dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurements For this task, the ALOS-1 PALSAR I4 dataset in Table  1  was processed by 8 groups, applying different processing chains listed in alphabetical order: DTU (  [25, 39] ), ENVEO  [3] , GAMMA-RS  [25] , GEUS  [25] , Swansea University  [41] , University of California, Irvine  [43] , and University of Washington  [13]. The raw SAR data were focused with the software of Werner et al. (  [25] ) and with that of  Rosen et al. ([36] ). The LoS and azimuth mean-velocity maps provided by the participants are shown in Figures  15  and 16  respectively. Since group 7 provided the horizontal velocity components along the Cartesian axes of a National Snow and Ice Data Center (NSIDC) Polar Stereographic North projection (EPSG 3413), the LoS and azimuth components were derived for this group by using the surface parallel flow assumption and the GIMP DEM  [21].
Groups 1, 4 and 6, which did not implement any of such strategies, achieved a limited coverage close to the termini of the outlet glaciers (panels a, d and f in Figures  15  and 16 ). Finally, coverage differences can be noticed in the overall azimuth length of the ice velocity maps in Figures  15  and 16. Besides group 4 (panel d), which unintentionally processed only part of the provided dataset, these differences are due to the processed azimuth bandwidth at the image borders, which is typically a user-configurable parameter during the image focusing step. Concerning the properties of the velocity measurements, the LoS maps differ primarily due to image-wide trends in the east-west direction. To highlight such differences all results were resampled to a common grid with a posting of 4. 5 arcsec in latitude and 13.
Remote Sens. 2018, 10, x FOR PEER REVIEW 25 of 36 Groups 1, 4 and 6, which did not implement any of such strategies, achieved a limited coverage close to the termini of the outlet glaciers (panels a, d and f in Figures  15  and 16 ). Finally, coverage differences can be noticed in the overall azimuth length of the ice velocity maps in Figures  15  and 16. Besides group 4 (panel d), which unintentionally processed only part of the provided dataset, these differences are due to the processed azimuth bandwidth at the image borders, which is typically a user-configurable parameter during the image focusing step. Concerning the properties of the velocity measurements, the LoS maps differ primarily due to image-wide trends in the east-west direction. To highlight such differences all results were resampled to a common grid with a posting of 4.
Multi Aperture Interferometry Concerning the MAI task (Section 3. 2), the best MAD accuracy achieved with respect to the GPS surveys was of 7 m/y (Table  6 ), and azimuth velocities maps with a spatial resolution of about 90 m × 90 m were generated. The algorithmic differences among groups, which most significantly affected performance, were the handling of ionospheric propagation errors  [10]  and the reduction of spatially uncorrelated noise due to loss of interferometric coherence. The mitigation of ionospheric streak errors was successfully carried out by one of the participants, although the procedure which was used cannot be considered of general applicability, since moving areas were excluded from the estimation, an empirically assigned threshold on the MAI phase. For systems with a sufficiently high range bandwidth, and datasets/areas meeting the above-mentioned conditions to reduce the occurrence of phase unwrapping errors, general ionospheric-streak correction approaches could be investigated based on the split-spectrum method  [48] [49] [50] , in which motion and ionospheric phase contributions are separated exploiting the frequency dependence of the latter.
Regarding the filtering of spatially uncorrelated noise, a difference among participants concerned the application of spatial filtering methods to each sub-band interferogram as opposed to their application to the final differential MAI interferogram. The superiority of the former approach was established for the case in which multi-looking is the only filtering method applied, based on analyses carried out on real datasets  [35] , as well as on theoretical derivations supported by numerical simulations  [51]. However, the intercomparison between groups 1 and 4 in Section 3. 2. 2 suggests that these conclusions should be re-examined for the common case in which adaptive filtering methods, such as  [27] , are used. Unlike multi-looking, these techniques can in fact be expected to perform better on the differential MAI interferogram, in which fringes common to both sub-band interferograms (e.
Offset Tracking The results of both offset-tracking experiments (Sections 3. 3 and 3. 4) were the most heterogeneous. The highest levels of agreement with the GPS measurements yielded LoS and azimuth MADs of 20 m/y and 40 m/y respectively for the ENVISAT ASAR dataset (task 3), and of 10 m/y in LoS and 20 m/y in azimuth for the ALOS PALSAR dataset (task 4). The spatial resolutions can be considered at best to approach the cross-correlation window spacing and at worst twice the size of the correlation window size  [52] , which on average amounted to values between 200 m × 170 m and 2. 7 km × 1. 7 km (ground range × azimuth) for task 3, and between 160 m × 120 m and 640 m × 1100 m for task 4. For both processing tasks the limited spatial-resolution of the measurements was one of the major error sources for areas with high velocity gradients, in agreement with the SAR vs.
GPS comparisons reported in  [23]. Furthermore, sub-optimal outlier culling and hole-filling procedures were also relevant in particular for the void-rich task 3 dataset, whereas GCP calibration errors as well as ionospheric streaks were noticeable for task 4, due to the larger spatial extent of the measurements and the lower (L-band) radar frequency. Finally, both offset-tracking tasks intentionally provided a challenge regarding the measurement of very high velocities (i. e. , >2 km/y) close to the glacier termini. Concerning the choice of the cross-correlation window sizes, which typically determine also the spacing at which the cross-correlations are computed, several groups chose large values, assigned based on analyst experience, and constant for the processing of the whole dataset. Although this approach typically succeeds in providing good-quality cross-correlations in ice-sheet regions characterized by different levels of coherence and intensity features, the optimal window sizes depend on surface and radar properties, which can vary significantly.
Within the validation area of the task 3 dataset (Section 3. 2. 3), the smallest window sizes which provide good quality measurements lie between 1. 0 km and 1. 5 km in both ground-range and azimuth, whereas for the task 4 dataset (Section 3. 4. 2), good cross-correlation peaks at the GPS locations are found already for window sizes between 300 m and 500 m in each dimension. Since these values cannot be known beforehand, a good compromise to preserve spatial resolution would seem to be the hierarchal approach of Joughin (  [13] ), in which cross-correlations are attempted for each measurement with increasing window sizes until quality criteria are satisfied. Concerning the latter, the use of an NCC threshold, as proposed by  Joughin ([13] ), could be complemented by a second threshold on cross-correlation SNR  [41] , to account also for off-peak cross-correlation values in the quality characterization.
Furthermore, since interferometric coherence is lost in fast moving areas, processing steps which enhance features with respect to radar speckle, such as the low-or high-pass filtering implemented by some of the participants, can be expected to improve the quality of the cross-correlation peaks in these areas. However, clearer indications on this can be provided by dedicated studies, such as  [41]. Regarding outlier culling, several groups obtained good validation results on static bedrock with approaches based on the thresholding of NCC magnitude and cross-correlation SNR, using the same thresholds for the datasets in tasks 3 and 4. Further culling steps based on the analysis of local velocity magnitude and/or flow-direction variations proved to be effective, but also very sensitive to analyst choices.
The results cover all SAR processing techniques currently used to carry out ice-motion measurements, namely DInSAR, MAI (azimuth split-bandwidth) and offset-tracking. Both the SAR-based results and the GPS data provide a unique benchmark to assess the performance of processing chains implementing these measurement techniques. Secondly, the algorithmic steps and/or parameters responsible for performance variability are analyzed in detail for each technique. Interestingly, the causes for sub-optimal performance are most often related to the application of procedures, which are strongly influenced by analyst choices, e. g. , concerning processing parameters. Useful indications to automate some key processing steps, while at the same time yielding improved performances, are obtained by analyzing the available processing results, as discussed in Section 4 and detailed in the inter-comparison and validation sections of each experiment (Section 3).
Finally, although this study was carried out on archive data from ESA and ESA Third Party Missions, the considerations presented in Section 4 concerning the processing algorithms are not sensor-specific and are thus equally applicable to current (e. g. , Sentinel-1a,b) and forthcoming (e. g. , NASA-ISRO) SAR missions. In particular, since the latter by design support the systematic application of interferometric techniques, this will allow the synergies between different measurement algorithms to be exploited to a much higher degree, thus contributing to define more robust and analyst-independent procedures for several critical processing steps, besides of course noticeably improving the spatial resolution and the accuracy of the final ice-velocity products.
And the possible constants determining the modified Newton's laws will therefore be dependent only on the galaxy considered and on the observer who collected the data. As a result, the intensities F M of the modified Newton's laws of gravitation have the following general form up to a multiplicative constant (⃗ r ≡ (x, y, z)): F M (⃗ r) ≡ m M (r) c 0 (⃗ r) + c x (⃗ r) x + c y (⃗ r) y + c z (⃗ r) z r 2 , (37) where M (r) is the mass enclosed in a disk of radius r and the functions c depend on ∆τ G C and r G C. It should be noted that this modification nevertheless preserves the action/reaction principle contrarily to the modifications given in MOND theories. 22 e use the isotropic formula (33) to make the fits and observational data provided by the SPARC database  23, 24  with explanations of how the surface brightness densities are obtained from the ARCHANGEL software.
25 Actually, we used two sort of files directly available online on the SPARC database website, namely, the Bulge-Disk Decompositions files 'XXXXXX. dens' and the Mass Models files 'XXXXXX rotmond. dat' all grouped together and stored respectively in the zip files BulgeDiskDec LTG. zip and Rotmod LTG. zip. Then, from the 'XXXXXX. dens' files, we have the surface brightness densities SBdisk and SBbulge of, respectively, the disks and the bulges of the galaxies XXXXXX with respect to the radius. These densities are also partially given in the files 'XXXXXX rotmond. dat' but these files give less points/data to make good fits. Therefore, we use in 'XXXXXX rotmond. dat' files only the rotational velocity values Vobs with their error bars errV as functions of the radius Rad, i. e. , the variable r.
24803 e -0. 50152 r + 740. 60414 e -0. 62407 r 2 -763. 48766 e -0. 08663 r-0. 54086 r 2 , ( and with coefficients α = 0. 0000 and β = 20. 8025. Lastly, among the 175 galaxies in the SPARC database, we have prioritized the least elliptical galaxies possible. We therefore mainly chose galaxies of types S0, SA, SAB or SB (unfortunately, there are no type E galaxies in the SPARC database) or those with significant experimental points for Vobs.
Moreover, the motivation for this publication comes from the fact, as indicated more precisely in the introduction, that we took at random only about ten galaxies, sometimes of very different types, and that each time the fit was possible. Consequently, it seemed to us highly unlikely statistically, among 175 galaxies in the SPARC database, that this modification of Newton's law was totally irrelevant; hence the motivation to publish these results. It is unlikely, a priori, that all fits are possible for other galaxies, but that for these statistical reasons, this is possible for the vast majority of them. Note that recent results 26 using the formula (33) in a purely phenomenological way and without any theoretical justifications allowed the good fits of 18 additional galaxies. Moreover, it would be interesting to see if there is a relationship between the dependence in cosmological time of the modified Newton's laws and models of accelerated expansion of the universe or galaxies clusters dynamics.
Acknowledgments The author would like to thank  Frederico Lelli  ( European Southern Observatory ) for his suggestions of formulas for the determination of total surface brightness density (SB) and his advices. The author would also like to thank  Ralf Hofmann  ( University of Heidelberg ) and  Thierry Grandou  ( Université de Nice -Sophia Antipolis ) for their criticisms and discussions on this subject at the ICNAAM conference where a non-detailed presentation 27 of this work was presented at the  Session-Workshop: 'Nonperturbative Quantum Field Theory III ,' organized by  Ralf Hofmann.
(2) Calibration data in JHK. Images collected by the VISTA 4m telescope as part of the VVV survey are excellent  [64]  but the current interface and data products are not satisfactory. Our approach is to download all VVV data and develop tools to extract data cubes of 6 arcmin centred on the target. Then we perform PSF photometry using PSFEx  [65]  and SExtractor  [66]  from the AstrOmatic. net suite of tools. We then calibrate the catalogues using 2MASS both for astrometry and photometry the JHK data. (3) Source estimates in H and/or K. If the microlensing target has been observed in IR, we extract its photometric light curve. The objective is to have measurements at different levels of amplification to be able to estimate accurately the source in IR bands. In the absence of such IR data, knowing the fitted V, I color of the source and extinction, we derive the color of the source and predict what would be its H and K magnitudes.
It can be done even if source and lens are separated only a fraction of the FWHM. Given the adaptive optics of PSF stability, we are currently limited to resolving source and lens with a separation of ∼60+ mas. The measurement will be done using for instance the NARROW camera at KECK (10 arcsec field of view). It can be attempted only under best observing conditions, when 45-60 mas images are being obtained. (7) Data processing. First, it is necessary to correct for the dark current and flat-fields in a standard way. We also need to observe a field with very few or no stars to estimate the sky contribution in the observations. A possibility is to target a dark cloud located about 400 arcsec north and 713 arcsec west of the Center. We then correct the science images. We then correct for the distortion of the images, using accurate maps provided by the observatory for the camera.
Acknowledgments: This work was supported by the  University of Tasmania  through the  UTAS Foundation  and the endowed Warren Chair in Astronomy. The author acknowledges his long term collaborators and in particular  Virginie Batista  (Paris),  David Bennett  (Goddard),  Andrew Cole  (UTAS),  Jean-Baptiste Marquette  (IAP). He acknowledges the financial support of  Programme National de Planétologie (CNRS, INSU) ,  PERSU  from  Sorbonne-Université  and the  CNES. Data presented herein were obtained at the  W. M. Keck Observatory  from telescope time allocated to the  National Aeronautics and Space Administration  through the agencys scientic partnership with the  California Institute of Technology  and the  University of California. The Observatory was made possible by the generous financial support of the  W.
Sample and Measurements The three independent datasets combined in this analysis are the orbital radius and excitation of rovibrational CO emission (Sect. 2. 1), the iron abundance Fe/H as measured on stellar photospheres (Sect. 2. 2), and the near-IR excess over the stellar flux in the SED (Sect. 2. 3). The sample includes the majority of well-  known Herbig Ae/Be stars within 200 pc, together with some at larger distances: 26 late-B, A, and F stars with temperatures T eff between 6,500 K and 11,000 K, masses between 1. 5 M and 4 M , and ages between 1 and 10 Myr. The sample is composed evenly by GI and GII disks (13 each), and we adopt the classification criterion from  Garufi et al. (2017) , where the flux ratio at 30 and 13 µ m is used to separate GI disks (with F30/F13 > 2. 2) from GII disks (with F30/F13 < 2.
Linked behavior between the datasets The datasets combined in this work show a linked behavior in the multi-dimensional parameter space illustrated in the four panels of Figure  1. The correlation between Fe/H and R co demonstrates a link between two observables that could in principle be independent of each other: iron is depleted from the stellar photospheres as R co recedes to larger radii in their inner disks. GII disks have, on average, smaller R co and higher Fe/H, while GI disks have the opposite, larger R co and lower Fe/H. A group of GI disks overlaps with GII disks at intermediate values of R co. F NIR values are found to lie between 5% and 34%, with only one disk showing a value as low as 0. 08% (HD141569, see Sect. 4. 2). All GII disks show F NIR in the narrow range between 14% and 19%.
2003 (Brittain et al. , 2007)). When Keplerian line profiles are modeled assuming a power-law radial brightness (e. g. ,  Salyk et al. 2011) , R co as defined here can be several times larger than R dust (taken as the innermost radius where CO gas can also exist). The model curve in Fig. 1c  shows R dust multiplied by 7, by assuming the median scaling factor between R co values and the R dust values from  Lazareff et al. (2017)  for this sample. By assuming devoid inner holes, this simple model provides only a trend of the lower boundary to the data points in the figure. Reproducing the high near-IR excess of some Herbig disks has been a long-standing modeling challenge that is still unsolved (see, e. g. ,  Dullemond et al. 2001; Vinković et al. 2006 ). New modeling efforts could be devoted to studying how F NIR can be increased toward values measured in the high-NIR GI disks (25-35%) by keeping an inner residual dust component while inner cavities are forming at larger disk radii.
Acknowledgements. We thank  A. Carmona  for providing CO data of HD169142, and acknowledge helpful feedback and discussions with  A. Bosman ,  G. Dipierro ,  U. Gorti ,  G. Mulders ,  P. Pinilla , and E. van Dishoeck. We also thank the anonymous referee for suggestions that helped improve the presentation of this work. M. B. acknowledges funding from  ANR of France  under contract number  ANR-16-CE31-0013  (Planet Forming Disks). This work is partly based on observations obtained at the  Infrared Telescope Facility , which is operated by the  University of Hawaii  under contract  NNH14CK55B  with the  National Aeronautics and Space Administration. This research has made use of the VizieR catalogue,  CDS, Strasbourg, France  ( A&AS 143 ,  23 ).
This activity was supported by Ageing@ Coimbra and the Administrative Authority of the Centre Region of Portugal. Another example of the collaborative work of Ageing@ Coimbra members within the scope of C2 Action Group is well represented by the project GrowMeUp, funded under the H2020 call PHC-19-2014 "Advancing active and healthy aging with ICT: service robotics within assisted living environments"; coordinated by the University of Coimbra and including the active partnership with Caritas Coimbra as an end-user organization (Grant Agreement 643647). 11  11 w w. growmeup. eu (Accessed: April 24, 2018).
This dementia care residence is a pilot unit established in 2013, including environmental, professional, therapeutics competences, recognized as a reference model for the care of patients with cognitive impairment and dementia. It includes 20 beds for mid-term care and rehabilitation (up to 90 days) and 30 beds for long-term care and maintenance (up to 180 days). "Knowledge" projects include: research and application of programs and new models; application of ICT solutions; collaborative models using telehealth and teleassistance; intergenerational approaches; infrastructures; equipment and access to knowledge; multimorbidity research and care; AHA and longevity, databases and population profiling. The winner was a project submitted by the Faculty of Medicine of the University of Coimbra and the Center for Neuroscience and Cell Biology: "NoMicro Technologies.
In this article, the tidal energy potential is determined by exploring the results of numerical models for The Copernicus Space Component. The numerical models shown in this research ran operationally and their results and forecasts can be accessed at  [22]. The CMEMS database has been widely used by several researchers to evaluate wave and tidal energies. This simulation has some weaknesses in terms of accurately representing some storm events, but it generally represents mean values quite well, and this database proposes homogeneous long-term data and a higher spatial coverage than that achieved with single-point observations. Moreover, data from the CMEMS database have previously been used to identify tidal energy potential in different regions  [23] [24] [25]. Three regions are analyzed for investigating their tidal energy potential, while their geographical coordinates and catalog using from the Copernicus Marine Environment Monitoring Service is provided in Table  1.
If requisite, improvements made to power output evaluate due to resulting changes in boundary conditions. This research analyses the tidal energy resource in Morocco using a 5-year series of data obtained from numerical modeling (hindcasting). The spatial distribution of tidal power is analyzed using data from 2 areas (Irish-Biscay-Iberia, Mediterranean sea). This allows a fast evaluation of the feasibility of tidal energy locations. The location of these regions is presented in Figure  2. Step 4 tidal parameters. If requisite, improvements made to power output evaluate due to resulting changes in boundary conditions. This research analyses the tidal energy resource in Morocco using a 5-year series of data obtained from numerical modeling (hindcasting). The spatial distribution of tidal power is analyzed using data from 2 areas (Irish-Biscay-Iberia, Mediterranean sea).
, 1985; Ion et al. , 2012). In order to help beekeepers, farmers, and land managers, some authors have developed lists with a ranking of the melliferous plants for several decades. These references can be global reviews and useful check lists for the melliferous potential of many species  (Crane, 1975; de Wilmars, Bruneau, & Evrard, 1989). Others offer a compilation of species, but do not discuss the values of the melliferous potential they report  (Janssens, Bruneau, & Lebrun, 2006). Ricciardelli d'Albore and Intoppa (1978)  conducted an exhaustive study to assess melliferous plants all over the Mediterranean basin. As a guiding tool for beekeepers, some data on the melliferous potential are also available in apicultural manuals, but the source of the values reported is generally not provided.