures that requires information fusion such as users credibility and Twitter styles categorization. And finally, we will consider more complex interaction patterns in the method, such as hashtags on a multi-relational graph.
In June 2014 the radiometer was installed in the Maïdo observatory, on La Réunion Island (21. 2 • S, 55. 5 • E). High resolution ozone spectra were continuously recorded during 7 months. Vertical profiles of ozone have been retrieved through an optimal estimation inversion process, using the Atmospheric Radiative Transfer Simulator ARTS2 as the forward model. The best estimate of the vertical profile is done by means of the optimal estimation method. The validation is performed against ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite, the ozone lidar located in the observatory and with ozone profiles from weekly radiosondes. Zonal and meridional winds retrieved from GROMOS-C data are validated against another wind radiometer located in situ, WIRA. In addition, we compare both ozone and winds with ECMWF model data.
Chem. Phys. Published: 11 March 2016 c Author(s) 2016. CC-BY 3. 0 License. GROMOS-C was installed on the terrace of the Maïdo observatory in June 2014, and remained until the end of January 2015. High temporal resolution ozone spectra were continuously recorded during those 7 months. The main goal of this measurement campaign was to validate the performance of GROMOS-C as an ozone and wind profiler by comparing to profiles from other reliable sources. Further, the high resolution ozone spectra constitute a valuable data set not previously available for this location. These data can be used to study the diurnal cycle of ozone in the tropics  (Studer et al. , 2014). An accurate knowledge of diurnal ozone variation is needed for reliable trend detection in the global ozone distribution. Merging ozone data from different satellites without properly accounting for the diurnal ozone cycle would result in a systematic bias in the calculated ozone trend.