dataesr/scientific-paragraphs-categorization huggingface dataset:
Concerning the eocyte data set, the model with 19 equilibrium frequencies per branch required about 522 h of calculation to converge to the ML optimum. Comparatively, the best COaLA model only required about 40 h of calculation. For the two other data sets (archaea and three domains), we cannot provide a precise comparison as the 19 equilibrium frequencies per branch model was stopped after 1 month of calculation before reaching the ML optimum. The best COaLA models used about 26 and 18 h of calculation, respectively, with a very stringent threshold of 10 -6 below which convergence is accepted. Tests on Single Gene Data Sets NH-COaLA is overparameterized for single-gene alignments. -From the 24 methanogenic archaeal genomes, we built all homologous gene families (see "Materials and Methods" section) and conserved the unicopy and nearly universal families, leading to 535 genes.
Even with rather small (5000 sites) phylogenomic data sets in the simulation experiments, and when the heterogeneity is similar to what one can observe with real data, the model is on average better than a homogeneous model. Overparameterization by the branch-wise approach in comparison with a homogeneous approach was detected in only 30% of the cases according to BIC with simulation experiments of sequence alignments having levels of compositional heterogeneity comparable with empirical data. With real data, three out of the four phylogenomic data sets were more efficiently fitted by the NH-NS branch-wise model than by other models. With more and more biological data coming from many and diverse sequencing projects, the data set sizes should increase as well. We observed that large, concatenated data sets are less frequently overparameterized by NH-NS models than single-gene data sets.
Acknowledgements The authors are grateful to  Manoel José da Silva  and all LabMar interns for assistance with the fieldwork. The authors would like to thank the anonymous reviewers for their comments and suggestions to improve the quality of the paper. The author was supported by the  Institutional Programme of Scientific Initiation (PIBIC)  with a scholarship granted by the  Research Support Foundation of the State of Alagoas (FAPEAL) ,  Brazil.
We present a technique for the multi-sensor registration of featureless datasets based on the photogrammetric tracking of the acquisition systems in use. This method is developed for the in situ study of cultural heritage objects and is tested by digitizing a small canvas successively with a 3D digitization system and a multispectral camera while simultaneously tracking the acquisition systems with four cameras and using a cubic target frame with a side length of 500 mm. The achieved tracking accuracy is better than 0. 03 mm spatially and 0. 150 mrad angularly. This allows us to seamlessly register the 3D acquisitions and to project the multispectral acquisitions on the 3D model.
Because of the complementary nature of the data acquired by these two techniques, conservators benefit from augmented 3D models with multispectral texture. The annotation of 3D models and the integration of complementary techniques is widely used for the study of cultural heritage  [2] [3] [4] [5]. There have been some attempts at creating integrated 3D/multispectral acquisition systems  [6] [7] [8] [9] [10]  for the study of cultural heritage objects. However, such integrated systems lack the flexibility necessary to study a variety of cultural heritage objects. Using separate systems for the 3D and multispectral (2D) acquisitions enables us to independently choose the most suitable for the given application. The drawback is that we then have to register these multiple datasets. The traditional approach is to use homologous points in the 2D and 3D data to retrieve the unknown intrinsic and extrinsic camera parameters, for example using the Tsai camera calibration method  [11] [12] [13].
White spheres were mounted on the high resolution scanner and the third task of the low resolution scanner was to track the position and orientation of this second scanner while in use. We extend this type of setup for the multisensor registration of featureless datasets. Our technique relies on close range photogrammetry to track 3D and multispectral acquisition systems. Photogrammetry is the science of measuring the position and shape of objects using photography. In stable and well calibrated setups off-line photogrammetry systems can potentially achieve a measurement precision of up to 1:500,000 with respect to the largest object dimension  [29]. To achieve such precision, the intrinsic camera parameters must be determined with great accuracy. These intrinsic parameters are also called the camera interior orientation (I.
Method Description Figure  1  represents the in situ acquisition setup: a group of cameras observe the acquisition systems as they successively digitize the surface under study from various positions. We refer to these as the "tracking cameras" to distinguish them from the multispectral cameras used throughout. The acquisition system is fixed to a target frame that enlarges it and increases the tracking accuracy. The acquisitions are performed simultaneously from the acquisition system in use and the tracking cameras. The registration is performed by projecting the acquired datasets in a single system. To ensure a precise tracking and subsequent registration, all optics and objects in play must be carefully calibrated. We introduce the following coordinate systems, linked to the materials in use: • C Si , (O Si , x Si , y Si , z Si ) is the coordinate system linked to acquisition system i.
Processing Software The processing of the calibration and acquisition photogrammetric bundles relies on two pieces of software. We use Gom's photogrammetric bundle adjustment software TriTop  [35]  to recognize the targets and perform an initial bundle adjustment. We thus estimate the cameras' internal and external orientation, the coordinates of the targets in a world system and the coordinates of the targets in the images. The output is then processed by a lab-developed software based on the AxOri library  [36] , i3mainzAxOri. We now perform the bundle adjustment with greater flexibility in differentiating the unknowns from the input parameters. For example, we can estimate the target coordinates or the object position by assuming the tracking cameras' interior and exterior orientation are known.
Results and Discussion The bulk of the calibrations were performed on the same day as the acquisitions. Only the camera calibrations were done at different moments: the tracking cameras were calibrated a few days earlier, while the multispectral camera was calibrated on the following day. The performed acquisitions and calibrations are listed in Table  3. This section first presents the results of the individual calibrations. We then evaluate the accuracy with which we track the target frame. Finally, we present the 3D registration results, followed by the integration of the 3D and multispectral datasets. Table  3. List of calibrations and acquisitions performed.
Tracking The target frame is tracked in 23 positions: 5 when the Gom Atos III digitizes the scene (G1 to G5) and 17 for the FluxData multispectral acquisitions (positions FD1 to FD17). The relative position of the target frame and the cameras is illustrated in Figure  3. Y G1 G2 G3 G4 G5 FD1 FD2 FD3 FD4 FD5 FD6 FD7 FD8 FD9 FD10 FD11 FD12 FD13 FD14 FD15 FD16 FD17 The spatial and angular tracking accuracy of the frame tracking for each position is given in Figure  4. The achieved tracking accuracy is compared with the simulation results in the best-case scenario and to the accuracy goal. We only reach the simulation results for the angular accuracy when the Gom is in the target frame. The spatial accuracy is also much better for these five positions, rarely exceeding 10 % more than the spatial accuracy reached during the simulations.
Conclusions and Perspectives Using a photogrammetric setup based on four tracking cameras and a cubic target frame with a side length of 500 mm, we were able to successively track the position and orientation of a fringe projection digitization system and a multispectral camera with an accuracy better than 0. 03 mm spatially and 0. 150 mrad angularly. The tracking results are used to register five 3D meshes together and to project seventeen multispectral acquisitions. These first experimental results show that our tracking method is adapted not only for the registration of 3D datasets, but also for the integration of multispectral texture on 3D models. The accuracy of the final registration relies on the success of a series of optical and geometrical calibrations. It is essential that the calibrated parameters are stable for the duration of the full acquisition process, typically from four to eight hours.
6 kg) and camera supports (30 kg) can be reduced by using lighter materials. In the current configuration, the weight of this material is comparable to that of a laser tracker (the Leica Absolute Tracker, for example, weighs 39 kg). A big advantage of this technique is that it can be used with any optical acquisition system that the user may already possess. The additional cost is thus only that of the tracking frame, tracking cameras and lens, which in our case is under 15,000 e. This is much less than the cost of a laser tracker, though this amount does not include software costs. A broader use of the technique would require the development of an independent and integrated software tool for data processing and display. The method is particularly cumbersome when digitizing highly three dimensional objects: eight tracking cameras may be necessary for a 1 m high statue.
As the number of cameras increase, so does the number of calibrations necessary and the difficulty of maintaining a stable setup. Compared with automatic alignment method, our system offers clear advantages when dealing with object with low spatial variability. Our system offers a solution to the multi-view and multimodal registration of smooth and featureless datasets. The registration accuracy is independent of the content of the acquired data, which means that the errors do not accumulate during the 3D registration of open surfaces, as would be the case using ICP, for example. These characteristics, including the fact that it is transportable, make our technique particularly suitable for the in situ study of cultural heritage objects. This registration method can also be used in industrial settings.
Acknowledgements This work was kindly funded by the  Conseil Regional de Bourgogne, France  and the  Fond Europeen de Developpement Regional (FEDER)  as well as i3mainz laboratory,  Germany. Gom  lent us a Tritop license for research purposes. This loan is most appreciated as it noticeably sped up and eased the target recognition and the data processing. Jens Bingenheimer  helped perform the measurements and calibrations. We greatly thank him for his meticulosity and his enthusiasm.
Current velocity was measured at different elevations above the bed depending on each instrument. The ADW S4 and Valeport current meters recorded current velocity at 0. 4 m and 0. 2 m above seabed respectively, while the ADCP measured current velocity at intervals of 0. 2 m through the water column from 0. 4 m above the bed to the water surface. Current velocity at 0. 2 m above the bed was estimated using the ADCP data by applying a logarithmic regression curve to the measured velocities obtained at different elevations in the water column. Beach morphology plays a major role in the variation of sediment transport rates, especially on bar-trough topography  [9, 10, 13, 32, 33]. Thus, during each field experiment, beach morphology was surveyed using a very high resolution Differential Global Positioning System (DGPS) with horizontal and vertical error margins of ± 2 cm and ± 4 cm respectively.
Potential effects of water depth Sediment flux measurements were performed in a water depth between approximately 1 m and 1. 5 m, but because of the excursion of the tide, these measurements were made at several locations on the foreshore (Figure  4 ). Calculation of sediment load at the same location on the field requires a lot of computer manipulations. So initially, the sediment loads are modelled in the middle of the digital domain. The water level in the middle of the simulated field rarely matches the exact water level measured during sampling. A test was therefore carried out to assess the potential effects of water depth on computed sediment flux using the data measured during the two field experiments at Hardelot beach (2009 and 2010). For the numerical simulations, bathymetry has been considered as stable during and between the two field campaigns.
Because sediment traps mainly collect sediments transported in suspension, future investigations using this data set will be aimed at de-coupling suspended and bed load transport calculation in the different sediment transport formulae in order to evaluate only the suspended sediment flux, which should result in more accurate comparisons between modeled and measured sediment transport rates. French Centre National de la Recherche Scientifique (CNRS) through the PLAMAR and MI-CROLIT projects of the Programme "Relief de la Terre". Additional funding was provided by the "Syndicat Mixte de la Côte d'Opale" through a doctoral scholarship to Adrien Cartier. The authors would like to thank Aurélie Maspataud, Vincent Sipka and Antoine Tresca as well as all the students and permanent staff for their help during the field experiments.
ruber (white) obtained from Atlantic sediment trap samples [T = (1/0. 09) Â ln(Mg/Ca/0. 449); Anand et al. , 2003]  using the modern sea water Mg/Ca value. [12] Our calculated Mg/Ca-derived SSTs rest on the assumption that the Mg/Ca ratio of seawater has remained constant over the studied time interval. For time intervals shorter than 1 Ma (as it is the case in our study: ~0. 4 Ma), an invariable Mg/Ca ratio of seawater is usually assumed because of residence times for both Mg and Ca that are longer than 1 Ma [e. g. ,  Fantle and DePaolo, 2006]. To account for temporal changes in seawater Mg/Ca for the Plio-Pleistocene, the available correction of Medina-Elizalde et al. [2008]  can be used. Applying this correction to our dataset results in higher Mg/Ca-derived SSTs (by ~2 C higher; see Figure  S1  in the supporting information) but does not affect the trend and therefore interpretation of our record.
A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12. 5 km was completed for the new emission scenarios RCP4. 5 and RCP8. 5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX.
Insignificant changes are not equivalent to the lack of agreement in the projected changes and should be interpreted differently, depending on the context. First, the direction of change was assessed. Changes in regions in which more than 66 % of the models agree in the direction of change were called robust changes. For the analyses of the significance, two different data preparation methods were used. For total precipitation and maximum, mean and minimum temperature, one value each per year and grid box was calculated (30 values per simulation per grid box). For heat waves, dry spells and heavy precipitation, only one value exists for each simulation and grid box, since they are defined over a 30-year period. Again they were calculated for all three time periods. For significance testing, the Mann-Whitney-Wilcoxon test was applied to the entire ensemble data set for the respective time periods.
Conclusions and outlook Regional climate change patterns for Europe projected by the high-resolution regional climate change ensemble within the EURO-CORDEX initiative for the new emission scenarios RCP4. 5 and RCP8. 5 have been compared to the state-of-the-art regional climate change data set from ENSEMBLES. The overall spatial patterns for temperature and precipitation changes and related indices are similar. There is a large degree of consistency between the ensembles of the three emission scenarios. The results clearly strengthen the previous findings obtained from the ENSEMBLES data set; however, there are some important new findings. Climate projections from the new ensemble indicate a reduced northwards shift of Mediterranean drying evolution and slightly stronger mean precipitation increases over most of Europe.
The EURO-CORDEX high-resolution data set will grow continuously and made available to the community. At the time of writing, more ensemble members are being conducted for both, the RCP4. 5 and RCP8. 5 scenarios. Most simulations will run until 2100 and will enlarge the ensemble. The EURO-CORDEX community agreed on common quality control and will provide a wealth of information for future climate research, impact assessment and adaptation.
Acknowledgments The ENSEMBLES data set used in this work was funded by the  EU   FP6  Integrated Project ENSEMBLES (Contract number  505539 ), whose support is gratefully acknowledged. Simulations at IPSL and INERIS were carried out thanks to the support of the CCRT/TGCC computing centre and allocation from the  GENCI  project on regional climate and air quality simulations over Europe. The CNRM-ALADIN simulations performed at Meteo-France/CNRM were carried out in the frame of Med-CORDEX and HyMeX programs and can be downloaded at www. medcordex. eu, the Med-CORDEX database hosted at ENEA. The CCLM simulations were supported by the  Federal Ministry of Education and Research (BMBF)  and performed under the ''Konsortial'' share at the  German Climate Computing Centre (DKRZ). The ETH Zurich simulations were supported by a Grant from the  Swiss National Supercomputing Centre (CSCS)  under project  ID s78.
The REMO simulations were supported by  CSC , MPIM, as well as BMBF and performed under the ''Konsortial'' share at the  German Climate Computing Centre (DKRZ) , which we are further thankful for their various support. The KNMI-RACMO2 simulations were supported by the 7th Framework EU-projects  IMPACT2C  ( FP7-ENV. 2011. 1. 1. 6-1Grant Nr 282746 ) and  ECLISE  ( FP7-ENV. 2010. 1. 1. 4-1Grant Nr 265240), and by the  Dutch Ministry of Infrastructure  and the Environment. The subregions (see Figure s1) used in this study are a result of the work carried out as part of the Vulnerability Assessment of the  EU  funded  Fifth Framework  project  ATEAM (Advanced Terrestrial Ecosystem Assessment and Modelling ). Part of SMHI contribution was done in the Swedish Mistra-SWECIA programme founded by  Mistra (the Foundation for Strategic Environmental Research).
We performed a quick analysis of CALIOP data for 2008 and found that the standard deviation of stratospheric backscatter at 30-34 km is consistently 3. 2 times larger over the SAA than over the rest of the globe. This has been proved particularly bothersome for the calibration of CALIOP signals based on stratospheric signals, which eventually had to ignore measurements over the area  (Powell et al. , 2009). Studies based on stratospheric CALIOP measurements require a careful attention to the area (e. g. Vernier et al. , 2011; Lopes et al. , 2013). Here we document how changes in the intensity and spatial extent of the SAA across six and a half years  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  affect levels of dark noise in CALIOP observations at 532 nm. We show that within those noise levels are information about the evolution of the SAA with time.
We have in addition identified an asyet unexplained yearly noise cycle that appears unrelated to the seasonal change in solar illumination, but is anticorrelated with the geomagnetic field activity. Acknowledgements. The authors wish to thank the NASA Langley CALIOP team for the CALIOP data, the ICARE data center for access to the data, and ClimServ for providing computing facilities. Data analysis was conducted using Python and Numpy  (Oliphant, 2007). Figures were created using Matplotlib  (Hunter, 2007).
Introduction Data calibration of spectra and waveforms issued from a search coil magnetometer is not a new problem. Among previous space physics missions using search coils magnetometers, let's mention GEOS-1 and GEOS-2 as the first ESA spacecraft dedicated to the study of waves and particles in the magnetosphere  (Knott, 1975; Jones, 1977). The GEOS wave consortium (S300 experiment) comprised a tri-axis searchcoil magnetometer build by the ancestry of the spatial team of the LPP. but the principle remains the same: how to calibrate magnetic waveforms issued from a search-coil rotating across a high ambient DC field, knowing that the transfer function is varying with the frequency? This kind of problems has been solved at this epoch for time-frequency studies  (Robert et al. , 1978 (Robert et al.
This has been done at the GEOS epoch, where the agreement found was ∼ 4 % on the magnitude and ∼ 4 • on the direction  (Robert, July 1979). As the CLUSTER data are archived and will be used a long time after the life duration of the instrument and corresponding PI and engineers, it was about time to do this work now to get the best possible calibration and to do needed checks of cross calibration. So a special care has been devoted to the calibration and the cross-calibration of the magnetic wave measurements, before launch on ground, then onboard during the commissioning phase and all along the mission. A special effort to compare STAFF-SC data with the FGM on board flux gate magnetometer data  (Balogh et al. , 1997 (Balogh et al. , , 2001) )  has been undertaken since the beginning of the mission until now.
Back Close Full in space is discussed  (Sect. 4). The transformation of the raw data, that are acquired in a spinning system (Cluster spacecraft are spin stabilised) into a fixed physically meaningful reference frame needs a series of successive coordinate transformations described in Sect. 5. Then the conversion of telemetry data into physical units, that is to say the calibration process itself, is presented for the wave form data (Sect. 6) and for the Spectrum Analyser data (Sect. 7). The continuity of spectral values as well as the similarity in the wave characterisation between the two STAFF experiments is presented in Sect. 8. Section 9 is devoted to the comparison between STAFF and FGM data and to discussion of the obtained results, followed by the conclusion.
From this a corrective transfer function has been established: FT CORR = 1. 1 × 1 + j f fch 1 + j f fcb Figures Back Close Full The new transfer functions thus obtained have been applied to STAFF data and compared again with FGM data, as will be shown later on in this paper, giving satisfactory results. Then it was estimated that we had found the error sources, and that we could not go further. This comparison seems to show that the facility loops had already undergone the deformation at the time of Cluster STAFF search coils calibration, in 1999, observed some years after. As the site magnetic quietness is not perfect, there remains some variations in the transfer function which were attributed to the environmental electric array, namely at 50 and 150 HZ. This lead to smooth the new transfer function, as can be seen on Fig.