dataesr/scientific-paragraphs-categorization huggingface dataset:
Radar Image Filtering Radar image filters play a crucial role in image processing, enhancing the quality of radar imagery for various applications. That is why Mullissa et al. [43]  developed a speckle filtering tool in GEE. However, this tool is not available in Google Colab. Therefore, it was rewritten in Python while developing this processing chain. Speckle filter performance is generally assessed by analysing how much speckle noise is reduced and how well features are preserved in the filtered image  [44]. Each filter is assessed in terms of the accuracy with which water bodies are detected. Five filters have been incorporated in the processing chain. These filters include the boxcar filter, which smooths images by averaging neighbouring pixels; the gamma map  [45] , a technique for adjusting brightness and contrast; the Lee filter  [46] , designed for speckle noise reduction; the Lee sigma filter  [47] , an enhanced version with adjustable noise reduction; the refined Lee filter  [48] , which further refines speckle noise reduction; and the Quegan multitemporal filter  [49] , utilising information from multiple radar images taken at different times to reduce noise.
Processing Chain Overview Figure  2  below shows a simplified overview of the processing chain. Flood detection begins by selecting satellite images covering the study area and applying a mask to identify flood-prone areas using HAND data. These selected images are then divided into preevent and post-event categories based on the flood event date. Subsequently, speckle filtering techniques are applied. Change detection is carried out by comparing each postevent image with multiple reference images acquired before the event, with a focus on selecting reference images that overlap with the post-event image. The results of the change detection process, involving multiple reference images, are integrated using a majority voting approach. The processing chain code is freely accessible and commented on Github: https://github.
Those products were compared to the results obtained by our processing chain for the same zones. For further validation, the results of the processing chain were compared to the results of the S1Chain developed by Alexandre et al. [52]. This processing chain downloads and extracts Sentinel-1 imagery within the Sentinel-2 tile footprints, incorporates multitemporal filtering, calculates the normalised difference ratio, and generates a mask delineating flooded regions. The data used were processed following the passage of Cyclone Batsirai in 2022, using the same Sentinel-1 data that we processed for this cyclone event. This makes it possible to compare the different chain outputs.
Frequent submersions are thus excluded from the flood detection results. To efficiently process the 30 scenes, we simultaneously submitted requests to Google Earth Engine (GEE) from the Google Colab session executing our Python code. GEE, equipped with automated resource allocation, managed concurrent processing seamlessly. Remarkably, all requests were completed in under an hour. The entire processing chain, from data processing to result visualisation, was implemented within a single Colab session  [53].
The HAND index can play a crucial role in supporting the flood detection process  [57]. We demonstrate the significance of utilising the existing global HAND dataset on GEE, emphasising its time-saving benefits in the flood detection process in addition to the accuracy improvement. The results show that several locations affected by flooding during the passage of Cyclone Batsirai were not mapped by traditional emergency services, thus demonstrating the value of this new processing chain able to systematically generate flood maps for cyclone events on the scale of an entire country. The maps used by national emergency management agencies, obtained from various sources, including through activation of the International Charter Space and Major Disasters, currently remain very limited in space and time.
New information provided by our processing chain can help to improve disaster management and response. To swiftly provide practical information to risk managers, we can enhance our analysis by integrating data from the European Space Agency (ESA) World Cover, which offers a global land cover map for 2021 at a 10-metre resolution, derived from Sentinel-1 and Sentinel-2 data. We can also utilise OpenStreetMap (OSM), a global open-source mapping platform that crowdsources geographic data, providing comprehensive, free maps for various applications. Additionally, Google Open Buildings can be used to incorporate building footprints using advanced deep learning algorithms and very high-resolution imagery. However, it is worth noting that while GEE offers data availability and processing resources, including computational power and dedicated codes, which is particularly beneficial for territories like Madagascar with vast expanses and limited resources, certain limitations need to be considered.
Sentinel-1 images are used for their ability to provide valuable information, even in unfavourable weather conditions. Our approach stands out for its speed, providing near-real-time data complementary to Copernicus products. The involvement of local stakeholders in data production and risk management enhances the capacity of communities to respond appropriately to floods. This collaboration contributes to more efficient flood management and rapid response in times of disaster. Furthermore, the utilisation of Google Colab significantly eases collaboration. The processing chain is easily shareable, requiring no installation, and the Python notebook contains both code and formatted text, facilitating comprehension. This seamless integration of collaborative tools enhances the accessibility of our approach, promoting wider engagement and cooperation among stakeholders in addressing flood-related challenges.
Conclusions This study developed a processing chain to identify flooded areas using radar imagery in Google Earth Engine. The chain was developed in Google Colab in the form of a Python notebook. We have shown that our processing chain is effective in detecting flooded areas throughout Madagascar. This processing over large areas was possible thanks to the computing power and the rapid access to the data offered by Google Earth Engine. The GEE platform is a real opportunity for work on large volumes of data, particularly for countries like Madagascar where bandwidth and the Internet network can be quite inconsistent. The results show that several locations affected by flooding during the passage of Cyclone Batsirai were not mapped by traditional emergency mapping services, thus demonstrating the value of systematically generating national flood maps for cyclone events.
The maps used by national emergency management agencies, obtained from various sources including through activation of the International Charter Space and Major Disasters, currently remain very limited. New information provided by our processing chain can help to improve disaster management and response. Cyclonic seasons give rise to clouds and atmospheric disturbances. Less affected than optical imagery, we have shown that radar Sentinel-1 data are a reliable solution for detecting flooding during these periods. It makes sense to continue working on these data, since ESA has announced the launch of Sentinel-1-C this year, and the rest of the programme is already planned. Building processing chains and operational tools based on Sentinel-1 data is part of a long-term vision. Code from the processing chain could easily be adapted to use other radar images from different satellite constellations and to complete or replace Sentinel-1 imagery when it is not available.
). The algorithms will also need to be adapted to process these types of data. The chain currently runs on Google Earth Engine. Adapting the script to work with multiple backends such as Digital Earth Africa  [58]  or Microsoft Planetary Computer  [59]  or other proposals like the ESA's  [60]  would be very interesting. The use of these different platforms could also provide access to different data sources important for rapid detection of all flooded areas. The good results obtained for the detection of areas under water and the replicable nature of the method raise the question of its use at an operational level. To this end, we have launched the SCO (Space Climate Observatory) Cimopolée project, a research and operational application project involving our teams and the Bureau National de Gestion des Risques et des Catastrophes à Madagascar (BNGRC).
Palatability Attack time (i. e. , the time taken by the bird to approach and attack the prey after seeing them) was used as a proxy for measuring the time the bird spends to attack after seeing the prey. Therefore, we tested for a difference in the attack time between control (peanuts) and experimental prey (adult moths or caterpillars). All peanuts offered at the beginning of a trial were removed before analysis. One bird (B36) refused nearly all prey items including peanuts and was removed from all analyses. For adults, attack time was tested for normality, using the Shapiro-Wilk normality test. Since the data are not normally distributed, we tested for differences in attack time using a Wilcoxon signed-rank test, with three comparisons: White-Control, Melanic-Control and White-Melanic. For caterpillars, since the refuse rate was high, all the birds that refused all the caterpillars were first removed, and a Wilcoxon signed-rank test was then used to compare attack times of caterpillars and controls.
Second, to include the whole behavioral variation and all the birds, a Wilcoxon signed-rank test was used to compare refuse rates of caterpillars and peanuts, and without removing birds that refused all caterpillars. In both cases, data were not normally distributed. A linear model was implemented to test whether birds with lower weight accepted caterpillars at a higher rate (refuse rate ~ weight). Finally, generalized linear mixed models were fitted to assess the effect of different variables on the refuse rate, with bird behaviour (eat/refuse) as response variable and logit link. Bird age, sex and trial duration were included as fixed effects. Bird identifier was included as a random factor to account for variance in bird behaviour. Only significant variables (p<0. 05) were retained for subsequent models.
Palatability Palatability data for adult moths were obtained using 266 individual prey presented to 27 birds tested. Out of 80 white and 78 melanic moths presented, 76 and 77 were eaten (respectively). Average attack time was 43 s for peanut, 20 s for the melanic moth and 14 s for the white moth. Attack time did not follow a normal distribution (Shapiro-Wilk normality test). No significant difference in attack time was observed between moths and peanut (Wilcoxon signed-rank test: control/melanic morph, p = 0. 5153, control/white morph, p = 0. 1731) or between moth morphs (Wilcoxon signed-rank test: white morph/melanic morph, p = 0. 1899) (Fig. 3 ). Palatability data for caterpillars were obtained using 458 prey (peanuts + caterpillars) presented to 40 birds. Out of 221 caterpillars presented, 73 were refused, 40 attacked but not eaten, 22 half-eaten, and 86 eaten.
Predation Preliminary analysis. Predation data were obtained using 1350 individual prey, tested with 45 birds. Moths were positioned randomly in the arena, and an absence of bias in moth positioning before the trials was confirmed (Chi-squared test, p = 0. 935). In the linear model fitted with raw data, only cell C1 emerged as occupied slightly significantly more often than others (p = 0. 045) (Table  S2 ). However, cells in the centre of the arena received markedly more attacks than cells at the edge (Chi-squared test, p < 0. 001, Table  1 ). The order of attack (i. e. whether the moth is attacked as first, second, etc. ) didn't explain attack probability for both morphs in the linear model (p = 0. 86). We found, however, that searching time increases significantly with order (i. e. , towards the end of the experience) (p < 0.
ACKNOWLEDGEMENTS We are deeply indebted with all people that helped us during experiments in the  Konnevesi Research Station , in particular to  Helinä Nisu  for her expertise on bird handling and training and to  Sanni Silvasti , for being the best Cicero we could have in station. We are also thankful to  Doris Gomez  who gave us useful advice about spectrometry data, to  Céline Teplitsky  and  Gaël Thébaud  foradvice on linear models, and to  Yann Le Poul  and  Pierre Lacoste  for help with statistical analysis. This project was funded by  ANR-18-CE02-0019-01  and  ERC-StG-243179  grants to MJ, and the  Academy of Finland , project no. 345091  to JM.
Model description In this study, the climatic effect of the darkening TP was investigated by a coupled land-atmosphere global climate model so-called the LMDZOR, which is a component of the IPSL-CM  21, 47. The model components of LMDZOR used here include the atmospheric module, the Laboratoire de Météorologie Dynamique atmospheric general circulation model with zoomed capability (LMDZ, version 5, v2076), and the land-surface component, the ORganizing Carbon and Hydrology In Dynamics EcosystEms (ORCHIDEE, version 1. 9. 5, v3035). LMDZ is a state-of-the-art atmospheric general circulation model including relevant dynamical and physical processes governing the atmosphere  48, 49. ORCHIDEE is an advanced terrestrial ecosystem model that simulates surface energy balance and water cycles of soil and vegetation and also considers the terrestrial carbon cycle  50.
However, the spatial resolution of 0. 5°× 0. 5°can still be too coarse to accurately mimic those TP land surface processes that are strongly influenced by topographic complexity (e. g. , ref. 51). At the edge of the zoomed area, LMDZOR can ensure the free exchange of atmospheric water, energy, and momentum. Previous studies have rigorously evaluated and shown the high reliability of LMDZOR in simulating regional climate in East Asia  52, 53. In this study, we further verified the capacity of LMDZOR for simulating the spatial patterns of annual and boreal summer (from May to September) hydrological variables and boreal summer atmospheric circulation over eastern Eurasia and the western Pacific by comparing results in CTL against five different observation-based and reanalysis datasets (see LMDZOR evaluation subsection and Supplementary Figs.
The parameters of the mass balance model in OGGM have been calibrated through the automated calibration procedure, making use of the geodetic mass-balance data from ref. 59 and pre-processed climatic research unit (CRU) temperature and precipitation  22. During the CRU climate data pre-processing procedure, the temperature and precipitation derived from the CRU time series (TS)  60  were downscaled to the CRU climatology (CL)  61  with the delta method (e. g. , ref. 62). This method first computed the deltas of CRU TS relative to its reference climate period (the overlapping part between two climate datasets), then applied these deltas to the CRU CL to obtain the downscaled climate data. The pre-processed CRU climate data would further be used to downscale the climate forcing data for our experiments (see Experimental design section below).
Experimental design Two equilibrium experiments were performed using LMDZOR. (I) The control experiment (CTL) is the current albedo experiment, forced by observed climatological surface albedo averaged over a 15-year period of this century (2003-2018 CE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43 product  64. The original albedo data at a spatial resolution of 500 m × 500 m was aggregated into 0. 5°× 0. 5°following ref. 65 to fit the model grid. Other input for CTL in LMDZOR included the average of the same period (2003-2018 CE) observational SST and SIC derived from the input datasets for Model Intercomparison Projects (input4MIPs), fixed global atmospheric CO 2 data (390 ppm) and the plant functional types (PFT) maps estimated from transient modeling in the project "Trends in net land-atmosphere carbon exchange" (TRENDY), the PFT, and the leaf area index (LAI) obtained from MODIS MCD15 product.
Acknowledgements This study was supported by the  Second Tibetan Plateau Scientific Expedition and Research (STEP) program  (grant No. 2019QZKK0208 ), the  National Natural Science Foundation of China (NSFC)  project  Basic Science Centre for Tibetan Plateau Earth System  ( 41988101-04 ), and the  Xplorer Prize. A. V. was supported through funding by the  Austrian Science Fund (FWF) -P30256  and the  German Research Foundation  ( DFG) -MA 6966/5-1. Co-authors from French institutions acknowledge  GENCI  for the allocation of computing resources.
Data availability The OGGM source code can be accessed at https://github. com/OGGM/ oggm and the pre-processed directories used in this study can be downloaded at https://cluster. klima. uni-bremen. de/~oggm/gdirs/ oggm_v1. 4/exps/CRU_new/elev_bands/qc0/pcp2. 5/match_geod_ pergla/. The projected climate data from the CMIP5 RCP8. 5 scenario can be accessed at https://data. ceda. ac. uk/badc/cmip5/data. The observed climatological surface albedo data from the MODIS MCD43 product and the LAI data from the MODIS MCD15 product can be downloaded at https://search. earthdata. nasa. gov/. The observational SST and SIC from input4MIPs are freely available at https://esgf-node. llnl. gov/projects/input4mips/. The global atmospheric CO 2 data and the PFT maps estimated from TRENDY can be downloaded at https:// sites.
exeter. ac. uk/trendy. The glacier attribution data from RGI v6 is freely available at http://www. glims. org/RGI/. The elevation data from GMTED2010 can be accessed at https://topotools. cr. usgs. gov/GMTED_ viewer/viewer. htm. The CRU TS and CL precipitation and temperature data can be downloaded at https://crudata. uea. ac. uk/cru/data/hrg/ and https://crudata. uea. ac. uk/~timm/grid/CRU_CL_2_0. html, respectively. The consensus estimate for the volume of all glaciers on Earth in ref. 71  is freely available at https://www. research-collection. ethz. ch/ handle/20. 500. 11850/315707, while the future projected glacier changes under the RCP8. 5 scenario in ref. 46 can be freely downloaded at https://doi. org/10. 1594/PANGAEA. 914503. The gridded thickness changes for glaciers in the Himalayans over the period 1975-2016 CE in ref.
76 can be accessed at: https://nsidc. org/data/HMA_Glacier_dH/ versions/1, while the ELA of glaciers in the TP and its surrounding ranges is estimated by ref. 77 can be accessed at https://zenodo. org/ record/5119153#. Yan0I7q-s2y. The processed output data of climate experiments and glacier simulations generated in this study have been deposited in the "Figshare" (https://figshare. com/articles/dataset/ Climate_and_glacier_data/21679409). The boundary of the Tibetan Plateau is derived from https://data. casearth. cn/sdo/detail/ 5feae826819aec33049b7ca9. Other maps used in this study are derived from MATLAB (see examples: https://ww2. mathworks. cn/ help/map/ref/worldmap. html?searchHighlight=worldmap&s_tid= srchtitle_worldmap_1). Other datasets supporting the findings of this manuscript are available in the main text or supplementary information.
Study sample This is a cross-sectional study using data from the ELSA-Brasil baseline, collected from 2008 to 2010. ELSA-Brasil is a multi-center cohort study of 15,105 active or retired public servants (support staff, administrative staff, and professors) of public teaching and research institutions from six Brazilian state capitals, aged from 35 to 74 years  13. In this study, only participants diagnosed type 2 diabetes according to the criteria chosen by ELSA-Brasil and those who self-declared race/skin color as white, mixed-race, black, or yellow (Figure  1 ) were included. To minimize the inclusion of type 1 diabetes, patients who had records of diabetes before the age of 30 and used insulin as the first medication were excluded. Records with missing data for the variables used in the statistical model were also excluded.
The effect modification of occupational social class on the association between sex and type 2 diabetes prevalence in the multiplicative and additive scales was measured  17. In the multiplicative scale, the prevalence ratio and the respective Wald 95%CI was estimated based on the exponents of the B parameters of the regression model. To calculate the measures on the additive scale, and their 95%CI -estimated by the delta method -the analysis was restricted to two specific occupational social class strata. That is, even though there were originally three possible strata (high, middle, and low), two databases were created: the first included only records from participants with high and middle occupational social classes, and the other, only records from participants with high and low occupational social classes.
0 (https://www. ibm. com/). Since ELSA-Brasil is a multi-center study, the ethics review committees of each participating institution and the National Ethics Research Committee approved the study research protocol. All participants signed an informed consent form before data collection. The Ethics Review Committee of the Institute of Collective Health Studies, Federal University of Rio de Janeiro approved this study (CAAE: 57801616. 4. 0000. 5286). Cad. Saúde Pública 2023; 39(4):e00150322.
PythonSimilar to R, Python is an interpreted language. It is beginner-friendly and viral outside of traditional software development and computer science. A major advantage is the large and active open source community maintaining a wide variety of packages (e. g. , 'pandas', 'matplotlib', 'plotly') supporting data analysis workflows. For analysing luminescence data with Python, we provide a work-in-progress version of a package called 'xlum-python' on GitHub TM (https://github. com/ SteveGrehl/xlum-python, last accessed: 2022-07-01). The package allows loading XLUM files with Python and conversion into pandas DataFrame objects (two-dimensional tabular data). This format is a starting point for further analysis, such as conversion to CSV files, export to Microsoft Excel TM or graphical output.
Interestingly, only 1 or 2 microtubules extended all the way from pole to pole. To quantify changes in the interaction of microtubules in the spindle midzone throughout anaphase in the one-cell stage C. elegans embryo we quantified the length of interactions between individual microtubules in 3 tomographic reconstructions of mitotic spindles at different stages in anaphase defined by the distance between the segregating chromosomes. The interactions were defined by a maximum center-to-center distance of 100 nm between interacting microtubules, based on our previous data on the spindle network  (Redemann et al. , 2017)  as well as the reported sizes of microtubule-associated proteins or molecular motors  (Mastronarde et al. , 1993; McDonald et al. , 1992; Peterman & Scholey, 2009). We further required a minimum interaction length of 100 nm (based on the detection limit of microtubules, which is 100nm) and an orientation angle of 0-90° between the two microtubules (Fig.
, 2006; Euteneuer & McIntosh, 1980; Heidemann et al. , 1980; Mastronarde et al. , 1993; J R McIntosh et al. , 1975; Saxton & McIntosh, 1987; Snyder & McIntosh, 1975). As our EM data is based on a relatively low sample number and because it is difficult to determine the microtubule polarity in our tomographic reconstructions, we used second harmonic generation (SHG) microscopy and two photon florescence (TP) microscopy simultaneously to measure the polarity of the collective microtubules throughout the spindle in vivo  (Yu et al. , 2014). SHG is a nonlinear optical process in which highly polarizable, non-centrosymmetric materials emit photons with half the wavelength of incident light. When an array of microtubules is imaged with SHG microscopy, the resulting SH signals depend on both the polarity of the microtubule array and the density of microtubules within a focal volume.