dataesr/scientific-paragraphs-categorization huggingface dataset:
Application to empirical graphs In this section, the datasets presented in sec. 2. 2-2. 3 are analyzed using the tools depicted in sec. 3. 1-3. 3, adapted to the bipartite setting. Tab. 1 shows that LCI networks are coherent, with an average q much lower than for other considered datasets, and are more likely in the loopless regime than other q τ LCI 0. 37 -1. 5 Metabolic 10 1. 8 datasets. This leaves room for fluctuation inside the LCI category. For example, the Ozlci dataset has a trivial structure, since it occupies only 2 layers, and hence q = 0. Fig. 4  presents two LCI networks embedded in 2d space ¶, with differing behaviors: the ELCD network is very coherent (q = 0. 21), and more coherent than the corresponding average randomized networks (q/q = 0. 03). The Bioenergiedat network is less coherent with value q = 0.
99, and q/q ratio just above 1. From the LCI literature, we expect a large number of feedback loops in the examined databases. Citing  [34] : "Feedback loops occur frequently in industrial systems. For instance, mining of coal needs electricity, while production of electricity needs coal". However, the majority of datasets are acyclic. Only Agribalyse and ELCD contain a small number of cycles compared to the edge number. In Agribalyse their occurrence is mostly related to seeds, which are both an input and output in plant growing. In ELCD, loops are associated with a few nodes, and filtering them is enough to remove all cycles, as explained in sec. 2. 2. This low number of loops has not been reported so far, up to our knowledge. It may arise from modeling bias, restricted scope, or from dataset selection bias.
In Fig. 5 , the leading eigenvalue λ 1 is plotted as a function of τ , for both datasets (LCI and non-LCI). Circles representing non-LCI data are well fitted by the dashed curve. Crosses representing LCI datasets are close to the exponential, except for the outlier value at (τ , λ 1 ) = (-10, 3) which represents the ELCD dataset, and seems inconsistent with the curve. After filtering as explained in sec. 2. 2 it is mapped to (τ , λ 1 ) = (-23, 0. 03), which gives a satisfactory fit. This case is reminiscent of a remark in  [20] : "typically loops are not independent as they can share some nodes. In particular, hubs are statistically more likely than other nodes to take part in loops". Indeed removing just a few nodes changed dramatically the behavior from a coherence point of view. However such cycle-removing is of course not relevant for acyclic datasets, while in Agribalyse cycles are scattered across the dataset rather than concentrated.
This raises the question of the robustness of coherence measures and will be discussed in sec. 4. In sec. 2. 2 the topic of monopartite projection was evoked as it is easier to use offthe-shelf tools than to extend a theory to the bipartite case. This is the approach taken for example in  [15] , that may be justified by the particular nature of LCI databases, as noted in sec. 2. 2. Several numerical experiments were run to try to find patterns in the effect of monopartite projection. First, this imposes the constraint that basal nodes must all belong to the same layer, more specifically to the layer chosen for projection. This problem may be mitigated using the new definitions of coherence in  [29]. In the easier case where k out = 1 for processes and projection is done on the flow layer, we were not able to evidence a predictable behavior for coherence quantities.
Conclusion Starting from the observation that little work had been devoted to production processes at the fine-grain level depicted by LCI in the complex networks community, we proposed a first original contribution in that direction. First, unlike earlier works, we proposed i) to keep the bipartite structure to avoid loss of information, ii) to look for a random model, iii) able to reproduce hierarchical features of the datasets. This was done by building on existing theory by Johnson and colleagues, upon extending some of their tools to the bipartite case. We report that: • the studied empirical networks built from LCI databases have high coherence compared to other existing datasets and low loop number. • the random "coherence ensemble" satisfactorily reproduces an important property of empirical dataset (the largest real part of the set of eigenvalues), which it closely related to other important aspects such as behavior of dynamical systems defined on networks as shown by several contributions in the literature.
In further work we plan to extend the number of studied LCI databases, to consider how these observations can extend to other classical properties (such as clustering, diameter,. ). Unbalanced bipartite basal model will be explored, as well as potential useful applications for LCI that can be derived from those findings. Furthermore, the new definitions of trophic coherence in  [29]  will be tested in the particular case of LCI networks. From those values, the variance q2 is: q2 = L B L L L B -1 2 + L -L B 2L -1 -1 2 (B. 1) Which yields: q = L L B - L B L (B. 2).
Data Collection, Management, and Privacy Data will be entered into a web-based secure database hosted at University College London (Research Electronic Data Capture  [REDCap] ). Collected data are anonymous. Case report forms were developed with peer review and national and international testing (tested in the United Kingdom, France, and the United States). Data range checks, data sense rules, and essential required variable alerts will be programed at outset to improve accuracy and completeness. In total, 10% of all records will be manually checked for sense and completeness. Manually identified errors will be coded as automated rules, and the whole database scanned or audited for errors. Identified errors will be alerted to sites for correction before database lock. Data errors and incomplete or missing follow-up data will be queried with sites on a patient-by-patient basis through a web-based data auditing app.
Randomization Sites will be randomized 1:1 in parallel arms when the submission of the minimum required retrospective data is complete. Sites will be randomized to either  (1)  no intervention (the site continues to submit data on consecutive eligible cases but without performance feedback and education during the study). Once the study is complete, these sites will be provided with performance and education feedback or (2) audit, feedback, and education (performance feedback and education provided before beginning further prospective phase of study). Sites will be randomized using the randomization app at the Centre for Healthcare Randomised Trials (CHaRT), University of Aberdeen, which developed the randomization sequence. This randomization app is available 24 hours a day, 7 days a week as a web-based app.
Sample Size The intervention will be delivered to centers, and so the primary outcomes will also be measured at the center level. The only published data about intersite achievement of the listed coprimary outcomes are the Scottish Quality Performance Indicator programme covering 11 sites in Scotland. The outcome with the largest SD (SI-IVC) was used to determine the overall sample size. With mean 0. 651 (SD 0. 2) for SI-IVC, recruiting 172 sites (86 per arm) would give 90% power to detect a difference of 0. 1 between the intervention and control groups at the 5% significance level. Where a smaller SD of 0. 1 (detrusor muscle in specimen) or 0. 15 (resection documenting) can be assumed, this would provide 90% power at the 5% significance level to detect differences of 0. 05 and 0. 075, respectively.
Acknowledgments We would like to acknowledge the input and expertise of our protocol external peer reviewers:  Christopher Blick ,  Ashish Kamat ,  Jim Catto ,  Hugh Mostafid ,  Richard Sylvester , and  John McGrath. This study was supported by the  Rosetrees Trust  grant  CF1\100002 , the  Urology Foundation ,  Action Bladder Cancer UK ,  Karl Storz  agreement, Photocure agreement,  Medac Pharma  agreement, and the  British Journal of Urology International Charity. None of the study funders had any academic input to the study design analysis or reporting. This study was sponsored by  British Urology Researchers in Surgical Training.
Acknowledgment The research leading to these results has received funding from the  Norwegian Research Council KLIMAFORSK  contract  325 326603/E10  and Coordination and Support Activity contract  310328/E10. It derives from exchanges and collaborations between participants in the  ACCLIMATE ERC  project (FP7/2007-2013 Grant agreement n°  339108 ) and ice2ice  ERC  project (FP7/2007-2013 Grant agreement n o  610055 ). We are indebted to  G. Isguder  for her micropaleontological expertise and help in sample preparation. We acknowledge  N. Smialkowski ,  L. Mauclair  and  L. Leroy  for processing the samples. We thank the  French Paul Emile Victor Institute (IPEV)  and the crew of the research vessel  Marion-Dufresne  for collecting core 330  MD16-3511Q. We also thank  P. Guyomard  to having given us access to core  CAR2013-PQP-CAR05  in the SHOM core repository.
After a training phase of the NNs, the NN-based observation operator is implemented in LDAS-Monde and the impact of assimilating ASCAT backscatter observations in ISBA is evaluated over southwestern France. The paper is organized as follows. Section 2 presents the different components of LDAS-Monde, starting with the interactive vegetation version of the ISBA LSM, as well as the data and methods used for NN training and assimilation. Details of the experimental setup and the specifics of the study area are also given in this section. Section 3 presents the results of the calibration of the NN hyperparameters and of the validation of the NN outputs. The impact of the assimilation on the ISBA simulations is shown. The results are discussed in Section 4. Section 5 summarizes the conclusions of this work.
The global database ECOCLIMAP-II provides the information for each patch  [47]. The grid cell LAI simulated by ISBA-A-gs is calculated by averaging the LAI of the different vegetation types weighted by the fractional area they cover. The model can be operated at different spatial scales, from local to global, and at different spatial resolutions. [15]  is a sequential data assimilation system embedded in the SURFEX modelling platform in an offline configuration (without interaction with the atmosphere). It allows the joint assimilation of global satellite SSM and LAI products into ISBA-A-gs. The assimilation of satellite products with LDAS-Monde is performed using an EKF  [48]  and allows the direct update of eight control variables of the ISBA model, including soil moisture from 1 to 100 cm depth (corresponding to layers 2 to 8) and LAI, with a 24 h assimilation window  [26].
ASCAT Data This paper focuses on the assimilation of ASCAT backscatter observations. ASCAT is a C-band (5. 255 GHz) radar installed on the MetOp satellites. The ASCAT instruments use vertically polarized antennae for both transmitting and receiving signals. Backscatter measurements are gathered at three azimuth angles: 45 • , 90 • , and 135 •. While the primary purpose of ASCAT is to measure wind speed and direction over oceans, these measurements can also be linked to other geophysical variables. In particular, over land, vegetation and surface soil moisture both affect the observed backscatter values. Retrieval methods have been developed to derive a soil moisture index, resulting in the creation of a surface soil moisture database  [6]. While the assimilation of this SSM retrieval into the ISBA model has demonstrated benefits  [15, 18] , the vegetation information contained in the backscatter observations remains untapped.
The period is divided in two parts. A training period, from 2007 to 2014 (8 years), is used to train the NN-based observation operator. Keras algorithms (https://keras. io/, accessed on 2 August 2023) with stochastic gradient descent using the Adam optimizer  [58]  are used to optimize the NN. The Relu activation function is used. The test period from 2015 to 2018 (4 years) is used to validate the NN and the analysis (Table  1 ). Masks for frozen soils, snow, complex topography, water bodies and urban areas are applied to both the model and the satellite data using information from ECOCLIMAP-II. As a result, 247 grid cells are considered. Three configurations of LDAS-Monde are used for the comparison (Table  1 ):.
The period is divided in two parts. A training period, from 2007 to 2014 (8 years), is used to train the NN-based observation operator. Keras algorithms (https://keras. io/, accessed on 2 August 2023) with stochastic gradient descent using the Adam optimizer  [58]  are used to optimize the NN. The Relu activation function is used. The test period from 2015 to 2018 (4 years) is used to validate the NN and the analysis (Table  1 ). Masks for frozen soils, snow, complex topography, water bodies and urban areas are applied to both the model and the satellite data using information from ECOCLIMAP-II. As a result, 247 grid cells are considered. Three configurations of LDAS-Monde are used for the comparison (Table  1 ): • Open loop (OL), a 12-year ISBA run without assimilation performed after a 20-fold spin-up of the initial year-2007.
• A novel configuration based on the assimilation of σ 0 40 (EKF). The two 4-year data assimilation runs start from the same initial conditions on 1 January 2015, obtained from the OL simulation. They follow the same framework as described in Albergel et al. [15]  and subsequent studies in terms of specified background error covariance matrix and run perturbations. The NN training is based on OL simulations, PROBA-V LAI observations, and ASCAT σ 0 40 observations from 2007 to 2014. NN performance is evaluated using the root-mean-square deviation (RMSD) of simulated vs. observed σ 0 40 values and the Pearson correlation coefficient (R). The influence of each predictor is evaluated by imposing a constant mean value of each predictor, one at a time, on the simulations of the previously trained NN.
The influence of soil temperature may be due to its influence on leaf water potential  [60]  and on below-ground hydraulic conductivity  [61]. In addition to its effect on the root biological activity, soil temperature has a direct effect on water surface tension and on gas volume in soils  [62]. Shan et al. [59]  investigated a broader range of predictors, including interception water storage by vegetation. We evaluated the use of interception water storage and found that this variable has a rather neutral effect on the performance of the NNs, but tends to induce instabilities in the data assimilation process.
Data Availability Statement: The PROBA-V LAI product is available from the CGLS portal (https: //land. copernicus. eu/global/products/lai, accessed on 2 August 2023). The ASCAT σ 0 40 is available from the EUMETSAT HSAF service (https://doi. org/10. 15770/EUM_SAF_H_0009, accessed on 2 August 2023). The SMOSMANIA in situ soil moisture data are available from the International Soil Moisture Network (ISMN) portal (https://ismn. earth/, accessed on 2 August 2023).
Conceptualization, all authors; methodology, T. C. , G. C. , B. B. , N. R. -F. and J. -C. C. ; investigation, T. C. , B. B. , N. R. -F. and J. -C. C. ; formal analysis, T. C. ; supervision, J. -C. C. and N. R. -F. ; writing-original draft preparation, T. C. ; writing-review and editing, J. -C. C. All authors have read and agreed to the published version of the manuscript. Funding: The PhD thesis of Timothée Corchia was funded by Météo-France and by CNES, France (Centre National d'Etudes Spatiales). This research received funding from the European Union Horizon 2020 Research and Innovation programme (grant agreement 958927 (CoCO2)) and Horizon Europe (grant agreement 101082194 (CORSO)).
Author Contributions: Conceptualization, all authors; methodology, T. C. , G. C. , B. B. , N. R. -F. and J. -C. C. ; investigation, T. C. , B. B. , N. R. -F. and J. -C. C. ; formal analysis, T. C. ; supervision, J. -C. C. and N. R. -F. ; writing-original draft preparation, T. C. ; writing-review and editing, J. -C. C. All authors have read and agreed to the published version of the manuscript. Funding: The PhD thesis of Timothée Corchia was funded by Météo-France and by CNES, France (Centre National d'Etudes Spatiales). This research received funding from the European Union Horizon 2020 Research and Innovation programme (grant agreement 958927 (CoCO2)) and Horizon Europe (grant agreement 101082194 (CORSO)).
Acknowledgments First of all, many thanks to all the authors for their wonderful and thoughtful writing: it was an honour to work with you. Many thanks to  Abigail Fields  and  Eden Almasude  for allowing the use of their artwork as this book's cover image; thanks to  Rosa Barotsi  and  Clio Nicastro  for helping me by reading this introduction and two of this volume's contributions when I felt unequipped to give the best feedback; thanks again to  Rosa and Clio, and to Anastasia (A) Khodyreva  and  Olga Cielemęcka  for the constant sensitive and intellectual inspiration, friendship and support; thank you to Anastassia and the team at  Logos  for providing the frame for a great collaboration, for encouraging different academic practices, and for their patience. Finally, I am grateful to the  Nordic Summer University  and the  Turku Institute for Advanced Studies (TIAS)  for generously funding this volume and its Open Access version.
Acknowledgements This work was funded by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  760801\Nemosine , and by  Fundação para a Ciência e a Tecnologia (FCT)  in the scope of the projects  UIDB/04028/2020  &  UIDP/04028/2020  (CERENA). Our thanks to  Karine Janel ,  Nathalie Marlin ,  Gérard Mortha  and  Jérémie Viguié  from  INP Grenoble , for helping us prepare the 20 cm paper sheets and perform the mechanical flexibility tests at Pagora (LGP2, UMR5518); to  Stéphane Bouvet  and  Eléonora Pelizzi  at  Bibliothèque Nationale de France   (BNF)  for access to the tensile testing equipment; to  Sabrina Paris Lacombe  for help in obtaining the right conditions for the tensile strength measurements; to  Georges Mouchaham  for helping to draw porous solids structures on Diamond at ESPCI (IMAP, UMR8004); to  Weidmann  for providing free samples of NFC.
Mobile system We conducted 24 mobile surveys (24 days) between October and September 2021. A cavity ring-down spectrometer (CRDS) model G2401 manufactured by Picarro Inc. (USA) was employed to measure CH 4 with 1 Hz time resolution  (Crosson, 2008). The analyzer was calibrated every month using the WMO X2004 scale (Yver  Kwok et al. , 2015). All the data reported in this study were quality controlled with the Integrated Carbon Observation System-Atmosphere Thematic Center (ICOS-ATC). The precision in measured CH 4 is 0. 7 ppb (Yver  Kwok et al. , 2015). The instrument was installed into a thermal-engine vehicle also equipped with a GPS device (NEO-M8N-0-10 U-Blox) and a sonic anemometer (150WX RS232 Weather Station Instrument) on the roof. In addition, the air inlet was added to the roof of the car, close to the anemometer (about 190 cm above the ground), as shown in Fig.
Measurement protocol and data collection Every month, two consecutive days of mobile survey were carried out around midday, when the air was well mixed in the planetary boundary layer. This allowed us to collect data in all seasons and under different wind conditions for each emitter. Each fieldwork day surveyed the three selected sites. Whenever CH 4 emission plumes were visible on the monitoring screen, 3-5 repeated transects, used to investigate gradual changes in CH 4 concentrations, were followed at a driving speed of 20-30 km h -1 , if the traffic condition permitted. This speed range has been identified optimal  (Lowry et al. , 2020)  during Gaussian plume peak shape characterization. Generally, the duration of each survey was 6-7 h. A pre-survey was performed at Aradippou to confirm this area is only for cattle farms and find proper driving paths to catch plumes for later mobile measurements.
34 Gg and 6. 74 Gg CH 4 in 2020 respectively, accounting for about 47 % of solid waste CH 4 emissions in Cyprus (NIR, 2022). The activity data used in this approach for Cyprus include disposed waste amounts, compositions and population (urban and rural) (NIR, 2022). The disposed waste amount in Koshi is estimated at 227. 63 Gg in 2020. The suggested default values for degradable organic carbon (DOC) cover the whole southern Europe region, and the methane generation rate constant is the default one for dry temperatures. Landfill CH 4 emissions are calculated by reducing the fraction of collected CH 4 and the fraction of oxidized CH 4 in the landfill cover soil from CH 4 generation. Thus, the uncertainty is related to CH 4 production/generation, variances in time collection efficiency and the part being oxidized  (Scheutz et al.
Acknowledgments We would like to thank the  Integrated Carbon Observation System (ICOS) teams at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE)  for their support on calibrations and data quality control for the instrument Picarro. We thank  Sara Defratyka  ( NPL ) for her support with the Gaussian plume model. We are grateful for all the technical support from the  Unmanned Systems Research Laboratory (USRL) at the Cyprus Institute (CyI). This work has been supported by the project  Eastern Mediterranean Middle East-Climate & Atmosphere Research Center (EMME-CARE ) which has received the funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 856612  and the  Cyprus Government , and the project  Air Quality Services for clean air in Cyprus  ( AQ-SERVE) INTEGRATED/0916/0016  is co-financed by the  European Regional Development Fund  and the  Republic of Cyprus  through the  Research and Innovation Foundation , and the project " Prototype System for a Copernicus CO 2 Service (CoCO2) " project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No.
Acknowledgements This work was financially supported by the  Russian Foundation for Basic Research  (grant  21-53-10005 ), by the  Royal Society  ( International Exchanges Award   IEC/R2/202035  to R. P. R. ), by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  823883 , and by the  UK Biotechnology and Biological Sciences Research Council  (grant  BB/X00158X/1  to R. P. R. ).
The physical assumptions behind the estimation of the OHC change are given in section 2. Section 3 describes the datasets used for the estimation of the OHC change and its validation. The preprocessing of the datasets used for the computation of OHC is explained in section 4. A subsection is specially dedicated to the validation of the estimation of steric sea level change from the space geodetic approach. Another subsection is dedicated to the description of the method used to propagate uncertainties. Results are presented in section 5. A validation work was also performed with in situ products (section 6). Finally, conclusions and perspectives are given in section 7.