dataesr/scientific-paragraphs-categorization huggingface dataset:
Aiming to complement existing planetary analogue rock and mineral libraries, the PTAL project  (Werner et al. , 2018)  shall represent a new resource for planetary scientists, namely a Planetary Terrestrial Analogues Library, in order to help the mineralogical characterization of planetary analogues through different techniques, some of which are used in space. The following techniques applied to characterize the samples are distributed over the partners of the PTAL consortium: X-ray Diffraction (XRD) and thin section analysis at Oslo University (Norway), point and imaging spectroscopy in NIR at Paris-Saclay University (France), Raman spectroscopy at Valladolid University (Spain), and Laser Induced Breakdown Spectroscopy (LIBS) at Toulouse University (France). Then spectroscopic data from all these techniques shall feed the PTAL database that will be publicly available by the end of the project (2020), with the ultimate goal of supporting the operations and the analyses of in situ investigations by the future rovers on Mars by assessing results of various instrument combinations.
The IAS team at Paris-Saclay University is in charge of the nearinfrared analysis within the PTAL project. Measurements are made with a commercial FTIR (Fourier Transform Infrared) instrument that mimics the spectral characterization of the rover point spectrometers, and with the flight spare model of MicrOmega/ExoMars. In this paper, we will focus on data acquired by the point spectrometer, while spectral analyses obtained from the imaging spectrometer MicrOmega will be presented in a forthcoming publication  (Loizeau et al. , in prep. ). The core of the PTAL analogue materials suite consisting in 94 powder samples is divided according to their site collection (Table  1 ). We also give here basic lithology information on the samples. Details on the field campaigns, sample preparation and analysis with XRD and thin sections will be presented in a forthcoming publication  (Dypvik et al.
, in prep. ). Note that experimental samples as well as ExoMars landing site analogues were included to be part of the PTAL collection during the project. These additional PTAL samples have been already presented in a separate paper  (Viennet et al. , 2017)  or will be described in a forthcoming paper  (Krzesinska et al. , in prep. ). After a brief presentation of the methodology (section 2), we present the spectral data and detailed mineralogical features for each sample in section 3. The results are then discussed in the context of the mineralogical properties of landing sites selected for Mars2020 and ExoMars.
Coordinated analyses are on-going, revealing pros and cons for each technique (the outcome of these analyses for typical and specific samples is foreseen to be submitted before the end of the PTAL project). Raman results are already available  (Veneranda et al. , 2019)  and LIBS analysis is ongoing. Coordinated analysis between the four spectroscopic techniques, together with thin section data, will be therefore performed. Another interesting comparison will be made between this study at macroscopic scale on homogeneous samples and the MicrOmega acquisitions at microscopic scale on bulks  (Loizeau et al. , in prep. ). New samples, specifically chosen to match Oxia Planum observations, have been recently acquired by the PTAL project and will be included in the spectral library as well. Data will be released and publicly available on the PTAL website database  (Veneranda et al.
3. Nonlinear resonances in the spontaneous symmetry breaking regime illustrated by plotting averaged modal intensities (blue and orange curves) recorded as the cavity detuning is ramped up in symmetric conditions (χ = 45 • , δ ∆ = 0). In (a) and (b), measurements are taken over several hundreds of scans, and the data are presented as color-coded %-age-of-occurrence histograms. (c),(d) Two individual scans highlighting the anti-correlated behavior of the modal intensities, selected to illustrate the mirror symmetry of the system. The total intensity is also shown as black curves. Note: the orange curves are associated with the component up-shifted by the AOM. Fig. 3. The system then always favors the same mode: resonance scans look either like the one presented in Fig. 3(c ), or the one in Fig.
3(d) , depending on the direction of the change  [24, 28]. A secondary state, an almost mirror image of the first one, is never excited spontaneously although it might be present in the system  [23, 29]. In order to probe its existence under asymmetric conditions, and to identify all the cw stationary solutions of the system for each set of asymmetry parameters (χ and δ ∆), we proceeded as follows. With the detuning locked and total driving power kept constant, we measured the instantaneous output intensities of the two polarization components with 10 GHz-bandwidth photodiodes that resolve individual pulses, and acquired the data over about 8000 successive cavity round-trips with a 40 Gsample/s oscilloscope. In the middle of these acquisitions, we apply strong perturbations to the two driving pulses through the polarization modulator (see top left of Fig.
We thank  Alexander Nielsen  for help with the last histogram measurements,  Andrus Giraldo  for advice on continuation software, and  Lewis Hill  for useful discussions. We acknowledge financial support from  The Royal Society of New Zealand , in the form of Marsden Funding ( 18-UOA-310 ), as well as  James Cook  ( JCF-UOA1701 , for S. C. ) and  Ruther-ford Discovery  ( RDF-15-UOA-015 , for M. E. ) Fellowships. J. F. thanks the  Conseil régional de Bourgogne Franche-Comté , mobility ( 2016-9201AAO050S01777 ).
Acknowledgments I would like to thank the editors  Tijana Krstić  and  Derin Terzioğlu , as well as  Yavuz Aykan ,  Aslıhan Gürbüzel ,  Cemal Kafadar ,  Ana Sekulić , and the anonymous reviewers for their feedback. The research for this article was supported by the  OTTOCONFESSION  project and the  Gerda Henkel Foundation. All translations are mine unless otherwise stated.
Acknowledgments This work was supported by the  European Research Council  for the Advanced Grants COXINEL (340015, PI: M. -E. Couprie) and X-Five (339128, PI: V. Malka), the  LABEX CEMPI  ( ANR-11-LABX-0007 ),  Ministry of Higher Education and Research, Hauts de France council and European Regional Development Fund (Contrat de Projets Etat-Region CPER Photonics for Society   P4S ).
Statistical analysis The nitrate and ammonium data obtained from each technique in the field and laboratory studies were checked for normality and equality of variances, and the appropriate parametric or non-parametric test applied to determine whether differences between methods were significant. In the field study, assessment of any differences (p-value b0. 05) in measured nitrate and ammonium from the three sampling methods were determined using the non-parametric Kruskal-Wallis rank sum test. If significant differences between the groups were identified, a Dunn test was performed to identify which groups were statistically different. In the laboratory study, significant differences (p-value b0. 05) in ammonium between sampling methods were determined using a paired t-test or the equivalent non-parametric Wilcoxon rank sum test.
4 ). 3. 2. 1. 1. 2. Vertical concentration profiles in the top 0. 15 m of the streambed. Descriptive statistics were calculated individually for each method from all data collected in the top 0. 15 m of the streambed as this represents the overlap of the window of detection for the sampling methods. The highest mean pore-water nitrate concentration was observed in the Minipoint samples (4. 08 mg l -1 ) and DET gel samples (4. 02 mg l -1 ), in comparison the mean pore-water nitrate concentration measured in the multilevel mini-piezometer samples was only 2. 53 mg l -1. The highest coefficient of variation and range were observed with the DET gels (173. 36 and 34. 23 mg l -1 , respectively), however, the lowest coefficient of variation was found in the Minipoint samples (135. 05) and the lowest range in the multilevel mini-piezometer samples (15.
2. 1. 2. 2. Vertical concentration profiles in the top 0. 15 m of the streambed. Descriptive statistics were calculated individually for each method from all data collected in the top 0. 15 m of the streambed as this represents the overlap of the window of detection for the sampling methods. The highest mean pore-water ammonium concentration was observed in the multilevel mini-piezometer data (3. 83 mg l -1 ), whereas the lowest was observed in the Minipoint sampler data (1. 05 mg l -1 ). The mean pore-water ammonium concentration observed with the DET gels was intermediate of these values (2. 32 mg l -1 ). The coefficient of variation was highest in the Minipoint samples (188. 57) and lowest in the multilevel mini-piezometer samples (74. 67), whereas, the range was highest in the multilevel mini-piezometer data (11.
Laboratory experiments A comparison of the mean pore-water ammonium concentration at each depth showed that the concentration in the DET gel samples was higher than in the multilevel mini-piezometer samples at all depths (Fig. 5 ). It should be noted, however, that pore-water ammonium concentrations were slightly higher in the multilevel mini-piezometer data than in the DET gel data in two samples (0. 14 and 0. 08 mg l -1 higher, high concentration solution, 25 mm depth). The differences in pore-water ammonium concentrations obtained by the two methods were statistically significant at all depths (p-value = 0. 02, 0. 02 and b0. 01 for 2. 5, 7. 5 and 12. 5 cm depths, respectively, Table  4 ). Porewater nitrate concentrations were not measured during these laboratory experiments as no nitrate was detectable in the DET gel samples after processing.
Samples were collected from multilevel mini-piezometers at a different time (9th July 2015) than those from the DET gels (17th June 2015) and Minipoint samplers (16-18th June 2015), and the Minipoint samplers were not co-located with the DET gels and multilevel mini-piezometers (see Fig. 2c ). Despite the sampling variations we believe the discussion remains valid due to co-located samplers requiring sufficient distance between them to prevent interference, therefore, even co-located samplers may not sample the same parcel of water. This is particularly important where there is large variability in nutrients at small-scales, which has been observed in the Hammer Stream  (Shelley et al. , 2017). The techniques were utilised individually to gather insight into the reach-scale streambed biogeochemistry inferred from nutrient profiles obtained from each method, therefore, all data from each sampling technique were compared rather than individual nutrient profiles.
025 to 0. 15 m) and the DET gels (0. 035 to 0. 135 m), while the Minipoint samplers had the highest vertical  resolution (25 mm) compared to the DET gels (50 mm) and the multilevel mini-piezometers (0. 1 to 0. 5 m, depending on depth). This resulted in the majority of the multilevel mini-piezometer data originating outside the top, biogeochemically reactive layer of the streambed, whereas all of the data from the Minipoints and DET gels were collected from within the top 0. 15 m. Additionally, the higher vertical resolution of the Minipoint data, and to a lesser extent the DET gel data, allows small-scale pore-water concentration dynamics to be observed. These combined may explain why pore-water nitrate was lower and pore-water ammonium was higher in the multilevel minipiezometer samples, as these concentration dynamics are often also observed with increasing depth below the sediment surface where typically anoxia increases and is therefore, accompanied by an increase in denitrification and decrease in nitrification  (Dahm et al.
Acknowledgements This research was funded by The  Leverhulme Trust  project " Where rivers, groundwater and disciplines meet: A hyporheic research network " and from the authors' institutions. Additional funding was also provided from  NERC  through a  Central England NERC Training Alliance Studentship ,  NERC  standard grant  NE/L004437/1 , and the  European Union 's  H2020-MSCA-RISE-2016  project  734317. Jay Zarnetske  and  Joseph Lee-Cullin  were partially supported by the  US NSF  Award Number  1446328. The authors would like to thank the  Leverhulme  project team for their help, guidance and insight. The  U. S. Geological Survey  loaned the Minipoint samplers used in this study and provided guidance for their operation. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the  U.
Cost of the energy loss A load flow calculation is adopted in this work to evaluate the power losses Plosses. To feed the load flow algorithm, line and bus data are constructed from the connection matrix (A) and from the submarines cables data used (the construction of A is detailed in the following section). The TECs are modeled as PQ-nodes. The algorithm provides the modules and phases voltages (V and α) at different nodes in the grid. The load flow calculation consists of solving both (2) and (3) using the universal method of Newton-Raphson  [11]. Pi,j=V 2 i (gi,j-Gi,j)+ViVj [Gi,j cos(αi-αj) + Bi,j sin(αi-αj)] (2) Qi,j=V 2 i (hi,j-Bi,j)+ViVj [Gi,j sin(αi-αj) -Bi,j cos(αi-αj)] (3) Where Pi,j and Qi,j are respectively the active and reactive power transmitted from the node "i" to node "j", yi,j=gi,j+hi,j is the parallel admittance in a π diagram and Yi,j=Gi,j+Bi,j is the global admittance of the network.
For scenarios 1 and 2, fishers traveled to a launch site, conducted four fishing trips, and then returned to Nouméa to sell their catch. We calculated revenue by predicting biomass of reef fish using boosted regression trees (BRT) for all species combined and for four catch categories (Coral trout--Plectropomus spp. , Unicornfish--Naso spp. , Parrotfish--Scarinae, and Emperors and Snappers--Lutjanus and Lethrinus sp. ). These groups were chosen based on prominence in the fish market in Nouméa and occurrence within transects (i. e. , rare but valuable species, such as Bolbometapon muricatum, were not included because of insufficient representation in data to create accurate models). All analysis was conducted in R (R Core Team, 2017). For details on BRT models, see the Supporting Information.
To estimate the potential upper and lower ranges of profitable travel time, we calculated 95% confidence intervals of annual means in fish and fuel prices and applied these to our framework. Predictor variables were chosen based on previous studies (D'agata,  Mouillot et al. , 2016; Maire et al. , 2016). Data on fish and fuel prices between 2005 and 2015 were obtained from the New Caledonia Direction des Affaires Maritimes and provincial fisheries departments, and downloaded from the website of the New Caledonia Direction de l'Industrie, des Mines et de l'Energie (www. dimenc. gouv. nc), respectively. See the Supporting Information for further technical details.
ACKNOWLEDGMENTS We thank the  PRISTINE and FUTURE-REEFS  projects funded by the TOTAL foundation for providing support and data in New Caledonia. We also thank the crew of the different oceanographic vessels used to collect these data: the  IRD oceanographic vessel "ALIS " and the  Government of New Caledonia oceanographic vessel "Amborella. "We also thank the  Secretariat of the Pacific Community (SPC)  for providing data collected in New Caledonia under the  European Union funded PROCFish-C program  and the.
In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.
Great care will be exercised to quantify both the systematic and random errors resulting from the new algorithm. Section 2. 1 will describe the optimal estimation methodology, adapted to the SIC retrieval. The database of passive microwave observations at 0% and 100% SIC will be presented (Section 2. 2). In Section 3, the sensitivity of the retrieval to the hypotheses will be analyzed. In Section 4, a data fusion method will be presented to improve the SIC estimations at high spatial resolution. Section 5 will conclude this study.
The Round Robin Data Package The Round Robin Data Package (RRDP)  [20]  has been developed for the European Space Agency (ESA) sea ice Climate Change Initiative (CCI) project. It is openly available (https://figshare. com/ articles/Reference_dataset_for_sea_ice_concentration/6626549). It contains an extensive collection of collocated satellite microwave radiometer data, relevant for computing and understanding the variability of the microwave observations over sea ice. It includes passive microwave observations from SMOS (1. 4 GHz), AMSR-E and AMSR2 (6 to 89 GHz). The analysis here will concentrate on the 6 to 36 GHz channels that are of interest for SIC estimates and that are planned for CIMR. The RRDP covers areas with 0% SIC (SIC0, corresponding to TB ocean ) and 100% SIC (SIC1, corresponding to TB ice ).
It includes different sea ice types (thin ice, first year ice, multi year ice), for all seasons including summer melt, for both north and south poles. To identify areas of 100% ice (SIC1), the method used in the RRDP is based on ice drift dataset derived from radar observation (ENVISAT ASAR, Radarsat2 and Sentinel-1). Areas of ∼100×100 km 2 with convergence in the ice drift pattern on two consecutive days are selected. During winter, this will correspond to areas of total ice cover (assuming that it starts on day 1 with near 100% ice). In summer, the percentage of ice may be uncertain, as opening in the ice may appear. Areas with 0% ice are selected at ∼100 km away from the ice edge using ice-chart data. Then satellite microwave radiometer data with the footprint inside the selected areas of 0% and 100% are chosen for collocation.
The locations of the SIC0 and SIC1 observations are summarized in Figure  1. Note the presence of SIC0 data located at low latitudes which are not representative of polar ocean/sea ice margins and that are filtered out for our study. SIC0 dataset totalizes 26,289 collocations, and SIC1 dataset totalizes 2008 collocations. The RRDP dataset gives direct access to the sensitivity of the observations to the characteristics of the sea ice. It has already been used in current SIC algorithms  [15]. We use this database to compute the tie points used in our methodology, with their respective variances and covariances at different frequencies and polarizations, and to derive the linear forward model. Depending on the season, hemisphere, or ice types, some variability is expected in the microwave observations.
Here, we will evaluate this variability to assess if the algorithm has to account for these environmental conditions or if a generic algorithm can be adopted. The SIC0 and SIC1 data are available for two different radiometers AMSR-E and AMSR2, depending on the observation year. We prefer to use AMSR2 (operational since 2012) data as it is the current radiometer whereas AMSR-E (2002-2011) is not operational anymore. The mean TB ice and TB ocean (the respective tie points for SIC1 and SIC0) and their Standard Deviations (StD) are calculated from the RRDP. First, we compute the tie points using the complete dataset. Then, they are calculated restricting the data to a given season (winter or summer) and hemisphere (north or south). The results are shown in Figure  2. There are respectively 39%, 35%, 22%, and 4% of the SIC1 data in the boreal winter, in the boreal summer, in the austral winter, and in the austral summer.
At 100% SIC (resp. , 0% SIC) in the RRDP (or a subset of it), the mean of the retrieved SIC should be 100% (resp. , 0% SIC) with a StD equal to the theoretical retrieval error StD shown in Figure  5 , when the tie points used in the forward model are representative of the whole database. This test is performed using different subsets of the RRDP data: (1) to calculate the tie points and their covariances, and (2) to apply the retrieval algorithm. Figures  6  and 7  illustrate the distribution of the retrieved SIC with our algorithm using different tie points applied to different subset of the RRDP for 100% and 0% SIC. The Appendix B provides the corresponding statistics for 100% SIC. Note that here the SIC values can be lower than 0% or larger than 100%. This is because we choose to not constrain the inversion in order to properly evaluate the precision of the algorithm.
Acknowledgements The authors wish to thank the anonymous referee for all the pertinent remarks she/he made. The authors's research is part of the  ANR  project  CAE-SARS  ( ANR-15-CE05-0024 ) and  PACMAN  ( ANR-16-CE05-0027 ) and of  PANORISK  project. The third author was partially supported by chaire  Risques Financiers de la fondation du risque, CMAP-Ecole Polytechniques, Palaiseau-France.
Climate Climate data for the region was supplied by the Cypriot Department of Meteorology (Republic of Cyprus Department of Meterology, 2019) and was collected from the nearest weather station at the Agriculture Research Institute in Saittas (Latitude 34°52'N, Longitude 32°55'E, at a distance of between 11 and16 km from the vineyard sites). Mean January Temperature (MJT) and Growing Degree Days (GDD) were calculated for the three seasons studied, as well as the long-term average (1955-2017) (Table  2 ). Rainfall was highly variable over the three seasons. In 2017, total rainfall was 481 mm compared with the long-term average of 735 mm. In 2018, the total rainfall was 941 mm, with large falls recorded in January, May, June and December of that year. In 2019, the total rainfall was 370 mm above the long-term average with large falls recorded in January, February, March, June, August and December (Figure  1 ).
ACKNOWLEDGEMENTS This project is supported through a  University of Adelaide  scholarship and funding from  Wine Australia. Wine Australia  is supported by  Australian  grape growers and winemakers with matched funds from the  Australian Government. Authors gratefully acknowledge support from  Dr Roberta De Bei  for her assistance in taking measurements, Cypriot vineyard owners for allowing their vineyards to be studied, as well as  Marleen Zambartas-Brouwer ,  Marcos Zambartas ,  Andreas Markantonis  and  Sophocles Vlassides. We would like to thank  Natalia Kataiftsi ,  Marie Tabar ,  Morphee Besseau ,  Jade Godmuse  and  Apostolis Grigoriou  for their additional assistance and vineyard information.
The GHS/QoL was scored from 0 to 100, with higher scores indicating better QoL. The scores were compared between the KdD and Kd arms using a restricted maximum likeli-hood&amp;ndash;based mixed-effects model for repeated measures under the assumption of missing data at random. A minimally important difference of 5 points between arms was prespecified. An exploratory time-by-time sensitivity analysis of covariance (ANCOVA) was used to evaluate the treatment effect on the GHS/QoL score among patients who remained on treatment until the specified visit (cycle 3 and every 3 cycles thereafter). Results: GHS/QoL completion rates from baseline to cycle 26 (study ongoing) for randomized patients who remained on treatment were &amp;gt;81% for both the KdD and Kd arms; the median extent of missing GHS/QoL data was 5.