dataesr/scientific-paragraphs-categorization huggingface dataset:
The result is then projected onto the point cloud, using a depth map  (Guo et al. , 2020). However, these methods are limited: switching to an intermediate representation involves a loss of information. Moreover, the result depends on the choice of projections or voxel sampling of the point cloud. Finally, these techniques are computationally very expensive  (Qi et al. , 2017a). Confronted with these challenges, approaches dealing directly with point clouds have been developed. The reference architectures are PointNet  (Qi et al. , 2017a)  and its improved version PointNet++  (Qi et al. , 2017b)  which considers local features. According to the authors, it is more efficient, fast and robust than methods based on intermediate representations. The quality of the database needed to train the neural network is a crucial issue, as it will directly influence its performance.
One of the main challenges of using neural networks for 3D tasks is the lack of reference databases, compared to those available for image processing. Moreover, manual labelling of a point cloud database is a very time-consuming process  (Zolanvari et al. , 2019). Faced with this problem, we argue that it is interesting to look at projection-based solutions, where labelled databases are abundantly available. This argument is also backed by other research results which show that object classes concerning building façade such as windows and doors are more difficult to detect using a purely 3D approach  (Malinverni et al. , 2019 , Pierdicca et al. , 2020). This problem mainly rose from the lack of training data for building opening classes, as well as the difficulty in distinguishing different classes in generally homogeneous and with reduced relief surfaces such as façades.
Automatic image segmentation is a research topic that has been widely covered in the artificial intelligence community, especially with the use of convolutional neural networks  (Kaushik and Kumar, 2019). Taking advantage of the rapid increase in the number of labelled image databases and the improvement of the computing capacities of graphics processor units, this type of architecture has given state-of-the-art results in various fields such as medical imaging, road transportation, product quality monitoring, etc. (Meyer et al. , 2018). There exists many architectures for CNNs; however, they are all based on three similar layers: convolutional, pooling and fully connected layer. The convolutional layer aims to learn the characteristics of the images, called feature maps. The pooling layer allows to reduce the dimensionality of the feature maps by extracting the most relevant features.
After a certain number of convolutional and pooling layers, a fully connected layer is implemented to perform the segmentation task  (Gu et al. , 2017). The architecture chosen for this study is the DeepLabv3+ neural network. It was used in  Tang et al. (2020)  where it was associated with a Faster-R-CNN architecture for liver segmentation on medical images. In the cited paper, DeepLabv3+ achieved better performance than other state-of-art methods used in this domain. The aim of this paper is therefore to utilise the DeepLabv3+ network and train it on a database of labelled building façade images. The trained network will thereafter be deployed on 2D orthoimages generated by photogrammetry, before backprojected to the 3D space later on.
Input data generation and pre-processing The first step of the study involves the creation of the three required input data. For the purposes of these experiments, the main façade of the Zoological Museum in Strasbourg, France was used as the test data. The façade was reconstructed using terrestrial photogrammetry using a Canon EOS 6D camera. The 3D point cloud was generated through dense matching using the software Agisoft Metashape. As the images were taken from an average camera-to-object distance of 10 m, this yields a Ground Sampling Distance (GSD) of roughly 4 mm. The point cloud used in this regard was generated using the "High" quality in Metashape, which implies the subsampling of the raw images with a factor of 2 for the dense matching process; the theoretical spatial resolution of the point cloud is therefore 8 mm.
Deep learning training and deployment As has been previously established, the DL part of the method is based on the transfer learning approach. In this study, a DeepLabv3+ network was created from a pre-trained ResNet-18 network  (He et al. , 2016). The choice of ResNet-18 as the initial network was empirical; more experiments and tests need to be performed to assess the potential results from other pre-trained networks. The pre-trained network was thereafter augmented using another dataset of labelled rectified images of façades prepared by the Center for Machine Perception (CMP) of the Czech Technical University, Prague  (Tyleček and Šára, 2013). This dataset consists of 606 rectified images of building façades (Figure  4 ) of various architectural styles, although the majority seems to belong to modern and/or European style buildings.
The images were classified into 12 classes, of which only six ("shops", "pillar", "door", "window", "façade", and "background") are retained in this study with other classes merged into these six. The CMP façade dataset can be accessed here: https://cmp. felk. cvut. cz/~tylecr1/facade/ (last accessed 2 February 2021). The choice of the classes included in the training was based on their existence in the test image of the Zoological Museum's façade orthophoto. While no freestanding pillars exist in the test dataset, several engaged columns can be observed on the second floor. "Shops" in this regard refer to business signs and plaques. For the purpose of the training, the labelled image dataset was randomly divided into training (80%) and validation data (20%). The training was performed for 100 epochs yielding a validation accuracy of 79.
RESULTS AND DISCUSSIONS The result of the semantic segmentation can be seen in Figure  5. Visually, while the algorithm managed to correctly determine the class of most of the pixels, some problems can also be observed. Indeed, misclassification is seen in the ground and second levels of the building. The texture data for the ground level was plagued by shadows and this may play an important role in explaining these results. As for the errors in the second floor, the quality of the original 3D reconstruction deteriorated as the point of view is farther from the ground. This is due to the terrestrial nature of the original data acqusition, therefore generating holes and textural distortions on higher positions. To add to these explanations, the orthophoto, unlike the rectified images of the training dataset, is a reconstructed raster.
First, the amount of training data concerning pillars was significantly lower because the input database mainly consists of building façades of architectural styles where pillars were not the main feature. Secondly, in the test dataset of the Zoological museum, no free-standing pillar was present. Instead, engaged pillars or faux pillars were present mainly as decorative adornments. In the context of our particular requirements, the detection of building façade openings i. e. the "window" and "door" classes are particularly interesting. In both classes, the results were quite satisfactory even though improvements may be necessary. The "window" class yielded an F1 score of 70. 98% while the "door" class fared worse with a score of 55. 52%. Several factors may explain these results. First of all, the confusion between doors and windows in this particular dataset mainly concerns both the second and ground levels of the museum.
Acknowledgements We thank  Géraldine Servant  for several precious discussions at many stages of this work, and for a careful reading of the manuscript. We thank  Benedict von Harling ,  Oleksii Matsedonskyi ,  Philip Soerensen ,  Ryusuke Jinno ,  Thomas Konstandin  and  Gilad Perez  for useful discussion, and  Maximilian Dichtl  for spotting typos in a previous version of this work. FS thanks  Olaf Behnke ,  Claudio Bonati ,  Roberto Contino ,  Yohei Ema ,  Michael Geller ,  Kyohei Mukaida ,  Davide Pagani ,  Frank Tackmann  and  Daniele Teresi  for additional useful discussion.
2018) , and the resulting estimate for the number of stars suitable for GR tests is of order 1  (Waisberg et al. 2018). The near-infrared interferometric instrument GRAVITY  (GRAVITY Collaboration et al. 2017 ) coupled to the four 8m telescopes of the VLT makes it possible to detect and trace such faint stars for the first time with an angular resolution that exceeds that of adaptive-optics assisted imaging on 8-10m telescopes by a factor of ∼ 20. Here, we report on the detection of a faint star (m K ≈ 18. 9) within 30 mas of Sgr A*. We use the classical radio interferometry data reduction/image reconstruction package Astronomy Image Process Software (AIPS, Greisen 2003) for our work. This detection does not yet explore the signal-to-noise ratio limit of GRAVITY, but is limited by our ability to model the point spread function (PSF) of the sparse 4telescope interferometer and the presence of other sources and their sidelobes in the field-of-view (FOV).
Data format conversion The standard GRAVITY data products after calibration are stored in the OIFITS format  (Duvert et al. 2017). In order to benefit from existing radio interferometry imaging reconstruction software and algorithms, we here converted the GRAVITY data product into the UVFITS format  (Greisen 2003) , which is commonly used in radio interferometry. The conversion was done with a python script which we adapted from the EHT-imaging package (Event Horizon Telescope  Collaboration et al. 2019). The essential data we read in from the calibrated OIFITS files are the VISAMP, VISPHI, MJD and U-V coordinates (UCOORD, VCOORD) columns from the OI_VIS table for the science channel output together with the corresponding OI_FLUX table. We then rewrite these following the UVFITS format convention.
Additional amplitude calibration Before writing out the UVFITS data product, we re-calibrate and re-scale the visibility amplitudes with the photometric flux of each baseline calculated from each telescope pair (using the OI_FLUX table). This allows us to get the correlated flux from each baseline instead of the default normalized visibility, which does not reflect the true brightness of the target. During this step, we also correct for the attenuation of the telescope flux due to different air mass and AO correction by fitting a polynomial function to the OI_FLUX of several S2 frames per telescope across one night and interpolating the correction per telescope accordingly. We then renormalize each visibility amplitude with that of the S2 frame used for calibration in the previous step. Thus, our visibility amplitudes are normalized such that a visibility amplitude of 1 equals S2's magnitude in the Ks band (m K = 14.
S62 and S29 Our data and identification are inconsistent with the conclusions of  Peissker et al. (2020) , who reported previously an orbit for S62 with a 9. 9-year period and extreme eccentricity. Their proposed orbit predicts a position of (RA, Dec) = (-89. -85, +98. +95) mas over the time span of our observations, with a proper motion of ≈ (+7, -5) mas yr -1. The position is outside the FOV of our GRAVITY data centered on Sgr A* and incompatible with our detection reported here. Hence, we address the question what object  Peissker et al. (2020)  have actually seen. To check the position proposed by  Peissker et al. (2020) , we pointed GRAVITY to the position in question on 13, 14 August 2019 and 13 September 2019. In all three epochs, we find a single dominant source in the field of view, for which we can derive dual-beam positions (for the methodology see GRAVITY  Collaboration et al.
As mentioned above in our data reduction process, the calibration is done by normalizing the Sgr A* complex visibility with that from an S2 frame. The visibility phase zero point is the position of S2 plus the preentered separation between Sgr A* and S2, which are calculated based on the S2 orbit. Any phase error here would affect all six baselines together. Meanwhile, our visibility amplitude has accounted for the photometric flux of each baseline from each telescope pair, which is a telescope-dependent time-variable quantity. We suspect that any variability here would introduce inconsistency of the visibility amplitude between different baselines, which cause deviations from our calculated synthesis beam. We show how well the amplitude of S2 is calibrated in Fig. 9. Ideally, the amplitudes should all be exactly unity; in practice the majority of our data has a deviation of the amplitude within 10%.
Acknowledgements. We thank the referee for a helpful report. F. G. thanks  W. D. Cotton  and  E. W. Greisen  for the help on AIPS and UVFITS data format. We are very grateful to our funding agencies (MPG,  ERC ,  CNRS  [PNCG, PNGRAM],  DFG, BMBF, Paris Observatory [CS, PhyFOG], Observatoire des Sciences de l'Univers de Grenoble , and the  Fundação para a Ciência e Tecnologia) , to ESO and the  ESO/Paranal  staff, and to the many scientific and technical staff members in our institutions, who helped to make NACO, SINFONI, and GRAVITY a reality. S. G. acknowledges the support from  ERC  starting grant No. 306311. F. E. and O. P. acknowledge the support from  ERC  synergy grant No. 610058. A. A. , P. G. , and V. G. were supported by  Fundação para a Ciência e a Tecnologia , with grants reference  UIDB/00099/2020  and  SFRH/BSAB/142940/2018.
We only keep frames where Sgr A* is relatively faint for later analysis, in which case Sgr A* is expected to have a near-infrared spectral index (νL ν ∼ ν α ) between -3 and -1 (Genzel et al. 2010; Witzel et al. 2018 ). Since we use S2 data to normalize the Sgr A* data, we take into account a spectral index of +2 from the Rayleigh-Jeans approximation of the black body radiation from S2: this would give us a spectral index.
A lot of research has followed a historical approach, focusing on the origins of the society and its members as well as its contributions to the development of meteorology  (Traumüller, 1885; Kington, 1974; Feldman, 1983; Cassidy, 1985; Wege and Winkler, 2004; Lüdecke, 2004 Lüdecke, , 2010;; Vinther et   2006). Data from the society have been used for climatological analysis, although mostly making use of single stations  (Przybylak et al. , 2014; Raicich et al. , 2015; Camuffo et al. , 2017; Häderli et al. , 2020)  and rarely looking at more than a couple  (Brázdil et al. , 2010; Yiou et al. , 2014). Aspaas and Hansen (2012)  analysed occurrences of the aurora borealis recorded by the different stations as well as the phenological comments in the SMP's annual reports. In his book The Weather of the 1780s over Europe,  Kington (1988)  used the available station data from this decade -including several stations from the SMP -to create hand-drawn daily synoptic weather maps, showing that prevailing weather situations over Europe at the time (with regard to moving pressure systems) could be reconstructed and analysed.
More recently, various data rescue projects, such as IMPROVE  (Camuffo and Jones, 2002) , HISTALP  (Auer et al. , 2007) , the ANR project  (CHEdaR, 2014) , and CHIMES  (Brugnara et al. , 2020b) , have incorporated larger portions of the data for reconstructing long high-quality series. Despite these important efforts, work on SMP data has been scattered and only a small fraction of the overall measurements has been used in modern climate research; much of the data had yet to be digitised or published in extenso. There is more to be done with regard to unlocking the full potential of the data gathered by the SMP over 230 years ago. This study takes an important step toward filling this perceived gap in research by making many of the SMP's observations available for future climate research. We provide an inventory of all meteorological observations made by the stations belonging to the network to assess data availability.
Most of these measurements have been digitised and are published with this paper. We also add converted and corrected temperature and pressure measurements from all SMP stations, which are here assessed for their quality, to this new dataset. Finally, we explore the potential of the recovered series for climate research by analysing two historical cases of extreme weather. Our work restates the importance of the society and its established role in the history of meteorology with regard to its methods of measuring and compiling weather observations. Following in the steps of previous data rescue projects, this project establishes a useful new historical climatological database, although it stands out from previous efforts due to the sheer quantity of digitised observations and processed series being published.
These observations will be important for studying past climatic variations and weather variability; they will also hold value for the improvement of reanalysis products that extend to the pre-industrial period. In fact, one of the main added values of this database in comparison with existing products is its sub-daily resolution; as such, it could contribute to research on high-resolution reconstructions and lead to increased knowledge about historical weather. To better explore the value of the database, this paper is organised as follows. In Sect. 2 we give some historical context to the society's network and explore the contents of its yearly publications; we then provide a detailed explanation of the data rescue procedure and the corrections applied to the temperature and pressure observations, including methods of comparison to explore their potential.
Nevertheless, what the SMP and its members achieved in the late 18th century in terms of coordination and discipline of observations meant that researchers today have a wealth of more detailed and organised information about the weather of the time. In the period from 1781 to 1792, a total of 37 stations contributed to Hemmer's project (see Table  1 ). The stations covered a wide area of mainland Europe, stretching from Rome in the south, La Rochelle in the west, and reaching as far north-east as St. Petersburg and Moscow (Fig. 1 ). As such, the data of the SMP are well placed for an extensive study to be made of Europe's climate variability and extreme events during that decade. Notably, there was a number of stations beyond mainland Europe that were also part of the SMP, which included Cambridge, Massachusetts in North America, Godthaab in Greenland, and Pyshminsk in Siberia.
The transformation of the digitised data from raw observations to scientifi-cally usable values begins with the unit conversion; for temperature observations the conversion from Réaumur to Celsius is straightforward: T C = T R × 1. 25. (1) At times, the SMP's Russian stations used thermometers in the Delisle scale, and the station in Cambridge (US) initially sent observations taken with a Fahrenheit thermometer. For these units, the conversion to Celsius is done as follows: T C = 100 -T D × 2/3 (2) T C = (T F -32)/1. 8. (3) In a second step, temperature values were corrected for a positive freezing-point bias due to the tendency of the freezing point of thermometers to gradually rise  (Winkler, 2009). One postulated explanation for this phenomenon was the rapid cooling of the glass walls of the thermometer after the manufacturing process; this would cause a realignment of the glass molecules over several years as the glass was being slowly compressed by external air pressure  (Bellani, 1808; Yelin, 1824).
The rise of the 0 • C point can be corrected easily by assuming that the correction used in  Winkler (2009)  can be applied to all stations; that is, a stepwise -0. 1 • C yr -1 extended over the first 6 years of observations. This correction was applied to all series with the exception of Cambridge (US), Dijon, Middelburg, Moscow, Stockholm, and St. Petersburg, which are known to have used instruments of their own, as well as the few stations that began sending data after 1785. Another known problem of the SMP thermometer is that it was only graduated until -16 • Ré, meaning that when the mercury dipped below this mark, the observers estimated the value by visual extrapolation, which makes lower temperatures less accurate. The correction of any larger, non-linear errors is mainly related to the exposure of the thermometer; these strongly depend on each station's specific configuration and are beyond the scope of this paper.
It is useful to plot c several times against multiple well-correlated reference series in order to better detect the inhomogeneity (or break point) and to avoid making bad corrections (see  Fessehaye et al. , 2019). According to  Venema et al. (2012) , the Craddock test allows for good accuracy compared with other break point detection tests, although the manual effort involved is higher, meaning that it is better suited to smaller datasets such as the one in this study. Furthermore, there is some subjectivity in the selection of break points: the choice to homogenise or not is linked to the specific approach of the researcher  (Brunetti et al. , 2006). Series with inhomogeneities were adjusted using a constant correction. These corrections were computed by calculating the mean difference between the candidate series and the reference series before and after the break point.
For instance, if the difference of a given pressure series with respect to the reference is on average 3 hPa before the break point and 7 hPa after, the correction is to be 4 hPa. The more reference series that are available to calculate this mean difference, the more accurate the estimated correction will be. Where possible, corrections were applied at the sub-daily level, and QC tests were performed again to ensure no further errors were being introduced. The results from the homogeneity tests as well as those from the quality control are presented in Sect. 3. 2. The corrected temperature and pressure series in our database are not only presented in their original resolution: daily mean values were computed from the sub-dailies and concatenated to daily means published by the SMP. For temperature, the calculation of daily means from sub-daily measurements was weighted considering the known observation times to represent the true means with respect to the Coordinated Universal Time (UTC) day.
Exploring potential Following the analysis of data availability and quality, we explored the potential of the corrected SMP temperature and pressure series with respect to two historical cases of extreme weather -namely, two devastating storms around the new year 1783-1784 and the anomalously cold March of 1785. The SMP data were used in combination with additional synoptic information from existing studies of preindustrial weather and climate. We used the CAP7 weather type reconstructions by  Schwander et al. (2017) , which extend the MeteoSwiss CAP9 classifications back to the year 1763  (Weusthoff, 2011) , a catalogue of recurrent dynamical weather patterns over central Europe (overall seven types) that can provide important information on the state of the atmosphere during these years.
A year later in 1789, Geneva and Tegernsee also stopped sending data. The last Ephemerides volume, for 1792, appeared in 1795, and it contained data recorded at 14 persevering stations. Overall, more than half of the 37 stations in the network sent data to Mannheim for longer than 7 years; 12 of these, mostly stationed in central Europe, had their data published in almost every Ephemerides issue. A total of 11 stations provided observations for 4 years or less (Fig. 3b ). Lists such as the one in Table  1  or the one provided in Cassidy's article  (Cassidy, 1985)  give a rough idea of which location participated in which year; however, they do not distinguish between the type, resolution, or quantity of data recorded for each location. A station that contributed only sub-daily wind force observations in a specific year but submitted sub-daily measurements for every parameter in another year has a different value to a researcher.