dataesr/scientific-paragraphs-categorization huggingface dataset:
s. l. (red dashed line in Figure  3a ) from 10:58 to 11:20 UTC. After that time, the highest part of the plume leaves the camera field of view until the end of the paroxysm. As derived from MWR and SEVIRI data, H T also increases at 09:30 UTC from 1 km above NSEC to reach maximal altitudes of 8. 9 ± 0. 3 km a. s. l. as seen with the MWR at 11:20 UTC, and 6. 1 km a. s. l. at 12:15 UTC using SEVIRI. Despite offering data only during the increase of the paroxysmal activity, ECV's height estimates agree with MWR heights as does the 7. 9 km height a. s. l. retrieved with MODIS at 12:30 UTC. On average, SEVIRI records display average H T that are 1-2 km lower than the MWR during the paroxysmal activity. Nevertheless, both instruments present similar H T values after the end of the paroxysm at 13:30 UTC with average values around 5.
9 ± 0. 4 km for MWR and 5. 0 ± 0. 6 km for SEVIRI until 15:15 UTC when H T starts to decrease. H T on 23 November 2013 as detected by ECV started to increase from 09:15 up to 09:55 UTC to reach altitudes up to 9 ± 0. 5 km a. s. l. (Figure  3b ). H T derived by the MWR and SEVIRI increased rapidly at 09:30 UTC up to similar top heights of 11. 7 km a. s. l. at 10:10 UTC and 12. 0 km a. s. l. at 10:07 UTC, respectively. The MWR and ECV-based H T increase 5-10 min before satellite estimates. Over the same eruptive period between 09:30 and 10:30 UTC, mean H T derived from the MWR, ECV and SEVIRI are close with values of 8. 2 ± 3. 2, 7. 4 ± 1. 5, and 6. 9 ± 3. 4 km a. s. l. , respectively. The X-band detection of the tephra plume ends at 10:40 UTC, ~20 min after the end of the paroxysm while visible and satellite thermal data last up to 11:30 and 12:02 UTC, respectively.
e. , 1 min to 15 min). A critical application of the three techniques, including the use of visible cameras at different locations  [22] , allows us to assess the best value of average H T as well as to evaluate the uncertainties associated with each remote sensor. In addition, it appears that satellite-based H T tend to be underestimated during weak and unstable paroxysmal activity; (3) exit velocities from V2B can be used in combination with exit velocities from infrasound to better constrain the vent radius used for MER calculations, based on the SFA. For Etna, a range of 10-13. 5 m was found as the best estimate of the NSEC radius. A combination of V2B and infrared camera signal with the existing early warning system based on infrasonic data at Etna [16] has also the potential to better characterize the MER in real-time at the beginning of the paroxysmal activity, i.
The archive of aerial photographs, dating [1935][1936][1937][1938][1939][1940][1941] and covering parts of north and central of Ethiopia, is one of the few archives with pre-1960 remotely sensed data in Africa. It allows adding 30 years of time depth for geographical studies, in contrast to the commonly known oldest imagery dating to 1964, sometimes 1958. These photographs were originally acquired by the Italian military forces, to obtain raw material for warfare purposes and the production of topographic maps. To make the archive accessible, it has been scanned and georeferenced. Procedures used in georeferencing and digitally archiving are detailed in this paper, as well as our experiences with orthorectification and use of the photosets in scientific research. This data set of aerial photographs of Ethiopia in the 1930s has been availed to the wider scientific community through the Pangaea website.
These APs are of great value for environmental and historical studies about the area they are covering, such as changes in land cover, hydrology or geomorphology. In the next step, these photographs have been digitally availed to Ethiopian universities, research institutes and to the broader scientific community. In this paper, we discuss the data set and its processing as well as procedures followed to semi-automatically avail it to end users. State-of-the-art information is also presented on ways to process and analyse subsets of the AP collection.
For sake of identification, a simple number consisting of. was used to allow unequivocally naming the photosets and linking them to coordinates. We have chosen to further maintain the naming given during the scanning stage in order to be able to trace back the photosets in case something would go wrong in renaming. This resulted in: Label numbers were constructed in the same way, adding '-label' at the end. Python scripts were developed in order to prepare the scanned photosets for publication on the website. In a first script (Appendix A), all filenames were checked on consistency using regular expressions. Log files were created by the script, indicating the files complying with the file naming proposed in (1) and the files needing manual adaptations to comply with (1). After preparing all files, a second script (Appendix B) was used to automatically list all.
TIF files in (2) YYYYMMDD-< serial No. > F I G U R E 6 Online interface to select and order historical APs. Selected APs in this screen-print cover areas in Amhara, Tigray, Benishangul-Gumuz, Oromo, Dire Dawa, Harari, Addis Ababa and Southern regions of Ethiopia and date back to 1935-1940. Base map: ©OSM contributors a directory and its subdirectories. All filenames consistent with (1) were renamed to the structure as given in (3) and converted to. JPG files using settings that reduce the file size to about 10% of the original with nearly no quality loss (~99%). Again, log files were created to indicate possible problematic files or failed conversions. Finally, a third script (Appendix D) was used to read in a. CSV file containing a table of dates, AP numbers and coordinates. Each converted.
| Online interface All aerial photographs that could be relocated have been made available to the wider scientific community through a webbased interface. The website is a fully responsive website  (Sarabadani Tafreshi et al. , 2017; Schade, 2014) , so that it is also convenient to use on smaller screens like tablets or smartphones. Although possibly less of an issue for an AP serving tool, fully responsive properties are important given that online search engines like Google or Bing have a mobile-first approach in search rankings (Google Search Guides, 2020). The technology used for creating the website is a mainly Javascript-focused stack, running on a VMWare virtual machine hosted by Ghent University. The backend is a Node. js (OpenJS Foundation, 2020) server protected by an NGINX (F2020 Inc.
, 2020) webserver (reverse proxy). The Leaflet. js map script (LeafletJS, 2020) in combination with some additional custom functionality (URL encoding, item listing, …) and responsible for the photo selection is written in client side Javascript. In order to stress the project's Ghent University origins, the UGent corporate identity was used to style all pages (Ghent University, 2020). Because of the email focused nature of this project, a database or database server was not necessary to implement, dramatically improving online security. For automated email communication between the user and the project owners, the third party email service MailJet is used  (MailJet, 2020). At present, the free tier is more than sufficient to satisfy all needs. Upgrade to a higher plan is relatively cheap should the need occur in the future.
Using these coordinates, the location of the photosets is visualized through Leaflet, an open-source JavaScript library for interactive maps (https:// leafl etjs. com). On the map, the area covered by the APs is schematically represented by resizable circular areas, four kilometre across, so that the photoset can be selected by simple clicking. Corresponding script is available as Appendix D. Visualization of the area of interest is possible as OpenStreetMap © imagery, OSM cycling map (renamed 'OSM topography', which is particularly of use in this mountainous country) and 'Satellite' (which is the ArcGIS online imagery). For the best visualization of the background map, it was chosen not to have names of photos on the map or while hovering over the map. The selection of APs takes place based on their location.
, 2015b; James & Robson, 2012)  to construct ortho-mosaics and 3D models from aerial photographs. The SfM part makes it possible to reconstruct an area based on an unordered collection of images. By detecting and matching textures in the photographs, they can be matched to each other. The algorithms calculate the camera parameters and the orientation of every picture. Particularly, SfM allows reconstructing the threedimensional scene geometry and the position of the cameras during the image acquisition period of the images captured around a scene or a landscape, even when imagery is already degraded or when the imagery lacks calibration information  (Sevara et al. , 2018). SfM allows this without using prior topographic data  (Peterson, 2017). To execute the process of SfM-MVS, the most common software PhotoScan, distributed by Agisoft, was used, which extends SfM with MVS.
The method was successfully applied in small areas in Tigray  (Frankl et al. , 2015a)  and in the Lake Tana basin  (Frankl et al. , 2019) , using vertical and low-oblique frames, all manually cropped to single images. The method was further developed for wider areas. The scanned photosets do not only contain the scene of interest (SOI), but are assemblages of four frames. The photographs each contain black borders; strips of the hardboard on which the photographs are glued are also visible on the scans. The scanned photosets were automatically cropped, divided into the individual frames (Appendix E) and contrast stretched using python scripts allowing batch processing. As the frames had been cropped before gluing on hardboard, there is no overlap between the vertical and low-oblique APs within the photosets, hence a lack of tie points between the frames which can hinder the correct alignment of the APs in a single geometrical model in the SfM processing.
An investigation of the optimal number of GCPs to be added to the mosaicking process in Agisoft Photoscan showed that with an increasing amount of GCPs, the RMSE in X and Y stagnated to an error of approximately 30 m. As the bearing and tilt information could, unfortunately, not yet be recovered and besides this, the scanners that were used for the scanning operations of the APs could not be calibrated in order to minimize distortions caused by the scanner  (Nyssen et al. , 2016) , a spline transformation was used to georeference the ortho-mosaics using numerous GCPs (www. pro. arcgis. com). On a sample data set, the accuracy was measured by calculating the difference between the X-coordinates (dX) and the Y-coordinates (dY) from a checkpoint on the georeferenced data set -this is a point on the data set that was not used as GCP -and its equivalent in the current landscape.
The checkpoints were chosen by means of a regular grid with 1,500 m between nodes. An estimation of the error in X and Y was then interpolated by means of the kernel density tool in ArcMAP. Errors in the data sets 1935-36 were less than 30 m in Y and less than 15 m in X (Figure  9 )  (Debever, 2019). Errors could be related to the non-uniform scale and geometry of the photographs (particularly for the low-oblique frames in mountain areas) or to random errors during the localization of ground control points. None of the georeferenced data sets show high systematic errors, but some areas have a clearly higher error than the others. The extent of the ortho-mosaics was determined by the quality of the scanned aerial photographs and the number of overlapping APs (Figure  10 ). Poor quality of some frames is for instance related to local differences in contrast, moisture-induced damage of photographs or presence of clouds.
This comparison was performed by a script to detect matches  (Hussnain et al. , 2016; Mustière & Devogele, 2008). The algorithm was based on Strahler's stream orders of the rivers  (Luo et al. , 2014; Molloy & Stepinski, 2007) , the angle difference between the historical an recent segments  (Borgefors, 1988)  and the scale between both networks (Forceville, 2018). Though the method was successful on regular imagery taken in 1964, the Italian imagery from the 1930s over Ethiopia could not be relocated through it. Indeed, the algorithm performs best with high stream orders and a long array of consecutive streams, but these were difficult to obtain from the chunks of flight lines available for the studied photosets. Another problem was that incorrect drainage networks were generated because of gaps in the generated DEM and artefacts induced by the borders of the frames (Forceville, 2018).
ACKNOWLEDGEMENTS Availing the collection of historical aerial photographs would not have been possible without the strong support by  Sultan Mohammed , director of EMA in 2012. The late  Gordon Petrie  ( University of Glasgow ) contributed a lot to sharpening our insights in the instrumentation of the 1930s. Further,  Koen Hufkens  ( Harvard University ),  Neil Munro  ( Mekelle University ) and  Maarten Wynants  ( University of Plymouth ) hinted available archives of historical aerial photographs covering parts of Africa. Last but not least, communities in many of the portrayed scenes ( Lake Tana basin, Dogu'a Tembien, Kemise graben ) are gratefully acknowledged for guiding team members to specific locations of ground control points and other geographical features in the APs.
| 3 NYSSEN Et al. T A B L E 1 Digital archives of pre-1960 aerial photographs of Africa Country Period Type of online database Online ordering and delivery Web address Number of APs Reference Durham University (2020) National Archives (2020) DRC Mining (2012) IWM (2020) WOSSAC (2020) WOSSAC (2020) Nyssen et al. (2020) NCAP (2020) NGI (2020) https://www. dur. ac. uk/libra ry/asc/sudan/ Approx. 20 https://www. archi ves. gov/resea rch/guide Unknown -fed-recor ds/group s/057. html http://www. drcmi ning. org/en/ 27,100 index_html www. iwm. ac. uk Approx. 50 www. wossac. com 44 × 4 www. wossac. com N/A http://www. ethio pia19 35. ugent. be/ 3,132 × 4 https://ncap. org. uk 3,478 http://www. cdngi portal. co. za/cdngi Many thousands porta l/ N N N Y Y Y Y Y Y Not georeferenced Georeferenced, search by coordinates Georeferenced Not georeferenced Georeferenced Georeferenced Georeferenced, web interface Georeferenced, web interface Georeferenced, web interface 1920-1959 1920-1959 1947-1959 1920-1959 1952 1952-3 1935-1941 1942-1955 1938-1959 Sudan, Egypt Africa, various D.
S9  or  Dhyani et al. , 2019). While road traffic clearly influences the diurnal pattern in PN, other sources, including cooking and domestic combustion, are likely to contribute. Small midday PN peaks were observed during the summer in the nucleation, Aitken, and coarse modes. Another study conducted in Delhi reported the same midday peaks in the warm season and the highest levels in the cold season  (Gani et al. , 2020) , which may be related to bus and LCV emissions at midday. Figure  3  shows the differences in the diurnal variations in total PN levels between the weekday and weekend. These are based upon a small dataset, and hence, the rather small differences within a season may not be meaningful. In winter, the PN levels on Saturday and Sunday are higher than on the weekdays during the night (from 20:00 to 10:00 LT the next day).
According to these criteria, a NPF event is considered when a distinctly new mode of particles appears in the nucleation mode size range, prevails for some hours, and shows signs of growth. These are the initial criteria used in identifying the events. Following that, as the dataset starts from a rather large size (15 nm), to be more confident about the events and not to confuse them with pollution events, high time resolution data for NO x and the fluctuations in the condensation sink were also used to identify pollution events affecting particle concentrations which were not considered. Hence, while we checked the particle size distributions for the NPF events, we also looked at the levels of pollutants to ensure that what was attributed to a NPF event was not particles from pollution/direct emissions.
When we focus on PNSD during the daytime, it can be clearly seen that the modes are manifest at different times. In winter, while the PNSD shows the same mode at approximately 100 nm from 8:00 to 14:00 LT, the mode in the afternoon (from 14:00 to 18:00 LT) drops slightly in size (70 nm). In the morning and afternoon, there are two small peaks at 60 and 40 nm for the Aitken fraction and 170 and 130 nm for the accumulation fraction in autumn. During the day in summer, there are two peaks at approximately 30 nm from 10:00 to 18:00. This may be associated with summer nucleation events and NPF on 3 June 2018 (Fig. 4 ). Furthermore it may be related to the growth in particles from 10:00 to 14:00 in autumn and summer. The full reasons for these changing PNSDs are not clear, and it would be unwise to attempt a detailed interpretation of a very small dataset.
There is little evidence for a contribution of new particle formation (although the summer season dataset is small), consistent with earlier work by  Gani et al. (2020). Another notable feature is the apparent complexity and seasonal variability in sources of NO, NO 2 , and BC, pollutants, which can often be used to identify or locate sources of emissions. This is reflected in the various particle fractions, which generally correlate poorly with the other pollutants and with fractions other than the proximate size. The diurnal variation in all particle fractions is strongly suggestive of a road traffic influence, especially in the winter campaign. This appears to be strongly influenced by the emissions of heavy duty diesel traffic, which is only able to access central Delhi at night. A size mode of < 15 nm may well be attributable to vehicles using LPG/CNG fuels.
Acknowledgements. We would also like to acknowledgement the  IIT Delhi team , under principal investigator  Mukesh Khare , who provided all the facilities, including space and logistical help, during the experiment. Financial support. We are thankful to the  Scientific and Technical Research Council of Turkey (TUBITAK ; grant no. 1059B191801445 ), who supported the work of  Ülkü Alver Şahin  for this ASAP project. This research has been supported by the  Natural Environment Research Council  (grant no. NE/P016499/1 ).
Identification of Studies We conducted two independent searches: one for food sovereignty and the other for the right to food. We obtained studies on each through a search in academic databases, a manual search of key organization's websites, and consultation with key experts. Search terms were based on literature and consultation with key experts (see Supplementary Material 1), and identified and tested in collaboration with librarians from Cornell University. Searches on academic databases were focused on PubMed, Web of Science, CAB Abstracts, and Agricola. To identify gray literature, a review team assembled a list of key organizations involved in food sovereignty research (Supplementary Material 2) and searched their websites for case studies related to FSN. Additionally, we assembled a list of key experts on both the right to food and food sovereignty, and requested any unpublished case studies from them by email.
For each study, the effect of a food sovereignty or right to food approach on FSN outcomes was recorded as positive (+), negative (-), neutral (0), or reverse positive (reverse+). Reverse positive scores referred to cases in which a reduction in food sovereignty or a lack of right to food policies leads to a reduction in FSN. Reverse positive results still indicated a positive relationship between rights-based approaches and FSN, but were tallied separately. We coded action types according to what we identified as the dominant action type in each study. Some studies involved more than one action type, yet we only assigned one principal action type to each study to avoid double counting studies. Data and code used for analysis and visualization are available at https:// github. com/devonds/rights_and_food_security.
Review Process and Literature Overview We identified a total of 4,873 books and articles on food sovereignty and 733 books and articles on the right to food through structured database searches. We found an additional 152 articles and reports on food sovereignty through other sources, including website searches of key food sovereignty organizations and consultations with key experts. Using similar methods, we found no additional literature on the right to food that was not also included in the article database search. Based on titles and abstracts, screeners excluded all but 497 studies on food sovereignty and 198 on the right to food. After excluding additional studies that were inaccessible or did not explicitly report a quantitative or qualitative assessment of FSN outcomes, we included and coded 162 studies on food sovereignty and 54 studies on the right to food (Figure  1 ).
ACKNOWLEDGMENTS We are thankful for the support of the library staff at  Cornell University, USA. We appreciate the work of  S. Ortiz , who contributed to the final stage of the coding process. This review was partially funded by the  United Nations Food and Agriculture Organization , the  UN Committee of World Food Security (CFS) , and the  World Agroforestry Centre  to support the writing of the HLPE expert report on Agroecological and other innovative approaches for sustainable agriculture and food systems that enhance food security and nutrition.
Monitoring of physicochemical parameters of the water At each site, temperature of the bottom water, at the same depth that the recruitment tiles were situated (see Recruitment monitoring below) was measured every hour with a HOBO pendant temperature logger (©ONSET) from which data were downloaded and calculated using the ONSET HOBOware Pro version 3. 7. 0  (Onset Computer Corporation, 2002 -2014). Salinity and water visibility were measured weekly using a refractometer and Secchi disc respectively.
Statistical analysis All the statistical analyses were performed using the R software (R Core Team, 2017). Descriptive statistics were calculated first. For physicochemical parameters, the annual mean, the mean per season and mean per site, as well as the minimum and maximum values were calculated. For planula, recruits and juvenile records, annual and/or monthly mean density were calculated per site. Normality of the data was determined using a Shapiro-Wallis test and homogeneity of the variance was calculated using Levene's test. The significance (or not) of the difference in means was determined using ANOVA, the t-test or the Kruskal-Wallis test (KW), and the Nemenyi test, at a level of 5 %.
Acknowledgments The authors thank the  Belgian "ARES CCD " that funded the  Polyaquaculture Research Unit  project, the  WIOMSA  who supported part of the study through a  MARG I  grant,  Jean Luc Randrianarison ,  Léonce Rabenjamina  and  Noelson  for their help in field works,  Paule Ratovoson  for her support in logistical organization,  Cynthia Lanjaniaina Fanomezana.
BRIDGING OBSERVATION SCALES WITH PHYSICALLY-BASED RADIATIVE TRANSFER MODELS AND MACHINE LEARNING FOR IMPROVED TRAIT ESTIMATION In field phenotyping studies, parametric regression approaches are typically applied to link vegetation indices derived from multispectral data with plant traits. These models are easy to implement and require little expert knowledge. However, large datasets are needed for calibration and validation and still face the limited transferability of the established models to other crops and different environmental conditions. Moreover, especially when hyperspectral data are used, parametric regressions tend to under-exploit the comprehensive information content hidden in the contiguous spectral data  (Verrelst et al. , 2019). Therefore, remote sensing scientists have developed radiative transfer models (RTMs) simulating the interactions of the full optical wavelength range with leaves and canopies based on physical laws.