dataesr/scientific-paragraphs-categorization huggingface dataset:
The ESA CCI Lakes LSWT dataset presented in this paper has been validated with in situ measurements on 79 lakes with a total of 207 sites. The geographical distribution of the sites is reported in Fig. 6  which shows that most of the sites are situated in North America and Europe, with 38% and 47% of the sites and 24% and 56% of the lakes respectively as shown in Table  13. As the in situ data are from a variety of sources, the frequency of the measurements varies greatly between the sources. Only for 90 of the 207 sites measurements with frequency of less than a measurement per day are available, mostly in North America. A portion of the lakes that have been used for the validation are small lakes, for which the LSWT retrieval is most challenging. In Table  13  we have reported the number of lakes for which the maximum distance to land  13  is less than 3 km.
Moreover, some of the locations of the in situ measurements are situated close to the shore even for large lakes, which means that the nearest water-filled pixels may not overlap the in situ measurement, thus increasing the uncertainty in the comparison from spatial representativity. The satellite-to-in situ-matches are created at the original satellite coordinates, at L2 spatially within 3 km and temporally within 3 hours for the in situ data where the measurement time was available. The LSWT of the L3S ESA CCI Lakes dataset are then directly validated to assess the products as seen by users. The validation of the LSWT is performed using conventional and robust statistics of the satellite minus in situ measurement difference, the latter being less sensitive to outliers and more descriptive of the majority of data.
86 returned in the 560 nm band  51. As for MODIS, significant linear correlations were also found for all of the 11 evaluated bands (from 412 nm to 748 nm) between POLYMER-corrected LWLR and in situ measurements, with the highest R = 0. 83 returned at 547 nm  78. Note that the validation with in situ data for Chla and turbidity was not independent, since the tuning algorithms adopted from the procedure described by Neil et al. 50 to generate Chla and turbidity have already used the same source of in situ data. In this context, a validation procedure of a weighted blended Chla product based on the fuzzy OWT classification framework was developed to demonstrate the performance of water quality products  51. Based on this uncertainty evaluation, it was found that the uncertainty (i. e. , ARU, %) of the blended algorithm result shows flat response to the OWT membership scores.
This indicates that our proposed procedure successfully removed the uncertainties across different OWTs by blending water quality products from outputs of several pre-assigned algorithms, and therefore making our product globally validated. Although the dataset used for the uncertainty characterization is not fully independent, the validation presented forms a global assessment. As for local performance, the validation results could be better or worse for a single lake, however, this would not alter the optimal configuration of the system nor the global validation. It is admitted that a lack of in situ reference data and a bias towards turbid, productive and large lakes in the datasets do exist, and is somewhat to overcome when the end-to-end validation of Chla and turbidity products derived from LWLR is concerned.
Also, a temporal-spatial sampling bias still exists with most of the available in situ data having been collected since the launch of MERIS and relatively close to shore. These inherent defects in the in situ dataset will add to uncertainties on the validation of LWLR products over a range of water types and lake geophysical and geospatial characteristics. For a full description of the validation procedure and results the reader can refer to Liu et al. 51  and the ESA CCI Lakes Product Validation Intercomparison Report  78. A separate follow-on validation of OLCI is still pending considering the limited matchups with in situ measurements from LIMNADES due to the relatively short operation period (2016-now), and that the similarities in radiometric performance and waveband configuration with MERIS would allow propagation of the present results to OLCI.
Consistency of the thematic ECV products in the dataset. The thematic ECVs in the dataset (L3S product) are generated independently with data originating from different satellite sensors. Nevertheless, physical processes link these observations together and create expectations to find some interdependencies and Fig. 7  LSWT satellite minus in situ temperature difference median (on the left hand side) and robust standard deviation (on the right hand side) per year (upper plot) and number of matches (lower plot) per quality level. cross-correlations. Moreover, the analysis of consistency between thematic ECVs provides a means for independent quality control of the primary observation data, related to unresolved observation challenges such as sub-pixel ice cover or poor atmospheric transmissivity, recorded differently between sensors and overpass times.
All results presented in this section are based on version 1 of the ESA CCI Lakes dataset. The major differences in version 2, other than wider spatio-temporal coverage, include the reprocessing of LIC with a new machine-learning based retrieval algorithm, which resolves the classification of dried up lake areas as ice. Also, the quality of LWLR products was enhanced by the introduction of two non-water reflectance signatures to classify land adjacency effects. For a full description of analyses and solutions towards consistency, please refer to the Consistency PVIR  79. Comparisons between LWL and Turbidity (derived from the LWLR product) and between LIC, LSWT and LWLR are reported in this section.
Comparisons are shown between lake ice cover classification, LWLR at 490 nm and LSWT in Fig. 10  where macro-pixels from 2002 to 2012 (MERIS) and 2016 to 2019 (OLCI) are considered. Only data points for which observations are obtained on the same day are shown, while there can be several hours between the overpass of individual sensors. The consistency between the three thematic products is defined as: • Ice only occurs when temperatures are close to or below freezing point, ignoring variations caused by lake salinity. • Where ice is observed, LSWT and LWLR cannot be observed. The identification of ice is the main focus in the LIC product based on MODIS data. All observations of LSWT and LWLR are screened for ice occurrence as well, but with different approaches and based on different sensors and observation times.
For the turbidity (lower plots), lake median of daily data is shown as blue dots, and the LOESS smoothing is reported as red lines. For LWL (upper plots) the values at the given temporal resolution (blue dots) are connected by the red line. observations of the two variables, confirm this interpretation. The distribution of LSWT data points which are also classified as ice by the LIC product (red histogram) is tilted towards the freezing point. The data points classified as ice also correspond to relatively high LWLR (histogram on the right, coinciding measurements of LWLR and LIC). These are highly likely observations containing ice, which have not been correctly identified by the pixel identification for MERIS and OLCI data. For the lake Amadjuak reported here and for other lakes analysed, we found that LSWT and LIC are consistent at a higher degree when compared with LWLR where the influence of observation artefacts can less easily be distinguished from noise.
Currently, globally validated algorithms to retrieve such quantities are available for chlorophyll-a and TSM or turbidity, and vertical transparency, with by far most of the attention in scientific literature dedicated to the retrieval of chlorophyll-a. Reading the products. The ESA CCI Lakes dataset is stored in NetCDF4 Classical format files, compliant to both CF (Climate and Forecast) metadata convention (v1. 8) and CCI Data Standards v2. 3. The consistency of the product is ensured by the use of a common land mask over a common grid. The main characteristics of the product are as follow: • The product consists of daily aggregations (the product is specified at the nominal time of 12:00:00 UTC). If a parameter is missing the field is filled with a default value. • The product provides data over a latitude/longitude grid of 0.
0083333° (1/120°), therefore each file contains 21600 number of rows and 43200 number of columns. • For LWL and LWE, for which the product consists on one value per lake, the value is set on each grid point belonging to a lake according to the common land mask. • There is a common lake identifier derived from existing databases (Global Land and Wetland Database (GLWD)  89  , Hydrolakes 14 , GloboLakes  13  ) and created specifically for the project. • The extent is -180 to 180 degrees longitude, -90 to 90 degrees latitude, where positive signs point north and east. The pixel coordinate is the centre of the pixel. • For each variable, the associated uncertainty is also available and for LSWT quality flags are also reported for a correct use use of the product. Quality level 4 and 5 are strongly recommended especially for climate studies.
A wide choice of software packages can be used to visualise or manipulate the NetCDF data. A list of software is provided on the Unidata web site (https://www. unidata. ucar. edu/software/netcdf/software. html). The ESA CCI Lakes files can also be visualised with the Climate Analysis Toolbox (Cate) https://climate. esa. int/en/ explore/analyse-climate-data), the reference software for visualising data developed within the CCI Program funded by ESA. Additionally, some scripts allowing to download data only on a selected area or for a selected lake are available in the GitHub of the project (https://github. com/cci-lakes/lakes_cci_tools). Downloading the products. The complete ESA CCI Lakes dataset is available at CEDA https://doi. org/10. 5285/a07 deacaffb8453e93d57ee214676304 and it is free after registration.
It is possible to download the whole dataset (almost 30 years of daily data) or to use scripts in diverse languages (e. g. Python) to download a well defined area in a certain period of time. Will the climate data record be extended in time?. The ESA CCI Lakes dataset described here covers the period from September 1992, when the first altimetry data became available, to the end of 2020. ESA has recently funded a continuation of the CCI Lakes project, which will allow the release of future version of the dataset with new thematic variables such as Lake Ice Thickness (LIT) and Colored Dissolved Organic Matter (CDOM) as well as increased temporal coverage. How should I refer to the products in publications?. The dataset is citable  52  and by using the data you agree to cite both dataset and this article.
Characteristics of level-2 data for the instruments used for LWL. missions, a constellation of nadir altimeters, do not cover the 2024 target lakes in the database. Moreover, different families of satellites (a set of satellites with the same orbit, as in the case of the Jason's or ENVISAT/ERS missions) are in different orbits and cover a different set of lakes. As a consequence, some lakes are partially covered in time, generating data gaps for some lakes over the whole 1992-2020 period of the dataset. Consequently, for many lakes, there are also data gaps with time series covering only a portion of the whole 1992-2020 period of the dataset.
acknowledgements The authors acknowledge the following funding for this work with gratitude. The  European Space Agency  supported the  Climate Change Initiative -New ECVs for Lakes , which has provided the majority of support leading to the outcomes herein described, via grant reference 4000125030/18/I-NB. Foundational work has been supported by the  Natural Environment Research Council (NERC ) GloboLakes grants:  NE/J023345/2 ,  NE/J02211X/1 ,  NE/J023396/1 ,  NE/J021717/1  and  NE/J022810/1. Use of the Centre  for Environmental Data Analysis computational facilities to generate LSWT has been supported in part by the  NERC National Centre for Earth Observation. The LIC work was enabled in part by support provided by  Compute Canada  (www. computecanada. ca). LWL and LWE product generation was also supported by the  Centre National d'Études Spatiales (CNES)  under the program TOSCA.
Increasing the coverage for LWL and collecting in situ data for validation of LWL and LSWT have also been supported by the  Copernicus Climate Change Service  ( C3S ), funded by the  European Commission. A particular thanks goes to the SERTIT team composed by  T. Ledauphin ,  J. Maxant  and  M. Studer  for their involvement in the LWE extraction methods analysis. The Pléiades/Sentinel-2 LWE comparison was supported by the  Surface Water and Ocean Topography (SWOT) CNES program. LWLR computation benefited from computing infrastructure of the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS). The authors are thankful to all the institutions, such as the  National Buoy Centre (NDBC) , the  Fisheries and Oceans Canada (FOC) , the  Swedish University of Agricultural Science (SLU) , the  Hungarian Met Service , NTL LTER, and UGLOS that have provided the in situ data for the validation of LSWT and in particular to  Enner Alcântara  ( Saõ Paulo State University, Brazil ),  Curtis DeGasperi  ( King County Water and Land Resources Division, Department of Natural Resources and Parks, Seattle, WA, USA ),  Margaret Dix  ( Universidad del Valle de Guatemala, Guatemala ),  Martin Dokulil  ( Mondsee, Austria ),  Claudia Dresti  ( CNR Institute for Water Research, Italy ),  Hilary Dugan  ( University of Wisconsin-Madison, USA ),  Gideon Gal  ( Israel Oceanographic  and  Limnological Research Institute, Israel) ,  Claudia Giardino  ( CNR Institute for Electromagnetic Sensing of the Environment, Italy ),  Badin Gibbes  ( The University of Queensland, Australia ),  Jacqui Hansson  ( Seqwater, Australia ),  April James  ( Nipissing University, Canada ),  Ilga Kokorite  ( University of Latvia and Latvian Environmental Geology and Meteorology Centre, Latvia ),  Alo Laas  ( Estonian University of Life Sciences, Estonia ),  Eric Leibensperger  ( State University of New York at Plattsburgh, USA ),  Alessandro Ludovisi  ( University of Perugia, Italy ),  Chris MacBride  ( NIWA, New Zealand ),  Shin-ichiro Matsuzaki  ( National Institute for Environmental Studies, Japan ),  Linda May  ( Centre for Ecology and Hydrology, Edinburgh, UK ),  Ghislaine Monet  ( UMR CARRTEL, France ),  Don Pierson  ( Uppsala University, Sweden ),  Sebastiano Piccolroaz  ( University of Trento, Italy ),  Tiina Nõges  ( Estonian University of Life Sciences, Estonia ),  Peeter Nõges  ( Estonian University of Life Sciences, Estonia ),  Antti Raike  ( SYKE, Finland ),  Alon Rimmer  ( Israel Oceanographic  and  Limnological Research Institute, Israel) ,  Michela Rogora  ( CNR Institute for Water Research, Italy ),  Geoffrey Schladow  ( UC-Davis Tahoe Environmental Research Center, USA ),  Eugene Silow  ( Irkutsk State University, Russia ),  Evangelos Spyrakos  ( University of Stirling, UK ),  Wim Thiery  ( KU Leuven, Belgium ),  Piet Verburg  ( NIWA, New Zealand ),  Caroline Wynne  ( Environmental Protection Agency, Ireland ),  Gesa Weyhenmeyer  ( Uppsala University, Sweden ).
The full time-series of the LWLR product is a combination of satellite data from MERIS, MODIS, and OLCI, for the period of Apr 29 th 2002 to Apr 7 th 2012, Apr 8 th 2012 to Apr 24 th 2016, and Apr 25 th 2016 to Dec 31 th 2020, respectively. The MERIS and OLCI LWLR data are provided for all of the 2024 lakes included in this dataset. In contrast, a subset of 38 lakes time series include MODIS data to fill the gap between MERIS and OLCI in the current product version, from approximately 250 evaluated for inter-sensor stability and other time-series remaining to be investigated for a future product version.
Study participants We used data from the population-based occupational and environmental health prospective cohort study (AMIGO) established in 2011/2012 to investigate environmental and occupational determinants of diseases and symptoms in the Dutch adult population. The rationale, study design and participant recruitment in AMIGO were described in detail previously  (Slottje et al. , 2014). In short, AMIGO participants were recruited from the general population in the Netherlands through the Primary Care Database of the Netherlands Institute for Health Services Research (NIVEL), which consists of routinely recorded data from health care providers to monitor health and utilization of health services in the Dutch population  (Nivel Primary Care Database|Nivel, 2022). The sample includes 14,829 adults (16 % of those invited), aged 31-65 years at the time of data collection (2011/2012), who were randomly selected within households based on their address.
Statistical analysis We calculated descriptive statistics for age, sex, the highest level of education attained, urbanicity level, and self-reported mobile phone use at baseline, and at each time point for perceived RF-EMF exposure, perceived RF-EMF risk, 4DSQ-S score, and MOS sleep index. We used one-way repeated measures ANOVA to compare group means of 4DSQ-S score and MOS sleep index, respectively, across all time points. The proportion of participants reporting IEI-RF was calculated at each time point T 0 , T 1 , and T 4 , whereas the proportion of those defining themselves as EHS was only available at T 4. We used the R package msm to calculate the observed transitions and transition probabilities of IEI-RF over different time intervals, and fit multi-state Markov models to our data  (Jackson, 2011).
This method, available in the msm package, compares observed and expected numbers of transitions between pairs of states for a series of transition starting times, transition time intervals and covariate categories, and it is intended for data which represent observations of the process at arbitrary times. In cases where there are several low expected counts in the resulting contingency tables, the number of observation time, time interval, or covariate categories may be reduced to improve the χ 2 approximation (Aguirre-  Hernández and Farewell, 2002; Jackson, 2011). We explored the association between EHS at T 4 and perceived RF-EMF exposure and risk, 4DSQ-S score, MOS sleep index assessed at T 0. We estimated four logistic regression models including perceived RF-EMF exposure (Model 5), perceived RF-EMF risk (Model 6), 4DSQ-S score (Model 7), MOS sleep index (Model 8) as independent variables, adjusted for sex and age.
This population-based cohort study was conducted according to an analysis plan developed a priori and defining in detail the planned statistical analysis (Supplementary Analysis Plan). Missing values (<1. 0 %) were replaced with the most common category (categorical variables) or with the mean value (continuous variables). All analyses were conducted with the R statistical software, version 4. 0. 4. Computing code related to all analyses presented is publicly available at https://github. com/eugeniotraini/ multistate_RF_EMF.
Furthermore, by collecting data on perceived RF-EMF exposure and risk, and non-specific symptoms (i. e. symptom reporting and sleep disturbances) over the 10-year followup, we were well positioned to investigate the dynamics of several individual factors possibly related to IEI-RF. Weakness of our study includes that it was not feasible to measure true exposure in our study participants and we were therefore not able to follow the time course of actual RF-EMF exposure. Because we asked for the "most important health complaint" attributed to RF-EMF exposure, we were also not able to reliably follow which exact symptoms were included into the attribution over time. Furthermore, given the sparseness of consistent "yes" data of IEI-RF over time, we did not estimate mutually adjusted multi-state Markov models.
We acknowledge financial support by The Netherlands Organization for Health Research (ZonMw) within the program on Epidemiological Perception Research in the field of Electromagnetic Fields and Health under grant numbers 8520000, 85200002 and 85200003, and the French Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES) under grant number EST-2018 RF-12 Impact. CRediT authorship contribution statement Eugenio Traini: Conceptualization, Investigation, Formal analysis, Writingoriginal draft. Astrid L. Martens: Writingreview & editing. Pauline Slottje: Writingreview & editing. Roel C. H. Vermeulen: Conceptualization, Supervision, Writingreview & editing. Anke Huss: Conceptualization, Supervision, Funding acquisition, Writingreview & editing.
Consequently, we expect our findings to support the implication elsewhere  (Zhao et al. , 2022) , and our hypothesis that models have to their detriment, drifted from their original process fidelity toward modeled parsimony as forewarned  (Raupach & Lu, 2004). For clarity, this work is less about which model is best (although a comparison is inevitable) and more about how dust emission modeling has been misled (weaknesses hidden) by the focus on calibrating dust cycle models to atmospheric DOD. For clarity, we recognize the valuable nature of calibrating dust cycle models to DOD. It is timely that a new dust emission point source (DPS) database for southwestern North America has been collated and has been used with the albedo-based dust emission model (AEM) to circumvent the simplifying assumptions about sediment entrainment and sediment supply  (Hennen et al.
( 3 ) This Q AEM does not require E (or VI data), R, z 0 , or z 0s and thereby has four primary parameters less than the exemplar TEM, and removes their associated sources of uncertainty. Soil moisture w was obtained from the same data as used in the TEM and calculated in the same way. The u s* is obtained directly from ω ns , using shadow from the normalized and rescaled albedo which describes the area-weighted land surface aerodynamic structure (partitioned between above canopy and soil surface) independent of waveband, making it highly suitable for the inclusion of dryland non-photosynthetic material 𝑢𝑢𝑠𝑠 * 𝑈𝑈ℎ = 0. 0311 ( exp -𝜔𝜔𝑛𝑛𝑠𝑠 1. 131 0. 016 ) + 0. 007. (4) The    *  ℎ is a coupled parameter which describes how the soil surface wind friction velocity is dependent on the wind speed at a given height (h), where that height is ideally at freestream.
Large-Scale Dust Emission Modeling, Mapping Spatial Variation, and Change Detection To implement vertical dust emission, we used contemporary (2001-2020) Earth observation data including spatially and temporally varying wind speeds (at 10 m height), soil moisture (0-7 cm depth), and soil surface temperature (to represent frozen ground which inhibits sediment flux) from the latest ERA5-Land data (Muñoz-Sabater et al. , 2021) (hourly; 0. 1°). The coverage of snow in a given pixel is an areal quantity and therefore ranges between 0 and 1. Consequently, we applied the MODIS Normalized Difference Snow Index  (Hall et al. , 2016 ) (MOD10A1 from Terra, daily at 500 m). We used soil surface temperature available in ERA5-Land and set a threshold of 273. 15 K above which sediment flux can occur. The use of these data does not imply priority over any other data.
For comparison, we used the AEM with soil surface wind friction velocity u s* /U h obtained directly from MODIS albedo (MCD43A3; Collection 6) varying daily, every 500-m pixel across the study area. MODIS is aboard polar-orbiting satellites which cause incomplete coverage. However, the variation in roughness at the daily-weekly scale is so small that we were able to smooth the available data to improve the coverage. Soil clay content was represented with a digital soil texture map  (Dai et al. , 2019)  and used in both models (see Section 2 and Text S1 and S2 in Supporting Information S1). All data are available from the catalog of the Google Earth Engine (GEE;  Gorelick et al. , 2017)  which then required no downloading and reformatting. We used the Java script coding environment to calculate daily dust emission (kg m -2 yr -1 ).