dataesr/scientific-paragraphs-categorization huggingface dataset:
KAP have been also used for teaching purposes as a creative tool for geography field training and as a medium to approach the complexity of readily available geodata  (Bogacki et al. , 2005). The Helikite system, a combination of both a helium balloon and kite wings, which allowed the acquisition of low-altitude aerial footage in very unstable wind conditions above a Roman quarry was described in  Verhoeven et al. , (2006). The authors highlighted the benefits for low-level photography represented the system during conditions in which a tethered balloon or a kite would fail to work properly. In Fera and Geiger (2018) a GIS based system project on Sai Island in Northern Sudan is presented. During the campaigns, aerial photography of the environment was conducted for topographical landscape recording in form of high resolution orthophoto-mosaics and Digital Elevation Models (DEM) by KAP.
Kite image dataset photogrammetric reconstruction The image-based modelling reconstruction of the UNESCO site of Khirokitia Vouni was created using four different datasets, collected specifically during the excavation seasons  2007, 2009, 2011, 2013. Four consumer grade digital compact cameras, not exceeding 13 Mpx resolution were used (Table  1 ). The effect of image resolution on the computed 3D object coordinates was investigated in  Nakada and Chikatsu (2003). The author examined the variation of accuracy observing analyzing the sensors resolution in terms of pixel. In  El-Hakim et al. , (2003)  the authors used small and large targets to assess the relationship between accuracy and image resolution on the Z coordinate using camera sensors ranging from 1. 5 to 14 megapixels. During the data collection step, the devices were set in auto-mode therefore the shutter speed and aperture parameters were determined automatically depending on the amount of light that goes into the camera and the ISO.
Starting from the estimated camera poses and orientation, it was possible to visually assess the un-ordered flight plan resulting from the kite survey (Figure  3 ). The main features highlighted were: -big variation in altitude (h); -small base to depth ratio (b/d). The statistic generated by Agisoft Metashape software didn't provide any specific hint concerning the camera network created from such an irregular data collection step, also due to the lack of a proper topographic survey. The 2007 dataset produced a Ground Control Pount (GCP) error of 1. 7 cm; however only four GCPs were used and this value must therefore be treated with care. Likewise, the 2009 dataset presented a very low error of 0. 8 cm in Metashape from six GCPs. This may be an indication of over-estimation of the precision by Metashape, considering the centimetric ground sampling distance.
The 2011 dataset generated a modest 7. 9 cm of GCP RMS error, while the 2013 dataset gave the value of 6. 3 cm. All of these values were obtained from few GCP points and may not necessarily reflect the overall quality of the respective projects; hence the motivation in using DBAT in this paper to generate more useful metrics to assess their quality. In principle, flight planning refers to the initial determination of flight geometry, given the area of interest and the required endproduct, and thus the desired accuracy. In the case of traditional nadir imagery, the parameters that are optimized are the flying height, the camera with its optics and the overlap pattern. (Förstner 1985; Ackermann 1992 , Kraus 1997 , 2007). Lately, oblique imaging started to be employed for different application fields  (Remondino and Gerke 2015, Rupnik et al.
Bundle Adjustment Assessment All image datasets were initially processed in Agisoft Metashape software following the standard workflow. Although being one of the most used commercial software for image-based 3D modeling, Agisoft Metashape is still considered, amongst others, a blackbox solution which provides limited knowledge of the BA metrics. Due to this reason, the Khirokitia image datasets were reprocessed in DBAT and the results were analyzed. DBAT outputs allowed to have a meaningful insight on the BA parameters computed, allowing the detection of potential errors or outliers in the project, and serving as a quality control tool for a more accurate and reliable a-posteriori photogrammetric reconstruction. (Figure  5 ). All datasets show a high correlation values between the different camera calibration parameters computed within the bundle adjustment.
This may depend on faulty physical assumptions due to the unordered camera network, as well as the lack of strong convergence. High correlations can result in a weak, or even false, calibration solution. Even worse, the calibration results might be block-dependent  (Tang and Fritsch, 2013). Despite showing an improvement from 2007 to 2013 datasets, the self-calibration procedure  (Clarke and Fryer, 1998)  may not be sufficient to properly calibrate the cameras interior parameters  (Luhmann et al. , 2006). The latter can indeed be reliably calculated if the image configuration and distribution of object points are well chosen  (Luhman et al. , 2014). Another reason which could have affected the weak camera self-calibration can be observed in the camera geometry changing over the acquisition period due to the auto shooting mode (i.
The assessment of the four analyzed datasets, shows that the 2013 image block achieve the best result in terms of BA metrics. The RMS point residuals, although still in the order of 10 cm is the lowest among all the results obtained reprocessing the project through DBAT. The different flight altitude of some camera stations reflects clearly in the point count and camera ray angles analysis. Although DBAT highlighted a set of images for the 2013 flight which contain high Spatial and Rotational Standard Deviation errors, this, compared with the other statistics, can be interpreted as an outlier produced by Agisoft Metashape software.
CONCLUSIONS The main aim of this study was to analyse and assess the usability and reliability of old image aerial datasets collected through kites for 3D photogrammetric reconstruction. Four multitemporal group of images acquired at the archaeological site of Khirokitia Vouni (Cyprus) have been used. All data have been initially run using the commercial software Agisoft Metashape, until the determination of the camera's pose was achieved, and then reprocessed using DBAT to obtain analytical metrics of the Bundle Adjustment. Due to the unordered camera network, the overall DBAT statistics are able to provide useful indications, highlighting the parts of the dataset (i. e. which images) which most likely contribute to weaken the photogrammetric 3D reconstruction. The main risk in processing aerial dataset, collected without following an a-priori designed flight plan and using blackbox commercial solutions, is that the user is not provided with enough and meaningful information and parameters for the estimation and the precision of the object points.
In this project, DBAT was instead able to highlight the blocks of images which presented the highest errors in terms of standard deviation, point count and ray angles in the Bundle Adjustment process. As expected, the images collected at different altitude weakened the camera network and the overall 3D reconstruction. In conclusion it can be affirmed that, although commercial software are able to produce detailed 3D models processing old images datasets collected using different platforms (i. e. kite, ballons, etc), an external assessment of the photogrammetric parameters is needed to evaluate the geometric reliability of the end-products.
Digital image correlation. DIC is an image processing technique that can be applied to two-dimensional images exhibiting a wide range of randomly distributed grey levels  43. The reference image, corresponding here to the sample at rest, is first divided into square regions called subsets. As each subset has a particular distribution of grey levels, it (and thus its position) can be tracked in subsequent images of the deformed sample. This yields the in-plane displacement fields within the analysed area, from which the strain fields can be derived. Comprehensive details about the DIC technique can be found in Sutton et al. 44. The software used in this study is CorrelManuV  45, 46. Even though DIC is regularly used to analyse strain fields at the macroscopic scale in bulk materials (polymers, cement) enhanced with carbon-nanotubes  [47] [48] [49] [50]  , only Maschmann et al.
Acknowledgements This research benefited from the support of  Ile-de-France SESAME program  and of the "École polytechnique fund raising -Smart environments:  Nanosensors and Nanoreliability Initiative ". Part of this work was carried out within the MATMECA consortium, supported by the  ANR  under contract number  ANR-10-EQPX-37 , and has benefited from the facilities of the  Laboratory MSSMat, CNRS, CentraleSupélec ,  Université Paris-Saclay, France. Luka Pavić  was funded by the  DGA Deflex project  under grant agreement  2012 60013. The authors also thank  Vincent de Greef  and  Alexandre Tanguy  for their technical support in developing the in-situ tensile machine and conducting the SEM tests, respectively, as well as  Michel Bornert  for fruitful discussions regarding his CorrelManuV software.
Polarization microscopy Purified sacculi were resuspended in PBS and placed on ethanol-cleaned No. 1. 5 coverslips under a cleaned glass slide. Polarization images were acquired on an inverted LC-PolScope mounted on a Nikon Ti-E equipped with a 60x/1. 4NA Plan Apo oil immersion objective and oil condenser with matching NA, and a Hamamatsu Photonics Flash4 camera. Z-stacks in 100 nm steps were taken of each sample using 50 msec acquisitions of 546/12 nm light. All image acquisition, processing, and display, including colored display and line maps were prepared using the OpenPolScope Hardware Kit and plugins for ImageJ/Micro-Manager from OpenPolScope. org. From the previously prepared sacculi, multiple slides were imaged and quantitated, with independent background calibrations. These gave similar results and were combined to yield the final data set.
Cell wall thickness measurements were performed using a custom-built MATLAB (Mathworks, MA) script. Image intensity profiles extracted from lines were drawn perpendicular to a user-input line defining the middle of the cell wall. The distance between the two lowest points below a threshold within 40 nm of the middle of the cell wall was measured as the cell wall thickness at ~30 points in each cell. This experiment was conducted once, using multiple cells for the analysis. Cell diameter was measured simultaneously as cell wall thickness using three user-input lines. The average of these three measurements was recorded.
Measurements of single-cell growth rate Cultures grown to an OD 600 below 0. 3 were concentrated by centrifugation at 6,000 × g for 30 sec. The cell pellet was resuspended in growth medium, and applied to No. 1. 5 glassbottomed dishes (MatTek Corp. , MA). All cells were imaged under a 2% agarose pad containing growth medium, with the top surface exposed to air, in a chamber heated to 37°C. Phase-contrast microscopy was performed using a Nikon Eclipse Ti equipped with a Nikon Plan Apo λ 100×/1. 4NA objective and an Andor camera. We used a custom-built package in MATLAB to perform segmentation on phase-contrast time-lapse movies, then calculated the growth rate of the surface area of single B. subtilis chains. Each data point for the single-cell growth rates (Figure  2d ) is the result of a single experiment; for each, >50 cells from multiple fields of view across different areas of the agarose pad were imaged and analyzed.
TIRF microscopy of Halo-PBP2a Cultures grown to an OD 600 below 0. 3 were labeled with 10 nM JF549  53  , and were concentrated by centrifugation at 6,000 × g for 30 sec. The cell pellet was resuspended in growth medium and applied to ethanol-cleaned No. 1. 5 coverslips. All cells were imaged under a 2% agarose pad containing growth medium with the top surface exposed to air, in a chamber heated to 37°C. TIRFM and phase-contrast microscopy were performed using a Nikon Eclipse Ti equipped with a Nikon Plan Apo λ 100×/1. 45NA objective and a Hamamatsu ORCA-Flash4. 0 V2 sCMOS camera. Fluorescence time-lapse images were collected by continuous acquisition with 300 msec exposures. All data are from a single experiment, where cells were induced at different levels, and tracks from >20 cells were used for analysis of each data point.
Simulation of directionally moving MreB The Image Correlation Spectroscopy 6 MATLAB package was used for the simulation of MreB moving around the cell. The following parameters were set for the MreB simulations: velocity, orientation, filament number, and filament length. The default velocity setting is 30 nm/sec and the default orientation is 0, which means MreB moves perpendicular to the central axis. The default filament length is set to 250 nm and each MreB monomer is assumed to be 5 nm. The cell width is set to 1 μm and the cell length is set to 4 μm. The pixel size is 65 nm and the time interval is 1 sec, which is the same as the TIRFM imaging obtained with our Nikon Eclipse Ti equipped with a Hamamatsu ORCA-Flash4. 0 V2 sCMOS camera. The particles are randomly distributed on the surface of the cell.
TIRF-SIM imaging of E. coli strains Cells were prepared as described in Media and culture conditions, above. Cells were placed under an LB agarose pad, on a cleaned No. 1. 5 coverslip, and imaged at 37°C. Live-cell SIM data were acquired as described previously  56  on a Zeiss Axio Observer. Z1 inverted microscope outfitted for structured illumination. An Olympus 100×/1. 49NA objective was used instead of the Zeiss 1. 45NA objective because the slightly larger NA of the Olympus objective gives higher tolerance for placing the excitation beams inside the TIRF annulus. Data was acquired at 1 sec frame rates, with 20 msec exposures from a 488 nm laser for each rotation. TIRF-SIM images were reconstructed as described previously  56.
TIRF-SIM imaging of B. subtilis strains Cells were prepared as described in Media and culture conditions, above. Cells were placed under a CH agarose pad in a No. 1. 5 glass-bottomed dish (MatTek Corp. , MA) for imaging. Images were collected on a DeltaVision OMX SR Blaze system in TIRF mode, using an Edge 5. 5 sCMOS camera (PCO AG, Germany) and a 60x objective. 75 msec exposures from a 488 nm diode laser were used for each rotation. Spherical aberration was minimized using immersion oil matching. Raw images were reconstructed using SoftWoRx (GE Healthcare, MA) software.
Particle tracking of JF549-Halo-PBP2a Particle tracking was performed using the software package FIJI 2 and the TrackMate v3. 8  54  plugin. For calculation of particle velocity, the scaling exponent α, and track orientations relative to the midline of the cell, only tracks persisting for 7 frames or longer were used. Particle velocity for each track was calculated from nonlinear least squares fitting using the equation MSD(t) = 4Dt + (vt)  2  , where MSD is mean squared displacement, t is time interval, D is the diffusion coefficient, and v is speed. The maximum time interval used was 80% of the track length. To filter for directionally moving tracks, we discarded those with a velocity lower than 0. 01 nm/sec. Tracks were also excluded if the R 2 for log MSD versus log t was less than 0. 95, indicating a poor ability to fit the MSD curve.
Osmotic shock experiments Overnight, exponentially growing cultures (described in Media and culture conditions) were diluted into fresh CH medium, grown at 37°C to an OD 600 of 0. 1 to 0. 2, then stained by growing in 100 μM Alexa Fluor 488-D-Lysine-NH2 for 1 hr. Without washing, cells were then loaded into a CellASIC microfluidic flow cell (MilliporeSigma, MA) pre-conditioned with media and washed in the chamber via channels 6 and 5. Media in channel 6 was replaced with 5 M sodium chloride, and the flow cell was resealed and imaged immediately. After collecting images across the whole chip pre-shock, 5 M sodium chloride was flowed into the chip via channel 6 and imaged immediately. This experiment was repeated in bulk cultures, yielding similar results as in the microfluidics. Each "Width" and "Length" data point (Supplementary Figure  6b ) is calculated from at least 145 cells (through >1000; see Supplementary Table  6 ) from multiple fields of view across different areas of the flow cell.
Acknowledgements We thank  Carl Wivagg ,  Ye Jin Eun , and  Leigh Harris , for helpful discussions,  Georgia Squyres  for  TIRF-SIM ,  Luke Lavis  for JF dyes, and  Z. Gitai  and  K. C. Huang  for strains. TIRF-SIM was performed at the  Advanced Imaging Center at Janelia Research Campus , a facility jointly supported by the  Gordon and Betty Moore Foundation  and  Howard Hughes Medical Institute. This work was funded by  National Institutes of Health  Grants  R01GM114274  to RO, and  DP2AI117923  to EG, and support from the  Volkswagen Foundation  to EG and  SVT. Some work was performed at the  Center for Nanoscale Systems at Harvard University , supported by  NSF   ECS-0335765.
GPP is calculated using a semi-empirical, Light-Use Efficiency (LUE) based canopy level model  (Mäkelä et al. , 2008; Peltoniemi et al. , 2015 , Minunno et al. , 2016) , which uses daily weather data and LAI as inputs. An empirical model was derived using this GPP and NFI-based NPP  (Neumann et al. , 2016a (Neumann et al. , , 2016b) )  for estimating the NPP:GPP ratio for different species and regions. Similarly, an empirical function for the ratio of stem growth to NPP was derived for species and European regions (Fig. 1 , Table  1 ). Stand level stemwood volume growth is obtained from the volume increment based on GPP and corresponding NPP allocation. This is divided by stand density to estimate mean tree growth, and empirically derived allometric functions are used to compute new values of H , D and H c from new volume and stand density.
Model equations The fraction of photosynthetically active radiation (f APAR ), which is actually absorbed by the forest depends on the forest structure approaching f APAR = 1 with a fully closed canopy. In FORMIT-M the f APAR is estimated based on leaf area index and an effective extinction coefficient as: ∑ = - ⎛ ⎝ ⎜ - ⎞ ⎠ ⎟ f k L 1 exp APAR i eff i i , (3) where L i denotes (all-sided) leaf area index of species i and k eff i , is effective extinction coefficient. L i is calculated based on stand foliage biomass (W F STAND i , , kg) and specific leaf area (S LA i , m 2 kg -1 DW) for the species as = × - L W S 10 i FS T A N D i L A i , , 4 (4) Constant species-specific values for k eff i , were used, which were estimated for each species from tree-level structural data following  Duursma and Mäkelä (2007 ): = -- ( ) k φS L e 1 eff A A k L φS ( / ) H A A (5) where S A , (m 2 ) is crown surface area, L A , (m 2 ) is leaf area and φ is an empirical parameter.
605 (14) Stand-level stem volume after one year growth (m 3 ha -1 ) is calculated as: = + - V V G ρ _ STAND STAND STEM BIOMASS wood ,2 ,1 1 (15) and stem volume of the mean tree after one year growth (m 3 ha -1 ) as: = V V N TREE STAND ,2 ,2 1 (16) where V STAND,1 and V STAND,2 are stand-level stem volumes (m 3 ha -1 ) in the beginning and end of the simulation year, respectively. G _ STEM BIOMASS is annual NPP allocated to stem growth (Eqn (  9 )) and ρ wood is wood density (kg m -3 ). V TREE,2 is mean tree stem volume (m 3 ) in the end of the year and N 1 is the total number of trees per hectare in the beginning of the simulation year. Stand mean diameter after one year growth (cm) is calculated using a model based on structural relationships of V , D and S DI fitted with NFI data (FORMIT project's tree-level NFI data, first measuring round): = D f V S ( , ) TREE DI 2 , 2 (17) where D 2 is mean tree's diameter (cm) and S DI is stand density index (Eqn.
The NFI plots to represent the rest of the silvicultural systems were selected randomly from the remaining plots, such that the total shares of each silvicultural system corresponded with those reported in the statistics, yet the location of the plots does not necessarily correspond to the real locations. The NFI plots were then simulated according to the management-scenario-specific definitions for each silvicultural system. As noted above, utilizable NFI data was only available for 11 European countries (Table  1 ). In order to obtain European-wide estimates, we extended the simulated results to those European countries where no NFI data was available to the project (henceforth called "non-NFI countries"), by multiplying their FMU areas by the nearest NFIcountry's simulated average result in the corresponding FMU (Supplementary Information).
The initial area of FMUs in each non-NFI country was obtained from a spatial analysis, where the FMU areas were calculated based on the Natura 2000 protected area map (European Environment Agency, 2011), species map and age class map produced using a k-NN algorithm  (Moreno et al. , 2017). The development of the age class distribution was calculated by transferring 1/20 of the age class area (age classes are defined in 20 year periods) to the next age class each year. Species and management class distributions were kept as they were initially. Further, the share of clear-cut plots in the simulations was used for determining which share of area is annually moved to the first age class in the non-NFI countries. As described above, daily weather data was required for aggregating the annual level maximum potential gross primary production to be used as input for the model.
The weather data was generated by the MPI-M-MPI-ESM-LR model version CCLM4-8-17, which was run by the "EU-consortium" (CLMcom CLM Community with contributions by BTU, DWD, ETHZ, UCD,WEGC) and provided by the knot of the German climate calculating centre (carbon. dkrz. de) (http://www. mpimet. mpg. de/en/science/models/mpi-esm/james-special-issue. html). We used three scenarios defined as RCP2. 6, RCP4. 5 and RCP8. 5 (Representative Concentration Pathways). The climate scenarios were run for 100 years from 2000 to 2100. For scenarios with current climate we repeated the scenario data from 2006 to 2010. The simulator is available at Mendeley Data (FORMIT-M simulator, https://doi. org/10. 17632/344n6ts3tg. 1).
2c  shows relatively high differences between modelled and actual forest increment in 2010. The over-estimation of increments is bigger in countries where the stocks have also been over-estimated, notably Finland, France and Spain (Fig. 2a ). This could be related to discrepancies between forest areas used for scaling (likely in Finland and France), or differences between the NFI data available to us and those used by FOREST EUROPE (likely in Spain). One possible cause for over-estimation by the model could be the method used for deciding and allocating site quality classes. Our scenarios of supply-limited cuttings in the Nordic case study demonstrated the largely hypothetical situation that all cuttings are done according to the assumed good management practices without delay. This gives surely an upper limit of harvests for a given set of management recommendationsthe level of cuttings obviously depends on the recommended intensity of thinnings and the recommended rotation lengths.
Acknowledgements Funding: This work was supported by the  European Union Seventh Framework Programme  [grant #  311970 ]; Estonian Ministry of. The Polish research was supported by funds for science in the years 2013-2016 allocated to an international co-financed project. The Check republic supported the research with institutional project " EXTEMIT-K ", No. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000433. The authors thank  Jari Liski  for making the Yasso model code available for the study.